Variabilidade Acústica E Respostas Evolutivas a Diferentes Pressões Seletivas No Canto De Anúncio De Anfíbios

UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE

CENTRO DE BIOCIÊNCIAS

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA

TESE DE DOUTORADO

VARIABILIDADE ACÚSTICA E RESPOSTAS EVOLUTIVAS A DIFERENTES PRESSÕES SELETIVAS NO CANTO DE ANÚNCIO DE ANFÍBIOS

David Lucas Röhr

Natal-RN

Maio/2015

David Lucas Röhr

VARIABILIDADE ACÚSTICA E RESPOSTAS EVOLUTIVAS A DIFERENTES PRESSÕES SELETIVAS NO CANTO DE ANÚNCIO DE ANFÍBIOS

Tese apresentada à Coordenação do Programa de Pós-Graduação em Ecologia, da Universidade Federal do Rio Grande do Norte, em cumprimento às exigências para obtenção do grau

de Doutor.

Orientador: Prof. Dr. Adrian Antonio Garda

Natal-RN

Maio/2015

Catalogação da Publicação na Fonte. UFRN / Biblioteca Setorial do Centro de Biociências

Röhr, David Lucas. Variabilidade acústica e respostas evolutivas a diferentes pressões seletivas no canto de anúncio de anfíbios. / David Lucas Röhr. – Natal, RN, 2015.

158 f.: il.

Orientador: Prof. Dr. Adrian Antonio Garda.

Tese (Doutorado) – Universidade Federal do Rio Grande do Norte. Centro de Biociências. Programa de Pós-Graduação em Ecologia.

1. Anura. – Tese. 2. Comunicação acústica. – Tese. 3. Canto de anúncio. – Tese. I. Garda, Adrian Antonio. II. Universidade Federal do Rio Grande do Norte. III. Título.

RN/UF/BSE-CB CDU 567.8

BANCA EXAMINADORA

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Dr. Adrian Antonio Garda – Universidade Federal do Rio Grande do Norte/Presidente – Orientador

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Dr. Carlos Roberto Sorensen Dutra da Fonseca – Universidade Federal do Rio Grande do Norte/ Membro Interno

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Dr. Márcio Zikán Cardoso – Universidade Federal do Rio Grande do Norte/ Membro Interno

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Dr. Carlos Barros de Araújo - Universidade Federal da Paraíba

Membro Externo

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Dr. Marcelo Felgueiras Napoli - Universidade Federal da Bahia

Membro Externo

AGRADECIMENTOS

Gostaria de começar agradecendo à minha família, devo tudo que sou a eles. Meu pai, Ferdinand Röhr, que sempre foi meu maior exemplo, por todos os conselhos muito sensatos (deveria ter seguido-os mais), por todo carinho e apoio em todas as fases da minha vida, especialmente durante o doutorado. Minha mãe, Gunde Schneider, por todo amor e cuidado que sempre teve por mim e o grande apoio, principalmente nos momentos de dificuldade. Minha irmã, Lucia Röhr, por ser minha melhor amiga e uma grande parceira na vida, juntamente com meu sobrinho, João Lucas, que tá pra nascer a qualquer momento e já o amo muito. A David Lemos, meu cunhado e amigo.

Gostaria também de agradecer fortemente ao meu orientador, Adrian Garda, e à minha co-orientadora, Flora Juncá, por todos os ensinamentos sobre bioacústica, herpetologia e ciência em geral, além de todo o apoio e amizade.

Agradeço muito ao meu grande amigo Gustavo Paterno. A convivência com ele durante esse doutorado foi fundamental para a elaboração desta tese. As muitas discussões sobre ciência, estudos de xadrez e a ajuda direta na confecção da tese, além de sua amizade e companheirismo, foram inestimáveis para minha vida em Natal.

Agradeço muito também ao Felipe Camurugi, sua amizade e sua disposição em me ajudar foram muito especiais pra mim.

Agradeço ao Marcelo Gehara e ao Pablo Martinez, pela grande contribuição em dois diferentes capítulos desta tese.

Agradeço a todos os bons amigos que fiz em Natal, especialmente todos com quem morei na república Tanquetão, vocês todos fizeram minha estadia nesta cidade muito feliz, me sentindo fazer parte de uma verdadeira família: Laura Fernandez, Gustavo Paterno; Anna Santos, Natália Pires, Andree Kimber, Nicolas Sebastian, Lucas Viegas e Marina Fagundes.

Agradeço a todos do Laboratório de Anfíbios e Repteis da UFRN, em especial, Felipe Camurugi, Vinicius São Pedro, Eliana Faria, Marcelo Gehara, Jéssica Fernanda,

Felipe Medeiros, Diego Santana, Sara Mângia, Emanuel Fonseca, Flávia Mól Lanna, Adrian Garda, Marília Lion, Alan Felipe e Thiago Pereira, que se tornaram bons amigos.

Agradeço a todos que foram a campo comigo, foram tantos que minha memória ridícula não permite listar todos.

Aos meus amigos do C.R.A, especialmente Bruno Caxias, Fausto Luiz, Gustavo Galvão, Mário Jarbas, Rodrigo Lapa, Cláudio Leandro, Leo Vidal, Rodrigo Almeida e Ricardo Quirino.

Agradeço a diversas pessoas que passaram por minha vida em diferentes fases e me tornaram uma pessoa melhor: Antônio Mattos, Washington Aarão, Alana Araújo, Denise Bacelar, Bruno Pilatus, Gustavo Paterno, Arturo Escobar, Vinícius São Pedro, Eliana Faria, Karol Marcal, Mario Jarbas, Juliana Fortes, Igor Alexandre, Welber Pina, Thiago Nilo e Thiago Mallman.

Agradeço a CAPES pela bolsa, ao CNPq por financiamento das coletas do segundo capítulo e ao projeto Sisbiota Herpeto-Helmintos por financiar as coletas no Amapá do primeiro capítulo.

Por último, gostaria de fazer um agradecimento especial à Alana Araújo, que há quatro anos topou o desafio de enfrentar esse doutorado junto comigo e, apesar da distância física, na maior parte do tempo, foi minha maior companheira durante todos esses anos. Sua importância pra mim é imensurável.

SUMÁRIO

Introdução Geral ...... 01 Referências Introdução Geral...... 04 Capítulo 01 - Variability in Anuran Advertisement Call: a Multi-level Study with the Genus Phyllomedusa (Hylidae: Anura)…………………… 07 Introdução...... 08 Material e Métodos...... 10 Resultados...... 12 Discussão...... 14 Referências Bibliográficas...... 17 Figuras...... 21 Tabelas...... 23 Material Suplementar...... 27 Capítulo 02 - Habitat Depended Variation in the Advertisement Call of Phyllomedusa nordestina (HYLIDAE: ANURA)...... 37 Introdução...... 38 Material e Métodos...... 41 Resultados...... 44 Discussão...... 45 Referências Bibliográficas...... 48 Figuras...... 52 Tabelas...... 55 Material Suplementar...... 57 Capítulo 03 - Background Noise as a Selective Pressure: Stream-breeding Anurans Call at Higher Frequencies...... 63 Introdução...... 65 Material e Métodos...... 66 Resultados...... 67 Discussão...... 68 Referências Bibliográficas...... 72 Tabelas...... 75 Figuras...... 76 Material Suplementar...... 77 Apêndice...... 89

Introdução Geral

Sinais reprodutivos podem ter um papel fundamental no processo de especiação, uma vez que isolamento reprodutivo pode ocorrer mediante variações na estrutura destes sinais e nas preferências dos possíveis parceiros sexuais (Turelli et al., 2001). A evolução destes sinais pode ser influenciada por uma gama de forças evolutivas, como seleção natural, seleção sexual e processos estocásticos (Erdtmann e Amézquita, 2009). Sinais acústicos são o principal meio de comunicação da maioria das espécies de anuros (Gerhardt e Huber, 2002). Dentre os diferentes tipos de vocalizações conhecidos para o grupo, o canto de anúncio, cuja principal função é atrair as fêmeas, tem sido o mais estudado (Gerhardt, 1994).

A evolução de vocalizações animais pode ser influenciada por processos estocásticos ou adaptativos que atuam diretamente no processo de especiação. A importância dos processos estocásticos em mudanças não-adaptativas nas vocalizações é atribuída à deriva genética (Gerhardt e Huber, 2002; Erdtmann e Amézquita, 2009) e à mudanças pleiotrópicas acompanhadas indiretamente por mudanças morfológicas (Cocroft e Ryan, 1995; Podos, 2001; Seddon, 2005; Erdtmann e Amézquita, 2009). Dentre os processos adaptativos, a importância do reconhecimento específico (Pfenning, 1998) e da seleção sexual (Ryan e Rand, 1993; Carson, 2003; Boul et al., 2007) têm sido os mais estudados.

Por outro lado, diferentes pressões impostas pelo ambiente onde a comunicação ocorre podem promover a evolução dos cantos, em distintas direções e intensidades (Boughman, 2002). Por exemplo, a presença de predadores (Tuttle e Ryan, 1981) ou parasitas (Bernal et al., 2006) no ambiente, os quais se utilizam dos sinais acústicos de suas presas para sua detecção/localização, podem exercer uma forte pressão seletiva sobre estes sinais.

Uma pressão seletiva pode favorecer sinais que minimizem a interferência do ruído ambiente oriundo dos sinais acústicos de outros animais ou de fatores abióticos (Wollerman e Wiley, 2002; Feng et al., 2006; Preininger et al., 2007). Na verdade, o ruído ambiente é um dos principais limitantes da comunicação acústica (Brumm e Slabberkoorn, 2005). Ambientes lóticos como riachos, que são utilizados por diversas espécies de anuros para reprodução, produzem um ruído ambiente constante que muitas vezes pode alcançar altas intensidades e apresenta predominância de energia em

1 espectros graves (Goutte et al., 2013) que se sobrepõe ao canto relativamente grave dos anuros (Wells, 2007). Desta forma, pode se esperar que anuros que vocalizem perto destes ambientes utilizem frequências mais altas com menor sobreposição espectral com o barulho ambiente. De fato, estudos mostram que a estrutura de comunidade de anuros próximos a riachos está associada à intensidade do barulho da água corrente (Goutte et al., 2013) e que a utilização de cantos mais agudos aumenta a capacidade de detecção e localização destes sinais nesses ambientes (Boonman e Kurniati, 2011). Além disso, todas as espécies de anuros que utilizam de comunicação ultrassônica cantam perto de corredeiras (Narins et al., 2004; Feng et al., 2006; Arch et al., 2008).

A eficiência da propagação do sinal acústico também está sob intensa pressão seletiva. Machos com vocalizações de maior alcance aumentam sua chance de atrair fêmeas. Desse modo, sinais acústicos que sofram o menor grau de atenuação (perda de energia) e degradação (perda de fidelidade) (Kime et al.,2000; Castellano et al., 2003) tendem a ser selecionados. A estrutura da vegetação pode ter um papel importante na propagação do som, uma vez que ondas sonoras se propagam com eficiências distintas em vegetações abertas ou fechadas. Assim, determinadas características acústicas dos sinais podem ser mais propícias à propagação em tipos específicos de ambientes (Wiley e Richards 1978; Kime et al., 2000).

Desta forma, é esperado que animais, especialmente naqueles grupos onde a comunicação acústica é amplamente difundida (ex. aves, mamíferos e anuros), modulem a estrutura de suas vocalizações para otimizar sua propagação, o que é conhecido como a ―Hipótese da Adaptação Acústica‖ (Morton, 1975; Rothstein e Fleischer, 1987; Ey e Fischer, 2009). Áreas de vegetação aberta apresentam características acústicas muito diferentes de áreas com vegetação densa (Wiley e Richards, 1978). Hábitats fechados tendem a impor maiores pressões seletivas sobre a sinalização acústica (Ey e Fischer, 2009), uma vez que nestes ambientes a comunicação visual é mais restrita, as condições acústicas são mais estáveis (Morton, 1975; Brown e Handford, 2000) e a reverberação e absorção das ondas sonoras são mais intensas (Wiley e Richards, 1978).

Existem várias predições sobre como as características dos sinais acústicos devem se comportar em hábitats abertos versus fechados. Como áreas de vegetação fechada são ambientes de propagação mais estáveis em relação a áreas abertas (maiores

2 flutuações de vento e temperatura), acredita-se que nestes hábitats as vocalizações devam apresentar um maior grau de estereotipia em relação aos hábitats abertos. Além disto, há várias características sonoras, relacionadas a diversos parâmetros acústicos temporais e espectrais, que favorecem uma propagação eficiente nestes dois ambientes. Por exemplo, espera-se que vocalizações em ambientes fechados apresentem uma maior duração, menor taxa de repetição de subelementos do canto, menor número de elementos de frequência modulados, frequências máximas e mínimas mais baixas, menores frequências média e dominante, além de bandas de frequências mais restritas. Dos estudos feitos até hoje com anuros, mamíferos e aves, parte corroboram algumas destas tendências (revisado em Ey e Fischer, 2009).

Existem basicamente duas metodologias para estudar a influência do ambiente nos sinais acústicos de diferentes animais: uma abordagem comparativa e outra experimental (Castellano et al., 2003). Em estudos de base comparativa, sinais acústicos de diferentes espécies dentro de um grupo monofilético são comparados controlando-se os possíveis efeitos da filogenia nas diferenças observadas (ver Ey e Fisher, 2009 e Peters e Peters, 2010). Com isso, é possível determinar se as correlações observadas são decorrentes da ancestralidade comum ou da evolução convergente em resposta a pressões seletivas semelhantes (Cosens e Falls, 1984; Wiley, 1991; Badyaey e Leaf, 1997). Em estudos de base experimental, para testar o efeito das diferenças ambientas e as variações nos sinais acústicos os pesquisadores podem testar de forma direta a eficiência de propagações em diferentes hábitats e analisar a quantidade de atenuação e/ou degradação com o aumento da distância (Ryan et al., 1990; Penna e Solis, 1998; Kime et al., 2000; Röhr e Juncá, 2013).

Estudos realizados com aves e primatas demonstram que, para diferentes grupos, uma propagação eficiente está sujeita a uma pressão seletiva significativa exercida pelo meio de propagação (e.g. Wiley, 1991; Brown et al., 1995; Badyaey e Leaf, 1997; Patten et al., 2004; Seddon, 2005; Kirschel et al., 2009; Ripmeester et al., 2010). Existem menos estudos realizados com anuros e os resultados não são conclusivos (Zimmerman, 1983; Ryan et al., 1990; Penna e Solis, 1998; Kime et al., 2000; Castellano et al., 2003; Bosch e De la Riva, 2004). Isso ocorre especialmente pelo fato de uma análise filogenética comparativa não ter sido conduzida para anfíbios, visto que hipóteses filogenéticas robustas para anfíbios só foram disponibilizadas recentemente.

Nesta tese, tentamos avançar sobre o conhecimento dos processos evolutivos que atuam sobre sinais acústicos, mais especificamente o canto de anúncio dos anuros. No primeiro capítulo, fizemos uma análise descritiva da variabilidade do canto de anúncio do gênero Phyllomedusa (Hylidae: Anura) em diferentes níveis: diferentes cantos de um mesmo indivíduo; cantos de diferentes indivíduos de uma mesma população; cantos de indivíduos de diferentes populações; cantos de diferentes espécies. Discutimos como a variabilidade dos diferentes parâmetros acústicos nesses níveis podem evidenciar processos evolutivos atuantes sobre essas vocalizações. Nos capítulos seguintes testamos se diferentes pressões seletivas estão associadas à diversidade intra e interespecífica nesta vocalização. No segundo capítulo, testamos a importância de barreiras de propagação sobre o canto de anúncio de Phyllomedusa nordestina. Para isto comparamos vocalizações de indivíduos de diferentes populações que ocorrem na Mata Atlântica e na Caatinga, além de testar se os parâmetros acústicos estão correlacionados à quantidade de vegetações no entorno do indivíduo. Já no terceiro capítulo, avaliamos se a frequência dominante do canto de anúncio das espécies que reproduzem em ambientes lóticos é mais alta em relação às espécies de ambientes lênticos. Para isto, utilizamos dados de literatura sobre espécies distribuídas por todo o globo.

Referências

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Castellano, S., Giacoma, C. & Ryan, M. J. 2003. Call degradation in diploid and tetraploid toads. Biological Journal of the Linnean Society, 78, 11-26. Cocroft, R. B. & Ryan, M. J. 1995. Patterns of advertisement call evolution in toads and chorus frogs. Animal Behaviour, 49, 283-303. Cosens, S. E. & Falls, J. B. 1984. A comparison of sound propagation and song frequency in temperate marsh and grassland habitats.Behavioral Ecology and Sociobiology, 3, 161-170. Erdtmann, L. & Amézquita, A. 2009. Differential evolution of advertisement call traits in dart-poison frogs (Anura: Dendrobatidae). Ethology, 115, 801-811. Ey, E. & Fischer, J. 2009. The ―Acoustic Adaptation Hypothesis‖ – a review of the evidence from birds, anurans and mammals. Bioacoustics, 19, 21-48. Feng, A. S., Narins, P. M., Xu, C., Lin., W., Yu, Z., Qiu, Q., Xu, Z. & Shen, J. 2006. Ultrasonic communication in frogs. Nature, 404, 333-336. Gerhardt, H. C. 1994. The evolution of vocalization in frogs and toads. Annual Review of Ecology and Systematics, 25, 293-324. Gerhardt, H. C. & Huber, F. 2002. Acoustic communication in insects and anurans. Chicago: University of Chicago Press. Goutte, S., Dubois, A. & Legendre, F. 2013. The importance of ambient sound level to characterise anuran habitat. Plos One, 8, e78020. Kime, N. M., Turner, W. R. & Ryan, M. J. 2000. The transmission of advertisement calls in Central American frogs. Behavioral Ecology,11, 71-83. Kirschel, N. G., Blumstein, D. T., Cohen, R. E., Buemann, W. Smith, T. B. & Slabbekoorn, H. 2009. Birdsong tuned to the environment: green hylia song varies with elevation, tree cover, and noise. Behavioral ecology, 20, 1089-1095. Morton, E. S. 1975. Ecological sources of selection on avian sounds. The American Naturalist, 109, 13-17. Narins, P. M., Feng, A. S., Lin, W. Y., Schnitzler, H. U., Denzinger, A., Suthers, R. A., & Xu, C. H. 2004. Old World frog and bird, vocalizations contain prominent ultrasonic harmonics. Journal of the Acoustical Society of America, 115, 910-913. Patten, M. A., Rotenberry, J. T. & Zuk, M. 2004. Habitat selection, acoustic adaptation, and the evolution of reproductive isolation. Evolution, 58, 2144-2155. Penna, M. & Solís, R. 1998. Frog call intensities and sound propagation in the South American temperate forest region. Behavioral Ecology and Sociobiology, 42, 371-381. Peters, G. & Peters, M. K. 2010. Long-distance call evolution in the Felidae: effects of body weight, habitat, and phylogeny. Biological Journal of the Linnean Society, 101, 487-500. Pfenning, K. S. 1998. The evolution of mate choice and the potential for conflict between species and mate- quality recognition. Proceedings of the Royal Society B: Biological Sciences, 265, 1743-1748. Podos, J. 2001. Correlated evolution of morphology and vocal signal structure in Darwin´s Finches. Nature, 409, 185-188. Preininger, D., Markus, B. & Hödl. 2007. Comparison of anuran acoustic communities of two habitat types in the Danum Valley Conservation Area, Sabah, Malaysia. Salamandra, 43, 129-138. Ripmeester, E. A. P., Mulder, A. & Slabbekoorn, H. 2010. Habitat-dependent acoustic divergence affects playbacks in urban and forest populations of the European blackbird. Behavioral Ecology, 21, 876-883. Röhr, D. L. & Juncá, F. A. 2013. Micro-habitat influence on the advertisement call structure and sound propagation efficiency of Hypsiboas crepitans (Anura: Hylidae). Journal of Herpetology, 47, 549-554. Rothstein, S. I. & Fleischer, R. C. 1987. Vocal dialects and the possible relation to honest status signaling in the Brown-Headed Cowbird. The Condor, 89, 1-23. Ryan, M. J. & Rand, A. S. 1993. Sexual selection and signal evolution: the ghost of biases past. Philosophical Transactions: Biological Sciences, 340, 187-195. Ryan, M. J., Cocroft, R. B. & Wilczynski, W. 1990. The role of environmental selection in intraspecific divergence of mate recognition signals in the cricket frog, Acris crepitans. Evolution, 44, 1869-1872. Seddon, N. 2005. Ecological adaptation and species recognition drives vocal evolution in neotropical suboscine birds. Evolution, 59, 200-215. Turelli, M., Barton, N. H. & Coyne, J. A. 2001. Theory and speciation. Trends in Ecology & Evolution, 16, 330-343. Tuttle, M. D. & Ryan, M. J. 1981. Bat predation and the evolution of frog vocalizations in the neotropics. Science, 214, 677-678. Wells, K. D. 2010. The ecology and behavior of amphibians. University of Chicago Press, Chicago. Wiley, R. H. 1991.Associations of song properties with habitats for territorial oscine birds of Eastern North America. The American Naturalist, 138, 973-993. Wiley, R. H. & Richards, D. G. 1978. Physical constraints on acoustic communication in the atmosphere: implications for the evolution of animal vocalizations. Behavioral Ecology and Sociobiology, 3, 69-94.

