Post on 11-Feb-2019
1 23
Brazilian Journal of Botany ISSN 0100-8404Volume 39Number 2 Braz. J. Bot (2016) 39:605-612DOI 10.1007/s40415-016-0273-z
Determinants of variation in heathvegetation structure on coastal dune fieldsin northeastern South America
Augusto C. Silva, José Luiz A. Silva &Alexandre F. Souza
1 23
Your article is protected by copyright and
all rights are held exclusively by Botanical
Society of Sao Paulo. This e-offprint is for
personal use only and shall not be self-
archived in electronic repositories. If you wish
to self-archive your article, please use the
accepted manuscript version for posting on
your own website. You may further deposit
the accepted manuscript version in any
repository, provided it is only made publicly
available 12 months after official publication
or later and provided acknowledgement is
given to the original source of publication
and a link is inserted to the published article
on Springer's website. The link must be
accompanied by the following text: "The final
publication is available at link.springer.com”.
Determinants of variation in heath vegetation structure on coastaldune fields in northeastern South America
Augusto C. Silva1 • Jose Luiz A. Silva1 • Alexandre F. Souza1
Received: 5 June 2015 / Accepted: 16 March 2016 / Published online: 29 March 2016
� Botanical Society of Sao Paulo 2016
Abstract Despite its implications for carbon storage,
animal conservation, and plant regeneration, the variation
in the structure of heath vegetation in South America is still
poorly studied. In this study, we aimed at examining the
edaphic and topographic determinants of this variation
along 85 plots (5 9 5 m) randomly distributed in a restinga
heath vegetation occurring on coastal dune fields in
northeastern Brazil. We carried out a PCA analysis to
reduce eleven vegetation descriptors into a small number of
structural gradients, which were then assessed by a step-
wise standard least-squares multiple regression to reveal
the effects of the abiotic environment on structure. The
three following hypotheses were tested: (1) both soils and
topography are important to explain variation in vegetation
structure at local scale; (2) herbaceous plants, cactus, and
woody plants show differential responses to soil and
topographic variations; and (3) soil acidity and salinity are
more important determinants of herbaceous cover than
woody plant variation. PCA analysis revealed three major
structural gradients related to biomass, herbaceous cover,
and leaning plants, respectively. These gradients were only
related to calcium and nitrogen contents, which partially
supports our first hypothesis. Our results also suggest that
different groups of plants have different responses to abi-
otic gradients that are exposed. The effect of the soil
acidity and salinity did not appear to present an immediate
strong influence on the herbaceous community. It seems
that a reduced number of edaphic factors promote the
variation in vegetation structure in the restinga heath
vegetation.
Keywords Communities � PCA � Soil � Topography �Woody plants
Introduction
The detection of patterns in the determinants of vegetation
structure has been necessary mainly to understand plant
ecological communities (Werger and Sprangers 1982; Gao
et al. 2014). Recent advances in this area have been reg-
istered regarding biomass variation in forested ecosystems,
due to its implications for global carbon balance and
warming effects (Houghton 2005; Colgan et al. 2012;
Rosenfield and Souza 2014). However, other aspects of
vegetation structure like height, diameter distribution, and
lateral growth provide important descriptors about plant
growth strategies (McGill et al. 2006), and the complexity
of vegetation structure has ecological consequences for
birds (Dıaz 2006) and mammals (Moore et al. 2014) in
local communities. Vegetation structure variation is
directly linked to plant species diversity (Gao et al. 2014)
and productivity (Clark and Clark 2000; Slik et al. 2013). A
solid understanding of vegetation structure variation is thus
key to the ecological knowledge of natural plant commu-
nities (Petersen and Drewa 2009; Rosenfield and Souza
2014) as well as for sustainable management (Gao et al.
2014) and conservation plans (Oliveira Filho et al. 2013).
Despite its importance, the variation in the structure of
restinga heath vegetation in South America is still poorly
Electronic supplementary material The online version of thisarticle (doi:10.1007/s40415-016-0273-z) contains supplementarymaterial, which is available to authorized users.
& Augusto C. Silva
agst.25@gmail.com
1 Departamento de Ecologia, CB, Universidade Federal do Rio
Grande do Norte, Campus Universitario, Lagoa Nova, Natal,
RN 59072-970, Brazil
123
Braz. J. Bot (2016) 39(2):605–612
DOI 10.1007/s40415-016-0273-z
Author's personal copy
studied. This understanding is particularly difficult due to
its high local variance along the coast plains (Magnago
et al. 2010). Brazilian restinga heath vegetation physiog-
nomy varies from herbaceous communities on mobile sand
dunes near the shore to a mosaic of open shrub-dominated
and short forest patches (Martins et al. 2008; Scarano 2009;
Oliveira Filho 2009). The heterogeneity of this vegetation
structure mosaic has been described in qualitative terms,
and its relationship with potentially determinant edaphic
factors remains unknown (Scarano 2002; Santos Filho et al.
