13 Spatial variation of fish communities among terra firme forest streams of the Colombian Amazonia

Spatial variation of fish communities among terra firme

forest streams of the Colombian Amazonia

Thesis Report MSc. in Tropical Ecology (60 ECTS)

Universiteit van Amsterdam 14-08-2006

Supervisors: Fernando Arbeláez Dr. Joost F. Duivenvoorden UvA student number: 0312924 Institute for Biodiversity and Ecosystem Dynamics [email protected] Faculty of Science Calle 95 # 16 – 23 A.601 Universiteit van Amsterdam +57 1 6107335 Javier A. Maldonado-Ocampo Inventories program Ichthyology collection coordinator Instituto Alexander von Humboldt

Arbeláez, F., Duivenvoorden, J.F. and J.A.Maldonado-Ocampo. 2006. Spatial variation of fish communities among terra firme forest streams of the Colombian Amazonia

2

––ACKNOWLEDGEMENTS––––––––––––––––––––––––––––––––––––––––––––––––––––

This work was possible thanks to the financial and logistical support of the following

institutions: Tropenbos-Colombia, Universiteit van Amsterdam – IBED, WWF-Education for

Nature- Russell E. Train Fellowship programme, Idea Wild, Instituto de Investigaciones

Alexander von Humboldt, El Zafire Research Station, Parque Natural Nacional Amacayacu and

Fundación Biodiversa Colombia.

I wish to thank my advisors, Joost Duivenvoorden and Javier Maldonado, for their constant help

and support during every phase of this project. Many thanks to María Cristina Peñuela, for her

great interest and her logistical support, to Iván Arce for his contribution to this project, to Juan

David Sánchez for his numerous advises in the field, to Juan David Bogotá for his help in Villa

de Leyva and to Dr. Donald Taphorn, for his help with the identification of Characiformes. To

my field guides Lizardo, Navilio, Richard, Ángel, Julio César, Leonel, Juan Carlos, Hernando,

José, Javier and Gerardo. For their hospitality, I am very grateful with the staff from El Zafire

and from Amacayacu National Park, to the communities of Santa Sofía, Mocagua and San

Martín, and with Isabella, Diana, Aquiles and Ana María.

…A la Tita Fanny


3

––INTRODUCTION–––––––––––––––––

In Amazonian terra firme (uplands that are

not seasonally flooded), soils tend to be

heavily leached and nutrient poor. Terra

firme forest streams (igarapés) are

characteristically acidic, due to the presence

of humic and fulvic acids, and poor in

nutrients and dissolved solids. As a result of

the lack of nutrients and the low light

penetration due to the forest canopy, the

primary productivity and the presence of

aquatic plants in these environments is

almost inexistent (Lowe-McConnell 1987,

Walker 1995, Mendonça et al. 2005).

However, they receive abundant and varied

contributions proceeding from the

surrounding forest (arthropods, leafs,

flowers, fruits and litter). These allow the

development of well-structured and very

complex fish communities (Knöppel 1970,

Lowe-McConnell 1987, Goulding et al.

1988). In a single small stream, more that

one hundred fish species can often be

collected, for what these systems might be

among the freshwater environments with the

highest number of fish species per area in

the world (Arbeláez et al. 2004).

A principal source for heterogeneity in

Amazonian upland soils is related to the

Tertiary and Pleistocene environmental

history and the influence of the Andes

Cordillera and the Guyana Shield. In this

regard, two main systems have been

recognized (Duivenvoorden and Lips 1993,

Hoorn 1994b, 1994a, Duivenvoorden and

Lips 1995). Shield-origin systems have

received sediments from ancient and

weathered soils, and are characterized by

very low soil elemental reserves. Andean-

origin upland systems are developed in

parent materials derived from relatively

young and less weathered sediments,

resulting in soils comparatively richer in

nutrients (though still poor for agricultural

production due to intensive leaching). Forest

plant composition (Duivenvoorden and Lips

1995), as well as terrain morphology (PAT

1997) clearly differs between these two

systems. Satellite imagery and surveys along

the border of Colombia and Peru suggest

that these two landscape units coincide in

the southern part of the so-called Trapecio

amazónico (PAT 1997), the Andes-origin

soils corresponding to the Pebas formation,

and the Shield-origin soils to the Terciario

Superior Amazónico formation. As proposed

in Hoorn (1994a), the latter formation will

be here referred, informally, as Mariñame

Sand Unit, because its original

chronostratigraphic name generates

confusion.


4

Landscape ecology studies improve the

understanding of landscape dynamics and

factors that affect fishery resources

(Schlosser 1991). Assessing the patterns of

diversity allows not only to have a deeper

understanding of the factors that govern the

species distribution and the maintenance of

diversity in the landscape, but is also an

important parameter in conservation

planning of protected areas (Crist et al.

2003). Partitioning of diversity components,

following the additive model, is an

important tool to assess these patterns of

diversity (Veech et al. 2002, Crist et al.

2003). According to the additive partitioning

model, γ diversity, or the total diversity in a

region, results of the addition of β (between-

habitat) and α (within-habitat) diversity.

Further subdivisions in different levels can

be made to assess the importance of

different sources and scales of spatial

heterogeneity in the total diversity.

Most of the ichthyological studies carried

out in the Amazon basin have focused on the

main rivers and their floodplains, as these

yield most of the commercial fisheries.

However, slight scientific attention has been

drawn to terra firme forest streams

(Mendonça et al. 2005), although unique and

highly interesting. Very little is known about

their composition and ecology, and about

the patterns that influence the distribution of

communities in a regional scale.

This study addressed two main hypotheses:

(1) The characteristics of soils and waters

are significantly different between Pebas

and Mariñame, which suggest that

Mariñame soils are significantly poorer than

Pebas, and thus, that they differ in origin (2)

Fish communities characteristics

(composition, richness, abundance and

biomass) differ significantly between

streams from Pebas and Mariñame

landscape units, which is related to their

difference in soil and water properties.

Unveiling β diversity patterns is of great

relevance for aquatic and terrestrial

management plans of the Amacayacu

National Natural Park, one of the areas of

study. Furthermore, some of the species

inhabiting these streams are often used as

ornamental fishes. Locating them and

assessing their populations are initial steps

for evaluating potential for sustainable

fisheries by local inhabitants.

This study was presented as Thesis project

to acquire the grade of MSc. in Tropical

Ecology at the University of Amsterdam.


5

––MATERIALS AND METHODS––––––

Study area

The fieldwork took place between

November 2005 and March 2006 in the

Western Amazonia, specifically, in the

southern Trapecio Amazónico, which is

located in the South-end of Colombia, in the

Amazonas Department, where it borders

Peru along the Amazon River (Fig. 1) .

The area is characterised by a humid, hot

equatorial climate and a southern

unimodal/bi-seasonal precipitation regime.

The annual precipitation in Leticia, the

Department capital, averages 3.400 mm

(between 1973 and 2004), with the rainiest

period from November to May, and the less

rainy season between June and October.

Temperature is relatively moderate and

constant along the year, averaging 25.7°C,

and humidity is high, with a 86% annual

mean (Rudas-Lleras and Prieto-Cruz 2005,

Galvis et al. 2006).

amplings were carried out in terra firme

forest streams belonging to the Mariñame

Sand Unit or the Pebas formations. The

original materials of Mariñame correspond

to Early to Middle Miocene, composed

mainly by medium to very coarse quartz

sands with low reserves of cations, P and N,

and have been suggested to have originated

in the Guyana Shield (Hoorn 1994a, 1994b,

Duivenvoorden 1995, Lips and

Duivenvoorden 1996, Duivenvoorden and

Lips 1998). As for the Pebas formation, its

original materials have been estimated from

the lower Miocene; they have predominantly

clay and silty/sandy clay textures, with fine

grain sand and higher nutrient reserves;

these are presumably of Andean origin

(Hoorn 1994a, Lips and Duivenvoorden

1996, Vonhof et al. 1998). In the study area,

Pebas formation is characterised by a

relatively undulated and uniform terrain,

while the surface in Mariñame formation is

dissected by deeper valleys (Rudas-Lleras

and Prieto-Cruz 2005).

Based on geological cartography (Fig. 1),

two large sampling areas were chosen,

where both landscape units were present.

The first sampling area, further on called

South-eastern, included the forest near the

village of Santa Sofia (S04°00’37’’,

W070°08’1’’) as the Pebas location, and El

Zafire biological station (S04°00’24’’,

W069°53’47’’) as the Mariñame location.

The second sampling area is contained in the

Amacayacu Natural National Park and

included the forest near Mata-matá station

(S03°49’8’’, W070°15’37’’) as the Pebas

location, and the headwaters of the Purité

basin (S03°41’53’’, W070°12’21’’) as the

Mariñame location.


6

Fig.1.. Location of the study area, large sampling areas, landscape units and sampling locations. Tsp=Pebas formation.

Tsa=Mariñame formation. Maps sources: Google Earth ® and PAT 1997 Zonificación ambiental para el plan modelo Colombo-

Brasilero (Eje Apaporis-Tabatinga: PAT). IGAC, Bogotá.

