Efeitos diretos da temperatura sobre as taxas de especiação não explicam o gradiente latitudinal de diversidade de pequenos mamíferos sul-americanos

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(1)UNIVERSIDADE FEDERAL DE SANTA MARIA CENTRO DE CIÊNCIAS NATURAIS E EXATAS PROGRAMA DE PÓS-GRADUAÇÃO EM BIODIVERSIDADE ANIMAL. Felipe Osmari Cerezer. EFEITOS DIRETOS DA TEMPERATURA SOBRE AS TAXAS DE ESPECIAÇÃO NÃO EXPLICAM O GRADIENTE LATITUDINAL DE DIVERSIDADE DE PEQUENOS MAMÍFEROS SUL-AMERICANOS. Santa Maria, RS 2019.

(2) Felipe Osmari Cerezer. EFEITOS DIRETOS DA TEMPERATURA SOBRE AS TAXAS DE ESPECIAÇÃO NÃO EXPLICAM O GRADIENTE LATITUDINAL DE DIVERSIDADE DE PEQUENOS MAMÍFEROS SUL-AMERICANOS. Dissertação apresentada ao Curso de Pós-Graduação em Biodiversidade Animal, da Universidade Federal de Santa Maria (UFSM, RS), como requisito parcial para obtenção do título de Mestre em Ciências Biológicas – Área Biodiversidade Animal.. Orientador: Prof. Dr. Cristian de Sales Dambros Coorientador: Prof. Dr. Nilton Carlos Cáceres. Santa Maria, RS 2019.

(3) ________________________________________________________ © 2019 Todos os direitos autorais reservados a Felipe Osmari Cerezer. A reprodução de partes ou do todo deste trabalho só poderá ser feita mediante a citação da fonte. Endereço: Rua Prof. Heitor da Graça Fernandes, 362, Apto 401, Bairro Camobi, Santa Maria, RS. CEP: 97105-140 Fone (55) 999800425; E-mail: [email protected].

(4) Felipe Osmari Cerezer. EFEITOS DIRETOS DA TEMPERATURA SOBRE AS TAXAS DE ESPECIAÇÃO NÃO EXPLICAM O GRADIENTE LATITUDINAL DE DIVERSIDADE DE PEQUENOS MAMÍFEROS SUL-AMERICANOS. Dissertação apresentada ao Curso de Pós-Graduação em Biodiversidade Animal, da Universidade Federal de Santa Maria (UFSM, RS), como requisito parcial para obtenção do título de Mestre em Ciências Biológicas – Área Biodiversidade Animal.. Aprovado em 15 de fevereiro de 2019:. __________________________________________________ Cristian de Sales Dambros, Dr. (UFSM) (Presidente/Orientador) __________________________________________________ Thiago F. Rangel, Dr. (UFG). __________________________________________________ Eliécer E. Gutiérrez Calcaño, Dr. (UFSM). Santa Maria, RS 2019.

(5) DEDICATÓRIA. Aos meus pais que não pouparam esforços para que esse sonho se tornasse realidade. Dedico a vocês esse trabalho..

(6) AGRADECIMENTOS. Um desafio tão grande quanto o desenvolvimento desta dissertação é descrever em breves agradecimentos as pessoas que contribuíram para a realização desse trabalho. Talvez, porque os verdadeiros amigos não se resumem a testes estatísticos e não é através de um valor de p que se descobre a real importância das pessoas em nossa trajetória. Enfim, agradeço a todos que direta ou indiretamente colaboraram para o êxito desse trabalho, em particular: Aos professores Cristian S. Dambros e Nilton C. Cáceres pela amizade, paciência, ensinamentos e confiança depositada. À CAPES pela concessão da bolsa de mestrado. Ao Centro de Computação (CLUSTER) da UFG, em especial ao Rhewter pela disponibilidade em resolver os problemas da Cluster. Ao Laboratório de Mastozoologia pelas conversas e parcerias em trabalhos. Ao Laboratório de Ecologia Teórica e Aplicada e o Laboratório de Conservação e Macroecologia Marinha: Anita, Gabriela, Isadora, Carine, Melina, Luiza (infelizmente, fui forçado a colocar teu nome) pela descontração, incentivos e rolês. Saibam que vocês tornaram o andamento desse trabalho muito mais leve. Ao Laboratório de Sistemática, Entomologia e Biogeografia: Andressa, Leandro e Marcelo pelas risadas, assuntos mais estocásticos possíveis e o tradicional café pós RU. Aos meus colegas de mestrado, em especial o Fernando, Amanda e Thaís pelas fofocas, cafés, bebedeiras (sei que tu vai se identificar aqui, Ramón) e parceria durante todo esse tempo. Por fim, mas muito longe de ser menos importante, o imprescindível agradecimento aos familiares, em especial a Cristina pelos mais diversificados “helps”. Aos meus pais João e Nelci, verdadeiros exemplos de bondade e humildade em um mundo egoísta e cruel. À minha irmã Juliane e meu cunhado Maurício pelo carinho e, principalmente, pelas caronas aos domingos. Muito obrigado!.