2013). Coastal sand plains are subjected to intense edaphic
gradients (Scheel-Ybert 2000; Gomes et al. 2007) due to
salinity and acidity variation in a background of overall
low nutrient availability. In tropical areas, the coastal
vegetation receives high levels of solar incidence and
elevated temperatures (Scarano 2002; Oliveira et al. 2014).
Topography variation influences vegetation structure
through direct and indirect mechanisms along coastal areas
due to the formation of dune fields (Cordeiro 2005). Local
elevation and slope alter vegetation exposure to wind and
solar incidences, as well as produce changes in soil mois-
ture (Moeslund et al. 2013), producing varying degrees of
environmental stress to plants (Menezes and Araujo 2000).
Thus, both soil- and topographic-related variables are
expected to play their respective roles in the variation of
restinga heath vegetation (Lane et al. 2008; Assis et al.
2011; Fenu et al. 2012; Tissier et al. 2013).
Here we aimed at examining abiotic determinants of
variation in the structure of restinga heath vegetation
occurring on coastal dune fields in northeastern Brazil.
Specifically, we tested the following hypotheses: (1) both
soil and topographic factors are important to explain varia-
tion in vegetation structure at local scale. However, due to
the overall low nutrient availability, topographic factors are
more important than soil-related factors (Lane et al. 2008;
Assis et al. 2011; Fenu et al. 2012); (2) herbaceous plants,
cactus, and woody plants show differential responses to soil
and topographic variations (Petersen and Drewa 2009;
Moeslund et al. 2013; Tissier et al. 2013); and (3) soil acidity
and salinity are more important determinants of herbaceous
cover than woody plant variation due to the closer associa-
tion of the former with open, near shore areas and because it
is the short life cycle of the organism that may have affected
their development by such aspects (Guedes et al. 2006; Lane
et al. 2008; Petersen and Drewa 2009).
Materials and methods
Study area
The study was carried out in the Barreira do Inferno Lauch
Center, Parnamirim municipality, Rio Grande do Norte
state, northeastern Brazil (5�540S, 35�100W, Fig. 1). The
Launch Center belongs to the Brazilian Air Force, and
human activities are restricted to aerospace research since
1965 (CLBI 2014). The Launch Center’s 1900 ha area
accompanies the roughly North–South coastline along
which a set of tall (ca. 80 m high) sand dunes for up to
2.0 km, where it is replaced by relatively flat sandy plain
(ca. 40 m a.s.l.). Soils are nutrient-poor white sand Neosols
with patches of red or yellow Latosols (SUDENE/DNPEA
1971). Climate is tropical with a severe dry season (Aw,
Peel et al. 2007). Mean annual temperature is 26 �C and
mean annual precipitation is 1746 mm, concentrated
between March and August (INMET 2014). Vegetation is a
mosaic of herbaceous, scrub and restinga forest. Myrtaceae
is the most abundant family in the study area. See Silva
et al. (2015) for a complete description of the plant com-
munity and list of woody species in the studied area.
Data collection
Data were collected in 85 (5 9 5 m) plots randomly dis-
tributed along 17 (100 m long) transects (five plots per
transect). Transects were placed perpendicularly to preex-
isting trails scattered through the distinct physiognomies. All
woody plants with diameter at soil level C3.0 cm were
numbered and measured for diameter at soil level, total
height, number of ramets, and visually classified as Leaning
when leaning at an angle C45�. Litter depth was measured to
the nearest cm at the corners of each plot. Seedling density as
well as grass and forb cover were measured at 1 m2 smaller
plots. We also estimated canopy openness through three
nonoverlapping wide-angle digital photographs at each plot.
The photographs were taken using a 16-mm lens and a digital
still camera (Sony a57, Sony Corporation, Japan) during
uniformly overcast sky conditions.
