amphibian community structure as a function of forest type in amazonian peru

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Amphibian community structure as a function of forest type in AmazonianPeru

Rudolf von May, Jennifer M. Jacobs, Roy Santa-Cruz, Jorge Valdivia, Jusmell M. Huamán and Maureen A. Donnelly

Journal of Tropical Ecology / Volume 26 / Issue 05 / September 2010, pp 509 - 519DOI: 10.1017/S0266467410000301, Published online: 30 July 2010

Link to this article: http://journals.cambridge.org/abstract_S0266467410000301

How to cite this article:Rudolf von May, Jennifer M. Jacobs, Roy Santa-Cruz, Jorge Valdivia, Jusmell M. Huamán and Maureen A. Donnelly (2010).Amphibian community structure as a function of forest type in Amazonian Peru. Journal of Tropical Ecology, 26, pp 509-519doi:10.1017/S0266467410000301

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Journal of Tropical Ecology (2010) 26:509–519. Copyright © Cambridge University Press 2010doi:10.1017/S0266467410000301

Amphibian community structure as a function of forest typein Amazonian Peru

Rudolf von May∗,1, Jennifer M. Jacobs†, Roy Santa-Cruz‡,§, Jorge Valdivia§, Jusmell M. Huaman#and Maureen A. Donnelly∗

∗ Department of Biological Sciences, Florida International University, 11200 SW 8th Street, OE-167, Miami, Florida, USA†Department of Integrative Biology, University of California, Berkeley, California, USA‡Museo de Historia Natural, Universidad Nacional de San Agustın de Arequipa, Arequipa, Peru§ Facultad de Ciencias Biologicas y Agropecuarias, Universidad Nacional de San Agustın de Arequipa, Arequipa, Peru# Facultad de Ingenierıa Forestal, Universidad Nacional Amazonica de Madre de Dios, Puerto Maldonado, Madre de Dios, Peru(Accepted 15 May 2010)

Abstract: The potential effect of forest type on the structuring of animal communities in western Amazonia remainspoorly understood. In this study, we tested the hypothesis that amphibian species richness, composition and abundancediffer across forest types in the lowland rain forest of south-eastern Peru. By using 320 individual transects, we comparedthe amphibian assemblages across four major forest types (floodplain, terra firme, bamboo and palm swamp) at eachof four sites separated by 3.5–105 km. We identified 1967 individuals of 65 species in 11 families and found that alarge proportion of the amphibian diversity in this region is attributed to habitat-related beta diversity. Overall, wefound that forest type is more important than site in predicting both species composition and abundance. We alsofound that, when analyses are conducted separately for each forest type and include species abundance data, similaritybetween assemblages decreases with increasing geographic distance. In contrast to studies that considered speciespresence/absence but ignored species abundances, our results highlight the importance of including abundance datain the assessment of animal diversity patterns in western Amazonia. We conclude that evaluating community structureacross forest types can improve our understanding of diversity patterns in this region.

Key Words: Amazon, beta diversity, frogs, habitat filtering, species sorting, tropical forest

INTRODUCTION

General assessments of biodiversity patterns in westernAmazonia have focused on a variety of taxa, but dataon animal community structure across major foresttypes remain scarce (Larsen et al. 2006, Pearson &Derr 1986, Peres 1997, Saaksjarvi et al. 2006). Mostprevious studies focusing on amphibian diversity inlowland Amazonia have compared assemblages acrossdifferent sites without taking into account the effect ofnaturally occurring forest types on community structure(Azevedo-Ramos & Galatti 2002, Blair & Doan 2009,Dahl et al. 2009, Doan & Arizabal 2002). In otherstudies, researchers have defined habitats based onthe degree of anthropogenic disturbance (e.g. primaryforest, secondary forest, plantation; Gardner et al. 2007a,

1 Corresponding author. Email: [email protected]

Pearman 1997) or focused on only one type of forest(Aichinger 1987). In some cases, results from a studyconducted in a single site and a single forest type (Allmon1991) were regarded as representative of entire SouthAmerican forests (Vasudevan et al. 2008).

A recent comparison of amphibian assemblages acrossfour major forest types in south-eastern Peru showed thatthese habitats may contribute to the local variation inamphibian species richness and composition (von Mayet al. 2009a). Because this comparison was made at onlyone site, and only species presence/absence data wereused, the next step is to evaluate whether similar patternsexist at other sites in the region. A large-scale comparison,incorporating abundance data (this paper), allows us toimprove our understanding of amphibian diversity in theregion. Moreover, the inclusion of additional sites allowsus to explore the relationship between species compositionand geographic distance. Some studies have illustrated anegative association between similarity and geographic

510 RUDOLF VON MAY ET AL.

distance (Azevedo-Ramos & Galatti 2002, Duellman &Thomas 1996), whereas other studies have found noassociation between similarity and geographic distance(Dahl et al. 2009, Doan & Arizabal 2002).

