Research ArticleFloristic Composition Structure and Species Associations ofDry Miombo Woodland in Tanzania
Ezekiel Edward Mwakalukwa12 Henrik Meilby1 and Thorsten Treue1
1 Department of Food and Resource Economics Faculty of Science University of Copenhagen Rolighedsvej 231958 Frederiksberg C Denmark
2Department of Forest Biology Faculty of Forestry and Nature Conservation Sokoine University of AgriculturePO Box 3010 Chuo Kikuu Morogoro Tanzania
Correspondence should be addressed to Ezekiel Edward Mwakalukwa ezedwayahoocom
Received 20 December 2013 Accepted 4 February 2014 Published 8 May 2014
Academic Editors M Drielsma H Ford M Tigabu and A Vina
Copyright copy 2014 Ezekiel Edward Mwakalukwa et al This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited
For the majority of forest reserves in Tanzania biodiversity is poorly documented This study was conducted to assess speciesrichness (woody species) diversity and forest structure and to examine relationships between species occurrence and topographicand edaphic factors in the Gangalamtumba Village Land Forest Reserve a dry Miombo woodland area in Tanzania A total of 35nested circular plots with radii of 5 15 and 20m were used to collect data on woody species and soil samples across the 6065 hacommunity-managed forest reserve Stumps were measured 20 cm above ground A total of 88 species belonging to 29 familieswere identified Generally forest structure parameters and diversity indices indicated the forest to be in a good condition and havehigh species richness and diversity Vegetation analysis revealed four communities of which two were dominated by the familyCaesalpiniaceae indicating large variation of site conditions and possible disturbances in the study area The high level of diversityof woody species and the high basal area and volume indicate that the forest is in good condition but the effect of anthropogenicactivities is evident and stresses the need for proper management to maintain or enhance the present species diversity
1 Introduction
Miombowoodland is themost widespread and dominant dryforest formation in Eastern Central and Southern Africa Itis characterized by an abundance of tree species in the legumesubfamily Caesalpinoideae including the three dominantgenera of Brachystegia Julbernardia and Isoberlinia [1 2]Covering an area of about 36 million km2 miombo wood-land supports the livelihoods of more than 100 million ruraland urban dwellers by providing a wide range of productssuch as firewood charcoal timber and forage and servicessuch as soil conservation and water catchment [3ndash5] How-ever due to the rapid population growth and the high level ofpoverty across theMiombo region the human pressure on itswoodlands has steadily increased over the last decades lead-ing to increasing deforestation and forest degradation [6ndash8]
The effects of increasing rates of deforestation and forestdegradation on biodiversity in developing countries have
been thoroughly studied [9ndash12] Habitat loss due to deforesta-tion reduces not only the number of species in the ecosystembut also the number and extent of places where speciescoexist Activities such as charcoal production firewood col-lection for subsistence use and for tobacco curing conversionof woodlands to farmland and seasonal forest fires are amongthe major drivers of deforestation and forest degradation inthe Miombo region [13ndash17] It is estimated that 14 millionha of woodlands is lost annually in the countries whereMiombo woodlands dominate leading to a loss of carbonstocks biodiversity and through soil degradation loss ofplant nutrients [4 5] Syampungani et al [5 p 151] stated thatldquoloss of biodiversity and extinction of most of the woodlandresources are imminent if the current intensive exploitation ofMiombo resources continues uncheckedrdquo More specificallyFAO (2000a cited by Syampungani et al [5]) reported that191 tree species in theMiombo ecoregion are endangered due
Hindawi Publishing CorporationISRN BiodiversityVolume 2014 Article ID 153278 15 pageshttpdxdoiorg1011552014153278
2 ISRN Biodiversity
N
PlotsVillagesDirt roads
Forest roadsGangalamtumba forest reserve
Tanzania
Iringa region 0 25 5 10
(km) To Iringa
Airport
Itagutwa
IkengezaTo Dodoma
Mfyome
Kiwele
Luganga
Figure 1 Map showing the location of the study area The inserted map of Tanzania shows the location of the Iringa region
to conversion of forest areas into agricultural lands or throughcharcoal production
GangalamtumbaVillage LandForest Reserve (GVLFR) inIringa rural district Tanzania which is owned and managedby the village of Mfyome was established in 2002 underTanzaniarsquos national participatory forest management pro-gramme and thus represents one of approximately 1500 Vil-lage Land Forest Reserves (covering some 24 million ha) theprogressive establishment of which is intended to promoteconservation of approximately 165 million ha of hithertounreserved forest on general and village land [18ndash21] Villagesrsquocontrol over Village Land Forest Reserves is conditional ontheir conservationprotection of these forests and the exec-utive management is performed by an environmentalforestmanagement committee whose members are directly electedfor five-year terms by all members of the village above theage of 18 [18] As such GVLFR is a typical example of anarea which at least until 2002 might have experienced lossof biodiversity due to increasing human activity includingcharcoal production and extraction of wood for tobaccocuring giving cause for concern with respect to the main-tenance of forest biodiversity [15] The extent to which theactivities have led to loss of biodiversity and deterioration ofthe plant community structure is so far unknown but for thedevelopment of sustainablewoodlandmanagement strategies
and for planning of future management and conservationinformation on these issues is urgently needed
Although many quantitative ecological studies have beenundertaken in places where Miombo dominates its exten-siveness and the large between-site variation which is causedby climatic and edaphic factors and anthropogenic activitiesappear to warrant further case studies [20 22ndash29] Ecologicalcase studies are particularly relevant when the informationgenerated is required for sound decision making about forestmanagement conservation strategies and determination ofsustainable harvesting levels Hence the objectives of thisstudy were (1) to provide a detailed assessment of the currentstanding stock species diversity richness and structure and(2) to understand the relationship between species abundanceand a range of environmental and topographic factors thatshape plant communities and species associations in theGVLFR
2 Materials and Methods
21 Study Site Gangalamtumba Village Land Forest Reserveis located in central-southern Tanzania (7∘351015840S 35∘351015840E)about 30 km north of Iringa town the administrative capitalof the Iringa region (Figure 1)The forest covers 6065 hectaresand is part of the Mfyome village area which is located in
ISRN Biodiversity 3
the ward of Kiwele The forest vegetation has been describedas dry Miombo woodland similar to the dry woodlandtype described from other countries such as Zimbabwe andMozambique [1] The forest is located in a relatively flat areaat an elevation of 850ndash1300 metres above sea level Theregion is characterised by distinct wet and dry seasons withalmost no rain in the four months of June-September andabout 80 of the annual precipitation falling in DecemberndashMarch Average rainfall data covering the last 50 years (1960ndash2010) were obtained from the meteorological station at Nduliairport which is located about 30 km from the forest andindicate that the area receives an average annual precipitation(mean plusmn standard error) of 617plusmn17mm (448ndash1085mm)Themean annual temperature is 198∘C and the average relativehumidities at 0600 and 1200GMT are 539 and 514respectively
The GVLFR is a production forest which is managedby the Mfyome village under a community-based forestmanagement (CBFM) arrangement established in 2002 com-pare above The primary economic activity in Mfyome issmallholder agriculture and the main economic uses of theforest are production of timber charcoal and firewood [20]The woodland is also used for grazing and is an importantsource of subsistence products such as firewood constructionmaterials fruit mushrooms wild vegetables and medicinalplants [18]
22 Vegetation Survey The field survey was conducted inJuly and August 2009 and involved establishment of a totalof 35 permanent nested circular sample plots distributedacross the entire forest Plots were established along transectlines and the distance between plots was approximately2 km (Figure 1) The radii of the nested circular plots were5m (00079 ha) 15m (00707 ha) and 20m (01257 ha) Thefollowing parameters were recorded within each of the 35plots within the 5m radius all small trees and shrubs(lt150 cm tall or ge150 cm but lt1 cm Dbh) were counted andtheir species were identified and medium-size trees andshrubs (ge1 cm Dbh but lt5 cm Dbh) were identified andmeasured with respect to diameter Within 15m radius thespecies were identified and the diameter was measured for alllarge trees and shrubs with Dbh ge5 cm Within 20m radiusall stumps of trees and shrubs were identified to specieslevel and measured for diameter 20 cm above ground Initialidentification of species for both standing treesshrubs andstumps relied on the knowledge of local botanists (usinglocal vernacular species names and features such as colorof the bark smell and leaves) and was later confirmed bybotanists from Tanzania Forest Research Institute (TAFORI)based at Lushoto Silviculture Research Station For speciesthat were difficult to identify in the field samples weretaken to the herbarium at Lushoto for reidentification Othermeasurements taken within the plots were geographicallocation (UTMcoordinates) and elevation (m) usingGPS andslope () using a Suunto clinometer
23 Soil Sampling Soil samples were collected from fivepoints which were located at the centre of the plot and 10mfrom the centre in the four cardinal directions (North East
South and West) At each point two samples were taken 0ndash15 cm and 15ndash30 cm below the surfaceThe five samples takenfrom each depth range were mixed in the field to obtain onecomposite sample per depth range and plot Thus 70 soilsamples (35 from each depth range) were collected from the35 plots In addition a soil core device with an inner diameterof 5 cm and a length of 5 cm was used for extracting soilbulk density samples from the centre of each plot and at eachdepth Hence a total of 70 samples were collected for bulkdensity determination
24 Laboratory Analyses In the laboratory all soil sam-ples were ground and passed through a 2mm sieve toremove stones and gravel Fine and coarse roots were alsoremoved Subsequently soil samples collected at 0ndash15 cmdepth were analysed for soil pH soil texture cation-exchangecapacity (CEC cmol(+)kg) available phosphorus (ppm)and exchangeable bases (Ca2+ Mg2+ and K+ cmol(+)kg)Samples from both depth ranges (0ndash15 and 15ndash30 cm) wereanalysed for percentages of organic carbon and total nitrogenStandard methods for soil analysis were used in order toobtain estimates for each of the mentioned variables that canbe compared with results reported in the literature [30ndash34]Soil pH was determined electrometrically using 10 g of soilsample diluted in 25mL distilled water that is using a 1 25ratio of soil to water Soil texture was determined by thehydrometer method and the textural classification was doneby the use of the soil texture triangle [35]
The Bray 1 method was used for the determination ofextractable P for acidic soils with pH less than 7 while theOlsen method was used for soils with pH above 7 (alkalinesoils) The ammonium acetate method at pH 7 was used indetermination ofCEC and by the use of an atomic absorptionspectrophotometer in a UNICAM 919 AA Spectrometer allexchangeable cations (Ca2+ Mg2+ and K+) were determinedSubsamples were finely ground into powder form (lt1mm) inan agate mortar and analyzed for total percentages of organicC and N by dry combustion (Dumas method) in a Leco CNS2000 analyzer [33] Samples for bulk density estimation wereoven-dried at 105∘C to constant weight and the weight wasrecorded (accuracy 001 g) The volume was calculated fromlength and cross-sectional area of the soil core and bulk den-sity was determined as dry weight (g) per unit volume (cm3)Most analyses were conducted at the Laboratory of ForestBiology Sokoine University of Agriculture (SUA) but C andN analyses were conducted at the Soil Science Laboratory attheDepartment of Forest andLandscape (nowDepartment ofGeosciences and Natural Resource Management) Universityof Copenhagen Denmark
25 Data Analysis Based on the data collected the followingmeasures were analysed species composition was expressedthrough species richness and diversity measures forest struc-ture was expressed through stem density basal area andvolume for plant communities species groups and diameterclasses Total species richness was computed as the totalnumber of species across all 35 plots Species diversity wascomputed using Shannonrsquos and Simpsonrsquos Diversity Indices
4 ISRN Biodiversity
[36]The volume of stumpswas calculated as cylinder volumewhile total volume for standing trees was calculated usinga regression equation developed for GVLFR by the authors[37 38] ln(V)= minus84554 + 23236 times ln(Dbh) (R2 = 0983RMSE = 0248 Dbh range 14ndash62 cm 119899 = 104) where V isvolume (m3tree) Dbh is diameter at breast height (ge1 cm)RMSE is the residual standard error R2 is the coefficientof determination n is the total sample size and ln is thenatural logarithmThe ImportanceValue Index (IVI) for eachspecies in each plot was calculated as the sum of relativedensity and dominance (basal area) and expressed in percent[39] Percentage base saturation (BS) was determined asthe ratio of total base cation concentration to CEC whilethe C N ratio was determined using the estimated elementalpercentages of carbon and nitrogen [35]
Using IVI for each species plots were classified byagglomerative hierarchical cluster analysis using Soslashrensenrsquosdistance measure and a group linkage method with flexible120573 of minus050 The 35 plots were ordinated by nonmetricmultidimensional scaling (NMS) using the PC ORDsoftwareversion 60 [40] Topographic variables (elevation and slope)and edaphic variables (pH bulk density texture extractableP CEC exchangeable base cations BS C N ratio Cand N) were correlated with the NMS ordination axesIndicatordominant species in each cluster were determinedusing percentage indicator values (IV) where values of 0correspond to no indication and 100 is perfect indication [4142] The first three to five names of these Indicatordominantspecies with the highest percentage indicator values (IV)constancy and significant indicator values (119875 lt 005) wereused to assign names to the clustersplant community types[29 41 42] The Steinhaus (SoslashrensenCzekanowski) coeffi-cient was used to assess the similaritydissimilarity of thespecies compositions of the plant communities [39]
3 Results
31 Species Richness Including all size categories a totalof 88 species (29 plant families) of standing trees andshrubssmall trees were identified in the GVLFR (Table 1)Trees contributed 60 (21 plant families) and shrubs 40(15 plant families) of the species For stumps a total of 42species (20 plant families) of trees and shrubssmall treeswith basal diameter ranging from 2 to 50 cm were identifiedFor stumps trees contributed 76 (13 plant families) of thespecies while shrubs contributed 24 (10 plant families)All species represented by stumps were also represented bystanding treesshrubs In general tree and shrub speciesfrom the family Caesalpiniaceae contributed most (13) tothe total number of species (standing individuals) followedby those from the families Mimosaceae (10) Rubiaceae(10) Fabaceae (9) and Euphorbiaceae (9) (Table 1)Among standing trees the greatest number of species wasfound in the four plant families Caesalpiniaceae (17)Mimosaceae (15) Fabaceae (13) and Combretaceae (9)while shrubssmall trees included most species from thefamilies Rubiaceae (26) Euphorbiaceae (17) and Cap-paraceae (9) For stumps tree and shrub species from
0 5 10 15 20 25 30 35
0
20
40
60
80
Sites
Spec
ies r
ichn
ess