(7) RESUMO. EFEITOS DIRETOS DA TEMPERATURA SOBRE AS TAXAS DE ESPECIAÇÃO NÃO EXPLICAM O GRADIENTE LATITUDINAL DE DIVERSIDADE DE PEQUENOS MAMÍFEROS SUL-AMERICANOS. AUTOR: Felipe Osmari Cerezer ORIENTADOR: Cristian de Sales Dambros COORIENTADOR: Nilton Carlos Cáceres. Um dos focos centrais em estudos ecológicos é compreender como as espécies estão distribuídas ao longo do espaço e os fatores que governam essa variação. O aumento de diversidade dos polos em direção aos trópicos (Gradiente Latitudinal de Diversidade) é um dos padrões geográficos mais estudados, embora os mecanismos subjacentes ainda permaneçam elusivos. Dezenas de hipóteses têm sido propostas para explicar essa variação latitudinal no número de espécies. A Teoria Metabólica da Ecologia sugere que os efeitos da temperatura sobre processos metabólicos podem explicar os padrões observados na natureza pelo aumento das taxas de especiação em ambientes mais quentes. Em adição, processos estocásticos de especiação, deriva ecológica e dispersão, como proposto pela Teoria Neutra da Biodiversidade, também podem afetar a diversidade de espécies. Entretanto, são poucos os estudos que avaliam de forma mecanística a relação entre diferentes processos ecológicos e evolutivos ao longo de gradientes latitudinais. Neste trabalho, irei avaliar a capacidade de processos metabólicos e neutros em explicar os padrões de riqueza de marsupiais didelfídeos e roedores histricomorfos e miomorfos ao longo do continente sul-americano. Ao final desse trabalho, pretende-se determinar o efeito de processos estocásticos locais e da temperatura sobre as taxas de especiação na riqueza de espécies desses grupos. Palavras-chave: Roedores. Marsupiais. Macroecologia. Neotropical. Teoria Neutra. Teoria Metabólica..

(8) ABSTRACT. DIRECT EFFECTS OF TEMPERATURE ON SPECIATION RATES DO NOT DRIVE THE LATITUDINAL DIVERSITY GRADIENT OF SOUTH AMERICAN SMALL MAMMALS. AUTHOR: Felipe Osmari Cerezer ADVISOR: Cristian de Sales Dambros CO-ADVISOR: Nilton Carlos Cáceres. One of the central focus in ecological studies is to understand how species are distributed throughout space and the factors that govern this variation. The increase of diversity from the poles to the tropics (Latitudinal Diversity Gradient) is one of the most studied geographic patterns, although the underlying mechanisms still remain elusive. Dozens of hypotheses have been proposed to explain this latitudinal variation in the number of species. The Metabolic Theory of Ecology suggests that the effects of temperature on metabolic processes may explain the patterns observed in nature by increasing speciation rates in warmer environments. In addition, stochastic processes of speciation, ecological drift, and dispersal, as proposed by the Neutral Theory of Biodiversity, may also affect species diversity. However, there are few studies that mechanistically evaluate the relationship between different ecological and evolutionary processes along latitudinal gradients. In this study, I will evaluate the capacity of metabolic and neutral processes to explain the richness patterns of didelphid marsupials and hystricomorph and myomorph rodents along the South American continent. At the end of this study, we intend to determine the importance of local stochastic processes and the effect of temperature on speciation rates in the species richness of these groups. Keywords: Rodents. Marsupials. Macroecology. Neotropical. Neutral Theory. Metabolic Theory..

(9) SUMÁRIO INTRODUÇÃO ............................................................................................................ 8 Estrutura da dissertação ................................................................................. 11 REFERÊNCIAS ......................................................................................................... 12 Research paper – Direct effects of temperature on speciation rates do not drive the Latitudinal Diversity Gradient of South American small mammals .............. 15 Abstract .................................................................................................................. 15 1. INTRODUCTION .................................................................................................................. 16 2. METHODS ............................................................................................................................. 18 2.1 Taxonomic groups .................................................................................... 18 2.2 Data acquisition ........................................................................................ 19 2.3 Statistical models ...................................................................................... 20 3. RESULTS ............................................................................................................. 24 4. DISCUSSION ....................................................................................................... 25 5. DATA ACCESSIBILITY ........................................................................................ 28 6. REFERENCES ..................................................................................................... 28 7. FIGURES ............................................................................................................. 35 8. SUPPORTING INFORMATION ............................................................................ 40.

(10) 8. INTRODUÇÃO A variação espacial na riqueza de espécies constitui um dos padrões ecológicos mais conspícuos (HUTCHINSON, 1959), sendo reconhecido que o número de espécies está distribuído de forma heterogênea sobre a superfície do globo (GASTON, 2000). Investigar a variação desses padrões de distribuição e suas causas é um dos focos centrais dos estudos macroecológicos (FIELD et al., 2008). Um dos fenômenos mais ubíquos no que diz respeito à diversidade biológica na terra é o aumento no número de espécies dos polos em direção aos trópicos (PONTARP et al., 2018; HILLEBRAND, 2004), geralmente chamado de Gradiente Latitudinal de Diversidade (GLD). Com poucas exceções (TEDERSOO, 2010; JANZEN, 1981; OWEN e OWEN, 1974), essa tendência latitudinal tem sido registrada em diversos grupos taxonômicos (WILLIG et al., 2003), ambientes (GASTON, 2000) e escalas temporais (MITTELBACH et al., 2007). No entanto, os mecanismos responsáveis por gerar e manter esse padrão ainda são alvos de intensos debates. Diversas hipóteses têm sido propostas para explicar a variação espacial na riqueza de espécies (BROWN, 2014), porém muitas são difíceis de serem testadas empiricamente e apresentam alto grau de colinearidade (WILLIG et al., 2003). Nas últimas décadas, houve um crescente aumento em estudos que avaliam o relacionamento entre riqueza de espécies e variáveis climáticas (FIELD et al., 2008). O clima, tendo a temperatura como variável mais expressiva, tem sido reconhecido como um dos preditores mais fortes da variação espacial de riqueza de espécies (HAWKINS et al., 2003). Um mecanismo proeminente que tenta explicar o relacionamento encontrado entre a diversidade de espécies e o clima é a hipótese de taxas-evolutivas ou clima-especiação (ROHDE, 1992). De acordo com essa hipótese, a disponibilidade de energia no ambiente pode influenciar diretamente as taxas evolutivas, com maiores temperaturas em regiões tropicais proporcionando um aumento nas taxas de especiação (ROHDE, 1992). No entanto, desafios práticos e teóricos têm sido encontrados para investigar como as taxas evolutivas variam em detrimento da disponibilidade de energia no ambiente (EVANS e GASTON, 2005). Através disso, para buscar uma explicação quantitativa mais precisa ao GLD, Brown et al. (2004) propuseram a Teoria Metabólica da Ecologia (TME). A TME destaca que a temperatura do ambiente, através de seus efeitos diretos sobre o metabolismo do organismo, pode afetar os processos de divergência.