Three topographic attributes were obtained for each plot:
elevation, terrain convexity, and slope. For the calculation of
elevation and convexity, 20 9 20 m virtual areas sur-
rounding each 5 9 5 m plot were used based on a Google
Earth 7.1.2.2014 satellite image. Elevation of a plot was
defined as the mean of the elevation values at its center and
four corners. Convexity was the elevation of the center of
the plot of interest minus the mean elevation of the four
corners (Legendre et al. 2009). Slope was measured in field
with a HEC Haglof digital clinometer perpendicular to the
elevation contour within each plot midline. Several edaphic
attributes were also collected. pH, Ca, Mg, Na, K, P, total N,
cation exchange capacity, organic matter, silt ? clay con-
tent, soil density, and moisture were obtained from three
1-kg soil surface samples (0–20-cm depth) collected using a
soil auger near the corners of each plot. The samples were
then bulked and subsampled to form 1-kg sample per plot.
The organic matter layer was removed before sampling. The
606 A. C. Silva et al.
123
Author's personal copy
laboratory methods used for soil analyses are detailed in
EMBRAPA/CNPS (1997). See Silva et al. (2015) for a
complete description of the abiotic setting.
Data analysis
To analyze the effect of environmental variables on veg-
etation structure, we conducted a principal components
analysis (PCA, function ‘principal’ from package ‘psych’
in software R 2.15.3) on a correlation matrix using vege-
tation structure descriptors (woody plant density, basal
area, average height, inclined plant, ramifications, forbs,
grassy, canopy openness, seedlings, cactus, and leaf litter
depth).Variables were log- or square root transformed
when necessary to achieve normality and standardized
prior to analysis. Significance of component loadings was
obtained from Hair et al. (1998) based on sample size
needed to attain significance based on a 0.05 significance
level, a power level of 80 % and standard errors assumed to
be twice those of conventional correlation coefficients.
Each PCA axis values were plotted considering longitude
and latitude as x-axis and y-axis in scatterplots to display
spatial variations in vegetation structure among sites, using
Rstudio Program v. 2.15.3.
We used stepwise standard least-squares multiple
regression to assess the effects of edaphic and topographic
variables on principal components. Variable selection was
performed using the Akaike Informaton Criterion through
function stepAIC of MASS library using direction ‘‘both,’’
and they were considered only for the final model of the
variables with a significance level of\5 % (R Core Team
2013). When more than one explanatory variable was
included in the model, both partial and standardized partial
regression coefficients are shown, since the latter can be
compared directly to show the relative standardized
strengths of the effects of several independent variables on
the same dependent variable.
Results
The soil and topographic environment
The soils of the study area have high sodium content, are
acidic, have low fertility, and have a mainly sandy texture
(Supplementary Material Table 1). Higher plots showed
looser soils that were also richer in aluminum and had
higher mineral nutrients as showed by positive correlations
between elevation and cation exchange capacity and sum
of bases. Organic matter was weakly related with elevation
but seemed to play an important role in nutrient availability
due to its positive correlation with most of mineral ion
concentrations, as well as with cation exchange capacity
and calcium in these environments. Soils with greater
Fig. 1 Geographical location of the study area in northeastern Brazil and the spatial distribution of the study transects in coastal vegetation areas.
Shades of gray correspond to mobile dunes (light gray), scrub (medium gray) and forest (dark gray) physiognomies
Determinants of variation in heath vegetation structure on coastal dune fields in… 607
123
Author's personal copy
silt/clay content were found in the lower plots (silt/clay
fraction was negatively correlated with elevation, Table 1).
Soil nutrients were, however, not related to silt/clay con-
tent, but were found mostly on dune slopes, as depicted by
the correlations of cation exchange capacity and phos-
phorus with elevation and slope, and the indirect relation-
ship between sum of bases and elevation through its
correlation with cation exchange capacity (Table 1). Soil
moisture increased with elevation, as well as sodium con-
tent. Soil nutrients (sum of bases and cation exchange
capacity) were lower in plots with higher density soils.
Vegetation structure
Vegetation descriptors revealed an overall short
(3.04 ± 1.50 m) and considerably open canopy
(36 % ± 0.32 canopy openness) (Table 1). The number of
stems was larger (20.17 ± 15.99 ramets/plot) than the
number of individuals (12.70 ± 8.88 ind./plot), and stem
leaning was also frequent among individuals
(0.32 ± 0.27 % ind./plot). The herbaceous covers were
quite variable, respectively, forbs (0.05 ± 0.17 % cover)
and grassy (0.03 ± 0.10 % cover). The soil was covered by
an average cm deep leaf litter (3.28 ± 2.51 cm).