Wollerman, L. & Wiley, R. H. 2002. Background noise from natural chorus alters female discrimination of male calls in a Neotropical frog. Animal Behaviour, 63, 15-22. Zimmerman, B. L. 1983. A comparison of structural features of calls of open and forest habitat frog species in the Central Amazon. Herpetologica, 39, 235-246.

CAPÍTULO 01

Variability in Anuran Advertisement Call: a Multi-level Study with the Genus Phyllomedusa (Hylidae: Anura).

David Lucas Röhr, Gustavo Brant Paterno, Marcelo Gehara, Felipe Camurugi; Flora Acuña Juncá, Guilherme Fajardo R. Álvares, Reuber Albuquerque Brandão, Adrian Antonio Garda

Abstract

Understanding variability of acoustic signals is a first important step for the comprehension of the evolutionary processes that led to present diversity. Herein, we evaluate the variability of the advertisement of the anuran genus Phyllomedusa at different levels: intra-individual; intra-population; inter-population; inter-specific. Analysis of coefficients of variation showed a continuum of variability between the acoustic parameters analyzed, from static to highly dynamic. Most of the variation was attributed to the inter-specific level, while the intra-individual varied the less, however, the variability at each level differed between parameters. While most temporal acoustic parameters were affected by environmental temperature, the spectral parameter was strongly influenced by body size. Only one acoustic parameter was correlated to the geographic distance between populations, while all presented a significant phylogenetic signal. Furthermore, the advertisement call for this genus showed a low potential for individual recognition. Based on these results, we discuss the possible importance of different evolutionary forces and the usage of this vocalization for taxonomy.

Keywords: Acoustic communication; Evolution; Individual recognition; Variation

Introduction

Understanding variability is fundamental for the comprehension of evolution (Hallgrímsson and Hall, 2011). Darwin´s observations on phenotypic variation were the basis for the development of the concept of natural selection (Darwin, 1859), and intra and inter-specific variation were central for the modern evolutionary synthesis (Mayr, 1963; Wright, 1968).

Acoustic signals are important for a large proportion of current fauna, and for most groups it has a predominant reproductive function (Gerhardt, 1994). Because these signals are involved in conspecific recognition, they may have a key role in diversification, and their importance as evolutionary forces has been the focus of many studies (Wilkins et al., 2013).

Acoustic signals are the predominant form of communication for the vast majority of anuran species. Although many species present more than one type of call, the advertisement call emitted by males with main function of attracting females can be considered the most important acoustic signal for this clade (Wells, 1977). Since the first bioacoustic studies with frogs, authors noticed that each species has a distinct advertisement call (Blair, 1958, 1964; Duellman and Pyles, 1983). Furthermore, it has been shown that females demonstrate strong preference for conspecific advertisement calls, even when considering sister taxa with relatively similar calls (Backwell and Jennions, 1993; Gerhardt, 1974). Thus, this vocalization is considered an important pre- zygotic reproductive barrier (Gerhardt and Huber, 2002) and has been used as an important taxonomic tool, helping overcome the lack of useful external morphological traits in frogs (Padial and De La Riva, 2009).

The anuran advertisement call is considered stereotyped, especially in comparison to other vertebrates such as birds and mammals. Still, there is considerable amount of intra-specific variation at the individual level (Howard and Young, 1998), at the population level (Sullivan, 1982), and among population of the same species (Sullivan, 1989). Although several studies clearly demonstrate this variation in each level separately, few studies have quantified how different acoustic parameters vary at different levels within a specific clade (Bee et al., 2010; Castellano et al., 2002). Different acoustic parameters from the same call are semi-independent and might encode distinct messages, thus evolving under differing selective pressures (Gerhardt

8 and Huber, 2002). Therefore, a broad comprehension of variability at different levels enables the proposition of various hypotheses about the evolutionary mechanisms that led to the present acoustic diversity.

In general, variation in acoustic signals can be related to pleiotropic effects (Podos, 2001), stochastic processes (Goicoechea et al., 2010), natural selection (Ryan et al., 1990), and sexual selection (Gerhardt, 2005). For anurans, pleiotropic effects of morphology and physiology are exemplified by the well-known influence of body size and temperature on acoustic parameters. The size of the vocal apparatus usually affects spectral parameters, while temperature often influences temporal characteristics of the calls (Gerhardt, 1994). Such relationships can affect the variability of different acoustic parameters at different levels. For example, acoustic parameters that are more dependent on temperature should vary more, especially intra-individually and in a shorter time scale. At the same time, parameters that are highly dependent on morphology should be more stereotyped, with most of the variation occurring at levels above individuals or ontogenetically. Therefore, it is important to carefully consider the influence of temperature and body size in any bioacoustic study.

Stochastic processes affect the evolution and variability of acoustic signals. For anurans, these effects are tested evaluating the geographic variation of acoustic parameters (Pröhl et al., 2007) or the effect of phylogeny on inter-specific variation (Goicoechea et al., 2010). While some acoustic parameters vary mostly in response to stochastic processes, others are not correlated with phylogeny or biogeography, possibly because they are under strong selective forces (Erdtmann and Amézquita, 2009). These different evolutionary pathways should also influence the amount of variation detected at different levels.

Selection may affect the variability of acoustic parameters in different ways (Wilkins et al., 2013). For example, acoustic parameters important for conspecific recognition are under selective pressure not to overlap with calls from sympatric species, possibly leading to more stereotypy and hence reducing the likelihood of hybridization (Lemmom, 2009). Beyond this, in diverse acoustic communities, calls might also be more stereotyped in order to use silent windows and reduce masking interference (Bee, 2008; Chek et al., 2003). At the same time, a higher stereotypy might be expected in habitats with dense vegetation coverage, because the acoustic

9 propagation scenario is more stable compared to open areas, which are more susceptible to wind and temperature shifts (Wiley and Richards, 1978; Ey and Fischer, 2009).

Nevertheless, most studies on the variability of anuran advertisement call discuss sexual selection (Gerhardt and Huber, 2002). One key measurement of acoustic variation in frogs has been the individual coefficient of variation. Based on this measure, extensive literature has shown that variability of different acoustic parameters follows a continuum from static to dynamic (Bee et al., 2010; Castellano et al., 2002; Gerhardt, 1991). Static parameters should be under stabilizing or weakly directional sexual selection (Gerhardt, 1991; Gerhardt and Huber, 2002). Indeed, most studies confirm that females tend to prefer advertisement calls with values close to the species/population mean for such acoustic parameters (Castellano et al., 1998; Rosso et al., 2006), and these parameters should be important for specific recognition and are more reliable taxonomic tools (Gerhardt and Huber, 2002). In contrast, acoustic parameters with high individual variability should be under directional selection, and females should prefer calls with more extreme values from the species distribution for these parameters (Bosch and Márquez, 2005; Castellano and Giacoma, 1998). These parameters can indicate male’s quality or facilitate localization (Gerhardt and Huber, 2002).

Herein, we evaluate the variability of different acoustic parameters of the advertisement call of 15 species of the anuran genus Phyllomedusa at different levels: intra-individual; intra-population; inter-population; intra-specific; inter-specific. We also estimated what percentage of the total variation detected can be explained by each levels. Furthermore, we tested: 1) the effect of environmental temperature and body size on the acoustic parameters, 2) if they present significant phylogenetic signal and 3) if they are correlated to geographic distance between populations. Finally, we evaluated the possibility of individual, population, and specific recognition considering the variation found in the acoustic parameters analyzed.

Material & Methods

The genus Phyllomedusa is composed of approximately 30 species, which are hylid frogs commonly known as monkey frogs and belong to the Subfamily Phyllomedusinae. We recorded the advertisement of species from the genus Phyllomedusa throughout Brazil. Most recordings were done using a Marantz PMD 660

10 digital recorder with a sampling rate of 48 kHz and 16 bit resolution, connected to a Sennheiser ME66 directional microphone. After each recording for the large majority of the individual we measured the environmental temperature and snout-vent length (See Table S1 for data on each recording: species; location; coordinates; recording equipment; temperature; body size; total number of calls analyzed). Acoustic parameters were measured in Raven Pro 1.4 and spectrograms produced as follows: FFT window width = 256; Frame = 100; Overlap = 50%.

We analyzed a total of five acoustic parameters for all calls recorded, which we believe are comparable at all levels, even between different species of this genus: dominant frequency (DF); total number of pulses in the call (PN); average pulse length considering all pulses from the call (PL); pulse rate (PR); total duration of the call (DU); interval between calls (CI).

To estimate the variability of the acoustic parameters we calculated the coefficient of variation (CV), which is a standardized measure of dispersion calculated through the ratio of the standard deviation to the mean, for all acoustic parameters at five different levels: 1) intra-individual (different calls from a single individual); 2) intra-population (calls from different individuals from a single population); 3) inter- population (calls from individuals from different populations); 4) intra-specific (calls from different individuals from the same species, independent from which population); 5) inter-specific (calls from individuals from different species).

To deal with different sample sizes at each level, we created a stratified hierarchical subsampling method in which we repeatedly drew from our data pool five different calls that were used to calculate the CV. We ensured that the calls were drawn from different entities forming the respective level of interest. For instance, before drawing five calls at the intra-individual level, we drew a species, than a population, than an individual, than five different calls. For the intra-population level we first drew a species, than a population, than five calls from different individuals and so on (see Table 1 for a stepwise description of all steps used to obtain the calls used for each level). We repeated each subsampling 1,000 times for each level to create five distributions of variation for each acoustic parameter. The CV for intra and inter- population was only calculated for Phyllomedusa nordestina due to the lack of sufficient population replicates for the other species.

Additionally, we performed a hierarchical ANOVA to calculate the amount of variation that can be attributed to each level. For this analysis we used only four levels in order to access the variability repartition for the entire genus: call; individual; population; species. To test the influence of body size and environmental temperature on acoustic parameters of Phyllomedusa we performed a regression model using the mean value from all calls from each individual and including the species as a block. To evaluate the geographic variation for the different populations of P. nordestina we applied a Mantel test for each acoustic parameter. In the correlation matrix we included the geographical coordinates of each locality and the mean acoustic parameters for each individual.

To quantify the phylogenetic signal strength for each acoustic parameter between the species of Phyllomedusa we recorded, we used Blomberg's K, which metric is based on Brownian motion model of evolution, where its significance is tested by permuting traits across a phylogenetic tree. When K is larger than one, related species present trait values more similar than expected from a Brownian motion and when it is smaller than one, relatives are less similar than expected (Blomberg et al., 2003).

Finally, we used a discriminant function analysis with all acoustic parameters to examine the extent to which calls can be assigned to the correct individual, population and species, and a cross-validation procedure was used to measure classification success.

All analyses were done on the R 3.1.2. environment using the following packages: ggplot2; reshape2; dplyr; gridExtra; ape; picante; caper; diversitree.

Results

We analyzed a total of 3,994 advertisement calls from 188 individuals of 15 different species of Phyllomedusa (see Table S1 for details on each individual recorded). Although we sampled calls from more than one locality for various species, we only obtained enough samples from different populations for P. nordestina, for which we recorded various individuals from 14 populations from different localities distributed throughout most of the species geographical distribution (Table S1). The advertisement call from all species recorded is relatively simple, being composed of

12 several similar pulses that can be grouped in different notes (See Table 2 for the mean values of the acoustic parameters for the specie recorded and Figure S1 and S2 for spectrograms of one call from each species).

The multiple-level analyses of variability using CV showed that all acoustic parameters presented the lowest variation intra-individually, but the intensity of this trend varied between parameters. Dominant frequency, pulse length, and pulse rate are the most static parameters, especially intra-individually. Pulse number and call duration present intermediate values, while call interval is highly dynamic and shows less difference between the intra-specific levels and the inter-specific level (Figure 1).

Call parameters also vary considerably below the interespecific level. In general, the variability is smallest at the intra-individual level for all parameters and a tendency for the intra-population level to show less variability than the inter-population, with the intra-specific intermediate. Furthermore, the three most static parameters (dominant frequency, pulse length, and call rate) show the largest difference inter-specifically compared to other levels (Figure 1).

The ANOVA confirmed the results from our multi-level CV evaluation, where most of the advertisement call variability for the 15 species of Phyllomedusa is explained by inter-specific differences. However, this trend also varies a lot between parameters, where there is a clear tendency for static acoustic parameters have a larger percentage of their variability credited to the inter-specific level, with intermediate parameters having a little less of their variability explained by this level (however more than 75%) and the dynamic parameter less than 50%. Below the inter-specific level, pulse rate had a considerable part of its variation attributed to the population level (Figure 2).

The correlation analyses showed that most of the acoustic parameters are significantly affected by body size and/or environmental temperature. While temperature affects mostly the temporal acoustic parameters measured (Table 3), most parameters where influenced by individuals body size, with a strong effect on the spectral parameter (dominant frequency) (Table 4). Only pulse rate and call duration were significantly correlated to the geographical distance between the populations of P. nordestina (Table 5). Conversely, all acoustic parameters tested presented a significant phylogenetic signal (Table 6). Finally, discriminant function analysis including all

13 acoustic parameters attributed 30.7% of the calls to the correct individual, 37.8% to the population, and more than 81.2% to the correct species.

Discussion

Our results corroborate most studies on acoustic variability for anuran advertisement calls, where the acoustic parameters present a continuum of variation (Bee et al., 2010; Castellano et al., 2002). The parameters that presented the lowest CV were mainly those that are considered important for frog specific recognition, especially dominant frequency and pulse rate (Gerhardt, 1994). Tests on female preference demonstrate that females generally prefer calls with medium values for those parameters, exerting a stabilizing sexual selection over the population (Castellano and Giacoma, 1998). Furthermore, studies on reproductive character displacement show that when populations occur in sympatry with sister taxa with similar advertisement calls these preferences are stronger, confirming the importance of these parameters for specific recognition and as reproductive barrier, diminishing hybridization (Lemmom, 2009). However, for some frog species females prefer calls with slightly lower dominant frequencies, exerting weak directional sexual selection, possibly choosing for larger males (Poole and Murphy, 2007; Ryan, 1980).

Considering the more variable parameters, call duration generally presents intermediate values of CV, while call interval/call rate are highly dynamic (Bee et al., 2010; Castellano et al., 2002; Gerhardt, 1991). Accordingly, females show directional preferences for these parameters, which are behaviorally controlled to some extent and are directly involved in the energetic expenditure of call production (Sullivan, 1992; Bosch and Márquez, 2005). These more extreme values from the population/species distribution could indicate higher fitness males or maybe just enhance probability of signal detection and facilitate localization, with this preference simply representing less energy cost and lower predation risk (Gerhardt, 1994).

As expected, the inter-specific level was the most variable and most of the advertisement call variability for Phyllomedusa is attributed to this level, while the lowest CV was detected intra-individually, except for call interval, which has a high variability at all levels (Figures 1 & 2). Furthermore, variability in levels that compare calls from different individuals of the same species also changed as predicted, parameters showing less variability at the intra-population level in relation to inter-

14 population, with intermediate values for intra-specific, probably because it includes calls from individuals which may or not be from the same population.

Most studies on geographical variation of anuran advertisement call detected significant differences between calls from individuals from distinct populations (Snyder and Jameson, 1965; Hasegawa et al., 1999; Castellano and Giacoma, 2000). This variation may be associated to stochastic processes and be directly related to gene flow or selection (Smith et al., 2003; Bernal et al., 2005, Pröhl et al., 2007; Ohmer et al., 2009), which may act directly on call characteristics or through pleiotropic effects (Castellano et al., 1999). When selective pressures are weak, call variation is expected to be correlated with geographical distance between populations or associated to an important dispersal barrier (Pröhl et al., 2006).

From all acoustic parameters evaluated for P. nordestina only pulse rate and call duration are related to geographical distance between populations, and pulse rate had a higher percentage of its variation explained by the population level when compared to the other static parameters. Possibly, less parameters were correlated to geographic distance because there is little variation between populations, both comparing with the inter-specific variation we detected for the species of Phyllomedusa and with other studies on geographical variation in anuran advertisement call (Snyder and Jameson, 1965; Hasegawa et al., 1999; Castellano and Giacoma, 2002; Bernal et al., 2005; Pröhl et al., 2007). This low variability between different populations may be related to conservative selective pressures acting upon all or most populations, to a large amount of gene flow, or because of recent population expansions. Other studies on geographic variation of anuran advertisement calls found contrasting results on the relationship between call variation and geographic distances (Snyder and Jameson, 1965; Hasegawa et al., 1999; Castellano and Giacoma, 2000; Smith et al 2003; Bernal et al., 2005; Pröhl et al., 2005; Ohmer et al., 2009).

Conversely, when considering the variation between the calls from the different species studied, all acoustic parameters presented a significant phylogenetic signal. These results corroborate most studies on phylogenetic signals for anurans (Cocroft and Ryan, 1995; Wollenberg et al., 2007; Erdtmann and Amézquita, 2009; Goicoechea et al., 2010). Although the anuran advertisement call traditionally is seen as a rapidly evolving trait and under strong selective forces, especially sexual selection (Gerhardt,

1994), recent studies have found surprisingly strong phylogenetic signals for most acoustic parameters tested, calling attention to the importance of stochastic processes, such as genetic drift, in the evolution of these vocalizations (Cocroft and Ryan, 1995; Wollenberg et al., 2007; Erdtmann and Amézquita, 2009; Goicoechea et al., 2010).

Analyzing acoustic signal variability in different parameters also enables evaluating the possibility of individual recognition (Bee, 2004; Bee et al., 2010; Bee and Gerhardt, 2001a; Bee et al., 2001; Feng et al., 2009a; Gasser et al., 2009). Besides attracting mates, the anuran advertisement call also presents a territorial function (Wells, 1977), and studies on individual recognition have focused on the dear enemy effect, because recognizing constant neighbors might reduce male’s aggressiveness (Bee, 2003, 2004; Bee and Gerhardt, 2001b). For at least some species it has been shown that males are capable of recognizing neighbor calls (Bee and Gerhardt, 2002; Davis, 1987; Feng et al., 2009b), and studies of variability using discriminant function analyses generally assign more than 70% of the calls to the correct individual (Bee, 2004; Bee et al., 2010; Bee and Gerhardt, 2001a; Bee et al., 2001; Feng et al., 2009b; Feng et al., 2009a; Gasser et al., 2009). For Phyllomedusa, probably individual recognition is not important (only about 30% of the calls were assigned to the correct individual). Furthermore, discriminant analyses confirms the relative low variability between populations (less than 40% of the calls were assigned to the correct population). Finally, only slightly more than 80% of the calls were assigned to the correct species, which could account for the apparent large amount of hybridization in this genus (Haddad et al., 1994).

Our results confirm that the advertisement call of the species studied from the genus Phyllomedusa may be used as a reliable taxonomic tool, because all acoustic parameters analyzed, except call interval, presented a much higher inter-specific variation in comparison to all intra-specific levels. However, considering the variation detected for the different intra-specific levels, it is important to have adequate replicates from different localities. Furthermore, it is recomended to consider temperature and body size, because most of the acoustic parameters were influenced by one or both variables. It is important to emphasize the influence of body size on the dominant frequency, considering that it was the only strong correlation recovered. CV at different levels shows that, between the analyzed acoustic parameters, dominant frequency could be considered the most reliable parameter for taxonomy. However, most of the inter-

16 specific variation is related to differences in body size, with a pleiotropic effect on the advertisement call.

Acknowledgements AAG and FAJ thank CNPq for financial support (Universal # 473503/2012-3 and #305704/2013-3, respectively). DLR also thanks Sisbiota Herpeto-Helmintos project for financial support.

References

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Figure 1: Variability of six acoustic parameters from the advertisement call of 15 species of Phyllomedusa at different levels. Coeficient of variation was calculated based on a stratified hierarchical subsampling method in which we repeatedly drew from our data pool five different calls. This procedure was repeated 1,000 times for each parameter at each level.

Figure 2: Results of the hierarchical ANOVA showing what percentage of the variability of the advertisement call of 15 species of Phyllomedusa is attributed to each level for all acoustic parameters: dominant frequency (df); number of pulses (pn); pulse length (pn); pulse rate (pr); call duration (dur); interval between calls (ci).