Here, our primary goal was to test the hypothesis thatamphibian species richness, composition and abundancediffer across forest types in the lowlands of south-easternPeru. We focused on four major forest types that arewidespread and cover most of the lowlands of south-western Amazonia: floodplain forest, terra firme forest,bamboo forest and palm swamp (Griscom et al. 2007,Mostacedo et al. 2006, Phillips et al. 1994, Pitman et al.1999). Given that other animal assemblages have beenshown to vary according to the type of forest (Larsenet al. 2006, Pearson & Derr 1986, Peres 1997), wepredicted that amphibian species richness and abundancedistribution patterns would also differ across forest types.Additionally, because geographic distance may influencethe patterns of community structure (Ernst & Rodel 2005,2008; Parris 2004), we tested the hypothesis that sitesclose to each other are more similar than sites fartheraway from each other.

MATERIALS AND METHODS

Study sites

We surveyed four major forest types at each of foursites in the Madre de Dios region of south-eastern Peru:Los Amigos Research Center (CICRA is the Spanishacronym), 12◦34′07′′S, 70◦05′57′′W, 270 m asl; Centrode Monitoreo 1 (CM1), 12◦34′17′′S, 70◦04′29′′W, c.250 m asl; Centro de Monitoreo 2 (CM2), 12◦26′57′′S,70◦15′06′′W, 260 m asl; Tambopata Research Center(TRC), 13◦08′30′′S, 69◦36′24′′W, 350 m asl. The firstthree sites are 3.5–25 km away from each other and thefourth site (TRC) is 80–105 km away from the otherthree sites. At CICRA, annual rainfall is variable andranges between 2700 and 3000 mm (http://atrium-biodiversity.org). The dry season (June–September) hasless rainfall and is slightly cooler than the wet season.The mean annual temperature ranges between 21 ◦C and26 ◦C (N. Pitman, pers. comm.). Details about our studysites can be found in Kratter (1997), Doan & Arizabal(2002) and maps are available at the Atrium BiodiversityInformation System site (http://atrium-biodiversity.org).

Forest types

We followed the general categories of forest typesrecognized by plant ecologists working in Madre de Diosand nearby regions (Griscom & Ashton 2006, Griscom

et al. 2007, Mostacedo et al. 2006, Olivier 2007, Pitmanet al. 1999, Silman et al. 2003). We did not follow thecategories proposed by Phillips (1993) and Phillips et al.(1994) because their classification was limited to a smallarea (112.4 km2) that represents only 0.13% of Madre deDios (85 300 km2) and does not include our study sites.

The floodplain forest can be classified in twogeneral categories: mature floodplain forest and primarysuccessional floodplain forest (Pitman et al. 1999). Wesampled only in mature floodplain forest (hereafterreferred to as floodplain), which exhibits high plantdiversity, a 25–35-m-tall canopy (except for gaps),numerous lianas and emergent tree species. Flooding mayoccur once a year or once every few years depending onriver level fluctuations; inundation varies from > 1.0 mnear the river to 0.1 m on more elevated terraces(Hamilton et al. 2007). Temporary bodies of water arecommon during the wet season as a result of rainwateraccumulation.

The terra firme forest (hereafter referred to as terrafirme) is found on higher terrain that is never flooded bythe river (Pitman et al. 1999). The terra firme at our sitesis 20–40 m above the floodplain and is primarily foundon flat upland terraces dissected by small permanent ortemporary streams. We sampled on these terraces andavoided streams and ravines bordering streams. The terrafirme has fewer temporary ponds than the floodplainbecause little rainwater is retained in the upper soil layers.The terra firme also exhibits high plant diversity, > 32 mtall canopy (except for gaps) and many species of emergenttrees (Griscom & Ashton 2006).

The bamboo forest (hereafter referred to as bamboo) ispatchily distributed and covers extensive areas dominatedby two native bamboo species, Guadua sarcocarpa andG. weberbaueri (Griscom et al. 2007, Olivier 2007). Atour sites, bamboo forms patches of variable size (c. 1 hato 100+ ha), is interspersed within the terra firme and itscanopy is lower (up to 25 m) than the terra firme canopy(Griscom & Ashton 2006).