(11) 9. genética e especiação (ALLEN e GILLOOLY, 2006). Dessa forma, devido à dependência dos processos metabólicos em relação à temperatura, uma maior riqueza de espécies em regiões tropicais seria o resultado de elevadas taxas de mutação em ambientes mais quentes (ALLEN et al., 2006). Em contrapartida, versões mais recentes da TME destacam que a temperatura pode afetar indiretamente o número de espécies através de efeitos positivos sobre a produtividade (STORCH, 2012), ou seja, o número total de indivíduos em um local pode ser contrastado pela disponibilidade de recursos (STORCH et al., 2018). Assim, locais com um maior número de indivíduos podem suportar mais espécies com populações viáveis (hipótese de mais indivíduos sensu Srivastava and Lawton, 1998), pois as chances de extinções estocásticas são reduzidas (CURRIE et al., 2004). Alguns estudos têm questionado a TME como uma resposta universal ao GLD (HAWKINS et al., 2007a), destacando que a sua capacidade explicativa pode variar em detrimento do grupo taxonômico (HAWKINS et al., 2007b). Devido ao fato de a TME sugerir que a taxa de especiação seja governada por processos metabólicos e que esses processos sejam contrastados pela temperatura do ambiente, é esperado que animais ectotérmicos e endotérmicos respondam de forma distinta à temperatura (BROWN et al., 2004). De fato, maiores taxas de evolução molecular (GILLOOLY et al., 2005) e especiação são observadas com o aumento da temperatura em ectotérmicos (ALLEN et al., 2006). Entretanto, endotérmicos são capazes de manter a temperatura corpórea constante e o metabolismo não está necessariamente associado à temperatura ambiental (STORCH, 2012). Recentemente, tem sido destacado que a riqueza de espécies em um local reflete variações em processos estocásticos de especiação, extinção e dispersão (VELLEND, 2016), tendo a Teoria Neutra da Biodiversidade (TNB) como ferramenta para modelar a importância desses processos na estruturação de comunidades. A TNB de Hubbell (2001) sugere que os padrões de distribuição e abundância não dependem de diferenças adaptativas entre as espécies (ADLER et al., 2007), mas que flutuações aleatórias no tamanho da comunidade (i.e. deriva ecológica) juntamente com especiação e dispersão, são capazes de gerar padrões similares aos observados na natureza (ALONSO, ETIENNE e MCKANE, 2006). Embora seja alvo de constantes críticas por desafiar abordagens baseadas em nicho (CLARK,.

(12) 10. 2012), estudos têm encontrado resultados que corroboram as predições da TNB (CASSEMIRO e PADIAL, 2008; MUNEEPEERAKUL et., 2008). Hipóteses evolutivas ressaltam que o GLD pode ser explicado a partir de um gradiente latitudinal nas taxas de especiação, e que este diminui com o aumento da latitude (ALLEN e GILLOOLY, 2006). No entanto, evidências sugerindo maiores taxas de especiação nos trópicos em detrimento de regiões temperadas são escassas (WEIR e SCHLUTER, 2007), demonstrando que o relacionamento entre as taxas de especiação e o GLD possa ser complexo (SCHLUTER e PENNELL, 2017). Diversos mecanismos têm sido propostos a fim de explicar o porquê as taxas de especiação podem diferir entre regiões tropicais e temperadas (MITTELBACH et al., 2007), tendo parâmetros ambientais (i.e. temperatura e produtividade) como fortes candidatos a essa variação. Dessa forma, utilizar uma abordagem que considera os efeitos integrados do clima sobre as taxas de especiação surge como uma alternativa atraente para explicar os padrões de diversidade de espécies. Seguindo essa lógica, Tittensor e Worm (2016) propuseram um modelo que unifica os princípios de duas teorias chaves em ecologia para explicar os padrões de riqueza de espécies, a Teoria Neutra da Biodiversidade (TNB; Hubbell, 2001) e a Teoria Metabólica da Ecologia (TME; Brown et al., 2004). Os modelos desenvolvidos a partir dessas duas teorias permitem quantificar e testar o efeito de múltiplos processos evolutivos e ecológicos sobre os padrões de diversidade em grandes escalas (TITTENSOR e WORM, 2016). No entanto, ainda não foi testado se processos neutros e metabólicos são capazes de explicar padrões empíricos de diversidade entre diferentes grupos taxonômicos ao longo do gradiente latitudinal. Marsupiais e roedores (pequenos mamíferos não voadores) compreendem o grupo de mamíferos mais diversificado do continente sul-americano. Com uma ampla variação em atributos biológicos (p. ex. massa corporal, metabolismo), pequenos mamíferos representam bons organismos para testar predições acerca dos processos que influenciam suas distribuições. Embora alguns estudos prévios tenham identificado parâmetros ambientais como fortes preditores do número de espécies de roedores sul-americanos (MAESTRI e PATTERSON, 2016; AMORI et al., 2012; TOGNELLI e KELT, 2004), o mecanismo pelo qual tais parâmetros afetam os padrões observados são ainda debatidos. Avaliar a riqueza de espécies de marsupiais e roedores sul-americanos a partir de uma perspectiva que integra a TNB e a TME pode auxiliar a desvendar os mecanismos responsáveis por regular a.

