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FISH COMMUNITY ASSEMBLY IN THE AMAZONIAN CREEKS:

INTEGRATING PHYLOGENY INTO COMMUNITY ECOLOGY

CSTOR GUISANDE.1*, CARLOS GRANADO-LORENCIO2, NGELA BOLIVAR3, EDGAR PRIETO3,

BERNARDO CORRALES3, MARCELANUEZ-AVELLANEDA4 AND SANTIAGO R. DUQUE3

1Facultad de Ciencias, Universidad de Vigo, Lagoas-Marcosende, 36200-Vigo, SPAIN

2Departamento de Biologa Vegetal y Ecologa, Facultad de Biologa, Universidad de Sevilla,

Sevilla, SPAIN

3 Instituto Amaznico de Investigaciones-IMANI, Universidad Nacional de Colombia, A.A.

215, Leticia, COLOMBIA

4Instituto Amaznico de Investigaciones Cientficas-SINCHI, Leticia, COLOMBIA

* e-mail: [email protected]


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Abstract. The aim of this study was to identify the factors that determine fish community

assembly in the creeks of the Colombian Amazon. We approached the study exploring the

phylogenetic basis of community niche structure. We used standard mitochondrial DNA

(mtDNA) markers and the amino acid composition (ACC) of fish eyes in both creeks to infer

the phylogenetic relationship among taxa and the genetic flow between the populations within

species between the creeks. Creeks positively contribute to high fish diversity in the Amazon

basin (high -diversity) because there is high local fish species diversity within creeks (high

-diversity) and, because the composition of fish communities is different among creeks

owing to increase among-locality compositional differences (high -diversity). One speciation

process responsible for the high -diversity was a differential adaptation to the hydrodynamic

conditions of the habitat, which leads to a spatial segregation among the species within the

creek. The fact that closely related lineages have similar color patterns but differ in the diet

indicates that sexual selection is another force that creates reproductive isolation, allowing

subsequent trophic differentiation. There was a positive significant relationship between AAC

separation within species between the creeks and the species richness in the creeks of the

genera to which the species belongs, corroborating the hypothesis that the low genetic flow

among communities favors the speciation rate and, indicating that low dispersal rate among

the creeks maintains dissimilarity among localities and, therefore, enhance -diversity. Sexual

selection by color patterns promotes reproductive isolation, but if this reproductive isolation is

combined with low dispersal rates, there is a high potential risk of species extinction.

Therefore, fishes of the Amazonian creeks are fashion victims.

Key words: fish, richness, Amazon basin, amino acids, ADN, trophic niche, sexual

selection.


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INTRODUCTION

Biodiversity degradation is one of the most important human effects on Earths

ecosystems (Vitoisek et al. 1997). The effect on biodiversity is not just the loss of species

with an intrinsic value, but the species individual traits and interactions contribute to maintain

the functioning and stability of ecosystems (Loreau et al. 2001). Biodiversity affects

ecosystem stability, nutrient dynamics, susceptibility to invasion, and it is not clear whether it

also affects productivity or if productivity depends on biodiversity (see Tilman 1999).

The restoration of degraded communities requires information about the processes that

determine which and how many species live in a specific habitat (community assembly). To

identify the main factor/factors that influence community assemblages is important because it

allows us to determine whether habitat biodiversity is supported by the diversity of local

communities (-diversity), site-to-site variation in local species composition (-diversity)

and/or regional diversity (-diversity) and, therefore, to decide whether restoration efforts

should be focused on returning local and/or regional process to their previous state (Chase

2003).

There is an intense debate about whether community assembly is mainly governed by

regional or local factors (Ehrlich 1997; Weiher and Keddy 1999; Lawton 2000; Hubbell 2001;

Bell 2001; Wootton 2001). If local factors determine the resulting community, community

assembly often leads to a single stable equilibrium. However, when the resulting community

depends on the regional factors, community assembly can lead to multiple stable equilibria.

Now it seems clear that it is not important to decide whether community assembly primarily

leads to a single or multiple stable equilibria, because many studies have shown that both

situations are equally possible (Chase 2003), but to identify the factors that determine the

assemblage of each specific community.


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Regional processes such as the size of the regional species pool (Gaston 2000), habitat

availability (Grossman et al. 1997), rate of dispersal (Mouquet and Loreau 2003), historical

sequences of species entering the locality and subsequent extinction rates (Robinson and

Edgemon 1988; Lockwood et al. 1997), and local processes such as abiotic environmental

conditions (Guisande et al. 2003), species interaction (Wootton 2001; Guisande et al. 2003),

primary production (Chase and Leibold 2002), resources heterogeneity within habitat

(Angermeier and Winston 1998), and rate of disturbance (Chase 2003) have been shown as

the main factors in determining community assembly. Some of these factor-diversity

relationships are scale-dependent (Chase and Leibold 2002), which means that the effect of

the factor on -diversity is different from the effect on -diversity.

Some of the ecological factors mentioned above may positively contribute to species

richness because as they have promoted in the past, or are now promoting, divergence among

closely related species. The integration of phylogeny into community ecology is, therefore,

necessary to fully understand community assembly (Losos 1996; Webb et al. 2002).

However, despite the potential importance of considering phylogeny in community ecology

studies, phylogenetic approaches have been little used to elucidate the factors determining

community structure. This is probably due to the difficulty of applying phylogenetic

information to community ecology, because different phylogenetic trees are obtained

depending on the methodology.

The high species richness of freshwater fishes that inhabit tropical areas has attracted

the attention of many scientists, although most of the studies have been carried out in Africa

(see Sturmbauer 1998). There is an agreement that both sexual selection and morphological

adaptation, particularly of the feeding apparatus, have been the factors responsible for the

explosive radiation of East African cichlids (Albertson et al. 1999). However, it is not clear

the relative timing and importance of these mechanisms. On one hand, it has been suggested


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that trophic differentiation have played an important role during the early speciation process,

whereas an opposite hypothesis suggests that adaptive morphological change is not the

primary cause of speciation but occurs after the establishment of genetic isolation by other

means (see Albertson et al. 1999).

Fish richness in the Amazonian basin is very high and creeks play an important role in

enhancing this fish richness (Fernandes et al. 2004). Human activities in the Amazon basin

have modified fish habitats and threaten species diversity mainly in creeks (Junk and Soares

2001). So far, land use has had probably the greatest effect, with climate and alien species

being minimal (Sala et al. 2000). Unfortunately, we are changing the Earth more rapidly than

we understand it, and the Amazonian ecosystem is not an exception, especially since the

mechanisms that maintain this high fish diversity in creeks are unknown. The aim of this

study was to identify the factors that determine local fish community assembly in the creeks

of the Colombian Amazon, by exploring the phylogenetic basis of the community niche

structure.

METHODS

Abiotic conditions of the creeks

The fish communities of two habitats, the Yahuarcaca and Arenosa creeks were

studied in the Colombian Amazon. Arenosa creek flows into Yahuarcaca creek, and then both

flow into Lake Yahuarcaca, which is linked to the River Amazon (Fig. 1). Weekly records,

between 1 and 3 p.m., of water level, temperature, conductivity, oxygen saturation and pH

were measured in both creeks from October 2003 to November 2004.