Figure 2 Species accumulation curve for large individuals (Dbhge5 cm) measured within circular plots with a radius of 15m in theGangalamtumba VLFR Vertical lines indicate standard deviations(range 0ndash49)
the family Mimosaceae contributed most (17) to the totalnumber of species followed by species from the familiesCaesalpiniaceae (14) Combretaceae (10) and Fabaceae(7) Among species categorised as trees the families Cae-salpiniaceae (19) and Mimosaceae (19) contributed equalnumbers of species followed by Combretaceae (13) andFabaceae (9) With respect to shrubs each of the familieswas represented by a single species (10 see Table 1)
When considering different size categories and includingboth trees and shrubs (small sizes Dbhlt 5 cm and large sizesDbh ge 5 cm) a total of 78 species (28 families) were foundamong large sizes with Caesalpiniaceae (13) Mimosaceae(12) and Fabaceae (10) being the most species-rich plantfamilies while among small sizes a total of 69 species (27families) were observed with Rubiaceae (13) Caesalpini-aceae (12) andMimosaceae (10) contributing the greatestnumber of species (Table 1) In general the average number ofspecies per plot was found to be 14 species (range 5ndash24 speciesper plot)
The species accumulation curve (Figure 2) shows thatthe 35 sitesplots used in this study were sufficient to covermuch (but not all) of the variation and species diversity ofthe study area At 35 plots the graph has not yet reached itsasymptotic level but is starting to converge implying that anyfurther increase of sample size would be expected to lead toinclusion of additional rare species However although thesample size was small (35 plots) and does not quite capturethe full woody plant biodiversity of the reserve the results arestill useful for characterizing the treeshrub species diversityand relationships between species and site
32 Species Diversity Shannon-Wiener diversity indices forlarge and small individuals were found to be 344 and 326respectively and the Simpson index for large individuals was
ISRN Biodiversity 5
Table1Ch
ecklist
oftre
eand
shrubspeciesrecordedin
Gangalamtumba
VLF
Rshow
ingfre
quency
()density
(meanplusmnSE
)basalarea(meanplusmnSE
)dispersio
nindex(D
I)and
Impo
rtance
ValueInd
ex(IVI)bothforthe
currentp
opulationof
largeind
ividuals(plotsize=
15m
radiusm
inim
umDbh
=5c
m)a
ndforstumps
(plotsize=
20m
radiusm
inim
umbasald
iameter
=2c
m)SH
shrub
ST
smalltreeandT
tree
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
1Mgiha
Dalbergia
arbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST60
247plusmn69136plusmn037
224
4765
1468plusmn41006plusmn005
170
2Mtono
Commiphora
afric
ana
Burseraceae
T77
110plusmn23159plusmn037
175
1142
1414plusmn06001plusmn001
111
3Mlama
Combretum
molleG
Don
Com
bretaceae
T74
116plusmn27069plusmn015
132
1523
3468plusmn19005plusmn002
307
4Mku
ngugu
Acacia
sp
Mim
osaceae
T23
40plusmn22102plusmn054
111
2873
920plusmn14001plusmn001
65
5Mkw
eeBrachyste
gia
spiciform
isBe
nth
Caesalpiniaceae
T37
58plusmn20118plusmn034
110
1731
2661plusmn24020plusmn009
308
6Mkalala
Albiziapetersiana
(Belle)O
liv
Mim
osaceae
T40
80plusmn30055plusmn020
85
2711
636plusmn29005plusmn004
94
7Mlyasenga
Combretum
zeyheri
Soun
dCom
bretaceae
T43
60plusmn20030plusmn011
741597
2020plusmn08001plusmn001
47
8Mkombivawo
Bauh
iniapetersiana
Caesalpiniaceae
SH34
54plusmn21029plusmn010
64
2000
9Muguvani
Markham
iaobtusifolia
Bign
oniaceae
T60
65plusmn13038plusmn010
64
647
605plusmn03
00
23
10Mdeke
Hym
enodictyon
parvifoliu
mOliv
Rubiaceae
ST31
50plusmn24048plusmn024
58
2749
302plusmn02
00
17
11Mub
wegele
Sclerocarryabirrea
sbspbirr
eaAnacardiaceae
T34
21plusmn6061plusmn023
58
441
605plusmn03001plusmn001
28
12Mmem
enam
ene
Margaritaria
discoidea(Bail)
Euph
orbiaceae
SH51
68plusmn24032plusmn013
57
2073
307plusmn07
00
06
13Mkambala
Acaciamellifera
(Vahl)Be
nth
Mim
osaceae
T29
14plusmn5049plusmn019
50
359
302plusmn02
00
08
14Mugegere
Dich
rosta
chys
cinerea
(L)Wight
ampArn
Mim
osaceae
ST31
53plusmn26017plusmn009
47
3086
2959plusmn19003plusmn002
178
15Mbata
AcaciaseyalD
elvar
seyal
Mim
osaceae
T34
30plusmn10019plusmn006
45
887
311plusmn11001plusmn001
46
16Mgulumo
Lann
easchw
einfurthiiAnacardiaceae
T43
24plusmn7032plusmn009
39
544
907plusmn04002plusmn001
22
17Muyom
boBrachyste
giaboehmii
Caesalpiniaceae
T11
11plusmn7019plusmn015
38
1309
607plusmn05001plusmn001
22
18Mmulim
uli
Cassiaabbreviata
Caesalpiniaceae
T34
31plusmn13017plusmn007
36
1353
916plusmn12001plusmn001
49
19Mdavi
Cordiasin
ensis
Lam
Boraginaceae
ST6
23plusmn23011plusmn010
34
5498
302plusmn02
00
06
6 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
20Mdw
endw
eTerm
inaliabrow
nii
Com
bretaceae
T17
14plusmn8019plusmn009
32
1129
305plusmn05
00
12
21Mfilafila
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T31
25plusmn8015plusmn005
30
646
605plusmn03
00
35
22Mkw
ata
Codyladensiflora
Caesalpiniaceae
T9
4plusmn2033plusmn019
28
282
334plusmn34004plusmn004
83
23Mgung
aAc
aciaabyssin
ica(H
ochst)
Mim
osaceae
T17
12plusmn7014plusmn007
24
1140
614plusmn12001plusmn001
39
24Mkole
Grew
iabicolor
Tiliaceae
ST26
21plusmn10008plusmn003
21
1078
605plusmn03
00
15
25Mparapand
eStrychnosp
otatorum
Lf
Loganiaceae
T23
23plusmn11014plusmn009
20
1331
607plusmn05
00
17
26Kitim
bwi
Orm
ocarpum
kirkii
Fabaceae
SH29
18plusmn6007plusmn003
19492
27Mtabagila
Lonchocarpus
capassa
Fabaceae
T26
11plusmn5014plusmn006
17550
302plusmn02
00
04
28Muw
otapon
ziOzoroainsig
nsssp
retic
ulata
Anacardiaceae
T14
10plusmn5010plusmn005
16564
1411plusmn05001plusmn000
31
29Msanzi
Gardeniaresin
iflua
Hiern
Rubiaceae
ST9
13plusmn8008plusmn005
161229
30Mkoga
Vitexpayos
Verbenaceae
ST17
12plusmn5011plusmn004
15605
302plusmn02
00
02
31Mpelem
eleGr
ewiaforbesiiHaw
Ex
Mast
Tiliaceae
ST14
16plusmn9005plusmn002
147
1246
32Mny
wenyw
eeDalbergiaboehmii
Fabaceae
T14
11plusmn7009plusmn007
151221
33Mulagavega
Albiziaam
ara(Roxb)
Boiv
Mim
osaceae
T14
9plusmn5004plusmn003
14712
34Mkola
Afzelia
quan
zensis
Caesalpiniaceae
T11
6plusmn3015plusmn009
14532
35Mugusi
Brachyste
giamanga
Caesalpiniaceae
T20
11plusmn6010plusmn007
13824
618plusmn13002plusmn002
25
36Mwaham
aShrebera
trichoclada
Oleaceae
T17
11plusmn6005plusmn002
12697
309plusmn09001plusmn001
15
37Mpingo
Dalbergia
mela
noxylon
Fabaceae
T11
11plusmn9004plusmn003
121655
314plusmn14001plusmn001
26
38Mub
aya
Strychnosinn
ocua
Loganiaceae
T17
8plusmn4011plusmn008
12375
302plusmn02001plusmn001
06
39Msisina
AlbiziaharveyiF
ournMim
osaceae
T14
5plusmn3008plusmn005
11359
305plusmn05002plusmn002
15
40Mlim
boEu
phorbiacuneata
Valh
Euph
orbiaceae
ST14
12plusmn6004plusmn002
11699
41Mwam
biElaeodendron
buchan
annii
Cela
steraceae
T3
8plusmn8003plusmn003
102000
42Mvembadand
aXe
roderris
stuhlmannii
Fabaceae
T14
4plusmn2011plusmn005
09
191
43Mtelela
Brachyste
giabu
ssei
Harms
Caesalpiniaceae
T6
2plusmn1012plusmn010
09
253
ISRN Biodiversity 7
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
44Mteresi
Prem
naholstiiGurke
Verbenaceae
ST9
8plusmn5003plusmn002
09
899
45Mkongolo
Commiphora
ugogensis
Burseraceae
T6
1plusmn1008plusmn006
08
097
302plusmn02001plusmn001
09
46Muw
isaBo
sciaangustifolia
A
Rich
varangustifo
liaCa
pparaceae
T17
8plusmn4004plusmn002
08
426
47Mnyalup
uko
Canthium
pseudoverticillatum
Hien
Rubiaceae
ST20
8plusmn4004plusmn002
07
414
48Mnyaluh
anga
Brideliascleu
roneura
MuellArg
Euph
orbiaceae
ST14
6plusmn3004plusmn002
07
341
49Mdide
Manilkaramochisia
(Bak)Dub
ard
Sapo
taceae
T9
5plusmn4002plusmn001
07
793
50Muh
emi
Erythrinaabyssin
icaFabaceae
T6
1plusmn1007plusmn006
07
163
51Msambarawe
Vangueria
infausta
BurchSspRo
tund
ataRu
biaceae
ST6
8plusmn5002plusmn002
06
925
52Mhehefu
Allophylu
sferrugineus
Taub
Sapind
aceae
SH9
5plusmn3002plusmn001
05
540
314plusmn14001plusmn001
14
53Mnyengrsquoenyengrsquoe
Excoecariabu
ssei
(Pax)
Euph
orbiaceae
ST11
4plusmn2001plusmn001
05
321
54Muganga
Berchemiadiscolor
Rham
naceae
T6
4plusmn3004plusmn003
04
717
55Mning
aPterocarpu
sangolensis
Fabaceae
T6
2plusmn1005plusmn004
04
253
309plusmn09006plusmn006
25
56Mtanang
we
Zizip
husm
ucronata
Rham
naceae
T3
4plusmn4003plusmn003
04
900
57Muh
ekele
Eucle
adivinorum
Hiern
Ebenaceae
ST3
3plusmn3001plusmn001
04
800
302plusmn02
00
49
58Mub
umila
Cassipourea
mollis
(REfr)Alstom
Rhizop
horaceae
T9
4plusmn3002plusmn001
04
526
302plusmn02
00
04
59Mkw
ambe
Flueggea
virosa
Willd
Euph
orbiaceae
SH6
3plusmn2003plusmn003
03
406
60Kivang
aZa
nhaafric
ana
(Radlk)Ex
ell
Sapind
aceae
T9
2plusmn1001plusmn001
02
212
302plusmn02
00
02
61Mning
amaji
Pterocarpu
stinctorius
Fabaceae
T3
0002plusmn002
02
100
62Mtosi
MaeruatriphyllaA
Rich
Capp
araceae
ST3
2plusmn2001plusmn001
02
600
63Mugosa
Ehretia
amoena
Boraginaceae
ST9
1plusmn1
00
01
094
64Mnyon
gamem
beStegan
otaenia
araliacea
Apiaceae
T6
1plusmn1
001
01
163
65Mvelevele
Vernoniaam
ygdalin
aAs
teraceae
SH3
2plusmn2001plusmn001
01
400
66Mpo
ngolo
Catunaregam
spinosa
(Thun
b)
Rubiaceae
ST3
1plusmn1001plusmn001
01
200
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
2 ISRN Biodiversity
N
PlotsVillagesDirt roads
Forest roadsGangalamtumba forest reserve
Tanzania
Iringa region 0 25 5 10
(km) To Iringa
Airport
Itagutwa
IkengezaTo Dodoma
Mfyome
Kiwele
Luganga
Figure 1 Map showing the location of the study area The inserted map of Tanzania shows the location of the Iringa region
to conversion of forest areas into agricultural lands or throughcharcoal production
GangalamtumbaVillage LandForest Reserve (GVLFR) inIringa rural district Tanzania which is owned and managedby the village of Mfyome was established in 2002 underTanzaniarsquos national participatory forest management pro-gramme and thus represents one of approximately 1500 Vil-lage Land Forest Reserves (covering some 24 million ha) theprogressive establishment of which is intended to promoteconservation of approximately 165 million ha of hithertounreserved forest on general and village land [18ndash21] Villagesrsquocontrol over Village Land Forest Reserves is conditional ontheir conservationprotection of these forests and the exec-utive management is performed by an environmentalforestmanagement committee whose members are directly electedfor five-year terms by all members of the village above theage of 18 [18] As such GVLFR is a typical example of anarea which at least until 2002 might have experienced lossof biodiversity due to increasing human activity includingcharcoal production and extraction of wood for tobaccocuring giving cause for concern with respect to the main-tenance of forest biodiversity [15] The extent to which theactivities have led to loss of biodiversity and deterioration ofthe plant community structure is so far unknown but for thedevelopment of sustainablewoodlandmanagement strategies
and for planning of future management and conservationinformation on these issues is urgently needed
Although many quantitative ecological studies have beenundertaken in places where Miombo dominates its exten-siveness and the large between-site variation which is causedby climatic and edaphic factors and anthropogenic activitiesappear to warrant further case studies [20 22ndash29] Ecologicalcase studies are particularly relevant when the informationgenerated is required for sound decision making about forestmanagement conservation strategies and determination ofsustainable harvesting levels Hence the objectives of thisstudy were (1) to provide a detailed assessment of the currentstanding stock species diversity richness and structure and(2) to understand the relationship between species abundanceand a range of environmental and topographic factors thatshape plant communities and species associations in theGVLFR
2 Materials and Methods
21 Study Site Gangalamtumba Village Land Forest Reserveis located in central-southern Tanzania (7∘351015840S 35∘351015840E)about 30 km north of Iringa town the administrative capitalof the Iringa region (Figure 1)The forest covers 6065 hectaresand is part of the Mfyome village area which is located in
ISRN Biodiversity 3
the ward of Kiwele The forest vegetation has been describedas dry Miombo woodland similar to the dry woodlandtype described from other countries such as Zimbabwe andMozambique [1] The forest is located in a relatively flat areaat an elevation of 850ndash1300 metres above sea level Theregion is characterised by distinct wet and dry seasons withalmost no rain in the four months of June-September andabout 80 of the annual precipitation falling in DecemberndashMarch Average rainfall data covering the last 50 years (1960ndash2010) were obtained from the meteorological station at Nduliairport which is located about 30 km from the forest andindicate that the area receives an average annual precipitation(mean plusmn standard error) of 617plusmn17mm (448ndash1085mm)Themean annual temperature is 198∘C and the average relativehumidities at 0600 and 1200GMT are 539 and 514respectively
The GVLFR is a production forest which is managedby the Mfyome village under a community-based forestmanagement (CBFM) arrangement established in 2002 com-pare above The primary economic activity in Mfyome issmallholder agriculture and the main economic uses of theforest are production of timber charcoal and firewood [20]The woodland is also used for grazing and is an importantsource of subsistence products such as firewood constructionmaterials fruit mushrooms wild vegetables and medicinalplants [18]
22 Vegetation Survey The field survey was conducted inJuly and August 2009 and involved establishment of a totalof 35 permanent nested circular sample plots distributedacross the entire forest Plots were established along transectlines and the distance between plots was approximately2 km (Figure 1) The radii of the nested circular plots were5m (00079 ha) 15m (00707 ha) and 20m (01257 ha) Thefollowing parameters were recorded within each of the 35plots within the 5m radius all small trees and shrubs(lt150 cm tall or ge150 cm but lt1 cm Dbh) were counted andtheir species were identified and medium-size trees andshrubs (ge1 cm Dbh but lt5 cm Dbh) were identified andmeasured with respect to diameter Within 15m radius thespecies were identified and the diameter was measured for alllarge trees and shrubs with Dbh ge5 cm Within 20m radiusall stumps of trees and shrubs were identified to specieslevel and measured for diameter 20 cm above ground Initialidentification of species for both standing treesshrubs andstumps relied on the knowledge of local botanists (usinglocal vernacular species names and features such as colorof the bark smell and leaves) and was later confirmed bybotanists from Tanzania Forest Research Institute (TAFORI)based at Lushoto Silviculture Research Station For speciesthat were difficult to identify in the field samples weretaken to the herbarium at Lushoto for reidentification Othermeasurements taken within the plots were geographicallocation (UTMcoordinates) and elevation (m) usingGPS andslope () using a Suunto clinometer
23 Soil Sampling Soil samples were collected from fivepoints which were located at the centre of the plot and 10mfrom the centre in the four cardinal directions (North East
South and West) At each point two samples were taken 0ndash15 cm and 15ndash30 cm below the surfaceThe five samples takenfrom each depth range were mixed in the field to obtain onecomposite sample per depth range and plot Thus 70 soilsamples (35 from each depth range) were collected from the35 plots In addition a soil core device with an inner diameterof 5 cm and a length of 5 cm was used for extracting soilbulk density samples from the centre of each plot and at eachdepth Hence a total of 70 samples were collected for bulkdensity determination
24 Laboratory Analyses In the laboratory all soil sam-ples were ground and passed through a 2mm sieve toremove stones and gravel Fine and coarse roots were alsoremoved Subsequently soil samples collected at 0ndash15 cmdepth were analysed for soil pH soil texture cation-exchangecapacity (CEC cmol(+)kg) available phosphorus (ppm)and exchangeable bases (Ca2+ Mg2+ and K+ cmol(+)kg)Samples from both depth ranges (0ndash15 and 15ndash30 cm) wereanalysed for percentages of organic carbon and total nitrogenStandard methods for soil analysis were used in order toobtain estimates for each of the mentioned variables that canbe compared with results reported in the literature [30ndash34]Soil pH was determined electrometrically using 10 g of soilsample diluted in 25mL distilled water that is using a 1 25ratio of soil to water Soil texture was determined by thehydrometer method and the textural classification was doneby the use of the soil texture triangle [35]
