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ADRIÁN GARCÍA-RODRÍGUEZ
DETERMINANTES ECOLÓGICOS DE
PROCESSOS MACRO E MICRO EVOLUTIVOS EM REGIÕES COMPLEXAS
Natal, Rio Grande do Norte - Brasil
2018
2
ADRIÁN GARCÍA-RODRÍGUEZ
DETERMINANTES ECOLÓGICOS DE PROCESSOS MACRO E MICRO EVOLUTIVOS
EM REGIÕES COMPLEXAS
Tese apresentada à Universidade Federal do Rio Grande do Norte, como parte das exigências do Programa de Pós-Graduação em Ecologia, para obtenção do título de Doutor.
Orientador
Dr. Gabriel Corrêa Costa
Co-orientador Dr. Adrian Antonio Garda
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ADRIÁN GARCÍA-RODRÍGUEZ
DETERMINANTES ECOLÓGICOS DE PROCESSOS MACRO E MICRO EVOLUTIVOS
EM REGIÕES COMPLEXAS
Tese apresentada à Universidade Federal do Rio Grande do Norte, como parte das exigências do Programa de Pós-Graduação em Ecologia, para
obtenção do título de Doutor.
Dr. Fabricio Villalobos Membro titular externo
Instituto de Ecología, A.C. (INECOL). México
Dr. Diogo Borges Provete Membro titular externo
UFMS
Dr. Adrian Antonio Garda Membro titular interno
UFRN
Dr. Sergio Maia Queiroz Lima Membro titular interno
UFRN
__________________________ Dr. Gabriel Corrêa Costa
Orientador Auburn University, Alabama, EU
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Universidade Federal do Rio Grande do Norte - UFRN Sistema de Bibliotecas - SISBI
Catalogação de Publicação na Fonte. UFRN -
Biblioteca Setorial Prof. Leopoldo Nelson -Centro de Biociências - CB
García-Rodríguez, Adrián.
Determinantes ecológicos de processos macro e
microevolutivos em regiões complexas / Carlos Adrián García
Rodríguez. - Natal, 2018. 160 f.: il.
Tese (Doutorado) - Universidade Federal do Rio Grande do
Norte. Centro de Biociências. Departamento de Ecologia.
Programa de Pós-Graduacão em Ecologia. Orientador: Prof. Dr. Gabriel Correa Costa.
1. Bioacústica - Tese. 2. Heterogeneidad climática - Tese. 3.
Complexidade topográfica - Tese. 4. Divergência genética - Tese.
5. Especiação - Tese. 6. Macroevolução - Tese. I. Costa, Gabriel Correa. II. Universidade Federal do Rio Grande do Norte. III.
Título.
Elaborado por KATIA REJANE DA SILVA - CRB-15/351
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Vive como si fueses a morir mañana. Aprende como si fueses a vivir para siempre.
Mahatma Gandhi La ciencia se compone de errores, que a su vez, son los pasos hacia la verdad
Julio Verne
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AGRADECIMIENTOS
Tras cuatro años de mucho aprendizaje y crecimiento personal, cierro aquí la
que puedo decir -sin temor a equivocarme- ha sido la experiencia más
enriquecedora de mi vida. Quedan plasmadas en estas páginas, las ideas que
poco a poco fui madurando durante este tiempo, pero más que un documento
que pretende hacer una pequeña contribución al conocimiento, este trabajo es
el vivo reflejo de un esfuerzo (y sacrifício) conjunto, y a la vez mi humilde
homenaje a todas las personas que me acompañaron en el camino.
Agradezco sobre todo a mi família, que es el centro de mi vida. A mis
padres, Carlos Alberto y Carmen María, por ser mi luz, mi ejemplo y mi más
grande apoyo en todo momento. Por su amor incondicional, por siempre
motivarme a perseguir lo que me apasiona, por acuerpar mis decisiones y por
enseñarme, desde que tengo memoria, a valorar la educación como el tesoro
más preciado que me podían dar. A mis hermanas Silvi, Lauri y Marce y mis
sobrinos Sofi, Ale y Amandita por su cariño, su comprensión, su complicidad,
su admiración y por ser mi fuerza y motivación aún en la distancia.
A mi orientador Gabriel Costa, por ser mi principal guía en este proceso.