(13) 11. diversidade desses grupos. Portanto, o objetivo desse estudo foi estimar valores de especiação e investigar o efeito da temperatura sobre esse processo em diferentes grupos de pequenos mamíferos sul-americanos. Estrutura da Dissertação Esta dissertação está estruturada em formato Research Paper, conforme as normas da revista Global Ecology and Biogeography..

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(17) 15. Direct effects of temperature on speciation rates do not drive the Latitudinal Diversity Gradient of South American small mammals. 1 2 3 4. Felipe O. Cerezer1*, Nilton C. Cáceres2, Cristian S. Dambros2. 5 6 7. 1. 8 9. 2. 10. Programa de Pós-Graduacão em Biodiversidade Animal, Departamento de Ecologia e Evolução, CCNE, Universidade Federal de Santa Maria, Santa Maria, RS, 97110-970, Brazil Departamento de Ecologia e Evolução, CCNE, Universidade Federal de Santa Maria, Santa Maria, RS, 97110-970, Brazil *Corresponding author: E-mail: [email protected]. 11 12. Abstract. 13. Aim: Broad-scale spatial patterns of species richness have been well documented in several groups.. 14. However, the mechanisms responsible for generating and maintaining spatial species richness. 15. gradients are still controvertial. Here, we investigate the importance of local stochastic processes and. 16. the effect of temperature on speciation rates on the latitudinal gradient of species richness.. 17. Location: South America.. 18. Time period: Present.. 19. Major taxa studied: Marsupials and rodents (non-flying small mammals).. 20. Methods: Using models that integrate both neutral and metabolic processes, we simulate the. 21. dynamics of speciation and ecological drift in the South American continent. Then, the simulated. 22. species richness was compared with empirical patterns using a Bayesian framework.. 23. Results: The simulated species richness by the neutral-metabolic model was strongly correlated with. 24. the observed species richness. Speciation rates estimated by the model vary among groups, with. 25. higher speciation rate for mammals with smaller body size and faster metabolism. However, we found. 26. that temperature has no effect on speciation rates.. 27. Main conclusions: Speciation rates in South American small mammals are not directly associated. 28. with environmental temperature. Thus, our findings suggest that extinction rates, potentially. 29. associated with changes in community size (ecological drift), are likely the major drivers of the. 30. latitudinal variation in species richness.. 31. KEYWORDS. 32. macroecology, marsupials, metabolic theory, neotropics, neutral theory, rodents.

(18) 16 33. 1 | INTRODUCTION. 34. One of the best known ecological patterns is the increase in the number of. 35. species towards the tropics (Hillebrand, 2004; Pontarp et al., 2018), generally. 36. referred to as the Latitudinal Diversity Gradient (LDG). Although there are few. 37. exceptions (Janzen, 1981; Tedersoo & Nara, 2010 ), this latitudinal pattern has been. 38. found in several groups, suggesting the existence of shared ecological and/or. 39. evolutionary processes (Kinlock et al., 2018). However, almost two centuries after the. 40. discovery of LDG, a conclusion with the regard to the main causal explanation. 41. remains elusive (Brown, 2014).. 42. Several hypotheses have been suggested to explain the differences in the. 43. number of species among tropical and temperate regions (Rahbek & Graves, 2001).. 44. Spatial variation in species richness emerges as a consequence of dispersal,. 45. speciation, and extinction processes (Vellend, 2016). Therefore, differences in the. 46. balance of these processes across the latitudinal gradients necessarily explain such. 47. differences in species richness. Although these high-level processes are important,. 48. most hypotheses focus on the simple association of climate (Hawkins et al., 2003) to. 49. the Latitudinal Gradient of Species Richness. However, few studies have shown that. 50. climatic variables could create the observed LDG through changes in the rates of. 51. speciation and extinction (Schluter & Pennell, 2017).. 52. Two prominent mechanisms that attempt to explain the climate-richness. 53. relationship are the climate-speciation (Rohde, 1992) and the energy-richness. 54. (Srivastava & Lawton, 1998) hypotheses. According to the climate-speciation. 55. hypothesis, the available energy in the environment can affect the evolutionary rates. 56. (Evans & Gaston, 2005), that is, higher temperatures in tropical regions would. 57. provide an increase in the speciation rates via direct effects on the metabolism.

(19) 17 58. (Rohde, 1992; Brown, Gillooly, Allen, Savage & West, 2004: Metabolic Theory of. 59. Ecology). Thus, an increase in metabolic processes at higher temperatures can. 60. accelerate mutation rates leading to genetic divergence and speciation (Allen,. 61. Gillooly, Savage & Brown, 2006). In addition, temperature can also indirectly affect. 62. species richness through its positive effects on productivity (Storch, 2012). In this. 63. way, the energy-richness hypothesis (also called the “more individuals hypothesis,. 64. Storch, Bohdalková & Okie, 2018) states that more productive areas support more. 65. individuals and therefore more species since stochastic extinctions are reduced. 66. (Srivastava & Lawton, 1998; Currie et al., 2004).. 67. Endotherms animals are able to keep body temperature constant so that. 68. temperature should not necessarily be associated with metabolism (Storch, 2012). In. 69. fact, some studies have shown that rates of molecular evolution are not associated. 70. with temperature in these groups (Bromham & Cardillo, 2003; Weir & Schluter, 2007;. 71. but see Gillman, Keeling, Ross & Wright, 2009). In contrast, as productivity increases. 72. with temperature along the latitudinal gradient (Luyssaert et al., 2007), the patterns of. 73. richness can be influenced by the productivity gradient and, consequently, by the. 74. variation in the number of individuals (Chu et al., 2018). However, empirical evidence. 75. that the spatial variation in species richness reflects a variation in the number of. 76. individuals has found mixed results (Storch, Bohdalková & Okie, 2018).. 77. Therefore, mechanistic models that incorporate climate effects on speciation. 78. rates (Allen, Gillooly, Savage & Brown , 2006) with metacommunities dynamics. 79. (Hubbell, 2001) may be excellent alternatives to explain macroecological patterns of. 80. species richness. Here, we integrated the neutral and metabolic models (based on. 81. Tittensor & Worm, 2016) into a Bayesian framework to estimate speciation rates and. 82. quantify the effect of temperature on these rates while taking into account the.