The Bray 1 method was used for the determination ofextractable P for acidic soils with pH less than 7 while theOlsen method was used for soils with pH above 7 (alkalinesoils) The ammonium acetate method at pH 7 was used indetermination ofCEC and by the use of an atomic absorptionspectrophotometer in a UNICAM 919 AA Spectrometer allexchangeable cations (Ca2+ Mg2+ and K+) were determinedSubsamples were finely ground into powder form (lt1mm) inan agate mortar and analyzed for total percentages of organicC and N by dry combustion (Dumas method) in a Leco CNS2000 analyzer [33] Samples for bulk density estimation wereoven-dried at 105∘C to constant weight and the weight wasrecorded (accuracy 001 g) The volume was calculated fromlength and cross-sectional area of the soil core and bulk den-sity was determined as dry weight (g) per unit volume (cm3)Most analyses were conducted at the Laboratory of ForestBiology Sokoine University of Agriculture (SUA) but C andN analyses were conducted at the Soil Science Laboratory attheDepartment of Forest andLandscape (nowDepartment ofGeosciences and Natural Resource Management) Universityof Copenhagen Denmark
25 Data Analysis Based on the data collected the followingmeasures were analysed species composition was expressedthrough species richness and diversity measures forest struc-ture was expressed through stem density basal area andvolume for plant communities species groups and diameterclasses Total species richness was computed as the totalnumber of species across all 35 plots Species diversity wascomputed using Shannonrsquos and Simpsonrsquos Diversity Indices
4 ISRN Biodiversity
[36]The volume of stumpswas calculated as cylinder volumewhile total volume for standing trees was calculated usinga regression equation developed for GVLFR by the authors[37 38] ln(V)= minus84554 + 23236 times ln(Dbh) (R2 = 0983RMSE = 0248 Dbh range 14ndash62 cm 119899 = 104) where V isvolume (m3tree) Dbh is diameter at breast height (ge1 cm)RMSE is the residual standard error R2 is the coefficientof determination n is the total sample size and ln is thenatural logarithmThe ImportanceValue Index (IVI) for eachspecies in each plot was calculated as the sum of relativedensity and dominance (basal area) and expressed in percent[39] Percentage base saturation (BS) was determined asthe ratio of total base cation concentration to CEC whilethe C N ratio was determined using the estimated elementalpercentages of carbon and nitrogen [35]
Using IVI for each species plots were classified byagglomerative hierarchical cluster analysis using Soslashrensenrsquosdistance measure and a group linkage method with flexible120573 of minus050 The 35 plots were ordinated by nonmetricmultidimensional scaling (NMS) using the PC ORDsoftwareversion 60 [40] Topographic variables (elevation and slope)and edaphic variables (pH bulk density texture extractableP CEC exchangeable base cations BS C N ratio Cand N) were correlated with the NMS ordination axesIndicatordominant species in each cluster were determinedusing percentage indicator values (IV) where values of 0correspond to no indication and 100 is perfect indication [4142] The first three to five names of these Indicatordominantspecies with the highest percentage indicator values (IV)constancy and significant indicator values (119875 lt 005) wereused to assign names to the clustersplant community types[29 41 42] The Steinhaus (SoslashrensenCzekanowski) coeffi-cient was used to assess the similaritydissimilarity of thespecies compositions of the plant communities [39]
3 Results
31 Species Richness Including all size categories a totalof 88 species (29 plant families) of standing trees andshrubssmall trees were identified in the GVLFR (Table 1)Trees contributed 60 (21 plant families) and shrubs 40(15 plant families) of the species For stumps a total of 42species (20 plant families) of trees and shrubssmall treeswith basal diameter ranging from 2 to 50 cm were identifiedFor stumps trees contributed 76 (13 plant families) of thespecies while shrubs contributed 24 (10 plant families)All species represented by stumps were also represented bystanding treesshrubs In general tree and shrub speciesfrom the family Caesalpiniaceae contributed most (13) tothe total number of species (standing individuals) followedby those from the families Mimosaceae (10) Rubiaceae(10) Fabaceae (9) and Euphorbiaceae (9) (Table 1)Among standing trees the greatest number of species wasfound in the four plant families Caesalpiniaceae (17)Mimosaceae (15) Fabaceae (13) and Combretaceae (9)while shrubssmall trees included most species from thefamilies Rubiaceae (26) Euphorbiaceae (17) and Cap-paraceae (9) For stumps tree and shrub species from
0 5 10 15 20 25 30 35
0
20
40
60
80
Sites
Spec
ies r
ichn
ess
Figure 2 Species accumulation curve for large individuals (Dbhge5 cm) measured within circular plots with a radius of 15m in theGangalamtumba VLFR Vertical lines indicate standard deviations(range 0ndash49)
the family Mimosaceae contributed most (17) to the totalnumber of species followed by species from the familiesCaesalpiniaceae (14) Combretaceae (10) and Fabaceae(7) Among species categorised as trees the families Cae-salpiniaceae (19) and Mimosaceae (19) contributed equalnumbers of species followed by Combretaceae (13) andFabaceae (9) With respect to shrubs each of the familieswas represented by a single species (10 see Table 1)
When considering different size categories and includingboth trees and shrubs (small sizes Dbhlt 5 cm and large sizesDbh ge 5 cm) a total of 78 species (28 families) were foundamong large sizes with Caesalpiniaceae (13) Mimosaceae(12) and Fabaceae (10) being the most species-rich plantfamilies while among small sizes a total of 69 species (27families) were observed with Rubiaceae (13) Caesalpini-aceae (12) andMimosaceae (10) contributing the greatestnumber of species (Table 1) In general the average number ofspecies per plot was found to be 14 species (range 5ndash24 speciesper plot)
The species accumulation curve (Figure 2) shows thatthe 35 sitesplots used in this study were sufficient to covermuch (but not all) of the variation and species diversity ofthe study area At 35 plots the graph has not yet reached itsasymptotic level but is starting to converge implying that anyfurther increase of sample size would be expected to lead toinclusion of additional rare species However although thesample size was small (35 plots) and does not quite capturethe full woody plant biodiversity of the reserve the results arestill useful for characterizing the treeshrub species diversityand relationships between species and site
32 Species Diversity Shannon-Wiener diversity indices forlarge and small individuals were found to be 344 and 326respectively and the Simpson index for large individuals was
ISRN Biodiversity 5
Table1Ch
ecklist
oftre
eand
shrubspeciesrecordedin
Gangalamtumba
VLF
Rshow
ingfre
quency
()density
(meanplusmnSE
)basalarea(meanplusmnSE
)dispersio
nindex(D
I)and
Impo
rtance
ValueInd
ex(IVI)bothforthe
currentp
opulationof
largeind
ividuals(plotsize=
15m
radiusm
inim
umDbh
=5c
m)a
ndforstumps
(plotsize=
20m
radiusm
inim
umbasald
iameter
=2c
m)SH
shrub
ST
smalltreeandT
tree
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
1Mgiha
Dalbergia
arbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST60
247plusmn69136plusmn037
224
4765
1468plusmn41006plusmn005
170
2Mtono
Commiphora
afric
ana
Burseraceae
T77
110plusmn23159plusmn037
175
1142
1414plusmn06001plusmn001
111
3Mlama
Combretum
molleG
Don
Com
bretaceae
T74
116plusmn27069plusmn015
132
1523
3468plusmn19005plusmn002
307
4Mku
ngugu
Acacia
sp
Mim
osaceae
T23
40plusmn22102plusmn054
111
2873
920plusmn14001plusmn001
65
5Mkw
eeBrachyste
gia
spiciform
isBe
nth
Caesalpiniaceae
T37
58plusmn20118plusmn034
110
1731
2661plusmn24020plusmn009
308
6Mkalala
Albiziapetersiana
(Belle)O
liv
Mim
osaceae
T40
80plusmn30055plusmn020
85
2711
636plusmn29005plusmn004
94
7Mlyasenga
Combretum
zeyheri
Soun
dCom
bretaceae
T43
60plusmn20030plusmn011
741597
2020plusmn08001plusmn001
47
8Mkombivawo
Bauh
iniapetersiana
Caesalpiniaceae
SH34
54plusmn21029plusmn010
64
2000
9Muguvani
Markham
iaobtusifolia
Bign
oniaceae
T60
65plusmn13038plusmn010
64
647
605plusmn03
00
23
10Mdeke
Hym
enodictyon
parvifoliu
mOliv
Rubiaceae
ST31
50plusmn24048plusmn024
58
2749
302plusmn02
00
17
11Mub
wegele
Sclerocarryabirrea
sbspbirr
eaAnacardiaceae
T34
21plusmn6061plusmn023
58
441
605plusmn03001plusmn001
28
12Mmem
enam
ene
Margaritaria
discoidea(Bail)
Euph
orbiaceae
SH51
68plusmn24032plusmn013
57
2073
307plusmn07
00
06
13Mkambala
Acaciamellifera
(Vahl)Be
nth
Mim
osaceae
T29
14plusmn5049plusmn019
50
359
302plusmn02
00
08
14Mugegere
Dich
rosta
chys
cinerea
(L)Wight
ampArn
Mim
osaceae
ST31
53plusmn26017plusmn009
47
3086
2959plusmn19003plusmn002
178
15Mbata
AcaciaseyalD
elvar
seyal
Mim
osaceae
T34
30plusmn10019plusmn006
45
887
311plusmn11001plusmn001
46
16Mgulumo
Lann
easchw
einfurthiiAnacardiaceae
T43
24plusmn7032plusmn009
39
544
907plusmn04002plusmn001
22
17Muyom
boBrachyste
giaboehmii
Caesalpiniaceae
T11
11plusmn7019plusmn015
38
1309
607plusmn05001plusmn001
22
18Mmulim
uli
Cassiaabbreviata
Caesalpiniaceae
T34
31plusmn13017plusmn007
36
1353
916plusmn12001plusmn001
49
19Mdavi
Cordiasin
ensis
Lam
Boraginaceae
ST6
23plusmn23011plusmn010
34
5498
302plusmn02
00
06
6 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
20Mdw
endw
eTerm
inaliabrow
nii
Com
bretaceae
T17
14plusmn8019plusmn009
32
1129
305plusmn05
00
12
21Mfilafila
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T31
25plusmn8015plusmn005
30
646
605plusmn03
00
35
22Mkw
ata
Codyladensiflora
Caesalpiniaceae
T9
4plusmn2033plusmn019
28
282
334plusmn34004plusmn004
83
23Mgung
aAc
aciaabyssin
ica(H
ochst)
Mim
osaceae
T17
12plusmn7014plusmn007
24
1140
614plusmn12001plusmn001
39
24Mkole
Grew
iabicolor
Tiliaceae
ST26
21plusmn10008plusmn003
21
1078
605plusmn03
00
15
25Mparapand
eStrychnosp
otatorum
Lf
Loganiaceae
T23
23plusmn11014plusmn009
20
1331
607plusmn05
00
17
26Kitim
bwi
Orm
ocarpum
kirkii
Fabaceae
SH29
18plusmn6007plusmn003
19492
27Mtabagila
Lonchocarpus
capassa
Fabaceae
T26
11plusmn5014plusmn006
17550
302plusmn02
00
04
28Muw
otapon
ziOzoroainsig
nsssp
retic
ulata
Anacardiaceae
T14
10plusmn5010plusmn005
16564
1411plusmn05001plusmn000
31
29Msanzi
Gardeniaresin
iflua
Hiern
Rubiaceae
ST9
13plusmn8008plusmn005
161229
30Mkoga
Vitexpayos
Verbenaceae
ST17
12plusmn5011plusmn004
15605
302plusmn02
00
02
31Mpelem
eleGr
ewiaforbesiiHaw
Ex
Mast
Tiliaceae
ST14
16plusmn9005plusmn002
147
1246
32Mny
wenyw
eeDalbergiaboehmii
Fabaceae
T14
11plusmn7009plusmn007
151221
33Mulagavega
Albiziaam
ara(Roxb)
Boiv
Mim
osaceae
T14
9plusmn5004plusmn003
14712
34Mkola
Afzelia
quan
zensis
Caesalpiniaceae
T11
6plusmn3015plusmn009
14532
35Mugusi
Brachyste
giamanga
Caesalpiniaceae
T20
11plusmn6010plusmn007
13824
618plusmn13002plusmn002
25
36Mwaham
aShrebera
trichoclada
Oleaceae
T17
11plusmn6005plusmn002
12697
309plusmn09001plusmn001
15
37Mpingo
Dalbergia
mela
noxylon
Fabaceae
T11
11plusmn9004plusmn003
121655
314plusmn14001plusmn001
26
38Mub
aya
Strychnosinn
ocua
Loganiaceae
T17
8plusmn4011plusmn008
12375
302plusmn02001plusmn001
06
39Msisina
AlbiziaharveyiF
ournMim
osaceae
T14
5plusmn3008plusmn005
11359
305plusmn05002plusmn002
15
40Mlim
boEu
phorbiacuneata
Valh
Euph
orbiaceae
ST14
12plusmn6004plusmn002
11699
41Mwam
biElaeodendron
buchan
annii
Cela
steraceae
T3
8plusmn8003plusmn003
102000
42Mvembadand
aXe
roderris
stuhlmannii
Fabaceae
T14
4plusmn2011plusmn005
09
191
43Mtelela
Brachyste
giabu
ssei
Harms
Caesalpiniaceae
T6
2plusmn1012plusmn010
09
253
ISRN Biodiversity 7
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
44Mteresi
Prem
naholstiiGurke
Verbenaceae
ST9
8plusmn5003plusmn002
09
899
45Mkongolo
Commiphora
ugogensis
Burseraceae
T6
1plusmn1008plusmn006
08
097
302plusmn02001plusmn001
09
46Muw
isaBo
sciaangustifolia
A
Rich
varangustifo
liaCa
pparaceae
T17
8plusmn4004plusmn002
08
426
47Mnyalup
uko
Canthium
pseudoverticillatum
Hien
Rubiaceae
ST20
8plusmn4004plusmn002
07
414
48Mnyaluh
anga
Brideliascleu
roneura
MuellArg
Euph
orbiaceae
ST14
6plusmn3004plusmn002
07
341
49Mdide
Manilkaramochisia
(Bak)Dub
ard
Sapo
taceae
T9
5plusmn4002plusmn001
07
793
50Muh
emi
Erythrinaabyssin
icaFabaceae
T6
1plusmn1007plusmn006
07
163
51Msambarawe
Vangueria
infausta
BurchSspRo
tund
ataRu
biaceae
ST6
8plusmn5002plusmn002
06
925
52Mhehefu
Allophylu
sferrugineus
Taub
Sapind
aceae
SH9
5plusmn3002plusmn001
05
540
314plusmn14001plusmn001
14
53Mnyengrsquoenyengrsquoe
Excoecariabu
ssei
(Pax)
Euph
orbiaceae
ST11
4plusmn2001plusmn001
05
321
54Muganga
Berchemiadiscolor
Rham
naceae
T6
4plusmn3004plusmn003
04
717
55Mning
aPterocarpu
sangolensis
Fabaceae
T6
2plusmn1005plusmn004
04
253
309plusmn09006plusmn006
25
56Mtanang
we
Zizip
husm
ucronata
Rham
naceae
T3
4plusmn4003plusmn003
04
900
57Muh
ekele
Eucle
adivinorum
Hiern
Ebenaceae
ST3
3plusmn3001plusmn001
04
800
302plusmn02
00
49
58Mub
umila
Cassipourea
mollis
(REfr)Alstom
Rhizop
horaceae
T9
4plusmn3002plusmn001
04
526
302plusmn02
00
04
59Mkw
ambe
Flueggea
virosa
Willd
Euph
orbiaceae
SH6
3plusmn2003plusmn003
03
406
60Kivang
aZa
nhaafric
ana
(Radlk)Ex
ell
Sapind
aceae
T9
2plusmn1001plusmn001
02
212
302plusmn02
00
02
61Mning
amaji
Pterocarpu
stinctorius
Fabaceae
T3
0002plusmn002
02
100
62Mtosi
MaeruatriphyllaA
Rich
Capp
araceae
ST3
2plusmn2001plusmn001
02
600
63Mugosa
Ehretia
amoena
Boraginaceae
ST9
1plusmn1
00
01
094
64Mnyon
gamem
beStegan
otaenia
araliacea
Apiaceae
T6
1plusmn1
001
01
163
65Mvelevele
Vernoniaam
ygdalin
aAs
teraceae
SH3
2plusmn2001plusmn001
01
400
66Mpo
ngolo
Catunaregam
spinosa
(Thun
b)
Rubiaceae
ST3
1plusmn1001plusmn001
01
200
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
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Advances in
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Environmental Chemistry
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ClimatologyJournal of
ISRN Biodiversity 3
the ward of Kiwele The forest vegetation has been describedas dry Miombo woodland similar to the dry woodlandtype described from other countries such as Zimbabwe andMozambique [1] The forest is located in a relatively flat areaat an elevation of 850ndash1300 metres above sea level Theregion is characterised by distinct wet and dry seasons withalmost no rain in the four months of June-September andabout 80 of the annual precipitation falling in DecemberndashMarch Average rainfall data covering the last 50 years (1960ndash2010) were obtained from the meteorological station at Nduliairport which is located about 30 km from the forest andindicate that the area receives an average annual precipitation(mean plusmn standard error) of 617plusmn17mm (448ndash1085mm)Themean annual temperature is 198∘C and the average relativehumidities at 0600 and 1200GMT are 539 and 514respectively
The GVLFR is a production forest which is managedby the Mfyome village under a community-based forestmanagement (CBFM) arrangement established in 2002 com-pare above The primary economic activity in Mfyome issmallholder agriculture and the main economic uses of theforest are production of timber charcoal and firewood [20]The woodland is also used for grazing and is an importantsource of subsistence products such as firewood constructionmaterials fruit mushrooms wild vegetables and medicinalplants [18]
22 Vegetation Survey The field survey was conducted inJuly and August 2009 and involved establishment of a totalof 35 permanent nested circular sample plots distributedacross the entire forest Plots were established along transectlines and the distance between plots was approximately2 km (Figure 1) The radii of the nested circular plots were5m (00079 ha) 15m (00707 ha) and 20m (01257 ha) Thefollowing parameters were recorded within each of the 35plots within the 5m radius all small trees and shrubs(lt150 cm tall or ge150 cm but lt1 cm Dbh) were counted andtheir species were identified and medium-size trees andshrubs (ge1 cm Dbh but lt5 cm Dbh) were identified andmeasured with respect to diameter Within 15m radius thespecies were identified and the diameter was measured for alllarge trees and shrubs with Dbh ge5 cm Within 20m radiusall stumps of trees and shrubs were identified to specieslevel and measured for diameter 20 cm above ground Initialidentification of species for both standing treesshrubs andstumps relied on the knowledge of local botanists (usinglocal vernacular species names and features such as colorof the bark smell and leaves) and was later confirmed bybotanists from Tanzania Forest Research Institute (TAFORI)based at Lushoto Silviculture Research Station For speciesthat were difficult to identify in the field samples weretaken to the herbarium at Lushoto for reidentification Othermeasurements taken within the plots were geographicallocation (UTMcoordinates) and elevation (m) usingGPS andslope () using a Suunto clinometer