Por su integridad profesional y su sincera amistad, por la admirable dedicación
y paciencia que tiene con sus alumnos. Por su inspiradora capacidad para sacar
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lo mejor de cada uno de nosotros y estar presente y disponible aún a 7000 km
de distancia. A mis co-autores y amigos Marcelo Araya, Andrew Crawford,
Adrian Garda, Carlos Guarnizo, Pablo Martinez, Brunno Oliveira y Alex Pyron
por toda su colaboración y críticas constructivas durante el desarrollo de estos
trabajos.
A mis mentores en Costa Rica, Cachí, Fede y GB por su influencia y
consejos durante mi formación como estudiante y después como profesional,
por su apoyo contínuo hasta el día de hoy. A la Escuela de Biología de la
Universidad de Costa Rica, especialmente a Gustavo Gutiérrez, Viviana Lang,
Elsa de la O y demás funcionarios que siempre me han apoyado a lo largo de
esta etapa, facilitando todos los procesos administrativos que permitieron
mantener un vínculo profesional con mi querida Álma Mater.
A todos mis amigos en Costa Rica (mis “Brothers” del cole, mis queridos
“Peleles” de pretil, y toda la chusma de Biolo) por siempre tener una sonrisa
para recibirme y un abrazo para despedirme, por desearme lo mejor y ante todo
por no dejar que la distancia nos separe. A Juanca y Eu, mis hermanitos ticos
que se embarcaron conmigo en esta aventura brasilera y fueron siempre mi
pedacito de Tiquicia en el exilio. A los ticos con los que en algún momento
coincidí estando Brasil: Sarita, Kabeto, Boris y Hellen, gracias por la ayuda, la
solidaridad y los buenos momentos. A mis grandes amigos y colegas, Pitillo,
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Erick, Victicor, Sofi Rodríguez, Sofi Granados y Bety por su ayuda en el campo
y su amistad sincera.
A mis amigos en Brasil, quienes hoy son mi família lejos de casa. Mis
colegas del lab Juampi, Bruninho, André, Brunnão, Tales y Felipe, por la
compañia, las buenas energias y la ayuda siempre desinteresada. A mis roomies,
con quiénes compartí mil historias y cuya compañia hizo todo más fácil desde
el inicio, principalmente a Camura y Anita mis hermanitos y cómplices, gracias
por tanto cariño y sonrisas compartidas, tamo junto sempre. A Vekinha, que le
tocó aguantarme en la recta final de esta tesis, gracias por el apoyo, la paciencia
y los chineos durante esta “labor de parto”. A quienes a ojos cerrados
literalmente me entregaron sus casas, sus carros y sus mascotas: mis hermanos
y consejeros Hélder y Carolzinha, mis amados Duka e Helo, mi otro hermano
Juampi y mis queridas Tamy e Isa, gracias no sólo por todo lo que facilitaron
mi vida, sino por esas grandes muestras de confianza, somos familia. A mis
grandes amigos y colegas Castiele, Eliana, Vinicius y Francisco, que haciendo
un esfuerzo gigantesco me visitaron en Costa Rica y también me acompañaron
en mis giras de campo, recuerdos para siempre mis queridos. A mis amigas,
consejeras, confidentes y hasta enfermeras Andressa y Nadia por cuidarme
cuando no estuve bien y escucharme siempre que lo necesité. A Gus y Serginho
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por su sincera amistad y por siempre tener esa energía leve y una palabra
adecuada para compartir.
Agradezco a todos los profesores, amigos y alumnos con quiénes coincidí en el
Programa de Posgraduacao em Ecologia de la UFRN. Ha sido un placer y un
honor ser parte del programa y un gusto inmenso haberlos encontrado en este
camino. Todos y cada uno de ustedes forman parte de mi historia, son el más
puro reflejo de la solidaridad y una muestra clara de que la amistad trasciende
idiomas y fronteras... saudades galera!
Agradezco infinitamente a este Brasil querido, por recibirme de brazos abiertos,
por presentarme algunos de los lugares más lindos y personas más especiales
que conocí en mi vida. Por desbordarme con su diversidad cultural y sus
bellezas naturales, por contagiarme de esa alegría que nunca acaba y hacerme
sentir en casa. Nada de esto hubiera sido posible sin el apoyo de la Coordenação
de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) que financió
durante 48 meses mi vida en Brasil y National Geographic Society que apoyó
mi trabajo de campo en Costa Rica.