Table 1: Detailed step by step procedure used to draw calls from our data pool in the stratified hierarchical subsampling method used to calculate the coefficient of variation of the advertisement call of Phyllomedusa at different levels for each acoustic parameter.

Level Step 1 Step 2 Step 3 Step 4 Step 5 Intra-individual Draw one Draw one Draw five Calculate CV Repeat 1,000 x specie individual different calls from this individual Intra- Draw one Draw five Draw one call Calculate CV Repeat 1,000 x population population of different from each P. nordestina individuals from individual this population Inter- Draw five Draw one Draw one call Calculate CV Repeat 1,000 x population different individual from from each populations of each population individual P. nordestina Intra-specific Draw one Draw five Draw one call Calculate CV Repeat 1,000 x species different from each individuals individual

Inter-specific Draw five Draw one Draw one call Calculate CV Repeat 1,000 x different individual from from each species each specie individual

Table 02: Advertisement call description for all species of Phyllomedusa recorded. Mean, standard deviation, maximum and minimum values for each acoustic parameter.

Number of Number of Call duration Dominant frequency Call interval Number of pulses Pulse rate Pulse lenght Specie individuals calls (s) (Hz) (s) (pulse/s) (s) 0.039 ± 0.017 2079.086 ± 193.94 9.085 ± 26.595 3.814 ± 0.913 109.906 ± 32.993 0.005 ± 0.001 Phyllomedusa nordestina 100 2227 (0.01-0.089) (1500-2625) (0.015-290.299) (2-8) (40-230.769) (0.0005-0.01) 0.029 ± 0.0014 2291.151 ± 59.219 21.599 ± 20.662 4 ± 0 135.705 ± 7.606 0.005 ± 0.0005 Phyllomedusa azurea 4 45 (0.026-0.034) (2153.3-2411.7) (1.432-88.384) (4-4) (111.111-150) (0.004-0.006) 0.311 ± 0.171 1141.304 ± 148.107 18.408 ± 14.256 14.495 ± 4.170 43.085 ± 9.245 0.014 ± 0.004 Phyllomedusa bahiana 10 115 (0.134-0.992) (937.5-1312.5) (0.986-74.14) (7-26) (20.618-63.492) (0.006-0.021) 0.039 ± 0.008 668.509 ± 105.429 5.812 ± 15.196 4.711 ± 1.117 112.587 ± 8.558 0.005 ± 0.0005 Phyllomedusa camba 5 260 (0.024-0.074) (562.5-1125) (0.087-121.42) (3-9) (88.888-142.857) (0.003-0.006) 0.230 ± 0.051 1299.226 ± 99.332 22.976 ± 12.378 6.832 ± 1.059 27.883 ± 3.640 0.017 ± 0.001 Phyllomedusa distincta 9 113 (0.134-0.35) (1125-1875) (9.2-82.562) (5-10) (15.244-35.714) (0.013-0.024) 0.027 ± 0.005 1970.339 ± 98.021 8.157 ± 19.462 4.070 ± 0.747 141.152 ± 14.440 0.005 ± 0.0009 Phyllomedusa hypocondrialis 11 354 (0.017-0.048) (1687.5-2250) (0.203-195.219) (3-7) (103.448-200) (0.003-0.008) 0.318 ± 0.065 1240.473 ± 132.010 17.350 ± 12.203 9.310 ± 1.798 27.735 ± 3.038 0.017 ± 0.003 Phyllomedusa iheringii 7 164 (0.19-0.494) (937.5-1500) (1.011-109.484) (6-14) (20.746-36.363) (0.012-0.025) 0.150 ± 0.072 2014.628 ± 189.398 37.132 ± 43.132 5.234 ± 2.397 33.164 ± 4.555 0.018 ± 0.003 Phyllomedusa rohdei 7 47 (0.078-0.415) (1687.5-2250) (2.773-183.164) (3-11) (25.157-46.391) (0.011-0.024) 0.033 ± 0.090 1182.335 ± 107.827 18.038 ± 13.749 9.280 ± 2 29.389 ± 3.812 0.015 ± 0.001 Phyllomedusa tetraploidea 7 121 (0.194-0.645) (937.5-1312.5) (2.24-77.324) (5-15) (20.634-38.095) (0.010-0.022) 0.043 ± 0.007 1609.375 ± 148.680 64.123 ± 65.685 7.916 ± 1.311 178.123 ± 15.675 0.004 ± 0.0006 Phyllomedusa vaillantii 4 12 (0.03-0.053) (1312.5-1875) (11.986-180.149) (5-10) (155.555-212.121) (0.002-0.005) 0.118 ± 0.010 1102.5 ± 154.0.65 8.396 ± 4.860 6.8 ± 0.447 51.450 ± 0.778 0.004 ± 0.0002 Phyllomedusa burmeisteri 1 5 (0.1-0.126) (1033.6-1378.1) (3.724-15.224) (6-7) (50.420-52.173) (0.004-0.004) 0.321 ± 0.085 1809.375 ± 139.717 42.742 ± 34.665 13.4 ± 3.589 39.782 ± 7.990 0.005 ± 0.0006 Phyllomedusa megacephala 2 20 (0.25-0.566) (1687.5-2062.5) (7.033-137.449) (8-22) (23.381-54.140) (0.004-0.006) 0.093 ± 0.039 1471.444 ± 167.530 7.3 ± 12.846 5.625 ± 1.135 71.520 ± 40.508 0.008 ± 0.002 Phyllomedusa ayeaye 5 96 (0.025-0.16) (1205.9-1722.7) (0.253-60.04) (3-10) (30.303-208.33) (0.003-0.015) 0.025 ± 0.005 1679.589 ± 153.0.89 15.362 ± 42.189 3.925 ± 0.641 1140.519 ± 23.248 0.004 ± 0.001 Phyllomedusa oreades 7 200 (0.018-0.068) (1125-2067.2) (0.192-352.912) (3-7) (79.365-285.714) (0.002-0.007) 0.037 ± 0.014 1271.514 ± 174.469 7.552 ± 12.433 4.423 ± 0.838 115.818 ± 28.474 0.004 ± 0,001 Phyllomedusa centralis 9 215 (0.019-0.099) (937.5-1550.4) (0.2-79.318) (3-7) (45.977-166.666) (0.002-0.008)

Table 3: Influence of the environmental temperature on the acoustic parameters of the advertisement call of 15 species of Phyllomedusa. Regression models included the mean value of the acoustic parameter for each individual and the species was included as a block.

Source n F p Call Interval 172 3.035 0.0833 Dominant Frequency 172 4.612 0.0332 Call Duration 172 19.329 <0.001 Pulse Length 172 9.499 0.002 Number of Pulses 172 2.393 0.123 Pulse Rate 172 21.142 <0.001

Table 4: Influence of body size on the acoustic parameters of the advertisement call of 15 species of Phyllomedusa. Regression models included the mean value of the acoustic parameter for each individual and the species was included as a block.

Source n F p Call Interval 165 0.501 0.479 Dominant Frequency 165 360.321 <0.001 Call Duration 165 63.311 <0.001 Pulse Length 165 44.384 <0.001 Number of Pulses 165 92.656 <0.001 Pulse Rate 165 28.451 <0.001

Table 5: Results of Mantel test evaluating which acoustic parameters of the advertisement call of Phyllomedusa nordestina are correlated to the geographic distance between populations.

Source r p Call Duration 0.064 0.0492 Dominant Frequency 0.0346 0.1896 Number of Pulses 0.0177 0.3174 Pulse Length -0.0169 0.6096 Pulse Rate 0.1099 0.0039 Call Interval -0.0038 0.4668

Table 6: Phylogenetic signal for acoustic parameters and body size of Phyllomedusa calculated through Blomberg’s K (Blomberg et al., 2003)

Source Blomberg´s K PIC.mean PIC.rdn.mean P Call Duration 0.735 0.0011 0.0043 0.009 Dominant Freuency 0.6418 16689.364 56643.053 0.007 Call Interval 0.691 23.480 74.192 0.016 Number of Pulses 0.535 0.964 2.696 0.023 Pulse Rate 0.794 198.234 729.991 0.004 Pulse Length 0.566 <0.001 <0.001 0.027 Body Size 0.355 58.681 64.193 0.462

Supplementary Material:

Table S1: Detailed information in each individual of Phyllomedusa recorded throughout Brazil

Location Geographical coordenates Temperature Body Size Number of Species ID (City/State) (Latitude/Longitude) (°C) (mm) calls Phyllomedusa nordestina 1 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.7 35.19 36 Phyllomedusa nordestina 2 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.3 34.55 35 Phyllomedusa nordestina 3 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.7 36 39 Phyllomedusa nordestina 4 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.1 32.45 39 Phyllomedusa nordestina 5 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.1 33.42 56 Phyllomedusa nordestina 6 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 21.1 34.34 11 Phyllomedusa nordestina 7 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 21.1 32.94 12 Phyllomedusa nordestina 8 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.5 34.05 08 Phyllomedusa nordestina 9 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.8 38.12 16 Phyllomedusa nordestina 10 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 21 31.27 10 Phyllomedusa nordestina 11 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 21.4 32.35 24 Phyllomedusa nordestina 12 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 21.4 36.6 24 Phyllomedusa nordestina 13 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.7 30.3 11 Phyllomedusa nordestina 14 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.7 32.45 14 Phyllomedusa nordestina 15 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.3 32.69 07 Phyllomedusa nordestina 16 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.3 35.41 13 Phyllomedusa nordestina 17 Estância/Sergipe 11°14’45‖S 37°27’49‖W 22.2 29.78 27 Phyllomedusa nordestina 18 Estância/Sergipe 11°14’45‖S 37°27’49‖W 23.2 34.93 19 Phyllomedusa nordestina 19 Estância/Sergipe 11°14’45‖S 37°27’49‖W 22.4 35.42 07 Phyllomedusa nordestina 20 Estância/Sergipe 11°14’45‖S 37°27’49‖W 22 35.16 04 Phyllomedusa nordestina 21 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 23.7 35.74 05 Phyllomedusa nordestina 22 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 21.1 37.28 04 Phyllomedusa nordestina 23 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 21.2 36.74 07 Phyllomedusa nordestina 24 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 21 36.02 02 Phyllomedusa nordestina 25 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 21.3 NA 15 Phyllomedusa nordestina 26 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 21.3 35.69 50 Phyllomedusa nordestina 27 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 19 NA 20 Phyllomedusa nordestina 28 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 19 35.39 16

Location in Brazil Geographical coordenates Temperature Body Size Number of Specie ID (City/State) (Latitude/Longitude) (°C) (mm) calls Phyllomedusa nordestina 29 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 18.5 33.37 20 Phyllomedusa nordestina 30 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 21.1 37.99 56 Phyllomedusa nordestina 31 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 21 32.86 23 Phyllomedusa nordestina 32 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 20.5 33.96 12 Phyllomedusa nordestina 33 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 18.8 32.09 37 Phyllomedusa nordestina 34 Jaguaquara/Bahia 13°28’28‖S 39°55’10.5‖W 20.5 40.28 34 Phyllomedusa nordestina 35 Jaguaquara/Bahia 13°28’28‖S 39°55’10.5‖W 20.5 35.82 24 Phyllomedusa nordestina 36 Jaguaquara/Bahia 13°28’28‖S 39°55’10.5‖W 19.5 37.13 60 Phyllomedusa nordestina 37 Jaguaquara/Bahia 13°28’28‖S 39°55’10.5‖W 19.5 38.69 15 Phyllomedusa nordestina 38 Jaguaquara/Bahia 13°28’28‖S 39°55’10.5‖W 19.5 40.01 24 Phyllomedusa nordestina 39 Jaguaquara/Bahia 13°28’28‖S 39°55’10.5‖W 18 36.31 19 Phyllomedusa nordestina 40 Jaguaquara/Bahia 13°28’28‖S 39°55’10.5‖W 18 35.02 25 Phyllomedusa nordestina 41 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 24.5 32.54 39 Phyllomedusa nordestina 42 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 24.6 30.8 40 Phyllomedusa nordestina 43 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 24.3 32.13 18 Phyllomedusa nordestina 44 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 24.4 30.97 16 Phyllomedusa nordestina 45 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 24 NA 33 Phyllomedusa nordestina 46 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 24 32.73 63 Phyllomedusa nordestina 47 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 23.1 34.95 44 Phyllomedusa nordestina 48 João Câmara/ Rio Grande do Norte 5°31’23.7‖S 41°49’17.7‖W 23.1 34.57 23 Phyllomedusa nordestina 49 Mamanguape/Paraíba 6°56’35.6‖S 35°7’23.7‖W 22.5 35.54 22 Phyllomedusa nordestina 50 Mamanguape/Paraíba 6°56’35.6‖S 35°7’23.7‖W 22 34.3 26 Phyllomedusa nordestina 51 Mamanguape/Paraíba 6°56’35.6‖S 35°7’23.7‖W 21 38.33 05 Phyllomedusa nordestina 52 Mamanguape/Paraíba 6°56’35.6‖S 35°7’23.7‖W 20 33.93 06 Phyllomedusa nordestina 53 Mamanguape/Paraíba 6°56’35.6‖S 35°7’23.7‖W 21.5 32.34 14 Phyllomedusa nordestina 54 Mamanguape/Paraíba 6°56’35.6‖S 35°7’23.7‖W 21.3 33.08 24 Phyllomedusa nordestina 55 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 18.8 37.87 30 Phyllomedusa nordestina 56 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 18 36.48 04 Phyllomedusa nordestina 57 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 18 36.4 32 Phyllomedusa nordestina 58 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 18 36.77 56 Phyllomedusa nordestina 59 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 18 35.8 37 Phyllomedusa nordestina 60 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 18 34.94 26 Phyllomedusa nordestina 61 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 16.8 35.16 04 Phyllomedusa nordestina 62 Mata de São João/Bahia 12°29’53.6‖S 38°18’35‖W 16.3 32.55 34

Location in Brazil Geographical coordenates Temperature Body Size Number of Specie ID (City/State) (Latitude/Longitude) (°C) (mm) calls Phyllomedusa nordestina 63 Paripiranga/ Bahia 10°41’22‖S 37°52’59.3‖W 20 34.27 21 Phyllomedusa nordestina 64 Paripiranga/ Bahia 10°41’22‖S 37°52’59.3‖W 20 40.31 10 Phyllomedusa nordestina 65 Paripiranga/ Bahia 10°41’22‖S 37°52’59.3‖W 20 36.07 44 Phyllomedusa nordestina 66 Paripiranga/ Bahia 10°41’22‖S 37°52’59.3‖W 20 39.14 36 Phyllomedusa nordestina 67 Paripiranga/ Bahia 10°41’22‖S 37°52’59.3‖W 20 36.78 20 Phyllomedusa nordestina 68 Paripiranga/ Bahia 10°41’22‖S 37°52’59.3‖W 20 NA 20 Phyllomedusa nordestina 69 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 22.1 33.31 11 Phyllomedusa nordestina 70 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 22.9 30.02 18 Phyllomedusa nordestina 71 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 21 31.01 41 Phyllomedusa nordestina 72 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 21 30.49 18 Phyllomedusa nordestina 73 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 19.5 34.35 51 Phyllomedusa nordestina 74 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 19.7 30.24 15 Phyllomedusa nordestina 75 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 19.7 29.57 20 Phyllomedusa nordestina 76 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 19.5 30.92 26 Phyllomedusa nordestina 77 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 19.5 31.25 38 Phyllomedusa nordestina 78 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 18.6 30.43 09 Phyllomedusa nordestina 79 São José da Tapera/ Alagoas 9°33’32.7‖S 37°23’25.9‖W 18.6 35.06 24 Phyllomedusa nordestina 80 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 25.4 35.77 20 Phyllomedusa nordestina 81 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 25.4 35.47 19 Phyllomedusa nordestina 82 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 25.4 34.45 16 Phyllomedusa nordestina 83 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 25.4 34.77 11 Phyllomedusa nordestina 84 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 25.4 34.12 05 Phyllomedusa nordestina 85 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 19.3 32.75 07 Phyllomedusa nordestina 86 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 19.3 33.16 19 Phyllomedusa nordestina 87 São Miguel dos Campos/ Alagoas 9°46’2.2‖S 36°2’19.5‖W 19.3 34.34 46 Phyllomedusa nordestina 88 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 19.4 33.16 07 Phyllomedusa nordestina 89 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 19.4 33.62 08 Phyllomedusa nordestina 90 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 19.3 32.98 21 Phyllomedusa nordestina 91 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 19.3 32.65 09 Phyllomedusa nordestina 92 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 18.7 33.56 07 Phyllomedusa nordestina 93 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 18 36.35 21 Phyllomedusa nordestina 94 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 17.4 NA 20 Phyllomedusa nordestina 95 Taquaritinga do Norte/ Pernambuco 7°46’54‖S 36°14’0.5‖W 17.8 NA 25 Phyllomedusa nordestina 96 Taquaritinga do Norte/ Pernambuco 7°53’21.4‖S 36°3’45‖W 17 33.32 32

Location in Brazil Geographical coordenates Temperature Body Size Number of Specie ID (City/State) (Latitude/Longitude) (°C) (mm) calls Phyllomedusa nordestina 97 Taquaritinga do Norte/ Pernambuco 7°53’21.4‖S 36°3’45‖W 17.4 35.94 14 Phyllomedusa nordestina 98 Taquaritinga do Norte/ Pernambuco 7°53’21.4‖S 36°3’45‖W 17 33.48 05 Phyllomedusa nordestina 99 Taquaritinga do Norte/ Pernambuco 7°53’21.4‖S 36°3’45‖W 16 35.51 12 Phyllomedusa nordestina 100 Taquaritinga do Norte/ Pernambuco 7°53’21.4‖S 36°3’45‖W 16 30.97 09 Phyllomedusa azurea 101 Cocalzinho/ Goiás 15°42’23.5‖S 48°49’48.2‖W 23 34.75 29 Phyllomedusa azurea 102 Cocalzinho/ Goiás 15°42’23.5‖S 48°49’48.2‖W 23.1 34.55 05 Phyllomedusa azurea 103 Cocalzinho/ Goiás 15°42’23.5‖S 48°49’48.2‖W 23.9 31.79 05 Phyllomedusa azurea 104 Cocalzinho/ Goiás 15°42’23.5‖S 48°49’48.2‖W 20 35.27 06 Phyllomedusa bahiana 105 Feira de Santana/ Bahia 12°7’60‖S 39°1’0.01‖W 20 76.62 21 Phyllomedusa bahiana 106 Feira de Santana/ Bahia 12°7’60‖S 39°1’0.01‖W 20 72.74 14 Phyllomedusa bahiana 107 Feira de Santana/ Bahia 12°7’60‖S 39°1’0.01‖W 19.8 77.67 07 Phyllomedusa bahiana 108 Paripiranga/ Bahia 10°41’22‖S 37°52’59.3‖W 20 75.42 08 Phyllomedusa bahiana 109 Macajuba/Bahia 12°7’55.8‖S 40°18’27.6‖W 15.3 72.21 14 Phyllomedusa bahiana 110 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 24.2 69.31 12 Phyllomedusa bahiana 111 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.5 77.74 14 Phyllomedusa bahiana 112 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.8 75.48 09 Phyllomedusa bahiana 113 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 24 77.56 10 Phyllomedusa bahiana 114 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.7 78.48 06 Phyllomedusa camba 115 Aripuanã/ Mato Grosso 10°8’29‖S 59°26’11.9‖W 24.1 79.49 26 Phyllomedusa camba 116 Aripuanã/ Mato Grosso 10°8’29‖S 59°26’11.9‖W 24.1 83.41 89 Phyllomedusa camba 117 Aripuanã/ Mato Grosso 10°8’29‖S 59°26’11.9‖W 22.1 82.5 04 Phyllomedusa camba 118 Aripuanã/ Mato Grosso 10°8’29‖S 59°26’11.9‖W 22.1 85.34 44 Phyllomedusa camba 119 Aripuanã/ Mato Grosso 10°8’29‖S 59°26’11.9‖W 22.1 84.4 97 Phyllomedusa distincta 120 Blumenau/Santa Catarina 26°50’19‖S 48°47’18‖W 16 50.33 08 Phyllomedusa distincta 121 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 19.7 53.16 10 Phyllomedusa distincta 122 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 18.3 50.44 07 Phyllomedusa distincta 123 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 18.3 49.61 21 Phyllomedusa distincta 124 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 19.8 47.9 18 Phyllomedusa distincta 125 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 19.5 53.98 09 Phyllomedusa distincta 126 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 18.5 53.53 15 Phyllomedusa distincta 127 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 18.3 48.15 15 Phyllomedusa distincta 128 Urussunga/ Santa Catarina 28°31’43.8‖S 49°20’23.7‖W 18.7 47.46 10 Phyllomedusa hypocondrialis 129 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 23.5 35.13 09 Phyllomedusa hypocondrialis 130 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.6 NA 65