Table 1. Sampling design, and name and specific location of the sampled streams.

Formation Large sampling area Pebas Mariñame

Santa Sofía El Zafire

South-eastern SSQ1. Q. Clímaco. S03°58’58’’, W070°07’38’’ SSQ2. Q. Tucuchira. S03°59’8’’, W070°06’50’’

ZAQ1. Q. Sufragio. (S04°00’26’’, W069°53’47’’) ZAQ2: Eufra (S03°59’5’’, W069°53’24’’)

Mata-matá Purité

Amacayacu Natural National Park

MAQ1. Q. Arriera. S03°48’23’’, W070°15’58’’ MAQ2. Q. Zanguijuela S03°47’53’’, W070°15’58’’

PUQ1. Q. Paujil S03°41’54’’, W070°12’24’’ PUQ2. Camarón S03°41’38’’, W070°12’27’’

In each sampling location, two terra firme

streams were chosen on the basis of local

informants’ knowledge. The most important

criteria for the stream selection were: (1) it

should spring inside a well-developed forest

(with a dense canopy cover) which lacked

signs of recent and severe human

disturbance; (2) the hydric pulse of the main


7

rivers should not affect its water levels near

to the sampling site; (3) it should not fall dry

in dry spells; and (4) its size should not be

too large (max. width 6m aprox.) to make

the sampling techniques ineffective. A total

of eight streams were sampled in four

locations, belonging to two large sampling

areas and to two landscape units (Table 1).

Soil and water sampling

In each stream, between three and five

700cm3 superficial soil samples (A horizon,

0-5 cm depth) were collected in the forest

surrounding the streams, at 5 to 10m

distance from the streambed and 10 to 15 m

apart from each other. Three 500ml water

samples from the middle part of the stream

were taken on sampling days 0, 2 and 4. The

samples were preserved as fresh as possible,

in a refrigerator, when available, or kept

inside running water. Furthermore, pH,

conductivity, dissolved oxygen and

temperature were measured in-field on

sampling days 0, 2 and 4, using a portable

multiparameter SensionTM156 HACH. The

soil and water samples were analysed at the

IGAC (Instituto Geográfico Agustín

Codazzi) soil laboratory in Bogotá. The

studied physical-chemical parameters are

listed in Table 2. The detailed results of

these analyses and general lab methods are

described in Appendix 2.

Table 2. Soil and water variables used in the PCA analyses.

Soil variables Water variables

Granulometry % of sand % of loam % of clay

pH Interchangeable acidity (I.A., meq/100g) % A.I. saturation (A.I.S., meq/100g) % Organic carbon (O.C.) Exchange complex (meq/100g)

Cationic Exchange Capacity (C.E.C.) Calcium (ln transformed) Magnesium Potassium Sodium Total bases (T.B.)

% bases saturation (B.S.) Phosphorous (ppm)

pH Electric conductivity (E.C., dS/cm) Sodium Adsorption Rate (S.A.R.) Cation content (meq/L)

Calcium Magnesium Potassium Sodium

Anion content (meq/L) Sulphates* Chlorides* Carbonates* Bicarbonates

Dissolved oxygen (D.O mg/l)** Temperature (°C)**

*Variables removed from the PCA for having too many undetected values. **Field measurements.


8

Fish assemblages sampling

In each stream, four daily sampling events

took place. Each sampling day consisted in a

standard routine from 14:30 to 19:30,

covering afternoon, dusk and night hours,

during which three fishing methods were

used: one cast net (multifilament, 1.8 m

radius, 1.5 cm2 mesh) for the five hours, two

dip nets (50 cm diameter, 0.5 mm mesh) for

two hours (14:30 to 16:30) and one seine net

(2 m x 3.5 m, 0.5 mm mesh) for three hours

(16:30 to 19:30). The samplings started from

a fixed station, alternating each day between

upstream and downstream one hundred

meters transects, and attempting to cover

every microhabitat for fish within the

transect. All captured individuals were fixed

in formalin (10%). In the Ichthiology

collection lab of the Humboldt Institute in

Villa de Leyva, Colombia, the fish were

preserved in ethanol (70%), identified,

counted, and finally deposited in the fish

collection of the Institute. The total catch of

each species per sampling day was weighted

using an electronic balance, after removing

the excess of alcohol. The weights were

approximated to the nearest integer in grams

and only the measurements higher than 10g

were recorded. A uniform weight of 5g was

assigned to measurements lower than that

value.

Data analyses

Soil and water data

In order to identify distribution patterns and

groups formed by streams, locations,

sampling areas and landscape units regarding

their soil and water physical-chemical

properties, two Principal Component

Analyses (PCA) were used to visualise these

multidimentional data. Non-detected values

were changed for 1/10 of the smallest value

for that variable. Constant variables and

those with high amount of non-detected

values were removed from the analyses

(Table 2). Averages of the variables for each

stream were used as inputs for the PCA. For

the water PCA, the variables used as inputs

were the data from lab analyses plus the

dissolved oxygen and the temperature from

the field measurements. Field pH and

conductivity were used as a reference to

identify and remove outliers from the lab

results.

All variables were tested for normality using

a Kolmogorov-Smirnov test with Lilliefors

significance correction. When necessary,

variables were transformed to achieve

normality, following Zar (1996).

For each main PCA axis (loading >20%), the

difference among sampling locations was

tested with a one-way ANOVA, verifying

the normality of the residuals. When this


9

difference was significant, a Tukey’s honest

significant difference post hoc test was

computed between the sampling locations. In

the same manner, the difference among

landscape units, sampling areas and their

interaction in each main axis was tested

using a two-ways ANOVA. These analyses

were performed in SPSS 11.0.1 statistical

software.

Fish assemblages

In order to evaluate representativeness of the

sampling in the whole area, species

accumulation curves and richness estimators

were computed for all the sampling days

using EstimateS (Version 7.5; Colwell 2005)

with 1000 randomisations without

replacement and shuffling of individuals

among samples within species. The mean

and confidence values of the Chao1 richness

estimator with bias correction was compared

to the number of observed species (Sobs

Mao Tau) and their confidence intervals

(95%), as computed by the software. The

abundance- based Chao1 estimator calculates

estimated richness based on the amount of

singletons (species with only one individual)

and doubletons (species with two

individuals) in each step of the sample

accumulation procedure (Colwell 2005).

As standardised fishing procedures and

capture effort were applied during all

samplings, the number of species per

sampling day was used as the index for

comparing diversity in different scales. The

difference in species richness, abundance and

total weight per sampling day was tested

among sampling locations, as well as among

landscape units, sampling areas and the

interaction between these. Consecutive

samplings from one stream are probably not

independent and, therefore, can not be

accounted as repetitions, since the catchment

from one day is likely to affect the results of

the following days. Therefore, one- and two-

ways ANOVAs with repeated measures were

performed, using the streams as repetitions

and the four sampling days as four levels of

variation of the within-streams “sampling”

factor.

In order to compare the variation of fish

assemblages among sampling days, streams,

locations, landscape units and sampling

areas, a Detrended Correspondence Analysis

(DCA) ordination technique was performed.

The species composition for each sampling

day, with rare species down-weighting, was

used as the input for the DCA, using

CANOCO for windows (Version 4.02)

ecological software. The differences among

locations, landscape units, sampling areas

and their interaction in the scores of each

DCA axis were tested using one- and two-


10

ways ANOVAS with repeated measures. The

DCA scores were also used for a hierarchical

cluster classification with the nearest

neighbour method, measuring the squared

Euclidian distance between sampling days.

As the distance between sampling days

shows how fish assemblages vary due to

environmental factors, daily fluctuations and

randomness, the minimum difference for

clustering was determined when at least all

samples of the same stream were grouped in

the same cluster. Clustering analysis and

ANOVAs were performed in SPSS.

In order to evaluate the α and β diversities

contribution in different grouping levels,

PARTITION ecological software was used.

The program first assesses the contribution

percentage of α and β diversities to the γ at

diversity at different levels, and then

generates expected values of beta diversity

according to a null model, under the

hypothesis that the observed partition of

diversity could be produced by random

allocation. The P values generated by the

software values are the proportion of null

values that are greater than the observed

values of α and β diversities (Crist et al.

2003). With a two-tailed probability of 0.05,

the observed diversity is significantly

different from the random distribution model

if P < 0.025 (when the observed value is

greater than expected) or if P > 0.975 (when

the observed value is smaller than the

expected value). The software then calculates

α and β diversity for each level, following

the additive model. For each organisation

level, α diversity is calculated as the mean

diversity index of all samples, while β

diversity is calculated as the total diversity (γ

diversity) minus the average α diversity

(Veech et al. 2002). The diversity indicators

used for these analyses were species richness

and the Simpson’s diversity index. The latter

expresses the probability that two individuals

randomly selected from any sample belong

to different species (Magurran 1988, Crist et

al. 2003).