(20) 18 83. processes of ecological drift and dispersal. The model was used in three small. 84. mammal groups that greatly differ in body size and metabolic rates (Jones et al.,. 85. 2009). We hypothesized that groups with larger body mass and lower metabolic rates. 86. would have lower speciation rates (1). However, as metabolic rates are independent. 87. of environmental temperature, we also hypothesized that observed variation in. 88. species richness across the latitudinal gradient should not result from the. 89. temperature-driven speciation rates (2).. 90 91. 2 | METHODS. 92 93. 2.1 | Taxonomic groups. 94. Marsupials and rodents represent two of the most diverse orders of South. 95. American non-volant mammals. Approximately 90% of didelphimorphid marsupials. 96. species on the American continent are endemic to South America (ca. 90 species;. 97. see Jansa, Barker & Voss, 2014). The South American rodent fauna is mostly. 98. represented by the suborders Hystricomorpha (ca. 250 species) and Myomorpha (ca.. 99. 600 species) (Patton, Pardiñas, & D’Elía, 2015). Although they belong to the same. 100. order, hystricomorph and myomorph rodents differ markedly in relation to some. 101. biological attributes directly related to their metabolism. Hystricomorph rodents have. 102. considerably larger body mass than myomorph rodents (μ = 1362.80 g, n = 146 spp.. 103. vs μ = 55.51 g, n = 235 spp., respectively; Jones et al., 2009). Consequently,. 104. hystricomorph rodents show a much lower mass-specific metabolic rate than the. 105. myomorph rodents (μ = 0.64 mLO2h-1g-1, n = 24 spp.; μ = 1.48 mLO2h-1g-1, n = 19. 106. spp., respectively; Jones et al., 2009). With respect to these characteristics, didelphid. 107. marsupials are situated between these two groups with greater body mass (μ =. 108. 211.16g, n = 60 spp) and lower mass-specific metabolic rate (μ = 0.66 mLO2h-1g-1, n.

(21) 19 109. = 11 spp) than myomorph rodents (Jones et al., 2009). These previously highlighted. 110. differences among such groups are directly linked to the predictions of this study.. 111 112. 2.2 | Data acquisition. 113. The richness of didelphid marsupials (n = 88 species), hystricomorph (n = 202. 114. species), and myomorph (n = 323 species) rodents were quantified along the. 115. continent of South America through geographic distribution maps from the. 116. International Union for Conservation Nature (IUCN 2017). These distribution maps. 117. were mapped within 2° x 2° (ca. 220 km at the equator) cells that cover the entire. 118. continent, totaling 385 cells. Thus, a binary matrix with the presence (1) and absence. 119. (0) values in each cell was constructed for each species. Finally, the richness of. 120. marsupials and rodents in each of the cells was calculated from the sum of all. 121. species occurring in that cell. We excluded cells with zero richness from the. 122. analyses. The definition of this spatial resolution (i.e. 2° x 2°) was employed with the. 123. intention of making the model computationally easier to be treated.. 124. Range maps are based on estimatives of species occurrences and might. 125. under or overestimate the presence of species (Hurlbert & White, 2005). However,. 126. these imperfections are likely to be more important at finer geographic scales and for. 127. individual species. The extraction of occurrence data from range maps has been. 128. widely used (Belmaker & Jetz, 2015; Marin et al., 2018), and community level. 129. patterns (e.g. species richness) obtained in our study were extremely similar to. 130. patterns reported in previous studies (Maestri & Patterson, 2016).. 131. Abundance data at large spatial scales are scarce, therefore we estimated. 132. local community size by calculating the fraction of a cell that is covered by the. 133. geographic distribution of each individual species. Each cell was divided into 100. 134. sub-cells and the number of sub-cells filled by the distribution of each individual.

(22) 20 135. species was counted. The sum of sub-cells of all species was used as a proxy for. 136. abundance. As range size and abundance are strongly correlated (Gaston,. 137. Blackburn & Lawton, 1997), the estimate of range within cell is likely to be. 138. representative of local abundance and similar procedures have been used recently. 139. (Keil et al., 2018) (see Figure S1).. 140. The annual mean temperature variable was obtained from the WorldClim. 141. database (Hijmans, Cameron, Parra, Jones & Jarvis, 2005), in a resolution of 2.5 arc-. 142. min (ca. 4.5 km at the equator). Thus, to evaluate the effects of temperature on. 143. speciation rates, the temperature values were extracted for each of the cells through. 144. geographic coordinates of the centroid.. 145 146. 2.3 | Statistical models. 147. We have implemented mechanistic models that simultaneously incorporate. 148. stochastic processes of speciation, ecological drift, and dispersal (neutral processes). 149. and the effect of temperature on speciation rate (metabolic processes) (Tittensor &. 150. Worm, 2016). The model starts with a derivation of the Neutral Theory of Biodiversity. 151. (Hubbell, 2001). In this model, each cell of South America represents a local. 152. community. Each local community, composed of Ji individuals of different species, is. 153. linked to all other communities in the domain Jj, constituting a spatially explicit. 154. metacommunity model (i.e., local communities are connected via dispersal). Based. 155. on this spatial context of communities, the dynamics of this model take place as. 156. follows. The model begins with local communities of size Ji. Ji denoting a vector. 157. containing the estimated community size in each cell for all species of a given group. 158. (e.g. marsupials). Next, in each generation, all individuals die and are synchronically. 159. replaced, i.e., non-overlapping generations (Wright-Fisher model). This model has. 160. been widely used due to its computational efficiency and because it generates similar.