Primary productivity

The rate of algal biomass accrual on artificial substrates in the absence of herbivory,

which has been used as an indirect indicator of primary productivity in aquatic ecosystems

(Chase and Leibold 2002), was measured in both creeks following the methodology described


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by Cascos and Toja (1994). The artificial substrates were put in the creeks the 12th of

December and two replicates were removed from the creeks at 6, 30 and 35 days for the

analysis of the chlorophyll.

Fish sampling

Sampling was carried out during May and November 2003 for 3-5 consecutive days in

each creek, taking two samples every day. The fishes collected during the first sampling in

May were used to estimate species richness, to analyze stomach content and morphology. The

second sampling in November was to confirm possible differences among creeks in species

richness (-diversity) and to determine whether the fish species community composition

varies over the year. Each day the first sample was collected from before sunrise to midday

and the second sample from before sunset to midnight. This sampling design allowed us

discriminate between species with nocturnal and diurnal behaviors. The fishes were collected

using gill nets, straight haul seine and handles. The fishes were subjected to general

anesthesia with tricaine (MS-222, 250 mg l-1) in a bath treatment with static water, and were

frozen if used for the morphological measurements. The diet and morphological features of 77

species (686 individuals) were analyzed, the phylogenetic relationship of 8 species was

studied using standard mtDNA and the AAC of the eyes of 18 species were also analyzed.

Stomach analysis

The overlap in diet among species was estimated by analyzing the stomach contents of

the fish. The stomachs of 5-10 individual specimens were analyzed for each species. The food

was divided into 8 different categories: fruits and seeds, aquatic invertebrates, terrestrial

invertebrates, periphyton, rest of vegetables, fishes, detritus and others. Fish with empty

stomachs were not considered in the analyses. The data were expressed as the volumetric

proportions of stomach content occupied by each of the food items.


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The Morosita index (CH) was used to measure the trophic niche overlap between

species

= =

=

+=n

i

n

ii

ikij

n

i

ikij

H

pp

pp

C

1 1

22

1

2

wherep is the proportion of the food item i consumed by the speciesj and k, and n is the

number of food items.

Morphological measurements

Figure 2 shows the morphological variables measured in the fishes, which were

standardized for the variable standard length. Fish color patterns were divided into different

categories: light, light with 1-2 spots, light with several spots, light speckled, light with

horizontal stripes, light with vertical stripes, dark with horizontal stripes, dark with vertical

stripes, dark speckled, dark with several spots, dark with 1-2 spots and dark. When the species

had any kind of intense color it was assigned to the dark category.

Species richness

The predicted species richness was estimated by using the indexes Jack1 (Burnham

and Overton 1978) and the Incidence-Based Coverage Estimator (ICE) (Chazdon et al. 1998)

with presence-absence matrices. These indexes were calculated by using the software

EstimateS 6 (Colwell 1997).

Analysis of amino acids

The adult eyes of the most abundant fishes were immediately removed and preserved

in HCl 6N. Amino acid analysis was performed on samples containing 1 or 2 eyes of adults of

each species per vial (between 4 and 6 samples of each species per creek). Amino acids were

measured by high-performance liquid chromatography (HPLC) using an Alliance system, a

474 scanning fluorescence detector, and a 15 x 3.9 Nova-Pak C18 column (van Wandelen and

Cohen 1997). Amino acid standard H NCI0180 PIERCE was used for identification and


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quantification. A total of 15 amino acids were analyzed: aspartic acid, serine, glutamic acid,

glycine, histidine, arginine, threonine, alanine, proline, tyrosine, valine, lysine, isoleucine,

leucine and phenylalanine.

Amino acid distance among species

The species scores on the discriminant analysis axes of the AAC were used to

calculate pairwise Euclidean distance (D) between species, and within species among creeks:

( )

n

XX

D

n

j

jkji

2

1

=

=

where n is the number of discriminant functions. For comparison of the AAC distance within

species among creeks,Xis the mean of the scores for the discriminant functionj (species

centroid) obtained from the discriminant analysis for each speciesin the creeks i and k. For

the comparison of the AAC distance between species,Xis the mean of the scores for the

discriminant functionj obtained from discriminant analysis of the species i and k.

Polar plot

To obtain a graphic representation of several discriminant functions obtained from the

discriminant analysis, a polar coordinate system was used to position fish species in the

diagram:

=

=n

j

jzabsX1

)180

cos()(

=

=n

j

jzabsY1

)180

sin()(

where X and Y are the positions in polar plot,zis the score of the discriminate functionj, is

the arbitrary angle of the discriminant functionj

in the polar plot andn

is the number of

functions considered from the discriminant analysis. Each discriminant function was divided

in two vectors, the positive and negative parts of the discriminant function, with the angle of

the negative part being 180 higher than that of the positive part. Therefore, for the polar

diagram the number of vectors was twice the number of discriminant functions obtained from


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the discriminant analysis. If, for example, there are 8 discriminant functions: the angle of

separation between each consecutive vector is 22.5

16

360; the angle of the positive part of

the discriminant function I is 22.5, and the angle of the negative part of the discriminant

function I is 202.5; the angle of the positive part of the discriminant function II is 45, and

the angle of the negative part is 225, etc.

Analysis of the standard mitochondrial DNA

Complete nucleotide sequences (840 pb) of the slightly overlapping mitochondrial

genes for ATP synthase subunit six (ATPase6) and subunit eight (ATPase8) (total 840 pb)

were obtained from eight species of characiforms (at least three samples for each species,from both localities: Yahuarcaca and Arenosa creeks) for a total of 30 sequences. Genomic

DNA was isolated from ethanol-preserved muscle tissue by standard proteinase K, phenol-

chloroform extraction (Sambrooket al. 1989). The mitochondrial ATPase6 and ATPase8

genes were amplified using the primers ATP 8.2_L8331

(5AAAGCRTYRGCCTTTTAAGC) and CO3.2_H9236 (5

GTTAGTGGTCAKGGGCTTGGRTC), according to the general methods (Lovette et al.

1988). The mitochondrial control region was amplified by polymerase chain reaction (PCR)

in 25 L reactions containing 5 L dNTPs (1mM), 2.5 L reaction buffer (200 mM Tris-HCl

pH 8.4, 500 mM KCL), 0.8 L MgCl (50 mM), 1 L of each primer (10 M), 0.2 L (2.5U)

of Taq DNA polymerase (Gibco BRL), 2, 6, or 8 L of template DNA (100 ng l-1) and 12.5,

8.5, or 6.5 L of H2O depending on the intensity of the band obtained in the chromatogram.

PCR conditions were as follows: 94C (2 min), 30 cycles of 94C (30 sec), 52C (30 sec) and

72C (1 min, 30 sec), followed by 72C (5 min). Samples were sequenced using the BigDye

terminator cycle sequencing ready reaction kit (Applied Biosystem Inc.) on an ABI 310

automated DNA sequencer. All templates were sequenced completely in both directions.