Location in Brazil Geographical coordenates Temperature Body Size Number of Specie ID (City/State) (Latitude/Longitude) (°C) (mm) calls Phyllomedusa hypocondrialis 131 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 23.5 36.73 17 Phyllomedusa hypocondrialis 132 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 23.4 37.31 09 Phyllomedusa hypocondrialis 133 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.5 35.77 36 Phyllomedusa hypocondrialis 134 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.2 32.67 24 Phyllomedusa hypocondrialis 135 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.3 37.39 10 Phyllomedusa hypocondrialis 136 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.3 34.82 33 Phyllomedusa hypocondrialis 137 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.3 37.6 91 Phyllomedusa hypocondrialis 138 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.3 34.26 37 Phyllomedusa hypocondrialis 139 Porto Grande/Amapá 0°43’51.8‖N 51°33’60‖W 22.4 36.84 23 Phyllomedusa iheringii 140 Santa Maria/Rio Grande do Sul 29°43’5‖S 53°43’37‖W 15.5 55.78 26 Phyllomedusa iheringii 141 Santa Maria/Rio Grande do Sul 29°43’5‖S 53°43’37‖W 15.5 54.1 18 Phyllomedusa iheringii 142 Santa Maria/Rio Grande do Sul 29°43’5‖S 53°43’37‖W 14.9 51.18 15 Phyllomedusa iheringii 143 Santa Maria/Rio Grande do Sul 29°43’5‖S 53°43’37‖W 15 58.13 10 Phyllomedusa iheringii 144 Santa Maria/Rio Grande do Sul 29°43’5‖S 53°43’37‖W 15 55.3 16 Phyllomedusa iheringii 145 Santa Maria/Rio Grande do Sul 29°43’5‖S 53°43’37‖W 16.7 55.67 09 Phyllomedusa iheringii 146 São Sapé/ Rio Grande do Sul 30°15’5.6‖S 53°35’7.4‖W 16.5 49.69 70 Phyllomedusa rohdei 147 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.7 45.76 02 Phyllomedusa rohdei 148 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.7 45.68 16 Phyllomedusa rohdei 149 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.8 42.65 04 Phyllomedusa rohdei 150 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.5 41.63 04 Phyllomedusa rohdei 151 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.3 46.36 03 Phyllomedusa rohdei 152 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.4 45.14 06 Phyllomedusa rohdei 153 Ilhéus/Bahia 14°47’44.4‖S 39°10’21.7‖W 23.2 41.84 12 Phyllomedusa tetraploidea 154 Palmas/Paraná 26°25’50‖S 51°57’31‖W 16 64.02 15 Phyllomedusa tetraploidea 155 Francisco Beltrão/Paraná 26°4’50.6‖S 53°5’13.4‖W 20.6 65.48 21 Phyllomedusa tetraploidea 156 Francisco Beltrão/Paraná 26°4’50.6‖S 53°5’13.4‖W 20.3 59.29 21 Phyllomedusa tetraploidea 157 Francisco Beltrão/Paraná 26°4’50.6‖S 53°5’13.4‖W 19.7 54.42 05 Phyllomedusa tetraploidea 158 Francisco Beltrão/Paraná 26°4’50.6‖S 53°5’13.4‖W 19.4 58.55 29 Phyllomedusa tetraploidea 159 Francisco Beltrão/Paraná 26°4’50.6‖S 53°5’13.4‖W 18.5 55.31 15 Phyllomedusa tetraploidea 160 Francisco Beltrão/Paraná 26°4’50.6‖S 53°5’13.4‖W 18.3 55.31 15 Phyllomedusa vaillantii 161 Porto Grande/ Amapá 0°57’51‖N 50°36’38‖W 23 51.9 03 Phyllomedusa vaillantii 162 Porto Grande/ Amapá 0°57’51‖N 50°36’38‖W 25 47.08 02 Phyllomedusa vaillantii 163 Porto Grande/ Amapá 0°57’51‖N 50°36’38‖W 22 49.21 04 Phyllomedusa vaillantii 164 Porto Grande/ Amapá 0°57’51‖N 50°36’38‖W 22.2 50.84 03

Location in Brazil Geographical coordenates Temperature Body Size Number of Specie ID (City/State) (Latitude/Longitude) (°C) (mm) calls Phyllomedusa burmeisteri 165 Carlos Chagas/ Minas Gerais 17°49’27.5‖S 40°55’25‖W 25.7 6.3 05 Phyllomedusa megacephala 166 Jaboticatubas/ Minas Gerais 19°8’26‖S 43°37’22‖W 23 38.56 14 Phyllomedusa megacephala 167 Jaboticatubas/ Minas Gerais 19°8’26‖S 43°37’22‖W 23 38.41 06 Phyllomedusa ayeaye 168 Poços de Caldas/ Minas Gerais 21°53’51‖S 46°32’42‖W 17.6 40.63 12 Phyllomedusa ayeaye 169 Poços de Caldas/ Minas Gerais 21°53’51‖S 46°32’42‖W 17.6 37.97 12 Phyllomedusa ayeaye 170 Poços de Caldas/ Minas Gerais 21°53’51‖S 46°32’42‖W 17.6 41.43 44 Phyllomedusa ayeaye 171 Poços de Caldas/ Minas Gerais 21°53’51‖S 46°32’42‖W 17.6 41.25 08 Phyllomedusa ayeaye 172 Poços de Caldas/ Minas Gerais 21°53’51‖S 46°32’42‖W 17.6 41.17 20 Phyllomedusa oreades 173 Minaçu/Goiás 13°47’07‖S 48°17’35‖W NA NA 60 Phyllomedusa oreades 174 Minaçu/Goiás 13°47’07‖S 48°17’35‖W NA NA 05 Phyllomedusa oreades 175 Minaçu/Goiás 13°47’07‖S 48°17’35‖W NA NA 05 Phyllomedusa oreades 176 Minaçu/Goiás 13°47’07‖S 48°17’35‖W NA NA 18 Phyllomedusa oreades 177 Minaçu/Goiás 13°47’07‖S 48°17’35‖W NA NA 41 Phyllomedusa oreades 178 Minaçu/Goiás 13°47’07‖S 48°17’35‖W NA NA 51 Phyllomedusa oreades 179 Minaçu/Goiás 13°47’07‖S 48°17’35‖W NA NA 20 Phyllomedusa centralis 180 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 31 Phyllomedusa centralis 181 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 10 Phyllomedusa centralis 182 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 28 Phyllomedusa centralis 183 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 25 Phyllomedusa centralis 184 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 28 Phyllomedusa centralis 185 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 28 Phyllomedusa centralis 186 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 43 Phyllomedusa centralis 187 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 17 Phyllomedusa centralis 188 Congonhas/Minas Gerais 15°31’23‖S 55°53’34‖W NA NA 05

Figure S1: Spectrogram (above) and ocillogram (below) for a sample advertisement call from all species of Phyllomedusa recorded.

Figure S1 (continued): Spectrogram (above) and ocillogram (below) for a sample advertisement call from all species of Phyllomedusa recorded.

Figure S2: Phylogenetic tree based on Pyron & Wiens (2011) including all species of Phyllomedusa recorded and a representation of their advertisement call (ocillogram)

CAPÍTULO 02

Habitat Dependet Variation in the Advertisement Call of Phyllomedusa nordestina

(HYLIDAE: ANURA)

David Lucas Röhr, Felipe Camurugi, Pablo Ariel Martinez, Flora Acuña Juncá &

Adrian Antonio Garda

Abstract

The Acoustic Adaptation Hypothesis (AAH) predicts that acoustic signals should be adapted to propagate more efficiently in the habitat through which they are normally transmitted. This hypothesis is fairly well tested for birds and primates.

However, the few studies on anurans indicate that this selective pressure may not be so important for frogs. Herein, we compare advertisement calls of Phyllomedusa nordestina from two contrasting habitats (Atlantic Rainforest and Caatinga) and test the influence of the amount of vegetation around calling sites on measured acoustic parameters. Our results indicate that different acoustic parameters from the same species advertisement call present diverging evolutionary paths: while the interval between pulses and the call rate are adapted to the environment of occurrence, individuals appear to be adapted to show flexible responses according to the amount of vegetation around calling site by changing their dominant frequency and number of pulses.

Keywords: Acoustic Communication; Acoustic Adaptation Hypothesis; Sound

Propagation; Anura; Evolution

Introduction

The efficiency of an acoustic signal is highly dependent on the environment where it is emitted, received and through which it propagates (Morton, 1975; Gehardt &

Huber, 2002). The acoustic environment may vary in biotic and abiotic background noise, presence and abundance of sound-guided predators and parasites, climatic conditions and presence and number of propagation barriers (Wiley & Richards, 1978;

Ryan & Tuttle, 1983; Brumm, 2013). Knowledge on the role of the acoustic environment on the evolution of an acoustic signal is still incipient and few studies have addressed how it is associated to present intra and inter signal variation (Wilkins et al.,

2013). The Acoustic Adaptation Hypothesis (AAH) predicts that acoustic signals should be adapted to propagate more efficiently in the habitat through which they are normally transmitted. Individuals whose sexual display signals will attenuate and degrade less and therefore be heard and recognized at farther distances will increase their probability of attracting a mate, leading to assortative mating and evolution through natural selection (Morton, 1975; Wiley & Richards, 1978; Ey & Fischer, 2009).

Acoustic signals are characterized by different spectral and temporal parameters.

Physics theories and experimental data predict that the efficiency of signal propagation depends on the values of these acoustic parameters and on habitat characteristics, indicating that transmission efficiency of different signals varies among contrasting habitats (Morton, 1975; Wiley & Richards, 1978; Kime et al., 2000). Traditionally, these predictions have been tested comparing signal characteristic and their efficiencies among habitats with different vegetation coverage (e.g. open vs. closed habitats), considering their clear difference in patterns of sound absorption, refraction, reflection, and scattering (Wiley & Richards, 1978). Although the theory of how these parameters

38 should respond to these habitats is well established, empirical data are scarce and diverging results for different clades have been recovered (Ey & Fischer, 2009).

Among the three highly vocal vertebrate clades, studies on birds and mammals highly outnumber those on anurans. A meta-analysis and other reviews show the importance of the AAH for the evolution of bird and mammals vocalizations

(Boncoraglio & Saino, 2007; Ey & Fischer 2009). However, results diverge between studies and there is a tendency for birds to have spectral parameters adaptations while temporal adjustments are more common for mammals (Ey & Fischer 2009). The few studies addressing anurans in which acoustic signals of species from contrasting environments were compared or where propagation efficiencies were experimentally tested failed to find clear evidences of the AAH (Zimmerman, 1983; Penna & Solís,

1998; Kime et al., 2000; Castellano et al., 2003; Bosch & de La Riva, 2004).

Nevertheless, a recent review (Erdtmann & Lima, 2013) discusses whether these results reflect that this selective pressure is not important for anurans and suppressed by other evolutionary forces or methodological weaknesses of the comparative and/or experimental designs used in such studies. Erdtmann & Lima (2013) argue that studies should include better vegetation quantifications, broader sampling covarege (avoiding pseudoreplications from the inclusion of many individuals from the same population), and adequate control for body size and phylogenetic relationships. Furthermore, most studies compare the vocalization from all species with available data from contrasting environments (Zimmerman, 1983; Penna & Solís, 1998; Kime et al., 2000; Bosch & de

La Riva, 2004), and it may be argued that the AAH may act as a micro-evolutionary process and, considering the many other possible selective forces, may be better detected using more restrictive monophyletic clades (Kime et al., 2000). Indeed, the single evidence for the AAH in anurans comes from the comparison of call

39 characteristics and propagation efficiency test between two closely related species, however lacking replicates (Ryan et al., 1990; Ryan & Wilczynski, 1991).

Additionally, plastic responses to the momentary propagation scenario should also be considered and studies should focus on intra-specific variation and include precise vegetation measures around calling sites. Acoustic environments are constantly changing and animals are expected to respond to these changes. While long-term changes in the environment should lead to adaptive responses, short-term alteration in the environment is expected to elicit plastic responses (Brumm, 2013). The vegetation between contrasting environments such as forests and more open areas can be considered as constantly different, however the amount of propagation barriers around a calling individual also varies within one habitat, especially considering changes in calling sites. According to the AAH, acoustic signals are fixed attributes and the selective pressure for propagation effectiveness should lead to long-term adaptive responses, guiding differences in signal design between populations and enabling sound-mediated speciation (Morton, 1975; Wiley & Richards, 1978). Still, frogs have been shown to use flexible responses to deal with the acoustic environment (Lardner & bin Lakin, 2002). For example, individuals of Hypsiboas pulchellus seem to perceive the amount of vegetation around their calling site and adjust temporal parameters for a more effective sound transmission (Ziegler et al., 2011).

Herein, we test the following hypotheses: 1) acoustic parameters are selected by the environment of occurrence according to the AAH; 2) frogs respond in a more flexible manner according to the amount of local vegetation around the individual. For this purpose, we studied intra-specific variation in the advertisement call of

Phyllomedusa nordestina. This tree-frog is widespread along Northeastern Brazil and occurs in two contrasting environments: Atlantic Rainforest and Caatinga. We recorded

40 the advertisement call of 101 individuals from 14 different localities, seven located in the Atlantic Rainforest and seven in the Caatinga dry forest (Figure 1) and analyzed

2227 calls. We measured the vegetation around 85 individuals and tested if spectral and temporal acoustic parameters are influenced by the habitat of occurrence and if they are correlated with the amount of vegetation around the frog, considering spatial distribution, body size, and environmental temperature.

Material and Methods

Organism

Phyllomedusa nordestina is a charismatic tree frog widespread along

Northeastern Brazil. It reproduces in small, and generally ephemeral ponds. Males call from perches in the vegetation where the amplexus takes place and eggs are laid in leaf- nests. When the eggs hatch, tadpoles fall into the pond where they develop until metamorphosis. The advertisement call of P. nordestina has been previously described

(Vilaça et al., 2011).

Study Sites

Recordings of calling males were made in 14 localities throughout Northeastern

Brazil, ranging from the northerly state of Rio Grande do Norte to the state of Bahia, in the south (Figure 1) (See Table S1 for details on each recording), covering most of the species distribution. Seven localities were in the Brazilian Atlantic Rainforest, and the remaining in the Caatinga, a semi-arid scrub and woodland characterized by low air humidity, and sparse irregular precipitation. Although its vegetation varies considerably, there is a predominance of shrubs, herbaceous plants, cacti, and sparse small trees (Leal et al., 2003). The Brazilian Atlantic Rainforest is the second largest rain forest from the American continent occupying most of Brazil's coast and reaching

Paraguay and Argentina at the southern part of its extension. It is characterized by high levels of precipitation and its vegetation is mostly composed of densely distributed high trees creating a vertical stratification (Tabarelli et al., 2005).

Field recordings

All recordings were made using a Marantz PMD 660 digital recorder with a sampling rate of 48 kHz and 16 bit resolution, connected to a Sennheiser ME66 directional microphone. Acoustic parameters were measured in Raven Pro 1.4 and spectograms produced as follows: FFT window width = 256; Frame = 100; Overlap =

50%. All recordings were of about five minutes and all calls of the focal individual were analyzed. For statistical analyzes the average value for each individual was used for all acoustic parameters. After each recording, environmental temperature was measured and snout-vent length (SVL) of the frog was taken in order to control for its effect on call structure (e.g. Sullivan, 1982). No frog was recorded more than once. The exact calling site was marked for vegetation measures that were taken on the following day.

Habitat characteristics

We took three different vegetation measures in each calling site. First, we measured the distance from the calling site to the nearest tree with a diameter at breast height larger than 30 cm. Two other measurements were taken at the exact calling site and along three random directions at distances of 1, 2, 4, 8, 16, and 32 m. In all 21 points we counted the number of woody plants inside a one-meter diameter circle and estimated canopy coverage using a 1 x 1 m square grid divided into 25 squares of 20 x

20 cm, placed above the head. To estimate coverage the numbers of squares with more than 50% coverage were counted.

Data Analysis

From all acoustic variables measured we chose four independent parameters identified after a correlation analysis and which have clear predictions in the AAH: 1) dominant frequency; 2) interval between pulses; 3) call rate; 4) number of pulses. In closed forest vegetation vocalizations are expected to be deeper, with longer intervals between call elements, and more redundant (higher call rate and longer) (Ey & Fischer,

2009). One of the main assumptions of most statistical analysis is the independence among observations. However, we sampled groups of individuals from various geographical localities, scenario which makes spatial autocorrelation very probable.

Data with spatial autocorrelation generates a super estimated number of independent observations, enhancing the probability of a false significant relationship between the explanatory and response variable, increasing the chances of type I error (Peres-Neto,

2009). We tested for spatial autocorrelation for each acoustic parameter (response variables) with Moran´s I test with the SAM 4.0 software (Rangel et al., 2006), considering that if the response variables do not present spatial autocorrelation the significance of the model would not be affected (Legendre et al., 2002). Considering the different statistical approaches that correct for spatial autocorrelation, the Generalized

Least Squares (GLS), which include spatial independence in the model error, has shown a good performance (Beale et al., 2010). For acoustic parameters with significant spatial dependence we used a GLS with an exponential function for the correlation matrix of the errors to test the influence of habitat and vegetation around calling site on call structure For the spatial independent parameters we used ordinary least squares (OLS) analyses. In every model we included body size (SVL) and environmental temperature as explanatory variables. The analyses were done using the nlme package (Pinheiro et al., 2007) in the R 3.1 environment.

For each acoustic parameter we ran two models, one including the habitat of occurrence (Atlantic Rainforest and Caatinga) and one including the vegetation coverage around each individual. Because many vegetation measurements were highly dependent, we did a Principal Components Analyses (PCA) based on a correlation matrix. In our analyses we included the two first components, which account for more than 42% of the variation (Figure S2). Finally, we analyzed spatial autocorrelation

(Moran´s I) for the residuals of the GLS models with SAM 4.0 software (See Figure S2 for autocorrelation diagnostics).

Results

Our results show that the environment has a significant influence on spectral and temporal acoustic characteristics of the advertisement call of P. nordestina, with different acoustic parameters being affected by habitat of occurrence and by the amount of vegetation around calling sites. Models testing habitat effect showed that frogs from the Atlantic Rainforest produce calls with longer interval between pulses (df = 90; F=

8.81; p = 0.004) and a slower call rate (df = 90; F= 5.98; p = 0.016) (Figure 2A,B).

Dominant frequency was influenced by body size (df = 90; F= 21.36; p < 0.001) but not by other variables. Number of pulses was not significantly affected by any of the variables (Table 1).

Models testing the influence of the amount of vegetation around calling site revealed the importance of this variable for dominant frequency and number of pulses.

The dominant frequency is strongly affected by the PC1 (Figure 3A) and also influenced by PC2 and body size, while the number of pulses was only influenced by

PC2 (Figure 3B). The correlation matrix between these acoustic parameters and all the vegetation variables measured indicate that when surrounded by denser coverage males

44 tend to use lower pitches and more pulse. Interval between pulses and call rate are not significantly influenced by any of the variables of these models (Table 2).

Discussion

Different acoustic parameters from the same call may present diverging evolutionary paths to cope with sound transmission. Two acoustic parameters were significantly affected by the habitat of occurrence, indicating that selective pressures differ between Atlantic Rainforest and Caatinga and are somehow driving the evolution of the advertisement call of Phyllomedusa nordestina. At the same time, two other parameters are correlated to the amount of vegetation around the calling site, indicating that individuals of this species have the ability to adjust their vocalization to different micro-habitats, presumably for more efficient sound propagation.

Pulse interval and call rate were significantly affected by the habitat of occurrence, indicating an adaptive response, yet only pulse interval varied accordingly to the AAH. As expected, individuals of P. nordestina from Atlantic Rainforest populations have advertisement calls with longer intervals between pulses.

Vocalizations with rapid amplitude modulations, where intervals between call elements are smaller, should suffer more sound degradation in closed vegetation habitats due the higher amount of reverberation in such habitats. At the same time, irregular amplitude fluctuations associated with wind and temperature gradients that characterize open areas should favor the propagation of fast amplitude modulated signals with smaller interval between elements (Morton, 1975; Richards & Wiley, 1980).

Conversely, call rate was affected by habitat contrary to expectations of the

AAH because individuals from the Caatinga called more frequently than those from the

Atlantic Forest. In forests it is predicted that animals would use acoustic signals at

45 higher rates, enhancing signal detection probability (Ey et al., 2009). We propose that this result is related to climatic differences between these environments and breeding characteristics of these frogs. The rainy season in the Caatinga is highly unpredictable and considerably shorter in comparison to the Atlantic Rainforest (Leal et al., 2003;

Tabarelli et al., 2005). Phyllomedusa nordestina relies on temporary puddles for tadpole development, and these habitats are much more ephemeral in the Caatinga, leading to shorter breeding periods. Production of advertisement calls requires an enormous amount of energetic expenditure for anurans, up to 25 times more than resting (Bevier,

1995). Therefore, populations from areas with shorter breeding period should increase calling effort.