(23) 21 161. predictions to models that use overlapping generation (Blythe & McKane, 2007;. 162. Etienne & Alonso, 2007). The vacant space in the community is occupied by (1) a. 163. new species (with speciation probability v, Figure 1), generated from an individual. 164. that underwent point speciation or (2) an offspring of a parental individual in the. 165. metacommunity (with replacement probability 1 – v, Fig. 1). In the later, the. 166. replacement is either from an offspring of an individual from a neighboring community. 167. (with dispersal probability mij) or an offspring of a randomly chosen individual from. 168. the local community (with dispersal probability 1 - mij). The probability of a. 169. descendant of the community j occupying the vacant space in community i (mij) is. 170. determined by the following equation:. 171. mij =. 172. e − zdij N. e. Equation (1). − zd ij. j =1. 173 174. where z is a constant defining the rate of the exponential decay in dispersal. 175. with the increase in geographic distance (d) between pairs of cells. N represents the. 176. total number of cells over the entire South American domain.. 177. It is important to emphasize two important assumptions of the model. First,. 178. speciation, births, deaths, and dispersal are equally likely for all individuals in a. 179. community, regardless of species identity (Ecological Equivalence; Hubbell, 2001).. 180. Second, communities are saturated in number of individuals (not species), that is, a. 181. given cell can support a fixed number of individuals (zero-sum dynamics; Hubbell. 182. 2001)..

(24) 22 183. Different from the original Hubbell’s (2001) model, in which speciation rates. 184. are equally likely to all individuals in all cells, speciation rate in our models depends. 185. on spatial location and is dependent on environmental temperature (Figure 1), as. 186. hypothesized by the Metabolic Theory of Ecology (Brown, Gillooly, Allen, Savage &. 187. West, 2004). The effect of temperature on speciation rate followed the equation:. 188. − bE 189. vcell i =. vbe. KT i. Equation (2). − bE. min(e. KT i ). 190 191. where vcelli represents the temperature-dependent speciation rate of. 192. individuals in the local community, and vb represents the basal speciation rate. The. 193. effect of temperature on speciation rate is given by the Boltzmann factor: e-E/KT,. 194. where E is the activation energy (c. 0.65 eV for respiration-limited organisms, such as. 195. animals), K is Boltzmann's constant (8.617 x 10-7 eV K-1) and Ti is the environmental. 196. temperature (degrees Kelvin) in the focal cell. A modification of the model proposed. 197. by Tittensor & Worm (2016) was the addition of a parameter (b) to the equation that. 198. designates the intensity of the temperature effect on speciation rate. The neutral-. 199. metabolic dynamics reached equilibrium after 100 generations, but was run for. 200. additional 50 generations (150 generations in total), considering one generation the. 201. time required to replace all individuals in the domain.. 202. We used the pattern-oriented modeling approach (Wiegand, Jeltsch, Hanski &. 203. Volker, 2003; Grimm & Railsback, 2012) to explore the sample space of parameters. 204. and evaluate the model explanatory power. Because neutral models are ergotic. 205. Markovian processes (equilibrium solution does not depend on the initial conditions),. 206. we started all communities from the observed distribution of species, and then.

(25) 23 207. estimated the parameters that reduced deviations from these conditions over time.. 208. This procedure greatly improved computational performance, reducing time required. 209. to run the models.. 210. Parameter estimation with neutral and metabolic effects was explored using a. 211. hierarchical Bayesian model and the posterior probabilities of the parameters were. 212. obtained using grid-approximation. We defined a range of different combinations of. 213. values for the basal speciation rate (vb), the parameter defining the effect of. 214. temperature on speciation rate (b) and the dispersal coefficient (z). Vb ranging from. 215. zero (no speciation) to one (all individuals speciate). The parameter b can. 216. continuously vary from zero (no association with temperature; equal speciation rate. 217. across all cells) to one (association with temperature, speciation rate vary as a. 218. function of the local temperature). The z values chosen define the probability of. 219. dispersal, which includes strong dispersal limitation (independent communities) to a. 220. weak dispersal limitation (a single large community).. 221. We used each combination of parameters to generate predicted values for. 222. species richness in each cell and estimated the probability of the parameters. 223. (posterior probability) to generate the observed species richness. The observed. 224. species richness was assumed to be a Poisson random variable with mean λ,. 225. defined as the predicted species richness averaged over 100 simulations for a given. 226. combination of parameters. We used flat priors to all parameters and converted. 227. Likelihood values into posterior probabilities using the equation:. 228 229. Pr ( Parm | data ). =. e Li − max( L ) n. e i =1. 230. Li − max( L ). Equation (3).