Gracias a todos, gracias por todo!
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SUMÁRIO
Agradecimientos................................................................6
Resumo..............................................................................12
Abstract..............................................................................14
Introdução Geral................................................................16
Capítulo 1. Faster amphibian speciation supports the role of mountains as
biodiversity pumps
Abstract……………………………………………….. 26
Introdução........................................................................ 27
Material e métodos........................................................... 30
Resultados........................................................................ 36
Discussão......................................................................... 44
Referências...................................................................... 50
Material suplementar....................................................... 61
Capítulo 2. Idiosyncratic responses to drivers of genetic differentiation in the
complex landscapes of Isthmian Central America
Abstract………………………………………………... 65
Introdução........................................................................ 67
Material e métodos........................................................... 72
Resultados........................................................................ 82
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Discussão......................................................................... 88
Referências...................................................................... 98
Material suplementar....................................................... 104
Capítulo 3. The role of geography, topography and climate in the acoustic
divergence of Neotropical Diasporus frogs
Abstract........................................................................... 116
Introdução....................................................................... 118
Material e métodos.......................................................... 121
Resultados........................................................................ 128
Discussão......................................................................... 136
Referências...................................................................... 144
Considerações finais......................................................... 158
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Resumo
As áreas de montanha do mundo cobrem menos de 15% da superfície terrestre; no entanto, elas concentram cerca de 90% dos hotspots de diversidade de espécies e 40% dos hotspots de endemismo. As evidências sugerem que fatores como a complexidade topográfica, a heterogeneidade climática e sua dinâmica histórica nas montanhas podem desempenhar um papel importante na evolução e manutenção de suas ricas biotas. Nesta tese, pretendi avaliar o papel de tais fatores tanto em escala macro (ou seja, nos padrões globais de especiação) quanto em escalas microevolutivas (ou seja, intraespecíficas de divergência genética e de traits) usando anfíbios como sistema de estudo. No primeiro capítulo, contrastei as taxas de especiação entre regiões de alta e baixa complexidade topográfica. Para este fim, usei uma filogenia quase completa de anfíbios contendo 7238 espécies (>90% da diversidade existente) para rodar uma Análise Bayesiana de Misturas Macroevolutivas (BAMM) que permite estimar as taxas de especiação. Posteriormente, projetei na geografia essa informação usando os mapas de distribuição disponíveis, para explorar padrões geográficos de especiação em anfíbios e avaliei sua associação com terrenos complexos, estimando um índice global de complexidade topográfica. Encontrei que, globalmente, as taxas de especiação são mais rápidas em regiões de alta complexidade topográfica independentemente da latitude. Desconstruí esse padrão repetindo as análises nas regiões Zoogeográficas de Wallace - levando em consideração as histórias evolutivas regionais independentes - e encontrei a mesma tendência em oito dos 11 reinos zoogeográficos. No segundo capítulo, avalio o papel relativo de diferentes componentes da paisagem na promoção da diversificação da linhagem na complexa topografia da América Central Ístmica (ACI: Costa Rica e Panamá), uma região geologicamente jovem, mas altamente biodiversa. Aqui usei DNA mitocondrial para estimar a divergência genética dentro de 11 espécies de anfíbios (9 anuros e 2 salamandras) com diferentes atributos ecológicos que ocorrem conjuntamente na região. Então, utilizei análises de Matriz Múltipla de Regressão com Randomização e Modelagem de Dissimilaridade Generalizada para quantificar o papel relativo do isolamento por distância, ambiente e resistência (topografia e adequação) na modelagem de padrões geográficos de estrutura genética dentro de cada espécie. Encontrei respostas idiossincráticas que podem refletir aspectos específicos de suas histórias de vida e poderiam dar uma visão sobre o papel da ACI como motor da especiação. No terceiro capítulo, testei se as barreiras climáticas e topográficas podem influenciar a variação dos sinais acústicos de duas espécies de sapos do gênero Diasporus. Este é um traço comportamental importante que possui características particulares que
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permitem o reconhecimento intra-específico e podem desempenhar um papel importante como mecanismo de isolamento reprodutivo. Para este capítulo, gravei vocalizações de anúncio de 170 machos de duas espécies de sapos do gênero Diasporus distribuídos na Costa Rica. Eu realizei gravações em 21 locais em todo o país, desde o nível do mar até 2800 metros de altitude. Com essa informação realizei análises bioacústicas para documentar a variação geográfica e análises correlativas de matrizes múltiplas para testar se a distância geográfica, as barreiras físicas ou climaticas entre populações, ou adaptação às condições locais podem moldar tais padrões. Para esse fim, eu incorporei análises espaciais (modelos de nicho, estimativas de rugosidade do terreno e teoria dos circuitos) para estimar níveis de isolamento das populações e ajustar um modelo de dissimilaridade generalizada para abordar esta questão. Nas duas espécies, encontrei altos níveis de variação acústica, assim como de isolamento entre populações, gerado pelos fatores testados. No entanto, somente as barreiras topográficas explicaram significativamente a variação acústica em D. diastema. Entretanto, a dissimilaridade climática e distância geográfica só possui associação marginal com os padrões de variação acústica encontrados. Em conclusão, consideramos forças que operam em uma escala local e de forma independente (por exemplo a seleção sexual, o deslocamento de caracteres ou mesmo deriva genética) poderiam então ser mais determinantes na evolução desses sinais nas espécies de estudo.