23 Soil Sampling Soil samples were collected from fivepoints which were located at the centre of the plot and 10mfrom the centre in the four cardinal directions (North East
South and West) At each point two samples were taken 0ndash15 cm and 15ndash30 cm below the surfaceThe five samples takenfrom each depth range were mixed in the field to obtain onecomposite sample per depth range and plot Thus 70 soilsamples (35 from each depth range) were collected from the35 plots In addition a soil core device with an inner diameterof 5 cm and a length of 5 cm was used for extracting soilbulk density samples from the centre of each plot and at eachdepth Hence a total of 70 samples were collected for bulkdensity determination
24 Laboratory Analyses In the laboratory all soil sam-ples were ground and passed through a 2mm sieve toremove stones and gravel Fine and coarse roots were alsoremoved Subsequently soil samples collected at 0ndash15 cmdepth were analysed for soil pH soil texture cation-exchangecapacity (CEC cmol(+)kg) available phosphorus (ppm)and exchangeable bases (Ca2+ Mg2+ and K+ cmol(+)kg)Samples from both depth ranges (0ndash15 and 15ndash30 cm) wereanalysed for percentages of organic carbon and total nitrogenStandard methods for soil analysis were used in order toobtain estimates for each of the mentioned variables that canbe compared with results reported in the literature [30ndash34]Soil pH was determined electrometrically using 10 g of soilsample diluted in 25mL distilled water that is using a 1 25ratio of soil to water Soil texture was determined by thehydrometer method and the textural classification was doneby the use of the soil texture triangle [35]
The Bray 1 method was used for the determination ofextractable P for acidic soils with pH less than 7 while theOlsen method was used for soils with pH above 7 (alkalinesoils) The ammonium acetate method at pH 7 was used indetermination ofCEC and by the use of an atomic absorptionspectrophotometer in a UNICAM 919 AA Spectrometer allexchangeable cations (Ca2+ Mg2+ and K+) were determinedSubsamples were finely ground into powder form (lt1mm) inan agate mortar and analyzed for total percentages of organicC and N by dry combustion (Dumas method) in a Leco CNS2000 analyzer [33] Samples for bulk density estimation wereoven-dried at 105∘C to constant weight and the weight wasrecorded (accuracy 001 g) The volume was calculated fromlength and cross-sectional area of the soil core and bulk den-sity was determined as dry weight (g) per unit volume (cm3)Most analyses were conducted at the Laboratory of ForestBiology Sokoine University of Agriculture (SUA) but C andN analyses were conducted at the Soil Science Laboratory attheDepartment of Forest andLandscape (nowDepartment ofGeosciences and Natural Resource Management) Universityof Copenhagen Denmark
25 Data Analysis Based on the data collected the followingmeasures were analysed species composition was expressedthrough species richness and diversity measures forest struc-ture was expressed through stem density basal area andvolume for plant communities species groups and diameterclasses Total species richness was computed as the totalnumber of species across all 35 plots Species diversity wascomputed using Shannonrsquos and Simpsonrsquos Diversity Indices
4 ISRN Biodiversity
[36]The volume of stumpswas calculated as cylinder volumewhile total volume for standing trees was calculated usinga regression equation developed for GVLFR by the authors[37 38] ln(V)= minus84554 + 23236 times ln(Dbh) (R2 = 0983RMSE = 0248 Dbh range 14ndash62 cm 119899 = 104) where V isvolume (m3tree) Dbh is diameter at breast height (ge1 cm)RMSE is the residual standard error R2 is the coefficientof determination n is the total sample size and ln is thenatural logarithmThe ImportanceValue Index (IVI) for eachspecies in each plot was calculated as the sum of relativedensity and dominance (basal area) and expressed in percent[39] Percentage base saturation (BS) was determined asthe ratio of total base cation concentration to CEC whilethe C N ratio was determined using the estimated elementalpercentages of carbon and nitrogen [35]
Using IVI for each species plots were classified byagglomerative hierarchical cluster analysis using Soslashrensenrsquosdistance measure and a group linkage method with flexible120573 of minus050 The 35 plots were ordinated by nonmetricmultidimensional scaling (NMS) using the PC ORDsoftwareversion 60 [40] Topographic variables (elevation and slope)and edaphic variables (pH bulk density texture extractableP CEC exchangeable base cations BS C N ratio Cand N) were correlated with the NMS ordination axesIndicatordominant species in each cluster were determinedusing percentage indicator values (IV) where values of 0correspond to no indication and 100 is perfect indication [4142] The first three to five names of these Indicatordominantspecies with the highest percentage indicator values (IV)constancy and significant indicator values (119875 lt 005) wereused to assign names to the clustersplant community types[29 41 42] The Steinhaus (SoslashrensenCzekanowski) coeffi-cient was used to assess the similaritydissimilarity of thespecies compositions of the plant communities [39]
3 Results
31 Species Richness Including all size categories a totalof 88 species (29 plant families) of standing trees andshrubssmall trees were identified in the GVLFR (Table 1)Trees contributed 60 (21 plant families) and shrubs 40(15 plant families) of the species For stumps a total of 42species (20 plant families) of trees and shrubssmall treeswith basal diameter ranging from 2 to 50 cm were identifiedFor stumps trees contributed 76 (13 plant families) of thespecies while shrubs contributed 24 (10 plant families)All species represented by stumps were also represented bystanding treesshrubs In general tree and shrub speciesfrom the family Caesalpiniaceae contributed most (13) tothe total number of species (standing individuals) followedby those from the families Mimosaceae (10) Rubiaceae(10) Fabaceae (9) and Euphorbiaceae (9) (Table 1)Among standing trees the greatest number of species wasfound in the four plant families Caesalpiniaceae (17)Mimosaceae (15) Fabaceae (13) and Combretaceae (9)while shrubssmall trees included most species from thefamilies Rubiaceae (26) Euphorbiaceae (17) and Cap-paraceae (9) For stumps tree and shrub species from
0 5 10 15 20 25 30 35
0
20
40
60
80
Sites
Spec
ies r
ichn
ess
Figure 2 Species accumulation curve for large individuals (Dbhge5 cm) measured within circular plots with a radius of 15m in theGangalamtumba VLFR Vertical lines indicate standard deviations(range 0ndash49)
the family Mimosaceae contributed most (17) to the totalnumber of species followed by species from the familiesCaesalpiniaceae (14) Combretaceae (10) and Fabaceae(7) Among species categorised as trees the families Cae-salpiniaceae (19) and Mimosaceae (19) contributed equalnumbers of species followed by Combretaceae (13) andFabaceae (9) With respect to shrubs each of the familieswas represented by a single species (10 see Table 1)
When considering different size categories and includingboth trees and shrubs (small sizes Dbhlt 5 cm and large sizesDbh ge 5 cm) a total of 78 species (28 families) were foundamong large sizes with Caesalpiniaceae (13) Mimosaceae(12) and Fabaceae (10) being the most species-rich plantfamilies while among small sizes a total of 69 species (27families) were observed with Rubiaceae (13) Caesalpini-aceae (12) andMimosaceae (10) contributing the greatestnumber of species (Table 1) In general the average number ofspecies per plot was found to be 14 species (range 5ndash24 speciesper plot)
The species accumulation curve (Figure 2) shows thatthe 35 sitesplots used in this study were sufficient to covermuch (but not all) of the variation and species diversity ofthe study area At 35 plots the graph has not yet reached itsasymptotic level but is starting to converge implying that anyfurther increase of sample size would be expected to lead toinclusion of additional rare species However although thesample size was small (35 plots) and does not quite capturethe full woody plant biodiversity of the reserve the results arestill useful for characterizing the treeshrub species diversityand relationships between species and site
32 Species Diversity Shannon-Wiener diversity indices forlarge and small individuals were found to be 344 and 326respectively and the Simpson index for large individuals was
ISRN Biodiversity 5
Table1Ch
ecklist
oftre
eand
shrubspeciesrecordedin
Gangalamtumba
VLF
Rshow
ingfre
quency
()density
(meanplusmnSE
)basalarea(meanplusmnSE
)dispersio
nindex(D
I)and
Impo
rtance
ValueInd
ex(IVI)bothforthe
currentp
opulationof
largeind
ividuals(plotsize=
15m
radiusm
inim
umDbh
=5c
m)a
ndforstumps
(plotsize=
20m
radiusm
inim
umbasald
iameter
=2c
m)SH
shrub
ST
smalltreeandT
tree
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
1Mgiha
Dalbergia
arbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST60
247plusmn69136plusmn037
224
4765
1468plusmn41006plusmn005
170
2Mtono
Commiphora
afric
ana
Burseraceae
T77
110plusmn23159plusmn037
175
1142
1414plusmn06001plusmn001
111
3Mlama
Combretum
molleG
Don
Com
bretaceae
T74
116plusmn27069plusmn015
132
1523
3468plusmn19005plusmn002
307
4Mku
ngugu
Acacia
sp
Mim
osaceae
T23
40plusmn22102plusmn054
111
2873
920plusmn14001plusmn001
65
5Mkw
eeBrachyste
gia
spiciform
isBe
nth
Caesalpiniaceae
T37
58plusmn20118plusmn034
110
1731
2661plusmn24020plusmn009
308
6Mkalala
Albiziapetersiana
(Belle)O
liv
Mim
osaceae
T40
80plusmn30055plusmn020
85
2711
636plusmn29005plusmn004
94
7Mlyasenga
Combretum
zeyheri
Soun
dCom
bretaceae
T43
60plusmn20030plusmn011
741597
2020plusmn08001plusmn001
47
8Mkombivawo
Bauh
iniapetersiana
Caesalpiniaceae
SH34
54plusmn21029plusmn010
64
2000
9Muguvani
Markham
iaobtusifolia
Bign
oniaceae
T60
65plusmn13038plusmn010
64
647
605plusmn03
00
23
10Mdeke
Hym
enodictyon
parvifoliu
mOliv
Rubiaceae
ST31
50plusmn24048plusmn024
58
2749
302plusmn02
00
17
11Mub
wegele
Sclerocarryabirrea
sbspbirr
eaAnacardiaceae
T34
21plusmn6061plusmn023
58
441
605plusmn03001plusmn001
28
12Mmem
enam
ene
Margaritaria
discoidea(Bail)
Euph
orbiaceae
SH51
68plusmn24032plusmn013
57
2073
307plusmn07
00
06
13Mkambala
Acaciamellifera
(Vahl)Be
nth
Mim
osaceae
T29
14plusmn5049plusmn019
50
359
302plusmn02
00
08
14Mugegere
Dich
rosta
chys
cinerea
(L)Wight
ampArn
Mim
osaceae
ST31
53plusmn26017plusmn009
47
3086
2959plusmn19003plusmn002
178
15Mbata
AcaciaseyalD
elvar
seyal
Mim
osaceae
T34
30plusmn10019plusmn006
45
887
311plusmn11001plusmn001
46
16Mgulumo
Lann
easchw
einfurthiiAnacardiaceae
T43
24plusmn7032plusmn009
39
544
907plusmn04002plusmn001
22
17Muyom
boBrachyste
giaboehmii
Caesalpiniaceae
T11
11plusmn7019plusmn015
38
1309
607plusmn05001plusmn001
22
18Mmulim
uli
Cassiaabbreviata
Caesalpiniaceae
T34
31plusmn13017plusmn007
36
1353
916plusmn12001plusmn001
49
19Mdavi
Cordiasin
ensis
Lam
Boraginaceae
ST6
23plusmn23011plusmn010
34
5498
302plusmn02
00
06
6 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
20Mdw
endw
eTerm
inaliabrow
nii
Com
bretaceae
T17
14plusmn8019plusmn009
32
1129
305plusmn05
00
12
21Mfilafila
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T31
25plusmn8015plusmn005
30
646
605plusmn03
00
35
22Mkw
ata
Codyladensiflora
Caesalpiniaceae
T9
4plusmn2033plusmn019
28
282
334plusmn34004plusmn004
83
23Mgung
aAc
aciaabyssin
ica(H
ochst)
Mim
osaceae
T17
12plusmn7014plusmn007
24
1140
614plusmn12001plusmn001
39
24Mkole
Grew
iabicolor
Tiliaceae
ST26
21plusmn10008plusmn003
21
1078
605plusmn03
00
15
25Mparapand
eStrychnosp
otatorum
Lf
Loganiaceae
T23
23plusmn11014plusmn009
20
1331
607plusmn05
00
17
26Kitim
bwi
Orm
ocarpum
kirkii
Fabaceae
SH29
18plusmn6007plusmn003
19492
27Mtabagila
Lonchocarpus
capassa
Fabaceae
T26
11plusmn5014plusmn006
17550
302plusmn02
00
04
28Muw
otapon
ziOzoroainsig
nsssp
retic
ulata
Anacardiaceae
T14
10plusmn5010plusmn005
16564
1411plusmn05001plusmn000
31
29Msanzi
Gardeniaresin
iflua
Hiern
Rubiaceae
ST9
13plusmn8008plusmn005
161229
30Mkoga
Vitexpayos
Verbenaceae
ST17
12plusmn5011plusmn004
15605
302plusmn02
00
02
31Mpelem
eleGr
ewiaforbesiiHaw
Ex
Mast
Tiliaceae
ST14
16plusmn9005plusmn002
147
1246
32Mny
wenyw
eeDalbergiaboehmii
Fabaceae
T14
11plusmn7009plusmn007
151221
33Mulagavega
Albiziaam
ara(Roxb)
Boiv
Mim
osaceae
T14
9plusmn5004plusmn003
14712
34Mkola
Afzelia
quan
zensis
Caesalpiniaceae
T11
6plusmn3015plusmn009
14532
35Mugusi
Brachyste
giamanga
Caesalpiniaceae
T20
11plusmn6010plusmn007
13824
618plusmn13002plusmn002
25
36Mwaham
aShrebera
trichoclada
Oleaceae
T17
11plusmn6005plusmn002
12697
309plusmn09001plusmn001
15
37Mpingo
Dalbergia
mela
noxylon
Fabaceae
T11
11plusmn9004plusmn003
121655
314plusmn14001plusmn001
26
38Mub
aya
Strychnosinn
ocua
Loganiaceae
T17
8plusmn4011plusmn008
12375
302plusmn02001plusmn001
06
39Msisina
AlbiziaharveyiF
ournMim
osaceae
T14
5plusmn3008plusmn005
11359
305plusmn05002plusmn002
15
40Mlim
boEu
phorbiacuneata
Valh
Euph
orbiaceae
ST14
12plusmn6004plusmn002
11699
41Mwam
biElaeodendron
buchan
annii
Cela
steraceae
T3
8plusmn8003plusmn003
102000
42Mvembadand
aXe
roderris
stuhlmannii
Fabaceae
T14
4plusmn2011plusmn005
09
191
43Mtelela
Brachyste
giabu
ssei
Harms
Caesalpiniaceae
T6
2plusmn1012plusmn010
09
253
ISRN Biodiversity 7
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
44Mteresi
Prem
naholstiiGurke
Verbenaceae
ST9
8plusmn5003plusmn002
09
899
45Mkongolo
Commiphora
ugogensis
Burseraceae
T6
1plusmn1008plusmn006
08
097
302plusmn02001plusmn001
09
46Muw
isaBo
sciaangustifolia
A
Rich
varangustifo
liaCa
pparaceae
T17
8plusmn4004plusmn002
08
426
47Mnyalup
uko
Canthium
pseudoverticillatum
Hien
Rubiaceae
ST20
8plusmn4004plusmn002
07
414
48Mnyaluh
anga
Brideliascleu
roneura
MuellArg
Euph
orbiaceae
ST14
6plusmn3004plusmn002
07
341
49Mdide
Manilkaramochisia
(Bak)Dub
ard
Sapo
taceae
T9
5plusmn4002plusmn001
07
793
50Muh
emi
Erythrinaabyssin
icaFabaceae
T6
1plusmn1007plusmn006
07
163
51Msambarawe
Vangueria
infausta
BurchSspRo
tund
ataRu
biaceae
ST6
8plusmn5002plusmn002
06
925
52Mhehefu
Allophylu
sferrugineus
Taub
Sapind
aceae
SH9
5plusmn3002plusmn001
05
540
314plusmn14001plusmn001
14
53Mnyengrsquoenyengrsquoe
Excoecariabu
ssei
(Pax)
Euph
orbiaceae
ST11
4plusmn2001plusmn001
05
321
54Muganga
Berchemiadiscolor
Rham
naceae
T6
4plusmn3004plusmn003
04
717
55Mning
aPterocarpu
sangolensis
Fabaceae
T6
2plusmn1005plusmn004
04
253
309plusmn09006plusmn006
25
56Mtanang
we
Zizip
husm
ucronata
Rham
naceae
T3
4plusmn4003plusmn003
04
900
57Muh
ekele
Eucle
adivinorum
Hiern
Ebenaceae
ST3
3plusmn3001plusmn001
04
800
302plusmn02
00
49
58Mub
umila
Cassipourea
mollis
(REfr)Alstom
Rhizop
horaceae
T9
4plusmn3002plusmn001
04
526
302plusmn02
00
04
59Mkw
ambe
Flueggea
virosa
Willd
Euph
orbiaceae
SH6
3plusmn2003plusmn003
03
406
60Kivang
aZa
nhaafric
ana
(Radlk)Ex
ell
Sapind
aceae
T9
2plusmn1001plusmn001
02
212
302plusmn02
00
02
61Mning
amaji
Pterocarpu
stinctorius
Fabaceae
T3
0002plusmn002
02
100
62Mtosi
MaeruatriphyllaA
Rich
Capp
araceae
ST3
2plusmn2001plusmn001
02
600
63Mugosa
Ehretia
amoena
Boraginaceae
ST9
1plusmn1
00
01
094
64Mnyon
gamem
beStegan
otaenia
araliacea
Apiaceae
T6
1plusmn1
001
01
163
65Mvelevele
Vernoniaam
ygdalin
aAs
teraceae
SH3
2plusmn2001plusmn001
01
400
66Mpo
ngolo
Catunaregam
spinosa
(Thun
b)
Rubiaceae
ST3
1plusmn1001plusmn001
01
200
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
4 ISRN Biodiversity
[36]The volume of stumpswas calculated as cylinder volumewhile total volume for standing trees was calculated usinga regression equation developed for GVLFR by the authors[37 38] ln(V)= minus84554 + 23236 times ln(Dbh) (R2 = 0983RMSE = 0248 Dbh range 14ndash62 cm 119899 = 104) where V isvolume (m3tree) Dbh is diameter at breast height (ge1 cm)RMSE is the residual standard error R2 is the coefficientof determination n is the total sample size and ln is thenatural logarithmThe ImportanceValue Index (IVI) for eachspecies in each plot was calculated as the sum of relativedensity and dominance (basal area) and expressed in percent[39] Percentage base saturation (BS) was determined asthe ratio of total base cation concentration to CEC whilethe C N ratio was determined using the estimated elementalpercentages of carbon and nitrogen [35]