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Fragments of the ATPase6 and ATPase8 mitochondrially encoded rRNA genes were obtained

for a subset of the taxa (Ort and Meyer 1997).

Phylogenetic analysis

Concatenated ATPase6 and ATPase8 sequences were aligned in ClustalX v.1.83.1 (6),

using the default settings and resetting all gaps before the alignment. The resulting alignment

was examined by eye, and the program Gblocks v.0.91b (Castresana 2000) was used to

identify regions ambiguously aligned, which were excluded from the analysis. The resulting

alignment was 749 nucleotides long. The best-fit model of nucleotide substitution was

selected by using the Akaike Information Criterion in Modeltest v.3.6 (Posada and Crandall

1998). A maximum likelihood tree was obtained under the best-fit model using the algorithm

implemented in Phyml v.2.4.1 (Guindon and Gascuel 2003), starting from a BIONJ tree

(Gascuel 1997). One thousand bootstrap replicates were used to assess phylogenetic

confidence (Felsenstein 1985). Pairwise phylogenetic distances were calculated by adding

together the branch lengths across the path between the pairs of taxa in the estimated

phylogeny, using TreeEdit v.1a10 (Rambaut and Charleston2002).

RESULTS

Species richness

Table 1 shows the list of species found in both creeks over the period studied. The

total number of species observed was 107. In Yahuarcaca creek the number of species

predicted by Jack 1 index (113) was very similar to the one predicted by ICE index (117), but

it seems that the real number of species is higher than observed because after 10 samples it

was not reached an equilibrium (Fig. 3a). In Arenosa creek the number of species predicted

by Jack 1 index (118) was also very similar to the one predicted by ICE index (115).

However, opposite to Yahuarcaca creek, the number of predicted species did not change after

the sampling number seven in Arenosa creek (Fig. 3b), indicating that the number of observed


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species was close to the number of predicted species. The percentage of shared species in

Arenosa creek was not 100% (Fig. 3c), which might indicate a relatively high -diversity.

Abiotic conditions

There were no differences between the two creeks for any of the environmental

variables that were measured on a weekly basis from October 2003 to November 2004 (Fig.4)

(Analysis of covariance, taking time as a covariable and the creeks as the factor, p > 0.261),

indicating that environmental conditions were not responsible for the potential high -

diversity observed in the creeks. The values of temperature, conductivity, oxygen saturation

and pH were within the normal range observed in water bodies and, therefore, abitotic

conditions do not restrict local fish diversity in the creeks (-diversity).


The rate of algal biomass accrual on artificial substrates was lower in Arenosa than in

Yahuarcaca (Fig. 5). Primary productivity was very low in both creeks and at the lower end of

the productivity values observed in a study carried out by Chase and Leibold (2002) in several

ponds (Chase and Leibold 2002). A low -diversity should be expected with this low primary

productivity (Chase and Leibold 2002). However, as mentioned above, local diversity is high

in both creeks.

Morphological measurements

A discriminant analysis of the morphological variables revealed that it was possible to

identify fish species by their morphological features, as 84.9% of cases were correctly

classified (by cross validation). For these morphological features, the first (type of teeth),

second (color patterns), third (length of dorsal fin) and fourth (length of anal fin) discriminant

functions accounted for 42.8%, 23.1%, 11.5% and 5.7% of variance, respectively (Fig. 6).

Therefore, in both creeks the species were discriminated mainly by the diet, their color

patterns (intrasexual and intersexual selection, hunting, warming, camouflage, etc.) and also


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by hydrodynamic characteristics, the length of dorsal and anal fins. These hydrodynamic

features discriminate among gymnotiformes (without dorsal fin and large anal fin),

siluriformes (small dorsal and anal fins), characiformes (medium length of both dorsal and

anal fins), and perciformes (large dorsal fin).

Diet of the species

There were not important differences among species in their diet, because the

discriminant analysis performed to the stomach contents of the species showed that only

10.6% of the species were correctly classified according to their diet (by cross validation). In

both creeks, most of the species fed mainly on detritus and, aquatic and terrestrial

invertebrates (Figure 7).

In some trophic groups, there were species with nocturnal or diurnal behavior (Table

3). This temporal segregation over the day partially offset the niche overlap among some

species that co-occurs in space and have similar diet, for instance the species of the genera

Moenkhausia.

Phylogenetic relationships among species

Some characiform species from the creeks were studied using standard mitochondrial

DNA (mtDNA) markers to infer the phylogenetic relationships among species and to quantify

population differentiation between the two localities. A phylogenetic analysis of the DNA

sequences is shown in Figure 8, which also includes information about the diet of the species.

Appropriate support for the relationships shown in the tree could not always be obtained.

Values below 5% for sequence divergence are usually found for individuals within species.

The ATPase sequence divergence ranged from 0-4.2% for most of the species. However,

three species showed higher intraspecific divergence values: T. argenteus (11.5%), M.

sanctafilomenae (21.7%), and N. marginatus (8.7%). In these species with high intraspecific


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divergence values, the differences were mainly between individuals of different creeks (Fig.

8), which may indicate a low genetic flow between creeks.

The construction of phylogenetic trees using molecular sequences of DNA has

sometimes been frustrated by the persistence of ancestral polymorphisms, because speciation

is more rapid than the fixation of alleles within species, and/or because phylogenies based on

a single gene reflect only the history of that marker and not the true relationships among

species (Kocher et al. 1993, Albertsonet al. 1999, Kocher2003). A different approach can be

undertaken using the amino acids. AAC is species-specific in marine fishes and, furthermore,

is a good tool for discriminating between subpopulations (Riveiro et al. 2003). The eyes are

one of the best parts of the fish body for basing discrimination analyses of fish

subpopulations, probably as, in water environments, vision is an important sense in both

feeding and social contexts (particularly sexual selection and species recognition).

Discriminant analysis of the AAC of the most abundant species (a total of 18 species)

showed that 66.9% of cases were correctly classified (by cross validation). The structure

matrix shows that there were no significant correlations between any of the amino acids and

the three first discriminant functions (Table 2). Glutamic acid, leucine, proline, arginine and

alanine were significantly correlated with discriminant functions 4 and 5. There was a positive

relationship between the amino acid Euclidean distance between the species pair, obtained

from the scores of the discriminant functions 4 and 5, and the patristic distance between the

species pair obtained from mtDNA markers (Fig. 9). A bootstrap method was used to evaluate

the statistical significance of this relationship, because each fish species pair was not

independent from the others. Regression was recalculated 1000 times using random series in

which only 50% of the data was used. In all cases, the slope of the regression was both

positive and significantly different from zero.