The palm swamp forms patches of variable size,typically between tens to hundreds of hectares, dominatedby the native palm Mauritia flexuosa. Palm swamp soilscan be permanently or seasonally flooded, are nitrogen-limited and have abundant organic matter (Householder2007, Kahn 1991). Slow decomposition results in acidicsoil and water (pH 4.5–5.5; J. Janovec, pers. comm.). Morethan 50% of the palm swamps were flooded (0.1–0.7 m)during the study.

Sampling methods

We conducted standardized sampling between 18January and 16 April 2008 (wet season). The averagerainfall, as measured between December 2007 and April

Beta diversity of Amazonian amphibians 511

2008, was 159.4 mm mo−1 in 2008 (http://atrium-biodiversity.org). We sampled on flat terrain in all foresttypes and avoided slopes that mark the transition betweenforest types. These slopes represent an ecotone and mayharbour a mix of species from different habitats. Toaccount for the interspersion of replicated samples, weestablished twenty 50-m transects per habitat at each site.This number was selected following our preliminary workat CICRA and published reports from other tropical forests(Veith et al. 2004). We selected habitat patches dissectedby at least 200 m of trail at each site and all transectswere established away from trails to avoid bias associatedwith potential trail effects (von May & Donnelly 2009).We used a random number table (Heyer et al. 1994)to determine the distance along the trail from whicheach transect began. All transects were perpendicularwith respect to the main trail, began 5 m away fromthe trail and were separated by at least 30 m. Becausetransects were established in several patches, transectsrepresenting each forest type were separated by up to3 km. Thus, transects included the variability associatedwith each habitat. We used a compass and a 50-m stringwith orange flagging marked at every 5 m to establisheach transect. Understorey vegetation was only disturbedwhen tangled vegetation blocked access; in those cases,we used a machete to clear a narrow path and waitedfor at least 3 d before sampling. Our sampling effort was320 transects, and each transect was sampled only onceto maintain independent sampling units (as opposed toother studies, where transects were re-sampled multipletimes).

We sampled all transects at night (19h00–01h30)because most amphibians are nocturnal, and nightsampling using visual encounter surveys (VES; Crump& Scott 1994) is more effective than other samplingmethods (Doan 2003). Our preliminary work showed thatnocturnal surveys were effective for finding both diurnaland nocturnal species. Moreover, previous research inother tropical forests has shown that some diurnal speciesare found more often at night than during the day(Lieberman 1986).

We used distance-and-time-constrained VES (50×4-mtransect in 30 min) as an alternative to the traditional VESmethod. To reduce the variation in species detectabilities,which can be considered an issue in VES (Pearmanet al. 1995), our search protocol included disturbanceof the substrate (in traditional VES, the substrate is noteffectively disturbed during search). While walking alonga transect, we first visually scanned the area using ourlights and then disturbed the substrate with a snakehook. Any frog that was not detected by our first visualassessment was eventually spotted as it jumped away fromits original location. All individuals located within 2 mon either side of the centre line of the transect, and onsubstrate up to 2 m in height, were captured. We placed all

encountered individuals in separate plastic bags that weretied to the string marking the centre line of the transect.All sampling was conducted by two or three observerswith headlamps. If there were three observers, only thefirst two actively searched while the third one recordeddata. If there were only two observers, data recording wasdone at the end of each survey. Each transect took 30 minto complete, i.e. 1 person-hour was effectively investedper transect. We identified, measured and released allcaptured individuals.

We collected voucher specimens only when fieldidentification was not possible. These specimens wereidentified and deposited at the Museo de Historia Naturalof Universidad Nacional Mayor de San Marcos, in Lima,Peru. Species nomenclature follows the on-line referenceto amphibian taxonomy, Amphibian Species of the World(http://research.amnh.org/herpetology/amphibia/index.php).

Data analysis

Because our primary interest was to evaluate amphibiancommunity structure across forest types, we grouped andanalysed most data with respect to forest type. In thispaper, the term ‘abundance’ refers to ‘relative abundance’under the assumption that the number of individualscounted in a transect represents the abundance in whichspecies occur in a particular place and time.