Three PCA axes summarized the 11 vegetation structure
descriptors, explaining together 69 % of the variation in the
data (Table 2). The first axis alone explained 41 % of the
total variation and was correlated with most variables
(Fig. 2): number of ramets (0.91), wood plant density (0.90),
basal area (0.82), tree height (0.78), litter depth (0.68), and
canopy openness (-0.89) (Table 2). The second axis
described a gradient in herbaceous cover, with positive
correlationswith grass (0.80) and forb (0.79) cover. The third
axis was positively correlated with the number of leaning
stems (0.76) and cactus basal area (0.55), and negatively
correlated with the number of woody plant regeneration
(-0.60). When PCA axes were plotted to display spatial
variation in vegetation structure among sites, high similari-
ties in restinga physiognomies at short scales were shown
(distances among plots or transects) that tended to decrease
at larger scales (distances among areas), as well as nonlinear
vegetation gradients along shore to inland (Fig. 3).
Relationship between abiotic factors and vegetation
structure
A large portion of the variation (57.3 %) in the main
vegetation structure gradient (PCA1) was explained by
variation in soil pH, Ca, total nitrogen, moisture, and with a
minimum contribution by elevation (F = 23.5;
P = 2.2 9 10-14) (Table 3). The vegetation complexity
and biomass depicted by this axis was explained by more
acidic soils (-0.51), richer in the macronutrients Ca (0.94)
and total N (0.48), and with higher soil moisture (0.27).
Even with a statistical significance of elevation to increase
the woody layer, it was not a biological significance (0.01).
Variation in herbaceous cover (PCA2) was weakly
(16.5 %) explained by the combined soil texture fractions
of silt and clay (-0.01), while the variation of cacti and
plants in the third inclined shaft PCA was weakly (6 %)
elevation explained by (-0.02).
Discussion
A growing body of research has shown that topographic
and soil conditions frequently act together not only in the
assembly of plant communities but also in the determina-
tion of vegetation structure (Lane et al. 2008; Colgan et al.
Table 1 Averages and standard deviations of measured topographic,
soil, and vegetation structure variables
Variable Value
Vegetation structure
Woody plant basal area (m) 0.06 ± 0.06
Cactus basal area (m) 0.001 ± 0.003
Wood plant density (ind./plot) 12.70 ± 8.88
Seedling density (ind. m2) 21.55 ± 32.81
Leaning plants (% ind./plot) 0.32 ± 0.27
Ramet density (ramets/plot) 20.17 ± 15.99
Height (m) 3.04 ± 1.50
Grass cover (% cover) 0.03 ± 0.10
Forb cover (% cover) 0.05 ± 0.17
Leaf litter depth (cm) 3.28 ± 2.51
Canopy openness (% pixels) 0.36 ± 0.32
Soil and topography
pH 5.64 ± 0.49
Humidity (%) 4.07 ± 2.32
Density (kg dm-3) 1.34 ± 0.08
Ca (cmolc dm3) 0.38 ± 0.21
Mg (cmolc dm3) 0.33 ± 0.24
Na (cmolc dm3) 0.13 ± 0.08
K (cmolc dm3) 0.09 ± 0.06
P (cmolc dm3) 16.59 ± 14.36
Total N (cmolc dm3) 0.67 ± 0.45
Sum of bases (cmolc dm3) 0.93 ± 0.39
Cation exchange capacity (cmolc dm3) 4.48 ± 2.21
Silt ? clay content (g kg-1) 40.88 ± 22.38
Organic matter (g kg-1) 23.80 ± 12.38
Slope (�) 9.38 ± 8.97
Elevation (m) 43.20 ± 16.11
Convexity (m) 0.41 ± 1.82
The table lists the variables collected accompanied by his unit of
measure and their respective means and standard deviations
608 A. C. Silva et al.
123
Author's personal copy
2012; Lin et al. 2012; Moeslund et al. 2013; Rosenfield and
Souza 2014). Contrary to results from other restingas, in
which both topography and soil characteristics showed to
be determinants of vegetation variation (Scarano 2002;
Guedes et al. 2006; Assis et al. 2011; Santos Filho et al.
2013; Silva et al. 2015), vegetation structure in the study
area was shown to be only affected by a reduced number of
soil factors. This result contradicts our first hypothesis,
which stated that both soil and topographic factors are
important to explain variation in vegetation structure at
local scale. It is likely that variations in vegetation structure
are composed of the differential responses of particular
species or species groups to edaphic variation (Petersen
and Drewa 2009), as the abundance of species of distinct
sizes and architectures respond in different ways to varia-
tions in the abiotic setting. Furthermore, the reduced
heterogeneity of topographic and edaphic characteristics in
the study area relative to the woody vegetation are
attributable to the young age of the sand dunes we studied
(Gomes et al. 2007; Assis et al. 2011; Lima et al. 2011).