Using IVI for each species plots were classified byagglomerative hierarchical cluster analysis using Soslashrensenrsquosdistance measure and a group linkage method with flexible120573 of minus050 The 35 plots were ordinated by nonmetricmultidimensional scaling (NMS) using the PC ORDsoftwareversion 60 [40] Topographic variables (elevation and slope)and edaphic variables (pH bulk density texture extractableP CEC exchangeable base cations BS C N ratio Cand N) were correlated with the NMS ordination axesIndicatordominant species in each cluster were determinedusing percentage indicator values (IV) where values of 0correspond to no indication and 100 is perfect indication [4142] The first three to five names of these Indicatordominantspecies with the highest percentage indicator values (IV)constancy and significant indicator values (119875 lt 005) wereused to assign names to the clustersplant community types[29 41 42] The Steinhaus (SoslashrensenCzekanowski) coeffi-cient was used to assess the similaritydissimilarity of thespecies compositions of the plant communities [39]
3 Results
31 Species Richness Including all size categories a totalof 88 species (29 plant families) of standing trees andshrubssmall trees were identified in the GVLFR (Table 1)Trees contributed 60 (21 plant families) and shrubs 40(15 plant families) of the species For stumps a total of 42species (20 plant families) of trees and shrubssmall treeswith basal diameter ranging from 2 to 50 cm were identifiedFor stumps trees contributed 76 (13 plant families) of thespecies while shrubs contributed 24 (10 plant families)All species represented by stumps were also represented bystanding treesshrubs In general tree and shrub speciesfrom the family Caesalpiniaceae contributed most (13) tothe total number of species (standing individuals) followedby those from the families Mimosaceae (10) Rubiaceae(10) Fabaceae (9) and Euphorbiaceae (9) (Table 1)Among standing trees the greatest number of species wasfound in the four plant families Caesalpiniaceae (17)Mimosaceae (15) Fabaceae (13) and Combretaceae (9)while shrubssmall trees included most species from thefamilies Rubiaceae (26) Euphorbiaceae (17) and Cap-paraceae (9) For stumps tree and shrub species from
0 5 10 15 20 25 30 35
0
20
40
60
80
Sites
Spec
ies r
ichn
ess
Figure 2 Species accumulation curve for large individuals (Dbhge5 cm) measured within circular plots with a radius of 15m in theGangalamtumba VLFR Vertical lines indicate standard deviations(range 0ndash49)
the family Mimosaceae contributed most (17) to the totalnumber of species followed by species from the familiesCaesalpiniaceae (14) Combretaceae (10) and Fabaceae(7) Among species categorised as trees the families Cae-salpiniaceae (19) and Mimosaceae (19) contributed equalnumbers of species followed by Combretaceae (13) andFabaceae (9) With respect to shrubs each of the familieswas represented by a single species (10 see Table 1)
When considering different size categories and includingboth trees and shrubs (small sizes Dbhlt 5 cm and large sizesDbh ge 5 cm) a total of 78 species (28 families) were foundamong large sizes with Caesalpiniaceae (13) Mimosaceae(12) and Fabaceae (10) being the most species-rich plantfamilies while among small sizes a total of 69 species (27families) were observed with Rubiaceae (13) Caesalpini-aceae (12) andMimosaceae (10) contributing the greatestnumber of species (Table 1) In general the average number ofspecies per plot was found to be 14 species (range 5ndash24 speciesper plot)
The species accumulation curve (Figure 2) shows thatthe 35 sitesplots used in this study were sufficient to covermuch (but not all) of the variation and species diversity ofthe study area At 35 plots the graph has not yet reached itsasymptotic level but is starting to converge implying that anyfurther increase of sample size would be expected to lead toinclusion of additional rare species However although thesample size was small (35 plots) and does not quite capturethe full woody plant biodiversity of the reserve the results arestill useful for characterizing the treeshrub species diversityand relationships between species and site
32 Species Diversity Shannon-Wiener diversity indices forlarge and small individuals were found to be 344 and 326respectively and the Simpson index for large individuals was
ISRN Biodiversity 5
Table1Ch
ecklist
oftre
eand
shrubspeciesrecordedin
Gangalamtumba
VLF
Rshow
ingfre
quency
()density
(meanplusmnSE
)basalarea(meanplusmnSE
)dispersio
nindex(D
I)and
Impo
rtance
ValueInd
ex(IVI)bothforthe
currentp
opulationof
largeind
ividuals(plotsize=
15m
radiusm
inim
umDbh
=5c
m)a
ndforstumps
(plotsize=
20m
radiusm
inim
umbasald
iameter
=2c
m)SH
shrub
ST
smalltreeandT
tree
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
1Mgiha
Dalbergia
arbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST60
247plusmn69136plusmn037
224
4765
1468plusmn41006plusmn005
170
2Mtono
Commiphora
afric
ana
Burseraceae
T77
110plusmn23159plusmn037
175
1142
1414plusmn06001plusmn001
111
3Mlama
Combretum
molleG
Don
Com
bretaceae
T74
116plusmn27069plusmn015
132
1523
3468plusmn19005plusmn002
307
4Mku
ngugu
Acacia
sp
Mim
osaceae
T23
40plusmn22102plusmn054
111
2873
920plusmn14001plusmn001
65
5Mkw
eeBrachyste
gia
spiciform
isBe
nth
Caesalpiniaceae
T37
58plusmn20118plusmn034
110
1731
2661plusmn24020plusmn009
308
6Mkalala
Albiziapetersiana
(Belle)O
liv
Mim
osaceae
T40
80plusmn30055plusmn020
85
2711
636plusmn29005plusmn004
94
7Mlyasenga
Combretum
zeyheri
Soun
dCom
bretaceae
T43
60plusmn20030plusmn011
741597
2020plusmn08001plusmn001
47
8Mkombivawo
Bauh
iniapetersiana
Caesalpiniaceae
SH34
54plusmn21029plusmn010
64
2000
9Muguvani
Markham
iaobtusifolia
Bign
oniaceae
T60
65plusmn13038plusmn010
64
647
605plusmn03
00
23
10Mdeke
Hym
enodictyon
parvifoliu
mOliv
Rubiaceae
ST31
50plusmn24048plusmn024
58
2749
302plusmn02
00
17
11Mub
wegele
Sclerocarryabirrea
sbspbirr
eaAnacardiaceae
T34
21plusmn6061plusmn023
58
441
605plusmn03001plusmn001
28
12Mmem
enam
ene
Margaritaria
discoidea(Bail)
Euph
orbiaceae
SH51
68plusmn24032plusmn013
57
2073
307plusmn07
00
06
13Mkambala
Acaciamellifera
(Vahl)Be
nth
Mim
osaceae
T29
14plusmn5049plusmn019
50
359
302plusmn02
00
08
14Mugegere
Dich
rosta
chys
cinerea
(L)Wight
ampArn
Mim
osaceae
ST31
53plusmn26017plusmn009
47
3086
2959plusmn19003plusmn002
178
15Mbata
AcaciaseyalD
elvar
seyal
Mim
osaceae
T34
30plusmn10019plusmn006
45
887
311plusmn11001plusmn001
46
16Mgulumo
Lann
easchw
einfurthiiAnacardiaceae
T43
24plusmn7032plusmn009
39
544
907plusmn04002plusmn001
22
17Muyom
boBrachyste
giaboehmii
Caesalpiniaceae
T11
11plusmn7019plusmn015
38
1309
607plusmn05001plusmn001
22
18Mmulim
uli
Cassiaabbreviata
Caesalpiniaceae
T34
31plusmn13017plusmn007
36
1353
916plusmn12001plusmn001
49
19Mdavi
Cordiasin
ensis
Lam
Boraginaceae
ST6
23plusmn23011plusmn010
34
5498
302plusmn02
00
06
6 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
20Mdw
endw
eTerm
inaliabrow
nii
Com
bretaceae
T17
14plusmn8019plusmn009
32
1129
305plusmn05
00
12
21Mfilafila
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T31
25plusmn8015plusmn005
30
646
605plusmn03
00
35
22Mkw
ata
Codyladensiflora
Caesalpiniaceae
T9
4plusmn2033plusmn019
28
282
334plusmn34004plusmn004
83
23Mgung
aAc
aciaabyssin
ica(H
ochst)
Mim
osaceae
T17
12plusmn7014plusmn007
24
1140
614plusmn12001plusmn001
39
24Mkole
Grew
iabicolor
Tiliaceae
ST26
21plusmn10008plusmn003
21
1078
605plusmn03
00
15
25Mparapand
eStrychnosp
otatorum
Lf
Loganiaceae
T23
23plusmn11014plusmn009
20
1331
607plusmn05
00
17
26Kitim
bwi
Orm
ocarpum
kirkii
Fabaceae
SH29
18plusmn6007plusmn003
19492
27Mtabagila
Lonchocarpus
capassa
Fabaceae
T26
11plusmn5014plusmn006
17550
302plusmn02
00
04
28Muw
otapon
ziOzoroainsig
nsssp
retic
ulata
Anacardiaceae
T14
10plusmn5010plusmn005
16564
1411plusmn05001plusmn000
31
29Msanzi
Gardeniaresin
iflua
Hiern
Rubiaceae
ST9
13plusmn8008plusmn005
161229
30Mkoga
Vitexpayos
Verbenaceae
ST17
12plusmn5011plusmn004
15605
302plusmn02
00
02
31Mpelem
eleGr
ewiaforbesiiHaw
Ex
Mast
Tiliaceae
ST14
16plusmn9005plusmn002
147
1246
32Mny
wenyw
eeDalbergiaboehmii
Fabaceae
T14
11plusmn7009plusmn007
151221
33Mulagavega
Albiziaam
ara(Roxb)
Boiv
Mim
osaceae
T14
9plusmn5004plusmn003
14712
34Mkola
Afzelia
quan
zensis
Caesalpiniaceae
T11
6plusmn3015plusmn009
14532
35Mugusi
Brachyste
giamanga
Caesalpiniaceae
T20
11plusmn6010plusmn007
13824
618plusmn13002plusmn002
25
36Mwaham
aShrebera
trichoclada
Oleaceae
T17
11plusmn6005plusmn002
12697
309plusmn09001plusmn001
15
37Mpingo
Dalbergia
mela
noxylon
Fabaceae
T11
11plusmn9004plusmn003
121655
314plusmn14001plusmn001
26
38Mub
aya
Strychnosinn
ocua
Loganiaceae
T17
8plusmn4011plusmn008
12375
302plusmn02001plusmn001
06
39Msisina
AlbiziaharveyiF
ournMim
osaceae
T14
5plusmn3008plusmn005
11359
305plusmn05002plusmn002
15
40Mlim
boEu
phorbiacuneata
Valh
Euph
orbiaceae
ST14
12plusmn6004plusmn002
11699
41Mwam
biElaeodendron
buchan
annii
Cela
steraceae
T3
8plusmn8003plusmn003
102000
42Mvembadand
aXe
roderris
stuhlmannii
Fabaceae
T14
4plusmn2011plusmn005
09
191
43Mtelela
Brachyste
giabu
ssei
Harms
Caesalpiniaceae
T6
2plusmn1012plusmn010
09
253
ISRN Biodiversity 7
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
44Mteresi
Prem
naholstiiGurke
Verbenaceae
ST9
8plusmn5003plusmn002
09
899
45Mkongolo
Commiphora
ugogensis
Burseraceae
T6
1plusmn1008plusmn006
08
097
302plusmn02001plusmn001
09
46Muw
isaBo
sciaangustifolia
A
Rich
varangustifo
liaCa
pparaceae
T17
8plusmn4004plusmn002
08
426
47Mnyalup
uko
Canthium
pseudoverticillatum
Hien
Rubiaceae
ST20
8plusmn4004plusmn002
07
414
48Mnyaluh
anga
Brideliascleu
roneura
MuellArg
Euph
orbiaceae
ST14
6plusmn3004plusmn002
07
341
49Mdide
Manilkaramochisia
(Bak)Dub
ard
Sapo
taceae
T9
5plusmn4002plusmn001
07
793
50Muh
emi
Erythrinaabyssin
icaFabaceae
T6
1plusmn1007plusmn006
07
163
51Msambarawe
Vangueria
infausta
BurchSspRo
tund
ataRu
biaceae
ST6
8plusmn5002plusmn002
06
925
52Mhehefu
Allophylu
sferrugineus
Taub
Sapind
aceae
SH9
5plusmn3002plusmn001
05
540
314plusmn14001plusmn001
14
53Mnyengrsquoenyengrsquoe
Excoecariabu
ssei
(Pax)
Euph
orbiaceae
ST11
4plusmn2001plusmn001
05
321
54Muganga
Berchemiadiscolor
Rham
naceae
T6
4plusmn3004plusmn003
04
717
55Mning
aPterocarpu
sangolensis
Fabaceae
T6
2plusmn1005plusmn004
04
253
309plusmn09006plusmn006
25
56Mtanang
we
Zizip
husm
ucronata
Rham
naceae
T3
4plusmn4003plusmn003
04
900
57Muh
ekele
Eucle
adivinorum
Hiern
Ebenaceae
ST3
3plusmn3001plusmn001
04
800
302plusmn02
00
49
58Mub
umila
Cassipourea
mollis
(REfr)Alstom
Rhizop
horaceae
T9
4plusmn3002plusmn001
04
526
302plusmn02
00
04
59Mkw
ambe
Flueggea
virosa
Willd
Euph
orbiaceae
SH6
3plusmn2003plusmn003
03
406
60Kivang
aZa
nhaafric
ana
(Radlk)Ex
ell
Sapind
aceae
T9
2plusmn1001plusmn001
02
212
302plusmn02
00
02
61Mning
amaji
Pterocarpu
stinctorius
Fabaceae
T3
0002plusmn002
02
100
62Mtosi
MaeruatriphyllaA
Rich
Capp
araceae
ST3
2plusmn2001plusmn001
02
600
63Mugosa
Ehretia
amoena
Boraginaceae
ST9
1plusmn1
00
01
094
64Mnyon
gamem
beStegan
otaenia
araliacea
Apiaceae
T6
1plusmn1
001
01
163
65Mvelevele
Vernoniaam
ygdalin
aAs
teraceae
SH3
2plusmn2001plusmn001
01
400
66Mpo
ngolo
Catunaregam
spinosa
(Thun
b)
Rubiaceae
ST3
1plusmn1001plusmn001
01
200
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
ISRN Biodiversity 5
Table1Ch
ecklist
oftre
eand
shrubspeciesrecordedin
Gangalamtumba
VLF
Rshow
ingfre
quency
()density
(meanplusmnSE
)basalarea(meanplusmnSE
)dispersio
nindex(D
I)and
Impo
rtance
ValueInd
ex(IVI)bothforthe
currentp
opulationof
largeind
ividuals(plotsize=
15m
radiusm
inim
umDbh
=5c
m)a
ndforstumps
(plotsize=
20m
radiusm
inim
umbasald
iameter
=2c
m)SH
shrub
ST
smalltreeandT
tree
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
1Mgiha
Dalbergia
arbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST60
247plusmn69136plusmn037
224
4765
1468plusmn41006plusmn005
170
2Mtono
Commiphora
afric
ana
Burseraceae
T77
110plusmn23159plusmn037
175
1142
1414plusmn06001plusmn001
111
3Mlama
Combretum
molleG
Don
Com
bretaceae
T74
116plusmn27069plusmn015
132
1523
3468plusmn19005plusmn002
307
4Mku
ngugu
Acacia
sp
Mim
osaceae
T23
40plusmn22102plusmn054
111
2873
920plusmn14001plusmn001
65
5Mkw
eeBrachyste
gia
spiciform
isBe
nth
Caesalpiniaceae
T37
58plusmn20118plusmn034
110
1731
2661plusmn24020plusmn009
308
6Mkalala
Albiziapetersiana
(Belle)O
liv
Mim
osaceae
T40
80plusmn30055plusmn020
85
2711
636plusmn29005plusmn004
94
7Mlyasenga
Combretum
zeyheri
Soun
dCom
bretaceae
T43
60plusmn20030plusmn011
741597
2020plusmn08001plusmn001
47
8Mkombivawo
Bauh
iniapetersiana
Caesalpiniaceae
SH34
54plusmn21029plusmn010
64
2000
9Muguvani
Markham
iaobtusifolia
Bign
oniaceae
T60
65plusmn13038plusmn010
64
647
605plusmn03
00
23
10Mdeke
Hym
enodictyon
parvifoliu
mOliv
Rubiaceae
ST31
50plusmn24048plusmn024
58
2749
302plusmn02
00
17
11Mub
wegele
Sclerocarryabirrea
sbspbirr
eaAnacardiaceae
T34
21plusmn6061plusmn023
58
441
605plusmn03001plusmn001
28
12Mmem
enam
ene
Margaritaria
discoidea(Bail)
Euph
orbiaceae
SH51
68plusmn24032plusmn013
57
2073
307plusmn07
00
06
13Mkambala
Acaciamellifera
(Vahl)Be
nth
Mim
osaceae
T29
14plusmn5049plusmn019
50
359
302plusmn02
00
08
14Mugegere
Dich
rosta
chys
cinerea
(L)Wight
ampArn
Mim
osaceae
ST31
53plusmn26017plusmn009
47
3086
2959plusmn19003plusmn002
178
15Mbata
AcaciaseyalD
elvar
seyal
Mim
osaceae
T34
30plusmn10019plusmn006
45
887
311plusmn11001plusmn001
46
16Mgulumo
Lann
easchw
einfurthiiAnacardiaceae
T43
24plusmn7032plusmn009
39
544
907plusmn04002plusmn001
22
17Muyom
boBrachyste
giaboehmii
Caesalpiniaceae
T11
11plusmn7019plusmn015
38
1309
607plusmn05001plusmn001
22
18Mmulim
uli
Cassiaabbreviata
Caesalpiniaceae
T34
31plusmn13017plusmn007
36
1353
916plusmn12001plusmn001
49
19Mdavi
Cordiasin
ensis
Lam
Boraginaceae
ST6
23plusmn23011plusmn010
34
5498
302plusmn02
00
06
6 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
20Mdw
endw
eTerm
inaliabrow
nii
Com
bretaceae
T17
14plusmn8019plusmn009
32
1129
305plusmn05
00
12
21Mfilafila
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T31
25plusmn8015plusmn005
30
646
605plusmn03
00
35
22Mkw
ata
Codyladensiflora
Caesalpiniaceae
T9
4plusmn2033plusmn019
28
282
334plusmn34004plusmn004
83
23Mgung
aAc
aciaabyssin
ica(H
ochst)
Mim
osaceae
T17
12plusmn7014plusmn007
24
1140
614plusmn12001plusmn001
39
24Mkole
Grew
iabicolor
Tiliaceae
ST26
21plusmn10008plusmn003
21
1078
605plusmn03
00
15
25Mparapand
eStrychnosp
otatorum
Lf
Loganiaceae
T23
23plusmn11014plusmn009
20
1331
607plusmn05
00
17
26Kitim
bwi
Orm
ocarpum
kirkii
Fabaceae
SH29
18plusmn6007plusmn003
19492
27Mtabagila
Lonchocarpus
capassa
Fabaceae
T26
11plusmn5014plusmn006
17550
302plusmn02
00
04
28Muw
otapon
ziOzoroainsig
nsssp
retic
ulata
Anacardiaceae
T14
10plusmn5010plusmn005
16564
1411plusmn05001plusmn000
31
29Msanzi
Gardeniaresin
iflua
Hiern
Rubiaceae
ST9
13plusmn8008plusmn005
161229
30Mkoga
Vitexpayos
Verbenaceae
ST17
12plusmn5011plusmn004
15605
302plusmn02
00
02
31Mpelem
eleGr
ewiaforbesiiHaw
Ex
Mast
Tiliaceae
ST14
16plusmn9005plusmn002
147
1246
32Mny
wenyw
eeDalbergiaboehmii
Fabaceae
T14
11plusmn7009plusmn007
151221
33Mulagavega
Albiziaam
ara(Roxb)
Boiv
Mim
osaceae
T14
9plusmn5004plusmn003
14712
34Mkola
Afzelia
quan
zensis
Caesalpiniaceae
T11
6plusmn3015plusmn009
14532
35Mugusi
Brachyste
giamanga
Caesalpiniaceae
T20
11plusmn6010plusmn007
13824
618plusmn13002plusmn002
25
36Mwaham
aShrebera
trichoclada
Oleaceae
T17
11plusmn6005plusmn002
12697
309plusmn09001plusmn001
15
37Mpingo
Dalbergia
mela
noxylon
Fabaceae
T11
11plusmn9004plusmn003
121655
314plusmn14001plusmn001
26
38Mub
aya
Strychnosinn
ocua
Loganiaceae
T17
8plusmn4011plusmn008
12375
302plusmn02001plusmn001
06
39Msisina
AlbiziaharveyiF
ournMim
osaceae
T14
5plusmn3008plusmn005
11359
305plusmn05002plusmn002
15
40Mlim
boEu
phorbiacuneata
Valh
Euph
orbiaceae
ST14
12plusmn6004plusmn002
11699
41Mwam
biElaeodendron
buchan
annii
Cela
steraceae
T3
8plusmn8003plusmn003
102000
42Mvembadand
aXe
roderris
stuhlmannii
Fabaceae
T14
4plusmn2011plusmn005
09
191
43Mtelela
Brachyste
giabu
ssei
Harms
Caesalpiniaceae
T6
2plusmn1012plusmn010
09
253
ISRN Biodiversity 7
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
44Mteresi
Prem
naholstiiGurke
Verbenaceae
ST9
8plusmn5003plusmn002
09
899
45Mkongolo
Commiphora
ugogensis
Burseraceae
T6
1plusmn1008plusmn006
08
097
302plusmn02001plusmn001
09
46Muw
isaBo
sciaangustifolia
A
Rich
varangustifo
liaCa
pparaceae
T17
8plusmn4004plusmn002
08
426
47Mnyalup
uko
Canthium
pseudoverticillatum
Hien
Rubiaceae
ST20
8plusmn4004plusmn002
07
414
48Mnyaluh
anga
Brideliascleu
roneura
MuellArg
Euph
orbiaceae
ST14
6plusmn3004plusmn002
07
341
49Mdide
Manilkaramochisia
(Bak)Dub
ard
Sapo
taceae
T9
5plusmn4002plusmn001
07
793
50Muh
emi
Erythrinaabyssin