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It was possible to establish a link between the phylogenetic relationship among species

and some of the morphological measurements that mainly discriminate the fish species. There

was a positive relationship between diet overlap and the amino acid Euclidean distance

between the species pair (Fig. 10a), indicating that those species with a higher phylogenetic

relationship have different diet. A bootstrap method, performed as mentioned above, shows

that in all cases the slope of the regression was both positive and significantly different from

zero. Moreover, it seems that closely related species have a similar shape and color (Figs. 8

and 10b).

Differences within species between creeks

A further discriminant analysis of the AAC, this time with different groups between

the creeks within the species, revealed that 71.2% of cases (by cross validation) were

correctly classified (Fig. 11). The first component of the discriminant analysis explained

28.1% of the variance and discriminated mainly between species. The second component

explained 23.4% of the variance and discriminated mainly between the two creeks. It is

possible, therefore, to identify each species according to its AAC and, furthermore, for some

species there were clear differences in their AAC between the two creeks. As the AAC of fish

eyes is a good indicator of the phylogenetic relationship among taxa (Fig. 9), we assume that

the difference in the AAC within species between the two creeks is an indicator of a low

genetic flow between populations.

The differences in the AAC within species between the two creeks were not constant.

For some species the AAC was very similar for both creeks, whereas for other species there

were important differences between the two creeks, indicating a low dispersal rate for some

species. The AAC distance within species between creeks was calculated using the centroids

of the axes with eigenvalues higher than 10 (axes I and II), in order to focus on the most

significant factors that conditioned AAC differences within species between the two creeks.


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There was a significant relationship between AAC separation within species between the two

creeks and the species richness in the creeks of the genera to which the species belongs (Table

4, Fig. 12).

DISCUSSION

Abiotic factors

The abiotic environment may influence community assembly by restricting which

species can establish at the habitat. In some extreme habitats, an environmental factor

(temperature, salinity, heavy metals, food, etc.) may even constrain community assembly

regardless of the level of the other factors. In same cases, there may be an interaction between

abiotic factors and species interaction. For instance, in mountain lakes where primary

production is very low and, hence, there is a strong food limitation for the zooplankton, food

resources partitioning (interespecific competition) is the most important factor in structuring

the zooplankton community in these oligotrophic lakes (Guisande et al. 2003).

Abiotic environment in the creeks studied does not constraint the local species

richness because the values of the environmental variables measured are within the normal

range observed in water bodies. Therefore, abiotic factors do not restrict -diversity in the

creeks. There were not important differences in most of the environmental variables measured

among creeks, indicating that abiotic environment was not responsible for the potential high

-diversity observed between creeks.

However, it does not mean that abiotic factors are not affecting fish -diversity in the

Amazon basin. We have only studied creeks, but there is a different fish fauna associated to

the river and the lakes. Different environmental conditions in a region clearly affect species

turnover and, hence, -diversity. This stamen is supported by some studies, which show that

environmental determinism explains a high proportion of the observed species turnover

(Condit et al. 2002; Tuomisto et al. 2003). Moreover, relationships between regional diversity


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and local diversity also support the idea that different habitats enhance diversity, because the

slope of the relationship between local and regional diversity is less than expected from null

model, probably indicating that spatial variation in environmental conditions leading to

species turnover among habitats (high -diversity) (Caley and Schluter 1997). In fishes, it has

been also observed that that total number of species is inversely correlated with total habitat

availability in creek fishes (Grossman et al. 1997). Therefore, part of the high regional fish

richness observed in the Amazon basin is clearly due to the presence of habitats with different

environmental conditions: the river, lakes and the creeks.

Stability of the abiotic conditions

At local spatial scales, diversity is often expected to have a hump-shaped

relationship with the rate of disturbance, with diversity highest at intermediate levels of

disturbance (Connell 1978). The most importance source of disturbance in the Amazon creeks

is flooding. The seasonal distribution of rainfall produces great fluctuations in the water level

of the Amazon River, with mean flood amplitude around 7-10 meters. However, fluctuations

in water level are rather regular. Therefore, the rate of disturbance is not so high to affect

negatively the local fish diversity in the creeks. All the contrary, species composition varied

over the year, probably because the seasonal distribution of rains promote habitat

heterogeneity and, hence, positively contribute to -diversity. Therefore, seasonal changes in

water level rather than stress the habitat promote habitat heterogeneity, enhancing species

richness.


Biodiversity is also affected by productivity in freshwater aquatic ecosystems (Chase

and Leibold 2002). At a local scale the relationship between -diversity and primary

production is hump-shaped where diversity peaks at intermediate productivity (Chase and

Leibold 2002). The primary productivity estimated by measuring the rate of algal biomass


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accrual on artificial substrates was very low in both creeks, which should lead to low local

species richness (Chase and Leibold 2002). However, the -diversity was high in both creeks.

It is probably due to there are other autotrophic carbon sources for the fish community of the

Amazon creeks. Forsberg et al. (1999) observed that central Amazon fish community

consumes a wide variety of living and dead autotrophic sources (phytoplankton, flooded-

forest trees, C3 and C4 macrophytes, periphyton and phytoplankton). Moreover, the input of

alloctonus material is high (Henderson and Walker 1986; McClain and Richey 1996), which

may be an important potential food for the fishes.

Therefore, it seems that the indirect method used to estimate primary productivity

based on the rate of algal biomass accrual on artificial substrates, which has been successfully

used in other aquatic ecosystems (Chase and Leibold 2002), it is not valid for the Amazon

creeks. The fact that few species fed on periphyton (Fig. 7) may explain why this primary

production estimation is not useful for the fish community of the Amazonian creeks.

Species interactions

In addition to physical environment, ecologist have usually sought to explain

differences in local diversity by the influence of local interactions among species as predation,

competition and parasitism (see Ricklefs 1987).

However, during the last two decades it has been questioned the importance of local

interactions on local diversity mainly for two reasons. First, local assemblages do not seem to

be saturated, in the way one might have expected if ecological interactions among species

limited local richness (Ricklefs 1987; Cornell and Lawton 1992; Caley and Schluter 1997;

Gaston 2000), because local diversity is linearly dependent on regional diversity over the

entire range of regional diversities without a ceiling above which local diversity it does not

rise despite continued increases in regional richness. Second, if local conditions determined

local diversity, variation in regional diversity should have little influence on local diversity.


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Again the relationship between local and regional diversity has been used to argue that almost

all the variation in local diversity among regions is accounted for by regional richness

(Ricklefs 1987; Caley and Schluter 1997). One aspect bias the outcome of the comparison of

species richness in local assemblages with the total number of species in the region, both

variables are not independent (Loreau 2000). The linear relation between local and regional

diversity is mainly driven by a strong impact of autocorrelation, that is increasing linearity

was observed when the sizes of local and regional areas became more similar (Hillebrand and

Blenckner 2002). Therefore, it seems that the spatial scale at which a local and regional

community is defined clearly affects the meaning of the relation between both variables and,

therefore, this relationship can not be always used to infer the impact of regional versus local

factors on local species richness.

