The Role of Environmental Gradients on the Diversity of Vertebrates

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MÁRIO RIBEIRO DE MOURA

The role of environmental gradients on the diversity of vertebrates

Tese apresentada à Universidade Federal de Minas Gerais, como parte das exigências do Programa de Pós-Graduação em Ecologia, Conservação e Manejo da Vida Silvestre, para obtenção do título de doutor.

BELO HORIZONTE MINAS GERAIS – BRASIL 2016

MÁRIO RIBEIRO DE MOURA

The role of environmental gradients on the diversity of vertebrates

Tese aprovada em 09 de junho de 2016 pela seguinte Banca Examinadora

Prof. Dr. Felipe S. F. Leite (UFV) Dr. Ricardo R. C. Solar (UFV) Prof. Dr. Adriano Paglia (UFMG) Dr. Ubirajara de Olveira (UFMG) Prof. Dr. Paulo C. A. Garcia (orientador)

Me perguntaram o que eu mais aprendi com o doutorado, a melhor resposta é:

“a imensidão do que eu não sei”

AGRADECIMENTOS

A realização deste trabalho contou com o apoio financeiro, intelectual e moral de diversas instituições e pessoas a quem sou imensamente grato: À Universidade Federal de Minas Gerais e ao Programa de Pós-Graduação em Ecologia, Conservação e Manejo da Vida Silvestre por me receberam como aluno. Ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) e Fundação Lemann pelas bolsas concedidas. Ao professor Paulo C. A. Garcia por ter me aceitado sob sua orientação, mesmo abordando temas fora de sua área de concentração. Obrigado pela paciência, amizade e confiança em mim depositados. Ao professor Walter Jetz por ter me recebido na Yale University, e ter propiciado uma vivência singular junto ao Global Biodiversity, Ecology and Conservation Lab. Ao professores Gabriel C. Costa, Fabricio Villalobos, e Ricardo R. C. Solar pela co- orientação indireta durante a execução dos trabalhos aqui apresentados. Todas as discussões e trocas de idéias foram essenciais para os resultados aqui alcançados. A todos os colegas da UFMG, UFV, UnB e UFG pelas eventuais discussões e trocas de idéias, especialmente ao amigo Henrique C Costa pela ajuda imprescindível na compilação e exploração das bases de dados aqui utilizadas. Aos colegas de Yale pelos produtivos brainstorms evolutivos, biogeográficos e macroecológicos, e também pelo auxílio nas revisões de manuscritos e de idioma, especialmente ao Ben Carlson, Christopher Trisos, Claire Baldeck, and Natham Upham. Obrigado também ao Sami Domisch, Giuseppe Amatulli, e Ajay Ranipeta pelas fantásticas dicas de programação em R e Pyton. Sério, sem essas dicas eu ainda estaria scriptando! Aos amigos Lucas Machado de Souza e ao Vinícius de Souza Moraes por terem me dado um teto durante as disciplinas externas cursadas em Brasília e Goiânia. A todos amigos, muitíssimo obrigado pela amizade e cumplicidade, pelos momentos de buteco e de ralação, fossem estes de Viçosa, Divinópolis, BH ou do mundo afora. A Ray pelo apoio, carinho e companheirismo durante grande parte do desenvolvimento desse doutorado. E finalmente a família, meus irmãos e irmã e especialmente a minha mãe Sônia, pelo exemplo de dignidade e perseverança, pelo heroísmo na minha criação e educação.

iii

SUMÁRIO

AGRADECIMENTOS ...... iii SUMÁRIO ...... iv INTRODUÇÃO GERAL ...... 1 CAPÍTULO 1 - Disentangling the Role of Climate, Topography and Vegetation on Species Richness Gradients ...... 8 CAPÍTULO 2 - Biogeographic regionalization of snakes in the Atlantic Forest hotspot: historical and ecological insights ...... 30 COVER PAGE ...... 31 ABSTRACT ...... 32 INTRODUCTION ...... 34 METHODS ...... 36 Study area ...... 36 Species assemblage data ...... 37 Interpolation Procedure ...... 42 Regionalization Procedure ...... 43 Contemporary and historical correlates ...... 43 Data Analysis ...... 46 RESULTS ...... 46 DISCUSSION ...... 50 REFERENCES ...... 55 SUPPORTING INFORMATION ...... 59 DATA ACCESSIBILITY ...... 59 BIOSKETCHES ...... 59 SUPPORTING INFORMATION ...... 60 CAPÍTULO 3 - Environmental filtering and spatial stochasticity as drivers of tropical snake assemblages ...... 108 COVER PAGE ...... 109 ABSTRACT ...... 110 INTRODUCTION ...... 112 MATERIAL AND METHODS...... 114 Study area and species assemblage data ...... 114 Quantification of variation in community composition ...... 115 Quantification of predictor variables ...... 116 Delineation of bioclimatic spaces ...... 118

Data Analysis ...... 118 RESULTS ...... 120 Snake diversity in the Brazilian Atlantic Forest ...... 120 Environmental correlates of snake assemblages ...... 121 Metacommunity paradigms and snake assemblages ...... 123 DISCUSSION ...... 125 Snake diversity in the Brazilian Atlantic Forest ...... 125 Environmental correlates of snake assemblages ...... 126 Metacommunity paradigms and snake assemblages ...... 128 CONCLUSION ...... 129 ACKNOWLEDGEMENTS ...... 130 REFERENCES ...... 130 DATA ACCESSIBILITY ...... 136 BIOSKETCH ...... 136 SUPPORTING INFORMATION ...... 137 CONCLUSÃO GERAL ...... 142

INTRODUÇÃO GERAL

The impact of anthropogenic factors in changing the environmental conditions of the planet has reached unprecedented levels. The remarkable influence of human-induced changes, including transformations in land cover and changing the composition of the atmosphere led the scientific community to name the current geologic time as

Anthropocene Epoc (Lewis & Maslin, 2015). The intensification of human activities around the planet has already increased the global temperatures in at least 1º C over the last century with consequences to the acceleration of global sea levels rise and changes in climatological regimes (Hansen et al., 2006; Rahmstorf, 2007). Although the environmental conditions on Earth vary naturally, the transformations induced by modern human society can impose several threats to biodiversity and to goods and services provided by nature. Understanding therefore how species respond to environmental gradients is crucial to predict the impact of global climate and landuse changes on biodiversity.

In the first chapter of this PhD thesis, I explore preexisting biodiversity datasets and recent developed environmental datasets to elucidate the role of distinct types of environmental gradients on biodiversity patterns. In the second chapter, I generate a fine scale dataset on snake assemblages in a major tropical forest and explore questions in ecological and historical biogeography of this major vertebrate group. Further, in the third chapter, I investigate the influence of environmental filtering and spatially stochastic processes in structuring tropical forest assemblages under distinct climatological regimes. Overall, the three topics addressed in this thesis investigate the influence of environmental gradients on (i) species richness, (ii) species pools, and (iii) species composition of one or more vertebrate groups.

Species richness and the synergistic associations among environmental gradients

The first chapter (Moura et al., 2016) address the relative influence of distinct types of environmental gradients in explaining species richness of Neotropical vertebrates. I explore the influence of climate, topography and vegetation as broad-scale drivers of amphibians, non-volant mammals, bat and birds. More importantly, I address the synergism among these three general categories of environmental gradients and trace a parallel of such synergestic associations with three major macroecological hypotheses invoked to explain broad-scale gradients of species richness: ambient-energy, productivity, and habitat heterogeneity. Further, I make a quantitative comparison of these synergistic association across groups with distinct dispersal ability, looking for generalities in my findings.

I first obtained the species richness of each vertebrate group using a grid cell of

110 × 110 km (ca. 1º × 1º at the equator) based on expert range maps

(BirdLifeInternational & NatureServe, 2014; IUCN, 2015). Then, I extracted environmental variables representing the climate (mean annual temperature, annual precipitation, temperature annual range and precipitation range), topography (mean elevation, elevational range and elevational roughness) and vegetation (standard deviation and range of forest canopy height range, and land cover diversity) and applied ordinary least square (OLS) and variation partitioning techniques to disentangle the contribution of each gradient type in explaining species richness. To account for spatial structure embedded within the environmental gradients, I used spatial eigenvector filtering analysis (spatial filters) to control autocorrelation in the OLS model residuals.

The shared contribution of different environmental gradients in explaining the variation in species richness is regarded as a quantitative measure of the synergistic association among such environmental gradients (Prunier et al., 2015).

Overall, vertebrate richness were mostly driven by the synergism between climate and vegetation, reinforcing the role of productivity in explaining broad-scale patterns of species richness. Climate was more important to good dispersers, such as bats and birds. The species richness of amphibians and non-volant mammals (relatively poor dispersers in comparison to bats and birds) presented a stronger spatial structure, corroborating the importance of limited dispersal in explaining cohesive structure of species richness gradients. Interestingly, topography and vegetation presented a weak synergistic association, suggesting caution in using topographic complexity as a surrogate of habitat (vegetation) heterogeneity.

Historical and contemporary drivers of species pools

In the second chapter, I explore the geographical organization one of the most neglect vertebrate groups, the reptiles. Since Neotropical reptiles lack of representativeness in online datasets, I first compile a comprehensive dataset on snake assemblages in one of the hottest hotspot in the world, the Brazilian Atlantic Forest (BAF). Then I ask what ecological and historical factors explain the species pools of snakes in this hyperdiverse tropical forest.

For this, I used unconstrained ordination techniques coupled with interpolation methods to produce a spatially contiguous representation of the snake faunal dissimilarity over the BAF. I applied non-hierarchical clustering techniques to classify the interpolated faunal dissimilarity into biogeographic subregions (species pools).

Then, I used multinomial logistic models in concert with deviance partitioning techniques to explore the contribution climatic stability, productivity, topographic complexity, and historical variation in climate in explaining the species pools of snakes.

Overall, I detected six biogeographic subregions (BSR) for snakes in the BAF.

The three coastal snake BSR were largely congruent with BSR and areas of endemism of other vertebrates, such as amphibians (Vasconcelos et al., 2014), mammals (Costa et al., 2000) and birds (Silva et al., 2004). The variability in BSR was explained mostly by the climatic stability and productivity. Interestingly, topographic complexity was effective to explain snake BSR only if historical variation in climate was dropped from the models. Thus, topographic gradients along this tropical forest actually reflect a historical legacy of Quaternary climate change. The substantial influence of Quaternary climate on the contemporaneous biogeographic species pool is most likely associated with the limited dispersal ability (Araújo et al., 2008). Thus, snake species might have not been able to track Quaternary climate changes, reinforcing their susceptibility do current changes in global climate.

Environmental drivers of species composition

In the third chapter, I explore the environmental gradients leading to the variation in community composition of tropical forest snake. This chapter is built upon the same dataset on snake assemblages in the BAF, although it focus on metacommunity paradigms, particularly the species-sorting (environmental filtering) and neutral- dynamics (spatial stochasticity) (Leibold et al., 2004). Due to modern patterns of air humid air circulation, the BAF presents two distinct climatological regimes, dry summers in its northern part and rainy summers in its southern half. Such scenario creates a singular opportunity to investigate the influence of climatological regimes on species composition of tropical ectotherms. We ask to what extent tropical snake assemblages differ in response to environmental filtering and spatially stochasticty under these distinct climatological regimes.

I first extracted dissimilarity matrices to represent the β-diversity, species- replacement and richness-difference (sensu Baselga, 2010; Legendre, 2014; Podani &

Schmera, 2016) between tropical snake assemblages. I used principal component analysis to classify the bioclimatic space within the BAF into two climatological units representing the forests in northern (dry summers and rainy winter) and southern (rainy summers and dry winters) BAF. I used a broad set of environmental variables to represent the climatic, topographic and vegetation complexity within BAF. To account for spatially structured processes, I used distance distance-based Moran’s eigenvector map analysis to produce spatially stochastic factors. I grouped the snake assemblages according to the bioclimatic spaces within the BAF, and then used constrained ordination in concert with variation partitioning to disentangle the influence of environmental filtering and spatially stochasticty in explaining the β-diversity, species- replacement and richness-difference for snake assemblages in all BAF extent, in the northern BAF, and in the southern BAF.

The β-diversity was mainly due to species-replacement between assemblages and richness-difference played only a minor role. The seasonality in temperature emerged as the most important environmental gradient in explaining variation in snake assemblage composition. However, the precipitation seasonality overtake the temperature seasonality when the snake assemblages were analysed separately according to northern and southern BAF. Overall, snake assemblages in southern BAF were better explained by environmental filtering than northern assemblages.

Conversely, spatial stochasticity presented similar contribution in explaining β-diversity and species-replacement in both northern and southern snake assemblages. Thus, the balance in thermo-hydric conditions affects the way in which the reptiles respond to environmental conditions. Concurrent dry and warm conditions can impose additional

5 constraints to ectothermic physiology, ultimately affecting the assembly structure of such organisms.

REFERENCES

Araújo, M.B., Nogués-Bravo, D., Diniz-Filho, J.A.F., Haywood, A.M., Valdes, P.J. &

Rahbek, C. (2008) Quaternary climate changes explain diversity among reptiles

and amphibians. Ecography, 31, 8–15.

Baselga, A. (2010) Partitioning the turnover and nestedness components of beta

diversity. Global Ecology and Biogeography, 19, 134–143.

BirdLifeInternational & NatureServe (2014) Bird species distribution maps of the

world.

Costa, L.P., Leite, Y.L.R., da Fonseca, G.A.B. & da Fonseca, M.T. (2000)

Biogeography of South American Forest Mammals: Endemism and Diversity in

the Atlantic Forest. Biotropica, 32, 872–881.

Hansen, J., Sato, M., Ruedy, R., Lo, K., Lea, D.W. & Medina-Elizade, M. (2006)

Global temperature change. Proceedings of the National Academy of Sciences,

103, 14288–14293.

IUCN (2015) IUCN Red List of Threatened Species. Version 2015.2,

www.iucnredlist.org.

Legendre, P. (2014) Interpreting the replacement and richness difference components of

beta diversity. Global Ecology and Biogeography, 23, 1324–1334.

Leibold, M.A., Holyoak, M., Mouquet, N., Amarasekare, P., Chase, J.M., Hoopes,

M.F., Holt, R.D., Shurin, J.B., Law, R., Tilman, D., Loreau, M. & Gonzalez, A.

(2004) The metacommunity concept: a framework for multi-scale community

ecology. Ecology Letters, 7, 601–613.

Lewis, S.L. & Maslin, M. a (2015) Defining the Anthropocene. Nature, 519, 171–180.

Moura, M.R., Villalobos, F., Costa, G.C. & Garcia, P.C.A. (2016) Disentangling the

Role of Climate, Topography and Vegetation in Species Richness Gradients. PLoS

One, in press.

Podani, J. & Schmera, D. (2016) Once again on the components of pairwise beta

diversity. Ecological Informatics, 32, 63–68.

Prunier, J.G., Colyn, M., Legendre, X., Nimon, K.F. & Flamand, M.C. (2015)

Multicollinearity in spatial genetics: separating the wheat from the chaff using

commonality analyses. Molecular Ecology, 24, 263–283.

Rahmstorf, S. (2007) A Semi-Empirical Approach to Projecting Future Sea-Level Rise.

Science, 315, 368–370.

Silva, J.M.C., Sousa, M.C. & Castelletti, C.H.M. (2004) Areas of endemism for

passerine birds in the Atlantic forest, South America. Global Ecology and

Biogeography, 13, 85–92.

Vasconcelos, T.S., Prado, V.H.M., Silva, F.R. & Haddad, C.F.B. (2014) Biogeographic

Distribution Patterns and Their Correlates in the Diverse Frog Fauna of the

Atlantic Forest Hotspot. PLoS One, 9, e104130.

CAPÍTULO 1 - Disentangling the Role of Climate, Topography and Vegetation on Species Richness Gradients

Artigo Publicado

MOURA, M.R., VILLALOBOS, F., COSTA, G.C., GARCIA, P.C.A. Disentangling the Role of Climate, Topography and Vegetation on Species Richness Gradients. PLoS ONE v. 11, n. 3, p. e0152468, 2016.

Supporting Information to

Moura, MR; Villalobos, F; Costa, GC; Garcia, PCA. 2016. Disentangling the Role of Climate, Topography and Vegetation in Species Richness Gradients. PLOS One, xxx– xxx.

S1 Fig. Moran’s index correlograms for species richness and residuals of the Spatial EigenVector Mapping models used in the variation partitioning analysis. Spatial correlograms for (A) amphibians, (B) non-volant mammals, (C) bats, and (D) birds.

S2 Fig. Variation in species richness explained by environmental gradients without considering spatial structure. Primary colors (red, green and blue) denote the proportion of variation explained by the unique fraction of topographic, biotic or climatic sets. Secondary colors (yellow, cyan, magenta) denote the variation commonly explained by two of the three types of environmental set. White color indicates the variation commonly explained by the biotic, climatic and topographic sets. Unexplained variation is omitted for simplicity (see S4 Table for further details on variation partitioning analyses). Each letter on the Venn diagram represents a fraction of the variation partitioning analysis and adds up to the total set of biotic [adfg], climatic [bdeg], and topographic [cefg] factors.

S1 Table. Results of principal components analysis using climatic, topographic and biotic sets of variables.

Climatic variables PC1 PC2 PC3 AMP 0.353 -0.287 0.393 PPR 0.360 -0.419 -0.269 TAR -0.365 -0.354 -0.277 APP 0.375 0.289 -0.304 AMP² 0.369 -0.249 0.385 TAR² 0.329 -0.434 -0.412 APP² -0.359 -0.326 -0.293 PPR² 0.314 0.420 -0.447 Proportion of variance (%) 67.16 13.02 11.44 Cumulative Proportion (%) 67.16 80.18 91.62

Topographic variables PC1 PC2 PC3 ElevM -0.504 0.232 -0.297 ElevR -0.515 -0.132 0.398 ElevCV -0.105 -0.662 -0.137 ElevM² -0.455 0.251 -0.597 ElevR² -0.504 -0.073 0.516 ElevCV² -0.088 -0.650 -0.335 Proportion of variance (%) 54.23 35.1 8.53 Cumulative Proportion (%) 54.23 89.33 97.86

Biotic variables PC1 PC2 PC3 FCR 0.518 0.016 -0.460 FCSD 0.330 0.521 0.316 LCD -0.355 0.503 -0.272 FCR² 0.512 -0.011 -0.498 FCSD² 0.323 0.519 0.370 LCD² -0.360 0.454 -0.480 Proportion of variance (%) 54.2 35.37 9.29 Cumulative Proportion (%) 54.2 89.57 98.86 Abbreviations: AMT = annual mean temperature; APP = annual precipitation; TAR = temperature annual range; PPR = precipitation range; ElevM = mean elevation; ElevR = elevational range; ElevCV = coefficient of variation of elevation; LCD = land cover diversity; FCR = forest canopy height range; FCSD = standard deviation of forest canopy height. The ² denote the quadratic term for the respective variable.

S2 Table. Variance Inflation Factor (VIF) and pairwise Pearson’s correlations for the first three axes of principal component analysis (PCA) using the climatic, topographic and biotic sets of explanatory variables.

Clim-PC2 Clim-PC3 Topo-PC1 Topo-PC2 Topo-PC3 Biotic-PC1 Biotic-PC2 Biotic-PC3 VIF

Clim-PC1 0.000 0.000 0.386 -0.131 0.126 0.696 -0.180 -0.079 2.56 Clim-PC2 0.000 -0.205 -0.138 0.003 0.137 0.005 -0.159 1.12

Clim-PC3 0.234 -0.054 0.028 -0.089 -0.123 0.011 1.09

Topo-PC1 0.000 0.000 0.094 -0.403 0.158 1.63

Topo-PC2 0.000 -0.189 -0.185 0.042 1.11

Topo-PC3 0.192 0.111 0.074 1.06

Biotic-PC1 0.000 0.000 2.26

Biotic-PC2 0.000 1.29

Biotic-PC3 1.10

Clim-PC, Topo-PC, and Biotic-PC refer to variables based on the first three axes of the PCA using climatic, topographic, and biotic variables, respectively. See S1 Table for individual contribution of each explanatory variable to each principal component.

S3 Table. Variation partitioning contributions of species richness of Neotropical vertebrates that can be explained by biotic, climatic, topographic and spatial sets. The identifiable fractions (adjusted R²) are designated by lowercase letters following the labels displayed in Fig. 5 (main document). Species richness Individual Non-volant contribution (%) Amphibians Bats Birds mammals [a] 2.35 3.71 1.87 4.74 [b] 15.10 17.93 27.25 14.47 [c] 0.20 2.58 2.36 0.40 [d] 16.31 30.97 6.10 12.51 [e] 29.39 18.84 32.68 32.72 [f] 5.57 -0.94 -0.77 5.21 [g] 0.11 0.71 0.69 1.51 [h] 3.04 1.98 -0.07 2.44 [i] -0.59 -5.99 11.87 2.26 [j] 2.24 -0.36 2.54 0.11 [k] 3.69 5.92 7.72 2.82 [l] -0.58 -0.50 0.51 3.47 [m] 3.59 1.96 -2.12 -0.61 [n] -1.11 -0.68 0.69 -0.01 [o] 9.56 1.62 1.31 4.19 [p] = Residuals 11.15 22.26 7.38 13.77

Total biotic set [aeghklno] 46.44 31.60 45.39 51.88 Total climatic set [befiklmo] 65.72 38.84 78.43 64.54 Total topographic set [cfgjlmno] 19.58 4.39 5.21 14.28 Total space set [dhijkmno] 36.72 35.41 28.04 23.71 Background colors in the cells of the first column follow the legend color of Fig. 5 (main document).

CAPÍTULO 2 - Biogeographic regionalization of snakes in the Atlantic Forest hotspot: historical and ecological insights

Artigo a ser submetido ao Journal of Biogeography

MOURA, M.R., ARGÔLO, A.J.S., COSTA, H.C. Biogeographic regionalization of snakes in the Atlantic Forest hotspot: historical and ecological insights. Unpublished.