icaFabaceae
T6
1plusmn1007plusmn006
07
163
51Msambarawe
Vangueria
infausta
BurchSspRo
tund
ataRu
biaceae
ST6
8plusmn5002plusmn002
06
925
52Mhehefu
Allophylu
sferrugineus
Taub
Sapind
aceae
SH9
5plusmn3002plusmn001
05
540
314plusmn14001plusmn001
14
53Mnyengrsquoenyengrsquoe
Excoecariabu
ssei
(Pax)
Euph
orbiaceae
ST11
4plusmn2001plusmn001
05
321
54Muganga
Berchemiadiscolor
Rham
naceae
T6
4plusmn3004plusmn003
04
717
55Mning
aPterocarpu
sangolensis
Fabaceae
T6
2plusmn1005plusmn004
04
253
309plusmn09006plusmn006
25
56Mtanang
we
Zizip
husm
ucronata
Rham
naceae
T3
4plusmn4003plusmn003
04
900
57Muh
ekele
Eucle
adivinorum
Hiern
Ebenaceae
ST3
3plusmn3001plusmn001
04
800
302plusmn02
00
49
58Mub
umila
Cassipourea
mollis
(REfr)Alstom
Rhizop
horaceae
T9
4plusmn3002plusmn001
04
526
302plusmn02
00
04
59Mkw
ambe
Flueggea
virosa
Willd
Euph
orbiaceae
SH6
3plusmn2003plusmn003
03
406
60Kivang
aZa
nhaafric
ana
(Radlk)Ex
ell
Sapind
aceae
T9
2plusmn1001plusmn001
02
212
302plusmn02
00
02
61Mning
amaji
Pterocarpu
stinctorius
Fabaceae
T3
0002plusmn002
02
100
62Mtosi
MaeruatriphyllaA
Rich
Capp
araceae
ST3
2plusmn2001plusmn001
02
600
63Mugosa
Ehretia
amoena
Boraginaceae
ST9
1plusmn1
00
01
094
64Mnyon
gamem
beStegan
otaenia
araliacea
Apiaceae
T6
1plusmn1
001
01
163
65Mvelevele
Vernoniaam
ygdalin
aAs
teraceae
SH3
2plusmn2001plusmn001
01
400
66Mpo
ngolo
Catunaregam
spinosa
(Thun
b)
Rubiaceae
ST3
1plusmn1001plusmn001
01
200
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
6 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
20Mdw
endw
eTerm
inaliabrow
nii
Com
bretaceae
T17
14plusmn8019plusmn009
32
1129
305plusmn05
00
12
21Mfilafila
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T31
25plusmn8015plusmn005
30
646
605plusmn03
00
35
22Mkw
ata
Codyladensiflora
Caesalpiniaceae
T9
4plusmn2033plusmn019
28
282
334plusmn34004plusmn004
83
23Mgung
aAc
aciaabyssin
ica(H
ochst)
Mim
osaceae
T17
12plusmn7014plusmn007
24
1140
614plusmn12001plusmn001
39
24Mkole
Grew
iabicolor
Tiliaceae
ST26
21plusmn10008plusmn003
21
1078
605plusmn03
00
15
25Mparapand
eStrychnosp
otatorum
Lf
Loganiaceae
T23
23plusmn11014plusmn009
20
1331
607plusmn05
00
17
26Kitim
bwi
Orm
ocarpum
kirkii
Fabaceae
SH29
18plusmn6007plusmn003
19492
27Mtabagila
Lonchocarpus
capassa
Fabaceae
T26
11plusmn5014plusmn006
17550
302plusmn02
00
04
28Muw
otapon
ziOzoroainsig
nsssp
retic
ulata
Anacardiaceae
T14
10plusmn5010plusmn005
16564
1411plusmn05001plusmn000
31
29Msanzi
Gardeniaresin
iflua
Hiern
Rubiaceae
ST9
13plusmn8008plusmn005
161229
30Mkoga
Vitexpayos
Verbenaceae
ST17
12plusmn5011plusmn004
15605
302plusmn02
00
02
31Mpelem
eleGr
ewiaforbesiiHaw
Ex
Mast
Tiliaceae
ST14
16plusmn9005plusmn002
147
1246
32Mny
wenyw
eeDalbergiaboehmii
Fabaceae
T14
11plusmn7009plusmn007
151221
33Mulagavega
Albiziaam
ara(Roxb)
Boiv
Mim
osaceae
T14
9plusmn5004plusmn003
14712
34Mkola
Afzelia
quan
zensis
Caesalpiniaceae
T11
6plusmn3015plusmn009
14532
35Mugusi
Brachyste
giamanga
Caesalpiniaceae
T20
11plusmn6010plusmn007
13824
618plusmn13002plusmn002
25
36Mwaham
aShrebera
trichoclada
Oleaceae
T17
11plusmn6005plusmn002
12697
309plusmn09001plusmn001
15
37Mpingo
Dalbergia
mela
noxylon
Fabaceae
T11
11plusmn9004plusmn003
121655
314plusmn14001plusmn001
26
38Mub
aya
Strychnosinn
ocua
Loganiaceae
T17
8plusmn4011plusmn008
12375
302plusmn02001plusmn001
06
39Msisina
AlbiziaharveyiF
ournMim
osaceae
T14
5plusmn3008plusmn005
11359
305plusmn05002plusmn002
15
40Mlim
boEu
phorbiacuneata
Valh
Euph
orbiaceae
ST14
12plusmn6004plusmn002
11699
41Mwam
biElaeodendron
buchan
annii
Cela
steraceae
T3
8plusmn8003plusmn003
102000
42Mvembadand
aXe
roderris
stuhlmannii
Fabaceae
T14
4plusmn2011plusmn005
09
191
43Mtelela
Brachyste
giabu
ssei
Harms
Caesalpiniaceae
T6
2plusmn1012plusmn010
09
253
ISRN Biodiversity 7
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
44Mteresi
Prem
naholstiiGurke
Verbenaceae
ST9
8plusmn5003plusmn002
09
899
45Mkongolo
Commiphora
ugogensis
Burseraceae
T6
1plusmn1008plusmn006
08
097
302plusmn02001plusmn001
09
46Muw
isaBo
sciaangustifolia
A
Rich
varangustifo
liaCa
pparaceae
T17
8plusmn4004plusmn002
08
426
47Mnyalup
uko
Canthium
pseudoverticillatum
Hien
Rubiaceae
ST20
8plusmn4004plusmn002
07
414
48Mnyaluh
anga
Brideliascleu
roneura
MuellArg
Euph
orbiaceae
ST14
6plusmn3004plusmn002
07
341
49Mdide
Manilkaramochisia
(Bak)Dub
ard
Sapo
taceae
T9
5plusmn4002plusmn001
07
793
50Muh
emi
Erythrinaabyssin
icaFabaceae
T6
1plusmn1007plusmn006
07
163
51Msambarawe
Vangueria
infausta
BurchSspRo
tund
ataRu
biaceae
ST6
8plusmn5002plusmn002
06
925
52Mhehefu
Allophylu
sferrugineus
Taub
Sapind
aceae
SH9
5plusmn3002plusmn001
05
540
314plusmn14001plusmn001
14
53Mnyengrsquoenyengrsquoe
Excoecariabu
ssei
(Pax)
Euph
orbiaceae
ST11
4plusmn2001plusmn001
05
321
54Muganga
Berchemiadiscolor
Rham
naceae
T6
4plusmn3004plusmn003
04
717
55Mning
aPterocarpu
sangolensis
Fabaceae
T6
2plusmn1005plusmn004
04
253
309plusmn09006plusmn006
25
56Mtanang
we
Zizip
husm
ucronata
Rham
naceae
T3
4plusmn4003plusmn003
04
900
57Muh
ekele
Eucle
adivinorum
Hiern
Ebenaceae
ST3
3plusmn3001plusmn001
04
800
302plusmn02
00
49
58Mub
umila
Cassipourea
mollis
(REfr)Alstom
Rhizop
horaceae
T9
4plusmn3002plusmn001
04
526
302plusmn02
00
04
59Mkw
ambe
Flueggea
virosa
Willd
Euph
orbiaceae
SH6
3plusmn2003plusmn003
03
406
60Kivang
aZa
nhaafric
ana
(Radlk)Ex
ell
Sapind
aceae
T9
2plusmn1001plusmn001
02
212
302plusmn02
00
02
61Mning
amaji
Pterocarpu
stinctorius
Fabaceae
T3
0002plusmn002
02
100
62Mtosi
MaeruatriphyllaA
Rich
Capp
araceae
ST3
2plusmn2001plusmn001
02
600
63Mugosa
Ehretia
amoena
Boraginaceae
ST9
1plusmn1
00
01
094
64Mnyon
gamem
beStegan
otaenia
araliacea
Apiaceae
T6
1plusmn1
001
01
163
65Mvelevele
Vernoniaam
ygdalin
aAs
teraceae
SH3
2plusmn2001plusmn001
01
400
66Mpo
ngolo
Catunaregam
spinosa
(Thun
b)
Rubiaceae
ST3
1plusmn1001plusmn001
01
200
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
ISRN Biodiversity 7
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
44Mteresi
Prem
naholstiiGurke
Verbenaceae
ST9
8plusmn5003plusmn002
09
899
45Mkongolo
Commiphora
ugogensis
Burseraceae
T6
1plusmn1008plusmn006
08
097
302plusmn02001plusmn001
09
46Muw
isaBo
sciaangustifolia
A
Rich
varangustifo
liaCa
pparaceae
T17
8plusmn4004plusmn002
08
426
47Mnyalup
uko
Canthium
pseudoverticillatum
Hien
Rubiaceae
ST20
8plusmn4004plusmn002
07
414
48Mnyaluh
anga
Brideliascleu
roneura
MuellArg
Euph
orbiaceae
ST14
6plusmn3004plusmn002
07
341
49Mdide
Manilkaramochisia
(Bak)Dub
ard
Sapo
taceae
T9
5plusmn4002plusmn001
07
793
50Muh
emi
Erythrinaabyssin
icaFabaceae
T6
1plusmn1007plusmn006
07
163
51Msambarawe
Vangueria
infausta
BurchSspRo
tund
ataRu
biaceae
ST6
8plusmn5002plusmn002
06
925
52Mhehefu
Allophylu
sferrugineus
Taub
Sapind
aceae
SH9
5plusmn3002plusmn001
05
540
314plusmn14001plusmn001
14
53Mnyengrsquoenyengrsquoe
Excoecariabu
ssei
(Pax)
Euph
orbiaceae
ST11
4plusmn2001plusmn001
05
321
54Muganga
Berchemiadiscolor
Rham
naceae
T6
4plusmn3004plusmn003
04
717
55Mning
aPterocarpu
sangolensis
Fabaceae
T6
2plusmn1005plusmn004
04
253
309plusmn09006plusmn006
25
56Mtanang
we
Zizip
husm
ucronata
Rham
naceae
T3
4plusmn4003plusmn003
04
900
57Muh
ekele
Eucle
adivinorum
Hiern
Ebenaceae
ST3
3plusmn3001plusmn001
04
800
302plusmn02
00
49
58Mub
umila
Cassipourea
mollis
(REfr)Alstom
Rhizop
horaceae
T9
4plusmn3002plusmn001
04
526
302plusmn02
00
04
59Mkw
ambe
Flueggea
virosa
Willd
Euph
orbiaceae
SH6
3plusmn2003plusmn003
03
406
60Kivang
aZa
nhaafric
ana
(Radlk)Ex
ell
Sapind
aceae
T9
2plusmn1001plusmn001
02
212
302plusmn02
00
02
61Mning
amaji
Pterocarpu
stinctorius
Fabaceae
T3
0002plusmn002
02
100
62Mtosi
MaeruatriphyllaA
Rich
Capp
araceae
ST3
2plusmn2001plusmn001
02
600
63Mugosa
Ehretia
amoena
Boraginaceae
ST9
1plusmn1
00
01
094
64Mnyon
gamem
beStegan
otaenia
araliacea
Apiaceae
T6
1plusmn1
001
01
163
65Mvelevele
Vernoniaam
ygdalin
aAs
teraceae
SH3
2plusmn2001plusmn001
01
400
66Mpo
ngolo
Catunaregam
spinosa
(Thun
b)
Rubiaceae
ST3
1plusmn1001plusmn001
01
200
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
8 ISRN Biodiversity
Table1Con
tinued
Sno
Vernacularlocal
name
Speciesbo
tanical
name
Family
Habitlife
form
Currentp
opulation
Stum
psFrequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
DI
Frequency
()
Density
stemha
Basalarea
(m2 ha)
IVI
67Mbo
liboli
Acaciadrepanolobium
(Harms)
Mim
osaceae
T3
000
01
100
68Mwim
akigulu
Maeruaangolen
sisCa
pparaceae
SH3
1plusmn1
00
01
300
69Mnyali
Tamarindu
sind
icaCa
esalpiniaceae
T6
1plusmn1001plusmn001
01
097
70Mgombw
ani
Combretum
aculeatum
Com
bretaceae
T6
1plusmn1
00
01
097
302plusmn02
00
04
71Msang
ala
Burkea
afric
anaHoo
kCa
esalpiniaceae
T3
0001plusmn001
01
100
302plusmn02
00
04
72Mkokonza
Opilia
amentacea
Roxb
Opiliaceae
SH3
000
00
100
311plusmn11
00
42
73Mfumbi
Kigelia
afric
ana
Bign
oniaceae
T3
000
00
100
74Mlang
ali
Euphorbia
cand
elabrum
Euph
orbiaceae
T3
000
00
100
75Mpu
lulu
Term
inaliaseric
eaCom
bretaceae
T3
000
00
100
76Mdaha
Diospyros
usam
barensisFWhite
Ebenaceae
T3
000
00
100
305plusmn05001plusmn001
15
77Mtund
wa
XimeniacaffraSond
Olacaceae
ST3
000
00
100
78Mwim
aperu
Tarenn
agla
veolens(S
Moo
re)B
rem
Rubiaceae
ST+
79Mwesa
Boscia
mossambicensisKl
Capp
araceae
ST+
80Musasam
ulo
Psychotria
schu
mmaniana
Rubiaceae
ST+
81Muh
anza
Senn
asin
gueana
Caesalpiniaceae
ST+
82Mtund
wahavi
Ximeniaam
erica
naOlacaceae
ST+
83Mtin
giligiti
Phyllanthu
sengler
iPax
Euph
orbiaceae
T+
84Msada
Vangueria
madagascarie
nsis
Gmel
Rubiaceae
ST+
85Mku
nung
uZa
nthoxylum
chalybeum
Rutaceae
T+
86Lu
kali
Croton
scheffleriP
axEu
phorbiaceae
SH+
87Kilim
andembw
eGardeniaternifolia
KSchu
mamp
Thon
nRu
biaceae
ST+
88MpelaM
buyu
Adan
soniadigitata
Bombacaceae
T++
Total(allspecies)
13511521plusmn5941355plusmn552
200
294
60plusmn30072plusmn049
200
+indicatesspecies
identifi
edam
ongsm
allerind
ividualswith
in5m
radius
plots(Dbhlt5c
m)and++
indicatesthata
largeind
ividualw
asidentifi
edwith
ina1
5-radius
plot
butw
asno
tincludedin
thea
nalyseso
fste
mdensitybasalareaand
volumea
sitw
asconsidered
anou
tlier
duetoits
tremendo
ussiz
e(Dbh
=340c
m)
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
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MeteorologyAdvances in
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Marine BiologyJournal of
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Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
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International Journal of
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BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
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ClimatologyJournal of
ISRN Biodiversity 9
005 and that of small individuals was 006 The followingspecies were observed to have the greatest contributionsto the Shannon-Wiener diversity index of large individualsDalbergia arbutifolia (contributing 030) Combretum molle(020) Commiphora africana (019) Albizia petersiana (016)and Margaritaria discoidea (014) while for smaller ones thegreatest contributions were found for Brachystegia spiciformis(026) Dalbergia arbutifolia (024) Grewia forbesii (023)Margaritaria discoidea (020) and Dichrostachys cinerea(016) In terms of frequency of occurrence for standingindividuals (large sizes) Commiphora africana was the mostfrequent species (77 of plots) followed by Combretummolle (74) and Dalbergia arbutifolia (60) while for smallsizes Margaritaria discoidea (66) Markhamia obtusifolia(63) and Dalbergia arbutifolia (57) were the most fre-quent species The Importance Value Index (IVI) for largeindividuals (Dbh ge 5 cm) shows that Dalbergia arbutifolia(224) Commiphora africana (175) and Combretum molle(132) were the most important species among standingindividuals while Brachystegia spiciformis (308)Combretummolle (307) and Dichrostachys cinerea (178) appeared to bethe most important among harvested individuals (stumps)These species were also found to have higher frequencies thanany other harvested species observed in the GVLFR (Table 1)
33 Stem Density The total mean stem density for large indi-viduals with Dbh ge5 cm was 1521 plusmn 594 stemsha (Table 1)and that of small individuals with Dbh lt5 cm (includingindividuals with Dbh lt1 cm) was 14318 plusmn 6956 stemshaAmong large individuals the most abundant species wereDalbergia arbutifolia (162 of 1521 stemsha) Combretummolle (8) Commiphora africana (72) and Albizia peter-siana (53) Among small individuals the most abundantspecies were Brachystegia spiciformis (13 of 14318 stemsha)followed by Dalbergia arbutifolia (11) and Grewia forbesii(10) For stumps the overall mean density was 60 plusmn38 stemsha with Combretum molle (12) Dalbergia arbuti-folia (12) Brachystegia spiciformis (10) and Dichrostachyscinerea (10) contributing the most (Table 1) Generally thedistribution of standing trees to size classes showed the usualreverse J shape which was also approximately observed forstumps (Figure 3) However for stumps the density of stemsin the 1ndash10 cm diameter class was slightly lower than whatwould be expected if tree felling had been a random event
34 Basal Area For the GVLFR as a whole the meanbasal areas for large (ge5 cm Dbh) and small individuals(lt5 cm Dbh) were 1355 plusmn 552m2ha (Table 1 Figure 4)and 305 plusmn 002m2ha respectively The species contributingmost to the basal area of large individuals were Commiphoraafricana (12) Dalbergia arbutifolia (10) and Brachystegiaspiciformis (9) while those contributing most to the basalarea of smaller individuals were Dalbergia arbutifolia (16)Grewia forbesii (15) and Dichrostachys cinerea (13) Themean basal area for stumps was 072m2ha with Brachystegiaspiciformis contributing the greatest individual proportion(28) 41 species made up the rest (Figure 4)
60542
3335
530
113
28 24
346166
34
11
02
01
10
100
1000
10000
100000
Diameter classes (cm)
Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Stem
s (ha
minus1)
Figure 3 Density of standing trees ge1 cmDbh and stumps ge1 cm bydiameter class in Gangalamtumba VLFR (119899 = 35) NB logarithmicscale on vertical axis
744477
241
103041 055
014025 018
010004
001
01
1
10
Diameter classes (cm)Standing treesStumps
10
ndash100
101
ndash200
201
ndash300
301
ndash400
401
ndash500
501
ndash600
Mea
n ba
sal a
rea (
m2
haminus1)
Figure 4 Distribution of basal area per hectare for standingtrees ge1 cm Dbh and stumps ge1 cm by diameter classes in theGangalamtumba VLFR (119899 = 35) NB logarithmic scale on verticalaxis
35 Volume The mean volumes for large (ge5 cm Dbh) andsmall individuals (lt5 cm Dbh) were 9217 plusmn 390m3ha and1257 plusmn 635m3ha respectively (not shown in tables) Thespecies contributing most to the volume of large individualswere Commiphora africana (12) Brachystegia spiciformis(10) Acacia sp (9) and Dalbergia arbutifolia (9) Forsmaller individuals the species that contributed most tovolume were Dalbergia arbutifolia (16) Grewia forbesii(14) and Dichrostachys cinerea (13) The mean remainingvolume of stumps was found to be 015 plusmn 01m3ha withBrachystegia spiciformis contributing the greatest individualpercentage (28) 41 species made up the rest
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
10 ISRN Biodiversity
36 Spatial Distribution Dispersion indices (DI) are pre-sented for individual species in Table 1 The dispersion indexvalues range from 094 indicating almost complete spatialrandomness or slight underdispersion to 5498 indicatingconsiderable overdispersion that is a patchy or clustereddistributionOut of 77 species excluding the single very largeAdansonia digitata that was considered an outlier 64 species(83) 9 species (12) and 4 species (5) were found tohave DI gt 1 DI = 1 and DI lt 1 respectively so the majorityof species are characterised by a patchy distribution acrossthe forest The species with the lowest estimated DI wasEhretia amoena (094) while the highest DI was estimated forCordia sinensis (5498)Themost abundant species includingDalbergia arbutifolia (DI = 4765) Combretum molle (DI =1523) and Commiphora africana (DI = 1142) are stronglyoverdispersed
37 Plant Communities and Species Association Four plantcommunities were identified through cluster analysis basedon the statistical significance (5) of the observed maximumindicator values (Table 2) Only two of these plant commu-nities were dominated by species from the family Caesalpini-aceae (Communities 1 and 24) The other two showed greatvariationoverlap between species of different plant families(Communities 3 and 5) The estimated Steinhaus similarityindices between pairs of plant communities varied from 32for Communities 1 and 3 and Communities 5 and 24 to 37for Communities 3 and 5 (not shown in tables)