The diversity contributions were analysed in

two different highest grouping levels: first,

streams and sampling locations were

grouped by landscape units (Pebas and

Mariñame) and second, by large sampling

areas (South-eastern and Amacayacu N.P.

areas). In both cases, four levels were used,

with the same first three levels: “among

samples”, “among streams” and “among

sampling locations”. The fourth level was

“among landscape units”, for the first

analysis, and “among large sampling areas”,

for the second. Finally, the samples from

each landscape unit, Pebas and Mariñame,

were analysed separately and considering


11

only the first three levels of organisation, to

assess possible differences in diversity

partitioning between sampling locations.

––RESULTS–––––––––––––––––––––––––

Soil analyses

The physicochemical soil and water data for

each stream is presented in Appendix 2.

In the soil PCA (Tables 3 and 4), including

the 15 physicochemical variables, the first

three axes explained 90% of the variance.

The first PCA axis significantly separated

Pebas and Mariñame geological formations

on the basis of soil fertility and texture.

The second axis, related among others to

phosphorous content and interchangeable

acidity, significantly divided the two

sampling areas: Amacayacu NNP and South-

eastern. Finally, the third axis separated

significantly Santa Sofia and El Zafire

regarding carbon content, CIC and cation

content. All the analysed variables had high

loading (>+/- 0.60) in at least one of the

three principal axes. The PCA plots (Fig. 2)

showed a tendency of streams from the same

location to be grouped together, especially in

axes 1 and 3 (Fig. 2b), as well as the

tendency of Mariñame streams to the left and

Pebas ones to the right of axis 1. In sum,

Mariñame soils had the tendency to be

sandier and with higher pH, while Pebas

soils tended to be loamier, with higher

interchangeable acidity and higher amounts

and saturation of bases.

Water analyses

Two water samples from El Zafire showed

unusually high values of conductivity (46.9

and 55.6 dS/cm), compared to the other

results and to the field measurements

(average 12.4, max. 23.8 dS/cm), for what

they were removed from the analyses.

Furthermore, three variables (sulphates,

chlorides and carbonates) were removed for

their high amount of non-detected values.

The water PCA (Tables 5 and 6), which

included 10 variables, showed that the two

first components explained 87% of the

variance. For both main components, the

effect of geological formations, sampling

areas and their interaction was highly

significant. The post-hoc test showed also a

significant difference between the two Pebas

locations (Santa Sofía and Mata-matá) in

axis 1 and, in axis 2, a separation between El

Zafire and the other sampling locations.

These differences, as well as the aggregation

of streams from the same sampling location,

were evident in the water PCA plot (Fig. 3).

All the analysed variables had high loading

(>+/- 0.75) in at least one of the three

principal axes.


12

Table 4. Results of one- and two ways ANOVAs and post hoc tests for soil PCA scores. Bold indicates a significant difference

Two-ways ANOVA PCA1 PCA2 PCA3 F 11.2 0.7 3.2 Landscape unit (N=4) p 0.03 0.45 0.15 F 0.1 17.4 0.2 Sampling Area (N=4) p 0.77 0.01 0.71 F 1.1 0.2 22.7 Interaction (N=2) p 0.35 0.67 0.01

One-way ANOVA F 4.2 6.1 8.7 Sampling locations (N=2) p 0.10 0.06 0.03

Santa Sofia X Mata-Matá X X El Zafire X X

Groups formed by Turkey’s HSD by location (p<0.05)

Purité

Table 3. Results of the PCA for physicochemical soil variables within streams, showing the loadings of each variable in the three principal axes and the contribution of each axis to the variance. Bold indicates high loading (>+/-0.6) Variables PCA1 PCA2 PCA3sand -0.80 -0.58 0.09 loam 0.85 0.47 -0.07 clay 0.56 0.72 -0.12 pH -0.60 0.34 -0.07 Interchangeable acidity 0.70 0.66 0.08 A.I. saturation -0.44 0.82 -0.18 Organic carbon -0.03 -0.52 0.82 Cationic Exchange Capacity 0.32 0.48 0.81 Calcium (ln) 0.96 -0.22 -0.04 Magnesium 0.94 -0.28 -0.12 Potassium 0.30 0.37 0.84 Sodium 0.04 -0.45 -0.80 Total bases 0.96 -0.22 0.05 bases saturation 0.81 -0.49 -0.31 Phosphorous 0.46 -0.73 -0.27 % Variance explained 43.2 27.2 19.5

X

Fig.2. Scatter plots for PCA results of 15 soil variables. a) Considering axes 1 and 2 and b) considering axes 1 and 3. Locations: ZA=El Zafire, SS=Santa Sofía, PU=Purité, MA-Mata-matá. Q=stream number. A solid line indicates a significant difference among landscape units. A dotted line indicates significant difference among sampling locations.

PCA axis 1

1.51.0.50.0-.5-1.0-1.5

PCA

axis

2

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

Location

ZAQ2

ZAQ1

SSQ2

SSQ1

PUQ2

PUQ1

MAQ2

MAQ1

Pebas soils

Mariñame soils

a.

PCA axis 1

1.51.0.50.0-.5-1.0-1.5

PCA

axis

3

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

LOCATION

ZAQ2

ZAQ1

SSQ2

SSQ1

PUQ2

PUQ1

MAQ2

MAQ1

Pebas soils

Mariñame soils

b.


13

In sum, Pebas waters appeared to have a

higher electric conductivity and greater

amount of nutrients and higher

temperatures than the Mariñame waters.

Among the Pebas locations, in Santa

Sofia these variables were significantly

higher. The difference in axis 2 implies

that certain characteristics of El Zafire

waters differed from the other locations,

such as higher pH, sodium absorption

rate and dissolved oxygen, and less

calcium content.

Table 6. Results of one- and two ways ANOVAs and post hoc tests for soil PCA scores. Bold indicates a significant difference.

Two-ways ANOVA PCA1 PCA2 F 229.8 15.33Landscape unit (N=4) p 0.00 0.02 F 85.3 86.2 Sampling Area (N=4) p 0.00 0.00 F 15.4 43.7 Interaction (N=2) p 0.02 0.00

One-way ANOVA F 110.2 48.4 Sampling locations (N=2) p 0.00 0.00

Santa Sofia X X Mata-Matá X X Purité X X


El Zafire X X

Table 5. Results of the PCA for

physicochemical water variables within streams, showing the loadings of each variable in the two principal axes and the contribution of each axe to the variance. Bold indicates high loading (>+/-0.6) Variables PCA1 PCA2

pH 0.52 0.84

Electric conductivity 0.98 -0.20

Sodium adsorption rate 0.38 0.72

Calcium 0.75 -0.61

Magnesium 0.88 -0.42

Potassium 0.96 0.05

Sodium 0.91 -0.16

Bicarbonates 0.82 0.37

Dissolved oxygen -0.02 0.89

Temperature 0.87 0.14

% Variance explained 58.7 28.0

PCA axis 1

2.01.51.0.50.0-.5-1.0-1.5

PCA

axis

2

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

Location

ZAQ2

ZAQ1

SSQ2

SSQ1

PUQ2

PUQ1

AMQ2

AMQ1

Pebas waters

Mariñame waters

Fig.3. Scatter plots for PCA results of ten water variables for the two main principal axes. Locations: ZA=El Zafire, SS=Santa Sofía, PU=Purité, MA-Mata-matá. Q=stream number. A solid line indicates a significant difference among landscape units. A dotted line indicates significant difference among sampling locations.


14

Icthyofauna thus, the observed richness accounted for

81% of the estimated species in the whole

area.

A total of 7656 fish individuals belonging to

120 species, eight orders and 27 families

were captured (Table 7). The orders

Characiformes and Siluriformes summed

Table 7. Distribution of the total capture by number of species and individuals by orders and families. ORDERS Species Individuals Characiformes 58 6229 Siluriformes 41 70Perciformes 8 45Gymnotiformes 8 248 Cyprinodontiformes 2 17 Synbranchiformes 1 3 Beloniformes 1 2 Lepidosireniformes 1 1 FAMILIES

2 7

Characidae 43 5845 Loricariidae 11 23Auchenipteridae 9 164 Cichlidae 7 45Heptapteridae 7 70Callichthyidae 6 109Curimatidae 4 55Lebiasinidae 3 177Aspredinidae 3 90Hypopomidae 3 90 Anostomidae 3 9 Sternopygidae 2 86 Gasteropelecidae 2 71 Gymnotidae 2 25 Trichomycteridae 2 18 Rivulidae 2 Erythrinidae 1 64Rhamphichthyidae 1 47 Pimelodidae 1 9Acestrorhynchidae 1 4 Chilodontidae 1 4 Cetopsidae 1 Synbranchidae 1 3 Belonidae 1 2Doradidae 1 1Lepidosirenidae 1 1 Polycentridae 1 1 Total 120 7659

8

6

17

3

82% of the total species. These orders were

also the most abundant, in particular

Characiformes, which accounted for 81% of

the captured individuals. The family

Characidae had the highest richness (36%)

and abundance (76%). In richness,

Characidae was followed by Loricariidae and

Auchenipteridae, and in abundance, it

preceded Cichlidae and Loricariidae. The

complete list of species with their

abundances in each stream is presented in

Appendix 1.