Palavras chave: Bioacústica, Complexidade Topográfica, Divergência Genética, Especiação, Genética da Paisagem, Heterogeneidade Climática, Isolamento, Macroecologia, Macroevolução, Montanhas
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Abstract
Mountain areas around the world cover less than 15% of global land surface; nevertheless, they concentrate around 90% of the hotspots of species diversity and 40% of the hotspots of endemism. Available evidence suggest that ecological factors such as landscape features (i.e topographic complexity, climatic heterogeneity and their historical dynamics) of mountains may play an important role in the evolution and maintenance of rich biotas at such regions. In my dissertation I aim to evaluate the role of such factors in both macro (i.e global speciation patterns) and microevolutionary (i.e intra-specific genetic and trait divergence) processes using amphibians as study system. In the first chapter, we tested in a global scale the Montane Pumps hypothesis, which proposes that speciation rates are faster in mountains explaining higher diversities in those regions. To this end we used a near complete Amphibian phylogeny containing 7238 species (>90% of the group’s extant diversity) and conducted a Bayesian Analysis in Macroevolutionary Admixtures (BAMM) to estimate speciation rates. Then we combined this information with available range maps to explore Amphibian geographic patterns of speciation and evaluated its association with complex terrains (mountains) by estimating a global index of topographic complexity. We found that globally, speciation rates are faster in regions of high topographic complexity independently of latitude. We repeated our analyses using the Wallace’s Zoogeographic regions, taking into account regional independent evolutionary histories, and found the same pattern in eight out of the total 11 zoogeographical realms. In a second chapter, we assess the relative role of different components of the landscape in promoting lineage diversification across the roughed topography of Isthmian Central America (Costa Rica & Panama), a geologically young but highly biodiverse region. Here we use available mitochondrial DNA to estimate genetic divergence within 10 amphibian species (8 anurans and 2 salamanders) with different biologies that co-occur in the region. Then, we use a Multiple Matrix of Regression with Randomization to assess the relative role of isolation by distance, by environment and by resistance (topography, current climate, and LGM paleoclimate) in shaping the geographic patterns of genetic structuration within each species. So far, we have not found a general force that explains genetic divergence in all studied species. Instead, we have found idiosyncratic responses that may reflect specific aspects of their life histories, such as dispersal capabilities, range size or reproductive potential. In the third chapter,
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we test how climatic and topographic barriers may influence variation in an important behavioral trait such as are advertisement calls. In anurans, such calls has species-specific features that play an important role in recognition. Then, variation in spectro-temporal features between populations has been proposed as a mechanism of reproductive isolation that may promote speciation in the long term. For this chapter I recorded advertisement calls of 170 males from 2 species of Diasporus frogs distributed in Costa Rica. I made recordings at 21 sites in all the country ranging from sea level to 2800 meters elevation. We use such information we conduct bioacoustics analyses to first document geographic variation and then test if the geographic distance, physical or ecological barriers between populations, or adaptation to local conditions could shape such patterns. To this end, we incorporate spatial analyses (niche models, terrain roughness estimations and circuit theory) to generate levels of population isolation and apply Generalized Dissimilarity Matrix test to address this question. In both species, I found high levels of acoustic variation and among population isolation derived by the tested factors. However, only topography significantly explained acoustic divergence in D. diastema while climatic dissimilarity and geographic distance are only marginally associated with the patters of acoustic variation in D. hylaeformis. In conclusion, other forces operating independently in the local scale -such as sexual selection, character displacement or genetic drift- may be more determinant in the evolution of acoustic signals in these species.