The ordination diagram (Figure 5) shows that one topo-graphic and six edaphic variables appear to be associatedwiththe four plant communities and the species distribution inthe study area The strongest correlation with communitycomposition was observed for elevation followed by soilpH at 0ndash15 cm depth base cation Ca2+ at 0ndash15 cm depthpercent base saturation (BS) at 0ndash15 cm depth percent clayat 0ndash15 cm depth C N ratio at 15ndash30 cm depth and percentsand at 0ndash15 cm depth The base cations magnesium (Mg2+)and potassium (K+) were not directly correlated with thecommunity composition but since they are included in thebase saturation percentage they are indirectly related Plantcommunities were ordered along the second ordination axiswhich was positively correlated with pH15 (119903 = 071) Ca15(119903 = 069) BS (119903 = 065) andClay15 (119903 = 052) and negativelycorrelated with Sand15 (119903 = minus046) Axis 1 of the ordinationwas positively correlated with Elev (119903 = 085) and CNrat30(119903 = 051) As shown in Table 3 and indicated graphically inFigure 5 correlations between many of the edaphic variablesare strong particularly between pH15 Sand15 Clay15 Ca15and BS whereas correlation between topographic and mostedaphic variables is small and nonsignificant However thereis a significant negative correlation (119903 = minus042) between Elevand Sand15 indicating that sandy soils are mostly found atlower elevations
4 Discussion
41 Species Composition The results reported in this studyshow that the composition of the vegetation types found in
Elev
pH15Ca15
Sand15
Clay15
CNrat30
BS
Axis 1
Axis 2
Community 1Community 3
Community 5Community 24
Figure 5 Compositional gradients and plant communities in NMSordination of 35 vegetation plots for trees ge5 cm in GangalamtumbaVLFR Four plant communities are recognized (cf Table 2) Com-munity 1 = Brachystegia spiciformis Diplorynchus condylocarponand Lannea schweinfurthii woodland Community 3 = Dalbergiaarbutifolia Commiphora africana and Albizia petersianawoodlandCommunity 5 = Acacia sp Acacia abyssinica and Albizia amarawoodland and Community 24 = Bauhinia petersiana and Shreberatrichoclada woodland Elev elevation (m) CNrat30 C N ratio at15ndash30 cm depth Sand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm Ca15 = Ca2+ (cmol(+)kg) at 0ndash15 cm pH15 = soil pH at 0ndash15 cm BS base saturation at 0ndash15 cm
the GVLFR especially the dominance of species from thefamily Caesalpiniaceae agreed well with previous descrip-tions and classifications of plant communities commonlyfound in miombo woodlands [1] However the observeddominance based on IVI of the genera Dalbergia Com-miphora and Combretum contrasts with patterns usuallyconsidered common for miombo woodlands The frequencyof species in these genera was also high compared to otherspecies observed in the GVLFR (Table 1) Similar deviationsexist between the results obtained by Banda et al [26]in the Katavi-Rukwa ecosystem where they observed thatTerminalia and Combretum were the dominant genera andthe findings of Giliba et al [27] and Njana [43] whoboth noted the dominance of the two common miombogenera Brachystegia and Julbernardia However Combretumalso occurred in their study areas The results suggest thaton a larger spatial scale the species diversity of miombowoodlands is very high and that the three common generaBrachystegia Julbernadia and Isoberlinia are not alwaysdominant at the local scale
The species richness observed in the GVLFR compareswell with miombo community studies in other areas of drymiombo in Tanzania and elsewhere which receive an average
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
ISRN Biodiversity 11
Table2Plantcom
mun
itiesandspeciesa
ssociatio
nsin
thed
ryMiombo
woo
dlands
ofGangalamtumba
VLF
RCom
mun
itynu
mbers135and24
representthe
different
plantassociatio
ns
119899speciesnu
mbero
fspecies
(77in
total)with
Dbhge5c
mIV+
speciesind
icator
valuemeanplusmnstd
errorand
BAisbasalarea
Com
mun
ityAs
sociated
species
Family
Habit
IV+
()
Con
stancy
()119875value
Stem
sha
BA m2 ha
MeanDbh
Brachyste
giaspiciform
ismdashDiplorhynchus
cond
ylocarponmdashLa
nnea
schw
einfurthiiw
oodland
1(n s
pecies
=47
nplots=9)
Sitevariables(meanplusmnSE
)Elev
=1200plusmn20pH15
=644plusmn013C
a15=604plusmn128
Sand
15=721plusmn431C
lay15=137plusmn188Silt15
=142plusmn335C
Nrat30=135plusmn05andBS
=526plusmn65
Brachyste
gia
spiciform
isCa
esalpiniaceae
T820
100
000
02164plusmn59356plusmn077138plusmn07
Diplorynchu
scond
ylocarpon
Apocyn
aceae
T759
89000
0866plusmn18044plusmn01382plusmn05
Lann
easchw
einfurthii
Anacardiaceae
T719
100
000
0449plusmn14072plusmn022126plusmn09
Bosciaangustifolia
Capp
araceae
T536
5600056
27plusmn12014plusmn00776plusmn06
Euphorbiacuneata
Euph
orbiaceae
ST428
4400200
42plusmn19012plusmn00665plusmn06
Other
species
mdashmdash
mdashmdash
mdash1075plusmn5741007plusmn647102plusmn03
Subtotal
1423plusmn174
1505plusmn178
109plusmn03
DalbergiaarbutifoliamdashCo
mmiphora
afric
anamdash
Albizia
petersiana
woo
dland
3(n
species=55n
plots=
16)Site
variables(meanplusmnSE
)Elev
=986plusmn19pH15
=648plusmn009C
a15=856plusmn109
Sand
15=767plusmn156C
lay15=135plusmn155Silt15
=97plusmn096C
Nrat30=106plusmn03andBS
=744plusmn44
Dalbergiaarbutifolia
Fabaceae
subfam
ilyPapilio
noideae
ST793
100
000
02500plusmn123263plusmn06477plusmn02
Commiphora
afric
ana
Burseraceae
T542
9400026
187plusmn40272plusmn064119plusmn06
Albiziapetersiana
Mim
osacea
T536
7500200
157plusmn57103plusmn04084plusmn02
Sclerocarryabirrea
Anacardiaceae
T504
6300186
40plusmn11128plusmn045168plusmn17
Other
species
mdashmdash
mdashmdash
mdash852plusmn449669plusmn37788plusmn03
Subtotal
1735plusmn166
1435plusmn071
92plusmn02
Acaciaspmdash
Acaciaabyssin
icamdash
Albiziaam
arawoo
dland
5(n
species=33n
plots=5)
Sitevariables(m
eanplusmnSE
)Elev
=986plusmn52pH15
=734plusmn023C
a15=139plusmn33
Sand
15=745plusmn678C
lay15=148plusmn47Silt15
=107plusmn26C
Nrat30=106plusmn07andBS
=963plusmn89
Acaciasp
Mim
osaceae
T981
100
000
02269plusmn111662plusmn286172plusmn14
Acaciaabyssin
icaMim
osaceae
T594
8000030
31plusmn9060plusmn03299plusmn08
Albiziaam
ara
Mim
osaceae
T552
60000
6654plusmn30023plusmn01671plusmn08
Grew
iabicolor
Tiliaceae
ST533
6000106
113plusmn52040plusmn01770plusmn04
Acaciaseyal
Mim
osaceae
T433
6000526
62plusmn34034plusmn01790plusmn05
Other
species
mdashmdash
mdashmdash
mdash665plusmn533552plusmn49872plusmn04
Subtotal
1194plusmn169
1367plusmn347
101plusmn05
Bauh
iniapetersiana
mdashShrebera
trichoclada
woo
dland
24(n
species=35n
plots=5)
Sitevariables(meanplusmnSE
)Elev
=1175plusmn25pH15
=73plusmn04C
a15=254plusmn69
Sand
15=522plusmn68C
lay15=331plusmn28
Silt15=147plusmn44C
Nrat30=961plusmn13andBS
=1041plusmn204
Bauh
iniapetersiana
Caesalpiniaceae
SH551
8000118192plusmn121111plusmn05075plusmn03
Shrebera
trichoclada
Oleaceae
T505
6000110
57plusmn32022plusmn01380plusmn05
Manilkaramochisia
Sapo
taceae
T376
4000332
34plusmn27014plusmn00969plusmn10
Dalbergia
mela
noxylon
Fabaceae
T370
4000380
68plusmn58026plusmn02266plusmn03
Ehretia
amoena
Boraginaceae
ST347
4000344
6plusmn4
002plusmn00170plusmn04
Other
species
mdashmdash
mdashmdash
mdash982plusmn759643plusmn47999plusmn05
Subtotal
1338plusmn137
817plusmn087
91plusmn04
Total
1521plusmn5941355plusmn55298plusmn01
Elevelevatio
n(m
)CN
rat30CN
ratio
at15ndash30c
mdepthSand
15sand(
)at0
ndash15c
mC
lay15clay
()at0
ndash15c
mSilt15silt
()at0
ndash15c
mC
a15Ca2+(cmol(+)kg)at0
ndash15c
mpH15soilp
Hat0ndash
15cm
BS
base
saturatio
nat0ndash
15cm
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
12 ISRN Biodiversity
Table 3 Pearson-correlation matrix for two topographic and eight edaphic variablesdagger associated with the classified plant communities in theGangalamtumba VLFR (119899 = 35) Applied significance levels lowast5 lowastlowast1 and lowastlowastlowast01 +not significant
Elev Slope pH15 Ca15 K15 N15 Sand15 Clay15 CNrat30Slope 019+
pH15 minus013+ minus033lowast
Ca15 002+ minus020+ 083lowastlowastlowast
K15 minus029+ 000+ 010+ 012+
N15 006+ 036lowast 010+ 041lowastlowast 033lowast
Sand15 minus042lowastlowast 003+ minus053lowastlowastlowast minus074lowastlowastlowast 006+ minus046lowastlowast
Clay15 031+ minus012+ 051lowastlowast 076lowastlowastlowast minus001+ 047lowastlowast minus086lowastlowastlowast
CNrat30 036lowast 010+ minus039lowast minus051lowastlowast minus015+ minus019+ 040lowast minus048lowastlowast
BS minus024+ minus017+ 084lowastlowastlowast 091lowastlowastlowast 015+ 037lowast minus058lowastlowastlowast 060lowastlowastlowast minus051lowastlowastdaggerElev elevation (m) pH15 soil pH at 0ndash15 cm Ca15 Ca2+ (cmol(+)kg) at 0ndash15 cm K15 K+ (cmol(+)kg) at 0ndash15 cm N15 N concentration at 0ndash15 cmSand15 sand () at 0ndash15 cm Clay15 clay () at 0ndash15 cm CNrat30 C N ratio at 15ndash30 cm depth BS base saturation at 0ndash15 cm 119905-value was calculated as119905 = 119903radic(119899 minus 2)(1 minus 119903
2) where 119903 is the Pearson-correlation coefficient and 119899 is the total sample size
Table 4 Species richness observed in other studies from drymiombo woodlands
Plot size(ha)
Totalnumberof plots
Total samplearea(ha)
Total numberof species References
0071 70 495 82 [43]0071 80 565 110 [27]0071 133 940 229 [26]0071 247 1746 102 [44]004 40 160 81 [45]025 14 350 69 [25]01 2 020 40 [28]01 152 1520 92 genera [22]0071 35 247 88 This study
annual rainfall of 565ndash1500mm (Table 4) Using plot sizes of004ndash025 ha and sample sizes of 2ndash247 plots a total numberof species ranging from40 (Dbhge 10 cm) to 229 (Dbhge 2 cm)have been reported fromMiombowoodlands [22 25ndash28 43ndash45]
The high number of species reported by Banda et al [26]Chidumayo [22] Isango [45] and Chamshama et al [44] islikely to be a consequence of the spatial scale and coverage ofthese studies as they all cover large areas include more thanone site and operate with large sample sizes For exampleChidumayo [22] included species from both wet and dryMiombo areas in Zambia The study by Sauer and Abdallah[15] who reported a total of 131 species occurring in thetobacco growing zone of Iringa rural district included twoforest reserves and three family-managed forests Thereforeconsidering the climatic conditions in the GVLFR (617mmof rainfall per year) and the sample size (35 plots) thespecies richness reported in this study can be ranked atleast as high or higher than those found in the studiesmentioned The selective sampling approach focusing onvarious microhabitat types adopted by Banda et al [26] mayhave contributed to the large number of species observed
in their study area Similarly the higher number of speciesfound by Giliba et al [27] was probably due to the presenceof riverine forest offering site conditions favourable formanyspecies
The values of the Shannon-Wiener (1198671015840 = 344) andSimpson indices (119863 = 005) reported for large individ-uals in this study are within the range observed for mostcommunities of particular life forms [36] For example 1198671015840usually does not exceed 5 although this maximum valuevaries depending on the type of the biological communitysampled and the sampling approach applied (eg minimumdiameter and size of sample units) A threshold value of 2 for1198671015840 has been mentioned as a minimum value above which
an ecosystem can be regarded as medium to highly diverse[27] Chamshama et al [44] reported three 1198671015840 values of31 32 and 33 from Kitulangalo Miombo forest in EasternTanzania while Njana [43] reported a 1198671015840 value of 340from dry Miombo forest in Western Tanzania The studiesby Sauer and Abdallah [15] and Giliba et al [27] reportedparticularly high values of 346 and 427 respectively Thiscould be attributed to the very large sample sizes used inboth studies and the presence of riverine forest where thechance of encountering many rare species is likely to be highThe forests examined in the mentioned studies receive anaverage annual rainfall of 700ndash1000mm compared to whichthe rainfall in the GVLFR is low (617mm on average per yearcf above)The relatively high diversity found inGVLFRmusttherefore be attributed to other factors than climate The soilanalyses indicate that the soil fertility level in the GVLFRis relatively high with considerable proportions of all 2 1lattice clay minerals (114) (ie illite montmorillonite andvermiculite) plus a good amount of organic matter whichmay suggest that the soil has large reserves of important plantnutrients for example K Mg and Fe The base saturationpercentage (BS) which is normally used to indicate the soilfertility status (Landon 1991) was also high 76 on averageand with 86 of the samples (30 samples) with BS valuesgt50 (fertile soil) and only 14 of the samples (5 samples)with BS below 50 (less fertile soil) As another indicationof high soil fertility status the mean C N ratio was 12 plusmn 043
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
ISRN Biodiversity 13
(range 7ndash19) for top soil (0ndash15 cm) and 11 plusmn 036 (range 5ndash16) for subsoil (15ndash30 cm) In general the values for both soilstrata fall within the expected range of 8ndash17 Soil pH values(mean 67 plusmn 01 range 567ndash852)were alsowithin the optimalrangewhere all the nutrients that aremost important for plantgrowth (Ca Mg K and P) are available and accessible toplants [46] In this study 97 of the soil samples had pHbelow 8 (one sample had a pH of 852) Fisher and Binkley[47] noted that high pH is almost never a problem in forestsas most trees do well across the full range of common pHvaluesTherefore considering the low average annual rainfallcompared to other forests the relatively high fertility of theGVLFR may be the main factor creating an environmentfavourable to many species thus leading to a quite highspecies diversity observed in this forest
Based on the Importance Value Index Brachystegia spi-ciformis and Combretum molle appeared to be the mostimportant species among harvested trees (stumps) in thestudy area This result agreed very well with the large valuesof basal area and volume and the information obtained fromlocal scouts and members of the village environmental com-mittee who said that the most frequently harvested speciesfor charcoal making include all Brachystegia species found inthe forest (ie Brachystegia spiciformis Brachystegia mangaBrachystegia boehmii and Brachystegia bussei) Among theCombretum species the most important one (based on IVI)Combretum molle is mostly used for house construction andfirewood rather than for charcoal making Apart from theBrachystegia species the only other species mentioned asimportant for charcoal making was Acacia mellifera Otherspecies are only used in case they happen to fall when fellingthe preferred species during the preparation of charcoal kilnsWith respect to stumps Brachystegia spiciformis Brachystegiamanga and Combretum molle are among the ten mostabundant species in terms of density basal area and volumeper hectare Stumps of the other species mentioned are notcommonly found a factwhichmay be due to the small samplesize being unable to capture sites where these species areharvested but may also be related to their limited distributionin the forest as indicated by the estimated dispersion indices(see Table 1)
42 Forest Structure Species densities reported from otherMiombo woodland areas in Tanzania are typically 348ndash1495stemsha for trees with Dbh gt4 cm [24 43 45] Comparedwith this the GVLFR is highly stocked as the estimateddensity is 2296 stemsha for trees with Dbh gt4 cmThemeanstem density of 1521 plusmn 594ha for trees with Dbh ge5 cmis also more than twice as large as values found in otherstudies [25ndash27 44 45 48] A similar pattern is seen whencomparing the density of regeneration between the studiesThe diameter distribution is characterised by a very cleartrend of decreasing stem density with increasing diameterThe shape of the distribution is thus an inverted ldquoJrdquo (Figure 3)which is a common feature of natural forests with activeregeneration and recruitment [49] However not many largetrees were captured by the sample whereas a considerablenumber of relatively large stumps were observed suggestingthat anthropogenic activities such as charcoal making may
have affected the structure and ecological balance of the forest(see Figure 3) Since the woodlands are often hit by fire whichtends to kill the seedlings (especially the late fires) and thusonly few seedlings can be expected to reach larger diameterclasses lack of mature trees and the resulting lack of seedsmay eventually threaten the biodiversity of the forest