We found that forested physiognomies in the heath
vegetation, as depicted by higher values in the first PCA
axis, was favored by more acidic soils richer in calcium
and total nitrogen. This is attributable to organic matter
accumulation in the forested areas, with the consequent
production of organic acids and biomass build up (Queiroz
et al. 2012). The fact that these same factors were not
relevant for the herbaceous cover in general confirms our
second hypothesis, namely, herbaceous plants, cactus, and
woody plants show differential responses to soil and
topographic variations (Moeslund et al. 2013; Santos Filho
et al. 2013; Tissier et al. 2013). These results indicate that
distinct plant groups and vegetation layers respond in very
different ways to the same edaphic gradients found in the
coastal sand plains, and thus that distinct mechanistic
explanations should be developed for each of these groups
(Petersen and Drewa 2009).
Woody plants on steep fields are exposed to varying
degrees of wind exposure, solar incidence, and water
availability that are to a large extent explained by undu-
lated topography (Moeslund et al. 2013). Windward slopes
and dune tops on coastal fields tend to lose water, nutrients,
and organic matter faster than more protected microsites
like leeward slopes of valleys between adjacent dunes
(Scarano 2009; Giaretta et al. 2013). Furthermore, low
wind erosion and stress in protected areas reduce physical
damage to established plants as well as promotes natural
regeneration (Moeslund et al. 2013). An increase in woody
plant biomass has been found to accompany increased soil
organic matter in plots further inland in other restingas
(Cordeiro 2005). In our study area, our results suggest two
possible explanatory scenarios: (1) topography influences
variation in soil properties which in turn shapes vegetation
structure; (2) edaphic variation results from a reinforce-
ment mechanism produced by the vegetation itself with
topographic effects reduced to its effect on vegetation due
to wind and moisture variations.
Fig. 2 Principal components ordination of restinga heath vegetation
in Parnamirim, northeastern South America. Dots correspond to plots
and arrows correspond to environmental variables
Table 2 Main results of the Principal Components Analysis on
vegetation structure variables
Axis 1 Axis 2 Axis 3
Eigenvalue 4.52 1.68 1.40
Explained variance (%) 41 15 13
Component loadings
Plant basal area 0.82 -0.22 -0.06
Cactus basal area -0.05 0.02 0.55
Plant density 0.90 0.04 -0.13
Seedlings density 0.50 0.14 -0.60
Leaning plants 0.20 0.24 0.76
Ramets density 0.91 -0.04 -0.07
Height 0.78 -0.35 -0.05
Grass cover -0.15 0.80 0.02
Forb cover -0.20 0.79 0.14
Leaf litter 0.68 -0.34 0.32
Canopy openness -0.89 0.25 -0.07
Eigenvalues and percentage of the variance explained by each axis of
the PCA are shown, as well as loading components (eigenvectors) for
each variable associated to each axis
Bold figures represent significant component loadings
Determinants of variation in heath vegetation structure on coastal dune fields in… 609
123
Author's personal copy
Soil acidity and salinity have been shown to play an
important role in the distribution of coastal herbaceous
plants (Magnago et al. 2010). Although herbaceous plants
in coastal regions frequently present strategies to deal with
increased soil salinity, too salty soil patches are frequently
found to reduce their abundance and occurrence, due to
root development impairment and consequent reduced
access to soil resources (Guedes et al. 2006). Acidic soils
reduce the availability of Ca, Mg, K, and P while
increasing the availability of compounds potentially toxic
to plants (Brady and Weil 2013). The lack of response of
the herbaceous cover to the topographic and soil factors we
measured suggests the existence of unmeasured gradients
in the study area, such as light and temperature gradients
(Pires et al. 2006; Guedes et al. 2006; Giaretta et al. 2013).
This result contradicts our third hypothesis, which stated
that soil acidity and salinity are more important determi-
nants of herbaceous cover than woody plant variation due
to the closer association of the former with open, near-
shore areas, and possible nutritional reduction and damage
that these the soil components can cause to the root system
these of plants (Guedes et al. 2006; Lane et al. 2008;
Petersen and Drewa 2009).