COVER PAGE

Journal of Biogeography – Original Article

Biogeographic regionalization of snakes in the Atlantic Forest hotspot: historical and

ecological insights

Mario R Moura1,3*, Antônio J S Argôlo2, Henrique C Costa3

1 Yale University, Department of Ecology and Evolutionary Biology. 165 Prospect St.

06511. New Haven, CT, USA

2 Universidade Estadual de Santa Cruz, Departamento de Ciências Biológicas. Rodovia Jorge

Amado, Km 16 Salobrinho. 45662-900. Ilhéus, BA, Brazil

3 Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Departamento de

Zoologia, Laboratório de Herpetologia. Avenida Antônio Carlos, 6627, Pampulha. 31270-

901. Belo Horizonte, MG, Brazil

*Corresponding author. E-mail: [email protected]

SRH: Biogeographic regionalization of Atlantic Forest snakes

Number of words in the Abstract: 256

Number of words in main body of the paper: 5041

Number of references: 41

ABSTRACT

Aim Snake faunal dissimilarity within tropical forests is not well characterized, nor are the processes underlying these patterns. Our aim was to disentangle the ecological and historical factors driving the biogeographic subregions (BSR) for snakes.

Location Brazilian Atlantic Forest (BAF).

Methods We compiled 274 snake inventories to build a species-by-site matrix and used unconstrained ordination to obtain a Euclidean representation of the faunal dissimilarity among sites. We performed a Euclidean-based clustering of the ordination axes to identify the k number of snakes BSR. We interpolated the ordination scores and clustered their estimates into k groups. We built multinomial logistic regression models to investigate the influence of contemporary climatic stability, productivity, topographic complexity, and historical climate shifts in explaining the BSR.

Results We identified 198 snake species organized into six BSR. Climatic stability presented the largest contribution in explaining the BSR for snakes, followed by productivity and historical variation in climate. Topography was important only if historical climates was excluded from the analysis.

Main Conclusions The highest rates of snake endemism within BAF were in the coastal BSR, as compared to the inland BSR that are mostly composed of open habitat specialists with a few regions of endemism in interior highland forests. Our findings indicate that topographic complexity in the BAF actually reflects the historical legacy of climate on snake distributions rather than simply providing a physical barrier.

Overall, the predominance of contemporary and historic stability in climate to explain

variability in the BSR reinforces the importance of niche conservatism in shaping species distributions in tropical forests.

Keywords: beta diversity, faunal dissimilarity, interpolation, regionalization, reptiles,

Serpentes, snakes, turnover

INTRODUCTION

The procedure in which regions are geographically organized according to compositional dissimilarity is the so-called biogeographic regionalization (Kreft &

Jetz, 2010). By performing a biogeographic regionalization, one obtains a spatially explicit framework to address questions in ecological and historical biogeography, as well as in conservation management (Kreft & Jetz, 2010; Ladle & Whittaker, 2011).

For example, in historical biogeography the boundaries dividing biogeographic regions may underlie physical features that act as barriers to dispersal among different regional biotas, whereas in ecological biogeography such boundaries may indicate environmental thresholds that limit species distribution (Cox & Moore, 2005;

Carstensen et al., 2013). From the perspective of biodiversity conservation, biogeographic regionalization can be used to design reserve networks that maximally represent the species pool within a set of protected areas (Ladle & Whittaker, 2011).

Most often, studies on biogeographic regionalization have been performed on global or continental scales (Kreft & Jetz, 2010; Rueda et al., 2010; Holt et al., 2013). Such studies provide important insights to patterns of biotic dissimilarity associated with continental landmasses; however, their results are usually not considered in conservation planning decisions that take place at smaller spatial extents (Jepson et al., 2011). Beside increasing the applicability of regionalization to conservation, performing these analyses at the extent of biomes or ecoregions also provides insights into historical and ecological biogeography at small-to-mid spatial scales

(Vasconcelos et al., 2014). To date, most studies have investigated the geographical regionalization of endotherm groups such as mammals and birds, or ectotherms with very specific ecological requirements, for example, amphibians (Kreft & Jetz, 2010;

Rueda et al., 2010; Holt et al., 2013). Ectotherm organisms with ecological requirement traditionally associated to temperature remains under investigated, particularly in tropical forests.

In South America, for instance, a preliminary classification of the Brazilian Atlantic

Forest (BAF) hotspot indicated six biogeographic subregions (BSR) based on the species composition of rodents, marsupials, and primates (Costa et al., 2000). A subsequent regionalization based on birds, primates, and butterflies revealed eight

BSR (Silva & Casteleti, 2003). Recently, Vasconcelos et al. (2014) proposed the regionalization of BAF into four BSR according to dissimilarity in amphibian assemblages. It is clear that the delineation of BSR is affected by organismal biology, and results for a particular animal group might not be applied to different ones (Cox &

Moore, 2005; Kreft & Jetz, 2010). Across taxa, the regionalization of BAF have revealed three coastal BSR with high rates of endemism, two of them in the northern part of BAF, and another one along the southeastern Brazilian coast (Silva &

Casteleti, 2003). Not coincidently, these three coastal BSR are frequently congruent with areas of endemism (Silva et al., 2004). The number of inland BSR tend to vary more among studies, but in general, the biota within inland BSR is considered a mix of forest-adapted species and species from open habitats in the Caatinga and Cerrado ecoregions (Silva & Casteleti, 2003; Vasconcelos et al., 2014). Different hypotheses have been raised to explain the regionalization and areas of endemism in BAF.

Ecological hypotheses are commonly associated with the heterogeneity of environmental conditions within BAF (Vasconcelos et al., 2014). Because of the large latitudinal extent, the BAF ranges widely in solar energy input and water availability, thereby affecting its productivity. Unlike most other tropical forests, BAF has high variation in climatic stability. Modern patterns of humid air circulation indicate two

distinct climatological regimes within BAF, dry summers in its northern part and rainy summers in the southern half (Grimm, 2003). The BAF also harbors three important mountain chains (Serra do Mar, Serra da Mantiqueira, and Serra do

Espinhaço, the latter partially) that promote high topographic complexity along its extension. The heterogeneity in productivity, climatic stability, and topography creates a wide range of environmental conditions that potentially shape the geographic distribution of BAF species, affecting therefore the biogeographic species pools within BAF (Silva & Casteleti, 2003).

Here we address the biogeographic regionalization of an ectotherm group in a hyperdiverse tropical forest. We compile inventory data on snake assemblages to identify cohesive BSR for snakes in the BAF. Then, we investigate the influence of productivity, climatic stability, topography, and historical variation in climate in explaining the biogeographic species pools of snakes in this hotspot.

METHODS

Study area

The BAF is recognized worldwide by its wealth of biodiversity, containing up to 20% of the world’s total plant and vertebrate species, and high threat from human economic activities to the persistence of species (Mittermeier et al., 2005; Zachos &

Habel, 2011). The BAF has two general classifications: (i) a narrow definition (sensu stricto) whose limit includes only the coastal rain forests in southeastern Brazil (Joly et al., 1999); and (ii) a broad definition (sensu lato) that encompass all forest types between the coastal rain forests and the dry corridor of open formations formed by

Cerrado, Caatinga and Chaco ecoregions (Oliveira-Filho & Fontes, 2000). The broader definition has been supported in several phytogeographic studies (see

Eisenlohr & Oliveira-Filho, 2015 and references therein). More importantly, it forms the basis of Brazilian environmental legislation concerning BAF conservation. We followed the sensu lato classification of the BAF established in the Brazilian Federal

Law 11,428/2006 whose boundaries includes hinterland forest enclaves and non- forest ecosystems, such as mangroves, restingas, montane savanna (campos rupestres and campos de altitude), among others, but only within Brazil (Fig. 1a). It does not cover the small sections of Atlantic Forest in southeastern Paraguay and northeastern

Argentina.

Species assemblage data

We searched the public literature for sources of snake inventories within BAF, published (articles, books) or not (thesis, dissertations, environmental impact assessments, management plans). We complemented data compilation with unpublished data from several researchers, including ourselves. Because of the great variety of data sources compiled, the resultant database was represented by short and long-term studies. To reduce possible biases due to methodological differences in sampling procedures, all short-term studies met the following criteria: (i) two out of eight different sampling methods (artificial shelter; funnel traps; local collectors; museum records; pitfall traps; quadrat plots; visual search; road survey/casual encounters); and (ii) two or more sampling periods, in dry and rainy seasons. We considered as long-term studies those inventories whose sampling period lasted four or more years. The sampling procedures in long-term studies did not necessarily cover more than one sampling method. As a general criterion, short and long-term studies were only included in our database if they presented at least five species. Whenever possible, we consulted the authors of each data source for more refined information

on geographical location and methods of their studies (Table S1.1 in Appendix S1,

Supporting Information). We critically reviewed the species composition of each inventory before entering them into the database. Whenever necessary, we consulted the authors and/or collectors tied to data sources to confirm the reliability of snake identifications. Species not identified to the species level were not included in the database. We did not consider inventories performed on islands farther than 300 m from the mainland, since ecological processes might have re-shaped diversity and species composition to a greater extent on such isolated islands.

Overall, we compiled 274 inventories encompassing 4077 species occurrence records for snakes in BAF. We found inventories in close proximity to one another or even multiple sources for an identical location. Since our focus here is on the variation of species assemblage composition at broad scales, we pooled the species occurrence data for those inventories ≤ 20 km from each other and used their average latitude and longitude as geographic coordinates. As a result, we obtained a presence-absence matrix for 218 sites containing 3606 species occurrence records. Each site in this final dataset represents the snake assemblages at the resolution of 10 arc-min or 20×20km

(Fig. 1b).

Figure 1 (a) The eight main forested ecoregions (sensu Olson et al., 2001) inserted within the Brazilian Atlantic Forest (grey colour indicates other ecoregions not named here). (b) The distribution of 274 snake inventories compiled in this study. Brazilian state abbreviations: AL = Alagoas, BA = Bahia, CE = Ceará, ES = Espírito Santo, GO = Goiás, MG = Minas Gerais, MS = Mato Grosso do Sul, PB = Paraíba, PE = Pernambuco, PI = Piauí, PR = Paraná, RJ = Rio de Janeiro, RN = Rio Grande do Norte, RS = Rio Grande do Sul, SC = Santa Catarina, SE = Sergipe, SP = São Paulo.

Biogeographic regionalization

Biogeographic regionalization is often performed using a species-by-sites matrix spatially continuous over the region of interest. Based on this matrix, the compositional dissimilarity is calculated among sites and partitioned into spatially cohesive biogeographic regions (Kreft & Jetz, 2010). However, our inventory data is spatially discontinuous, impeding the identification/visualization of cohesive BSR. To circumvent this issue, we interpolated the faunal dissimilarity to unsampled locations in order to map the compositional dissimilarity over a continuous surface. We then

performed the biogeographic regionalization based on this interpolated surface. The regionalization procedure here performed is summarized in four basic steps:

1. Perform an unconstrained ordination to obtain Euclidean representation of a

non-Euclidean compositional dissimilarity matrix.

2. Perform a Euclidean-based clustering using the Euclidean representation

obtained in step 1 to identify the number of BSR.

3. Interpolate the sites’ scores of a low-dimensional unconstrained ordination

over a spatially continuous surface that represents the region of interest.

4. Perform another clustering using the interpolated values from step 3 to

obtain a spatially cohesive representation of the BSR.

Detailed explanations on these four steps are described below.

Ordination Procedure

We choose the Simpson dissimilarity index (βsim hereafter) for our study since it has been recommended for biogeographic regionalization purposes (e.g. Kreft & Jetz,

2010; Dapporto et al., 2014) given its independence of richness difference (Koleff et al., 2003). Because βsim is a non-Euclidean metric (sensu Gower & Legendre, 1986), we performed a Principal Coordinate Analysis (PCoA) with Cailliez correction for negative eigenvalues to obtain a Euclidean representation of the βsim matrix (Legendre

& Legendre, 2012). We submitted the scores of all PCoA axes to a Euclidean-based clustering analysis to identify the number of BSR (see Clustering Procedure). Once the optimal number of clusters was established, we performed a three-dimension Non-

Metric Multidimensional Scaling (NMDS) using βsim to reduce the dimensionality of the dissimilarity matrix while retaining as much information as possible about distance relationships among sites. We extracted the NMDS scores of each site and

used them to interpolate the faunal dissimilarity over the BAF extent (see

Interpolation Procedure). These interpolated values were submitted to the

‘Regionalization Procedure’ describe below. All calculations were performed in R

3.2.3 (R Core Team, 2015). We used the betapart package (Baselga & Orme, 2012) to obtain βsim and the vegan package (Oksanen et al., 2015) for ordination procedures.

Clustering Procedure

Biogeographic regionalization is commonly achieved by hierarchical or non- hierarchical clustering methods that partition a dataset into different groups.

Hierarchical clustering facilitates the regionalization since the distance relationships among sites are arranged into dendrograms. However, the resulting hierarchical dendrogram may be uninformative if it is based on a distance matrix with many ties and zero values. Under this circumstance, the arrangement of sites in the dendrogram is affected by the order in which rows (sites) are entered in the analysis, allowing thus multiple possible solutions (Dapporto et al., 2013). Because our βsim has a high frequency of ties, we used a non-hierarchical clustering to produce a single partition of the faunal dissimilarity that optimizes within-group homogeneity. Here we applied

K-means partitioning to the PCoA scores obtained through ordination of βsim. K- means requires the user to specify in advance the k number of clusters. We identified k using the L-method proposed in Salvador & Chan (2004). Briefly, the L-method consists in performing a piecewise regression of the evaluation metric (here, the within-group sum of squares) against the number of k and finding the ‘knee’ in which the two regression lines minimize the root of the mean squared error (RMSE) in the scatterplot. However, the maximum number of groups (max. k) entered a priori in the piecewise regression affects the identification of the breakpoint. In entering different

values of max. k (range of the x-axis), one gets different optimal breakpoints in the piecewise regression (Salvador & Chan, 2004). One possible solution is to arbitrarily set the max. k and then identify the optimal k. We avoided this arbitrary choice by finding the optimal k for each possible value of max. k (from 4 to nsites−1). Since we performed piecewise regressions with different degrees of freedom (distinct max. k), we used the residual standard error (RSE) instead of RMSE to identify the optimal breakpoint. Overall, we performed 215 K-means partitioning analyses ( ; where i = max. k entered in each piecewise regression, n = number of sites), each of which with 100 iterations and 50 random starting points. We obtained 215 values of optimal k (including repeated values) and used the most frequent among them as the optimal number of clusters (Fig. S3.2). Calculations were performed in R 3.2.3 using the vegan package (Oksanen et al., 2015).

Interpolation Procedure

Among the most common interpolation techniques is Inverse Distance Weighting

(IDW), which employs a search window of variable size and a weighting parameter, i.e., the power of the inverse distance weighting function (1/ ; where d = distance between points i and j, p = power exponent) (Legendre & Legendre, 2012). The greater the value of p, the less influence distant points have on the estimated value.

We used the NMDS-scores as observed values to interpolate the snake dissimilarity over the whole BAF extent. Our previous simulations indicated that the exponent ‘2’ produce IDW estimations with high exactness in relation to the observed values (R² ≥

0.99 for the three NMDS axes). IDW interpolations using the exponent ‘1’ produced less exact values (R² = 0.87 on average) whereas exponents greater than ‘2’ did not produce better improvements. Thus, we performed all IDW interpolations using the

inverse square of the distance between sites. Computations were performed in R 3.2.3 using the gstat package (Pebesma, 2004).

Regionalization Procedure

To obtain discrete representation of the snake dissimilarity, we performed a new K- means partitioning using the interpolated NMDS-scores for each grid cell. To avoid local minima problems, K-means partitioning used 1,000 random starting points and

10,000 iterations, retaining the solution that minimized the total sum of squares

(Legendre & Legendre, 2012). We used the optimal k identified through the

‘Clustering Procedure’ as the number of groups. The final cluster arrangement was driven by the proximity of the grid cells in the Euclidean space of the interpolated

NMDS-axes. Calculations were performed in R 3.2.3 using the vegan package

(Oksanen et al., 2015).

To obtain a quantitative representation of the snake dissimilarity, we used the interpolated NMDS-scores to build a RGB raster. We assigned each interpolated

NMDS-axis to a raster band, and then used the plotRBG function of the raster package (Hijmans, 2015) to plot a spatial and quantitative representation of the snake dissimilarity. Spatial grid cells represented by similar colours are expected to have similar species composition, while grid cells with contrasting colours are predicted to be highly dissimilar in composition.

Contemporary and historical correlates

We used six environmental variables to represent the contemporary environmental conditions: annual mean temperature (AMT), temperature annual range (TAR = difference between max. temperature of warmest month and min. temperature of the

coldest month), annual precipitation (APP), precipitation range (PPR = difference in precipitation between the wettest and driest quarters), elevational range (ElevR) and elevation roughness (ElevCV = coefficient of variation of elevation). All environmental variables were downloaded at 30 arc-sec resolution (≈1 Km) from the

WorldClim database (Hijmans et al., 2005).

In addition to the six gradients representing the contemporary environment, four gradients were included to represent historical variation in climate. More specifically, we used the historical difference in annual precipitation (HDP, measured as the difference in annual precipitation between current climate and Last Glacial Maximum

– LGM condition), historical difference in annual mean temperature (HDT, measure as the difference in annual mean temperature between current climate and LGM condition. These two measures indicate the historical variation in energy input and water availability. We also included two other variables to account for variation in hydric and thermal conditions. By historical variation in hydric conditions (HHC), we are referring to the variation in the ‘hydric envelope’ in a region, such as annual precipitation, precipitation seasonality, and precipitation across wettest/driest/warmest/coldest. By historical variation in thermal conditions (HTC) we are referring to the overall variation in the ‘thermal envelope’, including changes in annual mean temperature, isothermality, temperature seasonality, and temperature across warmest/coldest/wettest/driest seasons. To measure the HHC and HTC, we calculated the difference between current and LGM conditions for each one of the 19 bioclimatic variables available at the WorldClim database (Hijmans et al., 2005). For example, ∆Bio1 = ∆AMT = AMTcurrent – AMTLGM. All ‘∆-bioclimatic’ variables were obtained at 2.5 arc-min resolution (≈5 km²). The 19 ‘∆-bioclimatic’ variables were separated into two groups, one group containing 11 ‘∆-bioclimatic’ variables related

to temperature (Bio1 to Bio11), and another group including eight ‘∆-bioclimatic’ variables related to precipitation (Bio12 to Bio19).

For each group of ‘∆-bioclimatic’ variables, we performed a principal component analysis (PCA) and extracted the PCA-scores for each grid cell in each PCA axis. The farther a grid cell is from the PCA-axes origin, the higher is the ‘∆-bioclimatic’ values in that cell. If a grid cell is positioned exactly over the PCA-axes origin, then, no variation between the current and past bioclimatic conditions is observed on that cell

(‘∆-bioclimatic’ = 0 for all variables). Thus, the Euclidean distance of a given grid cell to the axes origin in the multidimensional ordination space can be interpreted as a measure of historical variation in climate. That Euclidean distance derived from the

PCA-scores based on the 11 temperature-related ‘∆-bioclimatic’ variables indicates the historical variation in thermal conditions (HTC), while that one based on the eight precipitation-related ‘∆-bioclimatic’ variables denotes the historical variation in hydric conditions (HHC). The measures of HDP, HDT, HHC, HTC were obtained for each LGM general circulation models (CCSM4, MIROC-ESM, MPI-ESM-P), as available at the WorldClim database. These three measures were averaged to obtain consensus values for each particular variable.

To meet the resolution of the species assemblage data, we calculated the mean, range, or coefficient of variation of the predictors listed above using a buffer of 10-km radius centered on geographic coordinates of each site. The Pearson’s correlations among environmental variables ranged from −0.691 to 0.600 and Variance Inflation Factor

(VIF) was < 3.85 for all variables, indicating low multicollinearity (Table S3.4).

Calculations were performed in R 3.2.3 using the raster package (Hijmans, 2015).

Data Analysis

To determine the correlates of the biogeographic species pools for BAF snakes, we initially categorized each one of the 218 sites according to the BSR produced by the regionalization procedure. We grouped the explanatory variables into five predictor sets, three of them representing the contemporary environmental conditions: (i) productivity (Prod = AMT and

APP), (ii) climatic stability (Cstab = TAR and PPR), (iii) topographic complexity (Topo =

ElevR and ElevCV), and two predictor sets representing the overall (iv) current climate

(Cclim = AMP, APP, PPR, and TAR) and (v) historical variation in climate (Hclim = HDP,

HDT, HHC, and HTC). We built multinomial logistic regression (MLR) models for each explanatory variable, each predictor set, each combination of Prod/Cstab/Topo, each combination of Cclim/Hclim/Topo, and a model including all variables. We first performed single-predictor logistic regressions to identify important variables in explaining the deviance in BSR. We then used model selection to (i) identify the type of contemporary environmental conditions (Prod/Cstab/Topo) that explained the BSR the most, and to (iii) identify the role of

Cclim/Hclim/Topo in explaining the BSR. Model selections were based on the lowest value of Akaike’s Information Criterion corrected for small sample sizes (AICc). We used Akaike weights (wAICc) to evaluate the model selection uncertainty, with weights varying from 0

(no support) to 1 (complete support) (Burnham & Anderson, 2002). In each model selection, the model with the lowest AICc had its deviance partitioned to obtain the unique and shared contribution of different predictor sets in explaining BSR for snakes. Computations were performed in R 3.2.3 using nnet (Ripley & Venables, 2016) and MuMIn (Barton, 2013) packages.

RESULTS

We recorded a total of 198 snake species (Table S2.2) in the BAF inventories complied, belonging to 60 genera and nine families (see Comments on taxonomical nomenclature in

Appendix S2). The average richness per site was 16.5 species (median = 14; SD = 9.27; range

5–47). Another 22 snake species were detected through individual records, but without further information on other syntopic species (Table S2.3). Overall, we detected a minimum of 220 species within the BAF.

The K-means clustering using a Euclidean representation of βsim (PCoA axes) identified six

BSR for snakes. The subsequent three-dimensional NMDS produced high congruence between the observed and ordinated distances (non-metric fit R² = 0.966, Linear fit R² =

0.742) and stress value of 18.6. The K-means clustering of the interpolated NMDS-scores produced three BSR along the coast and three hinterland BSR (Fig. 2), from north to south, they are:

BSR1. Includes the Pernambuco Coastal/Interior Forests and northern region of Bahia

Coastal Forests ecoregion. BSR1 also encompasses the forest enclaves within Caatinga, including here the formations called Brejos de Altitude.

BSR2. All coastal forests from northern Bahia to southern Rio de Janeiro states. It also includes the seasonal dry forests in the transition zones among BAF, Caatinga, and Cerrado.