We first used sample-based rarefaction curves tocompare patterns of species richness among forest types.We pooled data collected at all sites and used the pro-gram EstimateS, version 8.0 (http://viceroy.eeb.uconn.edu/estimates) for this comparison (Gotelli & Colwell2001). We then used analysis of variance (ANOVA) tocompare the average species richness among forest types,and graphically compared the minimum and maximumnumber of species recorded in each forest type acrossall sites. We plotted rank-abundance curves to comparethe species abundance distributions among forest types.We arbitrarily defined as abundant species those thatwere represented by at least 20 individual observations(which correspond to approximately 1% of all identifiedindividuals).

We used the additive partitioning approach (Lande1996) to describe patterns of beta diversity across thelandscape. According to Lande (1996), gamma diversityis composed by the addition of the alpha and betacomponents, γ = α + β. We obtained γ by pooling thenumber of species recorded in all habitats ( = forest types),while α represented the mean number of species recordedin each habitat and β represented the mean number ofspecies not found in each habitat. We estimated β by β =γ − α. Researchers have used this approach to describe


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Figure 1. Rarefaction curves based on data collected at four sites (CICRA, CM1, CM2 and TRC). Each curve represents the expected number of speciesfor a given number of observed individuals, though the rarefaction was based on randomization of sample order. The bars indicate ± 1 SD. Thedotted vertical line indicates the point of comparison for the four curves.

amphibian diversity patterns across habitats (Gardneret al. 2007a, Pineda & Halffter 2004).

We used non-metric multidimensional scaling (nMDS)plots to visualize patterns of community structure. Forthis analysis, each site had four habitat patches (sensuLeibold et al. 2004) and each patch represented adifferent forest type. The nMDS plots were based ona Bray–Curtis dissimilarity matrix, first using speciespresence/absence and then using species abundance data(Clarke & Warwick 1994). We also ran an analysisof similarity (ANOSIM) to test for relationship amongforest types and calculated the similarity percentagecontribution (SIMPER) to evaluate which species weremost important in determining the dissimilarity betweenpairs of groups (Clarke & Warwick 1994). We appliedthe indicator species analysis procedure (Dufrene &Legendre 1997) to determine which species can be used asindicators of particular forest types. We used the statisticalsoftware Primer-E, version 5.0 (Clarke & Warwick 1994)to generate the nMDS plots and to run the ANOSIM andSIMPER, and we used PC-ORD version 5.0 (MjM Software,Gleneden Beach) for the indicator species analysis.

We used a Mantel test to evaluate the correlationbetween similarity and geographic distance. As in theprevious analyses, each site had four habitat patches. Weused a matrix containing presence/absence data (Jaccardsimilarity index) and a matrix with the geographicdistance among habitat patches. We also used a matrixcontaining abundance data (Bray–Curtis dissimilarityindex) and a matrix with the geographic distance amonghabitat patches. First, we tested whether there was acorrelation between similarity and distance when forest

types are not taken into account (as in previous studies).For this analysis, our matrix contained all possiblepairs of habitat patches. Second, we tested whetherthere was a correlation between similarity and distancewhen forest types are taken into account. For thisanalysis, we ran a separate Mantel test for each foresttype. We also performed Pearson correlations on thesame dataset to further assess the relationship betweensimilarity and distance (the values of distance, originallymeasured in km, were log-transformed in this case).We used an Excel spreadsheet integrated with PopTools(http://www.cse.csiro.au/poptools) to perform Manteltests and SPSS version 14.0 (SPSS Inc., Chicago) for thecorrelations.

RESULTS

We captured and identified 1967 individuals of 65amphibian species at four sites (Appendix 1). Fifty-oneindividuals (2.59%) escaped prior to identification andwere not included in the analyses. As is typical for mostamphibian communities in the Neotropics, the familyHylidae was the most species-rich (26 species). Tenother amphibian families were recorded, three of whichwere represented by only one species (Appendix 1). Theonly non-anuran family was Plethodontidae (lunglesssalamanders).

Our sample size was sufficient to characterize the speciesrichness and composition across forest types because thespecies accumulation curves approached an asymptote(Figure 1). Overall, we recorded more individuals and


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Figure 2. Mean (markers), minimum and maximum (bars) number ofamphibian species recorded in each forest type, based on data collectedat four sites (CICRA, CM1, CM2, TRC). Bars on top denote significantlydifferent groups in Student–Newman–Keuls post hoc comparisons.

species in the floodplain than in other forest types(Figure 1). Accordingly, the mean number of speciesin the floodplain was higher than in other forest types(ANOVA, F3,12 =5.37, P=0.014) and the maximum andminimum numbers of species in this habitat were alsohigher than in other habitats (Figure 2). We recordednearly the same number of individuals and species in theterra firme and bamboo, and both forest types exhibitedsimilar pattern of species accumulation. We recorded thelowest number of species in the palm swamp, althoughthis habitat ranked second in terms of total abundance.We found the same pattern when comparing species

richness based on a standardized abundance (e.g. 350individuals; Figure 1).