Keywords: Bioacoustics, Climatic Heterogeneity, Genetic Divergence, Isolation, Landscape Genetics, Macroecology, Macroevolution, Mountains, Speciation, Topographic Complexity
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INTRODUÇÃO GERAL
Um dos fenômenos naturais mais amplamente documentados é a distribuição
desigual que tem a diversidade em múltiplas dimensões (Menge and Sutherland
1976). Os padrões de riqueza de espécies, variam através do espaço, do tempo
e dos clados: algumas regiões são mais diversas que outras (Hillebrand 2004),
a composição da diversidade hoje não é a mesma que no passado (Johnson
2009) ainda, alguns grupos taxonômicos são muito diversos, outros contem
poucos representantes (Wiens 2011). Entender o porquê dessa variação tem se
tornado um dos maiores objetivos de pesquisa na intersecção da ecologia e
evolução, gerando hipóteses derivadas dessas duas áreas da ciência.
Na escala espacial, uma das mais extremas variações na distribuição da
diversidade acontece em áreas de topografias irregulares. Globalmente os
sistemas montanhosos tem uma distribuição desigual que abrange somente uma
oitava parte da superfície da terra (Antonelli 2015, Körner et al. 2017). No
entanto, essas regiões concentram altas riquezas de espécies, sendo que 90%
dos hotspots de diversidade e 40% dos hotspots de endemismo ocorrem em
áreas de montanha (Myers et al. 2000, Orme et al. 2005). A tendência que tem
as regiões de alta complexidade topográfica para suportar altos números de
espécies é um padrão bem documentado em diversos grupos animais e vegetais
(Ruggiero and Hawkins 2008). Porém, os determinantes ecológicos e os
17
mecanismos macro e micro evolutivos que geram essa diversidade biótica ainda
são pouco conhecidos.
Tem-se sugerido que as regiões montanhosas poderiam agir como
motores de especiação, pelo efeito duplo que as topografias complexas e os
fortes gradientes ambientais contidos nelas podem ter nos processos de
divergência genética (Funk et al. 2016). As configurações irregulares de topos
de montanha e vales alternados representam mosaicos de habitats favoráveis e
desfavoráveis (Kozak and Wiens 2006), que aumentam o isolamento entre
populações e em consequência a probabilidade de especiação alopátrica (Orr
and Smith 1998, Moritz et al. 2000, Rull 2005, Guarnizo et al. 2009).
Complementariamente, os amplos espectros ambientais representados em
curtas distâncias ao longo dos gradientes altitudinais (Graham et al. 2014,
Merckx et al. 2015), oferecem condições ideais em que a especiação ecológica
em parapatria pode ocorrer (Rundle and Nosil 2005). Nessas circunstâncias, as
pressões locais poder promover divergência entre populações, levando ao
surgimento de novas espécies, mesmo na ausência de barreiras físicas maiores
(Knox and Palmer 1995, Graham et al. 2004, Caro et al. 2013, Chapman et al.
2013).
Dentro de uma perspectiva macro ecológica, a montagem de
comunidades e riqueza de espécies numa região especifica, num dado momento,
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é determinada pelos processos de especiação, extinção e dispersão (Hutter et al.
2013). Portanto, a identificação dos fatores que potencialmente influenciam
nestes processos é crucial para entender a origem dos amplos padrões de
diversidade atual. Em escalas mais locais, as abordagens desenvolvidas nas
áreas da filogeografia e genética da paisagem tem sido úteis para abordar essa
questão com maior resolução espacial mas menor alcance taxonômico e
geográfico.