The Chao1 richness estimator (Fig. 4)

predicted that the number of species among

all the samples is 149 species, significantly

higher than the observed richness (120

species) with a 95% confidence interval;

100

120

140

160

180

200

Sobs (Mao Tau) Chao 1

Spec

ies

richn

ess

Fig.4. Means and 95% confidence intervals of observed and

Chao1 predicted richness.


15

Table 8. Results of ANOVAs with repeated samples (factor: sample with four levels) Two-ways ANOVA Richness Abundance Weight

F 122.51 0.32 16.45 Landscape unit (N=4) p 0.00 0.81 0.02 F 33.20 0.62 4.19 Sampling Area (N=4) p 0.00 0.82 0.11 F 2.47 0.46 1.98 Interaction (N=2) p 0.29 0.54 0.23

One-way ANOVA F 52.39 0.32 7.54 Sampling locations (N=2) p 0.00 0.81 0.04

Santa Sofia X X Mata-Matá X X X El Zafire X X


Purité X X

When analysing the total capture in the area,

most of the species had low abundances and

total and individual weights while very few

species showed high abundances and total

and individual weights (Fig. 5).

The number of individuals (Table 7) was not

significantly different among locations or

among landscape units, sampling areas and

their interaction (Table 8).

The difference of weight (Table 7), on the

contrary, was highly significant between

locations and between landscape units (Table

8). Santa Sofía showed a significantly higher

total weight than the two Mariñame

locations. In total, Pebas samples summed

more than twice the weight of the Mariñame

samples.

The number of species (Table 8) differed

significantly among sampling locations. The

two Pebas locations (Santa Sofia and

Amacayacu) formed a homogenous group

with the highest number of species, while the

location with fewest species was Purité.

Among landscape units and sampling areas,

the Pebas streams have a significantly higher

richness that the Mariñame streams;

likewise, the South-eastern sampling area

showed a higher richness than the

Amacayacu area.


16

0

200

400

600

800

1000

1200

1400

1600

1800

0 10 20 30 40 50 60 70 80 90 100 110 120 13

Tota

l wei

ght (

g)

0

Rank Species Total Weight (g) 1 Bujurquina mariae 1618 2 Bryconops cf inpai 1450 3 Cyphocharax spiluropsis 861 4 Bryconamericus sp. 854 5 Brycon melanopterus 853 6 Moenkhausia cf comma 720 7 Hyphessobrycon cf agulha 698 8 Charax cf. tectifer 600 9 Astyanax abramis 584

10 Gymnotus cf carapo 544

a.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 10 20 30 40 50 60 70 80 90 100 110 120 1

Tota

l abu

ndan

ce (i

ndiv

idua

ls

30

)

Rank Species Total abundance1 Bryconamericus sp. 1746 2 Tyttocharax cf cochui 989 3 Hyphessobrycon cf agulha 708 4 Bryconops cf inpai 499 5 Gephyrocharax sp. 375 6 Bujurquina mariae 291 7 Hemigrammus cf analis 288 8 Moenkhausia (lepidura-complex) sp. 165 9 Charax cf. tectifer 142

10 Nannostomus marginatus 115

b.

0

20

40

60

80

100

120

140

160

180

0 10 20 30 40 50 60 70 80 90 100 110 120 130

Rank of species

Ave

rage

wei

ght p

er in

divi

dual

(g)

Rank Species Average weight per individual (g)

1 Crenicichla sp. 1746 2 Auchenipteridae sp. 989 3 Pristobrycon sp.2 708 4 Hypostomus oculeus 499 5 Brycon melanopterus 375 6 Megalechis thoracata 291 7 Leporinus friderici 288 8 Scorpiodoras heckelii 165 9 Acestrorhynchus lacustris 142 10 Semaprochilodus insignis 115

c.

Fig. 5.Distribution of abundances(a), total weight (b) and average individual weight (c) for the total capture.


17

T

s

a

2

r

g

a

l

s

a

i

f

P

w

s

d

c

from the same stream were considered, three

clear aggregations were formed, separating

all the Mariñame samples, grouped in one

large cluster, from the Santa Sofia and

Amacayacu samples, which formed two

independent groups.

DCA axis 1

3.02.52.01.51.0.50.0-.5

DC

A ax

is 2

2.5

2.0

1.5

1.0

.5

0.0

-.5

Stream

ZAQ2

ZAQ1

SSQ2

SSQ1

PUQ2

PUQ1

MAQ2

MAQ1

Pebas landscape unit

Mariñame landscape unit

The DCA ANOVAs with repeated sampling

(Table 9) suggested comparable results as the

cluster analysis, although with a somehow

lower resolution. For both axes of the DCA,

samples from Mariñame and Pebas

formations were significantly different, but
Fig.6. Scatter plots for DCA results of fish composition per sample for the two main principal axes. Locations: ZA=El Zafire, SS=Santa Sofía, PU=Purité, MA-Mata-matá. Q=stream number. A solid line indicates a
he DCA using composition among

ampling days showed that the first two axes

ccount for 66% of the variation (43% and

2%, respectively). The graphic

epresentation of the DCA (Fig. 6) clearly

rouped sample events within each stream,

nd streams within sampling

ocations. The cluster analysis

howed a very strong aggregation

mong the Mariñame samples;

ndeed, some sub-clusters were

ormed by mixed samples of

urité and El Zafire streams,

hile some samples from the

ame stream were segregated in

ifferent subclusters. When only

lusters that included all samples

the effect of sampling areas or their

interaction was not significant. Among

sampling locations, only axis 1 shows a

significant difference, which occured

between Santa Sofía and the two Mariñame

locations (El Zafire and Purité).

Table 9. Results of one- and two- ways ANOVAs with repeated measures and post hoc tests for fish composition DCA scores. Bold indicates a significant difference.

Two-ways ANOVA DCA1 DCA2 F 15.62 8.07 Landscape unit (N=4) p 0.02 0.05 F 3.46 0.613 Sampling Area (N=4) p 0.14 0.478 F 5.95 1.83 Interaction (N=2) p 0.07 0.248

One-way ANOVA F 8.34 3.50 Sampling locations (N=2) p 0.03 0.13

Santa Sofia X Mata-Matá X X Purité X


El Zafire X


18

Table 10. Results of the diversity partitioning analysis with two different higher grouping: among landscape units and among large sampling areas.

Richness P Simpson P Total 120 0.910

α4 81.6 1 0.892 1 Level 4: Units (n=2) β4 38.4 0 0.018 0

α4 83.7 1 0.905 1 Level 4: Areas (n=2) β4 36.3 0 0.005 0

α3 56.1 1 0.881 1 Level 3: Locations (n=4) β3 25.5 0 0.011 0

α2 43.2 1 0.861 1 Level 2: Streams (n=8) β2 12.9 1 0.020 0

α1 27.7 1 0.848 1 Level 1: Samples (n=32) β1 15.5 1 0.013 0

The randomisation test PARTITION

software suggest that the distribution of α

and β diversity can not be explained by

random distributions of individuals among

samples (P<0.025 or P>0.975 in every case,

with 5000 randomisations, Table 10).

When richness was used as the diversity

index, the results were similar when

grouping locations by landscape units or by

large sampling areas (Table 10). Overall, α

diversity within streams accounted for 36%

of the total γ diversity, while β diversity

accounted for 64% (excluding “among

samples” diversity). Most of this β diversity

corresponded to the higher level of

organisation, i.e. among landscape units and

among large sampling areas (β4=38.4 and

36.3, respectively). The smallest contribution

is given by the “among streams” level

(β2=12.9) which is even less than the “among

samples” level (β1=15.5).

However, Simpson’s diversity index shows

different results for the higher grouping

levels. When grouping by landscape units,

the β4 was much higher that when grouping

by landscape units (0.018, 28% of the total β

diversity) than by large sampling areas

(0.005, 8% of the total β diversity).

The contribution of β diversity for species

richness among sampling locations is higher

when comparing the two Pebas locations,

Santa Sofia and Mata-Matá (β3=33.2), than

with the two Mariñame locations, El Zafire

and Purité (β3= 17.1; Table 11). However,

the same analysis for Simpson index does

shows similar results in the two landscape

units analyses.

Table 11. Results of the diversity partitioning analysis with the data either from Pebas either from Mariñame Only Pebas Only Mariñame

Richness P Simpson P Richness P Simpson P Total 95 0.8948 67 0.889

α3 61.8 1 0.8834 1 49.9 1 0.878 1 Level 3: Locations (n=2) β3 33.2 0 0.0114 0 17.1 0 0.012 0

α2 48.7 1 0.8691 1 37.1 1 0.852 1 Level 2: Streams (n=2) β2 13.1 0 0.0143 0 12.8 0 0.026 0

α1 31.7 1 0.8555 1 23.4 1 0.839 1 Level 1: Samples (n=16) β1 17 1 0.0136 0 13.7 1 0.013 0


19

––DISCUSSION––––––––––––––––––––––

Methodological considerations

This study confirms the already reported

high richness of fish species that inhabits

these streams ((Knöppel 1970, Lowe-

McConnell 1987, Arbeláez et al. 2004).