The gamma diversity, according to the additivepartitioning approach, can be expressed as: 65 [γ ] =18.2 [α] + 46.8 [β]. Within each forest type, beta diversitycontributed about half of the total gamma diversity(floodplain = 52%, terra firme = 54%, bamboo = 46%,palm swamp = 53%). Overall, mean diversity andevenness were higher in the floodplain than in the otherforest types (Appendix 1).

We found differences in species abundance distribu-tions among forest types, as indicated by different shapesof the rank-abundance distribution curves (Figure 3).We found more abundant species (i.e. those with > 20individuals) in the floodplain than in the other foresttypes, and the slope of the floodplain curve resemblesthe curve for all data combined. The species abundancedistributions in the terra firme and the bamboo resembleeach other and indicate that these forest types have lessabundant species compared with the floodplain. Theabundance distribution in the palm swamp also indicatesthat this forest type has less abundant species comparedwith the floodplain (Figure 3).

Our indicator species analysis confirmed the patternsexhibited by the rank-abundance distribution curvesand detected additional species that could be used tocharacterize each forest type. Overall, between one andsix species could be used to characterize each foresttype (these species are labelled with an asterisk inAppendix 1) and they contribute more than 50% to

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Figure 3. Rank-abundance distribution curves of species recorded across sites and forest types. The rank-abundance curve for all data combined isshown on top of the individual curves for each forest type. Each forest type includes pooled data from four sites. The most abundant species (> 20individuals observed across all sites) are labelled with particular letters in the curve for all data; only four of the 18 most abundant species werenot labelled, but they are N, O, P, Q (between labels M and R). For each forest type, the most abundant species (> 70%) are labelled in decreasingorder in parentheses. The relative abundance was transformed to log(abundance + 1), where abundance is the number of individuals recorded ineach forest type. Letter codes: A = Pristimantis reichlei, B = Leptodactylus (Adenomera) sp., C = Rhinella margaritifera, D = Pristimantis toftae, E =Engystomops freibergi, F = Leptodactylus petersii, G = Hamptophryne boliviana, H = Dendrophryniscus minutus, I = Chiasmocleis ventrimaculata, J =Ameerega hahneli, K = Hypsiboas cinerascens, L = Hypsiboas lanciformis, M = Phyllomedusa vaillanti, N = Noblella myrmecoides, O = Ameerega trivittata,P = Scinax ictericus, Q = Oreobates cruralis, R = Pristimantis carvalhoi, U = Dendropsophus minutus.



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Figure 4. Non-metric multidimensional scaling plot, four sites (CICRA,CM1, CM2 and TRC), four forest types, wet season 2008. Speciesabundance data were used for dissimilarity matrix and nMDS plot.

the total average dissimilarity between forest types.The SIMPER results show that the average dissimilaritybetween the floodplain and the terra firme was 73.2%.The average dissimilarity between the floodplain andbamboo was 75.1%, and for the floodplain and palmswamp, 81.0%. The terra firme and bamboo were themost similar habitats, as their average dissimilarity was57.2%. In contrast, both sites were very different from thepalm swamp, as the average dissimilarity was 86.7% and86.2%, respectively.

We found that community structure differs acrossforest types, as both the presence/absence and abundancematrices were effective at discriminating among foresttypes (Figure 4; only the nMDS plot based onthe abundance matrix is shown). When consideringabundance data, we found a significant pattern ofturnover across forest types (ANOSIM, Global R =0.524, P = 0.001; Figure 4). In contrast, there was nosignificant effect of site on assemblage turnover (GlobalR = 0.055, P = 0.262). Pairwise comparisons indicatedthat the floodplain differs from the other three foresttypes and that the palm swamp differs from both theterra firme and bamboo (all comparisons P < 0.029),but that the terra firme and bamboo do not differ fromeach other (P = 0.629). To verify whether this patternis maintained in the absence of uncommon species,we repeated the same analyses excluding 16 speciesrepresented by singletons and doubletons (Appendix 1).Again, we found a significant pattern of turnover acrossforest types (ANOSIM, Global R = 0.513, P = 0.001), butno effect of site (Global R = 0.030, P = 0.576).