BSR3. Serra do Mar Coastal Forest and the southernmost region of Bahia Coastal/Interior

Forests.

BSR4. The hinterland mountainous regions in the southwestern portion of the Bahia Interior

Forests and the lowland forests in eastern portion of the Alto Paraná Atlantic Forests.

BSR5. The western portion of the Alto Paraná Atlantic Forests. Similarly to BBS4, this BSR does not encompass coastal areas.

BSR6. Includes most of the Araucaria Moist Forests ecoregion, also encompassing the southern part of the Alto Paraná Atlantic Forests and northern of Uruguayan Savannas. It includes a small portion of coastal areas in the southernmost region of BAF.

Figure 2 Plots of the snake faunal dissimilarity in the Brazilian Atlantic Forest (BAF). (a) Regionalization of snakes into six biogeographic subregions (BSR) based on the K-means partitioning of interpolated values. (b) Quantitative representation of the interpolated faunal dissimilarity for snakes in BAF. Names of the main rivers within BAF in blue. Maps drawn in 10 arc-min resolution.

Overall, a BSR harbored on average 43% of the 198 snake species recorded within BAF

(Table S3.5). Approximately 33% (65 species) of the snake species occurred in only one of the six BSR, 26% (51 species) occurred in two BSR, and only 5% (10 species) occurred in all

BSR (Table S2.2).

The variability in BSR was explained mostly by variation in temperature (TAR) and energy input (AMT), followed by precipitation range (PPR) and historical difference in temperature

(HDT). Water availability (APP) and topographic-related variables presented similar

49 importance in explaining the BSR (Table 1). The model including Cstab, Prod and Topo sets was the best supported among those models representing the contemporary environment, explaining 85.5% of the variability in BSR (Table 1). When we included the Hclim set, the deviance explained increased up to 97%. However, the inclusion of Hclim weakened the contribution of Topo, and the model containing Cclim and Hclim was the best supported

(highest wAICc). Among the predictor sets representing the contemporary conditions, the unique contribution of Cstab accounted for the largest fraction (26.6%) of variability in BSR

(Fig 3a). After including Hclim, the unique contribution of Cclim kept the largest fraction of deviance explained, followed by the shared contribution between Cclim and Hclim (Fig. 3b).

Table 1 Multinomial logistic regression (MLR) models used to investigate the influence of environmental conditions in explaining the biogeographic subregions for snakes in the Brazilian Atlantic Forest.

MLR models – Single predictor Deviance expl. (%) AICc wAICc TAR 34.27 515.060 0.999 AMT 32.48 528.496 0.001 PPR 23.84 593.428 0.000 HDT 22.40 604.296 0.000 ElevR 12.86 675.976 0.000 ElevCV 12.59 678.006 0.000 HTC 12.20 680.911 0.000 APP 11.19 688.515 0.000 HDP 8.72 707.091 0.000 HHC 7.77 714.189 0.000

MLR models Deviance expl. (%) AICc wAICc Cstab + Prod + Topo 85.54 192.506 1.000 Cstab + Prod 76.69 231.955 0.000 Cstab + Topo 70.27 280.208 0.000 Cstab 55.93 363.605 0.000 Prod + Topo 58.98 365.075 0.000 Prod 37.71 500.517 0.000 Topo 26.11 587.698 0.000

Cclim + Hclim 92.52 170.279 0.523 Cclim + Hclim + Topo 97.01 170.460 0.477 Cclim + Topo 85.54 192.506 0.000 Cclim 76.69 231.955 0.000 Hclim + Topo 55.53 418.095 0.000

Hclim 44.17 476.385 0.000 Topo 26.11 587.698 0.000 Dev. Expl (%) = percentage of deviance explained, AICc = Akaike’s Information Criterion corrected for small sample sizes, ∆AICc, difference in Akaike's Information Criterion, wAICc = AICc weight. Cstab = contemporary climatic stability, Prod = productivity, Topo = topographic complexity, Cclim = contemporary climate (Cstab + Prod), Hclim = Historical variation in climate. See Methods for individual predictor abbreviation.

DISCUSSION

Our assessment of snake BSR finds moderate congruence with the regionalization recently reported for amphibians (Vasconcelos et al., 2014). For instance, our findings indicate two snake BSR within the northern BAF (BSR1–BSR2), in contrast with the single subregion found through regionalization of amphibians (Vasconcelos et al., 2014). Conversely, regionalization of the southern BAF is roughly similar for both ectothermic groups. Although ectotherms are highly influenced by climate, reptiles are more affected by temperature while precipitation plays a greater role for amphibians (Aragón et al., 2010). The evidence in hand suggests that snake composition is structured in both southern and northern climatological units of BAF, whereas amphibian composition may be structured mainly within the southern

BAF, where the warm and rainy seasons are coincident. It is worth noting, however, that the amphibian regionalization used expert range maps at coarser scales (50×50 km), in contrast to our study, which is based on local inventories at 20×20 km resolution. Further, the amphibian regionalization was performed using the Hellinger distance (Vasconcelos et al., 2014), which is not independent of richness difference, while we use βsim. These factors, and possibly others, may contribute to explain the distinct regionalizations observed between these two clades of ectotherms.

Figure 3 Deviance in biogeographic subregions (BSR) for snakes explained by environmental predictors. (a) Partitioning of contemporary environmental variation into climatic stability (Cstab), productivity (Prod) and topographic complexity (Topo). (b) Partitioning among contemporary climate (Cclim = Cstab + Prod), historical variation in climate (Hclim) and topographic complexity (Topo). Topo is presented for descriptive purposes in the bottom graph since this predictor set is not included in the final model after accounting for Hclim.

Overall, our regionalization scheme is congruent with the previous studies, particularly along the BAF coast. For instance, the snake BSR1 and BSR3 are largely congruent with BSR for birds, primates, and butterflies (Silva & Casteleti, 2003). Our BSR1, BSR2, and BSR3 have also been recognized as areas of endemism for mammals (Costa et al., 2000) and birds (Silva et al., 2004). Despite the similarities between our regionalization scheme and the published

52 literature, two major differences can be noticed. First, our regionalization merges the Atlantic

Dry Forests (ADF) and the Bahia Coastal/Interior Forests within the snake BSR2 (see Fig.

1a). The ADF has been indicated as a distinct BSR based on birds, primates and butterflies together (Silva & Casteleti, 2003). The few number of snake inventories over this ecoregion may have mislead its recognition as a different BSR. The ADF may also harbor distinct regional biotas for amphibians, marsupials, and rodents, as the previous regionalization of these animal groups adopted a BAF classification that excluded these seasonal dry forests

(Costa et al., 2000; Vasconcelos et al., 2014). Therefore, we recommend caution in interpreting the snake assemblages from the ADF as compositionally similar to snake assemblages within BSR2. The second major difference regards the inland BSR. Our regionalization indicated two completely hinterland BSR for snakes, BSR4 and BSR5. The regionalization for birds, primates, and butterflies merged these two regions (Silva &

Casteleti, 2003). In our study, BSR4 is mainly associated to mountainous areas, harboring snake assemblages from highland forests, while BSR5 is predominantly associated to lowland forests. Indeed, if we regionalized to seven snake BSR (the second most frequent optimal k), then BSR4 is separated into highland and lowland forests (Fig. S3.4). The topographic heterogeneity over BSR3 and BSR4 may not be an impediment for compositional exchange among assemblages of highly vagile animals, such as birds and butterflies, in contrast to snakes, which present limited dispersal ability. The regionalization of mammals, as separate from birds and butterflies, also differentiates the inland forests into two BSR (Costa et al.,

2000), which are roughly coincident with the BSR4 and BSR5.

While the coastal BSR often overlap with areas of endemism, the biota in inland BSR appear to be more closely associated with species adapted to open habitats in the Cerrado and

Caatinga (Silva & Casteleti, 2003; Silva et al., 2004). For instance, the BSR5 present seven endemic snake species (not registered in other snake BSR). All of them actually are typical

Cerrado species that marginally occur in the BAF (Table S3.5). Also, of the eight endemic taxa from BSR4, three are typical Cerrado species, and another four species correspond to high elevation endemism (Table S3.5). The remaining endemic taxon in BSR4 (Bothrops fonsecai) is poorly represented by the inventories compiled (only two records), but, in fact occurs in transition zones between BSR3 and BSR4 (Fenker et al., 2014). Thus, 73% of the endemic species associated with the inland BSR4–BSR5 are species adapted to open habitats.

Our findings indicate a strong influence of climatic variability in explaining the BSR.

Although the temperature (AMT) emerged among the most important factors, it must be noticed that due to the latitudinal extent of BAF, AMT is negatively correlated with TAR

(Table S3.4), which might block the dissociation between these two variables within BAF. In tropical regions, the thermoregulatory behaviour of reptiles emphasizes staying cool (Huey et al., 2009), which explains the primary role of TAR in driving snake BSR. The ability of ectotherms to buffer climatic variability is highly dependent upon their hydration status, sot that under dry conditions, ectotherms can have their activity time greatly reduced, threatening the viability of local populations (Kearney et al., 2013). Thus, low precipitation during the warmest periods may limit the capacity of some snakes to buffer thermal variations, which may therefore restrict the biogeographic species pools. Similar results are reported for BAF amphibians, whose regionalization in BAF are explained mostly by the AMT and precipitation seasonality (Vasconcelos et al., 2014).

Although the regionalization of snakes in BAF is largely explained by current climate, our findings also indicate a historic legacy of climate. After teasing apart the contribution of current climate, the effect size of historical variation in climate adds up to almost 12% of the deviance explained. In Europe, the large effect of Quaternary climate changes in explaining richness of amphibians and reptiles has been associated with the poor ability of narrow-range

54 species to track climatic shifts (Araújo et al., 2008). In this study, the number of occurrences in our dataset may approximate the distribution range for a given snake species. Indeed,

72.8% of snake species are registered in less than 22 sites (ca. 10% of total number of sites,

Table S2.2), suggesting limited colonization ability among BAF snakes. Interestingly, the influence of historical climate shifts occurs to the detriment of topography, indicating that topographic heterogeneity in BAF underlies a complex climatic history. It has been argued that the thermal stratification in tropical mountains in concert with niche conservatism might reflect historical opportunities for allopatric speciation along elevational bands (Janzen, 1967;

Cadena et al., 2012). Wide elevational ranges also promote the historical persistence of montane ectotherms as they enable species to easily track suitable ecoclimatic conditions along the elevational gradient (Sunday et al., 2014). Therefore, the influence of topographic complexity on BSR reflects the historical legacy of climate on snake distributions rather than simply providing a physical barrier.

In conclusion, we have advocated here for the biogeographic regionalization of BAF snakes through a clustering of interpolated values of snake dissimilarity to obtain a spatially cohesive representation of their faunal dissimilarity. Although these interpolated values are an abstract representation of the distance relationship among sites, our biogeographic regionalization scheme represents an important step towards conservation of BAF snakes.

Overall, the snake BSR are largely driven by climatic stability under both ecological and evolutionary time scales.

ACKNOWLEDGMENTS. We are immensely grateful to all the colleagues who kindly provided their unpublished data to our study; fortunately, the names are too many to list here, so we invite readers to check Appendix S1. Thanks go especially to A.L. Carvalho and three anonymous reviewers for comments in earlier drafts of this manuscript, and to N. Upham for

55 comments and reviewing our English. MRM thanks Conselho Nacional de Desenvolvimento

Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível

Superior (CAPES) and Fundação Lemann for fellowships granted (CNPq grants 141265/2013 and 2233356/2014-2); HCC thanks CAPES for PhD scholarship.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article:

Appendix S1 Snake inventories (Table S1.1) compiled for the Brazilian Atlantic Forest.

Appendix S2 Comments on taxonomical nomenclature and additional tables (Table S2.2– S2.3) containing the snake species list of the Brazilian Atlantic Forest and their known occurrence in each biogeographic subregion.

Appendix S3 Additional figures (Figure S3.1–S3.4) and tables (Table S3.4–3.5).

DATA ACCESSIBILITY

All R-scripts, the dissimilarity matrix (βsim), and the GIS layers generated for this study are available at the http://doi.pangaea.de/10.1594/PANGAEA.858333.

BIOSKETCHES

Mario R Moura has a background in herpetology, biogeography, and macroecology. He is interested in geographical ecology of terrestrial vertebrates. His recent work has involved investigating the synergistic associations among environmental gradients when driving biodiversity patterns.

Antônio J S Argôlo has a background in zoology, with emphasis on herpetology. His main interests are natural history and geographical distribution of snakes, endangered species and epidemiology of snakebite.

Henrique C Costa has a background in herpetology, taxonomy, and wildlife assessments. He is one of the co-authors of the annual checklist of Brazilian reptiles and also works with science disclosure for children. His main interests are taxonomy, systematics, biogeography, and conservation of South American reptiles.

Author contributions: MRM conceived the ideas; MRM, HCC, and AA compiled the data; MRM analysed the data; MRM and HCC led the writing. All authors contributed in the form of discussions and suggestions, and approved the final manuscript.

SUPPORTING INFORMATION

Moura, M.R., Argôlo, A.J.S., Costa, H.C. (2016) Biogeographic regionalization of snakes in the Atlantic Forest hotspot: historical and ecological insights. Journal of Biogeography, XXX–XXX.

Appendix S1 Snake inventories (Table S1.1) compiled for the Brazilian Atlantic Forest.

Table S1.1. List of 274 snake inventories compiled for the Brazilian Atlantic Forest. Geographic coordinates, sampling methods, period of sampling, and references. Species composition on those sites distant less than 20 km from each other (same value at the column ‘Site number’) was pooled in the species-by-sites matrix. Sampling methods: AS = artificial shelter; CE = casual encounters; LC = local collectors; MU = museum records; PT = pitfall trap; QP = quadrat plot; VS = visual search; FT = funnel traps.

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period Taíba, São Gonçalo do Amarante, CE 1 -38.9188 -3.5153 VS; PT 2007-2009 [1] Jardim Botânico and Dunas, São Gonçalo do Amarante, CE 1 -38.8733 -3.5599 VS; PT 2007-2009 [1] Planalto de Ibiapaba, Ubajara, CE 2 -40.9038 -3.8444 VS; PT; CE 2007-2009 [2], D. Loebmann, pers. comm. Floresta Nacional de Palmares, Altos, PI 3 -42.5947 -5.0564 CE; PT; VS 2010-2011 [3] Campus URFN, Natal, RN 4 -35.2054 -5.8423 VS; MU 2008 [4–6] RPPN Mata Estrela, Baía Formosa, RN 5 -35.008 -6.394 VS; PT; LC 2007-2008 [7], B. França, pers. comm. Reserva Biológica Guaribas, SEMA II, Rio Tinto, PB 6 -35.1790 -6.7260 CE; LC; VS; PT 2010-2012 [8,9], R. França, pers. comm. Reserva Biológica Guaribas, SEMA III, Rio Tinto, PB 6 -35.1010 -6.7990 CE; LC; VS; PT 2010-2011 [8], R. França, pers. comm 2008; 2012- Floresta Nacional de Nísia Floresta, Nísia Floresta, RN MU; VS [10,11] 7 -35.1817 -6.0827 2014 Reserva Ecológica Mata do Pau Ferro, Areia, PB 8 -35.7539 -6.9640 LC 1983-2009 [12–15] Mata do Buraquinho, João Pessoa, PB 9 -34.8650 -7.1450 PT; VS 2004-2005 [16,17] Parque Riacho do Meio, Barbalha, CE 10 -39.2837 -7.3339 LC 2004-2012 [18,19] Campo de Instrução Marechal Newton Cavalcanti, Abreu e -35.1000 -7.8333 PT; VS 2008 [20] Lima, PE 11 Estação Ecológica de Caetés, Paulista, PE 12 -34.9322 -7.9322 PT; VS 2007-2008 [21] RVS Mata do Engenho Tapacurá, São Lourenço da Mata, PE 13 -35.1001 -8.0041 PT; VS; CE 2006 [20], F. Amorim, pers. comm. 1983-1984; Estação Ecológica do Tapacurá, São Lourenço da Mata, PE -35.2210 -8.0490 PT; VS; MU [20,22] 13 2007-2009 Reserva Ecológica Mata do Sistema Gurjaú, Cabo de Santo 14 -35.0590 -8.2246 PT; VS 2002-2003 [20]

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period Antônio Agostinho, PE Brejo dos Cavalos, Caruaru, PE 15 -35.9666 -8.2667 LC 1985-1997 [12,13] Brejo de Agrestina, Agrestina, PE 16 -35.9280 -8.4513 LC 1972-1985 [12,13] Forest Fragments, Ribeirão, PE 17 -35.3029 -8.5009 PT; VS 2013 D.J. Santana, pers. comm. RPPN Frei Caneca + RPPN Pedra D'Antas, Lagoa dos Gatos, CE; LC; MU; [20,23], I.J. Roberto and S.P. -35.8548 -8.6965 2007-2013 PE 18 PT; VS Lima, pers. comm. Mata de Murici, Flexeiras, AL 19 -35.8000 -9.2333 VS; MU 1993-1997 [24–29] [26,30], I.J. Roberto, pers. Reserva Biológica de Pedra Talhada, Quebrangulho, AL -36.4302 -9.2474 PT; VS 2012-2013 20 comm. 1993-1994; Mata do Cedro, Rio Largo, AL -35.9189 -9.5166 VS; MU [25] 21 1996 Mata da Salva, Rio Largo, AL 21 -35.8296 -9.5188 VS; MU 1994-1996 [4,24,25,31–34] RPPN Usina Porto Rico, Campo Alegre, AL 22 -36.2359 -9.7589 PT; VS; CE 2006-2007 [35,36] Mata do Catolé, Maceió, AL 23 -35.7167 -9.6667 VS; MU 1995-1996 [25–27,31] RPPN Madeira, Teotônio, AL 24 -36.3714 -9.9388 PT; VS 2010-2011 [13,37], A.O. Silva, pers. comm. Refúgio de Vida Silvestre Mata do Junco, Capela, SE 25 -37.0583 -10.5417 PT; VS 2007-2009 [38,39] [40], L.O. Drummond, pers. Reserva Biológica Santa Isabel, Pirambu, SE -36.7270 -10.6390 LC; VS 2008 26 comm. Parque Nacional Serra de Itabaiana, Areia Branca, SE 27 -37.3419 -10.7488 LC 1997-2005 [13,41] Povoado Boieiro, Lagarto, SE 28 -37.7175 -10.8660 LC; VS 2008-2009 [42,43] Barra do Itariri, BA 29 -37.6113 -11.9478 CE; LC; MU; VS 2010-2013 R. Marques, pers. comm. Baixio, BA 30 -37.7062 -12.1123 CE; LC; MU; VS 2010-2013 R. Marques, pers. comm. Massarandupió, BA 31 -37.8404 -12.3172 CE; LC; MU; VS 2010-2013 R. Marques, pers. comm. [44–46], M.A. Freitas, pers. Universidade Estadual Feira de Santana, Feira de Santana, BA -38.9711 -12.2008 VS; MU; LC 1996-2000 32 comm. Porto Sauípe, Mata de São João, BA 31 -37.9061 -12.4064 VS; LC; PT 2003-2006 [44], M.A. Freitas, pers. comm. Instituto da Mata, Mata de São João, BA 33 -38.2346 -12.4501 CE; LC; MU; VS 2010-2013 R. Marques, pers. comm. Restinga de Imbassaí, Mata de São João, BA 34 -37.9589 -12.4678 PT; VS 2008-2009 [47,48] Praia do Forte, Mata de São João, BA 34 -38.0147 -12.5748 CE; LC; MU; VS 2010-2013 [48], R. Marques, pers. comm. Universidade Federal do Reconcâvo Bahiano, Cruz das Almas, [44], M.A. Freitas, pers. comm., -39.0860 -12.6625 LC 1990s-2000s BA 35 A.J.S. Argôlo, pers. obs. Forest fragment, São Francisco do Conde, BA 36 -38.6342 -12.7181 VS; LC 1991-2000 [49], M.A. Freitas, pers. comm. Arembepe beach, Camaçari, BA 37 -38.1416 -12.7236 CE; LC; MU; VS 2010-2013 [50], R. Marques, pers. comm. [44,50], M.A. Freitas, pers. Forests fragments, Camaçari, BA -38.2058 -12.7920 VS; CE; LC 2005-2008 38 comm. Forests fragments, Simões Filho, BA 38 -38.4231 -12.7810 LC 1990s-2000s [44,45], M.A. Freitas, pers.

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period comm. Busca Vida beach, Camaçari, BA 38 -38.2708 -12.8630 CE; LC; MU; VS 2010-2013 [50], R. Marques, pers. comm. VS; PT; CE Parque Municipal de Pituaçu, Salvador, BA -38.4154 -12.9566 2000s [51–53] 39 Campi UFBA Ondina and Federação, Salvador, BA 40 -38.5092 -13.0036 LC 1988-2011 [54] VS; PT; CE; Serra do Timbó, Amargosa, BA -39.6689 -13.1114 2006-2008 [44], M.A. Freitas, pers. comm. 41 MU; Caraíbas, distrito de Cascavel, Mucugê, BA 42 -41.3823 -13.1641 AS; CE 2000-2008 [55] M.A. Freitas, pers. comm., Fazenda de Joselito, Valença, BA -39.3364 -13.2541 LC, MU 1996-2009 43 A.J.S. Argôlo, pers. obs. Fazenda Califórnia, Ituberá, BA 44 -39.1808 -13.6792 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Lagoa Santa, Ituberá, BA 44 -39.2006 -13.7703 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Reserva Biológica da Michelin, BA 44 -39.1667 -13.8333 CE; PT 2007 [57] Fazenda Reunidas São Francisco and surroundings, -39.4736 -13.9074 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Ibirapitanga, BA 45 Fazenda Flor de Ouro, Camamu, BA 46 -39.3125 -13.9539 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Mariposa and surroundings, Camamu, BA 46 -39.1240 -13.9620 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Santa Rita and surroundings, Camamu, BA 46 -39.2147 -13.9847 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda São Salvador, Ibirapitanga, BA 45 -39.4175 -14.0131 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Oceania, Itagibá, BA 47 -39.7692 -14.1675 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Barra do Cedro, Itagibá, BA 47 -39.8836 -14.2833 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Reduto and surroundings, Aureliano Leal, BA 48 -39.5572 -14.3640 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda São Domingos and surroundings, Boa Nova, BA 50 -40.0824 -14.3654 LC 1999-2006 A.J.S. Argôlo, pers. obs. Fazenda Santa Maria, Ilhéus, BA 49 -39.1667 -14.3833 LC 1986-1999 [56] Fazenda Altamira and surroundings, Boa Nova, BA 50 -40.1187 -14.4144 LC 2000-2007 A.J.S. Argôlo, pers. obs. Fazenda Formosa, Ilhéus, BA 49 -39.1833 -14.4333 LC 1986-1999 [56] Fazenda Inveja and surroundings, Poções, BA 51 -40.2956 -14.6290 LC 1999-2007 A.J.S. Argôlo, pers. obs. 1998-1999; Fazenda Veneza, Iguaí, BA -40.0050 -14.7108 LC A.J.S. Argôlo, pers. obs. 52 2006-2011 1997-1999; Fazenda Princesa Ester, Iguaí, BA -40.0436 -14.7464 LC A.J.S. Argôlo, pers. obs. 52 2006-2014 Fazenda Lagoa Formosa, Vitória da Conquista, BA 53 -40.7747 -14.7589 LC 2000-2006 A.J.S. Argôlo, pers. obs. CEPLAC, Ilhéus, BA 54 -39.2167 -14.7667 LC 1986-1999 [56,58] Fazenda Morro Redondo and surroundings, Barro Preto, BA 55 -39.4252 -14.7783 LC 1980s-1990s [56,59], A.J.S. Argôlo, pers. obs. Fazenda Venturosa, Ibicaraí, BA 56 -39.6494 -14.8108 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Canaã, Vitória da Conquista, BA 53 -40.7817 -14.9178 LC 1999-2006 A.J.S. Argôlo, pers. obs.