Our findings suggest that species interactions, specifically spatial segregation within

the habitat, sexual selection and food resource partitioning, are affecting fish community

assembly in the Amazon creeks. It is in agreement with other studies carried out with cichlid

fishes of Africans lakes, where the rapid diversification of this group has been attributed to

morphological adaptation, particularly of the feeding apparatus, and sexual selection

(Seehausen et al. 1997; Albertson et al. 1999).

The variations in size of dorsal and anal fins are adaptations to the hydrodynamic

conditions where the species lives: surface water, medium column water, bottom, bank,

within the macrophites, etc. The fact that the species are discriminated according to

hydrodynamic features seems to indicate that habitat segregation within the creek is an

important factor affecting fish community assembly.

Color plays an important role in intrasexual (agonistic interactions among males),

intersexual selection (female preference for colorful males), recognition of partners, hunting,

warning, and camouflage (Rodd and Reznick 1991; Evans and Norris 1996; Gong 1997;


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Brosset 1997; Kodric-Brown 1998). Intersexual selection generated by sensory drive

contributes to fish speciation (Boughman 2001), and it has been suggested that intersexual

sexual selection may be the driving force behind speciation in the haplochromine cichlids of

Lake Victoria, because mate choice of females for differently colored males maintains

reproductive isolation between sympatric species and color morphs (Seehausen et al. 1997).

The importance of fish color patterns on local fish richness may help explain the

negative effects of human activities on fish diversity in the Amazonian creeks. Land use has

probably the greatest effect on diversity in tropical areas (Sala et al. 2000) and, in particular,

may have an important effect on fish diversity, as it may tend to enhance turbidity of the

water. Water transparency affects female mate-preference criteria (Endler and Houde 1995).

When visibility becomes poor, females are no longer able to distinguish males of sibling

species from those of their own, and hybridize with males from other species, causing a

breakdown of reproductive barriers (Seehausen et al. 1997). Fish species diversity declined in

the Lake Victoria has been explained in terms of increasing turbidity of the water, which it

might favors the formation of hybrids (Seehausen et al. 1997).

In addition to spatial segregation and reproductive isolation, the third important

interaction that promotes speciation was food competition. The high diet overlap among the

species (most of the species are feeding on the predominant food, the macroinvertebrates),

which has been previously observed in tropical fishes (Winemiller et al. 1991; Hori 1991;

Winemiller et al. 1995), could be interpreted as evidence for a predominant role for habitat

filtering (phenotypic attraction). However, the results obtained from mtDNA and AAC show

that closely related taxa have different diets (phenotypic repulsion) (Figs. 8 and 10), which is

when the species composition of a community is the result of past or present competition

(Webb et al. 2002). Therefore, food resource partitioning was also an important component

structuring the assemblage of the fish community in the creeks.


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Connectance among localities

In the Amazon creeks studied, there were differences in the AAC of populations of the

same species among the creeks, indicating a low genetic flow among creeks for some species.

It is agreement with the restricted-movement paradigm which states that some adult fish

species in creeks are sedentary and spend most of their lives in short reaches of creek

(Rodrguez 2002), and shows that species differ in their dispersal ability. Moreover, our

results show that those genera with a higher number of species are those in which the gene

flow among populations is lower. These findings corroborate the concept that some lineages

are inherently more likely to speciate than others (Losos 1996), and that a low dispersal rate

in fishes leads to divergence and speciation (see Smith and Sklason 1996).

High rates of connectance among localities (high dispersal rates) usually reduce -

diversity (Hastings and Gavrilets 1999) and, hence, increase similarity among localities (low

-diversity). As regional diversity (-diversity) is a function of and diversities, where =

+ (Whittaker 1972) or = (Veech et al. 2002), high dispersal rates should generally

reduce -diversity. Therefore, the low dispersal rate observed for some species enhances -,

-diversity and -diversity

Invasion patterns

Species richness and community composition vary also according to invasion patterns:

invasion rate, the timing and invasion order (Robinson and Edgemon 1988; Drake 1990).

Timing and invasion order may also affect community assembly (Robinson and

Edgemon 1988; Lockwood et al. 1997), although some models predict that the role of history

in the process of assembly is less important that might have been expected from earlier

research (Law and Morton 1996). When sequence effects (invasion order and timing) affect

community assembly, a regional species list would not translate to a single community

structure and, it may not possible to reconstruct a degraded community because the history of


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the relationship between species that are no longer there may not allow to understanding

extant community structure (Sammuels and Drake 1997). Invasion order seems to be more

influential where immigration rates are relatively low than where dispersal from outside

sources is high (Robinson and Edgemon 1988). Moreover, the history of species invasion may

be important when the regional species pool is large, as it is the case in the Amazon basin. As

the species composition is different among creeks but the functional groups are the same, it

seems to indicate that the invasion order was important on structuring the final species

composition of each community.

The fact that, despite the fish community composition is different between the creeks,

in both creeks the species are discriminated mainly by color patterns, morphological features

associated with the hydrodynamic and the diet, it might indicate that is a system governed by

guild- or functional group-level assembly rules (Fox and Brown 1993). Therefore, it seems

that fish community assembly in the creeks may operate on traits rather than species, and one

might consider functional groups as collections traits. In a guild-level assembly rule the

precise species composition of the community is unpredictable, but the rule narrows down the

number of possible outcomes from a given species pool, maintaining equal representation of

functional groups, termed favored states (Fox and Brown 1993). Therefore, fish community

composition would be different among creeks, but morphological diversity (Roy and Foote

1997) seems to be the same.

Integrating phylogeny into community ecology

We have been able to integrate phylogeny into community ecology by showing that

there is a link between phylogenetic relationship among species and the morphological

features that mainly discriminate the fish species.

We distinguish in our hypothetical speciation process of the Amazon fishes from a

common ancestor, between adaptive processes that lead to a niche differentiation and relax


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competition, and nonadaptive processes that promote reproductive isolation and, therefore,

favor speciation.

One divergence seems to be associated to a morphological adaptation to the

hydrodynamic conditions of the habitat (adaptative process). The length of dorsal and anal

fins discriminate mainly among gymnotiformes, siluriformes, characiformes and perciformes.

The length of dorsal and anal fins are adaptations to the hydrodynamic conditions where the

species lives in the creek and, hence, suggesting a habitat differentiation among the species

within the creek (spatial segregation).

Other speciation process seems to be related with a reproductive isolation promoted by

the different color patterns (non adaptive process). Color patterns were similar among closely

related lineages (Figure 12). However, a large number of speciation events and mating

barriers produced by sexual selection might not be enough to maintain species diversity (Galis

and Metz 1998). Therefore, in addition to the maintenance of reproductive isolation by sexual

selection, it is necessary a niche differentiation and, therefore, reproductive isolation and

adaptive radiation must go together. The fact that closely related lineages have similar color

patterns but differ in the diet indicate that sexual selection is the force that creates

reproductive isolation, allowing subsequence trophic differentiation (adaptive process).

Therefore, trophic niche segregation is the ecological mechanism that, in addition to a

behavioral mechanism (sexual selection), are partially responsible for the little or no

hybridization among the fish species and, therefore, contribute to the high local fish richness

observed in the creeks.