In the Mantel tests, we only found a correlation betweenassemblage similarities and geographic distance whenforest types and abundance data were included in theanalyses. First, when forest types were not considered inthe analysis, we found no correlation between similarity

and geographic distance. The lack of correlation wasobserved both with presence/absence data (Jaccardsimilarity index; Mantel test, r = − 0.078, P = 0.255)and abundance data (Bray–Curtis dissimilarity; Manteltest, r = 0.217, P = 0.068). When we conducted theanalysis separately for each forest type, but only includedpresence/absence data, we found no correlation betweensimilarity and geographic distance (Jaccard similarityindex; Mantel tests and Pearson correlations, P > 0.05 foreach forest type). Only when we conducted the analysisseparately for each forest type and included speciesabundance data, we found a significant correlationbetween similarity and geographic distance in bothfloodplain and terra firme (Bray–Curtis dissimilarity;Figure 5a, b). We found no correlation in the bambooand the palm swamp (Figure 5c, d). However, thetrend observed in the bamboo suggests that it would bepremature to exclude the possibility that similarity andgeographic distance are correlated in this forest type. Alinear function best fitted the data in the floodplain (y =9.85x+48.0, R2 =0.78) and the terra firme (y=23.7x+18.8, R2 =0.97), where y=Bray–Curtis dissimilarity andx = distance.

DISCUSSION

Our results support the prediction that amphibian speciesrichness, composition and abundance differ across foresttypes in the heterogeneous landscape of south-easternPeru. Previous researchers have also shown that forestheterogeneity is important for maintaining amphibiandiversity (Ernst & Rodel 2006, 2008; Gardner et al.2007a), but they often focused on different habitat‘states’ such as primary and secondary forest. Here, wefocused on differences among naturally occurring foresttypes in a region where patterns of amphibian diversityhave not been studied in detail. Our results corroboratesome general patterns (e.g. high species diversity in thefloodplain; Crump 1971, Rodrıguez 1992) and improvethe knowledge of amphibian community structure acrossother poorly studied habitats, especially bamboo and palmswamp.

We found that a large proportion of amphibiangamma diversity in south-eastern Peru is attributed tohabitat-related beta diversity. Although the numericallydominant species may vary across habitats or sites, ourresults indicate that forest type is more important thansite location in predicting both species composition andabundance. The observation that bamboo and terra firmeassemblages do not clearly differ from each other (exceptfor a few species; Appendix 1) is not surprising as bamboohabitats are physically nested within a larger land areacovered by terra firme. This pattern is consistent withfindings by Silman et al. (2003), who demonstrated that


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Mantel testr = 0.95, P = 0.037Pearson correlationr = 0.88, P = 0.020

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(b) Terra firme

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Figure 5. Relationship between Bray–Curtis dissimilarity and geographic distance, for all possible pairwise comparisons, analysed separately for eachforest type.

plant species composition in bamboo and terra firme insouth-eastern Peru are similar to each other. However,more research is needed to make better generalizationsabout how the bamboo forest differs from terra firme interms of animal communities.

The patterns of community structure across foresttypes that we have observed in south-western Amazoniaresemble those observed in other tropical regions (Ernst& Rodel 2005, 2008; Gardner et al. 2007b, Watling2005). For example, East African amphibian assemblagesexhibit a significant association with forest types on asimilar geographic scale (Gardner et al. 2007b). Althoughthe number of species recorded in East Africa is muchlower than in Amazonia, the patterns observed inboth regions suggest that species-sorting across foresttypes (sensu Leibold et al. 2004) is important. Ourresults, and results from the studies cited above, suggestthat habitat heterogeneity is important for maintainingphylogenetically distant amphibian faunas.

Perhaps even more remarkable is the similarityof patterns exhibited by the amphibian assemblagesin south-eastern Peru with those reported for treeassemblages in the same region (Pitman et al. 1999).Pitman et al. (1999) showed that, for most tree species,habitat preferences are driven by abundance distributionsinstead of restricted affinity to a given habitat: ‘habitatpreferences of Amazonian plants are a matter of degree,

and not as strict as suggested by earlier researchers’(Pitman et al. 1999: p. 2657). Likewise, most amphibianspecies we encountered occur in more than one foresttype but usually exhibit high abundance in only oneforest type. Hence, the results of indicator species analysisshould be taken with caution because observing aparticular species does not tell observers which foresttype they are in. The indicator species analysis can beuseful to characterize particular forest types, but it doesnot imply that those taxa are specialists to those foresttypes. The only exception might be Ranitomeya biolat, apoison frog that is strongly associated with the bamboobecause it is the only anuran in the region that usesbamboo internodes as a reproduction and retreat site (vonMay et al. 2009b) and does not successfully breed in otherforest types (R. von May, pers. obs.).