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period Fazenda Recanto da Adriana, Barra do Choça, BA 57 -40.5478 -14.9601 LC 1999-2006 A.J.S. Argôlo, pers. obs. Fazenda Cangussu, Barra do Choça, BA 57 -40.6481 -14.9619 LC 1999-2003 A.J.S. Argôlo, pers. obs. Fazenda Trindade and surroundings, Buerarema, BA 58 -39.2936 -15.0636 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Fazenda Altamira, Caatiba, BA 59 -40.1200 -15.1106 LC 1994-2000 A.J.S. Argôlo, pers. obs. Parque Nacional Cavernas do Peruaçu, Januária, MG 60 -44.2139 -15.1800 VS; LC 2003 [60], R.N. Feio, pers. comm. Fazenda Campos, Itapetinga, BA 59 -40.1486 -15.2689 LC 1994-1997 A.J.S. Argôlo, pers. obs. Fazenda Caprichosa, Camacan, BA 61 -39.5356 -15.3231 LC 1980s-1990s [56,59], A.J.S. Argôlo, pers. obs. Fazenda Boa Esperança and surroundings, Ribeirão do Largo, -40.7932 -15.3735 LC 2000-2003 [61], A.J.S. Argôlo, pers. obs. BA 62 Fazenda Santa Bárbara, Pau Brasil, BA 61 -39.6206 -15.4553 LC 1994-2010 [62,63], A.J.S. Argôlo, pers. obs. Fazenda Brasil, Mascote, BA 63 -39.4011 -15.4900 LC 1980s-1990s [56,64], A.J.S. Argôlo, pers. obs. Fazenda Violeta, Belmonte, BA 64 -38.8928 -15.8339 LC 1995-1999 A.J.S. Argôlo, pers. obs. [65–67], M.A. Freitas, pers. Fazenda Palmeiras, Itapebi, BA -39.6277 -15.9508 MU; PT; VS 2001-2004 65 comm. Complexo Limoeiro, Almenara, MG 66 -40.8538 -16.0277 LC; MU; VS 2000-2004 [68,69] Fazenda Rio Preto and surroundings, Belmonte, BA 67 -38.9687 -16.0386 LC 1993-1999 A.J.S. Argôlo, pers. obs. Estação Veracruz, Santa Cruz Cabrália, BA 68 -39.1700 -16.3772 MU; PT; VS 1994 [70] Complexo Cairi, Santa Maria do Salto, MG 69 -40.0566 -16.4102 LC; MU; VS 2000-2004 [68,69] Forest fragments, Itabela, BA 70 -39.5249 -16.6045 VS; PT 2008-2011 [64], T.M. Castro, pers. comm. Forest fragments, Itinga, MG 71 -41.8821 -16.7234 CE; LC; VS 2011-2012 M.R. Moura, pers. obs.. Associação da Lapinha, Itamarajú, BA 72 -39.6225 -16.8708 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Associação Córrego da Barriguda, Itamarajú, BA 72 -39.4961 -16.8914 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Forest fragments, Bocaiúva, MG 73 -43.8735 -17.4583 VS; CE; PT 2012 M.R. Moura, pers. obs. Forest fragments, Carlos Chagas, MG 74 -40.9654 -17.6039 PT; VS; LC 2013-2014 T. Pezzuti, pers. comm. Fazenda Nova Estrela and surroundings, Mucuri, BA 75 -39.9692 -18.0406 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Rancho Alegre, Faz. Pombal, Mucuri, BA 76 -39.5944 -18.0664 LC 1980s-1990s [56], A.J.S. Argôlo, pers. obs. Parque Estadual de Itaúnas, Conceição da Barra, ES 77 -39.7401 -18.4028 VS; LC 2002-2003 [71] APA Conceição da Barra, Conceição da Barra, ES 78 -39.7697 -18.6753 VS; PT 2012 [72] Forest fragments, Serro, MG 79 -43.4205 -18.7532 CE; PT; VS 2012-2014 M.R. Moura, pers. obs. Parque Estadual do Pau Furado, Araguari, MG 80 -48.1607 -18.7657 CE; VS 2009 [73] [74,75], I.M. Soares, pers. APA Pico do Ibituruna, Governador Valadares, MG -41.9372 -18.8533 PT; VS 2014 81 comm. Forest Fragments, Virginópolis, MG 82 -42.6964 -18.8916 CE; PT; VS 2014-2015 J. Thompson, pers. comm. Paranauinha at Distrito de Itacolomi, Conceição do Mato L.B. Nascimento & R.A.M. -43.5833 -19.0000 VS; PT; CE 2009-2011 Dentro, MG 83 Fonseca, pers. comm.

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period Reserva Biológica de Sooretama, Sooretama, ES 84 -40.1555 -19.0064 LC; MU 2004-2014 [76] M.A.S. Canellas & R. Filogonio, Fazenda Bonaparte and surroundings, Periquito, MG -42.1385 -19.0487 PT; VS; CE 2007-2010 85 pers. comm. Forest Fragments, Dores de Guanhães, MG 89 -42.8775 -19.0501 CE; PT; VS 2014-2015 J. Thompson, pers. comm. AHE Foz do Rio Claro, São Simão, GO 86 -50.6534 -19.1010 PT; VS 2008-2009 [77] Reserva Natural Vale, Linhares, ES 87 -39.9447 -19.1324 LC; MU 2004-2014 [76] 2008-2009, Forest fragments, Conselheiro Pena, MG -41.3989 -19.1942 PT; VS M.R. Moura, pers. obs. 88 2012-2013 M.A.S. Canellas & R. Filogonio, Fazenda Santa Luzia and surroundings, Sobrália, MG -42.1981 -19.2036 PT; VS; CE 2007-2010 85 pers. comm. Forest fragments, Ferros, MG 89 -42.9281 -19.2169 VS; MU 2008 E.T. Silva, pers. comm. Pontal do Ipiranga, Linhares, ES 90 -39.7332 -19.3952 VS; PT; MU 2009-2012 [78,79] C.S. Carvalho Neto, pers. Barra Mansa, Alvarenga, MG -41.6210 -19.4208 LC; VS 2008 91 comm. Forest fragments, Linhares, ES 92 -40.2066 -19.4471 CE; PT; VS 2012-2013 M.R. Moura, pers. obs. Povoação beach and surroundings, Linhares, ES 93 -39.7883 -19.5787 LC 1991-2004 [79] Regência, Linhares, ES 93 -39.8398 -19.6507 LC 1987-2010 [79,80] [81–84], C. R. Rievers, pers. Parque Estadual do Rio Doce, Marliéria, MG -42.5730 -19.6553 MU; PT; VS 2008-2009 94 comm. RPPN Feliciano Miguel Abdala, Caratinga, MG 95 -42.1064 -19.7572 CE; LC; PT; VS 2000-2001 [85,86] Parque Municipal de Caratinga, Caratinga, MG 95 -42.1160 -19.8310 CE; PT; VS 2006-2007 [79], S. Genelhu, pers. comm. Estaleiro Jurong and surroundings, Aracruz, ES 96 -40.0741 -19.8608 LC 2009-2012 [79], T.M. Castro, pers. comm. Estação Ambiental Peti, São Gonçalo do Rio Abaixo, MG 97 -43.3500 -19.8833 CE; VS 2002-2004 [87] Estação Biológica de Santa Lúcia, Santa Teresa, ES 98 -40.5392 -19.9765 LC 1986-2011 [79] Forest fragments, Santa Bárbara do Leste, MG 99 -42.1173 -19.9832 VS; PT 2012; 2014 E.T. Silva, pers. comm. Fazenda São João, Turmalina, SP 100 -50.4303 -20.0070 CE; LC; PT; VS 2008-2009 [88,89] RPPN Mata do Sossego, Simonésia, MG 99 -42.0702 -20.0728 CE; MU; PT 2007-2013 [86] Jacareípe beach and surroundings, Serra, ES 101 -40.2020 -20.0871 VS; LC 2014 T.M. Castro, pers. comm. Instituto Inhotim, Brumadinho, MG 102 -44.2183 -20.1240 CE; LC; PT; VS 2008-2009 [83,90] Forest fragment, Santa Maria de Jetibá, ES 103 -40.8949 -20.1495 VS; MU; CE 2015 [79,91], A. Mônico, pers. comm. H.C. Costa & M.R. Moura, pers. Forest fragments, Ouro Preto, MG -43.5219 -20.1812 CE; VS 2012-2015 104 obs. Forest fragments, Cariacica, ES 105 -40.4370 -20.2753 VS; PT 2006-2008 T.M. Castro, pers. comm. Reserva Biológica de Duas Bocas, Cariacica, ES 105 -40.5219 -20.2811 CE; MU; PT 2007-2008 [92] Parque Estadual da Fonte Grande and surroundings, Vitória, CE; MU; VS; 2004-2006; -40.3327 -20.2968 [93–95] ES 105 LC; PT 2006-2008

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period Estação Ecológica de Corumbá, Arcos, MG 106 -45.6208 -20.3154 CE; PT; VS 2012 [96] Estação Ecológica do Tripuí, Ouro Preto, MG 107 -43.5758 -20.3958 CE; LC; MU; VS 2000-2003 [97] Reserva Ecológica de Jacarenema, Vila Velha, ES 108 -40.3261 -20.4065 VS; MU; CE 2000-2002 [98] Sítio Três Marias and surroundings, Marechal Floriano, ES 109 -40.8238 -20.4100 LC 1993-1997 [79,84,99–102] Parque Estadual do Itacolomi, Ouro Preto, MG 107 -43.4879 -20.4184 CE; LC; MU; VS 2000-2003 [59,83,97] Parque Estadual de Pedra Azul, Domingos Martins, ES 110 -41.0050 -20.4208 LC; VS 2003 [26,103] Fazenda Brunoro, Venda Nova do Imigrante, ES 110 -41.1819 -20.4400 VS; PT 2007-2008 [79,104,105] Forest fragments, Congonhas, MG 111 -43.9126 -20.4806 VS; CE; MU 2007-2014 J.V.A. Lacerda, pers. comm. Fazenda Vista Bonita, Barretos, SP 112 -48.8317 -20.4846 CE; LC; PT; VS 2008-2009 [88,89] Fazenda Primavera, Votuporanga, SP 113 -50.0879 -20.5123 CE; LC; PT; VS 2008-2009 [88,89,106–109] [79,110], T.M. Castro, pers. Parque Estadual Paulo César Vinha, Guarapari, ES -40.4028 -20.5720 MU; PT; VS 1992-2007 114 comm. RPPN Mata da Serra, Vargem Alta, ES 115 -40.9690 -20.6306 VS; MU; CE 2010 [111] CE; LC; MU; Serra do Brigadeiro - Central region, Araponga, MG -42.4833 -20.7167 2009-2011 [112] 116 PT; VS Universidade Federal de Viçosa, Viçosa, MG 117 -42.8695 -20.7580 LC; MU 1930's-2009 [113] Estação Ambiental Meirelles, Cachoeiro de Itapemirim, ES 118 -41.1127 -20.8489 VS; PT; CE 2007-2008 [114], H. Rabello, pers. comm. Serra do Brigadeiro - Southern region, Ervália, MG 116 -42.5351 -20.8790 CE; LC; MU; VS 2009-2011 [112] Fazenda Boa Vista, União Paulista, SP 119 -49.9256 -20.9218 CE; LC; PT; VS 2008-2009 [88,89] CE; LC; MU; Serra da Bandeira, Ritápolis, MG -44.3044 -21.0006 2005-2006 [79,115] 120 PT; VS Fazenda Taperão, Planalto, SP 119 -49.9792 -21.0050 CE; LC; PT; VS 2008-2009 [88,89] Eucalyptus monoculture, Coronel Xavier Chaves, MG 120 -44.2466 -21.0403 VS; PT. FT 2013-2014 [116] RPPN Foz do Aguapeí, Castilho, SP 121 -51.7158 -21.0550 PT; VS 2012-2014 D.H. Morais, pers. comm. Forest fragment, São João Del Rei, MG 120 -44.2914 -21.0664 VS; PT; FT 2013-2014 [79,116,117] [79,118], C.H.V. Rios, pers. Serra de São José, Tiradentes, MG -44.1798 -21.1014 PT; FT; EO; VS 2009-2010 120 comm. Parque Estadual do Aguapeí, Monte Castelo, SP 121 -51.5870 -21.1837 VS 2008 [119] Forest fragments, Presidente Kennedy, ES 122 -41.1217 -21.2277 VS; PT 2006-2008 [79], T.M. Castro, pers. comm. Parque Ecológico Quedas do Rio Bonito, Lavras, MG 123 -44.9715 -21.3309 VS; PT; LC 2005-2006 [120] 2009; 2010- Reserva Biológica Unilavras Boqueirão, Ingaí, MG -44.9908 -21.3464 PT; VS [79,121–124] 123 2011 RPPN Alto da Boa Vista, Descoberto, MG 124 -42.9312 -21.3743 VS; MU 2009-2010 [79,125], B. Sousa, pers. comm. Fazenda Santa Lúcia, Taquaritinga, SP 125 -48.6890 -21.4019 CE; LC; PT; VS 2008-2009 [88,89] Fazenda Águas Claras, Sales, SP 126 -49.4993 -21.4056 CE; LC; PT; VS 2008-2009 [88,89] Parque Estadual do Rio do Peixe, Ouro Verde, SP 127 -51.7675 -21.5972 VS; LC 2000s-2010s [126]

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period PT; FT; VS; LC; Fazenda Fortaleza de Sant'Anna, Chácara, MG -43.2194 -21.6728 2010-2011 [79,127] 128 MU Parque Estadual de Ibitipoca, Lima Duarte, MG 129 -43.8931 -21.7086 LC 1993-2010 [79,128], B. Sousa, pers. comm. Universidade Estadual do Norte Fluminense, Campos dos -41.3294 -21.7555 CE; LC; VS 2009-2010 C.H.O. Nogueira, pers. comm. Goytacazaes, RJ 130 2002-2003; [79,129,130], V.A. São-Pedro, Forest fragments, Poços de Caldas, MG -46.5228 -21.7844 PT; EO; VS 131 2007-2008 pers. comm. [131], S.C. Gomides, pers. Forest fragments, Juiz de Fora, MG -43.3703 -21.7920 FT; PT; VS 2008-2009 128 comm. Parque Estadual Porto Ferreira, Porto Ferreira, SP 132 -47.4310 -21.8513 LC; PT; VS 1940-2003 [132] 2001-2002; [79,129,130], V.A. São-Pedro, RPPN Retiro Branco, Poços de Caldas, MG -46.5092 -21.8670 PT; EO; VS; LC 131 2007-2008 pers. comm. 2002-2007; Parque Estadual de Nova Baden, Lambari, MG -45.3172 -21.9431 VS; PT; CE; LC 2008; 2009- [133–136] 133 2010 H.C. Costa & M.R. Moura, pers. Forest Fragments, Aiuruoca, MG -44.6053 -22.0056 MU; PT; VS 2009 134 obs. Forest fragments, Quissamã, RJ 135 -41.6438 -22.1177 VS; PT 2006-2008 T.M. Castro, pers. comm. Estrela, Antônio João, MS 136 -55.7911 -22.2130 LC 1956-1979 G. Puorto, pers. comm. Fazenda Primavera, Nova Andradina, MS 137 -53.2870 -22.2140 LC 1959-1967 G. Puorto, pers. comm. [79,131,133], A. Hudson, pers. FLONA Passa Quatro, Passa Quatro, MG -44.9461 -22.3896 FT; LC; CE 2007 138 comm. 2004, 2007- Reserva Ecológica de Guapiaçu, RJ -42.7333 -22.4000 CE; PT; QP; VS [137] 139 2014 Serra dos Órgãos, Rio de Janeiro, RJ 140 -43.0009 -22.4733 CE 2008-2015 [138] Parque Estadual Morro do Diabo, Teodoro Sampaio, SP 141 -52.3158 -22.5948 VS; MU 1990s-2000s [79,139,140] AS; CE; LC; PT; Fazenda Santa Elisa and surroundings, Munhoz, MG -46.2333 -22.6000 2002-2007 [141–143] 142 VS Parada Angélica and Taquara, Duque de Caxias, RJ 143 -43.2142 -22.6144 MU; VS 2006-2009 [144] Jardim Primavera and Campos Elísios, Duque de Caxias, RJ 143 -43.3128 -22.6928 MU; VS 2006-2009 [144] Serra do Mendanha, Nova Iguaçu, RJ 144 -43.5000 -22.8167 PT; VS 2002-2006 [145–147] CE; LC; MU; [148,149], M.A. Passos & B. Mata de Santa Genebra, Campinas, SP -47.1166 -22.8310 2010-2011 145 PT; VS Mantovani, pers. comm. Núcleo Experimental de Iguaba Grande, Iguaba Grande, RJ 146 -42.1925 -22.8499 CE; PT; VS 2008-2009 [150,151] Forest fragments, Niterói, RJ 147 -43.1036 -22.8833 MU 1956-2014 [152] Forest fragments, Roseira, SP 148 -45.2898 -22.9166 VS; MU; CE 2011-2012 [130,153], J.T.M. Portillo, pers.

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period comm. Ilha de Itacuruçá, Mangaratiba, RJ 149 -43.8803 -22.9329 VS; LC 2005-2006 [154] [153], J.T.M. Portillo, pers. Forest Fragments, Taubaté, SP VS; MU; CE 2011-2012 150 -45.5625 -23.0625 comm. 2002-2003; Restinga da Marambaia, Mangaratiba, RJ -43.9907 -23.0889 VS; MU; LC [154,155] 151 2004-2006 Parque Estadual Mata São Francisco, Santa Mariana, PR 152 -50.5691 -23.1591 CE; LC; VS * [156] Campus UEL, Londrina, PR 153 -51.2011 -23.3219 VS; MU 1992-2003 [79,157] Parque Estadual da Serra do Mar - Núcleo Santa Virgínia, -45.1167 -23.3667 CE; LC; VS 2003-2004 [79,158] Ubatuba, SP 154 Forest fragments, Maringá, PR 155 -52.0667 -23.3667 PT; VS 2009 [159] Parque Estadual da Serra do Mar - Núcleo Pinciguaba, CE; LC; MU; -44.8333 -23.3833 2001-2002 [160] Ubatuba, SP 156 PT;VS Forest fragments, Iperó, SP 157 -47.5797 -23.4458 CE; PT; VS 2011-2014 [161], M. Dixo, pers. comm. Parque Estadual Mata dos Godoy, Londrina, PR 153 -51.2544 -23.4492 * * [162,163] Boqueirão (North coast), Ubatuba, SP 154 -45.0750 -23.5000 VS; PT 2008-2009 [164] Reserva Legal da Pedreira Itapeti, Mogi das Cruzes, SP 158 -46.2448 -23.5243 VS; PT; CE 2009-2010 [79,130,161,165] Parque Estadual das Fontes do Ipiranga, São Paulo, SP 159 -46.6194 -23.6413 LC; PT; VS 2011-2015 [166] Reserva Florestal de Morro Grande, Cotia, SP 160 -46.9543 -23.6706 PT; MU 2002-2003 [79,167,168] Parque Natural Municipal Nascentes de Paranapiacaba, Santo -46.2833 -23.7667 CE; MU; PT; VS 2009-2011 [169] André, SP 161 Fazenda João XXIII, Pilar do Sul, SP 162 -47.6886 -23.8886 VS; LC; CE 2001-2006 [170,171] CE; LC; MU; Parque Estadual do Jurupará, SP -47.4038 -23.9483 2005-2007 [172] 163 PT; VS Ilha Porchart, Guarujá, SP 164 -46.3696 -23.9798 VS; MU 2000-2005 [173] Forest Fragments, Miracatu, SP 165 -47.1701 -24.0044 CE; PT; VS 2015 [167], R. Gaiga, pers. comm. CE; LC; PT; 2008; 2010- [174–176], G. Souza-Filho, pers. UHE Mauá (A), Telêmaco Borba, PR -50.7040 -24.0580 166 MU; VS 2015 comm. Parque Estadual Carlos Botelho, São Miguel Arcanjo, SP 167 -47.9395 -24.0744 LC; PT; VS 2005-2006 [177] 1990; 1999- Parque Estadual Cerrado, Jaguariaíva, PR -49.6610 -24.1730 LC; VS; PT [178], S. Morato, pers. comm. 168 2000; 2002 [176], G. Souza-Filho, pers. UHE Mauá (B), Telêmaco Borba, PR -50.6168 -24.2996 CE; PT; MU; VS 2010-2015 169 comm. Estação Ecológica Juréia-Itatins, Iguape, SP 170 -47.2500 -24.5333 VS; CE; LC; MU 1993-1996 [179] Núcleo Caverna do Diabo, Eldorado, SP 171 -48.4017 -24.6381 PT; VS 2005-2006 [180] Forest Fragments, Nova Cantu, PR 172 -52.5878 -24.7582 VS; LC 2009 [181]