While pulse interval and call rate seem adapted to habitat of occurrence, males of P. nordestina seem to respond with call flexibility to the amount of vegetation around calling site, altering dominant frequencies and number of pulses. Active alteration of call properties is well documented for anurans, however it is generally associated to changes in the social context where males are calling (Bee et al., 2000). Only two studies found evidences of advertisement call flexibility for more efficient acoustic propagation (Lardner & bin Lakim, 2002; Ziegler et al 2011). Ziegler et al. (2011) found a significant correlation between temporal parameters of the advertisement call of

Hypsiboas pulchellus and the amount of vegetation around each individual from a single population. Using an experimental approach, they found that some individuals alter temporal call parameters after being enclosed by artificial propagation barriers, indicating signal plasticity. Herein, we found that males of P. nordestina surrounded by higher vegetation densities emit advertisement calls with a higher number of pulses.

Calls with more pulses are more suitable in dense vegetation because longer calls have a higher probability of being detected (Ey & Fischer, 2009). This result endorses the

46 change in temporal acoustic parameters as a way of frogs to deal with different vegetation coverages (Ziegler et al., 2011). Increases in call duration in forests habitats have also been demonstrated for other vertebrates, such as primates (Ey et al., 2009).

The dominant frequency of anuran advertisement call is known to be under strong morphological constrains, where larger frogs have lower pitches. During territorial encounters, males of several species actively lower the pitch of their call

(Davies & Halliday, 1978). This acoustic parameter is also important in sexual selection for many species, where females prefer low or median frequencies (Ryan, 1980).

Although low frequencies suffer less excess attenuation, especially in closed habitats, tests of the AAH have failed to show adaptive responses to more efficient propagation in this parameter for anurans (Zimmerman, 1983; Bosch & de La Riva, 2004).

However, males of Metaphrynella sundana apparently adjust call frequency to explore the resonance effect of tree holes where they reproduce, enhancing propagation distance

(Lardner & bin Lakim, 2002). We found that the dominant frequency of the advertisement call of P. nordestina is affected by the amount of vegetation around each calling individual, having a stronger influence than body size.

The advertisement call of P. nordestina is strongly influenced by the environment in which it is emitted, as all acoustic parameters tested where somehow affected, two of them by the habitat type and two by the amount of vegetation around calling sites. These results contrast with most tests of the AAH for anurans, which fail to detect its importance on acoustic variation. The significant influence of the amount of vegetation around calling site underscores the importance of studies testing flexible responses for more efficient propagation and the evolution of vocal plasticity in vertebrates. We believe future studies should focus on other restricted groups, testing variation in single species or strongly related monophyletic clades, having adequate

47 number of replicates and using phylogenetic comparative methods which also account for the intra-specific variation.

Acknowledgements

We thank Márcio Zikan Cardoso and Renata Sousa-Lima for suggestions on the manuscript and fruitful discussions. AAG and FAJ thank CNPq for financial support

(Universal # 473503/2012-3 and #305704/2013-3, respectively).

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Figure 1: Geographic distribution of the recorded individuals of Phyllomedusa nordestina along Northeaster Brazil from the Caatinga (red squares) and Atlantic

Rainforest (green circle). The number beside each locality represents the number of individuals recorded from this population.

Figure 2: Difference between the advertisement call from Phyllomedusa nordestina from the Atlantic Rainforest (MA) and Caatinga (CA). A) Interval between pulses (PI). B) Call Rate (CR).

Figure 3: Influence of the amount of vegetation (represented by the two components with highest explanatory power after a principal component analyses: PC1;

PC2) surrounding the calling site of Phyllomedusa nordestina on its advertisement call.

A) Dominant frequency (DF). B) Number of pulses.

Table 1. Influence of habitat of occurrence (HB), body size (BS) and environmental temperature (TE) on the acoustic parameters of Phyllomedusa nordestina: dominant frequency (DF); number of pulses (NP); interval between pulses

(PI); and call rate (CR). For acoustic parameters with significant spatial correlation

(Moran’s I) we used generalized least squares (GLS) model and for the other ordinary least squares (OLS).

Parameter Spatial Correlation Variable df F-value p-value DF Yes HB 90 1.108 0.295 BS 90 21.362 <0.001 TE 90 2.205 0.141 NP No HB 90 0.016 0.899 BS 90 3.78 0.054 TE 90 2.89 0.093 PI Yes HB 90 8.813 0.004 BS 90 0.852 0.356 TE 90 0.695 0.407 CR Yes HB 90 5.987 0.016 BS 90 1.726 0.192 TE 90 0.009 0.923

Table 2. Influence of the amount of vegetation around calling site (represented by the two components with highest explanatory power after a principal component analyses: PC1; PC2), body size (BS), and environmental temperature (TE) on the acoustic parameters of Phyllomedusa nordestina: dominant frequency (DF); number of pulses (NP); interval between pulses (PI); and call rate (CR). For acoustic parameters with significant spatial correlation (Moran’s I) we used generalized least squares (GLS) model and for the other ordinary least squares (OLS).

Parameter Spatial Correlation Variable df beta p-value DF Yes PC1 76 -503.54 0.002 PC2 76 153.51 0.371 BS 76 -18.4 0.021 TE 76 5.58 0.539 NP No PC1 76 -1.01 0.137 PC2 76 -1.53 0.040 BS 76 0.04 0.221 TE 76 -0.08 0.035 -7.38E- PI Yes PC1 75 06 0.942 -9.43E- PC2 75 05 0.385 BS 75 4.45E-08 0.993 -9.72E- TE 75 06 0.121 CR Yes PC1 76 -0.06 0.358 PC2 76 0.014 0.848 BS 76 0.004 0.219 TE 76 0.003 0.419

Supplementary Material

Figure S1: Principal component analysis for the vegetation measured around each calling male of Phyllomedusa nordestina at different distances (0, 1, 2, 4, 8, 16, 32m). Number of woody plants in a one meter radius (NT); canopy coverage (CD); distance to the closest tree (DA).

Figure S2: Spatial autocorrelation analysis (Moran´s I) for the acoustic parameters of Phyllomedusa nordestina (left). For all parameters with a strong spatial dependence we performed a generalized least squares model (GLS) and tested the spatial autocorrelation of the models residuals (right).

Table S1: Detailed information in each individual of Phyllomedusa nordestina recorded throughout Northeastern Brazil

Location in Brazil Geographical coordenates Temperature Body Size Number of Species ID (City/State) (Latitude/Longitude) (°C) (mm) calls Phyllomedusa nordestina 1 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.7 35.19 36 Phyllomedusa nordestina 2 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.3 34.55 35 Phyllomedusa nordestina 3 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.7 36 39 Phyllomedusa nordestina 4 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.1 32.45 39 Phyllomedusa nordestina 5 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.1 33.42 56 Phyllomedusa nordestina 6 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 21.1 34.34 11 Phyllomedusa nordestina 7 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 21.1 32.94 12 Phyllomedusa nordestina 8 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.5 34.05 08 Phyllomedusa nordestina 9 Bezerros/ Pernambuco 8°17’27‖S 35°45’36‖W 19.8 38.12 16 Phyllomedusa nordestina 10 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 21 31.27 10 Phyllomedusa nordestina 11 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 21.4 32.35 24 Phyllomedusa nordestina 12 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 21.4 36.6 24 Phyllomedusa nordestina 13 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.7 30.3 11 Phyllomedusa nordestina 14 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.7 32.45 14 Phyllomedusa nordestina 15 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.3 32.69 07 Phyllomedusa nordestina 16 Cuité/Paraíba 6°29’28.4‖S 36°9’28.1‖W 20.3 35.41 13 Phyllomedusa nordestina 17 Estância/Sergipe 11°14’45‖S 37°27’49‖W 22.2 29.78 27 Phyllomedusa nordestina 18 Estância/Sergipe 11°14’45‖S 37°27’49‖W 23.2 34.93 19 Phyllomedusa nordestina 19 Estância/Sergipe 11°14’45‖S 37°27’49‖W 22.4 35.42 07 Phyllomedusa nordestina 20 Estância/Sergipe 11°14’45‖S 37°27’49‖W 22 35.16 04 Phyllomedusa nordestina 21 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 23.7 35.74 05 Phyllomedusa nordestina 22 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 21.1 37.28 04 Phyllomedusa nordestina 23 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 21.2 36.74 07 Phyllomedusa nordestina 24 Igarassu/Pernambuco 7°49’1.2‖S 34°57’19.2‖W 21 36.02 02 Phyllomedusa nordestina 25 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 21.3 NA 15 Phyllomedusa nordestina 26 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 21.3 35.69 50 Phyllomedusa nordestina 27 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 19 NA 20 Phyllomedusa nordestina 28 Igrapiúna/Bahia 13°47’45.5‖S 39°10’3.3‖W 19 35.39 16

CAPÍTULO 3

Background Noise as a Selective Pressure: Stream-breeding

Anurans Call at Higher Frequencies

David Lucas Röhr* a, Gustavo Brant Paterno a, Felipe Camurugi b, Flora

Acuña Juncá c, and Adrian Antonio Garda a

a Programa de Pós-graduação em Ecologia, Universidade Federal do Rio Grande do

Norte, Lagoa Nova, 59072–970, Natal, RN, Brazil. b Programa de Pós-Graduação em Ciências Biológicas (Zoologia), Departamento de

Sistemática e Ecologia, Centro de Ciências Exatas e da Natureza, Universidade Federal da Paraíba, João Pessoa, 58059–900, PB, Brazil c Departamento de Ciências Biológicas, Universidade Estadual de Feira de Santana, BR

116, Km 03, Campus Universitário, 44031–460, Feira de Santana, Bahia, Brazil.

*Corresponding author: [email protected]

Running Headline: Stream Noise and Frog Advertisement Calls

Summary

Acoustic signals are an important part in the behaviour of many species and may play a key role in speciation. However, little is known about the importance of natural selection on the evolution of such signals. Background noise from streams is a constant source of masking interference for anuran species reproducing in these environments.

Herein, we test if the noise of flowing water habitats has favoured advertisement calls with higher dominant frequencies in frogs. Phylogenetic generalized least square model analysis revealed a significant influence of reproductive environment and body size on dominant frequency, with no significant interaction between habitat and body size.

While stream breeders call at higher dominant frequency, this acoustic parameter decreases with body size in both environments. We discuss the biological consequences of long-term adaptive shift in this acoustic parameter and possible trade-offs with other evolutionary forces.

Key-words: Acoustic Communication; Advertisement Call; Comparative Methods;

Evolution; Masking Interference.

Introduction

Acoustic signals are a fundamental part in the communication system of many species, having evolved independently several times in different clades (Gerhardt & Huber

2002). Stochastic processes, pleiotropic effects, and sexual and natural selection may drive the evolution of sound communication in animals (Wilkins, Seddon & Safran

2013). The role of stochastic evolution has been quantified contrasting molecular and acoustic variation with the aid of recent phylogenetic hypotheses (Goicoechea, De La

Riva & Padial 2010). Morphological and physiological constrains affect signal characteristics, and selective pressures on these may lead to pleiotropic signal divergence (Podos 2001). Sexual selection is the best-studied evolutionary force shaping acoustic signal evolution. Females may show strong preference for specific acoustic parameters, resulting in differential mating success (Ritchie 1996) and ultimately leading to species divergence. In contrast, much less is known about the role of natural selection on acoustic signal evolution (Wilkins, Seddon & Safran 2013).

Background noise is one of the main constraints on acoustic communication, limiting the active space of every natural communication system (Brumm 2013). Short duration noise is circumvented by plastic responses, whereas more predictable and constant noise should result in long-term adaptive processes (Brumm 2013). While short-term plastic responses to background noise have been fairly well studied, less is known about long- term adaptive processes in constantly noisy environments (Brumm & Slabbekoorn

2005).

Stream noise is characterized by constancy, often high intensities, and emphasized energy in low frequency bands (Goutte, Dubois & Legendre 2013) that overlap with low frequency anuran vocalizations (Wells 2007). Hence, higher frequencies should be favoured in stream breeders by reducing the energy expenditure needed to diminish

65 interference by increasing intensity. Indeed, flowing water noise pressure level is one of the best predictors of anuran community composition in the vicinity of streams (Goutte,

Dubois & Legendre 2013) and communicating in high frequencies near streams improves signal detection and discrimination (Boonman & Kurniati 2011). Still, the role of flowing water noise as a selective pressure on anuran advertisement calls is contentious (Boeckle, Preininger & Hödl 2009; Hoskin, James & Grigg 2009). One analysis using 110 species in five families found that stream species use slightly higher dominant frequencies, but this trend vanished in analyses controlling for body size and phylogeny (Vargas-Salinas & Amezquita 2014).

Herein, we test if masking interference from low frequency noise of streams has favoured advertisement calls with higher dominant frequencies in frogs reproducing near these environments. To do so, we gathered information on calls of 509 species from 31 frog families in all biogeographic realms. We test this hypothesis using phylogenetic comparative methods controlling for adult male body sizes.

Materials and methods

Database was constructed composed of mean advertisement call dominant frequencies

(the frequency band with the greatest amount of energy) and maximum male body sizes

(snout-vent length – SVL) reported for each species from data available in the literature.

Only species reproducing exclusively in flowing or still waters (leaving out species that use both habitats and species which reproduce independently from water bodies) and that were also included in the phylogeny proposed by Pyron and Wiens (2011) were included. In order to achieve a large data set, practical and pre-established criteria were used for data inclusion, where for multiple literature hits on the same species the most recently reported mean dominant frequency and the overall largest male body size was

66 used. However, to ensure that these criteria do not include a bias in the analyses, a random subset of the data was tested demonstrating that there is no significant difference in dominant frequency between older and recent publications and that maximum and mean SVL are highly correlated (more than 98%) (Supporting

Information Sections 2.4 & 2.5) For a few species where authors reported a dominant frequency range, the average between maximum and minimum values was used. In rare cases where the publication did not report values for dominant frequency but included a spectrogram with a straight and clearly identifiable emphasized spectrum, a visual estimation of this parameter was included.

The phylogenetic signal of our data set was evaluated using Blomberg’s K, which varies from zero to infinity and indicates the strength of phylogenetic signal under Brownian motion model of evolution ((Blomberg, Garland & Ives 2003). Next, a phylogenetic generalized least square model (PGLS) was used, which takes into account the non- independence of observations due to phylogeny and assumes a Brownian motion model of evolution (Freckleton et al., 2002). The dominant frequency was included as the response variable and reproduction habitat (still/flowing) and SVL as the explanatory variables to test if dominant frequency was affected by reproduction environment.

Dominant frequencies and body sizes were log transformed (natural logarithm) before the analysis. To optimize branch length transformation, the lambda value was set by maximum likelihood (Orme et al. 2012). All statistical analyses were performed in R

3.1.2 using the packages Caper (Orme et al. 2012) and Picante (Kembel et al. 2010).

Results

A dataset of 509 species was compiled representing 31 of the 54 currently recognized anuran families (See Supporting Information Section 2 for phylogenetic tree and dataset; see Appendix 1 for complete table with references). Stream reproducing species

(N=177) have a mean dominant frequency of 3.37 ± 2.04 kHz (range 0.42–15.97) and a mean SVL of 41.7 ± 20 mm (range 20–138), while still water reproducing species

(N=332) average dominant frequency and SVL were 2.18 ± 1.26 kHz (range 0.18–9.17) and 51.2 ± 29.3 mm (range 15–245), respectively.

Dominant frequency (K = 0.37, Z variance = -4.16, p < 0.001) and SVL (K = 0.44, Z variance = -4.45, p < 0.001; Table 1) presented a significant phylogenetic signal. PGLS analysis revealed a strong influence of reproductive environment and body size on dominant frequency (R2 = 0.38), with no significant interaction between habitat and body size (Table 2; Fig. 1; see Section 4.5 in Supporting Information for model diagnostic). While stream breeders call with higher frequencies than still water species, dominant frequency decreases with body size in both environments (ß = -0.874, standard error = 0.052). Model residuals showed a non-significant phylogenetic signal

(K = 0.055, Z variance = 1.301, p = 0.888).

Discussion

As predicted, frogs reproducing in streams use higher dominant frequencies, suggesting that these species have evolved to diminish masking interference from background noise caused by flowing waters by using higher frequencies. This hypothesis was previously corroborated in studies on a single community (Preininger, Boeckle & Hödl

2007) and one specific family (Boeckle, Preininger & Hödl 2009), but also contradicted in another analysis (Hoskin, James & Grigg 2009), all of which used a limited taxonomic sampling and did not account for phylogeny. Conversely, an analysis including a wider taxonomic sampling (110 species from five families) and controlling for phylogenetic relationships recovered significant effects of body size but not of environment on advertisement calls (Vargas-Salinas & Amezquita 2014).

The authors suggest that habitat filtering, through restrictions imposed by ambient noise on the communication of large bodied frogs, would be responsible for the differences in dominant frequencies between environments. Our data shows that when a more comprehensive taxonomic coverage is considered, body size alone cannot account for all the variation observed in dominant frequencies, and differences in frequencies between environments are likely to have evolved in response to long-term exposure of frogs to ambient noise. Although Vargas-Salinas and Amezquita (2014) found different results, they only included about one fifth of the species used in our analysis. More importantly, the fact that their analysis failed to detect a significant environment effect might be related to the restricted number of families used in the study (five), highly diminishing the number of phylogenetic contrasts in the analyses (monophyletic clades with species from both habitats) and hindering extrapolation to all anurans.

Furthermore, when only the three most representative families with species from both habitats are tested separately, the habitat effect is not significant for one family and the size effect is very different between the two others (Supporting Information Section 4.7)

Although environmental influence on dominant frequency is highly significant in our analysis, its effect is small when compared to body size (Table 2). This is expected considering the inverse relationship between vocal apparatus mass and call frequency

(Martin, 1971), which makes the variation in this acoustic parameter limited by morphological constraints. Indeed, our complete PGLS model accounts for about 40% of the variation in this parameter and other selective forces might be important (see discussion below). Moreover, the importance of environmental noise can vary among different anuran clades and future studies should focus on more restrictive groups (such as a single family) with better representation of its species and accurate measures of sound pressure levels. Even though the environment is not the main driver of dominant

69 frequency variation, there is a mean difference of nearly 1200 Hz between environments. Thus, considering the importance of this parameter for anuran reproduction (Gerhardt & Huber 2002), its biological relevance should not be overlooked.

In large species, the increase in dominant frequency within physical constraints on sound production mechanisms may not overcome the emphasized spectrum from background stream noise. Furthermore, because advertisement calls are crucial for anuran reproductive behaviour, other evolutionary forces besides pleiotropic effects of body size and noise interference might be involved in the establishment of dominant frequency differences. For instance, some species/clades may evolve different strategies to cope with such interference, such as visual communication (Hödl & Amézquita

2001). Therefore, complex trade-offs may also be involved because of the pervasive role of this vocalization in anuran biology.

Attractiveness and detectability may be selected by opposing forces in streams. During aggressive acoustic encounters dominant frequency might be determinant for the outcome (Davies & Halliday, 1978), and territorial males may lower call frequency in the presence of intruders (Wagner, 1989) (Bee & Bowling 2002). Meanwhile, females may show increased phonotaxis for low or median values of dominant frequency, leading to directional or stabilizing selection (Gerhardt 1991), but see Gerhardt and

Schwartz (2001) for further discussion on female preference for dominant frequency.

Hence, males near streams may face a trade-off between the need to increase call frequency to enhance signal detection at the expense of reducing attractiveness.

Furthermore, a trade-off between sound propagation and detectability in forested stream environments is also expected. Low frequency calls are more efficient in habitats with many physical barriers compared to higher frequencies (Ey & Fischer 2009). Therefore,

70 species reproducing in forest streams should face opposite selective pressures, where low dominant frequencies suffer less attenuation and degradation but high dominant frequencies experience less masking interference. Furthermore, community composition may promote additional limits and selective pressures by driving the evolution of anuran advertisement call dominant frequencies in two distinct manners. First, the presence of sympatric phylogenetically related taxa with similar vocalizations may lead to sexual character displacement to decrease hybridization probability (Lemmon 2009).

Second, in highly diverse acoustic habitats calls may evolve to fill different acoustic niches and spectral silent windows should be favoured (Chek, Bogart & Lougheed

2003). In either case, dominant frequency changes favoured by these scenarios could reinforce or counterbalance the selective forces of flowing water masking interference.