Besides being associated with abiotic variables, vege-
tation structure has been shown to be associated with sig-
nificant compositional (Sundarapadian and Swamy 1999;
Peterson and Reich 2008; Gao et al. 2014) and productivity
gradients (Clark and Clark 2000). Biomass variation has
important implications for the structure of trophic webs
(Uetz 1991) and for the provisioning of distinct habitat
types for the fauna (Tews et al. 2004; Dıaz 2006; Moore
et al. 2014). These effects seem to be stronger in olig-
otrophic ecosystems like heath vegetation (Oliveira Filho
et al. 2013). Vegetation complexity usually accompanies
variation in vegetation biomass, and modulates the
ecosystem services provided by the vegetation like the
fixation of sand dunes (Menezes and Araujo 2000; Tabalipa
and Fiori 2008). All the above-mentioned effects make
vegetation structure an important plant community
descriptor, mainly when considering the reduced sampling
effort needed to study vegetation structure relative to spe-
cies composition (Werger and Sprangers 1982).
Our results suggest that the combined effect of topog-
raphy and soil nutrients, which has been shown to produce
structuring effects on the heath vegetation floristic gradi-
ents, is not important in the shaping of vegetation structure.
Fig. 3 Spatial distributions of
a sample plots. Ellipses indicate
clusters of transects used as
small scales; b magnitude of
variation in the structure
according to the size of the
circles related to PCA axis 1,
and the woody, respectively;
c variation structural relative to
the axis 2, concerning
herbaceous plants; d variations
related to axis 3, cactus, and
leaning plants in the Parnamirim
restinga
610 A. C. Silva et al.
123
Author's personal copy
Calcium and nitrogen showed to be limiting factors for the
development of vegetation structure and biomass build up
in the restinga. Woody and herbaceous plants show dif-
ferential responses to the same abiotic gradients found in
the restinga. Herbaceous plants seem to respond to envi-
ronmental gradients other than those related to topographic
and soil nutrient ones. It seems that a reduced number of
edaphic factors promote the variation in vegetation struc-
ture in the restinga heath vegetation.
Acknowledgments Financial support was provided by Fundacao de
Apoio a Pesquisa do Estado do Rio Grande do Norte (FAPERN)
through the Grant Edital N8005/2011 – Programa Primeiros Projetos
IV, by CAPES through a master scholarship to JLAS, and by CNPq
through a scientific initiation scholarship to ACS. The authors thank
the Brazilian Air Force, Aviator Cel. Luiz Guilherme S. Medeiros,
and Glauberto Leilson for facilitation of access to the Barreira do
Inferno Launch Center. The authors are thankful to Amarilys D.
Bezerra, Atila D.E. Melo, Angelica A. Souza, and Morvan Franca for
their invaluable help in the field. Comments by Adriano C. F. Silva
and Luiz A. Cestaro greatly helped to improve an earlier version of
this manuscript.
References
Assis MA, Prata EMB, Pedroni F, Sanchez M, Eisenlohr PV et al
(2011) Florestas de restinga e de terras baixas na planıcie
costeira do sudeste do Brasil: vegetacao e heterogeneidade
ambiental. Biota Neotrop 11:103–121
Brady NC, Weil RR (2013) Elementos da natureza e propriedades do
solo, 3rd edn. Bookman, Porto Alegre
Clark DB, Clark DA (2000) Landscape-scale variation in forest
structure and biomass in a tropical rain forest. Forest Ecol Manag
137:185–198
CLBI (2014) Centro de Lancamento da Barreira do Inferno. http://
www.clbi.cta.br/new/index.php
Colgan MS, Asner GP, Levick SR, Martin RE, Chadwick OA (2012)
Topo-edaphic controls over woody plant biomass in South
African savannas. Biogeosciences 9:1809–1821
Cordeiro SZ (2005) Composicao e distribuicao da vegetacao herbacea
em tres areas com fisionomias distintas na Praia do Pero, Cabo
Frio, RJ, Brasil. Acta Bot Bras 19:679–693
Dıaz L (2006) Influences of forest type and forest structure on bird
communities in oak and pine woodlands in Spain. Forest Ecol
Manag 223:54–65
EMBRAPA/CNPS (1997) Manual de Metodos de Analise de Solos,
2nd edn. EMBRAPA/CNPS, Rio de Janeiro
Fenu G, Carboni M, Acosta ATR, Bacchetta G (2012) Environmental
factors influencing coastal vegetation pattern: new insights from
the Mediterranean Basin. Folia Geobot 48:493–508
Gao T, Hedblom M, Emilsson T, Nielsen AB (2014) The role of
forest stand structure as biodiversity indicator. Forest Ecol
Manag 330:82–93
Giaretta A, Menezes LFT, Pereira OJ (2013) Structure and floristic
pattern of a coastal dunes in southeastern Brazil. Acta Bot Bras
27:87–107
Gomes FH, Vidal-Torrado P, Macıas F, Gherardi B, Perez XLO
(2007) Solos sob vegetacao de restinga na Ilha do Cardoso (SP).