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period Núcleo Cedro, Barra do Turvo, SP 173 -48.4319 -24.9686 PT; VS 2005-2006 [180] 2000-2005; Boqueirão do Sul, Ilha Comprida, SP -47.9097 -25.0310 VS; PT; LC [58,164,182] 174 2008-2009 1994-1995; Parque Estadual Ilha do Cardoso, Cananéia, SP -47.9614 -25.1125 VS; PT; MU [58,173,183] 174 2000-2005 Parque Estadual Vila Velha, Ponta Grossa, PR 175 -50.0102 -25.2373 CE; LC; PT; VS 1983-1984 [161,184] Porto de Cima, Morretes, PR 176 -48.8858 -25.4317 MU 1980s-1990s [185] 1992; 1998- Parque Estadual do Rio Guarani, Três Barras do Paraná, PR -53.1420 -25.4380 VS; LC [186], S. Morato, pers. comm. 177 1999; 2002 Forest Fragmnets, Virmond, PR 178 -52.2163 -25.4906 LC; PT; VS 2011-2014 F.L. Trein, pers. comm. CE; MU; PT; FT; [187], G. Souza-Filho, pers. Complexo Energético Fundão (A), Pinhão, PR -51.9662 -25.6342 2010-2012 180 VS comm. Pontal do Paraná, PR 179 -48.4955 -25.6436 CE; PT; VS 2014-2015 [79], F.L. Trein, pers. comm. CE; MU; PT; FT; [187], G. Souza-Filho, pers. Complexo Energético Fundão (B), Pinhão, PR -51.9772 -25.6774 2010-2012 180 VS comm. Colônia Pereira, Paranaguá, PR 179 -48.5889 -25.6917 MU 1980s-1990s [185,188] Serra da Prata, Limeira, PR 179 -48.6417 -25.7167 MU 1980s-1990s [185] Usina Hidrelétrica de Guaricana, Morretes, PR 181 -48.9667 -25.7167 MU 1980s-1990s [185,189] Parque Florestal do Rio da Onça, Matinhos, PR 179 -48.5333 -25.7833 MU 1980s-1990s [185] Parque Estadual do Monge, Lapa, PR 182 -49.6879 -25.7923 VS; LC 2001-2002 [190] Colônia Castelhanos, São José dos Pinhais, PR 181 -48.8889 -25.8167 MU 1980s-1990s [185] Morro da Boa Vista, Joinville, SC 183 -48.8258 -26.2917 PT; VS; MU 2010 [79,191,192] RPPN Taipa do Rio Itajaí, Itaiópolis, SC 184 -49.9498 -26.5606 LC 2004-2012 [193] [194], P.A. Hartmann, pers. UHE Quebra Queixo, Ipuaçu, SC -52.5466 -26.6597 VS; LC 2002-2003 185 comm. [195], F. Dallacorte, pers. Parque Natural Municipal Nascentes do Garcia, Blumenau, SC -49.1119 -27.0603 PT; VS 2007-2008 186 comm. 1998-2002; Floresta Nacional de Chapecó - Gleba I, Guatambú, SC -52.7801 -27.1046 LC [196,197] 187 2006-2007 Faxinal do Bepe, Indaial, SC 186 -49.2029 -27.1128 PT; VS 2014-2015 F. Dallacorte, pers. comm. Forest fragments, Botuverá, SC 186 -49.2075 -27.2213 PT; VS; CE 2014 [198], G. Casan, pers. comm. Parque Estadual Turvo, Derrubadas, RS 188 -53.8803 -27.2224 VS; MU; CE 2000-2001 [79,199] 2008; 2011- Forest fragments, Ouro, SC -51.6260 -27.3411 VS; PT [200,201] 189 2012 Colégio Agrícola, Frederico Westphalen, RS 190 -53.4304 -27.3902 LC 1991-2000 G.M.F. Pontes, pers. comm. Parque Estadual Rio Canoas, Campos Novos, SC 191 -51.1830 -27.5760 VS; MU 2002-2004 [79,202], G.M.F. Pontes, pers.

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period comm. São Leonardo district, Alfedro Wagner, SC 192 -49.1710 -27.6836 CE; LC; MU; VS 2005-2007 T.S. Kunz, pers. comm. [79,197], M. Wachlevski, pers. Caldas da Imperatriz, Santo Amaro da Imperatriz, SC -48.8140 -27.7413 PT;VS 2007-2010 193 comm. Parque Municipal da Lagoa do Peri, Florianópolis, SC 194 -48.5253 -27.7491 CE; MU; PT; VS 2005-2007 [203], T.S. Kunz, pers. comm. Vale do Rio Pelotas, Anita Garibaldi, SC 195 -51.1454 -27.7903 VS; PT; CE 2006-2009 [204,205] [192], M. Wachlevski, pers. 2007-2010; Baixada do Maciambu and surroundings, Palhoça, SC -48.6241 -27.8461 PT;VS;CE comm., F.H. Llanos, pers. 2014-2015 196 comm. Forest fragments, Paulo Lopes, SC 196 -48.6562 -27.9519 VS; CE 2012-2013 F.H. Llanos, pers. comm. RPPN Emílio Einsfeld Filho, Campo Belo do Sul, SC 197 -50.8414 -28.0315 VS; PT; CE 2006-2009 [192,204,205] Parque Municipal de Sertão, Sertão, RS 198 -52.2244 -28.0419 CE; PT; VS 2008-2011 [206,207] Forest fragments, São Martinho, SC 199 -48.9588 -28.1109 VS; CE 2013-2015 F.H. Llanos, pers. comm. UHE São José, Cerro Largo, RS 200 -54.7678 -28.1550 PT; VS; MU 2008-2011 [208–217] Fazenda da Brigada Militar, Passo Fundo, RS 201 -52.3283 -28.2442 CE; PT; VS 2001-2003 [130,218,219] Forest fragments, Bom Jesus, SC 202 -50.7144 -28.2958 VS; PT; CE 2006-2009 [204,205] Complexo Eólico Lagunar, Laguna, SC 203 -48.8156 -28.5300 PT, VS, FT 2012-2013 [220], F.H. Llanos, pers. comm. [221,222], F.H. Llanos, pers. Reserva Biológica do Costão da Serra, Siderópolis, SC -49.6125 -28.5710 VS; PT; LC 2007-2008 204 comm. Parque Estadual Tainhas, Jaquirana, RS 205 -50.3605 -29.0806 VS; LC 2006 [223] Parque Nacional Aparados da Serra, Cambará do Sul, RS 206 -50.1156 -29.1759 AS; VS; CE 2001-2005 [197,224–226] 2005; 2007- Parque Estadual de Itapeva, Torres, RS -49.7540 -29.3626 LC; MU; CE [84,227–232] 207 2008 Universidade Federal de Santa Maria, RS 208 -53.7191 -29.7133 CE; MU; VS 2001-2002 [219,233] Santa Maria, RS 208 -53.7500 -29.7333 AS; CE; PT; VS 2001-2004 [219,234] RPPN Fazenda Morro Sapucaia, Sapucaia do Sul, RS 209 -51.1014 -29.8349 VS; PT; MU 2007-2008 [235] Forest fragment, São Sepé, RS 210 -53.6064 -30.0932 VS; LC; CE 2010-2011 [236,237] Águas Claras, Viamão, RS 211 -50.8980 -30.1355 LC 1985-1996 [79,197] [238], G.M.F. Pontes, pers. Fazenda Trevo, Balneário Pinhal, RS -50.3031 -30.2126 LC 2001-2006 212 comm. [238], G.M.F. Pontes, pers. Magistério, Balneário Pinhal, RS -50.2617 -30.2927 VS; LC 1998-2004 212 comm. [238], G.M.F. Pontes, pers. Fazenda Atlântica, Balneário Pinhal, RS -50.3370 -30.2954 LC 2001-2004 212 comm. Fazenda Novosares, São Jerônimo, RS 213 -51.9021 -30.3711 LC; VS 2004-2006 [239] Parque Estadual de Itapuã, Viamão, RS 214 -51.0000 -30.3833 LC; MU; VS 2003-2004 [240]

Site Sampling Sampling Locality name, Municipality, State Abbreviature Longitude Latitude Reference number methods period [79], G.M.F. Pontes, pers. Balneário Dunas Altas, Palmares do Sul, RS -50.3065 -30.3888 LC 1998-2003 212 comm. [241], R.B. Oliveira, pers. Pontal do Anastácio & Buraco Quente, Palmares do Sul, RS -50.6600 -30.4720 MU;VS 2003 215 comm. [241], R.B. Oliveira, pers. Lagoa das Capivaras, Tapes, RS -51.2830 -30.4790 MU;VS 2003-2004 216 comm. Mata da Estrada Velha, Rio Grande, RS 217 -52.0587 -32.0587 AS; VS; PT; FT 2005-2007 [242] Balneário Cassino, Rio Grande, RS 218 -52.3482 -32.1318 AS; CE; PT; VS 2009-2010 [243]

ACKNOWLEDGMENTS We are grateful to all those people who shared information or data about their inventories: F. Amorim (UFBA), B.E. Assis (EcosSistema Cons. Amb.), M.A.S. Canellas (Århus University), G. Cansan (Biometria Cons. & Proj.), C.S. Carvalho- Neto (Lyon Eng.), T.M. Castro (PSG), F. Dallacorte (BioTeia), M. Dixo (Probiota), L.O. Drummond (UFRJ), R.N. Feio (UFV), R. Filogonio (Herpeto Cons. Amb.), B. França (UFPB), R. França (UFPB), M.A. Freitas (ICMBio), R. Gaiga (Biotropica), S. Genelhú (UNEC), S.C. Gomides (UFMG), P.A. Hartmann (Unipampa), A. Hudson (ICMBio), A.A. Jeronimo (ULBRA), T.S. Kunz (UFRS), J.V.A. Lacerda (UFMG), S.P. Lima (SINCIE), C.S. Lisboa (Fundação Parque Zoológico de São Paulo), F.H. Llanos (UNESC), D. Loebmann (FURG), B. Mantovani (UNICAMP), R. Marques (UESC), A. Mônico (UVV), D.H. Morais (UNESP), S.A.A. Morato (STCP), J.C. Moura-Leite (MHNCI), R.A. Murta- Fonseca (MNRJ), L.B. Nascimento (PUC-MG), C.H.O. Nogueira (UENF), G.C. Novakowski (UEM), L.E. Oliveira (UFU), R.B. Oliveira (FZBRS), M.A. Passos (PróAmbiente), T.L. Pezzuti (UFMG), G.M.F. Pontes (PUC-RS), J.T.M. Portillo (USP), G. Puorto (Inst. Butantan), H. Rabello (CUSC), C.R. Rievers (EcosSistema Cons. Amb.), C.H.V. Rios (Probiota), I.J. Roberto (URCA), D.J. Santana (UFMS), V.A. São Pedro (UFRN), A.O. Silva (UFRPE), E.T. Silva (UFMG), I.M. Soares (ICMBio), B. Sousa (UFJF), F. Souza (UFMS), G. Souza-Filho (Hori Cons. Amb.), J. Thompson (Biocev), F.L. Trein (Zenith Geoambiental), and M. Wachlevski (UFERSA).

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SUPPORTING INFORMATION TO

Moura, M.R., Argôlo, A.J.S., Costa, H.C. (2016) Biogeographic regionalization of snakes in the Atlantic Forest hotspot: historical and ecological insights. Journal of Biogeography, XXX–XXX.

Comments on taxonomical nomenclature

Taxonomical nomenclature follows the last edition of the Brazilian List of Reptiles (Costa & Bérnils, 2015). Many Brazilian snakes are polytypic, and there are some cases of two or more subspecies of the same species occurring in the BAF (e.g., Dipsas indica, Erythrolamprus miliaris, E. poecilogyrus, E. reginae, E. viridis, Leptophis ahaetulla, Micrurus lemniscatus, Spilotes pullatus, S. sulphureus). The validity of those subspecies has not been tested or has not been published yet. Additionally, most inventories do not present identification at subspecies level, and usually a subspecies cannot be identified based only on geographic distribution. Due to those hindrances, subspecies were not considered in this work.

Taxa not completely identified (i.e., ‘cf.’, ‘aff.’, and ‘sp.’) were not considered in analyses except for a few cases. As an example, Dendrophidion aff. dendrophis cited by Freire (2001), and Liotyphlops sp. cited by Argôlo (2004), were later described as new species, D. atlantica (Freire et al., 2010) and Liotyphlops trefauti (Freire et al., 2007). We have also changed the species identification when a taxonomical review came to light, affecting taxon nomenclature. The best example is the description of Chironius brazili after the taxonomic review of C. flavolineatus (Hamdan & Fernandes, 2015) – most records of C. flavolineatus from southeastern and southern BAF where re-identified to C. brazili. There were few cases where species identification was changed after consulting authors of inventories or due to biogeographic inconsistences (e.g., Epicrates cenchria from São Gonçalo do Amarante, Ceará (Borges-Leite et al., 2014) was re-identified as E. assisi (J. Borges-Leite pers. comm.); Leptotyphlops macrolepis, an Amazonian species cited for Ituberá, Bahia (Lima & Juncá, 2008) was re-identified as Trilepida salgueiroi, a morphologically similar taxon from the Atlantic Forest.

Table S2.2 Snake species recorded after compilation of 274 inventories in the Brazilian Atlantic Forest. Species presence within each biogeographic subregion (BSR) for snakes. Threatened status follow the Brazilian Red List (MMA, 2014).

Taxon Number of sites BSR Redlist Status Anomalepididae 1. Liotyphlops beui (Amaral, 1924) 8 4, 6 LC 2. Liotyphlops trefauti Freire, Caramaschi & Argôlo, 2007 3 1, 2 LC 3. Liotyphlops wilderi (Garman, 1883) 5 2, 3 LC

Typhlopidae 4. Amerotyphlops arenensis Graboski, Pereira-Filho, Silva, Prudente & Zaher, 2015 3 1 LC 5. Amerotyphlops brongersmianus (Vanzolini, 1976) 55 1, 2, 3, 5, 6 NE 6. Amerotyphlops paucisquamus (Dixon, 1979) 6 1 VU

Leptotyphlopidae 7. Epictia borapeliotes (Vanzolini, 1996) 4 1, 2 LC 8. Epictia munoai (Orejas-Miranda, 1961) 3 6 LC 9. Trilepida jani (Pinto & Fernandes, 2012) 2 4 LC 10. Trilepida koppesi (Amaral, 1955) 1 5 LC 11. Trilepida salgueiroi (Amaral, 1955) 16 2, 3 LC

Tropidophiidae 12. Tropidophis grapiuna Curcio, Nunes, Argôlo, Skuk & Rodrigues, 2012 2 2 VU 13. Tropidophis paucisquamis (Müller in Schenkel, 1901) 4 3, 4 LC 14. Tropidophis preciosus Curcio, Nunes, Argôlo, Skuk & Rodrigues, 2012 1 4 DD

Boidae

15. Boa constrictor Linnaeus, 1758 66 1, 2, 3, 4, 5 LC 16. Corallus hortulanus (Linnaeus, 1758) 47 1, 2, 3 LC 17. Epicrates assisi Machado, 1945 9 1, 2 LC 18. Epicrates cenchria (Linnaeus, 1758) 37 1, 2, 3, 4, 5, 6 LC 19. Epicrates crassus Cope, 1862 7 4, 5 LC 20. Eunectes murinus (Linnaeus, 1758) 12 1, 2, 5 LC

Taxon Number of sites BSR Redlist Status

Colubridae 21. Chironius bicarinatus (Wied, 1820) 66 1, 2, 3, 4, 6 LC 22. Chironius brazili Hamdan & Fernandes, 2015 9 2, 4, 6 NE 23. Chironius carinatus (Linnaeus, 1758) 8 1, 2 LC 24. Chironius diamantina Fernandes & Hamdan, 2014 1 2 NE 25. Chironius exoletus (Linnaeus, 1758) 62 1, 2, 3, 4, 6 LC 26. Chironius flavolineatus (Jan, 1863) 29 1, 2, 4, 5 LC 27. Chironius foveatus Bailey, 1955 20 2, 3 LC 28. Chironius fuscus (Linnaeus, 1758) 32 1, 2, 3, 4 LC 29. Chironius laevicollis (Wied, 1824) 27 2, 3, 4 LC 30. Chironius quadricarinatus (Boie, 1827) 15 2, 3, 4, 5 LC 31. Dendrophidion atlantica Freire, Caramaschi & Gonçalves, 2010 4 1 DD 32. Drymarchon corais (Boie, 1827) 18 1, 2, 5 LC 33. Drymoluber brazili (Gomes, 1918) 3 2, 4 LC 34. Drymoluber dichrous (Peters, 1863) 32 1, 2, 3, 4 LC 35. Leptophis ahaetulla (Linnaeus, 1758) 39 1, 2, 3, 5 LC 36. Mastigodryas bifossatus (Raddi, 1820) 52 1, 2, 3, 4, 5, 6 LC 37. Mastigodryas boddaerti (Sentzen, 1796) 2 1 LC 38. Oxybelis aeneus (Wagler in Spix, 1824) 44 1, 2, 3 LC 39. Simophis rhinostoma (Schlegel, 1837) 2 3, 4 LC 40. Spilotes pullatus (Linnaeus, 1758) 89 1, 2, 3, 4, 5, 6 LC 41. Spilotes sulphureus (Wagler in Spix, 1824) 35 1, 2, 3 LC 42. Tantilla boipiranga Sawaya & Sazima, 2003 3 2, 3, 4 LC 43. Tantilla melanocephala (Linnaeus, 1758) 39 1, 2, 3, 4, 6 LC

Dipsadidae 44. Apostolepis assimilis (Reinhardt, 1861) 7 4, 5 LC 45. Apostolepis cearensis Gomes, 1915 7 1, 2 LC 46. Apostolepis dimidiata (Jan 1862) 3 4, 5 LC 47. Apostolepis flavotorquata (Duméril, Bibron & Duméril, 1854) 1 4 LC 48. Atractus caete Passos, Fernandes, Bérnils & Moura-Leite, 2010 1 1 EN 49. Atractus francoi Passos, Fernandes, Bérnils & Moura-Leite, 2010 1 3 DD

Taxon Number of sites BSR Redlist Status 50. Atractus guentheri (Wucherer, 1861) 5 2 LC 51. Atractus maculatus (Günther, 1858) 4 1, 2 DD 52. Atractus pantostictus Fernandes & Puorto, 1994 5 3, 4, 5 LC 53. Atractus paraguayensis Werner, 1924 7 6 LC 54. Atractus potschi Fernandes, 1995 3 1 DD 55. Atractus reticulatus (Boulenger, 1885) 9 3, 6 LC 56. Atractus ronnie Passos, Fernandes & Borges-Nojosa, 2007 1 1 EN 57. Atractus serranus Amaral, 1930 1 3 VU 58. Atractus thalesdelemai Passos, Fernandes & Zanella, 2005 1 6 EN 59. Atractus trihedrurus Amaral, 1926 3 3, 4 LC 60. Atractus zebrinus (Jan, 1862) 7 3, 4 LC 61. Boiruna maculata (Boulenger, 1896) 13 3, 4, 6 LC 62. Boiruna sertaneja Zaher, 1996 4 1, 2 LC 63. Caaeteboia amarali (Wettstein, 1930) 5 2, 3 LC 64. Clelia plumbea (Wied, 1820) 29 1, 2, 3, 4, 6 LC 65. Coronelaps lepidus (Reinhardt, 1861) 8 2, 3 LC 66. Dipsas albifrons (Sauvage, 1884) 13 1, 2, 3, 4 LC 67. Dipsas alternans (Fischer, 1885) 11 2, 3, 4, 6 LC 68. Dipsas bucephala (Shaw, 1802) 2 4 LC 69. Dipsas catesbyi (Sentzen, 1796) 11 2 LC 70. Dipsas indica Laurenti, 1768 20 2, 3, 4 LC 71. Dipsas sazimai Fernandes, Marques & Argôlo, 2010 4 1, 3 LC 72. Dipsas variegata (Duméril, Bibron & Duméril, 1854) 25 1, 2, 3 LC 73. Ditaxodon taeniatus (Peters in Hensel, 1868) 1 6 VU 74. Echinanthera amoena (Jan, 1863) 3 3 LC 75. Echinanthera cephalomaculata Di-Bernardo, 1994 1 1 VU 76. Echinanthera cephalostriata Di-Bernardo, 1996 15 1, 2, 3, 4 LC 77. Echinanthera cyanopleura (Cope, 1885) 20 3, 4, 6 LC 78. Echinanthera melanostigma (Wagler in Spix, 1824) 13 2, 3, 4 LC 79. Echinanthera undulata (Wied, 1824) 11 3, 4 LC 80. Elapomorphus quinquelineatus (Raddi, 1820) 21 2, 3, 4 LC 81. Elapomorphus wuchereri Günther, 1861 14 2, 3 LC 82. Erythrolamprus aesculapii (Linnaeus, 1766) 51 1, 2, 3, 4, 5 LC