(26) 24 231 232. where Parm represents a given combination of parameters and L is the loglikelihood.. 233. All these procedures were done in the R environment, version 3.3.1, through. 234. the packages vegan (Oksanen et al., 2016), maptools (Bivand & Lewin-Koh, 2017). 235. and raster (Hijmans & van Etten, 2014).. 236 237. 3 | RESULTS. 238. The observed richness of South American marsupials and rodents was higher. 239. at tropical latitudes (Figure S2). In general, all groups exhibited greater peaks of. 240. richness in lowland Amazon and Atlantic rainforests (Figure 2a; 2d; 2g) and the. 241. tropical region of the Andes (from Colombia to the north of Argentina).. 242. In spite the high peaks of species richness in tropical regions, values were. 243. lower than expected from the neutral-metabolic model (i.e. overestimated) in lowland. 244. Amazon and in the Atlantic forest portions, whereas the model underestimated. 245. species richness for all groups in the Andes (Figure 2c; 2f; 2i). Despite these. 246. differences, the observed species richness was strongly correlated to predictions by. 247. the neutral-metabolic model for all groups (Figure 2b; 2e; 2h), although the predictive. 248. power was weaker for myomorph rodents (Figure S3).. 249. Basal speciation rate was lower for hystricomorph rodents and similar between. 250. didelphid marsupials and myomorph rodents (Figure S4). However, there was a. 251. weak/nonexistent effect of temperature on speciation rates for all groups (Figure 3–. 252. 5). Although there is a high probability that dispersal is limited at the continental scale. 253. (Figure S5), we were not able to distinguish dispersal rates (z) among the three small. 254. mammal groups..

(27) 25 255. The neutral-metabolic model explained a large portion of the variation of. 256. species richness for didelphid marsupials (r² = 0.81), hystricomorph rodents (r² =. 257. 0.88), and myomorph rodents (r² = 0.53).. 258 259. 4 | DISCUSSION. 260. Based on differences of biological attributes (i.e. body mass and metabolism). 261. between groups of small mammals, we hypothesized that basal speciation rates. 262. would be distinct among them. However, due to the ability to regulate body. 263. temperature, we also hypothesized that the environmental temperature does not. 264. directly modulate the speciation rates in the focal groups. We found higher basal. 265. speciation rate for groups with smaller body mass and faster metabolism (Figure S4).. 266. Furthermore, as predicted, our results suggest that speciation rates are not directly. 267. associated with environmental temperature.. 268. Despite the strong predictive capacity of the neutral-metabolic model (Figure. 269. S3), were found patterns of residual variation (observed minus predicted species. 270. richness) not explained by the model that are congruent among groups. In general,. 271. the neutral-metabolic model underestimated species richness along the Andean. 272. region (Figure 2c; 2f; 2i). By establishing barriers to dispersal, isolating populations,. 273. and providing a variety of montane habitats (Ribas, Moyle, Miyaki & Cracraft, 2007;. 274. Antonelli, Nylander, Persson & Sanmartín, 2009; Mutke, Jacobs, Meyers, Henning &. 275. Weigend, 2014), the Andes have been recognized an important agent shaping. 276. patterns of species diversity in South American (Matthew et al., 2008; Rangel et al.,. 277. 2018). Thus, a lower estimated richness for this region may be caused by the lack of. 278. topographical heterogeneity in our model (Rangel et al., 2018). Although. 279. underestimated richness along the Andes is evident for all groups, it is important to.

(28) 26 280. note that this underestimation was stronger for myomorph rodents. This may be a. 281. consequence of the limited dispersal capacity (Schloss, Nuñez & Lawler, 2012) and. 282. relativelly high basal speciation rate (Figure S4) of the group, which cause high. 283. endemicity in this region (Caviedes & Iriarte, 1989; do Prado et al., 2015).. 284. Although differences in basal speciation rates could explain the higher. 285. richness of myomorph rodents, the pattern was the opposite for didelphid marsupials. 286. and hystricomorph rodents—i.e., higher observed richness for the group with lower. 287. basal speciation rate (see Figure S4). Because species diversity is afected by the. 288. balance between speciation and extinction (Mittelbach et al., 2007), the higher. 289. richness of hystricomorph could also be created by differences in extinction rates via. 290. ecological drift. Metacommunities with a larger number of individuals are less prone. 291. to stochastic extinctions (ecological drift; Orrock & Watling, 2010), as in the case of. 292. hystricomorphs (Figure S1). In this study, didelphid marsupials exhibited a smaller. 293. metacommunity size than hystricomorph rodents (Figure S1); hence, didelphids. 294. might be more susceptible to events of stochastic extinctions (see Gilbert & Levine,. 295. 2017). Indeed, evidence suggests an important role of extinctions in the evolutionary. 296. history of didelphid marsupials (Jansa, Barker & Voss, 2014). Thus, it is likely that. 297. differences in extinction rates (by ecological drift), not just speciation, may explain. 298. disparities in species richness between these small mammals groups.. 299. Changes in speciation and extinction rates along environmental gradients may. 300. explain spatial variations in species richness (Schluter & Pennell, 2017), such as. 301. LDG. It has been proposed that speciation rates are faster in hot environments. 302. (speciation-climate hypothesis sensu Rohde, 1992). This hypothesis is based on the. 303. idea that in warmer regions (i.e. tropics) mutation rates are higher (Gillooly, Allen,. 304. West & Brown, 2005), as a consequence of the increase of metabolic processes..