However, the number of fish species

captured in this study was more than twice

the reported by other works in terra firme

streams of the Brazilian and Ecuadorian

Amazonia (e.g. Crampton, Knöppel 1970,

Saul 1975, Penczak et al. 1994, Buhrnheim

and Cox-Fernandes 2003, Mendonça et al.

2005). The North-western Amazonia has

been suggested, in general, to support a

particularly high diversity of plants species

(e.g. Duivenvoorden et al. 2002, Kreft et al.

2004, Ter Steege et al. 2004, Wittmann et al.

2006), thus a similar pattern could occur

with fish species. However, the higher

richness here found, compared for example

to other works in Western Amazonian (e.g.

in Ecuador) suggest that this is certainly not

the result of a particularly higher richness of

Colombian igarapés. It is more likely to be

more the consequence of a more effective

sampling methodology used in this and other

works in Colombia ((Prieto 2000, Arbeláez

et al. 2004, Galvis et al. 2006). This

includes: diverse and effective (non-

destructive) fishing arts; covering as many

microhabitats as possible along transects;

fishing during different activity times for

fish; and alternating sampling transects in

consecutive days. The daily sampling routine

hereby presented proved to be effective and

highly practical in the field; furthermore, it

allowed comparison among samplings,

taking into account daily variations. This

methodology is therefore recommended for

further similar studies in fish communities of

Amazonian terra firme forest streams.

When doing a fish inventory, using several

sampling techniques, as was performed in

this work, allows capturing a wider range of

species in the community. However, as

sampling techniques vary in effectiveness

and specificity, this introduces biases in the

observed structure of the community and

therefore in diversity indexes based on it

(e.g. Shannon and Simpson indexes).

However, as the daily sampling methodology

was as standardised as possible for each

stream, it allowed comparison among

streams of sampled fish assemblages and

richness by capture effort unit: sampling

days.

Ichtyofauna

Although the total captured number of

species was still significantly lower than the

estimated one by Chao1, it could be


20

considered high enough (>80%) to assume

an adequate representativeness of the

sampling on the area.

The distribution of taxa, as much in richness

as in abundance, agrees with most

ichthyological studies carried out in

Amazonian streams, where Characiformes is

usually the dominant group, followed by

Siluriformes and Perciformes (mainly

Cichlidae), while Gymnotiformes are usually

common (e.g. (Goulding et al. 1988,

Arbeláez et al. 2004, Mendonça et al. 2005,

Galvis et al. 2006).

The distribution of abundances among the

whole community showed a very clear

pattern of distribution (Fig. 5). Most of the

fish species inhabiting these streams had

lower abundances (1-20 individuals) while

few species had high abundances. This

agrees with the reports for different

Amazonian freshwater systems (e.g.

(Goulding et al. 1988, Saint-Paul et al. 2000,

de Oliveira et al. 2003, Arbeláez et al. 2004,

L. 2004) and seems to be more pronounced

in poor water than in rich water systems

(Saint-Paul et al. 2000, Silvano et al. 2000).

A similar distribution of species was found

for the total and individual weight. The latter

is related to a common phenomenon that

occurs in the Amazon basin, miniaturaization

of fish species, as the result of rich biological

interactions among fish and resources

partitioning, and closely related to the

environment characteristics (Costa and Le

Bail 1999). Reduction of body- and of

population- sizes in Amazonian poor water

systems are examples of the adaptive

processes that might occur in fish species,

related to strategies to reduce inter- and

intra-specific competition, in order to allow

resource and habitat partitioning and

overlapping (Costa and Le Bail 1999,

Arbeláez et al. 2004).

Soils and waters among landscape units

Duivenvoorden (1995) and Duivenvoorden

& Lips (1995, 1998) suggested that upland

soils from the middle Caquetá area differed

in their physicochemical characteristics. The

authors reported clayey soils with high

exchange capacity, interchangeable acidity

and soil nutrient reserves of the Pebas

formation (Ali-Acrisols), which they

attributed to be of Andean origin. They also

reported soils with coarser textures and lower

CEC and IA and low levels of the soil

nutrient reserves of the Mariñame Sand Unit

formation and the sandstone plateaus (Ali-

Ferrasols), probably originated in the

Guyana shield. The analyses carried out in

this work suggested similar differences

among the soils of the Pebas and the


21

Mariñame formations in the southern

Trapecio Amazónico. The soils of Mariñame

appear to be sandier and with lower amount

of nutrients than the ones of Pebas. This was

also evidenced in the water characteristics

that drain these soils, as the electric

conductivity and the loads of dissolved

nutrients in the water were significantly

higher in Pebas streams. These results

support the hypothesis that Amazonian

upland soils differ in physicochemical

characteristics, and that it could be related to

the origin of their sediments, in this case,

Pebas formation from richer and younger

sediments of the Andes, and Mariñame

formation from older and poorer sediments

of the Guyana shield.

It is common that the poorer Amazonian

soils, such as the ones that drain the Rio

Negro basin, have a tendency to be acidic,

which results also in waters with very low

pH (Goulding et al. 1988). However, this

was not the case when comparing Mariñame

and Pebas formations. The water pH was not

an important variable in the water PCA to

difference the two landscape units, and the

relation was even inversed in the soil pH,

with Mariñame showing higher pH. In

Duivenvoorden & Lips (1998), pH was also

not an important variable to account for the

difference in soils between these two

formations. Nevertheless, other important

variables in the soil analysis, related to base

content and base saturation, with much

higher loading than the pH, suggest that

there is indeed a difference in the bases

content.

A significant difference was found between

Santa Sofia and El Zafire soils in the third

axis of the soils PCA (Fig. 2). Although

there are no clearly defined groups for this

axis, as there is for the first axis or for the

axes of the waters PCA, there seems to be a

range that goes from the Santa Sofia soils

with high organic content and CEC, to El

Zafire soils were these values are very low,

together with a trade-off between sodium and

potassium. It was also found that the waters

seemed to differ in nutrient content between

the two Pebas locations, being Santa Sofia

streams significantly richer than Mata-matá

streams. These results suggest that there

might be other processes and trade-offs

involved in the regional differentiation of

soils and waters than just the separation

between geological formations. For example,

the higher human intervention of the forest

close to the large village of Santa Sofia,

compared to the other locations, might be

playing an important role.

It is interesting to notice that the results on

water analyses appeared to be more clearly


22

interpretable (streams from same locations

close to each other, clear separation between

landscape units) than the soil results. This

could occur because localised differences in

soil composition were perhaps amplified due

to the small size and number of soil samples.

These localised differences are “averaged” in

the stream water that has drained them, for

what the water physicochemical

characteristics appeared to give a better

image of the general soil characteristics in

the area area.

Fish community characteristics among

landscape units

Physicochemical factors and habitat

differences have been suggested to influence

the distribution of fish species (Angermeier

and Karr 1982, Henderson and Crampton

1997, Mendonça et al. 2005). This study also

evidenced that differences in soil properties

and in water chemistry were reflected in the

fish communities; fish communities

appeared to be very particularly sensitive to

water chemistry. In this case, the differences

in the fish communities accounted for

species richness, carrying capacity and

composition.

Species richness

(Duivenvoorden et al. 2005) did not find

significant differences in tree diversity

among poorer and richer upland forests in

the middle Caquetá. This works suggest,

however, that Pebas formation streams

support higher fish species richness than the

Mariñame formation streams (Table 8). As

the number of individuals captured was not

significantly different (Fig. 6b), this result is

not likely to be the consequence of

undersampling in the latter. Conditions of

extreme acidity and of low conductivity can

directly affect fishes, since such

environments influence their ionic and acid-

basic regulation (Gonzalez 1996, cited in

Mendoça 2005). The extremely low

conductivity (<6 dS/cm in average) in poorer

upland soils may require more specialised

species that can tolerate them, which might

be a limiting factor for species richness

(Towsend et al. 2003).

Carrying capacity

The fish carrying capacity in aquatic systems

has often been related to the load of nutrients

in their waters, for example when comparing

Amazonian white-water and black-water

systems, in relation to size (Galvis et al.

2006) and biomass (Henderson and

Crampton 1997, Saint-Paul et al. 2000). In

this study, a similar pattern was observed

comparing similar systems that differed in

concentration of elements in the water: Pebas

streams, richer in elements, showed a much


23

higher carrying capacity than Mariñame

streams, reflected in the total weight of the

catchments (nearly twice as much).

Fish composition

The DCA analyses on fish composition

showed a strong relation between differences

in soil and water characteristics and the fish

assemblages. First, the streams from

Mariñame, which had similar soil and water

characteristics seemed to have equally

undifferentiated fish communities. Second,

the streams from Pebas appeared to be

segregated in relation to the sampling

locations, Santa Sofia and Mata-matá, both

in water properties and in fish assemblages.

These differences and similarities in fish

composition could already be evidenced

from the field with the resemblance between

the two Mariñame locations, Purité and El

Zafire, separated nearly by 50 km. Some

species (i.e. Bunocephalus sp., Moenkhausia

tridentata and Otocinclus sp.; see Appendix

1) were collected in those two locations but

in neither of the Pebas locations. Even

closely related species, such as Heptaterus

sp.1 and Heptaterus sp.2, were segregated in

the same area between the two landscape

units, in locations 25 km away from each

other (Santa Sofia and El Zafire). This

general distribution pattern might suggest

that the difference in composition could be

more a reflection of ecological preferences of

the fish species than biogeographical

segregation, though further data is required

to assert this statement (further sampling and

genetic analyses).