Nesta tese avalio em diferentes escalas geográficas de que forma as
paisagens complexas determinadas por regiões montanhosas possuindo perfis
climáticos heterogêneos influenciam em processos evolutivos que contribuem
para a formação dos padrões biológicos que observamos. O meu interesse foi
primeiramente abordar essa questão tentando obter o ‘big picture’ da
generalidade de certos padrões macro evolutivos em escala global; ao mesmo
tempo, que procurei aprofundar numa maior resolução, testando o rol que tem
certos atributos físicos e ecológicos da paisagem na geração de pressões locais
que influenciam os processos micro evolutivos de diferenciação genética e
divergência acústica no espaço. Para atingir esses objetivos eu incorporei
diversas análises evolutivas, conceitos de genética de populações, abordagens
da ecologia do comportamento e ferramentas de machine learning para projetar
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no espaço múltiplos padrões de variação e avaliar quais são as forças que lós
explicam melhor.
No primeiro capítulo, testei em escala global se existe uma relação entre
taxas de especiação mais rápidas e regiões topograficamente complexas, que
potencialmente poderia explicar maiores diversidades nessas regiões. Para este
fim, usei uma filogenia quase completa de anfíbios para estimar dinâmicas
evolutivas. Posteriormente, espacializei essa informação para explorar padrões
geográficos de especiação em anfíbios e avaliei sua associação com terrenos
complexos, estimando um índice global de complexidade topográfica. No
segundo capítulo, avalio o papel relativo de diferentes componentes da
paisagem na promoção da diversificação da linhagem na complexa topografia
da América Central Ístmica (ACI: Costa Rica e Panamá). Aqui usei DNA
mitocondrial para estimar a divergência genética dentro de 11 espécies de
anfíbios que ocorrem conjuntamente na região. Posteriormente quantifiquei o
papel relativo do isolamento por distância, ambiente e resistência (topografia e
adequação bioclimatica) na modelagem de padrões geográficos de estrutura
genética dentro de cada espécie. No terceiro capítulo, testei como as barreiras
climáticas e topográficas podem influenciar a variação nas chamadas de
anuncio de duas espécies de sapos do gênero Diasporus. Para este capítulo,
gravei vocalizações de anúncio de 170 machos em 21 locais na Costa Rica,
20
desde o nível do mar até 2800 metros de altitude. Com essa informação eu
documentei a variação acústica intraespecifica e testei se a distância geográfica,
o isolamento gerado pela topografia e o clima, ou a adaptação às condições
locais podem moldar tais padrões.
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24
CAPÍTULO I*
Faster amphibian speciation supports the role of mountains as biodiversity pumps
25
Faster amphibian speciation supports the role of mountains as biodiversity pumps
Adrián García-Rodríguez1,2, Pablo A. Martínez3, Brunno F. Oliveira1,4, R.
Alexander Pyron5 & Gabriel C. Costa6
1 Departamento de Ecologia, Universidade Federal do Rio Grande do Norte, Natal
- RN, Brasil, 59078-900
2 Escuela de Biología, Universidad de Costa Rica, San Pedro, 11501-2060 San José,
Costa Rica.
3 PIBi Lab. (Laboratorio de Pesquisas Integrativas em Biodiversidade), Programa
de Pós-Graduação em Ecologia e Conservação, Universidade Federal do Sergipe,
São Cristóvão, Brasil
4 Department of Wildlife Ecology and Conservation, University of Florida,
Gainesville, FL 32611-0430, USA
5 Department of Biological Sciences, The George Washington University, 2023 G
Street NW, Washington, DC 20052, USA
6 Department of Biology, Auburn University at Montgomery, Montgomery, AL
36124, United States of America.
*Corresponding author; email: [email protected]
26
ABSTRACT
Continental mountain areas cover less than 15% of global land surface; nevertheless,
around 90% of the hotspots of species diversity and 40% of the hotspots of
endemism are concentrated in these regions. Such high diversities could be
explained by higher diversification rates in regions of high topographic complexity,
giving mountains the character of speciation pumps. We specifically focused on
testing whether speciation is faster in mountains by conducting macro evolutionary
analyses on a near complete Amphibian phylogeny and evaluating geographic
patterns of this evolutionary rate. We accounted for the role of topographic
complexity on speciation patterns across the globe and within zoogeographic realms.
We found that globally, speciation rates are higher in mountainous areas. At a
regional scale, we found the same pattern for most zoogeographical realms.