The third speciation process is promoted by other nonadaptive mechanism, the low

dispersal rate observed in the species of some genera. This hypothesis is supported by the

positive relationship between ACC separation within species among creeks and the species


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richness of the genera to which belongs the species (Figure 12), suggesting that a lower

genetic flow among populations enhances speciation rate.

Finally, coexistence of closely related species (species of the same genera) that

temporal and spatially co-occur is probably due to their abundance is low and/or there is a

temporal segregation over the day (i.e. in the genera Moenkhausia), which both relax

competition. Moreover, in some genera, i.e.Leporinus, species clearly differ in color pattern,

suggesting that reproductive isolation by sexual selection was favoring in the past but it is still

promoting at the present the speciation process. It may explain the contradictory results

obtained in several studies, which have showed that either trophic differentiation or genetic

isolation have played an important role during the early speciation process. Our findings show

that is not really important to determine the relative timing of the mechanisms responsible for

the high diversity, because any of them may promote speciation at any time over the

evolutionary process.

Conclusions

The creeks positively contribute to the high fish diversity that occur in this region

(high -diversity) because there is a high local fish species diversity within creeks (high -

diversity) and, the composition of fish communities are different among creeks owing to

increase among-locality compositional differences (high -diversity). Our results show that in

the Amazon creeks there are multiple stable equilibriums, with a dissimilar species

composition among localities but with similar functional groups. Chase (2003) mentioned that

multiple stable equilibria should be more prominent in systems with large species pools, low

levels of dispersal, high productivity, and low rates of disturbance, which is agreement with

our findings.

The local factors that mainly contribute to the high -diversity is habitat segregation

within the creek, reproductive isolation, which is promoted by a behavioral mechanism


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(sexual selection), food resource partitioning and perhaps also temporal segregation over the

day. Other local factors also important are the wide variety of food resources within the

habitat (detritus, periphyton, seeds and macroinvertebrates), abiotic conditions within a range

of normal values and low levels of disturbance. The main factor that maintains dissimilarity

among localities and, therefore, enhance -diversity, is the low dispersal rate among the

creeks, because favors divergence and speciation. Low dispersal rate probably also

contributes to -diversity because promotes reproductive isolation and, hence, enhances

speciation rate.

From our findings, it is clear that diversity of the fish community in the Amazon

creeks is extremely vulnerable. As mentioned above, one consequence of the high diversity is

species with small populations and, therefore, with a higher probability of extinction that if

the population abundance were high (Hanski et al. 1995). Moreover, although low dispersal

rates enhances regional species richness, the potential risk of extinction of the species is

higher because local populations that are linked by dispersal are less susceptible to extinction

than isolated populations (Hanski et al. 1994). Therefore, low rates of connectance among

localities enhance diversity, but also the vulnerability of the habitat. Finally, reproductive

isolation due to sexual selection is combined with low dispersal rates, there is a high potential

risk of species extinction (Hanski et al. 1994). Therefore, fishes of the Amazonian creeks are

highly susceptible to extinction.

The identification of the main factors that govern fish community assembly in the

Amazon creeks will allow a better conservation and restoration of degraded communities.

Moreover, our results give an insight into a topic, the timing and importance of the

mechanisms responsible for the explosive radiation of freshwater tropical fishes, which has

been focus of considerable debate.

ACKNOWLEDGMENTS


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We thank D. D. Ackerly, C. Rico, C. O. Webb and K. Winemiller for their comments

on the earlier version of this manuscript, G. Ort and A. Roa for the analysis of the

mitochondrial DNA, D. Posada for the phylogenetic analysis and KALUA for logistic

support. This project was supported by the Junta de Andaluca (AD 38/2), the Universidad de

Sevilla, the Xunta de Galicia and UVI.

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Table 1. List of species found in the creeks during both sampling periods.

Species Code Arenosa Yahuarcaca

May November May November

ORDEN BELONIFORMES

Family Belonidae

Potamorhamphis guyanensis 83 + +ORDEN CHARACIFORMES

Family Anostomidae

Leporinus friderici 4 + + + +Family Characidae

Acestrorhynchus falcirostris 24 + + + +Astyanax abramis 29 + + +Brachychalcinus sp 40 +Brycon melanopterus 87 +Bryconops melanurus 2 + + + +Bryconops sp. 1 1 + + + +Bryconops sp. 2 35 +Charax leticiae 94 + +Ctenobrycon hauxwellianus 14 + + + +Cyphocharax spirulopsis 60 + + +Gephyrocharax sp 58 +Gnathocharax sp 12 + +Gymnocoyimbus thayeri 33 + + +

Hemigrammus analis 72 + Hemigrammus bellottii 56 + + Hemigrammus ocellifer 6 + + + +Hemigrammus pulcher 95 + +Hemigrammus sp 1 98 + +

Hemigrammussp 2

69 + + +

Hemigrammus sp 3 104 + +Hyphessobrycon copelandi 22 + + + +Iguanodectes spilurus 101 + +Knodus cfmoenkhausii 85 + +Microschemobrycon sp 71 + + +Moenkhausia comma 45 +Moenkhausia lepidura 8 + + + +Moenkhausia melograma 9 + + + +Moenkhausia cfnaponis 78 + +Moenkhausia sanctafilomenae 10 + + + +Myleus rubripinis 31 +

Phenacogaster pectinatus 52 + + +Serrasalmus rhombeus 43 +Raphiodon gibbus 44 +Tetragonopterus argenteus 11 + + + +Triportheus angulatus 47 + +Tyttocharax cf madeirae 23 + + + +Family Crenuchidae

Characidium sp 1 41 + +Characidium sp 2 7 + + + +


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Characidium sp 3 77 + +Crenuchus spilurus 55 + + +

Elacocharax cf pulcher 75 + + +Leptocharacidium sp 92 + +Microscharacidium 48 +Odontocharacidium aphanes 93 + +

Odontocharacidium sp. 30 + + +Family Curimatidae

Chilodus punctatus 81 + +Curimata sp 37 +Curimatopsis macrolepis 68 +Curimatella alburna 88 +Steindachnerina guentheri 16 + + + +Family Ctenolucidae

Boulengerella maculata 99 +Family Erythrinidae

Hoplias malabaricus 28 + + + +Family Gasteropelecidae

Carnegiella strigata 17 + + + +Family Lebiasinidae

Copella nattereri 63 + +Nanostommus eques 106 +Nanostommus marginatus 26 + + + +Nanostommus trifasciatus 18 + + + +Pyrrhulina laeta 66 + + +Family Prochilodontidae

Semaprochilodus insignis 86 +GroupGlandulocaudinidae 25 + + +GroupXenurobryconini 80 + +ORDEN CIPRINODONTIFORMES