Duivenvoorden and Lips (1995) reported

differences in the forest composition

between Ali-Acrisols from Pebas and Ali-

Ferrasols from Mariñame upland soils.

Likewise, the hereby-presented results

suggest that the general properties of the

soils are an important component for fish

species differentiation. As these forest stream

ecosystems are completely dependent on the

inputs form the surrounding forest, a very

strong correspondence between forest

composition and fish assemblages would be

expected. To unveil these patterns, forest

inventories along the streams, together with

the fish samplings, should be carried out.

The results of the diversity partitioning

analysis were not as clear as the

correspondence analysis. Some differences

and similarities of the fish composition were

very evident in the DCA and corresponded to

soils and water analyses, but were not

reflected in a different contribution of the β

diversity; in particular, this occurred with the

difference between large sampling areas and

among the two Pebas locations. The fact that

the software does not estimates statistical


24

significance of the diversities contributions

makes them unreliable for comparison.

Furthermore, the two analysed indexes,

species richness and Simpson index led to

different conclusions. Perhaps the approach

of getting an idea of the β diversity as the

difference between γ diversity and the

average α diversity is too simplistic and does

not really reflect the spatial turnover of

species in a regional scale, at least at the

temporal and spatial scale used in this study.

It is possible that the differentiation of

landscape units is reflected also in other

characteristics of the fish community, such

as trophic levels structure, reproductive

strategies and behaviour, as well as

physiological and morphological

adaptations. Further and more specific

studies in these areas are required to gain a

deeper understanding on these aspects.

Importance for conservation

Regional and landscape ecology studies are

of high relevance for environmental

management and planning of natural areas

(Groves 2003, Groom et al. 2006), and

improve the understanding of land-use

disturbances on fishery resources (Schlosser

1991). This is especially important protected

areas, such as the Amacayacu Natural

National Park. Some results of the present

work reflect that relation:

1) The identification of a clear fish β

diversity pattern in the region, conforming

two landscape units with different soil and

water characteristics allows wiser planning

and management of land and aquatic

resources in the Park.

2) The results show a significantly lower

carrying capacity for one of these units, the

Mariñame formation, which might indicate

that it may be less suitable for exploitation,

and particularly sensitive to destructive

fishing methods, such as ichtyocides, which

are still commonly used in certain areas

(Peñuela pers. com.).

3) The whole fish community shows a very

high frequency of rare species. Since rare

species are particularly vulnerable to habitat

changes, rarity patterns in the biota reflect

the degree of integrity of the environment

(de Oliveira et al. 2003). For one side, this

reinforces the importance of preserving these

environments, and for the other, changes in

these distribution patterns could be used as

indicators of habitat change and human

influence at different ecological scales.

4) The high sensitivity to water

characteristics observed in these fish

communities could indicate that the might be

very vulnerable to small habitat changes that


25

could alter those characteristics, such as

contamination, deforestation and change of

land use, among others.

The results of this study also suggested that

other non-identified factors seemed to be

involved in the differentiation of the soil and

water characteristics and in the fish

assemblages. One clear case of this was the

separation of between the two locations of

Pebas landscape unit: Santa Sofia and Mata-

matá. More sampling should be carried out

in other areas where the two landscape units

coincide, in this and other areas of the

Amazonia, e.g. Caquetá, and using the

hereby-proposed methodology. However,

further studies, involving other techniques,

such as molecular biology, and other groups

of organisms, will help us improve the

knowledge of these patterns of regional

diversity.

––REFERENCES ––––––––––––––––––––

Angermeier, P. L., and J. R. Karr. 1982. Fish communities along environmental gradients in a system of tropical streams. Environmental biology of fishes 9:117-135.

Arbeláez, F., G. Gálvis, J. I. Mojica, and S. R. Duque. 2004. Composition and richness of the ichthyofauna in a terra firme forest stream of the Colombian Amazonia. Amazoniana 18:109-123.

Buhrnheim, C. M., and C. Cox-Fernandes. 2003. Seasonal variation of fish

communities in Amazonian rain forest streams. Ichthyological Exploration of Freshwaters 12:65-78.

Colwell, R. K. 2005. EstimateS: Statistical estimation of species richness and shared species from samples. User guide.

Costa, W. J., and P. Y. Le Bail. 1999. Fluviphylax palikur: A New Poeciliid from the Rio Oiapoque Basin, Northern Brazil (Cyprinodontiformes: Cyprinodontoidei), with Comments on Miniaturization in Fluviphylax and Other Neotropical Freshwater Fishes. American soc. ichthyologist & herpetologist:1027-1034.

Crampton, W. G. R. Os peixes da Reserva Mamirauá: diversidade e história natural na planície alagável da Amazônia. Pages 10–36 in H. L. Queiroz and W. G. R. e. Crampton, editors. Estratégias para manejo de recursos pesqueiros em Mamirauá. Sociedade Civil Mamirauá/CNPq, Brasília.

Crist, T. O., J. A. Veech, J. C. Gering, and K. S. Summerville. 2003. Partitioning Species Diversity across Landscapes and Regions: A Hierarchical Analysis of alpha, beta, and gamma Diversity. The American Naturalist 162:734-743.

de Oliveira, E. F., E. Goulart, and C. V. Minte-Vera. 2003. Patterns of dominance and rarity of fish assemblage along spatial gradients in the Itaipu Reservoir, Paraná, Brazil. Acta Scientiarum 25:71-78.

Duivenvoorden, J. F. 1995. Tree species composition and rain forest–environment relationships in the middle Caqueta area, Colombia, NW Amazonia. Vegetatio 120:91–113.

Duivenvoorden, J. F., A. J. Duque, J. Cavelier, A. García, C. Grández, M. J. Macía, H. Romero-Saltos, M.


26

Sánchez, and V. R. 2005. Density and diversity of plants in relation to soil nutrient reserves in well-drained upland forests in the north-western Amazon Basin. in I. Friis and B. H. (eds.), editors. Symposium on plant diversity and complexity patterns: local, regional and global dimensions. The Royal Danish Academy of Sciences and Letters, Denmark.

Duivenvoorden, J. F., and J. M. Lips. 1993. Landscape ecology of the Middle Caquetá Basin. Explanatory notes to the maps. Pages 301 in Estudios en la Amazonia Colombiana. Tropenbos-Colombia, Bogotá.

Duivenvoorden, J. F., and J. M. Lips. 1995. A Land Ecological Study of Soils, Vegetation and Plant Diversity in Colombian Amazonia. The Tropenbos Foundation, Wageningen.

Duivenvoorden, J. F., and J. M. Lips. 1998. Mesoscale Patterns of tree spcecies diversity in Colombian Amazonia. in D. Dallmeier and J. A. Comiskey, editors. Forest biodiversity in North, Central and South America, and the Caribbean. Research and Monitoring. UNESCO/ The Partenon Publishing Group, Paris.

Duivenvoorden, J. F., J. C. Svenning, and S. J. Wright. 2002. Beta Diversity in Tropical Forests. Science 295:636-637.

Galvis, G., J. I. Mojica, J. Lobón-Cerviá, C. Granado-Lorencio, S. R. Duque, C. Castellanos, P. Sánchez-Duarte, M. Arce, Á. Gutiérrez, L. F. Jiménez, M. Santos, S. Vejarano, F. Arbeláez, E. Prieto, and M. Leiva, editors. 2006. Peces del Alto Amazonas - Región de leticia. Conservación Internacional, Bogotá.

Goulding, M., M. Leal-Carvalho, and F. E.G. 1988. Rio Negro, rich life in poor water. Amazonian diversity and

foodchain ecology as seen through fish communities. SPB Academic Publishing, The Hague.

Groom, M. J., G. K. Meffe, and C. R. Carroll. 2006. Principles of Conservation Biology (3rd ed.). Sinauer Associates, Sunderland, MA.

Groves, C. R. 2003. Drafting a conservation blueprint. A practitioner’s guide to planning for biodiversity. Island Press, Washington, D. C.

Henderson, P. A., and W. G. R. Crampton. 1997. A comparison of fish diversity and density from nutrient rich and poor waters lakes in the Upper Amazon. J. Trop. Ecol. 13:175-198.

Hoorn, C. 1994a. An environmental reconstruction of the paleo-Amazon River system (Middle-Late Miocene, NW Amazonia). Palaeogeography, Palaeoclimatology, Paleoecology 112:187-238.

Hoorn, C. 1994b. Fluvial palaeo-environments in the intracratonic Amazonas Basin (Early Miocene - early Middle Miocene, Colombia). Palaeogeography, Palaeoclimatology, Paleoecology 109:1-54.

Knöppel, H. A. 1970. Food of Central Amazonian fishes. Contribution to the nutrient-ecology of Amazonian rainforest-streams. Amazoniana 2:257-352.