Although background noise is probably the main difference in the acoustic scenario between still and flowing water habitats, these environments also vary in a myriad of other factors that might affect its acoustic community and should be considered in future studies, such as: 1) community of sound-guided predators; 2) vegetation coverage which might act as propagation barriers; 3) sympatric species with prominent acoustic signals; 4) tadpole development environment leading to differences in adult body size and steroid hormones.

Finally, even with this complex evolutionary scenario we found a significant trend for anuran species calling near streams to use higher dominant frequencies. Other advertisement call characteristics may respond similarly. For example, sound intensity and call rate are expected to be higher in frogs reproducing in such habitats and using dominant frequencies similar to local noise. Patterns for other variables, such as call duration and complexity, are less clear. Testing predictions for these variables, however, is much harder because of the lack of appropriate descriptions of these parameters in the

71 literature. Background noise from streams is clearly determinant for the evolution of anuran advertisement calls and future work should explore the generality of these results for other groups of animals.

Acknowledgements

We thank Carlos Roberto Fonseca, Alex Pyron, Pablo Martinz, Marcelo Gehara, and

Frank Burbrink for suggestions on the manuscript and fruitful discussions. AAG and

FAJ thank CNPq for financial support (Universal # 473503/2012-3 and #305704/2013-

3, respectively).

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Table 1. Phylogenetic signal for dominant frequency (DF) and body size (SLV) calculated through Blomberg’s K (Blomberg, Garland & Ives 2003Blomberg, Garland

& Ives 2003).

Source K PIC.mean PIC.rdn.mean P

logDF 0.3660 0.00754 0.02918 0.001

logSVL 0.4387 0.00321 0.01501 0.001

Residuals 0.1002 0.00013 0.00016 0.173

Table 2. Anova table for the phylogenetic generalized least square model {log(DF) ~ habitat * log(SLV)} evaluating the effects of body size (SVL) and habitat on advertisement call dominant frequencies.

Source df SQ MSQ F P

Habitat 1 0.0764 0.0764 33.0 < 0.001

SVL 1 0.6461 0.6461 279.4 < 0.001

Habitat*SVL 1 0.0008 0.0008 0.3 0.5674

Residuals 505 1.1675 0.0023 — —

Figure 1. Relationship between dominant frequency and body size (SVL) for species calling in still and flowing waters (n = 509). Lines represent PGLS regressions.

ELETRONIC SUPPLEMENTARY MATERIAL Background Noise as a Selective Pressure: Stream- breeding Anurans Call at Higher Frequencies

David Lucas Röhr; Gustavo B. Paterno; Felipe Camurugi; Flora A. Juncá; Adrian A. Garda;

May, 2015

This doccument follows the principles of reproducible research (Peng, 2011). All Data and code required to repeat the analysis bellow are linked at Github. To dowanload the source code used to generate all figures, tables and analysis in the paper, please see: source code. This documment was generated in R studio with kintr package.

1. Packages versions: We used R version 3.2.0 (2015-04-16) and the following packages: library(ape);library(caper);library(knitr) library(dplyr);library(ggplot2);library(picante);library(gridExtra)

Please check the Packages versions, for details.

2. Data structure: 2.1 Species data

• To download raw data: link.

• See Appendix 1 to download complete table with references used.

The species dataset contains six variables (see Methods for detailed information on data collection). variable discription fam family sp species name environment Reproductive environment (still or flowing) DF Dominant frequency (hertz) SVL snout-vent length (mm) logDF log of dominant frequency (DF) logSVL log of snout vent length (SVL)

Last six rows of the species dataset:

environme SV fam sp nt DF L logDF logSVL 50 Rhacophorid Rhacophorus_schle still 190 43 7.5496 3.7612 4 ae gelii 0 09 00 50 Rhinophrynid Rhinophrynus_dors still 152 75 7.3297 4.3174 5 ae alis 5 50 88 50 Scaphiopodid Scaphiopus_couchii still 165 72 7.4085 4.2766 6 ae 0 31 66 50 Scaphiopodid Scaphiopus_holbro still 142 78 7.2619 4.3567 7 ae okii 5 27 09 50 Scaphiopodid Scaphiopus_hurterii still 150 67 7.3132 4.2046 8 ae 0 20 93 50 Scaphiopodid Spea_multiplicata still 130 49 7.1701 3.8918 9 ae 0 20 20 2.2 Phylogentic tree The phylogenetic tree used in this paper was pruned from: Pyron and Wiens (2011) anura super tree. * To dowanload the pruned tree with study species (509): Study Tree.

## ## Phylogenetic tree with 509 tips and 508 internal nodes. ## ## Tip labels: ## Hadromophryne_natalensis, Heleophryne_purcelli, Heleophryne_regis, Calyptocephallela_gayi, Neobatrachus_pelobatoides, Neobatrachus_sudell i, ... ## Node labels: ## 209.59, 206.29, 195.63, 151.99, 47.93, 9.02, ... ## ## Rooted; includes branch lengths.

Figure S1: Phylogeny for 509 anuran species sampled in this study extracted from Pyron and Wiens (2011) original tree. Black circles represent pond-breeding species (N = 332) and red circles stream-breeding species (N= 177)

2.3 Summary metrics for Dominant frequency and Sout- vent length environment meanDF sdDF meanSVL sdSVL flowing 3377.322 2036.938 41.70932 20.03995 still 2180.557 1263.536 51.27440 29.35337

Figure S2: Distribution histograms for Dominant Frequency (log) and Snout-vent length (log) 2.4 Testing for bias in Dominant Frequency between old and new publications To construct a large data set, we difened some practical, pre-established criteria of data inclusion that enabled us to use more than 500 species. We agree that the most recent publication is not always the best, however the recent improvement of recording equipment and sound analysis software is unquestionable. Furthermore, judging the merit of multiple papers takes a large amount of time because factors such as the number of individuals recorded, geographical location of individuals included and recording equipment used must be considered. We believe that in multiple occasions these decisions can be subjective, leading to uncertainty about possible bias in the resulting dataset. Our pre-established criterion explicitly avoids this, leaving no space to speculations about such biases. To be sure, we collected data on dominant frequency from different papers for 30 randomly-selected species from our dataset and found no significant differences between the most recent compared to the older data:

## ## Paired t-test ## ## data: DFpaired.data[, 1] and DFpaired.data[, 2] ## t = 1.0339, df = 31, p-value = 0.3092 ## alternative hypothesis: true difference in means is not equal to 0 ## 95 percent confidence interval: ## -101.573 310.448 ## sample estimates: ## mean of the differences ## 104.4375

This provides strong evidence that our criterion does not affect our results and conclusions.

2.5 Testing correlation between mean and maximum SVL In large comparative studies it is always hard to choose the best approach to represent species, however, we do not believe that the choice of using maximum body size affected the results presented in the manuscript.

1. The decision to use maximum SVL was a pre-established criterion applied to all species, thus we find very unlikely that this choice could cause any non-random tendency in our analysis.

2. We decided to use maximum SVL because data for most species was available in multiple publications and it would therefore be hard to calculate a mean value from many sources. It would be possible to calculate the weighted mean among publications, however this approach would be unpractical considering the number of species included (509). Furthermore, we could also use the mean value provided from one specific publication, however, this could include subjectivity to the analysis, diminishing its reproducibility. Thus, choosing the maximum value among papers excludes bias and represents the adult potential size of species considering multiple publications. 3. Finally, in order to consider the concerns about our choice of maximum SVL: 1. Re-collected data for maximum and mean SVL for 30 random species of our previous survey.

2. Performed a Pearson's correlation analysis between maximum and mean SVL (see results below).

SVL_m ax SVL_me an SVL_max 1.0000000 0.9817694 SVL_mean 0.9817694 1.0000000 The correlation coefficient between maximum and mean SVL values was higher than 0.95, thus we truly believe that our choice of maximum SVL by no means affected any aspect of our main results or final conclusions.

3. Phylogenetic signal 3.1 Domiant Frequency and Snout-vent length We used K statistics to test the phylogentic signal for Dominant Frequency (logDF) and Snout- vent length(logSVL)(for details about the method, see Blomberg et al (2003)). k.signal <- multiPhylosignal(select(comp.data$data,logDF,logSVL),comp. data$phy,reps=999) kable(k.signal)

K PIC.variance.o PIC.variance.rnd.me PIC.variance PIC.variance

bs an .P .Z logDF 0.366009 0.0075415 0.0292139 0.001 -4.246941 9 logSV 0.438748 0.0032133 0.0149448 0.001 -4.609946 L 0 Dominant Frequency and Snout-Vent Length show significant phylogenetic signal, however, K values are low. 3.2 Checking the phylogenetic signal of the residuals from stantard OLS regression In order to check the need to include the phylogeny in our analysis, first it is important to check if there is phylogenetic signal in the residuals of an Ordinary Least Square regression (OSL) (Kamilar & Cooper, 2013; Freckleton, 2009). mod.osl <- lm(logDF ~ environment*logSVL,anura.data) # Extracting residuals from the model: comp.data$data$lm.res <- residuals(mod.osl) osl.resi.sig <- phylosignal(comp.data$data$lm.res,reps=999,comp.data$p hy) kable(osl.resi.sig)

K PIC.variance.obs PIC.variance.rnd.mean PIC.variance.P PIC.variance.Z 0.1166795 0.0103429 0.0146007 0.01 -1.617337 Because the residuals from OSL regression show phylogenetic signal k = 0.12, it is necessary to correct for phylogenetic non-independence in data.

4. Data analysis We used a phylogenetic generalized least square model (PGLS) with dominant frequency as the response variable and reproduction habitat (lentic/lotic) and SVL as the explanatory variables to test if dominant frequency was affected by reproduction environment. Dominant frequencies and body sizes were log transformed before the analysis. To optimize branch length transformation, the lambda value was set by maximum likelihood (see Freckleton et al., 2002; Orme et al., 2013 for details). PGLS analysis were performed with the function pgls from the package caper. 4.1 Data preparation: Using the function comparative.data we combined our phylogenie with the species dataset comp.data <- comparative.data(phy=study.tree,data=anura.data,names.col ="sp",vcv=T,vcv.dim=3)

4.2 Phylogenetic generalized least square model (PGLS) Fitting pgls model with with lambda adjusted by maximum likelihood:

82 mod.pgls <- pgls(logDF ~ environment*logSVL, data=comp.data,lambda="ML ") summary(mod.pgls) ## ## Call: ## pgls(formula = logDF ~ environment * logSVL, data = comp.data, ## lambda = "ML") ## ## Residuals: ## Min 1Q Median 3Q Max ## -0.169581 -0.034834 -0.003471 0.024159 0.162009 ## ## Branch length transformations: ## ## kappa [Fix] : 1.000 ## lambda [ ML] : 0.889 ## lower bound : 0.000, p = < 2.22e-16 ## upper bound : 1.000, p = < 2.22e-16 ## 95.0% CI : (0.823, 0.933) ## delta [Fix] : 1.000 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 11.222857 0.439315 25.5462 <2e-16 *** ## environmentstill -0.420810 0.399058 -1.0545 0.2922 ## logSVL -0.918595 0.093136 -9.8630 <2e-16 *** ## environmentstill:logSVL 0.060128 0.104687 0.5744 0.5660 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 0.04808 on 505 degrees of freedom ## Multiple R-squared: 0.3825, Adjusted R-squared: 0.3788 ## F-statistic: 104.3 on 3 and 505 DF, p-value: < 2.2e-16 4.3 ANOVA table Df Sum Sq Mean Sq F value Pr(>F) environment 1 0.0763674 0.0763674 33.0318903 0.0000000 logSVL 1 0.6460504 0.6460504 279.4422042 0.0000000 environment:logSVL 1 0.0007571 0.0007571 0.3274965 0.5673916 Residuals 505 1.1675238 0.0023119 NA NA 4.4 Lambda estimation profile.lambda <- pgls.profile(mod.pgls) plot(profile.lambda)

Figure S3: Confidence interval for lambda estimation

4.5 Model diagnostic 4.5.1 Diagnostic graphs

Figure S4: Standard graphic methods for model diagnostics

Residulas do not show any tendency.

4.5.2 Phylogenetic signal of model residuals After performing PGLS analysis it is important to check the phylogenetic signal of model residuals. k.residuals <- phylosignal(mod.pgls$phyres,reps=999,comp.data$phy) kable(k.residuals)

K PIC.variance.obs PIC.variance.rnd.mean PIC.variance.P PIC.variance.Z 0.1054035 0.0001429 0.0001544 0.395 -0.4206921 Results above shows that the residuals do not present significant phylogenetic signal.

4.6 Model comparison: OSL vs PGLS kable(AIC(mod.osl,mod.pgls))

df AIC mod.osl 5 671.3566 mod.pgls 4 485.3776 AIC comparison shows that PGLS model has much lower AIC value (485) tham OSL model (671). Thus, PGLS model is a better fit for the data. 4.7 Phylogenetic generalized least square model (PGLS) within families To test if the environment effect on Dominant Frequency is independent of taxonomic group, we performed pgls models for the three families in this dataset with more tham 30 species (Bufonidae, Ranidae and Hylidae).

4.7.1 Bufonidae: Fitting pgls model with with lambda adjusted by maximum likelihood: mod.pgls.bufo <- pgls(logDF ~ environment*logSVL, data=comp.data.bufo, lambda="ML") kable(anova(mod.pgls.bufo))

Df Sum Sq Mean Sq F value Pr(>F) environment 1 0.0017044 0.0017044 0.6756231 0.4154320 logSVL 1 0.1610608 0.1610608 63.8456240 0.0000000 environment:logSVL 1 0.0029574 0.0029574 1.1723362 0.2846895 Residuals 45 0.1135197 0.0025227 NA NA

4.7.2 Ranidae: Fitting pgls model with with lambda adjusted by maximum likelihood: mod.pgls.rani <- pgls(logDF ~ environment*logSVL, data=comp.data.rani, lambda="ML") kable(anova(mod.pgls.rani))

Df Sum Sq Mean Sq F value Pr(>F) environment 1 0.1111967 0.1111967 30.416038 0.0000037 logSVL 1 0.0044243 0.0044243 1.210182 0.2790242 environment:logSVL 1 0.0094783 0.0094783 2.592628 0.1166087 Residuals 34 0.1242992 0.0036559 NA NA

4.7.2 Hylidae: Fitting pgls model with with lambda adjusted by maximum likelihood: mod.pgls.hyli <- pgls(logDF ~ environment*logSVL, data=comp.data.hyli, lambda="ML") kable(anova(mod.pgls.hyli))

Df Sum Sq Mean Sq F value Pr(>F) environment 1 0.0196581 0.0196581 7.828985 0.0057858 logSVL 1 0.1412524 0.1412524 56.254873 0.0000000 environment:logSVL 1 0.0003487 0.0003487 0.138868 0.7099115 Residuals 157 0.3942170 0.0025109 NA NA

Figure S5: Regression plots for three anura families (Hylidae, Ranidae, Bufonidae).

5. References

1. Pyron, A. R., & Wiens, J. J. (2011). A large-scale phylogeny of Amphibia including over 2800 species, and a revised classification of extant frogs, salamanders, and caecilians. Molecular Phylogenetics and Evolution, 61(2), 543-583.

2. Blomberg, S. P., Garland, T., & Ives, A. R. (2003). Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution, 57(4), 717-745.

3. Orme, D., Freckleton, R., Thomas, G., Petzoldt, T., Fritz, S., Isaac, N. and Pearse, W. (2013). caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2. http://CRAN.R-project.org/package=caper

4. Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1618), 20120341.

5. Freckleton, R. P., Harvey, P. H., & Pagel, M. (2002). Phylogenetic analysis and comparative data: a test and review of evidence. The American Naturalist, 160(6), 712-726.

6. Freckleton, R. P. (2009). The seven deadly sins of comparative analysis. Journal of Evolutionary Biology, 22(7), 1367-1375.

When using the data available in this paper, please cite the original publication. Contact [email protected] for any further information.

Appendix1. Dominant frequencies, snout-vent length (SVL) and literature sources for

species included in the analysis. Asterisk indicates stream-breeding species.

Taxa SVL (mm) DF (Hz) Source Allophrynidae Allophryne ruthveni 20.6 4645 (Lynch & Freeman, 1966; Lescure & Marty, 2000) Alsodidae Alsodes nodosus* 75.85 1140 (Penna et al., 1983; Palma, 2013) Alytidae Alytes cisternasii* 41 1491 (Márquez, 1995) Arthroleptidae Cardioglossa occidentalis* 27.3 4100 (Blackburn et al., 2008) Leptopelis argenteus 45 1750 (Channing, 2001) Leptopelis bocagii 52 600 (Channing & Howell, 2006) Leptopelis concolor 34 1750 (Schiøtz, 1999) Leptopelis kivuensis 37 1480 (Roelke et al., 2011) Batrachylidae Batrachyla antartandica 36.7 1951 (Penna & Solis, 1998; Penna & Meier, 2011) Hylorina sylvatica 56 1252 (Barrio, 1967; Penna & Veloso, 1990) Bufonidae Ansonia hanitschi* 32 5700 (Malkmus et al., 2002) Ansonia leptopus* 40 3933 (Matsui, 1982a; Wood Jr. et al., 2008) Ansonia longidigita* 50 3500 (Malkmus et al., 2002) Ansonia platysoma* 25 8000 (Malkmus et al., 2002) Atelopus chiriquiensis* 34 2025 (Lötters et al., 1999; Savage, 2002) Atelopus flavescens* 30 2725 (Lescure & Marty, 2000) Atelopus franciscus* 21 3320 (Lescure & Marty, 2000) Atelopus peruensis* 38.5 1950 (Lötters et al., 1999; Coloma et al., 2000) Atelopus pulcher* 29.3 2429 (Lötters et al., 2002) Atelopus spumarius* 29 3375 (Cocroft et al., 1990; Lima et al., 2006) Atelopus varius* 41 2350 (Savage, 1972; Cocroft et al., 1990) Atelopus zeteki* 40 1820 (Cocroft et al., 1990; Savage, 2002) Bufo arenarum 112 1100 (Straneck et al., 1993) Bufo asper* 100 1031 (Inger & Bacon Jr, 1968; Preininger et al., 2007) Bufo beebei 51 3150 (Gallardo, 1965; Tárano, 2010) Bufo brauni* 65 1120 (Channing & Howell, 2006) Bufo calamita 71 1477 (Arak, 1988) Bufo castaneoticus 37.7 1650 (Köhler & Lötters, 1999b) Bufo coccifer 62 2512 (Mendelson III et al., 2005) Bufo cognatus 103 2307 (Wright & Wright, 1949; Cocroft & Ryan, 1995) Bufo empusus 76 1549 (Schawrtz, 1972; Alonso & Rodríguez, 2003) Bufo granulosus 70 2906 (Lima et al., 2006; São-Pedro et al., 2011) Bufo gundlachi 34 2669 (Schawrtz, 1972; Alonso & Rodríguez, 2003)

Taxa SVL (mm) DF (Hz) Source Bufo gutturalis 90 1150 (Channing, 2001; Harper et al., 2010) Bufo houstonensis 77 2151 (Dodd Jr., 2013) Bufo ibarrai 82 1700 (Porter, 1966; Mendelson III et al., 2005) Bufo juxtasper* 138 718 (Inger, 1964; Matsui, 1982b) Bufo longinasus* 29 2329 (Schawrtz, 1972; Alonso & Rodríguez, 2003) Bufo luetkenii 96 1700 (Porter, 1966; Savage, 2002) Bufo marmoreus* 83 1825 (Porter, 1966; Suazo-Ortuño et al., 2007) Bufo melanochlorus* 65 1700 (Savage, 2002) Bufo nebulifer 98 1475 (Blair, 1956; Dodd Jr., 2013) Bufo ocellatus 53 1378 (Caldwell & Shepard, 2007) Bufo pantherinus 91 2500 (Channing, 2001) Bufo pardalis 110 650 (Cherry & Francillon-Vieillot, 1992; Cherry & Grant, 1994) Bufo quercicus 32 4750 (Dodd Jr., 2013) Bufo raddei 67 1513 (Kuzmin & Ischenko, 1997; Stöck et al., 2000) Bufo retiformis 47 3113 (Bogert, 1962; Sullivan et al., 2000) Bufo schneideri 180 700 (Norman, 1994; Köhler et al., 1997) Bufo siculus 86.6 1600 (Stöck et al., 2008; Lo Valvo & Giacalone, 2013) Bufo speciosus 78 2621 (Wright & Wright, 1949; Cocroft & Ryan, 1995) Bufo steindachneri 48.3 2142 (Largen et al., 1978) Bufo taladai* 138 768 (Schawrtz, 1959; Alonso & Rodríguez, 2003) Bufo terrestris 82 2000 (Wright & Wright, 1949; Leary, 2001) Bufo valliceps 77.8 1480 (Sullivan & Wagner Jr., 1988; Wagner Jr. & Sullivan, 1995) Dendrophryniscus minutus 17 3900 (Lescure & Marty, 2000; Lima et al., 2006) Pedostibes tuberculosus* 38 3782 (Gururaja & Ramachandra, 2006) Pelophryne misera 21 4000 (Malkmus et al., 2002) Schismaderma carens 88 450 (Channing & Howell, 2006) Calyptocephalellidae Calyptocephallela gayi 120 866 (Penna & Veloso, 1990) Centrolenidae Centrolene buckleyi* 34.7 2500 (Bernal et al., 2004; Guayasamin et al., 2006) Centrolene daidaleum* 24.1 6120 (Ruiz-Carranza & Lynch, 1991; Cardozo-Urdaneta & Señaris, 2012) Centrolene geckoideum* 77 3828 (Lynch et al., 1983; Grant et al., 1998) Centrolene hesperium* 27.3 3630 (Cadle & McDiarmid, 1990) Centrolene peristictum* 20.6 6878 (Lynch & Duellman, 1973; Maldonado, 2012) Centrolene savagei* 24.1 6214 (Díaz-Gutiérrez et al., 2013) Centrolene venezuelense* 33.8 4050 (Señaris & Ayarzagüena, 2005; Guayasamin et al., 2009) Cochranella euknemos* 25 4200 (Savage, 2002) Cochranella granulosa* 29 4250 (Savage, 2002) Cochranella mache* 24 5410 (Guayasamin & Bonaccorso, 2004; Ortega-Andrade et al., 2013) Espadarana prosoblepon* 28 5758 (Jacobson, 1985; Savage, 2002) Hyalinobatrachium carlesvilai* 23.6 4838 (Castroviejo-Fisher et al., 2009) Hyalinobatrachium 27 4500 (Starrett & Savage, 1973; Savage, 2002) colymbiphyllum*