(29) 27 305. From a scenario in wich the energy flow regulates microevolution rates, temperature. 306. can affect speciation rates and generate greater diversity of species in the tropics. 307. (Brown, Gillooly, Allen, Savage & West, 2004). However, our results suggests that 1). 308. species richness patterns emerge even when speciation rates are not affected by. 309. environmental temperature (Figure 3-5) and 2) that temperature is unlikely to be. 310. directly associated with speciation rates in small mammals.. 311. Although temperature was not associated to speciation rates, we observed a. 312. strong correlation between environmental temperature and community size (Figure. 313. S6) and the effects of ecological drift on community size explained between 53%. 314. (myomorphs) and 88% (hystricomorphs) of the LDG (Figure S3). Our results support. 315. more recent versions of the Metabolic Theory of Ecology, which argue that. 316. temperature indirectly affects species richness through its effects on primary. 317. productivity (Allen, Gillooly & Brown, 2007; Chu et al., 2018). Warmer and more. 318. productive areas support larger number of individuals per species (more-individual. 319. hypothesis), reducing the risks of stochastic extinctions (Currie et al., 2004; Storch,. 320. Bohdalková & Okie, 2018). Therefore, this result suggests that temperature may be. 321. influencing the richness of these groups through a mechanism linked to the number. 322. of individuals.. 323. Numerous potential hypotheses have been proposed to explain latitudinal. 324. variation in species richness (Brown, 2014), with temperature being considered the. 325. main determinant of diversity at large geographic scales. However, a plethora of. 326. empirical studies evaluate the richness-temperature relationship using correlative. 327. models (Evans, Warren & Gaston, 2005), without considering the direct/indirect. 328. mechanistic links that generate and maintain species diversity at broad scales. 329. (Pontarp et al., 2018). By the use of a mechanistic model combining the effect of.

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(37) 35. 7. FIGURES. Figure 1 Illustration of neutral-metabolic model dynamics in two cells of South America. Each cell contains a fixed number of individuals of different species. At each step of the simulation, all the individuals on the cell are selected to die (non-overlapping model). The spaces left by the death of these individuals may be occupied either by (a) an offspring of a parental from the local community (with probability 1 - mij) or by an individual that dispersed from the metacommunity (with probability m ij, see Eq. 1) or (b) by an individual of a new specie that has undergone speciation (with probabilit vb). In our model, this probability of speciation will be modeled as a function of local temperature (T i), as predicted by the metabolic equation (Eq. 2). Symbols represent hypothetical species of the community. Symbols in red represent possible substitution scenarios after the death of an individual. The figure does not represent the non-overlapping model, and it only shows the substitution of a single individual for simplicity. Si represents the observed richness in the focal cell, vb the basal speciation rate, b the effects of temperature on speciation rates, z is the exponential decay rate in dispersal with geographic distance, D is a distance matrix, Ni is the focal cell of the South American domain, and Ti is the temperature of the focal cell..

(38) 36. Figure 2 Observed species richness, predicted species richness, and residuals for South American small mammals. Columns represent species richness observed (a, d, and g), predicted by the neutralmetabolic model (b, e, and h) and residuals (difference between observed and predicted: c, f, and i) for the groups of mammals studied here: didelphid marsupials (a-c), hystricomorph rodents (d-f) and myomorph rodents (g-i)..

(39) 37. Figure 3 Probability of speciation rates and the effect of temperature on speciation given the latitudinal pattern of small mammal species richness. (a) Three-dimensional plot indicating the Likelihood values of the interaction between the parameters basal speciation rate (vb) and temperature effects on speciation rate (b) in didelphid marsupials. (b) The region of higher posterior probability predicted by the model for the parameter basal speciation rate (vb). (c) The region of higher posterior probability predicted by the model for the parameter temperature effects on speciation rate (b). In b and c log-likelihood values were converted into a posterior probabilities (see Eq. 3)..

(40) 38. Figure 4 Probability of speciation rates and the effect of temperature on speciation given the latitudinal pattern of small mammal species richness. (a) Three-dimensional plot indicating the Likelihood values of the interaction between the parameters basal speciation rate (vb) and temperature effects on speciation rate (b) in hystricomorph rodents. (b) The region of higher posterior probability predicted by the model for the parameter basal speciation rate (vb). (c) Region of higher posterior probability predicted by the model for the parameter temperature effects on speciation rate (b). In b and c log-likelihood values were converted into a posterior probability (see Eq. 3)..

(41) 39. Figure 5 Probability of speciation rates and the effect of temperature on speciation given the latitudinal pattern of small mammal species richness. (a) Three-dimensional plot indicating the Likelihood values of the interaction between the parameters basal speciation rate (vb) and temperature effects on speciation rate (b) in myomorph rodents. (b) The region of higher posterior probability predicted by the model for the parameter basal speciation rate (vb). (c) Region of higher posterior probability predicted by the model for the parameter temperature effects on speciation rate (b). In b and c log-likelihood values were converted into a posterior probability (see Eq. 3)..

(42) 40. 8. SUPPORTING INFORMATION. Figure S1 Measure used as a proxy for abundance of species (for more details see the methods section). Estimated community size for (a) didelphid marsupials, (b) hystricomorph rodents, (c) myomorph rodents..

(43) 41. Figure S2 The relationship among observed species richness and latitude for small mammals. (a) Didelphid marsupials, (b) hystricomorph rodents, (c) myomorph rodents. Each point represents a map cell..

(44) 42. Figure S3 Regression fit between observed and predicted species richness for small mammals. (a) Didelphid marsupials (R-Square = 0.81), (b) hystricomorph rodents (R-Square = 0.88), (c) myomorph rodents (R-Square = 0.53). Each point represents a map cell..

(45) 43. Figure S4 Basal speciation rates (vb) estimated by the neutral-metabolic model for the different groups of small mammals. Bars indicate the regions with the higher posterior probability for this parameter..

(46) 44. Figure S5 Exponential decay of the dispersal with the geographical distance between pairs of cells for small mammals. (a) Didelphid marsupials, (b) hystricomorph rodents, (c) myomorph rodents. Dashed line represents the boundaries between neighboring cells. Note that although there is a strong dispersal limitation, it is not possible to discriminate accurately differences of this process between the groups..

(47) 45. Figure S6 Relationship between community size and temperature for small mammals. (a) Didelphid marsupials (R-Square = 0.30), (b) hystricomorph rodents (R-Square = 0.34), (c) myomorph rodents (R-Square = 0.10). Each point represents a map cell..

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