Kreft, H., N. Koster, W. Kuper, J. Nieder, and W. Barthlott. 2004. Diversity and biogeography of vascular epiphytes in Western Amazonia, Yasuni, Ecuador. Journal of Biogeography 31:1463-1476.

L., C. 2004. Fish assemblage structure in a first order stream, Southeastern Bra-zil: Longitudinal distribution, seasonality, and microhabitat diversity. Biota Neotropica 5:75-83.

Lips, J. M., and J. F. Duivenvoorden. 1996. Fine litter input to terrestrial humus


27

forms in Colombian Amazonia. Oecologia 108:138-150.

Lowe-McConnell, R. H. 1987. Ecological Studies in Tropical Fish Communities.

Magurran, A. E. 1988. Ecological diversity and its measurement. Princeton University Press, Princeton, N.J.

Mendonça, F. P., W. E. Magnusson, and Z. J. 2005. Relationships between habitat characteristics and fish assemblages in small streams of Central Amazonia. Copeia:751-764.

PAT. 1997. Zonificación ambiental para el plan modelo Colombo-Brasilero (Eje Apaporis-Tabatinga: PAT). IGAC, Bogotá.

Penczak, T., A. A. Agostinho, and E. K. Okada. 1994. Fish diversity and community structure in two small tributaries of the Paraná River, Paraná State, Brazil. Hydrobiologia 294:243-251.

Prieto, E. 2000. Estudio ictiológico de un caño de aguas negras de la Amazonia Colombiana, Leticia. Universidad Nacional de Colombia, Bogotá.

Rudas-Lleras, A., and A. Prieto-Cruz. 2005. Flórula Del Parque Nacional Natural Amacayacu, Amazonas, Colombia. Missouri Botanical Garden, Saint Louis, MO.

Saint-Paul, U., J. Zuanon, M. A. Correa, M. Garcia, N. N. Fabre, U. Berger, and W. J. Junk. 2000. Fish communities in central Amazonian white-and blackwater floodplains. Environmental biology of fishes 57:235-250.

Saul, W. G. 1975. An ecological study of fishes at a site in upper Amazonian Ecuador. Proc. Acad. Nat. Sci. Philad. 127:93-134.

Schlosser, I. J. 1991. Stream Fish Ecology: A Landscape Perspective. BioScience 41:704-712.

Silvano, R. A., B. D. do Amaral, and O. T. Oyakawa. 2000. Spatial and temporal patterns of diversity and distribution of the Upper Jurua River fish community (Brazilian Amazon). Environmental biology of fishes 57:25-35.

Ter Steege, H., N. Pitman, D. Sabatier, H. Castellanos, P. Van Der Hout, D. C. Daly, M. Silveira, O. Phillips, R. Vasquez, and T. Van Andel. 2004. A spatial model of tree α-diversity and tree density for the Amazon. Biodiversity and Conservation 12:2255-2277.

Towsend, C., M. Begon, and J. Harper. 2003. Essentials of ecology. Second Edition. Blackwell, Malden.

Veech, J. A., K. S. Summerville, T. O. Crist, and J. Gering. 2002. The additive partitioning of species diversity: recent revival of an old idea. Oikos 99:3-9.

Vonhof, H. B., F. P. Wesselingh, and G. M. Ganssen. 1998. Reconstruction of the Miocene western Amazonian aquatic system using molluscan isotopic signatures. Palaeogeography, Palaeoclimatology, Palaeoecology 141:85-93.

Walker, I. 1995. Amazonian streams and small rivers. in J. G. Tundisi, C. E. M. Bicudo & T. Matsumura-Tundisi (eds.), editor. Limnology in Brazil. Sociedade Brasileira de Limnologia/Academia Brasileira de Ciencias, Brazil.

Wittmann, F., J. Schongart, J. C. Montero, T. Motzer, W. J. Junk, M. T. F. Piedade, H. L. Queiroz, and M. Worbes. 2006. Tree species composition and diversity gradients in white-water forests across the Amazon Basin. Journal of Biogeography 33:1334-1347.


28

––A P P E N D I X 1 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– LIST OF ESPECIES AND ABUNDANCES

MA: Mata-Matá; PU: Purité; SS: Santa Sofía; ZA: El Zafire. Q- stream number

Order Family Species MAQ1 MAQ2 PUQ1 PUQ2 SSQ1 SSQ2 ZAQ1 ZAQ2 Total

Beloniformes Belonidae Potamorrhaphis guianensis 2 2

Characiformes Acestrorhynchidae Acestrorhynchus lacustris 1 1 1 1 4

Anostomidae Leporinus af friderici 2 1 1 1 5 Leporinus agassizi 1 1 Leporinus cf natereri 1 1

Characidae Astyanax abramis 5 3 2 10 Astyanax anterior 2 1 1 4 Axelrodia stigmatias (cf.) 1 1 Brycon melanopterus 2 2 4 Bryconops inpai 1 1 8 5 3 11 9 38 Characidium cf. pellucidum 1 3 2 3 2 2 13 Characidium sp.1 1 7 5 1 2 16 Characidium sp.2 8 6 1 15 Charax leticiae 2 2 Charax tectifer 10 12 5 3 8 3 3 44 Creagrutus cochui 12 7 19 Crenuchus spilurus 1 5 6 Ctenobrycon hauxwellianus 3 3 Gephyrocharax sp. 8 10 8 6 8 10 10 8 68 Gymnocorymbus thayeri 1 4 5 Hemibrycon sp. 3 3 Hemigrammus analis 1 7 4 8 20 Hemigrammus cf gracilis 5 5 Hemigrammus levis 2 5 1 8 Hemigrammus sp. 2 2 Hyphessobrycon cf agulha 7 11 7 8 2 3 12 8 58 Knodus breviceps 7 7 5 9 14 10 7 7 66 Melanocharacidium cf nigrum 1 1 6 4 12 Microschemobrycon cf geisleri 1 2 1 4 Moenkhausia cf collettii 6 10 16 Moenkhausia cf dichroura 5 4 9 Moenkhausia comma 8 10 1 3 22 Moenkhausia lepidura-complejo sp. 9 9 8 26 Moenkhausia oligolepis 4 5 3 2 1 15 Moenkhausia sp. 3 3 Moenkhausia sp.2 1 2 3 Moenkhausia tridentata 7 9 3 19 Odonthocharacidium aphanes (cf.) 1 1 Phenacogaster af pectinatus 6 11 3 6 26 Pristobrycon sp.1 3 1 4 Pristobrycon sp.2 2 1 3 Roeboides myersii 1 1 Serrasalmus sp. 2 1 3


29

Steindachnerina guentheri 2 2 4 Tetragonopterus argenteus 1 3 4 Triportheus cf angulatus 3 6 9 Triportheus pictus 1 1 Tyttocharax cochui 7 6 5 9 11 7 10 9 64

Chilodontidae Chilodus punctatus 1 2 3

Curimatidae Curimatella alburna 2 2 Cyphocharax pantostictos 1 3 4 Cyphocharax spiluropsis 5 5 Semaprochilodus insignis 1 1

Erythrinidae Hoplias malabaricus 9 8 3 3 2 2 3 30

Gasteropelecidae Carnegiella strigata 5 2 3 1 3 14 Gasteropelecus maculatus 3 8 11

Lebiasinidae Copeina guttata 1 1 Nannostomus marginatus 8 8 1 1 18 Pyrrhulina laeta 5 8 5 6 5 29

Cyprinodontiformes Rivulidae Rivulus sp. 4 1 1 6 Rivulus sp.2 2 2

Gymnotiformes Gymnotidae Gymnotus cf carapo 6 5 11 Gymnotus javari 5 2 7 Gymnotus sp. (revisar) 1 1

Hypopomidae Brachyhypopomus sp. 7 7 2 1 17 Brachyhypopomus sp.2 4 4 Steatogenys elegans 2 1 1 4

Rhamphichthyidae Gymnorhamphichthys rondoni 1 3 7 5 1 3 20

Sternopygidae Eigenmannia virescens 5 8 3 2 6 24 Sternopygus macrurus 4 1 1 4 1 1 12

Lepidosireniformes Lepidosirenidae Lepidosiren paradoxa 1 1

Perciformes Cichlidae Apistogramma sp.1 7 7 1 2 17 Apistogramma sp.2 1 5 8 6 20 Apistogramma sp.3 3 3 Biotodoma sp. 1 2 3 6 Bujurquina mariae 9 11 9 8 6 9 12 11 75 Crenicichla cf.alta 6 2 2 3 1 3 1 18 Crenicichla sp. 1 1

Polycentridae Monocirrhus polyacanthus 1 1

Siluriformes Aspredinidae Bunocephalus coracoideus 1 1 Bunocephalus sp. 2 7 6 15 Pterobunocephalus sp. 9 6 15

Auchenipteridae Auchenipteridae sp. 1 1 Tatia intermedia 6 1 2 2 11 Tatia perugiae 3 7 2 1 13 Tatia sp.2 12 15 27 Tatia sp.3 2 2 Tatia sp.X 1 1 Tatia sp.Y 1 1 1 1 4 Tetranematichthys quadrifilis 1 1