I—Caracterizacao e classificacao. Rev Bras Cienc Solo
31:1563–1580
Guedes D, Barbosa LM, Martins SE (2006) Composicao florıstica e
estrutura fitossociologica de dois fragmentos de floresta de
restinga no Municıpio de Bertioga, SP, Brasil. Acta Bot Bras
20:299–311
Hair JF Jr, Anderson RE, Tathan RL, Black WC (1998) Multivariate
data analysis, 5th edn. Prentice Hall, Upper Saddle River
Houghton RA (2005) Aboveground forest biomass and the global
carbon balance. Glob Chang Biol 11:945–958
INMET (2014) Instituto Nacional de Meteorologia. http://www.
inmet.gov.br
Lane C, Wright SJ, Roncal J, Maschinski J (2008) Characterizing
environmental gradients and their influence on vegetation
Table 3 Multiple regression models between vegetation structure principal components and topographic and soil variables
Coefficients Estimate std. Error t value P([|t|)
PCA1 (initial model AIC: 185.79; final model AIC: AIC: 176.76)
(Intercept) 2.38 1.14 2.08 0.039*
Elevation (m) 0.01 (0.02) 0.00 3.12 0.002**
pH -0.51 (-0.79) 0.16 -3.13 0.002**
Ca (cmolc dm3) 0.94 (0.98) 0.36 2.60 0.01*
Humidity (%) 0.27 (0.24) 0.08 3.15 0.002**
Total N (cmolc dm3) 0.48 (0.47) 0.11 4.25 0.000***
R2 adjusted = 0.57; F = 23.5; P = 2.2 9 10-14
PCA2 (initial model AIC: 241.49; final model AIC: 229.89)
(Intercept) 0.75 0.20 3.67 0.000***
Silt ? clay (g kg-1) -0.01 (-0.01) 0.00 -4.19 0.000***
R2 adjusted = 0.16; F = 17.57; P = 6.8 9 10-5
PCA3 (initial model AIC: 250.71; final model AIC: 239.42)
(Intercept) 0.74 0.30 2.46 0.01*
Elevation (m) -0.02 (-0,02) 0.00 -2.62 0.01*
R2 adjusted = 0.06; F = 6.90; P = 0.010
Standardized regression coefficients and nonstandard (in parentheses) are shown, with significant values detached by asterisks
Determinants of variation in heath vegetation structure on coastal dune fields in… 611
123
Author's personal copy
zonation in a subtropical coastal sand dune system. J Coast Res
24:213–224
Legendre P, Mi X, Ren H, Ma K, Yu M, Sun IF et al (2009)
Partitioning beta diversity in a subtropical broad-leaved forest of
China. Ecology 90:663–674
Lima RAF, Oliveira AA, Martini AMZ, Sampaio D, Souza VC et al
(2011) Structure, diversity, and spatial patterns in a permanent
plot of a high restinga forest in Southeastern Brazil. Acta Bot
Bras 25:633–645
Lin D, Lai J, Muller-Landau HC, Mi X, Ma K (2012) Topographic
variation in aboveground biomass in a subtropical evergreen
broad-leaved forest in China. PLOS One 7:e48244
Magnago LFS, Martins SV, Schaefer CEGR, Neri AV (2010)
Gradiente fitofisionomico-edafico em formacoes florestais de
Restinga no Sudeste do Brasil. Acta Bot Bras 24:734–746
Martins SE, Rossi L, Sampaio PSP, Magenta MAG (2008) Caracter-
izacao florıstica de comunidades vegetais de Restinga em
Bertioga, SP, Brasil. Acta Bot Bras 22:249–274
McGill BJ, Enquist BJ, Weiher E, Westoby M (2006) Rebuilding
community ecology from functional traits. Trends Ecol Evol
21:178–185
Menezes LFT, Araujo DSD (2000) Variacao da biomassa aerea de
Allagopteraarenaria (Gomes) O. Kuntze (Arecaceae) em uma
comunidade arbustiva de Palmae na restinga de Marambaia, RJ.