Taxon Number of sites BSR Redlist Status 83. Erythrolamprus almadensis (Wagler in Spix, 1824) 21 1, 2, 3, 4, 5, 6 LC 84. Erythrolamprus atraventer (Dixon & Thomas, 1985) 2 3, 4 LC 85. Erythrolamprus frenatus (Werner, 1909) 2 5 LC 86. Erythrolamprus jaegeri (Günther, 1858) 17 3, 4, 5, 6 LC 87. Erythrolamprus maryellenae (Dixon, 1985) 2 2 LC 88. Erythrolamprus miliaris (Linnaeus, 1758) 103 1, 2, 3, 4, 5, 6 LC 89. Erythrolamprus mossoroensis (Hoge & Lima-Verde, 1973) 3 1 LC 90. Erythrolamprus poecilogyrus (Wied-Neuwied, 1825) 106 1, 2, 3, 4, 5, 6 LC 91. Erythrolamprus reginae (Linnaeus, 1758) 25 1, 2, 3, 4, 5 LC 92. Erythrolamprus semiaureus (Cope, 1862) 9 6 LC 93. Erythrolamprus taeniogaster (Jan, 1863) 19 1, 2 LC 94. Erythrolamprus typhlus (Linnaeus, 1758) 11 2, 3, 4 LC 95. Erythrolamprus viridis (Günther, 1862) 11 1, 2 LC 96. Gomesophis brasiliensis (Gomes, 1918) 4 3, 4, 6 LC 97. Helicops angulatus (Linnaeus, 1758) 10 1 LC 98. Helicops carinicaudus (Wied, 1825) 24 2, 3, 5, 6 LC 99. Helicops infrataeniatus (Jan, 1865) 22 4, 5, 6 LC 100. Helicops leopardinus (Schlegel, 1837) 8 1 LC 101. Helicops modestus Günther, 1861 8 2, 3, 4, 6 LC 102. Hydrodynastes bicinctus (Hermann, 1804) 1 5 LC 103. Hydrodynastes gigas (Duméril, Bibron & Duméril, 1854) 6 1, 5 LC 104. Imantodes cenchoa (Linnaeus, 1758) 30 1, 2, 3, 4 LC 105. Leptodeira annulata (Linnaeus, 1758) 49 1, 2, 3, 4, 5 LC 106. Lygophis anomalus (Günther, 1858) 2 6 LC 107. Lygophis dilepis Cope, 1862 5 1, 2 LC 108. Lygophis flavifrenatus (Cope, 1862) 7 6 LC 109. Lygophis meridionalis (Schenkel, 1901) 2 2, 6 LC 110. Mussurana montana (Franco, Marques & Puorto, 1997) 4 3, 4 LC 111. Mussurana quimi (Franco, Marques & Puorto, 1997) 3 3, 4, 6 LC 112. Oxyrhopus clathratus Duméril, Bibron & Duméril, 1854 47 2, 3, 4, 6 LC 113. Oxyrhopus formosus (Wied, 1820) 3 2 LC 114. Oxyrhopus guibei Hoge & Romano, 1978 54 1, 2, 3, 4, 5 LC 115. Oxyrhopus melanogenys (Tschudi, 1845) 1 1 LC

Taxon Number of sites BSR Redlist Status 116. Oxyrhopus petolarius (Linnaeus, 1758) 72 1, 2, 3, 4, 5 LC 117. Oxyrhopus rhombifer Duméril, Bibron & Duméril, 1854 30 1, 2, 3, 4, 6 LC 118. Oxyrhopus trigeminus Duméril, Bibron & Duméril, 1854 54 1, 2, 3, 4, 5 LC 119. Paraphimophis rusticus (Cope, 1878) 4 6 LC 120. Phalotris lativittatus Ferrarezzi, 1994 2 5 - 121. Phalotris lemniscatus (Duméril, Bibron & Duméril, 1854) 7 6 LC 122. Phalotris matogrossensis Lema, D’Agostini & Cappellari, 2005 1 5 LC 123. Phalotris mertensi (Hoge, 1955) 5 4, 5 LC 124. Phalotris reticulatus (Peters, 1860) 2 4, 6 LC 125. Philodryas aestiva (Duméril, Bibron & Duméril, 1854) 23 2, 3, 4, 6 LC 126. Philodryas agassizii (Jan, 1863) 5 6 LC 127. Philodryas arnaldoi (Amaral, 1933) 2 3, 6 LC 128. Philodryas laticeps Werner, 1900 1 2 DD 129. Philodryas nattereri Steindachner, 1870 21 1, 2 LC 130. Philodryas olfersii (Liechtenstein, 1823) 121 1, 2, 3, 4, 5, 6 LC 131. Philodryas patagoniensis (Girard, 1858) 69 1, 2, 3, 4, 6 LC 132. Philodryas viridissima (Linnaeus, 1758) 1 2 LC 133. Phimophis guerini (Duméril, Bibron & Duméril, 1854) 7 1, 2 LC 134. Pseudoboa haasi (Boetteger, 1905) 3 3, 6 LC 135. Pseudoboa nigra (Duméril, Bibron & Duméril, 1854) 61 1, 2, 3, 4, 5 LC 136. Pseudoboa serrana Morato, Moura-Leite, Prudente & Bérnils, 1995 1 4 LC 137. Psomophis joberti (Sauvage, 1884) 4 1 LC 138. Psomophis obtusus (Cope, 1864) 2 6 LC 139. Ptychophis flavovirgatus (Gomes, 1915) 2 4, 6 LC 140. Sibon nebulatus (Linnaeus, 1758) 4 1 LC 141. Sibynomorphus mikanii (Schlegel, 1837) 37 1, 2, 3, 4, 5 LC 142. Sibynomorphus neuwiedi (Ihering, 1911) 72 1, 2, 3, 4, 6 LC 143. Sibynomorphus ventrimaculatus (Boulenger, 1885) 9 6 LC 144. Siphlophis compressus (Daudin, 1803) 28 1, 2, 3 LC 145. Siphlophis leucocephalus (Günther, 1863) 5 2 DD 146. Siphlophis longicaudatus (Andersson, 1901) 7 2, 3, 4, 6 LC 147. Siphlophis pulcher (Raddi, 1820) 9 2, 3 LC 148. Sordellina punctata (Peters, 1880) 3 3 LC

Taxon Number of sites BSR Redlist Status 149. Taeniophallus affinis (Günther, 1858) 36 1, 2, 3, 4, 6 LC 150. Taeniophallus bilineatus (Fischer, 1885) 12 2, 3, 4, 6 LC 151. Taeniophallus occipitalis (Jan, 1863) 27 1, 2, 3, 4, 6 LC 152. Taeniophallus persimilis (Cope, 1869) 6 3, 4 LC 153. Taeniophallus poecilopogon (Cope, 1863) 4 6 LC 154. Thamnodynastes chaquensis Bergna & Alvarez, 1993 1 5 LC 155. Thamnodynastes hypoconia (Cope, 1860) 31 1, 2, 3, 4, 5, 6 LC 156. Thamnodynastes longicaudus Franco, Ferreira. Marques & Sazima, 2003 2 3, 4 LC 157. Thamnodynastes nattereri (Mikan, 1828) 44 1, 2, 3, 4 LC 158. Thamnodynastes pallidus (Linnaeus, 1758) 10 1, 2 LC 159. Thamnodynastes rutilus (Prado, 1942) 1 4 LC 160. Thamnodynastes strigatus (Günther, 1858) 33 2, 3, 4, 5, 6 LC 161. Tomodon dorsatus Duméril, Bibron & Duméril, 1854 33 3, 4, 6 LC 162. Tropidodryas serra (Schlegel, 1837) 8 2, 3 LC 163. Tropidodryas striaticeps (Cope, 1869) 25 2, 3, 4 LC 164. Uromacerina ricardinii (Peracca, 1897) 9 2, 3, 6 LC 165. Xenodon dorbignyi (Duméril, Bibron & Duméril, 1854) 7 6 LC 166. Xenodon merremii (Wagler in Spix, 1824) 82 1, 2, 3, 4, 5, 6 LC 167. Xenodon neuwiedii Günther, 1863 54 1, 2, 3, 4, 6 LC 168. Xenodon rabdocephalus (Wied, 1824) 22 1, 2, 3 LC 169. Xenopholis scalaris (Wucherer, 1861) 8 1, 2, 4 LC 170. Xenopholis undulatus (Jensen, 1900) 7 1, 2, 4, 5 LC

Elapidae 171. Micrurus altirostris (Cope, 1859) 25 3, 4, 6 LC 172. Micrurus corallinus (Merrem, 1820) 74 1, 2, 3, 4, 6 LC 173. Micrurus decoratus (Jan, 1858) 4 3, 4 LC 174. Micrurus frontalis (Duméril, Bibron & Duméril, 1854) 11 2, 3, 4, 5 LC 175. Micrurus ibiboboca (Merrem, 1820) 38 1, 2 DD 176. Micrurus lemniscatus (Linnaeus, 1758) 14 1, 2, 3, 4 LC 177. Micrurus potyguara Pires, Silva, Feitosa, Prudente, Pereira Filho & Zaher, 2014 3 1 NE 178. Micrurus silviae Di-Bernardo, Borges-Martins & Silva, 2007 2 6 LC

Taxon Number of sites BSR Redlist Status Viperidae 179. Bothrops alternatus Duméril, Bibron & Duméril, 1854 20 4, 5, 6 LC 180. Bothrops bilineatus (Wied, 1821) 23 1, 2 LC 181. Bothrops cotiara (Gomes, 1913) 5 6 LC 182. Bothrops diporus Cope, 1862 4 6 LC 183. Bothrops erythromelas Amaral, 1923 4 1, 2 LC 184. Bothrops fonsecai Hoge & Belluomini, 1959 2 4 LC 185. Bothrops itapetiningae (Boulenger, 1907) 1 3 NT 186. Bothrops jararaca (Wied, 1824) 122 1, 2, 3, 4, 6 LC 187. Bothrops jararacussu Lacerda, 1884 39 2, 3, 4, 5, 6 LC 188. Bothrops leucurus Wagler in Spix, 1824 55 1, 2 LC 189. Bothrops lutzi (Miranda-Ribeiro, 1915) 2 1 LC 190. Bothrops mattogrossensis Amaral, 1925 1 5 LC 191. Bothrops moojeni Hoge, 1966 6 4, 5 LC 192. Bothrops muriciensis Ferrarezzi & Freire, 2001 1 1 EN 193. Bothrops neuwiedi Wagler in Spix, 1824 16 2, 3, 4, 6 LC 194. Bothrops pauloensis Amaral, 1925 1 4 LC 195. Bothrops pirajai Amaral, 1923 4 2 EN 196. Bothrops pubescens (Cope, 1870) 8 6 LC 197. Crotalus durissus (Linnaeus, 1758) 64 1, 2, 3, 4, 5, 6 LC 198. Lachesis muta (Linnaeus, 1766) 17 1, 2 LC

Table S2.3 Snake species reported in the Brazilian Atlantic Forest but not recorded by the inventories compiled. Threatened status follows the Brazilian Red List (MMA, 2014).

Taxon Redlist status Reference Anomalepididae 1. ǂ Liotyphlops caissara Centeno, Sawaya & Germano, 2010 LC Centeno et al. (2010) 2. Liotyphlops schubarti Vanzolini, 1948 NT Centeno et al. (2010) 3. Liotyphlops ternetzii (Boulenger, 1896) LC Centeno et al. (2010)

Taxon Redlist status Reference Typhlopidae 4. Amerotyphlops amoipira (Rodrigues & Juncá, 2002) DD Brito & Freire (2012)

Boidae 5. # Corallus cropanii (Hoge, 1953) VU Machado-Filho et al. (2011) 6. Eunectes notaeus Cope, 1862 LC Santos et al. (2013)

Dipsadidae 7. ǂ Amnesteophis melanauchen (Jan, 1863) LC Myers (2011) 8. † Apostolepis ammodites Ferrarezzi, Barbo & Albuquerque, 2005 LC Lema & Renner (2007) 9. # Apostolepis quirogai Giraudo & Scrocchi, 1998 EN Lema & Cappellari (2001) 10. # Atractus spinalis Passos, Teixeira, Sena, Dal Vechio, Pinto, Mendonça, Cassimiro & Rodrigues, 2013 LC Passos et al. (2013) 11. † Calamodontophis paucidens (Amaral, 1935) EN Franco et al. (2001) 12. # Calamodontophis ronaldoi Franco, Cintra & Lema, 2006 EN Franco et al. (2006) 13. ǂ Cercophis auratus (Schlegel, 1837) LC Hoogmoed (1997) 14. Clelia hussami Morato, Franco & Sanches, 2003 LC Morato et al. (2003) 15. † Mussurana bicolor (Peracca, 1904) LC Zaher (1996) 16. † Phalotris tricolor (Duméril, Bibron & Duméril, 1854) LC Lema et al. (2005) 17. Rhachidelus brazili Boulenger, 1908 LC Smith et al. (2013) 18. Xenodon histricus (Jan, 1863) DD Alves et al. (2013)

Viperidae 19. * Bothrops alcatraz Marques, Martins & Sazima, 2002 CR Marques et al. (2002) 20. * Bothrops insularis (Amaral, 1922) CR Guimarães et al. (2014) 21. * Bothrops otavioi Barbo, Grazziotin, Sazima, Martins & Sawaya, 2012 CR Barbo et al. (2012) 22. * Bothrops sazimai Barbo, Gasparini, Almeida, Zaher, Grazziotin, Gusmão, Ferrarini & Sawaya, 2016 - (Barbo et al., 2016) ǂ Species known only from the holotype # Species known from less than 10 individuals in no more than five localities. † Species with core distribution in another biome (Chaco, Cerrado, Pampa), with only a couple of records in marginal Atlantic Forest areas. * Species endemic from islands off the coast of the state of Espírito Santo (B. sazimai) and São Paulo (the others), southeastern Brazil.

References Alves S.S., Bolzan A.M.R., Santos T.G., Gressler D.T., & Cechin S.Z. (2013) Rediscovery, distribution extension and defensive behaviour of Xenodon histricus (Squamata: Serpentes) in the state of Rio Grande do Sul, Brazil. Salamandra, 49, 219–222. Argôlo A.J.S. (2004) As Serpentes dos Cacauais do Sudeste da Bahia. Editora da UESC, ilhéus. Barbo F.E., Gasparini J.L., Almeida A.P., Zaher H., Grazziotin F.G., Gusmão R.B., Ferrarini J.M.G., & Sawaya R.J. (2016) Another new and threatened species of lancehead genus Bothrops (Serpentes, Viperidae) from Ilha dos Franceses, Southeastern Brazil. Zootaxa, 4097, 511–529. Barbo F.E., Grazziotin F.G., Sazima I., Martins M., & Sawaya R.J. (2012) A New and Threatened Insular Species of Lancehead from Southeastern Brazil. Herpetologica, 68, 418–429. Borges-Leite M.J., Rodrigues J.F.M., & Borges-Nojosa D.M. (2014) Herpetofauna of a coastal region of northeastern Brazil. Herpetology Notes, 7, 405–413. Brito P.S. de & Freire E.M.X. (2012) New records and geographic distribution map of Typhlops amoipira Rodrigues and Juncá, 2002 (Typhlopidae) in the Brazilian Rainforest. Check List, 8, 1347–1349. Centeno F.C., Sawaya R.J., & Germano V.J. (2010) A New Species of Liotyphlops (Serpentes: Anomalepididae) from the Atlantic Coastal Forest in Southeastern Brazil. Herpetologica, 66, 86–91. Costa H.C. & Bérnils R.S. (2015) Répteis brasileiros: Lista de espécies 2015. Herpetologia Brasileira, 4, 75–93. Franco F.L., de Carvalho Cintra L.A., & de Lema T. (2006) A new species of Calamodontophis Amaral, 1963 (Serpentes, Colubridae, Xenodontinae) from southern Brazil. South American Journal of Herpetology, 1, 218–226. Franco F.L., E.L.Salomão, Borges-Martins M., Di-Bernardo M., Meneghel M., & Carreira S. (2001) New records of Calamodontophis paucidens (Serpentes, Colubridae, Xenodontinae) from Brazil and Uruguay. Cuadernos de Herpetologia, 14, 155–159. Freire E.M.X. (2001) Composição, Taxonomia, Diversidade e Considerações Zoogeográficas sobre a Fauna de Lagartos e Serpentes de Remanescentes da Mata Atlântica do Estado de Alagoas. Universidade Federal do Rio de Janeiro, Freire E.M.X., Caramaschi U., & Argôlo A.J.S. (2007) A new species of Liotyphlops (Serpentes: Anomalepididae) from the Atlantic Rain Forest of Northeastern Brazil. Zootaxa, 1393, 19–26. Freire E.M.X., Caramaschi U., & Gonçalves U. (2010) A new species of Dendrophidion (Serpentes: Colubridae) from the Atlantic Rain Forest of Northeastern Brazil. Zootaxa, 2719:, 62–68. Guimarães M., Munguía-Steyer R., Doherty P.F., Martins M., & Sawaya R.J. (2014) Population Dynamics of the Critically Endangered Golden Lancehead Pitviper, Bothrops insularis: Stability or Decline? PLoS ONE, 9, e95203. Hamdan B. & Fernandes D.S. (2015) Taxonomic revision of Chironius flavolineatus (Jan, 1863) with description of a new species (Serpentes: Colubridae). Zootaxa, 4012, 97–119. Hoogmoed M.S. (1997) Rediscovery of a forgotten snake in an unexpected place and remarks on a small herpetological collection from southeastern Brazil. Zoologische Mededelingen, 71, 63–81. Lema T. & Cappellari L.H. (2001) Geographic Distribution: Apostolepis quirogai.

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Herpetological Review, 32, 121. Lema T. de, D’Agostini F.M., & Cappellari L.H. (2005) Nova espécie de Phalotris, redescrição de P. tricolor e osteologia craniana (Serpentes, Elapomorphinae). Iheringia. Série Zoologia, 95, 65–78. Lema T. & Renner M.F. (2007) Contribuição ao conhecimento de Apotolepis ammodites (Serpentes, Colubridae, Elapomorphinae). Biociências, 15, 126–142. Lima T.M. & Juncá F.A. (2008) A herpetofauna de serrapilheira da reserva ecológica da Michelin, Ituberá, Bahia, Brasil. Sitientibus Série Ciências Biologicas, 8, 316–321. Machado-Filho P.R., Duarte M.R., L.F. Carmo, & Franco F.L. (2011) New record of Corallus cropanii (Boidae, Boinae): a rare snake from the Vale do Ribeira, State of São Paulo, Brazil. Salamandra, 47, 112–115. Marques O.A. V., Martins M., & Sazima I. (2002) A new insular species of pitviper from Brazil, with comments on evolutionary biology and conservation of the Bothrops jararaca group (Serpentes, Viperidae). Herpetologica, 58, 303–312. MMA - Ministério do Meio Ambiente (2014) Portaria no 444 de 17 de dezembro de 2014. Diário Oficial da União, 245, 121–126. Morato S.A.A., Franco F.L., & Sanches E.J. (2003) Uma nova espécie de Clelia (Serpentes, Colubridae) do sul do Brasil. Phyllomedusa, 2, 93–100. Myers C.W. (2011) A New Genus and New Tribe for Enicognathus melanauchen Jan, 1863, a Neglected South American Snake (Colubridae: Xenodontinae), with Taxonomic Notes on Some Dipsadinae. American Museum Novitates, 3715, 1– 36. Passos P., Teixeira Junior M., Recoder R.S., Sena M.A., Dal Vechio F., Pinto H.B. de A., Mendonça S.H.S.T., Cassimiro J., & Rodrigues M.T. (2013) A new species of Atractus (Serpentes: Dipsadidae) from Serra do Cipó, Espinhaço Range, Southeastern Brazil, with proposition of a new species group to the genus. Papeis Avulsos de Zoologia, 53, 75–85. Santos G.S., Lema T. de, Winck G.R., Cechin S.Z., & Boelter R.A. (2013) Distribution extension of the yellow anaconda Eunectes notaeus Cope, 1862 (Squamata: Boidae) in the state of Rio Grande do Sul, Brazil. Check List, 9, 660– 662. Smith P., Scott N., Cacciali P., & Atkinson K. (2013) Rhachidelus brazili (Squamata: Serpentes): first records from Paraguay and clarification of the correct spelling of the generic name. Salamandra, 49, 56–58. Zaher H. (1996) A new genus and species of pseudoboine snake, with a revision of the genus Clelia (Serpentes, Xenodontinae). Bollettino del Museo Regionale di Scienze Naturali, 14, 289–337.

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SUPPORTING INFORMATION TO

Moura, M.R., Argôlo, A.J.S., Costa, H.C. (2016) Biogeographic regionalization of snakes in the Atlantic Forest hotspot: historical and ecological insights. Journal of Biogeography, XXX–XXX.

Appendix S3 Additional figures (Figure S3.1–S3.4) and tables (Table S3.4–3.5), and details on Computations of historical variation in climate.

Figure S3.1 Shepard Diagram for ordination methods using the Simpson Dissimilarity Index (βsim). Euclidean distance among sites in the (a) multidimensional space of the Principal Coordinate Analysis (PCoA) or (b) 3-D space of the Non- Metric Muldimensional Scaling (NMDS) ordination, plotted against the βsim distance.

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Figure S3.2 (a) The variation in the optimal number of clusters (k) identified by the L-method algorithm according to increasing number of maximum k (i.e. the number of points in the piecewise regression). (b) Histogram for the values of optimal k selected when varying the maximum k from 4 to [nsites – 1]. Red bar indicate the optimal k = 6.

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Figure S3.3 Interpolated scores of the Non-Metric Muldimensional Scaling (NMDS) ordination using βsim dissimilarity matrix. (a) First, (b) second, and (c) third axes of the interpolated NMDS-scores. Maps drawn in 10 arc-min resolution.

Figure S3.4 Plots of the snake faunal dissimilarity in the Brazilian Atlantic Forest (BAF). (a) Regionalization of snakes into seven biogeographic subregions (BSR) based on the K-means partitioning of interpolated values. (b) Quantitative representation of the interpolated faunal dissimilarity for snakes in BAF. Names of the main rivers within BAF in blue. Maps drawn in 10 arc-min resolution.

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Table S3.4 Variance Inflation Factor (VIF) and pairwise Pearson’s correlations for the variables representing the environmental conditions at the 218 sites within the Brazilian Atlantic Forest. Variables APP ElevCV ElevR PPR TAR HDP HDT HHC HTC VIF AMT -0.287 0.232 -0.326 0.155 -0.691 -0.367 -0.465 0.368 -0.024 3.85 APP 0.228 0.169 0.145 0.144 -0.255 0.060 0.306 0.177 2.29 ElevCV 0.127 -0.056 -0.251 -0.060 -0.309 0.315 0.600 2.36 ElevR 0.268 0.223 -0.051 0.027 -0.298 -0.107 1.56 PPR 0.003 -0.295 0.021 -0.249 -0.368 1.58 TAR 0.348 0.600 -0.412 -0.075 2.83 HDP -0.029 -0.142 0.302 2.05 HDT -0.312 -0.264 2.07 HHC 0.520 2.78 HTC 3.20 Variable abbreviations: AMT = annual mean temperature; APP = annual precipitation; ElevCV = coefficient of variation of elevation; ElevR = elevational range; PPR = precipitation range; TAR = temperature annual range; HDP = historical difference in annual precipitation; HDT = historical difference in annual mean temperature; HHC = historical variation in hydric conditions; HTC = historical variation in thermal conditions.