Callichthyidae Corydoras elegans 5 5 Corydoras rabauti 1 3 4 Corydoras semiaquilus 10 10


30

Corydoras sp. 5 4 9 Denticetopsis praecox 1 5 2 5 1 5 6 25 Megalechis thoracata 1 1

Cetopsidae Helogenes marmoratus 1 1 1 3

Doradidae Scorpiodoras heckelii 1 1

Heptapteridae Heptapterido (revisar) 1 1 Heptapterus sp. 1 1 2 Heptapterus sp.2 1 1 2 2 6 Mastiglanis asopos 3 1 4 Mastiglanis sp.2 1 1 1 1 4 Pimelodella cf steindachneri 3 1 4 Pimelodella geryi 2 1 2 5 1 2 4 17

Loricariidae Ancistrus sp. 4 3 2 2 8 9 9 10 47 Farlowella oxyrryncha 1 2 1 2 3 9 Farlowella platoryncha 1 1 Hypostomus oculeus 1 1 2 4 Limatulichthys griseus 7 7 14 Loricarido trompa redonda 4 4 Loricarido trompudo 4 4 Otocinclus sp. 4 6 5 15 Otocinclus sp.2 1 1 2 Rineloricaria castroi 2 3 2 7 Rineloricaria cf lanceolata 1 1

Pimelodidae Pimelodus sp. 1 3 4

Trichomycteridae Trichomycteridae sp. 3 1 4 Vandellia cirrhosa 1 1

Synbranchiformes Synbranchidae Synbranchus marmoratus 1 1 1 3

Total 188 201 117 114 219 191 164 153 1347


31

––A P P E N D I X 2 –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– DETAILED PHYSICOCHEMICAL DATA OF SOILS AND WATERS

FROM EACH STREAM

Soil physicochemical analyses South eastern area Santa Sofia El zafire SSQ1 n=5 SSQ2 n=5 ZAQ1 n=3 ZAQ2 n=3 Mean SD Mean SD Mean SD Mean SD

sand (%) 40.5 9.6 54.6 21.6 66.1 17.9 62.8 10.4

loam (%) 38.1 6.5 29.9 14.1 20.8 11.3 22.1 6.3

Gra

nulo

met

ry

clay (%) 21.3 3.8 15.6 7.6 13.1 7.2 15.1 4.6

pH 3.4 0.2 3.5 0.1 3.7 0.1 3.5 0.1 I.A. (meq/100 g) 6.0 1.2 5.7 3.0 3.3 1.3 4.9 1.7

A.I.S. (%) 82.2 6.6 91.2 0.6 83.6 6.1 87.6 1.6 O.C. (%) 2.3 0.7 1.5 0.7 3.2 1.4 3.6 1.6

C.I.C. 13.1 2.2 10.3 6.5 15.1 7.2 16.5 6.9 Ca 0.5 0.3 0.2 0.1 0.1 0.0 0.1 0.0

Mg 0.5 0.1 0.2 0.1 0.2 0.1 0.2 0.1 K 0.2 0.0 0.1 0.1 0.2 0.1 0.2 0.1

Na 0.1 0.0 0.0 0.0 0.2 0.2 0.1 0.1

Exch

ange

com

plex

(m

eq/1

00g)

T.B. 1.3 0.3 0.6 0.3 0.6 0.3 0.7 0.2 B.S. (%) 10.0 4.0 5.8 1.1 4.6 2.6 4.4 0.8 P (ppm) 8.6 2.2 6.1 1.7 6.1 6.8 2.7 0.7

Amacayacu NNP area Mata-mata Purite AMQ1 n=5 AMQ2 n=5 PUQ1 n=3 PUQ2 S03 n=3 Mean SD Mean SD Mean SD Mean SD

sand (%) 20.9 3.0 30.9 3.1 58.1 3.1 52.4 8.7 loam (%) 51.8 3.0 45.8 2.3 25.0 3.1 23.8 4.3

Gra

nulo

met

ry

clay (%) 27.3 3.2 23.3 1.2 16.9 3.1 23.8 5.2 pH 3.7 0.2 3.6 0.2 4.0 0.1 3.7 0.1 I.A. (meq/100 g) 8.0 0.6 9.0 0.6 3.5 0.3 5.6 0.5

A.I.S. (%) 87.8 2.8 92.4 1.6 90.1 1.4 93.8 0.4 O.C. (%) 1.7 0.4 2.3 0.7 1.6 0.5 1.7 0.3

C.E.C. 16.5 1.4 19.9 1.3 11.3 1.9 15.1 3.1 Ca 0.5 0.1 0.2 0.1 0.1 0.0 0.0 0.0

Mg 0.4 0.1 0.2 0.1 0.1 0.0 0.1 0.0 K 0.2 0.1 0.3 0.0 0.2 0.0 0.2 0.0

Na 0.1 0.0 0.1 0.0 0.1 0.0 0.1 0.0

Exch

ange

com

plex

(m

eq/1

00g)

T.B. 1.1 0.3 0.7 0.2 0.4 0.1 0.4 0.1 B.S. (%) 6.7 1.3 3.7 0.7 3.4 0.4 2.5 0.4 P (ppm) 3.4 1.9 3.7 1.3 4.1 2.1 0.6 0.0


32

Soil physicochemical analyses

South eastern area Santa Sofía El Zafire SSQ1 SSQ2 ZAQ1 ZAQ2 Average SD n Average SD n Average SD n Average SD npH 6.655 0.262 2 6.493 0.488 3 6.815 0.007 2 6.875 0.318 2Electric conductivity (E.C., dS/cm)

22.45 3.748 2 23.8 8.879 3 6.525 4.469 2 5.85 0.156 2

Calcium 0.065 0.021 2 0.07 0.026 3 0.001 0.028 2 0.001 0 2Magnesium 0.035 0.007 2 0.04 0.017 3 0 0.014 2 0.005 0 2Potassium 0.04 0 2 0.03 0.01 3 0.015 0 2 0.01 0 2Sodium 0.055 0.007 2 0.073 0.021 3 0.02 0.021 2 0.02 0 2Bicarbonates 0.215 0.021 2 0.233 0.112 3 0.13 0.014 2 0.13 0 2Sodium Adsorption Rate (S.A.R.)

0.235 0.007 2 0.303 0.015 3 0.42 0.078 2 0.335 0.021 2

Dissolved oxygen (D.O. µS.cm-1)** 5.043 0.251 2 3.955 0.24 2 5.26 0.368 2 5.418 0.032 2

Temperature (°C)** 25.725 0.389 2 26.1 0 2 25.425 0.247 2 24.425 0.177 2

Amacayacu NNP area Mata-matá Purité MAQ1 MAQ2 PUQ1 PUQ2 Average SD n Average SD n Average SD n Average SD npH 5.877 0.129 3 5.693 0.051 3 5.027 0.231 3 5.077 0.136 3Electric conductivity (E.C., dS/cm)

15.333 1.436 3 13.467 0.058 3 6.113 2.792 3 5.343 1.236 3

Calcium 0.047 0.006 3 0.03 0 3 0.033 0.032 3 0.033 0.021 3Magnesium 0.02 0 3 0.027 0.006 3 0.013 0.006 3 0.007 0.005 3Potassium 0.023 0.01 3 0.02 0 3 0.004 0.001 3 0.005 0.004 3Sodium 0.057 0.006 3 0.053 0.006 3 0.01 0.001 3 0.009 0.001 3Bicarbonates 0.07 0 3 0.053 0.006 3 0.05 0.035 3 0.053 0.006 3Sodium Adsorption Rate (S.A.R.)

0.313 0.05 3 0.32 0.052 3 0.07 0.017 3 0.07 0.026 3

Dissolved oxygen (D.O. µS.cm-1)** 3.998 0.384 2 3.742 0.012 2 4.393 0.118 2 3.657 1.655 2

Temperature (°C)** 24.725 0.247 2 24.875 0.318 2 24.575 0.106 2 24.525 0.106 2


33

Methods: Soils Granulometry: Bouyoucos pH Interchangeable acidity (I.A., meq/100g): with KCl; % A.I. saturation (A.I.S.) % Organic carbon (O.C.): Walkley - Black; Cationic Interchange Capacity (C.I.C.) and bases: Ammonium acetate 1 normal and neutral meq Calcium/100g x 200 = ppm. meq Magnesium/100g x 120 = ppm. meq Potassium/100g x 391 = ppm meq Sodium/100g x 230 = ppm Percentage (%) = ppm / 10.000 Phosphorous (ppm) : Bray II; Methods: waters pH: Potentiometric Electric conductivity (E.C., dS/cm) Sodium Adsorption Rate (S.A.R.) Calcium, Magnesium (meq/L): Atomic absorption Potassium, Sodium: atomic emission Anion content (meq/L) Sulphates: turbidimetric Chlorides, Carbonates, Bicarbonates: Potentiometric titulation Dissolved oxygen (D.O. µS.cm-1): field measurements Temperature (°C): field measurements

13 Spatial variation of fish communities among terra firme forest streams of the Colombian Amazonia

Documents

Transcript of 13 Spatial variation of fish communities among terra firme forest streams of the Colombian Amazonia