Rivulus sp 1 67 + + +Rivulus sp 2 84 +ORDEN GYMNOTIFORMES

Family Hypopomidae

Brachyhypopomus sp 51 + + +Steatogenys elegans 50 + + +Family Gymnotidae

Gymnotus cf carapo 38 + + +Family Sternopygidae

Eigenmannia virescens 13 + + + +Sternopygus macrurus 20 + +

Family RhamphichthidaeGymnorhamphichthys hypostomus 100 +ORDEN PERCIFORMES

Family Cichlidae

Aequidens sp 107 +Aequidens vittatus 90 +Apistograma agassizi 91 + +Apistograma cfortmani 27 + + +Apistograma sp 76 + + +


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Biotodoma wavrini 53 +Bujurquina mariae 15 + + + +Cichlasoma sp 57 + +Crenicara sp 89 +Crenicichla saxatilis 42 + + +

Hypselacara bitaeniatum 21 + +

Satanoperca jurupari 5 + +Family Eleotridae

Microphilypnus sp 97 + +Family Nandidae

Monocirrhus polyacanthus 61 + + +ORDEN SILURIFORMESFamily Auchenipteridae

Tatia intermedia 96 +Tatia perugiae 70 + + +Family CallichthyidaeCallichthys callichthys 103 +Family Cetopsidae

Pseudocetopsis cf macilenta 59 + +Helogenes marmoratus 82 +Family Heptapteridae

Myoglanis sp 19 + + +Heptapterus mustelinus 79 +Family Loricariidae

Ancistrus sp 34 + +Cochliodon oculeus 36 +Corydoras cfarcuatus 102 +Corydoras cfpastasensis 62 + +

Farlowella amazona 39 + + +Farlowella smithi 74 + + + +Farlowella oxyrryncha 65 + + + +Hypoptopoma sp 105 +Limathulychtys punctatus 3 + + + +Loricaria sp 73 + + + +Otocinclus vestitus 54 + + + +

Rineloricaria sp 49 +Family Pimelodidae

Pimelodella cristata 32 + +Pimelodus blochii 64 + +


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Table 2. Structure matrix of the discriminate analysis performed to the AAC of 18 species

indicating the intra-groups correlations between the discriminant variables and the

discriminant functions (* significant correlations) and the percentage of variance explained by

each discriminate function.

Variables Function1 (43.9%) 2 (21.1%) 3 (11.8%) 4 (8.8%) 5 (5.1%)

Glutamic acid .016 -.209 -.313 .746(*) .206Leucine -.233 .077 -.434 -.486(*) -.176Proline .075 -.085 -.296 -.484(*) -.171Arginine -.069 .087 .218 -.060 -.713(*)Alanine -.026 .370 -.181 -.273 .478(*)

Lysine .004 .150 -.114 -.177 .280Valine -.152 .148 .025 -.271 -.252Isoleucine -.023 .312 -.037 -.313 -.232Serine .003 .025 .224 -.319 -.133Histidina -.181 -.096 .091 .062 .050Glycine .203 -.156 -.210 -.111 -.156Tyrosine .081 -.347 .296 -.213 -.037Threonine .017 .363 .227 .036 .358Aspartic acid -.003 -.025 .164 .136 -.056Phenylalanine -.032 .005 .193 -.440 -.387


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Table 3. Species activity (diurnal or nocturnal) in each trophic group.

Food Day Night

Fishes H. malabaricus (28) A. falcirostris (24)

Aquatic andterrestrialinvertebrates

Bryconops sp (1), M.melograma (9) and M.comma (45)

M. lepidura (8) and M.sanctafilomenae (10)

Aquaticinvertebrates

B. mariae (15),Brachycalcinus sp (40) andN. marginatus (26),

Characidium sp 1 (41),Characidium sp 2 (7),Characidium sp 3 (77), S.elegans (50), Myoglanis sp.(19),A. cfortmani (27) andE.cfvirescens (13),Odontocharacidium sp (30)

Terrestrialinvertebrates

A. abramis (29), C. strigata(17) andB. malanurus (2)

S. jurupari (5), and S.rhombeus (43)

Detritus

O. oculeus (36),L. punctatus(3), S. guentheri (16), T. cfmadeirae (23) and C.hauxwellianus (14)

F. amazona (39) andR.marmoratus (46)

Rest of vegetablesG. thayeri (33),L. cf

friderici (4),P. cristata (32)

PeriphytonN. trifasciatus (18),Rineloricaria sp (49), H.copelandi (22)


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Table 4. Species richness observed in the creeks considering this study and previous ones

(Prieto 2000; Arbelaez 2000; Castellanos 2000) of the genera to which belongs the species

that it has been analyzed their amino acid composition

Code Species Species of the genera

24 Acestrorhynchus falcirostrisA. falcatus, A. falcirostris, A. microlepiandA. lacustris

27 Apistograma cfortmaniA. cf agassizi, A. cfgeisleri, A.bitaeniata andA. cfortmani

2 Bryconops melanurus B. melanurus and Bryconops sp.

15 Bujurquina mariae B. mariae

7 Characidium sp. C. fascidorsale, C. pellucidium andCharacidium sp.

14 Ctenobrycon hauxwellianus C. hauxwellianus

6 Hemigrammus ocelliferH. analis, H. bellottii, H. cf. gracilis,H. luelingui, H. ocellifer, H.schmardae andH. pulcher

22 Hyphessobrycon copelandi H. cfcopelandi

3 Limahtulychtys punctatus L. punctatus

19 Myoglanis sp Myoglanis sp.

8 Moenkhausia lepidura9 Moenkhausia melograma

10 Moenkhausia sanctafilomenae

M. comma, M. dichroura, M. lepidura,M. melogramma, M. naponis, M.megalops and M. sanctafilomenae

26 Nanostommus marginatus18 Nanostommus trifasciatus

N. marginatus, N. trifasciatus and N.eques

16 Steindachnerina guentheri S. bimaculata and S. guentheri

11 Tetragonopterus argenteus T. argenteus

23 Tyttocharax madeirae T. madeirae


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Figure 1. Map of the studied area with the sampling stations.

Figure 2. Plot of the morphological variables measured in the fishes. 1 standard length, 2 pre-

dorsal fin length, 3 head length, 4 dorsal fin length, 5 ventral fin length, 6 pectoral fin length,

7 anal fin length, 8 caudal fin length, 9 maximun body depth, 10 minimum body depth, 11

dorsal fin depth, 12 pectoral fin depth, 13 ventral fin depth, 14 anal fin depth, 15 caudal fin

depth, 16 eye diameter, 17 head depth, 18 maximum body width, 19 minimum body width, 20

anus-caudal fin length, 21 snouth-eye length, 22 eye position, 23 body form, 24 lower barbells

length, 25 upper barbells length, 26 mouth depth, 27 mouth width, 28 mouth position and 29

the kind of jaw tooth (absents, unicupide, bicuspide, conic, canine, sawed, spatula).

Figure 3. Accumulative number of species observed ()) and predicted (, Jack1 index SD)

in Yahuarcaca (A) and Arenosa (B) creeks, and the percentage of shared species in

Yahuarcarca ()) and Arenosa () considering the species observed (C).