We found that, when analyses are conducted separatelyfor each forest type and include species abundance data,similarity between assemblages decreases with increasinggeographic distance. Our results stand in contrast tofindings by Dahl et al. (2009), who did not find acorrelation between similarity and distance in south-western Amazonia despite the fact that their sites wereseparated by up to 400 km. However, Dahl et al. (2009) didnot standardize their sampling with respect to forest typeand did not use abundance data (J. Moravec, pers. comm.).Likewise, we found no correlation between similarity


and distance when forest type and abundance data werenot included in the analysis. Thus, our results illustratethat abundance data and forest type should alwaysbe included in the analysis of amphibian communitystructure at relatively small regional scales (as small as100 km in our study). Researchers working in othertropical and subtropical regions (and who included bothabundance data and habitat characteristics in theiranalyses) also found that geographic distance is importantfor structuring amphibian assemblages (Keller et al. 2009,Parris 2004).

The four forest types we studied are relativelydiscrete in western Amazonia and are, in part, definedby vegetation and different environmental conditionsincluding soil type and flooding regime. Treating eachforest type as a separate unit has many advantagesand allows researchers to understand which majorlandscape features influence the structuring of taxonomicassemblages. An alternative method is to analyse thevariation of amphibian communities across fine-scaledenvironmental gradients (e.g. soil type, humidity), withthe aim of identifying which habitat characteristics aremost relevant for community structure. Preliminarywork on this topic has shown that amphibian speciesmay respond individualistically to some substratecharacteristics (e.g. soil pH, leaf-litter mass; Menin et al.2007, Van Sluys et al. 2007, R. von May unpubl. data).More research is needed to link environmental gradientswith animal diversity patterns in western Amazonia.

In conclusion, evaluating community structure acrossforest types can improve our understanding of diversitypatterns in Amazonian landscapes. At the same time,this type of information can aid in conservation. Ifdifferent areas are set aside as corridors or smallpreserves, the inclusion of each forest type will maximizethe amount of protected biodiversity in the region.Finally, given that many threatened amphibian speciesin Peru might be found outside protected areas, moreinformation on species’ habitat requirements is neededto develop strategies for habitat conservation and reservedesign.

ACKNOWLEDGEMENTS

We thank Kelsey Reider, Lisseth Flores, Valeriano Quispe,Jerry Martınez, Raul Thupa, Hernan Collado, Jorge Perez,Mario Napravnik, Kurt Holle, Jesus Ramos and the staffat CICRA, CM1, CM2 and TRC for help in field work andlogistics. We thank Jesus Cordova and Cesar Aguilar forproviding access to the herpetological collection in theMuseo de Historia Natural Universidad de San Marcos.We thank Nigel Pitman, James Watling, Paul Fine,Alessandro Catenazzi, Evelyn Gaiser, Steve Oberbauer,Kyle Summers, Zhenmin Chen, Vivian Maccachero,

Monica Isola, Justin Nowakowski, Robert Hegna, KelseyReider, Seiichi Murasaki, Steven Whitfield, Tiffany Doanand two anonymous reviewers for providing constructivecomments on the manuscript. We also thank EvelynGaiser, Tom Philippi and Zhenmin Chen for providingstatistical advice. Collection of data and voucherspecimens was authorized by an IACUC permit (Number05-013) issued by Florida International University(FIU) and collection and export permits issued by theInstituto Nacional de Recursos Naturales (INRENA), Peru(permit numbers 11-2008-INRENA-IFFS-DCB and 09C/C-2008-INRENA-IANP). We thank Karina Ramırezand Carmen Jaimes for advice with permit applications.Funding for this study was provided by the AmazonConservation Association, Wildlife Conservation Society,Tinker Foundation, Graduate Student Association andLatin American and Caribbean Center at FIU. RvM thanksFIU’s University Graduate School for a Doctoral YearFellowship. This paper is contribution number 181 toFIU’s programme in tropical biology.

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Appendix 1. Species of amphibian and number of individuals recorded in each forest type. Data from the four study sites were pooled (N = 20transects per habitat per site). The asterisk(s) next to the number of individuals denotes that the species could be used as an indicator for thathabitat (indicator species analysis: two asterisks, P < 0.05; one asterisk, 0.05 < P < 0.10). Diversity measures (mean ± SE) are included at thebottom of table.