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Table S3.5 Summary information for biogeographic subregions (BSR) for snakes within the Brazilian Atlantic Forest. Number Species BSR List of endemic species (registered in only one BSR) Threatened speciesa of records richness 5 spp.: Amerotyphlops 17 species: Amerotyphlops arenensis, Am. paucisquamus, Dendrophidion paucisquamus (VU), Atractus atlantica, Mastigodryas boddaerti†, Atractus caete, At. potschi, At. ronnie, caete (EN), At. ronnie (EN), BSR1 668 88 Echinanthera cephalomaculata, Erythrolamprus mossoroensis, Helicops Echinanthera cephalomaculata angulatus†, He. leopardinus, Oxyrhopus melanogenys†, Psomophis joberti†, (VU), Bothrops muriciensis Sibon nebulatus†, Micrurus potyguara, Bothrops lutzi†, Bo. muriciensis (EN) 10 species: Tropidophis grapiuna, Chironius diamantina, Atractus guentheri, 2 spp.: Tropidophis grapiuna BSR2 1139 110 Dipsas catesbyi, Erythrolamprus maryellenae†, Oxyrhopus formosus, (VU), Bothrops pirajai (EN) Philodryas laticeps, Ph. viridissima, Siphlophis leucocephalus, Bothrops pirajai 5 species: Atractus francoi, At. serranus, Echinanthera amoena, Sordellina 1 species: Atractus serranus BSR3 765 101 punctata, Bothrops itapetiningae† (VU) 8 species: Trilepida jani, Tropidophis preciosus, Apostolepis flavotorquata†, BSR4 425 97 Dipsas bucephala†, Pseudoboa serrana, Thamnodynastes rutilus†, Bothrops fonsecai, Bothrops pauloensis† 7 species: Erythrolamprus frenatus†, Hydrodynates bicinctus†, Phalotris BSR5 135 47 lativittatus†, Ph. matogrossensis†, Thamnodynastes chaquensis†, Bothrops mattogrossensis†, Trilepida koppesi† 18 species: Epictia munoai, Atractus paraguayensis, At. thalesdelemai, Ditaxodon taeniatus, Erythrolamprus semiaureus, Lygophis anomalus, Ly. 2 species: Atractus flavifrenatus, Paraphimophis rusticus, Phalotris lemniscatus, Philodryas BSR6 474 69 thalesdelemai (VU), Ditaxodon agassizii, Psomophis obtusus, Sibynomorphus ventrimaculatus, Taeniophallus taeniatus (EN) poecilopogon, Xenodon dorbignyi, Micrurus silviae, Bothrops cotiara, Bo. diporus, Bo. pubescens a According to the Brazilian Red List of Threatened Species (MMA, 2014). Conservation status between parentheses. See Table S2.3 for additional threatened species not included in our data compilation. † Species with core distribution in another biome, with few records in Atlantic Forest areas (Freire, 1998; Franco & Ferreira, 2003; Montingelli, 2009; Nogueira et al., 2011; Brandão, 2013; Moura et al., 2013; Guedes et al., 2014).

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References Brandão A.L.R. (2013) Posição Taxonômica de Oxyrhopsu aff. guibei e Variações dos Padrões de Oxyrhopus melanogenys (Tschudi, 1845) (Serpentes: Dipsadidae). Universidade Federal do Rio de Janeiro / Museu Nacional, Rio de Janeiro. Franco F.L. & Ferreira T.G. (2003) Descrição de uma nova espécie de Thamnodynastes Wagler, 1830 (Serpentes, Colubridae) do nordeste brasileiro, com comentários sobre o gênero. Phyllomedusa, 1, 57–74. Freire E.M.X. (1998) Geographic Distribution: Sibon nebulata. Herpetological Review, 29, 178. Guedes T.B., Sawaya R.J., & Nogueira C.C. (2014) Biogeography, vicariance and conservation of snakes of the neglected and endangered Caatinga region, north-eastern Brazil. Journal of Biogeography, 41, 919–931. Hijmans R.J., Cameron S.E., Parra J.L., Jones P.G., & Jarvis A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965–1978. MMA - Ministério do Meio Ambiente (2014) Portaria no 444 de 17 de dezembro de 2014. Diário Oficial da União, 245, 121–126. Montingelli G.G. (2009) Revisão taxonômica do gênero Mastigodryas Amaral, 1934 (Serpentes: Colubridae). Universidade de São Paulo, São Paulo. Moura M.R., Pirani R.M., & da Silva V.X. (2013) New records of snakes (Reptilia: Squamata) in Minas Gerais, Brazil. Check List, 9, 99–103. Nogueira C., Ribeiro S., Costa G.C., & Colli G.R. (2011) Vicariance and endemism in a Neotropical savanna hotspot: distribution patterns of Cerrado squamate reptiles. Journal of Biogeography, 38, 1907–1922.

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CAPÍTULO 3 - Environmental filtering and spatial stochasticity as drivers of tropical snake assemblages

Artigo em preparação para submissão na Global Ecology and Biogeography

MOURA, M.R., COSTA, H.C., ARGÔLO, A.J.S., GARCIA, P.C.A. Environmental filtering and spatial stochasticity as drivers of tropical snake assemblages. Unpublished.

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COVER PAGE

Global Ecology and Biogeography

Environmental filtering and spatial stochasticity as drivers of tropical snake

assemblages

1,2* 2 3 2 Mario R Moura , Henrique C Costa , Antônio J S Argôlo , Paulo C A Garcia

1 Yale University, Department of Ecology and Evolutionary Biology. 165 Prospect St.

06511. New Haven, CT, USA

2 Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Departamento de

Zoologia. Avenida Antônio Carlos, 6627, Pampulha. 31270-901. Belo Horizonte, MG, Brazil

3 Universidade Estadual de Santa Cruz, Departamento de Ciências Biológicas. Rodovia Jorge

Amado, Km 16 Salobrinho. 45662-900. Ilhéus, BA, Brazil

*Corresponding author. E-mail: [email protected]

SRT: Contemporary drivers of snake assemblages in the Atlantic Forest hotspot

Keywords: Brazilian Atlantic Forest, beta diversity, metacommunity, neutral dynamics, reptiles, Serpentes, snakes, species replacement, species-sorting, turnover

Number of words in the Abstract: 300

Number of words in main body of the paper: 4167

Number of references: 52

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ABSTRACT

Aim Understanding factors leading to variation in species composition is critical to ecosystem management and conservation biology as it elucidates potential outcomes of global change to biodiversity. We aim to disentangle the relative contribution of species-sorting (environmental filtering) and neutral-dynamics (spatial stochasticity) in structuring snake assemblages in a hyperdiverse tropical forest.

Location Brazilian Atlantic Forest (BAF).

Methods We used a comprehensive dataset of 218 snake assemblages covering 198 species to obtain β-diversity, species-replacement and richness-difference components between snake assemblages, and then we apply constrained ordination techniques to determine their environmental correlates. To account for spatially structured processes we used eigenvector spatial filters. We perform variation partitioning to compute the unique and shared contribution of environmental filtering (Envset) and spatial stochasticity (Spfset) in explaining the variation in snake assemblages. Because the

BAF presents two distinct climatological regimes, we repeat our analyses using snake assemblages from areas with rainy winters (northern forests) and rainy summer

(southern forests).

Results Snake β-diversity is mainly due to species-replacement. The primary correlates of β-diversity and species-replacement between snake assemblages are climatic stability, and water availability and mean temperature during the warmest periods. Richness-difference is mostly driven by vegetation complexity. The shared contribution between Envset and Spfset explain most of the β-diversity and species- replacement between assemblages. Envset present greater contribution in explaining

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snake assemblages in southern than northern part of the biome whereas Spfset present similar contribution.

Main Conclusions The thermo-hydric conditions show a strong influence in structuring snake assemblages. The higher contribution of Envset in the southern forests indicates that snake assemblages there achieved a more stable state with environment than in the northern forests. Our results suggest that snake assemblages are less prone to thermoregulatory constraints in southern BAF, where in despite of the higher variability in temperature, there are regular and constant rainfall rates.

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INTRODUCTION

Understanding factors that lead to variation in community composition is critical for conservation biogeography as it elucidate potential outcomes of global change to biodiversity (Whittaker et al., 2005; Logue et al., 2011). Four metacommunity paradigms have been commonly invoked to explain variation in ecological communities: species-sorting, mass-effect, neutral dynamics and patchy-dynamics

(Leibold et al., 2004). The first two paradigms have niche-based assumptions. The species-sorting assumes that community composition responds to environmental conditions and ecological communities are thus structured by environmental filtering

(Chase & Leibold, 2003), while the mass-effect also considers species dispersal to be high enough to override local dynamics (i.e. source-sink system mediated by dispersal structure ecological communities). Conversely, the neutral and patchy-dynamics assume no role of environment in community structure and rely on the importance of limited-dispersal in generating spatially structured ecological communities. Under the neutral paradigm, species are ecologically equivalent (i.e. do not differ in their niche), in contrast to patch-dynamics that assumes different species abilities, with highly vagile species presenting low dominance (i.e. good colonisers and bad competitors versus poor colonisers and good competitors) (Leibold et al., 2004). Although these four metacommunity paradigms are non-mutually exclusive, the investigation of species-sorting and neutral-dynamics is more common at broad scales, since these two paradigms do not depend on data on abundance and dispersal rates of species (Logue et al., 2011).

Despite the importance of metacommunity studies to ecosystem management and conservation biology, the knowledge on factors structuring the ecological

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communities is virtually inexistent for many regions in the planet. The vast majority of studies focusing on metacommunity dynamics is performed in non-terrestrial habitats, with small to medium spatial extent (< 500 km) and using invertebrates and plants (Cottenie, 2005; Logue et al., 2011). Among the groups least studied under the metacommunity context are the reptiles. While not a monophyletic clade, reptiles as comprised of lizards, snakes, turtles, amphisbaenians, crocodiles, and the tuatara together form the second richest group of terrestrial vertebrates, with over 10,300 extant species identified (Uetz & Hošek, 2015), of which almost 20% are listed as globally threatened (Böhm et al., 2013). They also play an essential role in the trophic structure of many assemblages, often occupying the middle region of food webs, as prey and predators of a wide range of organisms (Vitt & Caldwell, 2013). Reptiles can be used as bioindicators of environmental changes (Manolis et al., 2002), and several populations have experienced global decline in recent years (Sinervo et al., 2010).

Despite their remarkable importance, reptiles are still underrepresented in metacommunity studies. For instance, none of the 158 datasets and 98 studies respectively reviewed by Cottenie (2005) and Logue et al. (2011) address structure of reptile assemblages. Here, we take a step forward to fill this gap of knowledge and explore the role of species-sorting (environmental filtering) and neutral-dynamics

(spatial stochasticity) in structuring the snake assemblages in one of the top-five hotspots in the world, the Brazilian Atlantic Forest (BAF) (Mittermeier et al., 2005;

Zachos & Habel, 2011).

Unlike most others tropical forests, the BAF presents complex climatic heterogeneity.

Patterns of humid air circulation separate the BAF into two climatological regimes: rainy winters in northern forests and rainy summers in southern forests (Grimm,

2003). Such scenario creates a singular opportunity to investigate the influence of

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climatological regimes on species composition of tropical ectotherms. The concomitant warm and wet conditions in the southern BAF might provide more opportunities to ectotherms to avoid overheating there, in contrast to northern BAF, where warm and dry season concur. It has been shown that fully hydrated ectotherms can considerably extend their activity time, increasing nutrient budget and fitness, ultimately enhancing population survival (Kearney et al., 2013). If ectotherms respond differently to the climatological regimes in BAF, we can expect different influence of species-sorting in structuring snake assemblages in southern and northern

BAF. Conversely, under assumptions of environmental equivalent conditions, we can expect similar influence of neutral-dynamics in structuring snake assemblages in both northern and southern BAF. Hence, besides exploring here the effect of environmental filtering and spatial stochasticity on snake assemblages, we also ask to what extent environmental conditions structure tropical forest snake assemblages under distinct climatological regimes.

MATERIAL AND METHODS

Study area and species assemblage data

We followed the definition of BAF established in the Brazilian Federal Law

11,428/2006, which considers all forest types between the coastal rain forests and the diagonal of South America’s open vegetation biomes. This definition also includes non-forest ecosystems historically associated with those forest types, such as mangroves, restingas, montane savannas (campos rupestres and campos de altitude).

The dataset used here represents a comprehensive compilation of snake inventories in the BAF, spanning for more than 80 years of field research efforts. The dataset was compiled at 10 arc-min resolution (20×20 km), and comprises 3606 species

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occurrence records from 198 snake species, distributed across 218 sites. All snake assemblages included in this dataset have at least five snake species identified at the species level (see Moura et al., unpublished data, for complete description of this dataset).

Quantification of variation in community composition

The variation in species composition across sites is broadly called as β-diversity and can result from (i) species-replacement (turnover) between sites within a region, and

(ii) richness-difference, when one species assemblage includes a larger number of species than another. The richness-difference is called nestedness if the species assemblage in on site is a strict subset of another richer site (Baselga, 2010). To address these different β-diversity components, recent studies have partitioned dissimilarity indices according to two frameworks (Legendre, 2014). On the one hand, the approach by Baselga (2010, 2012) partitions Jaccard (DJ) or Sørensen (DS) index into nestedness-resultant and species-replacement components. On the other hand, the approach by Podani & Schmera (2011) and Carvalho et al. (2012) partitions DJ or DS into species-replacement and richness-difference components. In both approaches, the species-replacement and richness-difference/nestedness-resultant components add up to DJ or DS, but differing in the way in which those components are relativized (see

Legendre, 2014; Baselga & Leprieur, 2015; Podani & Schmera, 2016).

In this study, we partitioned DS using both frameworks. Notation of β-diversity components follow Legendre (2014). We obtained the species-replacement (ReplBS) and nestedness-resultant (NesBS) components derived from Baselga’s framework, and their counterparts from Podani’s framework (species-replacement, ReplS; and richness-difference, RichS). Throughout the Methods, we collectively refer to these

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symmetric matrices (DS, ReplBS, NesBS, ReplS, RichS) as response matrices. All response matrices were obtained in R 3.2.3 (R Core Team, 2015) using the beta.div.comp function provided in Legendre (2014).

Quantification of predictor variables

The environmental conditions are here represented by 10 predictors: mean temperature of warmest quarter (TWQ), precipitation of warmest quarter (PWQ), temperature seasonality (TS), precipitation seasonality (PS), mean elevation (ElevM), elevational range (ElevR), elevation roughness (ElevCV, coefficient of variation of elevation), forest canopy height range (FCR), coefficient of variation of forest canopy height (FCCV), and land cover diversity (LCD, as the Shannon index of land use cover classes). As a note, the use of annual mean temperature and annual precipitation could overlook the differences in climatological regimes within the BAF (i.e. rainy summers and dry winters versus dry summers and rainy winters), thus we expect

TWQ and PWQ to better indicate potential thermoregulatory constraints influencing tropical forest reptiles. To calculate the climatic and topographic variables, we used the WorldClim database (Hijmans et al., 2005), and for vegetation-related variables, we used the Global Vegetation Map database (Simard et al., 2011) and the Global

Consensus Land Cover database (Tuanmu & Jetz, 2014). We downloaded all environmental variables at 30 arc-sec resolution (≈1 Km). To meet the resolution of the species assemblage data, we calculated the mean, range, or coefficient of variation of the predictors listed above using a buffer of 10-km radius centred on geographic coordinates of each site (Fig. S1). Computations were performed in R 3.2.3 (R Core

Team, 2015) using the raster package (Hijmans, 2015).

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The use of all environmental predictors unsurprisingly leads to high multicollinearity, which also increases due the addition of quadratic terms to assess hump-shaped relationship between predictors and response matrices. To allow non-linear relationships and to circumvent the collinearity between linear and quadratic terms, we calculated two orthogonal polynomials (first and second degree) for each environmental predictor. To further reduce multicollinearity, we computed the

Variance Inflation Factor (VIF) for the complete set of 20 orthogonal polynomials and excluded those variables with VIF > 5 (Kutner et al., 2004). For instance, only ElevM was dropped due its high collinearity with TWQ. The final set of environmental predictors included 19 variables with pairwise Pearson’s correlations ranging from

−0.518 to 0.598 and VIF ≤ 4 for all variables (Table S1).

To account for spatially stochastic processes, we applied a distance-based Moran’s eigenvector map (dbMEM) analysis (Borcard & Legendre, 2002; Legendre et al.,

2015). In this analysis, a matrix of geographic distances is computed among the sites and then truncated according to a distance threshold (Borcard & Legendre, 2002). A principal coordinate analysis (PCoA) is performed using the truncated (geographic) distance matrix, and the principal coordinates corresponding to the positive eigenvalues are retained as Moran’s eigenvector (spatial filters, hereafter) (Legendre

& Legendre, 2012). The maximum distance from the minimum spanning tree linking all sites was used to produce the connectivity (truncated) matrix, and the geographic distances larger than this threshold were replaced by four times the threshold value

(Borcard & Legendre, 2002). The spatial structure embedded in stochastic processes

(or in the environment) is expected to produce positive spatial correlation in species distribution and therefore assemblage composition. To examine the spatial correlation in the each spatial filter, we produced a spatial correlogram of Moran’s I coefficients

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using 21 geographic distance classes. We then performed a Moran’ I test for significant spatial correlation in each distance class, retaining only those spatial filters with significant spatial correlation (P ≤ 0.05 in Moran’ I test) in at least one distance class. For instance, 61 spatial filters were retained to represent the spatially stochastic processes.

Delineation of bioclimatic spaces

To determine the bioclimatic spaces in the BAF we initially rasterized the BAF boundaries at 2.5 arc-min resolution (≈5 Km) and extracted the values of TWQ,

PWQ, TS and PS for each grid cell within the BAF. We performed a principal component analysis (PCA) using these four bioclimatic variables and extracted all

PCA-scores for each grid cell. Then, we performed a K-means partitioning analysis using the PCA-scores to cluster the BAF into two bioclimatic spaces. Not surprisingly, the clustering of PCA-scores returned two spatially cohesive groups matching the climatological regimes observed between northern and southern BAF

(Fig. 1a). We categorized snake assemblages according to these two bioclimatic spaces. All subsequent analyses were performed using the northern, the southern and all snake assemblages.

Data Analysis

To determine the primary correlates of assemblage composition, we performed distance-based redundancy analysis (dbRDA; Legendre & Anderson, 1999). In a traditional dbRDA, a PCoA is performed to extract a Euclidean representation (i.e. the principal coordinates) of a symmetric response matrix to be used in a RDA (Legendre

& Legendre, 2012). If a non-Euclidean response matrix (sensu Gower & Legendre,

1986), is used, the principal coordinates produced have positive and negative

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eigenvalues, the latter corresponding to eigenfunctions based on complex numbers. In ignoring these complex numbers, one inflates the total sum of squares of the non-

Euclidean response matrix, affecting therefore the model explanatory power

(McArdle & Anderson, 2001). To circumvent this issue, McArdle & Anderson (2001) introduced a multi-response permutation (MRP) test which provides corrected statistics for dbRDA using non-Euclidean matrix. We applied this MRP to each response matrix (DS, ReplBS, NesBS, ReplS, RichS) using all 19 environmental predictors. If the full model presented significant relationship for a given response matrix (Blanchet et al., 2008), we used forward selection to identify a more parsimonious set of variables that present an explanatory power (adjusted R2) similar to the model containing all predictors (full model). To be selected, a given predictor had to (i) increase the adjusted R2 the most, while (ii) achieving a significant relationship (P ≤ 0.05), and (iii) could not overtake the adjusted R² of the full model

(Blanchet et al., 2008). Since the assessment of a response matrix via MRP test does not produce ordination axes, we also performed a traditional dbRDA to enable visual ordination plots, while acknowledging that results from MRP test and traditional dbRDA are not quantitatively identical although qualitatively similar. Response matrices were square-root transformed to reduce their non-Euclidean nature before producing the ordination plots (Legendre, 2014).

To disentangle the relative importance of species-sorting (environmental filtering) and neutral dynamics (spatially stochastic processes), we grouped the explanatory variables into two distinct predictor sets according to (i) environmental variables

(Envset) and (ii) spatial filters (Spfset). We used variation partitioning (Borcard et al.,

1992) to assessed the relative importance of each predictor set, quantifying therefore the unique and shared contributions of Envset and Spfset in explaining a given response

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matrix. We obtained the variation partitioning fractions through the adjusted R² derived from MRP tests. Computations were performed in R 3.2.3 using the dbRDA.D function provided in Legendre (2014) and adonis function in the vegan package

(Oksanen et al., 2015).

RESULTS

Snake diversity in the Brazilian Atlantic Forest

Snake β-diversity was mainly due to species replacement between assemblages, with nestedness-resultant (NesBS) component representing on average 12% of the pairwise dissimilarity (DS) between all assemblages. Conversely, richness-difference (RichS) component represented on average 37% of DS value between all assemblages. Overall, the mean α-diversity, β-diversity, and β-diversity components were roughly similar between northern and southern snake assemblages. The proportion of DS due to RichS and NesBS was slightly higher for snake assemblages in northern than those southern

BAF, while still qualitatively similar to values observed for all assemblages together

(Table 1).

Table 1 Summary of the snake assemblage data in the Brazilian Atlantic Forest

(BAF) and in each bioclimatic space within this hotspot.

Metric Northern BAF Southern BAF Whole BAF Number of sites 102 116 218 Mean α-diversity 17.9 15.35 16.5 γ-diversity 129 152 198 Mean pairwise DS 0.731 0.778 0.802 Mean pairwise ReplBS 0.601 0.672 0.705 Mean pairwise NesBS 0.130 0.106 0.097 Mean pairwise ReplS 0.425 0.488 0.503 Mean pairwise RichS 0.306 0.290 0.299

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Environmental correlates of snake assemblages

Almost 30% of the variation in DS was explained by the environmental gradients. We observed similar results for the constrained ordination of the species-replacement components derived from Baselga’s (ReplBS) and Podani’s (ReplS) frameworks. From the 12 to 13 environmental gradients explaining ReplBS and ReplS, 11 are common to both response matrices (Table 2). On average, the environmental gradients explained

39% of the species-replacement components. Conversely, environmental gradients explained less than 9% of the variation in RichS and presented no contribution in explaining NesBS. The strong non-Euclidean nature of RichS and NesBS reduced the efficiency of ordination techniques to constrain these matrices (Legendre, 2014). Not surprisingly, only the DS, ReplBS, and ReplS could be appropriately represented in the

Euclidean space of ordination plots (Fig. 1).