Taxa SVL (mm) DF (Hz) Source Hyalinobatrachium 24 4300 (Myers & Donnelly, 1997) crurifasciatum* Hyalinobatrachium eccentricum* 23 4160 (Myers & Donnelly, 2001) Hyalinobatrachium fleischmanni* 25.5 5395 (Kime et al., 2000; Savage, 2002) Hyalinobatrachium fragile* 24.5 3846 (Guayasamin et al., 2009; Wen et al., 2012) Hyalinobatrachium iaspidiense* 20.1 4940 (Lescure & Marty, 2000) Hyalinobatrachium ignioculus* 23 4920 (Noonan & Bonett, 2003; Barrio-Amorós & Castroviejo-Fisher, 2008) Hyalinobatrachium mondolfii* 22.5 5106 (Señaris & Ayarzagüena, 2001) Hyalinobatrachium orientale* 24.5 4819 (Cannatella & Lamar, 1986; Castroviejo-Fisher et al., 2008) Hyalinobatrachium orocostale* 25.5 3435 (Castroviejo-Fisher et al., 2008; Guayasamin et al., 2009) Hyalinobatrachium pallidum* 22.4 3259 (Cardozo-Urdaneta & Señaris, 2012; Rojas-Runjaic et al., 2012) Hyalinobatrachium pellucidum* 21.4 5039 (Castroviejo-Fisher et al., 2009; Wen et al., 2012) Hyalinobatrachium taylori* 21.6 4495 (Lescure & Marty, 2000) Hyalinobatrachium valerioi* 24 7250 (Starrett & Savage, 1973; Savage, 2002) Nymphargus griffithsi* 24.2 4107 (Hutter & Guayasamin, 2012) Nymphargus pluvialis* 26.5 3941 (Catenazzi et al., 2009) Rulyrana spiculata* 27.7 4639 (Catenazzi et al., 2009) Teratohyla midas* 26.8 4680 (Zimmerman, 1983; Guayasamin et al., 2009) Teratohyla pulverata* 29 5950 (Savage, 2002) Teratohyla spinosa* 20 7000 (Savage, 2002) Vitreorana antisthenesi* 27 5432 (Guayasamin et al., 2009; Wen et al., 2012) Vitreorana eurygnatha* 22.5 4850 (Heyer et al., 1990) Vitreorana gorzulae* 22.5 4787 (Kok & Castroviejo-Fisher, 2008; Castroviejo-Fisher et al., 2009) Vitreorana helenae* 20.4 4495 (Señaris & Ayarzagüena, 2005; Guayasamin et al., 2009) Ceratophryidae Ceratophrys cornuta 72 2176 (Duellman & Lizana, 1994; Marquez et al., 1995) Ceratophrys cranwelli 130 1715 (Valetti et al., 2013) Ceratophrys ornata 125.8 2000 (Basso, 1990; Salas et al., 1998) Chacophrys pierottii 53.1 4357 (Lascano, 2011) Cycloramphidae Cycloramphus boraceiensis* 51.3 1950 (Heyer et al., 1990) Thoropa miliaris* 71.5 600 (Heyer et al., 1990) Dicroglossidae Euphlyctis cyanophlyctis 45 1651 (Roy & Elepfandt, 1993) Euphlyctis ehrenbergii 66 937 (Roy & Elepfandt, 1993; Khan, 1997) Fejervarya limnocharis 43 2119 (Prakash, 1988; Márquez & Eekhout, 2006) Fejervarya sakishimensis 55.5 1030 (Matsui et al., 2007) Fejervarya syhadrensis 29.3 3350 (Kuramoto et al., 2007) Hoplobatrachus occipitalis 104 1250 (van den Elzen & Kreulen, 1979; Rödel, 2000) Hoplobatrachus tigerinus 120 520 (Roy & Elepfandt, 1993; Glaw & Vences, 2007) Limnonectes arathooni* 42 3380 (Smith, 1927; Brown & Iskandar, 2000) Limnonectes blythii* 105.4 428 (Matsui, 1995) Limnonectes gyldenstolpei* 53 1085 (Garcia-Rutledge & Narins, 2001) Limnonectes microdiscus* 48.6 875 (Inger, 1954; Kurniati et al., 2010)

Taxa SVL (mm) DF (Hz) Source Paa spinosa* 101 834 (Yu & Zheng, 2009) Sphaerotheca breviceps 48 1950 (Minton, 1966; Kanamadi et al., 1994) Heleophrynidae Hadromophryne natalensis* 45 1500 (Channing, 2001) Heleophryne purcelli* 47 2250 (Channing, 2001) Heleophryne regis* 43 1800 (Channing, 2001) Hylidae Agalychnis annae* 73.9 1165 (Duellman, 1970) Agalychnis callidryas 59 2006 (Duellman & Pyles, 1983; Savage, 2002) Agalychnis litodryas 72.2 1664 (Duellman, 1970) Agalychnis saltator 54 1800 (Duellman & Pyles, 1983; Leenders, 2001) Agalychnis spurrelli 75.6 893 (Ortega-Andrade, 2008) Aplastodiscus albofrenatus* 40 2500 (Lutz, 1973; Heyer et al., 1990) Aplastodiscus albosignatus* 52 2630 (Lutz, 1973; Abrunhosa et al., 2005) Aplastodiscus arildae* 41.6 2847 (Heyer et al., 1990; Carvalho Jr et al., 2006) Aplastodiscus callipygius* 50.7 1040 (Cruz & Peixoto, 1985; Abrunhosa et al., 2005) Argenteohyla siemersi 70 1882 (Cei, 1980; Cajade et al., 2010) Bokermannohyla circumdata* 70 1460 (Lutz, 1973; Carvalho et al., 2012) Bokermannohyla hylax* 61.5 2000 (Heyer et al., 1990; Carvalho et al., 2012) Cyclorana brevipes 45 1930 (Tyler & Martin, 1977) Cyclorana cryptotis 41 1060 (Tyler & Martin, 1977) Cyclorana cultripes 52 1879 (Tyler & Martin, 1977) Cyclorana manya 29.9 2500 (Van Beurden & McDonald, 1980) Dendropsophus allenorum 21.4 3430 (Duellman, 2005) Dendropsophus anceps 40 3244 (Lutz, 1973; Gomes & Martins, 2006) Dendropsophus berthalutzae 21 4041 (Lutz, 1973; Forti et al., 2012) Dendropsophus bifurcus 28 2958 (Duellman, 1978; Márquez et al., 1993) Dendropsophus bipunctatus 25 5426 (Lutz, 1973; Wogel & Pombal Jr, 2007) Dendropsophus brevifrons 22 4254 (Duellman, 1978; Duellman & Pyles, 1983) Dendropsophus carnifex 27.7 2490 (Duellman, 1969; De la Riva et al., 1997) Dendropsophus ebraccatus 27.8 2577 (Duellman & Pyles, 1983) (Duellman, 1970) Dendropsophus elegans 29.6 3500 (Lutz, 1973; Bastos & Haddad, 1995) Dendropsophus giesleri 25 3300 (Heyer, 1980; Weygoldt & Peixoto, 1987) Dendropsophus koechlini 24 4500 (Duellman, 2005) Dendropsophus labialis 43 1750 (Amézquita, 1999; Guarnizo, 2011) Dendropsophus leali 23 6200 (Duellman, 2005) Dendropsophus leucophyllatus 36 2482 (Márquez et al., 1993; Rodríguez & Duellman, 1994) Dendropsophus marmoratus 44 1514 (Duellman & Pyles, 1983; Rodríguez & Duellman, 1994) Dendropsophus microcephalus 24.5 6210 (Duellman, 1970; Tárano, 2010) Dendropsophus minusculus 20.6 9186 (Duellman & Pyles, 1983; Duellman, 1997) Dendropsophus nanus 22 4200 (Lutz, 1973; Nunes et al., 2007) Dendropsophus rhodopeplus 24.2 5670 (Duellman, 2005) Dendropsophus riveroi 20 5314 (Márquez et al., 1993; Rodríguez & Duellman, 1994)

Taxa SVL (mm) DF (Hz) Source Dendropsophus robertmertensi 26.4 5388 (Duellman & Fouquette, 1968; Duellman, 1970) Dendropsophus rubicundulus 28.3 3500 (Cardoso & Vielliard, 1985; Napoli & Caramaschi, 1999) Dendropsophus sanborni 17 5100 (Lutz, 1973; Martins & Jim, 2003) Dendropsophus sarayacuensis 29 2990 (Duellman & Pyles, 1983; Rodríguez & Duellman, 1994) Dendropsophus sartori 26 3217 (Duellman & Fouquette, 1968; Duellman, 1970) Dendropsophus seniculus 37.7 3969 (Heyer et al., 1990; Hepp et al., 2012) Dendropsophus triangulum 28 2298 (Duellman & Pyles, 1983; Rodríguez & Duellman, 1994) Duellmanohyla rufioculis* 30 2320 (Duellman, 1970) Exerodonta melanomma* 29.9 2336 (Duellman, 1970) Exerodonta sumichrasti* 27.7 2510 (Duellman, 1970) Hyla andersonii 51 1100 (Gerhardt, 1974; Conant & Collins, 1998) Hyla arborea 50 2122 (Castellano et al., 2002; Arnold, 2003) Hyla chinensis 32 4000 (Kuramoto, 1980; Fei et al., 1999) Hyla chrysoscelis 51 2400 (Wright & Wright, 1949; Gerhardt, 2005) Hyla femoralis 37 2600 (Wright & Wright, 1949; Blair, 1958b) Hyla gratiosa 68 1840 (Wright & Wright, 1949; Oldham & Gerhardt, 1975) Hyla intermedia 50 2388 (Castellano et al., 2002; Arnold, 2003) Hyla meridionalis 50 2750 (Oliveira et al., 1991; Arnold, 2003) Hyla molleri 45 2750 (Schneider, 1974) Hyla squirella 36 3344 (Wright & Wright, 1949; Taylor et al., 2007) Hyla versicolor 51 2200 (Wright & Wright, 1949; Gerhardt, 2005) Hylomantis granulosa* 45.8 1828 (Pimenta et al., 2007) Hylomantis lemur 40.8 2396 (Duellman, 1970) Hyloscirtus armatus* 68.5 3600 (Duellman, 1997) Hyloscirtus colymba* 37 3520 (Duellman, 1970; 1972b) Hyloscirtus lindae* 68.1 1553 (Duellman & Altig, 1978; Coloma et al., 2012) Hyloscirtus pacha* 60.8 1854 (Duellman & Hillis, 1990; Coloma et al., 2012) Hyloscirtus palmeri* 45 2500 (Duellman, 1970; Ibáñez et al., 1999) Hyloscirtus pantostictus* 63 1570 (Duellman & Berger, 1982; Coloma et al., 2012) Hyloscirtus phyllognathus* 34 2588 (Duellman, 1972b) Hyloscirtus tapichalaca* 63.8 1469 (Kizirian et al., 2003; Coloma et al., 2012) Hypsiboas albopunctatus 60 2168 (Lutz, 1973; Kwet et al., 2002) Hypsiboas bischoffi 46.1 1750 (Marcelino et al., 2009; Pombal Jr., 2010) Hypsiboas cinerascens 44 1390 (Duellman & Pyles, 1983; Rodríguez & Duellman, 1994) Hypsiboas ericae* 34 2700 (Caramaschi & Cruz, 2000; Garcia & Haddad, 2008) Hypsiboas fasciatus 40.3 2530 (Lescure & Marty, 2000; Duellman, 2005) Hypsiboas joaquini* 56.4 1700 (Garcia et al., 2003) Hypsiboas microderma* 34 3300 (Pyburn, 1977; Rodríguez & Duellman, 1994) Hypsiboas ornatissimus 31 2855 (Lutz, 1973; Lescure & Marty, 2000) Hypsiboas pardalis 69 1133 (Lutz, 1973; Rodrigues, 2009) Hypsiboas pellucens 61.6 800 (Cochran & Goin, 1970; Duellman, 1971) Hypsiboas roraima* 44.9 2616 (Barrio-Amorós et al., 2011) Hypsiboas rufitelus 49.2 1600 (Duellman, 1970)

Taxa SVL (mm) DF (Hz) Source Hypsiboas semiguttatus* 46.1 2450 (Garcia et al., 2007) Hypsiboas semilineatus 51 1014 (Lingnau & Bastos, 2003; Lisboa et al., 2011) Isthmohyla pseudopuma 41.4 956 (Duellman, 1970) Isthmohyla rivularis* 34 2420 (Duellman, 1970) Isthmohyla tica* 34.1 2228 (Duellman, 1970) Litoria gracilenta 38 2750 (Peters, 1869; Günther & Richards, 2000) Litoria impura 42 1240 (Peters & Doria, 1878; Kraus & Allison, 2004) Litoria inermis 33 3800 (Davies et al., 1983) Litoria latopalmata 39 2900 (Davies et al., 1983) Litoria leucova* 35.4 4500 (Johnston & Richards, 1994) Litoria pallida 34 1963 (Davies et al., 1983) Litoria raniformis 79.9 900 (Ford, 1989; Hamer & Organ, 2008) Litoria rheocola* 35.5 2500 (Hoskin & Goosem, 2010) Litoria spartacus* 37.5 2919 (Richards & Oliver, 2006) Litoria thesaurensis 65 1774 (Tyler & Davies, 1978; Kraus & Allison, 2004) Litoria tornieri 33 2725 (Doughty, 2011) Litoria wollastoni* 42.7 2950 (Menzies & Zweifel, 1974) Myersiohyla kanaima* 37.8 3600 (Goin & Woodley, 1969) Nyctimystes cheesmani* 40 1900 (Kraus, 2012) Osteocephalus taurinus 85 1501 (Trueb & Duellman, 1971; De la Riva et al., 1995) Osteopilus septentrionalis 89 2300 (Blair, 1958a; Savage, 2002) Pachymedusa dacnicolor 82.6 1727 (Duellman, 1970) Phyllomedusa atelopoides 37.4 1150 (Duellman et al., 1988) Phyllomedusa azurea 43.46 2333 (Guimarães et al., 2001; Álvares, 2008) Phyllomedusa bahiana 74.5 960 (Pombal Jr. & Haddad, 1992; Silva & Juncá, 2006) Phyllomedusa bicolor 103 940 (Zimmerman, 1983; Lima et al., 2006) Phyllomedusa burmeisteri 75.8 1500 (Pombal Jr. & Haddad, 1992; Abrunhosa & Wogel, 2004) Phyllomedusa camba 74 860 (Duellman, 2005; Rodrigues et al., 2011) Phyllomedusa centralis* 42 1451 (Bokermann, 1965; Brandão et al., 2009) Phyllomedusa distincta 66 1250 (Haddad et al., 1994) Phyllomedusa duellmani 54.2 950 (Cannatella, 1982) Phyllomedusa hypochondrialis 43.73 2047 (De la Riva et al., 1995; Álvares, 2008) Phyllomedusa megacephala* 43.2 1722 (Caramaschi, 2006; Giaretta et al., 2007) Phyllomedusa neildi 63.8 762 (Barrio-Amoros, 2006) Phyllomedusa nordestina 42.1 2076 (Caramaschi, 2006; Vilaça et al., 2011) Phyllomedusa palliata 43.8 1500 (Duellman, 1974; Zimmerman, 1983) Phyllomedusa sauvagii 89.2 2000 (Halloy & Espinosa, 2000; Rodrigues et al., 2007) Phyllomedusa tarsius 90 870 (Zimmerman, 1983; Lima et al., 2006) Phyllomedusa tetraploidea 63.8 1700 (Pombal Jr. & Haddad, 1992) Phyllomedusa tomopterna 54 1790 (Zimmerman, 1983; Lima et al., 2006) Phyllomedusa trinitatis 81 800 (Barrio-Amoros, 2006) Plectrohyla cyclada* 39.5 2072 (Duellman, 1970) Pseudacris brachyphona 32 2394 (Wright & Wright, 1949; Cocroft & Ryan, 1995)

Taxa SVL (mm) DF (Hz) Source Pseudacris clarkii 29 3072 (Duellman, 1970; Cocroft & Ryan, 1995) Pseudacris feriarum 27.3 2977 (Schawrtz, 1957; Cocroft & Ryan, 1995) Pseudacris fouquettei 29.8 3138 (Lemmon et al., 2008) Pseudacris illinoensis 43 2168 (Tucker, 1998; Owen & Tucker, 2006) Pseudacris kalmi 38.1 2972 (Harper, 1955; Cocroft & Ryan, 1995) Pseudacris nigrita 28 3149 (Wright & Wright, 1949; Cocroft & Ryan, 1995) Pseudacris ornata 39 2651 (Cocroft & Ryan, 1995; Lannoo, 2005) Pseudacris triseriata 32 2776 (Wright & Wright, 1949; Cocroft & Ryan, 1995) Pseudis bolbodactyla 45 1937 (Caramaschi & Cruz, 1998; Vaz-Silva et al., 2007) Ptychohyla euthysanota* 37.3 3100 (Duellman, 1970) Ptychohyla leonhardschultzei* 35.6 2750 (Duellman, 1970) Ptychohyla spinipollex* 41.2 4300 (Duellman, 1970) Scinax berthae 22.2 4886 (Faivovich, 2005; Pereyra et al., 2012) Scinax boesemani 31.1 1420 (Duellman & Pyles, 1983; Duellman, 1997) Scinax boulengeri 48.7 1615 (Duellman, 1970) (Duellman & Pyles, 1983) Scinax crospedospilus 33.3 1350 (Heyer et al., 1990) Scinax cruentommus 28 3300 (Duellman, 1972a; 1978) Scinax fuscovarius 47.1 855 (De la Riva, 1993; Fonte, 2010) Scinax nasicus 37 1064 (Lutz, 1973; Fonte, 2010) Scinax proboscideus 39.8 2012 (Duellman, 1972b; Lima et al., 2004) Scinax rostratus 45.7 3050 (Duellman, 1970; 1972b) Scinax ruber 41.2 700 (Duellman & Wiens, 1993; Bernal et al., 2004) Scinax staufferi 29 3056 (León, 1969; Duellman, 1970) Scinax sugillatus 42 2023 (Duellman, 1973) Scinax uruguayus 25.8 4350 (Langone, 1990; Lingnau, 2009) Scinax x-signatus 42.5 1110 (Heyer et al., 1990; Tárano, 2010) Smilisca baudinii 76 350 (Duellman, 1970) Smilisca fodiens 62.6 2230 (Duellman, 1970) Smilisca phaeota 65.5 372 (Duellman, 1970) Smilisca puma 38.1 740 (Duellman, 1970) Smilisca sordida* 45 2695 (Duellman, 1970) Sphaenorhynchus lacteus 41.5 2240 (Duellman, 2005) Sphaenorhynchus orophilus 32 2350 (Heyer et al., 1990) Tlalocohyla loquax 44.7 2323 (Duellman, 1970) Tlalocohyla picta 21.4 2661 (Duellman, 1970) Trachycephalus coriaceus 63 2090 (Rodríguez & Duellman, 1994; De la Riva et al., 1995) Trachycephalus mesophaeus 85 1014 (Lutz, 1973; Prado et al., 2003) Trachycephalus nigromaculatus 86 1990 (Cochran, 1955; Abrunhosa et al., 2001) Triprion petasatus 60.8 2096 (Duellman, 1970) Hylodidae Crossodactylus caramaschii* 25.8 5000 (Bastos & Pombal, 1995) Crossodactylus schmidti* 30 3306 (Caldart et al., 2011) Hylodes dactylocinus* 27 2973 (Pavan et al., 2001)