Rev Bras Biol 60:147–157
Moeslund JE, Arge L, Bøcher PK, Dalgaard T, Svenning JC (2013)
Topography as a driver of local terrestrial vascular plant
diversity patterns. Nord J Bot 31:129–144
Moore TL, Valentine LE, Craig MD, Hardy GSJ, Fleming PA (2014)
Does woodland condition influence the diversity and abundance
of small mammal communities? Aust Mammal 36:35–44
Oliveira AA, Vicentini A, Chave J, Castanho CT, Davies SJ et al
(2014) Habitat specialization and phylogenetic structure of tree
species in a coastal Brazilian white-sand forest. J Plant Ecol
7:134–144
Oliveira Filho AT (2009) Classificacao das fitofisionomias da
America do Sul Cisandina tropical e subtropical: proposta de
um novo sistema—pratico e flexıvel—ou uma injecao a mais de
caos? Rodriguesia 60:237–258
Oliveira Filho AT, Budke JC, Jarenkow JA, Eisenlohr PV, Neves
DRM (2013) Delving into the variations in tree species
composition and richness across South American subtropical
Atlantic and Pampean forests. J Plant Ecol. doi:10.1093/jpe/
rtt058
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of
the Koppen-Geiger climate classification. Hydrol Earth Syst Sci
11:1633–1644
Petersen SM, Drewa PB (2009) Are vegetation—environment
relationships different between herbaceous and woody ground-
cover plants in barrens with shallow soils? Ecoscience
16:197–208
Peterson DW, Reich PB (2008) Fire frequency and tree canopy
structure influence plant species diversity in a forest-grassland
ecotone. Plant Ecol 194:5–16
Pires LA, Britez RM, Martel G, Pagano SN (2006) Producao,
acumulo e decomposicao da serapilheira em uma restinga da Ilha
do Mel, Paranagua, PR, Brasil. Acta Bot Bras 20:173–184
Queiroz EP, Cardoso DBOS, Ferreira MHS (2012) Composicaoflorıstica da vegetacao de restinga da APA Rio Capivara, Litoral
Norte da Bahia, Brasil. Sitient serie Cienc Biol 12:119–141
R Core Team (2013) R: a language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna
Rosenfield MF, Souza AF (2014) Forest biomass variation in
Southern most Brazil: the impact of Araucaria trees. Rev Biol
Trop 62:359–372
Santos Filho FS, Almeida EB Jr, Zickel CS (2013) Do edaphic aspects
alter vegetation structures in the Brazilian restinga? Acta Bot
Bras 27:613–623
Scarano FR (2002) Structure, function and floristic relationships of
plant communities in stressful habitats marginal to the Brazilian
Atlantic rain forest. Ann Bot 90:517–524
Scarano FR (2009) Plant communities at the periphery of the Atlantic
rain forest: rare-species bias and its risks for conservation. Biol
Conserv 142:1201–1208
Scheel-Ybert R (2000) Vegetation stability in the Southeastern
Brazilian coastal area from 5500 to 1400 14C yr BP deduced
from charcoal analysis. Review Palaeobot Palyno 110:111–138
Silva JLA, Souza AF, Jardim JG, Goto BT (2015) Community
assembly in harsh environments: the prevalence of ecological
drift in the heath vegetation of South America. Ecosphere 6:111.
doi:10.1890/ES14-00548.1
Slik JWF, Paoli G, McGuire K, Amara I, Barroso J et al (2013) Large
trees drive forest aboveground biomass variation in moist
lowland forests across the tropics. Glob Ecol Biogeogr. doi:10.
1111/geb.12092
SUDENE/DNPEA (1971) Levantamento exploratorio: Reconheci-
mento dos solos do estado do RN, 1st edn. SUDENE/DNPEA,
Recife
Sundarapadian SM, Swamy PS (1999) Litter production and leaf-litter
decomposition of selected tree species in tropical forests at
Kodayar in the Western Ghats, India. Forest Ecol Manag
123:231–244
Tabalipa NL, Fiori AP (2008) Influencia da vegetacao na estabilidade
de taludes na bacia do Rio Ligeiro (PR). Geociencias
27:387–399
Tews J, Brose U, Grimm V, Tielborger K, Wichmann MC et al (2004)
Animal species diversity driven by habitat heterogeneity/diver-
sity: the importance of keystone structures. J Biogeogr 31:79–92
Tissier EJ, Mcloughlin PD, Sheard JW, Johnstone JF (2013)
Distribution of vegetation along environmental gradients on
Sable Island, Nova Scotia. Ecoscience 20:361–372
Uetz GW (1991) Habitat structure and spider foraging. In: Bell SS,
McCoy ED, Mushinsky HR (eds) Habitat structure, vol 8.
Springer, Berlin, pp 325–348
Werger MJA, Sprangers JTC (1982) Comparison of floristic and
structural classification of vegetation. Vegetatio 50:175–183
612 A. C. Silva et al.
123
Author's personal copy