The climatic stability (TS, PS) in concert with water-availability and energy input during the warmest quarter (PWQ and TWQ) emerged as the top-4 most important environmental predictors in explaining DS, ReplBS and ReplS for all snake assemblages

(Table 2). When snake assemblages were investigated separately between northern and southern BAF, PS emerged as the most important factors in explaining DS, ReplBS and ReplS, in both northern and southern snake assemblages. Moreover, TS emerged as an important driver of DS, ReplBS and ReplS only for assemblages in the northern

BAF, while TWQ explained DS, ReplBS and ReplS only in southern BAF (Table S2).

Environmental gradients related to forest canopy (FCR, FCR²) and water-availability

(PWQ²) emerged as the most important in explaining RichS for assemblages in the whole BAF and in the northern forests (Tables 2 and S2).

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Figure 1 Results of the first two axes of the constrained ordinations for snake assemblages in the Brazilian Atlantic Forest (BAF). (a) Snake assemblages used in this study and the distribution of bioclimatic spaces in the BAF. Distance-based

Redundancy Analysis (dbRDA) of (b) snake β-diversity (DS), and species- replacement components from (c) Baselga’s (ReplBS) and (d) Podani’s (ReplS) frameworks. For all plots, numbers from 1 to 218 denote the snake assemblages latitudinally ordered from north to south, following the same designation of snake assemblages as in Moura et al. (unpublished data). Colours indicate the bioclimatic spaces in which each snake assemblage is inserted. See Methods for variable abbreviations.

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Table 2 Results of the forward selection procedure based on the cumulative adjusted

2 2 R (Cum. R adj × 100) for environmental variables explaining each response matrix for snake assemblages in the Brazilian Atlantic Forest (BAF).

DS ReplBS ReplS RichS Order Cum. Cum. Cum. Cum. Variable 2 Variable 2 Variable 2 Variable 2 R adj R adj R adj R adj 1 TS 10.95 TS 16.35 TS 15.34 PWQ² 2.98 2 PWQ 15.39 PWQ 23.03 PWQ 21.41 FCR² 4.97 3 PS 18.55 PS 26.83 TWQ 24.82 FCR 6.60 4 TWQ 20.91 TWQ 30.14 PS 27.36 PS 8.16 5 ElevCV 22.53 ElevR 32.52 ElevR 29.37 6 TS² 24.16 TS² 34.82 TS² 31.25 7 TWQ² 25.30 ElevM² 36.45 ElevM² 32.65 8 FCR² 26.38 TWQ² 37.81 TWQ² 33.78 9 ElevM² 27.08 ElevCV 39.01 PS² 34.81 10 ElevCV² 27.73 PS² 39.82 ElevCV 35.52 11 PS² 28.16 FCCV² 40.48 LCD 36.05 12 ElevR² 28.57 ElevR² 41.01 FCCV² 36.57 13 FCCV² 28.96 ElevCV² 41.37 14 PWQ² 29.31 Variables abbreviations: ElevM = mean elevation; ElevR = elevational range; ElevCV

= coefficient of variation of elevation; FCR = forest canopy height range; FCCV = coefficient of variation of forest canopy height; LCD = land cover diversity; PS = precipitation seasonality; PWQ = precipitation of warmest quarter; TS = temperature seasonality; TWQ = mean temperature of warmest quarter. The ² denote the quadratic term for the respective variable. See Table S2 for results of snake assemblages in the northern and southern BAF.

Metacommunity paradigms and snake assemblages

The environmental variables (Envset) and spatial filters (Spfset) accounted for almost

39% of variation in DS for all assemblages in the BAF. The total contribution of both predictor sets combined was even higher for ReplBS (54%) and ReplS (51%). In contrast, the total contribution of Envset and Spfset accounted for only 9.5% of the variation in RichS. Overall, most of the variation in the response matrices (DS, ReplBS,

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ReplS, RichS) is attributed to the shared fraction between Envset and Spfset (Fig. 2,

Table S3). The unique contribution of Spfset explained more variation in DS, ReplBS and ReplS than the unique contribution of Envset. The unique contribution of Envset and Spfset were similar in explaining the variation in RichS for all snake assemblages.

Figure 2 Variation partitioning of (a) snake β-diversity (DS), species-replacement components of (b) Baselga’s (ReplBS) and (c) Podani’s (ReplS) frameworks, and (d) richness-difference (RichS). Predictor sets refers to environmental variables (Envset), and spatial filters (Spfset). See Table S3 for further details on variation partitioning fractions.

When analysed separately, the snake assemblages in northern and southern BAF showed some differences that deserve attention. For instance, the environmental

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variables and spatial filters explain more variation of DS, ReplBS and ReplS in the snake assemblages in southern than northern BAF (Fig. 2, Table S3). Although the total contribution of Envset differed between snake assemblages in northern and southern BAF, the total contribution of Spfset was highly similar, particularly for DS,

ReplBS and ReplS (Table S3). Nevertheless, the synergistic association between Envset and Spfset was smaller in the northern BAF, where the unique contribution of Spfset was generally higher. Interestingly, the Envset explained the variation in RichS only for northern BAF, presenting no contribution in explaining richness-difference between assemblages in southern BAF.

DISCUSSION

We have presented for the first time a detailed examination of metacommunity paradigms shaping reptile assemblages in a tropical forest. We explored the influence of distinct types of environmental gradients in explaining the β-diversity, species- replacement and richness-difference between snake assemblages in one of the hottest hotspots in the world, the BAF. Moreover, we address the relative importance of environmental filtering and spatially stochastic processes in structuring the snake assemblage. We also investigate the influence of species-sorting and neutral-dynamics under distinct climatological regimes, contributing to the discussion of processes underlying the ectothermic responses to global climate change.

Snake diversity in the Brazilian Atlantic Forest

A typical snake assemblage in BAF have on average 16.5 species, which represents less than a tenth of the γ-diversity of snakes in this hotspot (Table 1). The difference observed between the partitioning of DS according to ReplBS/NesBS and ReplS/RichS indicates that poor snake assemblages are not necessarily nested subsets of richer

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sites. That is, sites with low snake richness may sustain rare species, resulting therefore in high values of ReplBS and intermediate to high values of ReplS. The greater contribution of species-replacement component from Baselga’s framework than that from Podani’s framework to the partition of β-diversity is also reported for

BAF amphibians (Silva et al., 2014). Our findings have strong conservation implications for reptiles in BAF. In order to achieve maximum representation of snake diversity within a set of protected areas, reserve networks in BAF cannot focus solely on richer sites but also in poorness-rarity regions.

Environmental correlates of snake assemblages

The tropical snake assemblages here investigated are notoriously structured by climatic variability, indicating the strong role of thermal and hydric tolerances to assembly processes. In tropical regions, the thermoregulatory behaviour of reptiles is mostly about staying cool (Huey et al., 2009). Because of the upper thermal niche conservatism (Araújo et al., 2013), ectothermic species need to rely on the thermoregulatory behaviour to buffer unsuitable climate during particular daily hours and/or annual seasons (Kearney et al., 2009; Sunday et al., 2014). However, the efficacy of behavioural thermoregulation for the maintenance of ectothermic populations is limited, since excessive and endless heat will restrict their available time to forage and interact, ultimately resulting in energetic shortfalls that increase the extinction rates (Huey et al., 2010; Sinervo et al., 2010). The high influence of PWQ in explaining β-diversity and species-replacement of snakes in the BAF is in line with biophysical ecology models, which point water-availability during the warmest periods as a critical condition to enable ‘dry skinned’ ectotherms to buffer thermoregulatory constraints (Kearney et al., 2013). The emerging of PS as the most

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important driver of DS, ReplBS, and ReplS in both northern and southern BAF indicates that water variability is important under a relatively homogenous climatological regime, even if drier conditions are not concurrent with warmer periods in BAF.

The environmental conditions explain β-diversity and species-replacement of snake assemblages better in the southern than northern BAF. The smaller contribution of environment in explaining snake assemblages in the northern BAF suggests a major role of historical biogeographical dynamics (Leibold et al., 2010). Indeed, a similar pattern is reported to phylogeographic endemism of BAF vertebrates (mostly ectotherms), for which the current climate better explains phyloendemism in the southern part of BAF, whereas historical forest stability better explains these phyloendemism in the northern part of this hotspot (Carnaval et al., 2014).

Simulations from global climate models (e.g. Hijmans et al., 2005) indicate that the northern BAF presented wetter conditions during the Last Glacial Maximum (LGM) which in concert with large patches of LGM-forests might have contributed to structure snake assemblages in the past. Additional research on responses of tropical ectotherm assemblages to Quaternary climatic change can help to elucidate this issue.

If thermoregulatory needs is a major mechanism determining snakes’ responses to environmental conditions, we can expect a greater difficult to avoid overheating in heavily fragmented landscapes, where the amount of habitats providing shade is limited. Global predictions from biophysical models indicate the necessity of 25–50% of shade requirement for ectothermic thermoregulation over the BAF extent (Kearney et al., 2009). Such estimates are particularly worrying for BAF reptiles, given that only 12% of this tropical forest remains (Ribeiro et al., 2009). When snake assemblages are analysed separately for northern and southern BAF, gradients related

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to forest canopy (potential indicators of shade availability) explain variation in snake assemblages mostly in the northern BAF. Interestingly, variables related to vegetation complexity explain most of the variation in richness-difference between snake assemblages, especially in the northern forests (Tables 2 and S2). An alternative to outrun the heating would be migrating uphill (Colwell et al., 2008; Sunday et al.,

2014). Unfortunately, this is not an option for most reptile species in the northern

BAF, since the main mountain ranges in this biome are south located (Fig S1). To add another complicating factor, most of the forest cover located at low elevations (< 1200 m) has been converted into human modified or anthropogenic landscapes, and the remaining lowland forests are mostly unprotected (Tabarelli et al., 2010). Therefore, reptile species in this hotspot might already be seriously threatened by feedback loops of climate warming, habitat loss and habitat fragmentation.

Metacommunity paradigms and snake assemblages

Our findings indicate that most of the variation in β-diversity and species-replacement of snake assemblages in the BAF is accounted for environmental gradients spatially structured. A substantial part of DS, ReplBS and ReplS is explained by the unique fraction of the Spfset. Such unique spatial fraction is usually associated with neutral- dynamics, such as random dispersal and ecological drift (Hubbell, 2001). We however acknowledge that this fraction can also depict spatially structured factors (ecological and historical) not included in the analysis (Legendre & Legendre, 2012). This may be the case when the shared contribution of Envset and Spfset is high, reinforcing the synergistic association between deterministic and stochastic processes in structuring ecological assemblages. Recent discussions have also pointed out to confounding effects of both deterministic and neutral processes underpinning this unique spatial

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fraction (Smith & Lundholm, 2010). Even so, it seems reasonable to assume that spatially stochastic processes are linked, at least partially, to neutral-dynamics operating within ecological communities (De Cáceres et al., 2012).

The spatial stochasticity present similar contribution in explaining snake assemblages in the northern and southern BAF. As expected, limited dispersal is a common share between snake species in northern and southern forests. Thus, the greater contribution of Envset in explaining DS, ReplBS and ReplS in the southern BAF indicates that southern forests provides more ‘optimal’ conditions for ectotherms than those currently observed in northern forests. In addition, the higher independent contribution of Spfset in explaining DS, ReplBS and ReplS in the northern BAF might actually reflect spatially structured historical processes not included in our analyses, as already indicated to explain phyloendemism of vertebrates in the northern part of

BAF (Carnaval et al., 2014).

CONCLUSION

We have addressed the environmental drivers of the non-directional β-diversity of a reptilian clade in tropical forests. In doing so, our study uncovers the importance of thermal and hydric conditions in structuring snake assemblages in a hyperdiverse tropical forest. We have showed that even within a same tropical forest, the differences in climatological regimes can substantially change the snakes’ response to environmental conditions. In conclusion, the capacity of tropical forest snakes to adjust their geographical distribution to climate change is intrinsically related to the synergism between thermal and hydric conditions. If the climate becomes warm and dry, we can expect a disequilibrium between reptile assemblages and environment.

We recommend additional efforts to incorporate explicit phylogenetic approaches to

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link evolutionary and historical processes to ecological determinants of reptilian diversity in tropical forests.

ACKNOWLEDGEMENTS

We are grateful to all colleagues who kindly provided their unpublished data to our study. To R.R.C. Solar for comments in earlier drafts of this manuscript. MRM thanks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq),

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and

Fundação Lemann for fellowships granted (CNPq grants 141265/2013 and

2233356/2014-2); HCC thanks CAPES for PhD scholarship.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article:

Appendix S1 Additional tables (Tables S1–S3) and additional Fig. S1.

DATA ACCESSIBILITY

All R-scripts and response matrices generated for this study are available at the (DOI will be provided if the paper got an acceptance).

BIOSKETCH

Mario R Moura has a background in herpetology, biogeography, and macroecology.

He is interested in geographical ecology and conservation of terrestrial vertebrates.

His recent work has involved investigating the synergistic associations among environmental gradients in explaining biodiversity patterns.

136

SUPPORTING INFORMATION

Moura, M.R., Costa, H.C., Argôlo, A.J.S. (2016) Environmental filtering and spatial stochasticity as drivers of tropical snake assemblages. Global

Ecology and Biogeography, XXX–XXX.

Appendix S1 Additional tables (Tables S1–S3) and additional Fig. S1.

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Table S1. Variance Inflation Factor (VIF) and pairwise Pearsons correlations for the orthogonal polynomial variables representing the environmental conditions.

Variable ElevR FCCV FCR LCD PS PWQ TS TWQ ElevCV² ElevM² ElevR² FCCV² FCR² LCD² PS² PWQ² TS² TWQ² VIF ElevCV 0.127 0.222 -0.021 0.233 -0.197 0.071 0.048 0.368 0.000 0.266 0.043 -0.140 -0.007 0.165 -0.179 0.228 0.073 -0.012 3.06 ElevR -0.095 0.321 0.056 0.175 0.441 0.116 -0.356 -0.082 -0.040 0.000 0.043 0.348 -0.127 0.013 0.090 -0.277 -0.066 2.40

FCCV -0.137 0.133 -0.022 -0.036 0.034 0.140 -0.117 0.172 0.050 0.000 -0.103 0.019 -0.085 0.013 0.053 0.050 1.15

FCR 0.008 0.068 0.316 -0.090 -0.234 0.007 -0.023 -0.300 0.088 0.000 -0.037 -0.042 0.012 -0.217 -0.054 1.63

LCD -0.003 0.064 0.176 -0.128 0.015 0.367 0.019 -0.103 -0.106 0.000 0.136 -0.051 -0.042 0.189 1.43

PS 0.085 -0.452 0.114 0.071 0.073 -0.022 -0.067 -0.040 0.012 0.000 0.242 -0.394 0.145 2.39

PWQ 0.468 -0.488 0.205 0.058 -0.066 -0.002 0.275 0.067 -0.061 0.000 -0.499 -0.041 3.03

TS -0.518 0.186 -0.111 -0.021 0.093 0.111 -0.012 0.200 -0.272 0.000 -0.215 3.23

TWQ -0.325 -0.034 0.142 -0.110 -0.135 0.009 -0.183 0.159 0.233 0.000 4.01

ElevCV² -0.102 -0.180 -0.016 -0.024 0.079 0.100 -0.066 -0.234 -0.096 1.60

ElevM² 0.221 -0.117 -0.098 0.170 -0.049 0.130 0.050 0.598 2.49

ElevR² -0.015 -0.081 0.071 -0.147 0.000 0.077 0.196 1.34

FCCV² 0.147 -0.095 0.082 -0.084 0.099 -0.092 1.11

FCR² -0.156 -0.008 0.064 -0.051 0.067 1.46

LCD² -0.057 -0.003 0.008 0.091 1.17

PS² -0.062 0.387 -0.078 1.66

PWQ² 0.079 0.169 1.39

TS² -0.073 2.63

TWQ² 1.92

Variables abbreviations: ElevM = mean elevation; ElevR = elevational range; ElevCV = coefficient of variation of elevation; FCR = forest canopy height range; FCCV = coefficient of variation of forest canopy height; LCD = land cover diversity; PS = precipitation seasonality; PWQ = precipitation of warmest quarter; TS = temperature seasonality; TWQ = mean temperature of warmest quarter. The ² denote the quadratic term for the respective variable.

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Table S2 Results of the forward selection procedure based on the cumulative adjusted

2 2 R (Cum. R adj × 100) for environmental variables explaining each response matrix of snake assemblages in the northern and southern Brazilian Atlantic Forest (BAF).

Northern BAF DS ReplBS ReplS RichS Order Cum. Cum. Cum. Cum. Variable 2 Variable 2 Variable 2 Variable 2 R adj R adj R adj R adj 1 PS 7.02 PS 10.09 PS 8.98 FCR² 5.57 2 TS 12.36 TS 17.97 ElevM² 15.58 FCR 10.11 3 ElevM² 16.64 ElevCV² 24.98 TS 21.20 PWQ² 13.31 4 ElevCV² 18.35 ElevR 28.39 ElevCV² 24.33 5 FCCV² 19.81 FCCV² 29.92 PWQ 26.04 6 ElevR 20.82 PS² 27.56 7 FCCV 28.70

Southern BAF DS ReplBS ReplS RichS Order Cum. Cum. Cum. Cum. Variable 2 Variable 2 Variable 2 Variable 2 R adj R adj R adj R adj 1 PS 8.49 PS 13.21 PS 12.99 - - 2 PS² 14.07 PS² 21.68 PS² 20.92 3 TWQ 17.70 ElevR 27.37 TWQ 26.78 4 ElevCV 21.38 TWQ 32.02 ElevR 31.04 5 ElevCV² 23.38 ElevCV 34.63 TWQ² 33.33 6 ElevM² 25.31 ElevR² 36.84 ElevCV 35.60 7 TWQ² 26.26 TWQ² 38.55 FCR² 37.34 8 FCR² 27.15 ElevCV² 39.96 TS² 38.58 9 ElevR² 27.88 ElevM² 41.27 PWQ 39.57 10 PWQ 28.42 PWQ 42.27 LCD² 40.48 11 PWQ² 29.02 Variables abbreviations: ElevM = mean elevation; ElevR = elevational range; ElevCV

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Table S3 Results of the variation partitioning of snake β-diversity (DS), species- replacement (ReplBS and ReplS) and richness-difference (RichS) components.

Response matrix Individual contribution (%) DS ReplBS ReplS RichS All assemblages in the BAF Unique environmental 6.63 8.92 7.32 1.63 Shared 22.69 32.45 29.25 6.53 Unique spatial 9.36 12.69 14.46 1.40 Unexplained 61.32 45.94 48.97 90.44

Total environmental set 29.31 41.37 36.57 8.16 Total spatial set 32.05 45.14 43.71 7.93 Total environmental + spatial sets 38.68 54.06 51.03 9.56

Assemblages in the northern BAF Unique environmental 7.10 4.72 0.00 8.77 Shared 13.72 25.21 31.88 4.54 Unique spatial 17.95 22.31 15.74 2.22 Unexplained 61.23 47.76 52.38 84.47

Total environmental set 20.82 29.92 31.88 13.31 Total spatial set 31.67 47.52 47.62 6.76 Total environmental + spatial sets 38.77 52.24 47.62 15.53

Assemblages in the southern BAF Unique environmental 6.75 6.98 10.73 0 Shared 22.27 35.29 29.75 0 Unique spatial 9.09 12.94 20.08 2.42 Unexplained 61.88 44.79 39.44 97.58

Total environmental set 29.02 42.27 40.48 0 Total spatial set 31.36 48.23 49.83 2.42 Total environmental + spatial sets 38.12 55.21 60.56 2.42

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Figure S1 Spatial representation of the environmental gradients in the Brazilian Atlantic Forest

(BAF). (a) Bioclimatic spaces in the BAF indicating the northern (red) and southern forests (blue);

(b) TWQ = mean temperature of warmest quarter; (c) PWQ = precipitation of warmest quarter; (d)

TS = temperature seasonality; (e) PS = precipitation seasonality; (f) ElevM = mean elevation; (g)

ElevR = elevational range; (h) ElevCV = coefficient of variation of elevation; (i) FCR = forest canopy height range; (j) FCCV = coefficient of variation of forest canopy height; (k) LCD = land cover diversity. Gray lines denote boundaries of Brazilian state covered by the BAF.

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CONCLUSÃO GERAL

I have demonstrated in this thesis how broad scale patterns of species richness, species pools, and species composition are affected by environmental gradients. Of particular interest are the synergistic associations between distinct types of environmental gradients, particularly those representing climatic stability, solar energy input and water availability. Although these climatic gradients are commonly used to represent the ‘contemporary climate’, it is worth noting that mechanistic bases underlying variability and availability in particular climatic conditions are not the same (Fine, 2015). On the one hand, water availability and energy input are often associated with productivity hypothesis, which is translated as the increase of energy flow through trophic cascades leading to an increase of populations, and thereby reduction of extinction rates (Wright, 1983;

Evans et al., 2005). On the other hand, climatic stability hypothesis rely on the decrease of extinction rates under stable conditions, which, given enough time, may promote specialization and increase speciation rates (Pianka, 1966; Evans et al., 2005). Although water-energy was important to explain variation in tropical forest snake assemblages, the contemporary climatic stability emerged as the most important factor for both species pools and species composition.

I have also explored an additional interpretation to the role of water availability and energy input in structuring ecological communities. Although these environmental factors are traditionally associated to productivity, it seems that thermoregulatory constraints explain better the distribution of ectotherms than processes related to increase of energy flow through trophic cascades. However, by thermoregulatory constraints I refer not only to the influence of solar energy input but also to the importance of water availability to buffer unfavorable conditions in climate, principally in the tropics where the rule is staying cool (Kearney et al., 2009). Currently, ectotherms are already dependent of behavioral thermoregulation to survive under unfavorable conditions during particular daily hours (very sunny noonday) or annual periods (e.g. dry summers) (Sunday et al., 2014).

Currently, ectotherms are already dependent of behavioral thermoregulation to survive under unfavorable conditions during particular daily hours (very sunny noonday) or annual periods (e.g.

142 dry summers). Therefore, changes in climatological regimes that lead to untypical warmer and drier periods may threaten the survival of tropical forest ectotherm populations.

In conclusion, several ecological and evolutionary hypotheses have been proposed to explain geographic variation in species diversity, and it is difficult for many of them to isolate the potential effects of underlying mechanisms (Fine, 2015). Overall, I encourage future research to explore the synergism among different environmental gradients and take into consideration the distinct mechanisms associated with each factor, particularly broad-scale climatic gradients.

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