The mean basal area of 1355 plusmn 552m2ha observedin this study is slightly above the range of values typicallyreported from the miombo region 75ndash126m2ha [26 28 4344 50] However the basal areas reported by Isango [45] werea bit higher (1504ndash1563m2ha) than observed in this study
Themean total volume inGVLFRwas estimated at 9217plusmn390m3ha for trees with Dbh ge5 cm Other studies in dryMiombo woodlands have reported mean volumes of 167ndash7603m3ha [24 44 45 50] The current standing volume ofGVLFR is thus slightly higher than values typically reportedfor forests in the Miombo ecoregion A plausible reason forthis may be that compared to other forests the GVLFR isstill well stocked despite ongoing human activities such ascharcoalmakingThebasal area observed for stumpswas only072m2ha so there is no indication of intensive extractionover many years This corresponds well with the findings ofTreue et al [20] who conclude that the estimated 04m3haannual extraction of woody biomass from GVLFR is consid-erably below its estimated annual increment of 15m3ha andthat the local forest managers seem capable of regulating theharvesting activities which in addition to village membersrsquocollection for subsistence uses also included commercialcharcoal production by external companies that pay a fee perbag of charcoal extracted to the village government
43 Plant Communities and Species Associations Except forBrachystegia spiciformis the majority of the Brachystegia andJulbernardia species that are common to miombo wood-lands elsewhere are not among the species most commonlyobserved in this study A similar pattern was observed byBanda et al [26] in the Katavi-Rukwa ecosystem wherethe genera that are most common in miombo woodlandsBrachystegia Julbernardia and Isoberlinia were not commonin their study sites Furthermore the four plant communi-ties distinguished in this study are comparable with thosereported by other studies in Tanzania including the work byMunishi et al [29] in theMiombowoodlands of Rukwa basinChunya district Tanzania and the study by Banda et al [26]in the Katavi-Rukwa ecosystem
Elevation was noted by Munishi et al [29] to be themost important factor shaping the species communities intheir study area This study found a similar pattern with thehighest coefficient of correlation observed for elevation sug-gesting that topographical variation is among the strongestdeterminants of community composition in dry Miombowoodlands hence influencing the spatial distribution ofspecies strongly However in our study area edaphic factorsalso influenced the species distribution directly Two plantcommunities occurredmainly on clayey soils with high pH atintermediate elevations one on sandy soils with lowpHat lowelevations and one on sandy-clayey soils at higher elevationsand with neutral soil pH (Figure 5) Specifically it appeared
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
14 ISRN Biodiversity
that the Acacia woodland category (Community 5) grows atlow elevations around temporary streams (or at least wherethe ground water table is relatively high) and the Bauhiniawoodland category (Community 24) appearsmostly in placeswith a relatively high percentage of clay (and therefore highvalues of pH Ca and BS) By contrast it appeared that theDalbergia woodland category (Community 3) is located atmore sandy sites (with low values of pH Ca and BS) andthe Brachystegia woodland category (Community 1) seemedto be located mostly at higher elevations where the CN-ratio is quite high Correlations between elevation and thebasic soil variables pH Ca and percentages of sand andclay were low (cf Figure 5) Thus the results indicate thatthe plant communities of the dry miombo woodlands inGVLFR are not only shaped by the topographic variation(elevation) and the groundwater level but also by basic soilcharacteristics The results thus confirm findings from otherstudies detecting effects of elevation and soil characteristicson species composition [51 52]
5 Conclusion
Considering that the sample size used in this study wassmaller than samples used in other studies in dry miombowoodlands the results show that the species diversity inGVLFR is relatively high compared to other forest reservesThe vegetation of GVLFR is characterised by high densitybasal area and volume and despite the scarcity of largediameter trees this indicates that the forest is in a goodcondition The effect of anthropogenic activities is neverthe-less evident and stresses the need for proper managementespecially for economically important species preferred forcharcoalmaking (egBrachystegia spiciformis) if the currentspecies diversity is to be maintained or enhanced A repeatedfuture study would be needed to assess whether the currentcommunity-based management regime yields this intendedoutcome but the available information and analyses allow forsome optimism in this respect
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper
Acknowledgments
The authors wish to thank people from Mfyome villagefor their invaluable assistance during field work Mzee IddiKaheya Damian Chotamasege AugustinoMkwama Libera-tus Simime and Mawazo Nyangwa Furthermore thanks aredue to Venancia Mlelwa from Sokoine University and MadsMadsen Krag from University of Copenhagen for doing thelaboratory work The authors also wish to thank GoodluckMoshi their driverMarcoNjana andNassoroHMagogo theTAFORI botanist for assisting with species identification andKlaus Dons for preparing a study site map Financial supportwas provided by Danida through the ENRECA Project
ldquoParticipatory forest management for rural livelihoods forestconservation and good governance in Tanzaniardquo (no 725)
References
[1] P G H Frost ldquoThe Ecology of Miombo Woodlandsrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 11ndash57 1996
[2] N Burgess J Hales E Underwood et al Terrestrial Ecoregionsof Africa and Madagascar A Conservation Assessment IslandPress World Wildlife Fund 2004
[3] J Clarke W Cavendish and C Coote ldquoRural householdsand miombo woodlands use value and managementrdquo in TheMiombo in Transition Woodlands and Welfare in Africa BCampbell Ed pp 101ndash135 1996
[4] B M Campbell A Angelsen A Cunningham Y KaterereA Sitoe and S Wunder ldquoMiombo woodlandsmdashopportunitiesand barriers to sustainable forest managementrdquo httpsener-gypediainfoimages66dMiombo2007pdf
[5] S Syampungani P W Chirwa F K Akinnifesi G Sileshi andO C Ajayi ldquoTheMiombo woodlands at the cross roads poten-tial threats sustainable livelihoods policy gaps and challengesrdquoNatural Resources Forum vol 33 no 2 pp 150ndash159 2009
[6] J I O Abbot and K Homewood ldquoA history of change causesof miombo woodland decline in a protected area in MalawirdquoJournal of Applied Ecology vol 36 no 3 pp 422ndash433 1999
[7] E J Luoga E T F Witkowski and K Balkwill ldquoLand coverand use changes in relation to the institutional frameworkand tenure of land and resources in eastern Tanzania Miombowoodlandsrdquo Environment Development and Sustainability vol7 no 1 pp 71ndash93 2005
[8] FAO The State of Food and Agriculture 2010-2011 Forest andAgriculture Organization Rome Italy 2010
[9] A H Gentry ldquoTropical forest biodiversity distributional pat-terns and their conservational significancerdquo Oikos vol 63 no1 pp 19ndash28 1992
[10] O E Sala F S Chapin III J J Armesto et al ldquoGlobalbiodiversity scenarios for the year 2100rdquo Science vol 287 no5459 pp 1770ndash1774 2000
[11] H J Geist and E F Lambin ldquoProximate causes and underlyingdriving forces of tropical deforestationrdquo BioScience vol 52 no2 pp 143ndash150 2002
[12] R S DeFries ldquoTropical forests deforestation and ecosystemsservices Contributions of remote sensingrdquo in GeoinformaticsFor Tropical Ecosystems P S Roy Ed pp 1ndash32 2003
[13] B M Campbell P Frost and N Bryon ldquoMiombo woodlandsand their use overview and key issuesrdquo in The Miombo inTransition Woodlands and Welfare in Africa B Campbell Edpp 1ndash10 2006
[14] H J Geist ldquoGlobal assessment of deforestation related totobacco farmingrdquo Tobacco Control vol 8 no 1 pp 18ndash28 1999
[15] J Sauer and J M Abdallah ldquoForest diversity tobacco produc-tion and resource management in Tanzaniardquo Forest Policy andEconomics vol 9 no 5 pp 421ndash439 2007
[16] E N Chidumayo and K J Mbata ldquoShifting cultivation ediblecaterpillars and livelihoods in the Kopa area of northernZambiardquo Forests Trees and Livelihoods vol 12 no 3 pp 175ndash1932002
[17] E N Chidumayo and L Kwibisa ldquoEffects of deforestation ongrass biomass and soil nutrient status in miombo woodlandZambiardquo Agriculture Ecosystems and Environment vol 96 no1ndash3 pp 97ndash105 2003
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
ClimatologyJournal of
ISRN Biodiversity 15
[18] J F Lund and T Treue ldquoAre we getting there Evidence ofdecentralized forest management from the Tanzanian miombowoodlandsrdquoWorld Development vol 36 no 12 pp 2780ndash28002008
[19] T Blomley and S Iddi Participatory Forest Management inTanzania 1993ndash2009mdashLessons Learned and Experiences toDate Ministry of Natural Resources and Tourism Forestry andBeekeeping Division Dar es Salaam Tanzania 2009
[20] T Treue Y M Ngaga H Meilby et al ldquoDoes participatoryforest management promote sustainable forest utilisation inTanzaniardquo International Forestry Review vol 16 no 1 pp 1ndash162014
[21] Ministry ofNatural Resources andTourismParticipatory ForestManagement in Tanzania Facts and Figures Forestry andBeekeeping Division Dar es Salaam Tanzania 2008
[22] E N Chidumayo ldquoSpecies structure in Zambian miombowoodlandrdquo Journal of Tropical Ecology vol 3 no 2 pp 109ndash1181987
[23] J D Lowore ldquoCoppice regeneration in some miombo wood-lands of Malawirdquo FRIM Report 99001 Forestry ResearchInstitute of Malawi 1999
[24] E J Luoga E T F Witkowski and K Balkwill ldquoHarvestedand standing wood stocks in protected and communal miombowoodlands of eastern Tanzaniardquo Forest Ecology and Manage-ment vol 164 no 1ndash3 pp 15ndash30 2002
[25] M Williams C M Ryan R M Rees E Sambane J Fernandoand J Grace ldquoCarbon sequestration and biodiversity of re-growing miombo woodlands in Mozambiquerdquo Forest Ecologyand Management vol 254 no 2 pp 145ndash155 2008
[26] T Banda NMwangulango BMeyer et al ldquoThewoodland veg-etation of the Katavi-Rukwa ecosystem in western TanzaniardquoForest Ecology andManagement vol 255 no 8-9 pp 3382ndash33952008
[27] R A Giliba E K Boon C J Kayombo E B Musamba AM Kashindye and P F Shayo ldquoSpecies composition richnessand diversity in Miombo Woodland of Bereku Forest ReserveTanzaniardquo Journal of Biodiversity vol 2 no 1 pp 1ndash7 2011
[28] D D Shirima P K T Munishi S L Lewis et al ldquoCarbonstorage structure and composition of miombo woodlands inTanzaniarsquos Eastern Arc Mountainsrdquo African Journal of Ecologyvol 49 no 3 pp 332ndash342 2011
[29] P K T Munishi R A P C Temu and G Soka ldquoPlant com-munities and tree species associations in a Miombo ecosystemin the Lake Rukwa basin Southern Tanzania implications forconservationrdquo Journal of Ecology and Natural Environment vol3 no 2 pp 63ndash71 2011
[30] J Dewis and F Freitas ldquoPhysical analysis of soilsrdquo in Physicaland Chemical Methods of Soil andWater Analysis FAO Bulletinno 10 pp 51ndash57 2nd edition 1970
[31] S R Olsen and L E Sommers ldquoPhosphorusrdquo inMethods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 403ndash430 American Societyof Agronomy Madson Wis USA 1982
[32] J D Rhoades ldquoCation exchange capacityrdquo in Methods of SoilAnalysis Part 2 Agronomy Monograph No 9 A L Page R HMiller and P R Keeney Eds pp 149ndash157 American Society ofAgronomy Madson Wis USA 1982
[33] IMatejovic ldquoDetermination of carbon hydrogen and nitrogenin soils by automated elemental analysis (dry combustionmethod)rdquo Communications in Soil Science and Plant Analysisvol 24 no 17-18 pp 2213ndash2222 1993
[34] J P Moslashberg Soil Analysis Manual Revised Edition Laboratoryof Soil Sciences Department of Soil Science SokoineUniversityof Agriculture Morogoro Tanzania 2001
[35] N C BradyThe Nature and Properties of Soils Macmillan 8thedition 1974
[36] C J Krebs Ecological Methodology Hamper Collins PublishersNew York NY USA 2nd edition 1999
[37] B Husch T W Beers and J A Kershaw Forest MensurationJohn Wiley amp Sons Hoboken NJ USA 4th edition 2003
[38] E E Mwakalukwa H Meilby and T Treue ldquoVolume andaboveground biomass models for dry Miombo Woodland inTanzaniardquo In press
[39] M Kent Vegetation Description and Analysis A PracticalApproach Wiley-Blackwell John Wiley amp Sons Hoboken NJUSA 2nd edition 2012
[40] B McCune and M J Mefford PC-ORD Multivariate Analysisof Ecological Data Version 6 MjM Software Gleneden BeachOre USA 2011
[41] MDufrene andP Legendre ldquoSpecies assemblages and indicatorspecies the need for a flexible asymmetrical approachrdquo EcologyMonography vol 67 pp 777ndash795 1997
[42] J E PeckMultivariate Analysis for Community Ecologists Step-by-Step using PC-ORD MjM Software Design Gleneden BeachOre USA 2010
[43] M A Njana Arborescent species diversity and stocking inMiombo Woodland of Urumwa forest reserve and their contri-bution to Livelihoods Tabora Tanzania [MS thesis] SokoineUniversity of Agriculture Morogoro Tanzania 2008
[44] S A O Chamshama A G Mugasha and E Zahabu ldquoStandbiomass and volume estimation for Miombo woodlands atKitulangalo Morogoro Tanzaniardquo Southern African ForestryJournal no 200 pp 59ndash70 2004
[45] J Isango ldquoStand structure and tree species composition ofTanzania miombo woodlands a case study from miombowoodlands of community based forest management in Iringadistrictrdquo in MITMIOMBOmdashManagement of Indigenous TreeSpecies For EcosystemRestoration andWood Production in Semi-Arid Miombo Woodlands in Eastern Africa Proceedings ofthe 1st MITMIOMBO Project Workshop held in MorogoroTanzania 6th-12th February 2007 Working Papers of theFinnish Forest Research Institute no 50 pp 43ndash56 2007
[46] J R Landon Booker Tropical Soil Manual A Handbook ForSoil Survey and Agricultural Land Evaluation in the Tropics andSubtropics Longman Scientific amp Technical and John Wiley ampSons 1991
[47] R F Fisher and D Binkley Ecology and Management of ForestSoils John Wiley amp Sons 3rd edition 2000
[48] W A Rees ldquoPreliminary studies into bush utilization by Cattlein Zambiardquo Journal of Applied Ecology vol 11 pp 207ndash214 1974
[49] S M Philip Measuring Trees and Forests CAB InternationalWallingford UK 2nd edition 1994
[50] J D Lowore P G Abbot and M Werren ldquoStackwood volumeestimations formiombowoodlands inMalawirdquoCommonwealthForestry Review vol 73 no 3 pp 193ndash197 1994
[51] D Kubota T Masunaga Hermansah et al ldquoSoil environmentand tree species diversity in tropical rain forest West SumatraIndonesiardquo in Soils of Tropical Forest Ecosystems CharacteristicsEcology and Management A Schulte and D Ruhiyat Eds pp159ndash167 1998
[52] P K T Munishi T H Shear T Wentworth and R A PC Temu ldquoCompositional gradients of plant communities insubmontane rainforests of EasternTanzaniardquo Journal of TropicalForest Science vol 19 no 1 pp 35ndash45 2007
Submit your manuscripts athttpwwwhindawicom
Forestry ResearchInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental and Public Health
Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
EcosystemsJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
MeteorologyAdvances in
EcologyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Marine BiologyJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom
Applied ampEnvironmentalSoil Science
Volume 2014
Advances in
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Environmental Chemistry
Atmospheric SciencesInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Waste ManagementJournal of
Hindawi Publishing Corporation httpwwwhindawicom Volume 2014
International Journal of
Geophysics
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Geological ResearchJournal of
EarthquakesJournal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
BiodiversityInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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OceanographyInternational Journal of
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014
Journal of Computational Environmental SciencesHindawi Publishing Corporationhttpwwwhindawicom Volume 2014
Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014
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