Species Floodplain Terra firme Bamboo Palm swamp All sites

AromobatidaeAllobates conspicuus 0 8 2 0 10Allobates femoralis 3 0 0 0 3Allobates trilineatus 11 0 0 0 11BufonidaeDendrophryniscus minutus 5 1 0 169∗ 175Rhinella margaritifera 38 39 16 77 170Rhinella marina 0 1 2 0 3CeratophrynidaeCeratophrys cornuta 3 1 0 1 5DendrobatidaeAmeerega hahneli 18 7 1 33 59Ameerega trivittata 5 8 12 2 27Ranitomeya biolat 0 0 6∗∗ 0 6HemiphractidaeHemiphractus scutatus 0 1 0 0 1HylidaeDendropsophus koechlini 2 0 0 0 2Dendropsophus leali 4 0 0 0 4Dendropsophus minutus 0 1 13 3 17Dendropsophus parviceps 2 0 0 0 2Dendropsophus rhodopeplus 6 1 0 0 7Dendropsophus schubarti 3 0 0 1 4Hypsiboas boans 1 0 0 0 1Hypsiboas cinerascens 10 3 0 33 46Hypsiboas fasciatus 13∗ 4 0 1 18Hypsiboas geographicus 0 0 1 3 4Hypsiboas lanciformis 2 1 7 27∗ 37Osteocephalus buckleyi 0 0 0 3 3Osteocephalus cf. pearsoni 1 0 1 0 2Osteocephalus leprieurii 1 2 2 0 5Osteocephalus sp. 1 4 0 0 5Osteocephalus taurinus 0 1 3 5 9Phyllomedusa camba 0 1 0 0 1Phyllomedusa palliata 2 1 0 1 4


Appendix 1. Continued.

Species Floodplain Terra firme Bamboo Palm swamp All sites

Phyllomedusa vaillanti 29 0 4 1 34Scarthyla goinorum 0 0 0 3 3Scinax garbei 13 0 0 1 14Scinax ictericus 11 6 6 4 27Scinax pedromedinae 5 1 0 0 6Scinax ruber 4 3 10∗ 1 18Trachycephalus coryaceus 1 0 0 0 1Trachycephalus venulosus 0 0 1 0 1LeiuperidaeEngystomops freibergi 111∗ 10 15 0 136Edalorhina perezi 2 0 0 0 2LeptodactylidaeLeptodactylus (Adenomera) sp. 33 55 78 25 191Leptodactylus didymus 2 0 3 0 5Leptodactylus knudseni 1 0 0 0 1Leptodactylus lineatus 0 2 0 0 2Leptodactylus pentadactylus 2 0 0 0 2Leptodactylus petersii 39 7 5 79∗∗ 130Leptodactylus rhodomystax 1 1 0 0 2Leptodactylus rhodonotus 1 0 0 0 1MicrohylidaeChiasmocleis bassleri 2 0 0 0 2Chiasmocleis ventrimaculata 69∗∗ 6 10 2 87Ctenophryne geayi 11∗ 3 0 0 14Elachistocleis bicolor 2 0 0 0 2Hamptophryne boliviana 87∗∗ 33 3 1 124Syncope antenori 4 3 9 0 16PlethodontidaeBolitoglossa altamazonica 1 0 9∗∗ 3 13StrabomantidaeNoblella myrmecoides 9 7 8 5 29Oreobates cruralis 12 9 2 1 24Pristimantis altamazonicus 1 0 1 5 7Pristimantis buccinator 4 6 0 1 11Pristimantis carvalhoi 21 0 2 0 23Pristimantis divnae 0 3 1 0 4Pristimantis fenestratus 3 0 0 0 3Pristimantis ockendeni 0 3 4 0 7Pristimantis reichlei 23 118∗∗ 89 4 234Pristimantis skydmainos 12 0 0 0 12Pristimantis toftae 89∗∗ 13 33 3 138Number of species 51 37 32 30 65Number of individuals 736 374 359 498 1967Diversity, Shannon (H′) 2.60 ± 0.14 2.05 ± 0.13 2.09 ± 0.11 1.70 ± 0.20Diversity, Simpson (1 – λ′) 0.89 ± 0.02 0.80 ± 0.04 0.80 ± 0.01 0.70 ± 0.07Evenness, Pielou (J′) 0.81 ± 0.04 0.73 ± 0.03 0.74 ± 0.20 0.65 ± 0.08

amphibian community structure as a function of forest type in amazonian peru

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