Taxa SVL (mm) DF (Hz) Source Hylodes meridionalis* 37.5 4400 (Pombal Jr. et al., 2002; Lingnau et al., 2013) Hylodes perplicatus* 39.8 3400 (Haddad et al., 2003) Hylodes phyllodes* 31.4 5000 (Heyer & Cocroft, 1986) Hylodes sazimai* 28.5 4750 (Heyer, 1982; Haddad & Pombal Jr, 1995) Hyperoliidae Afrixalus delicatus 22.2 4525 (Pickersgill, 2005) Afrixalus dorsalis 28 3587 (Schiøtz, 1999; Köhler et al., 2005) Afrixalus fornasini 38 2450 (Channing, 2001) Afrixalus knysnae 25 3800 (Schiøtz, 1999; Channing, 2001) Afrixalus laevis* 23 3000 (Schiøtz, 1999) Afrixalus paradorsalis 34 2864 (Schiøtz, 1999; Köhler et al., 2005) Afrixalus stuhlmanni 21.1 4650 (Pickersgill, 2005) Heterixalus andrakata 29 3200 (Glaw & Vences, 1991; 2007) Heterixalus betsileo 28 3000 (Glaw & Vences, 1993; 2007) Heterixalus boettgeri 25 2850 (Glaw & Vences, 1993; 2007) Heterixalus carbonei 26.4 3200 (Vences et al., 2000) Heterixalus luteostriatus 28 3500 (Andreone et al., 1994; Glaw & Vences, 2007) Heterixalus madagascariensis 35 3375 (Glaw & Vences, 1993; 2007) Heterixalus tricolor 26 3000 (Glaw & Vences, 1993; 2007) Heterixalus variabilis 31 3500 (Glaw & Vences, 1993; 2007) Hyperolius alticola* 33 2250 (Schiøtz, 1999) Hyperolius argus 34 2000 (Schiøtz, 1999) Hyperolius baumanni 30 3000 (Schiøtz, 1999) Hyperolius cinnamomeoventris 28 3246 (Schiøtz, 1999; Sinsch et al., 2012) Hyperolius cystocandicans 28 2813 (Schiøtz, 1999; Köhler et al., 2005) Hyperolius guttulatus* 35 2000 (Schiøtz, 1999) Hyperolius kivuensis 33 2649 (Schiøtz, 1999; Sinsch et al., 2012) Hyperolius montanus 28 2750 (Schiøtz, 1999) Hyperolius phantasticus 37 2529 (Schiøtz, 1999; Köhler et al., 2005) Hyperolius picturatus* 31 3850 (Schiøtz, 1999) Hyperolius puncticulatus 33 2000 (Channing & Howell, 2006) Hyperolius pusillus 21 4950 (Channing, 2001) Hyperolius torrentis* 38 3500 (Schiøtz, 1999) Hyperolius viridiflavus* 30 4250 (van den Elzen & Kreulen, 1979; Schiøtz, 1999) Kassina maculata 65 1700 (Channing, 1976; Schiøtz, 1999) Kassina senegalensis 49 1000 (Channing & Howell, 2006) Phlyctimantis verrucosus 52 1600 (Schiøtz, 1999) (Channing & Howell, 2006) Semnodactylus wealii 44 2000 (Schiøtz, 1999) Leptodactylidae Edalorhina perezi 30.2 2700 (Duellman, 2005) Engystomops coloradorum 24.1 1060 (Cannatella & Duellman, 1984; Ryan & Drewes, 1990) Engystomops guayaco 19.4 2114 (Ron et al., 2005) Engystomops pustulatus 29.9 947 (Ryan & Drewes, 1990; Ron et al., 2004)

Taxa SVL (mm) DF (Hz) Source Engystomops pustulosus 34 546 (Ryan, 1983; Kime et al., 2000) Engystomops randi 18.56 1116 (Ron et al., 2004) Eupemphix nattereri 42.7 710 (Silva et al., 2008) Leptodactylus bufonius 53.6 1500 (Heyer, 1978) Leptodactylus chaquensis 99.7 365 (Heyer & Giaretta, 2009) Leptodactylus didymus 46.7 780 (Heyer et al., 1996; Köhler & Lötters, 1999b) Leptodactylus diedrus 40.4 820 (Heyer, 1994; 1998) Leptodactylus elenae 45.2 1160 (Heyer, 1978; Heyer et al., 1996) Leptodactylus fuscus 52.1 560 (Bernal et al., 2004) Leptodactylus gracilis 47.8 1970 (Heyer, 1978; Köhler & Lötters, 1999a) Leptodactylus griseigularis 51 2770 (Heyer, 1994; Heyer & de Carvalho, 2000) Leptodactylus knudseni 159 500 (Heyer, 2005) Leptodactylus labyrinthicus 170 391 (Zina & Haddad, 2005) Leptodactylus leptodactyloides 48 1200 (Heyer, 1994; Duellman, 2005) Leptodactylus longirostris 40 2315 (Heyer, 1978; Lescure & Marty, 2000) Leptodactylus melanonotus 46 1840 (Heyer, 1970; Heyer & de Carvalho, 2000) Leptodactylus mystaceus 52.2 1056 (Heyer et al., 1996) Leptodactylus mystacinus 65 2275 (Heyer et al., 2003) Leptodactylus notoaktites 50.8 1230 (Heyer, 1978; Heyer et al., 1996) Leptodactylus ocellatus 120 175 (Heyer et al., 1990; Salas et al., 1998) Leptodactylus pentadactylus 177 545 (Kime et al., 2000; Savage, 2002) Leptodactylus podicipinus 44.7 1970 (Silva et al., 2008) Leptodactylus rhodonotus 79 2000 (Heyer, 1979; Duellman, 2005) Leptodactylus spixi 43 1634 (Heyer, 1983; Bilate et al., 2006) Leptodactylus vastus 180.3 430 (Heyer, 2005) Lithodytes lineatus 47 2600 (Duellman, 2005) Paratelmatobius cardosoi 17.9 2550 (Pombal & Haddad, 1999) Physalaemus albonotatus 32.4 2399 (Marquez et al., 1995; Rodrigues et al., 2004) Physalaemus barrioi 29.45 2265 (Provete et al., 2012) Physalaemus biligonigerus 45 568 (Lynch, 1970; Marquez et al., 1995) Physalaemus cuvieri 25.9 690 (Heyer et al., 1990) Physalaemus ephippifer 28 840 (Martins, 1998; Kaefer et al., 2011) Physalaemus signifer 25.9 1210 (Wogel et al., 2002) Pleurodema bibroni 38 1960 (Duellman & Veloso, 1977; Kolenc et al., 2009) Pleurodema brachyops 49 732 (Duellman & Veloso, 1977; Kime et al., 2000) Limnodynastidae Neobatrachus pelobatoides 39.6 816 (Roberts et al., 1991) Neobatrachus pictus 55 1300 (Roberts, 1997b) Neobatrachus sudelli 40 1509 (Roberts, 1997a) Mantellidae Aglyptodactylus laticeps 45.4 2350 (Glaw et al., 1998) Blommersia blommersae 21.3 6000 (Glaw & Vences, 2002) Blommersia domerguei 17 4350 (Vences et al., 2002; Glaw & Vences, 2007)

Taxa SVL (mm) DF (Hz) Source Blommersia kely 15.9 4700 (Vences et al., 2002) Blommersia sarotra 16.8 4592 (Vences et al., 2002) Boophis boehmei* 30 2500 (Glaw & Vences, 2007) Boophis goudotii 70 750 (Glaw & Vences, 2007) Boophis luteus* 40 3250 (Glaw & Vences, 1992; 2007) Boophis marojezensis* 27 5583 (Glaw et al., 2001) Boophis rappiodes* 25.1 3000 (Vences & Glaw, 2002) Boophis sibilans* 32.5 3300 (Glaw & Thiesmeier, 1993) Boophis vittatus* 25.5 7250 (Glaw et al., 2001) Boophis xerophilus 38.7 2700 (Glaw & Vences, 1997) Guibemantis tornieri 49 1850 (Glaw et al., 2000; Glaw & Vences, 2007) Mantella viridis* 25 4350 (Glaw & Vences, 2007) Mantidactylus argenteus* 27 4250 (Vejarano et al., 2006; Glaw & Vences, 2007) Mantidactylus charlotteae* 26.2 2900 (Vences & Glaw, 2004) Mantidactylus opiparis* 26.1 3150 (Vences & Glaw, 2004) Megophyidae Leptobrachium gunungense* 65 1200 (Malkmus et al., 2002; Malkmus, 2006) Leptobrachium montanum* 62.7 800 (Malkmus et al., 2002) Leptolalax arayai* 29.6 5588 (Matsui, 1997) Leptolalax oshanensis* 30.7 4512 (Jiang et al., 2002; Ohler et al., 2011) Leptolalax pictus* 36 7000 (Malkmus et al., 2002) Megophrys nasuta* 105 2250 (Malkmus et al., 2002) Oreolalax omeimontis* 58.4 1071 (Jiang et al., 2002; Nguyen et al., 2013) Xenophrys minor* 32 3456 (Stejneger, 1926; Jiang et al., 2002) Microhylidae Calluella guttulata 49 1950 (Parker, 1934; Heyer, 1971) Chiasmocleis hudsoni 17 3795 (Parker, 1940; Rodrigues et al., 2008) Chiasmocleis shudikarensis 24.5 3565 (Lescure & Marty, 2000; Peloso & Sturaro, 2008) Dermatonotus muelleri 62.4 1758 (Giaretta et al., 2013) Elachistocleis ovalis 36 3838 (Lavilla et al., 2003; Thomé & Brasileiro, 2007) Gastrophryne elegans 25.8 3100 (Nelson, 1963; Nelson, 1973) Gastrophryne olivacea 30.8 4420 (Nelson, 1973; Loftus-Hills & Littlejohn, 1992) Glyphoglossus molossus 77 700 (Parker, 1934; Heyer, 1971) Hamptophryne boliviana 35 2208 (Hödl, 1990; De la Riva et al., 1996) Hypopachus variolosus 48 2750 (Parker, 1934; Lee, 2000) Kaloula pulchra 70 2500 (Malkmus et al., 2002) Microhyla butleri 26 2850 (Parker, 1934; Heyer, 1971) Microhyla pulchra 35 1750 (Parker, 1934; Heyer, 1971) Micryletta inornata 30 5450 (Parker, 1934; Heyer, 1971) Phrynomantis bifasciatus 53 1800 (Channing & Howell, 2006) Phrynomantis microps 47.3 1260 (Grafe, 1999; Rödel, 2000) Scaphiophryne boribory 60 700 (Vences et al., 2003; Glaw & Vences, 2007) Scaphiophryne gottlebei 30 950 (Andreone et al., 2005; Glaw & Vences, 2007)

Taxa SVL (mm) DF (Hz) Source Scaphiophryne madagascariensis 49 1000 (Vences et al., 2002; Vences et al., 2003) Scaphiophryne menabensis 42 850 (Glos et al., 2005) Scaphiophryne spinosa 48 800 (Vences et al., 2003; Glaw & Vences, 2007) Myobatrachidae Crinia riparia* 25.2 2735 (Littlejohn & Martin, 1965; Odendaal et al., 1983) Geocrinia victoriana 27 2676 (Littlejohn & Harrison, 1985) Pseudophryne bibronii 30 2640 (Chambers et al., 2006; Byrne, 2008) Uperoleia laevigata 35 2260 (Robertson, 1986) Nyctibatrachidae Nyctibatrachus major* 55 1250 (Boulenger, 1882; Kuramoto & Joshy, 2001) Odontophrynidae Macrogenioglottus alipioi 100 515 (Abravaya & Jackson, 1978; Dixo & Verdade, 2006) Odontophrynus achalensis* 49.4 933 (Salas & Di Tada, 1988; Rosset et al., 2007) Odontophrynus americanus 58 1035 (Martino & Sinsch, 2002) Odontophrynus cultripes 60 660 (Caramaschi & Napoli, 2012) Odontophrynus occidentalis* 60 820 (Martino & Sinsch, 2002) Proceratophrys avelinoi* 29.2 1600 (Kwet & Faivovich, 2001; Kwet & Baldo, 2003) Proceratophrys bigibbosa* 43.8 1050 (Kwet & Faivovich, 2001) Proceratophrys 57.1 818 (Santana et al., 2010) concavitympanum* Proceratophrys cristiceps* 50.2 940 (Nunes & Juncá, 2006; Cruz et al., 2012) Proceratophrys cururu* 43.1 800 (Eterovick & Sazima, 1998) Proceratophrys goyana* 46.5 1006 (Martins & Giaretta, 2013) Proceratophrys melanopogon* 49.9 1179 (Prado & Pombal Jr, 2008; Mângia et al., 2010) Pelobatidae Pelobates cultripes 85 1075 (Lizana et al., 1994) Petropedetidae Petropedetes yakusini* 73 1300 (Channing & Howell, 2006) Phrynobatrachidae Phrynobatrachus acridoides 28 2000 (Channing, 2001) Phrynobatrachus dendrobates* 31 3800 (Channing & Howell, 2006) Pipidae Xenopus ruwenzoriensis 44 1500 (Channing & Howell, 2006) Ptychadenidae Hildebrandtia ornata 65 1400 (Channing, 2001) Ptychadena anchietae 51 1750 (van den Elzen & Kreulen, 1979; Harper et al., 2010) Ptychadena mahnerti 42 3000 (Perret, 1996) Ptychadena oxyrhynchus 53 1715 (Rödel, 2000; Bwong et al., 2009) Ptychadena porosissima 44.1 4091 (Sinsch et al., 2012; Dehling & Sinsch, 2013) Ptychadena taenioscelis 35 2450 (Bwong et al., 2009; Harper et al., 2010) Pyxicephalidae Cacosternum boettgeri 19 4500 (Boulenger, 1882; Channing et al., 2013) Cacosternum capense 31.5 2300 (Channing, 2001; Channing et al., 2013) Cacosternum nanum 15 3600 (Channing et al., 2013)

Taxa SVL (mm) DF (Hz) Source Microbatrachella capensis 15 4900 (Boulenger, 1910; Channing, 2001) Poyntonia paludicola* 30 2200 (Channing & Boycott, 1989; Channing, 2001) Pyxicephalus adspersus 245 225 (Du Preez, 1996; Boeckle et al., 2009) Pyxicephalus edulis 120 525 (Channing et al., 1994; Rödel, 2000) Strongylopus grayii 35 2300 (Channing, 2001) Tomopterna cryptotis 45 3450 (Channing, 2001) Tomopterna delalandii 44.8 2500 (Passmore & Carruthers, 1975) Tomopterna krugerensis 44.8 2500 (Passmore & Carruthers, 1975) Tomopterna tuberculosa 40 2600 (Channing, 2001) Ranidae Amnirana galamensis 78 2500 (Channing, 2001) Amolops chunganensis* 39 3425 (Matsui et al., 1993; Liu et al., 2000) Amolops larutensis* 30 5033 (Van Kampen, 1923; Matsui et al., 1993) Amolops panhai* 34 6500 (Matsui & Nabhitabhata, 2006) Amolops wuyiensis* 47 2232 (Zhang et al., 2013) Huia cavitympanum* 52 15966 (Inger & Stuebing, 2005; Arch et al., 2008) Huia masonii* 37.8 12000 (Stuart & Chan-Ard, 2005; Boonman & Kurniati, 2011) Hylarana nicobariensis 46.2 1825 (Inger, 1954; Jehle & Arak, 1998) Meristogenys jerboa* 44 5650 (Inger & Gritis, 1983; Matsui et al., 1993) Meristogenys orphnocnemis* 37.3 7205 (Matsui, 1986; Preininger et al., 2007) Odorrana schmackeri* 46 3479 (Liu, 1950; Zhou et al., 2014) Odorrana tormota* 33 7000 (Feng et al., 2002) Rana adenopleura 51 650 (Matsui & Utsunomiya, 1983) Rana amamiensis* 69 2850 (Matsui, 1994) Rana baramica 55.6 2000 (Leong et al., 2003; Zainudin et al., 2010) Rana curtipes 51.4 1222 (Krishna & Krishna, 2005) Rana dalmatina 59 682 (Lesbarrères & Lodé, 2002; Hettyey et al., 2005) Rana erythraea 48 2459 (Brown & Alcala, 1970; Roy et al., 1995) Rana forreri 90 850 (Frost, 1982; Savage, 2002) Rana glandulosa 55 1380 (Brown, 1902; Grafe et al., 2008) Rana hosii* 68 4950 (Manthey & Grossmann, 1997; Kurniati et al., 2010) Rana laterimaculata* 39 3245 (Leong et al., 2003) Rana lessonae 57 1831 (Wycherley et al., 2002) Rana livida* 54 5250 (Ao et al., 2003; Shen et al., 2011) Rana luctuosa 68 1500 (Kueh et al., 2010; Zainudin et al., 2010) Rana nigromaculata 81 2043 (Mu & Zhao, 1990; Khonsue et al., 2001) Rana picturata* 54 2500 (Zainudin et al., 2010; Zainudin & Sazali, 2012) Rana plancyi 50 1840 (Lue, 1990; Ueda, 1994) Rana pretiosa 75 1000 (Briggs, 1987; Cushman & Pearl, 2007) Rana septentrionalis 63.1 605 (Leclair & Laurin, 1996; Bevier et al., 2004) Rana supranarina* 76.8 1840 (Matsui, 1994) Rana taylori 78 1350 (Savage, 2002) Rana temporalis* 55.3 3100 (Kadadevaru et al., 2000; Hampson & Bennett, 2002)

100

Taxa SVL (mm) DF (Hz) Source Rana utsunomiyaorum* 48.1 2326 (Matsui, 1994) Staurois latopalmatus* 47.7 5149 (Boeckle et al., 2009) (Boeckle et al., 2009) Staurois natator* 35.5 4746 (Emerson, 1997; Preininger et al., 2007) Staurois parvus* 23.6 5578 (Matsui et al., 2007; Grafe et al., 2012) Staurois tuberilinguis* 30.2 5240 (Matsui et al., 2007; Boeckle et al., 2009) Rhacophoridae Buergeria robusta* 52 1850 (Kuramoto, 1986; Huang et al., 2001) Chiromantis doriae 26.5 4300 (Aowphol et al., 2013) Chiromantis rufescens 49 1650 (Channing & Howell, 2006) Chiromantis vittatus 25.2 3900 (Aowphol et al., 2013) Chiromantis xerampelina 75 1700 (Channing & Howell, 2006) Ghatixalus variabilis* 48 3135 (Bossuyt & Dubois, 2001; Kanamadi et al., 2001) Kurixalus idiootocus 24.9 2300 (Kuramoto & Wang, 1987) Polypedates leucomystax 63 1528 (Sheridan et al., 2010) Polypedates maculatus 57 1550 (Kanamadi et al., 1993; Daniel, 2002) Rhacophorus arboreus 60 1400 (Kasuya et al., 1992; Wilkinson, 2003) Rhacophorus chenfui 41 2329 (Matsui & Wu, 1994; Wang et al., 2012) Rhacophorus dennysi 96 1321 (Wang et al., 2012) Rhacophorus dugritei 40.8 1600 (Matsui & Wu, 1994; Ohler et al., 2000) Rhacophorus kio 79.1 890 (Ohler & Delorme, 2006; Wildenhues et al., 2011) Rhacophorus malabaricus 48.3 1260 (Das, 2000; Hampson & Bennett, 2002) Rhacophorus omeimontis 70 972 (Matsui & Wu, 1994; Liao & Lu, 2011) Rhacophorus reinwardtii 52.5 1300 (Ohler & Delorme, 2006; Kurniati et al., 2010) Rhacophorus schlegelii 43 1900 (Maeda & Matsui, 1990; Matsui & Wu, 1994) Rhinophrynidae Rhinophrynus dorsalis 75 1525 (Savage, 2002) Scaphiopodidae Scaphiopus couchii 72 1650 (Capranica & Moffat, 1975; Vásquez & Pfennig, 2007) Scaphiopus holbrookii 78 1425 (Blair, 1958b; Dodd Jr., 2013) Scaphiopus hurterii 67 1500 (Strecker, 1910; Awbrey, 1968) Spea multiplicata 49 1300 (Pfennig, 2000; Pfennig & Pfennig, 2005)

101

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