Figure 4. Monthly values (means SD) of temperature, pH, conductivity, oxygen saturation,

depth and transparency in Yahuarcaca ()) and Arenosa () from October 2003 to November

2004.

Figure 5. Chlorophyll concentration (means SD) in the artificial substrates in Yahuarcaca

()) and Arenosa () at different days.

Figure 6. Polar diagram of the first, second, third and fourth functions of the discriminate

analysis of the morphological measurements of the species. Code of the species in table 1

Figure 7. Volumetric percentages of stomach content occupied by each of the food items.

Code of the species in table 1.

Figure 8. Maximum likelihood tree for the concatenated ATPase6 and ATPase8 sequences

(log likelihood = -5507.48834). The best-fit model used in the analysis was TN93++. The

estimated base frequencies were f(A)=0.30, f(C)=0.27, f(G)=0.12, f(T)=0.31. The estimated

transition/transversion ratio for purines was 5.90, while the same ratio for pyrimidines was


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6.07. The estimated proportion of invariable sites was 0.32, while the estimated shape of the

-distribution for rate heterogeneity among sites was 1.00, which indicates moderate rate

variation among sites. Only bootstrap values above 50% are shown. Letters next to the names

of species indicate individuals sampled in Arenosa (A) or Yahuarca (Y) creeks (several letters

appear when more than one individual had the same nucleotide sequence). Information about

the main diet of the species is also included.

Figure 9. Relationship between the mean SD amino acid distances obtained from the

discriminant analysis and the patristic distances obtained from the phylogenetic tree (r= 0.82,

P= 0.001).

Figure 10. Relationship between the mean amino acid distances obtained from the

discriminant analysis comparing only the genera of the family Characidae and the diet overlap

obtained using the Morisita index (r= 0.62,P< 0.001) (all SE were lower than 0.76 for the

amino acid distances and 0.18 for the Morisita index) (A), and plot of the discriminant

functions 4 and 5 performed to the AAC of the species of the family Characidae. Code of the

species in table 1.

Figure 11. Plot of the first two discriminant functions for the amino acids of the species

(mean SD of the scores). Yahuarcaca creek (solid symbols) and Arenosa creek (white

symbols). Code of the species in table 1.

Figure 12. Relationship between the amino acid distance (mean SD) within species

between creeks and the species richness in the creeks of the genera to which the species

belongs (r= 0.96,P= 0.009).


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41

Figure 1

Amazonas River

Yahuarcaca Lake

Yahuarcaca stream

4 08 S 69 56 W

Arenosa stream

4 07 S 69 57 W

COLOMBIA Amazon river


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42

Figure 2

1

9

23 20

10

14

19

15

8

28

AB

26

27

2425

18

5

13

7

2

11

12

4

17

22

1621 3

6


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Figure 3

Samples

Numberofspecies

0

25

50

75

100

125

150

Samples

Numberofspecies

0

25

50

75

100

125

150

Samples

0 2 4 6 8 10

%sh

aredspecies

0

25

50

75

100

A Yahuarcaca

B Arenosa

C


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Figure 4

Oxygensaturation(%

)

0

2

4

6

8

Temperature(C)

22

24

26

28

30

Conductivity(s)

0

20

40

60

80

pH

4.5

5.0

5.5

6.0

6.5

7.0

7.5

S O N D J F M A M J J A S O N

2003 2004

Depth(cm)

0

30

60

90

120

150

180

Transparency(m)

0

50

100

150

S O N D J F M A M J J A S O N

2003 2004


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Figure 5

Colonization time (days)

0 10 20 30 40

Chlorophyl(gcm

-2)

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035


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Figure 6

1

2

3

4

5

6

7

8

9 1011

12

13

14

15

16

17

18

19

20

21

22

23

24

25 26

27

28

29

30

31

32

33

34

35

36

37

38

39

40 41

42

43

44

45

46

47

48

49

50

51

52

5354

55

56

57

58

5960

61

62

63

64

65

66

67

68

69

70

71

72

73 74

75

76

77

Discriminat

component I

Discriminatcomponent IV

Discriminatcomponent II

Discriminat

component III

Adaptive process

Habitat segregation

Non adaptive process

Reproductive isolationby color patterns

Adaptive process

Trophic differentiation


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Figure 7

0

20

40

60

80100

0

20

40

60

80

100

0

20

40

60

80

100

Volumetricpercentagesofstomachcontent

occupiedbyeachofthefooditems

0

20

40

60

80

100

0

20

40

60

80

100

Code of the species

0

20

40

60

80

100

Fruits and seeds

Aquatic invertebrates

Terrestrial invertebrates

Periphyton

Rest of vegetables

Fishes

Code of the species

0 1 2 3 4 5 6 7 8 9 10 1 1 12 1 3 14 1 5 16 1 7 18 1 9 20 2 1 22 2 3 24 2 5 26 2 7 28 2 9 30 3 1 32 3 3 34 3 5 36 3 7 38 3 9 40 4 1 42 4 3 44 4 5 46 4 7 48 4 9 50 5 1 52 5 3 54 5 5 56 5 7 58 5 9 60 6 1 62 6 3

0

20

40

60

80

100Detritus


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Figure 8

100

100

100

100

100

1006073

98

85

100

0.05 substitutions /site

100

10075

7575

100

A

A

A

YAA

A

A A

A

A

A

A

Y

A

A

AAY

Y

Y

Y

Y

Y

Y

Y

Y

Y

Bryconops melanurus(terrestrial invertebrates)

Moenkhausia melograma(aquatic and terrestrial invertebrates)

Moenkhausia sanctafilomenae(aquatic and terrestrial invertebrates)

Tetragonopterus argenteus (rest of vegetables)

Carnegiella strigata(terrestrial invertebrates)

Tyttocharax cf. madeirae(detritus and aquatic invertebrates)

Leporinus friderici(rest of vegetables)

Nanostommus marginatus (aquatic invertebrates)


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Figure 9

Phylogenetic distance

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Aminoaciddistance

0.0

0.5

1.0

1.5

2.0

2.5

3.0


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Figure 10

Amino acid distance

0 1 2 3 4 5

Dietoverlap

0.0

0.2

0.4

0.6

0.8

1.0

A

Discriminant component IV

-4 -3 -2 -1 0 1 2 3 4

DiscriminatcomponentV

-3

-2

-1

0

1

2

3

4

22

166

8

11

23

2

9

24

10

B

14

Light

Light with

horizontal stripes

Dark with

1-2 spots

Dark

Light with

1-2 spots


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Figure 11

Discriminant function I

-10 -8 -6 -4 -2 0 2 4 6 8 10

DiscriminantfunctionII

-6

-4

-2

0

2

4

6

8

10

3

3

22

16

6

6

8

14 15

15

14 11

11

26

26

23

232

2

9

9

24

24

27

27

16

18

18

10

10

22

19 19

87

7


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Figure 12

Species richness of the genera

0 1 2 3 4 5 6 7 8

ACCdistancewithinspeciesamon

gstreams

0

1

2

3

4

5

6

7

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Borr_artquebrad2005