Moreover, clades showing the fastest speciation rates are groups with predominantly
montane distributions. Our study bolsters the importance of mountains as engines of
speciation at different geographical scales. Due to their remoteness, the real
contribution of such areas to the origin and maintenance of global biodiversity is
probably still underestimated. These facts and the risk these regions face from global
change suggests that mountains around the globe should be conservation priorities
in local and regional agendas.
Keywords: Amphibians, BAMM, Macroecology, Macroevolution, Topographic
Complexity
27
BACKGROUND
Nearly one-third of the world’s terrestrial species diversity is concentrated in regions
of high topographic complexity [1]. High diversity in mountain regions is a well-
documented pattern [2–4], reported for numerous taxa and regions [5]. In Central
and North America for example, mammal diversity is greater in regions dominated
by mountains and complex reliefs [6]. Likewise, peaks of species richness and
endemism of Afrotropical avifauna occurs within mountains and mountain-lowland
complexes [7] . Worldwide, most global centres of vascular plant richness (>5000
species per 10,000 km2) are located in regions dominated by mountainous areas such
as Costa Rica-Chocó, Tropical Eastern Andes, Atlantic Brazil, Northern Borneo and
New Guinea [8]. As a consequence, despite continental mountain areas covering less
than 15% of global land surface [9], around 90% of the hotspots of species diversity
and 40% of the hotspots of endemism [10,11] are concentrated in these regions.
Although this pattern has been reported for several taxa and across different
regions [5], we still lack a comprehensive understanding of the mechanisms that
drive higher diversity in mountains [12]. From an evolutionary perspective, montane
systems have been hypothesized to be engines of diversification, because of their
potential to drive speciation, both in allopatry and parapatry [13]. Evidence of
allopatric speciation [14] promoted by the vicariant settings implicit in complex
topographies have been widely documented in a variety of taxa [15–17]. For many
28
groups, the irregular configuration of alternate mountaintops and valleys represent
mosaics of favourable and unfavourable habitats [18] that increases isolation among
populations, thereby increasing opportunities for allopatric speciation [19].
Moreover, the distribution of such suitable regions has varied in response to
historical climatic oscillations, increasing allopatric diversification in the mountains
[20]. Other features of mountains are the wide environmental spectrums they cover
in short distances along their elevational gradients [12,21]. These transitions offer
ideal conditions where ecological speciation in parapatry can take place [22]. In
these circumstances, local pressures can drive adaptive divergence between
populations, leading to the formation of new species, in the absence of hard
geographic barriers [23–26].
Whether by allopatric or parapatric speciation, the idea that mountains act as
cradles of biodiversity has been supported in several studies that linked the
chronology of orogenic events to radiations of clades. For instance, the rise of the
Tibetan Plateau seems to have triggered the rapid radiation of glyptosternoid
catfishes [27]; ranid frogs [28] and plants of the families Asteraceae and Fabaceae
[29,30]. Similarly, accumulating evidences suggests that the Andes uplift impacted
evolutionary dynamics of Neotropical taxa such as hummingbirds from the genus
Adelomyia [31], butterflies from the subtribe Oleriina [32], and a variety of
angiosperm clades [33–36].
29
Since the processes of speciation, extinction and dispersal are the ultimate
determinants of diversity occurring in a given geographic region [37], identifying
potential factors that drives these processes is crucial to understand the origin and
distribution of past, present, and future biodiversity. Recently, several hypotheses
based on this evolutionary framework have been proposed to explain the rich biotas
in montane regions [12]. One of them is the Montane species pump hypothesis,
which predicts that clades occurring at mountains have higher rates of net
diversification [38] likely as consequence of their higher rates of speciation.
Evidence supporting this model has been reported for Mesoamerican hylid frogs as
well as for tanagers and butterflies from the Andes; in these cases, montane clades
showed higher speciation rates than those whose ranges are restricted to lowlands
[38–40].
However, the few studies testing whether complex topographic regions are
speciation pumps were too restrictive in terms of their phylogenetic scope (i.e. few
specific clades were analysed) and geographical extent (i.e. explored only local to
regional scales), which limits our ability to determine the generality of topographic
complex regions as speciation pumps. Here, we assess the prediction that complex
topographies promote faster speciation rates. To do this, we use amphibians as a
study system, and integrate global information on species distributions, terrain
complexity and novel analyses on evolutionary dynamics across a nearly complete
30
phylogeny of the group. Amphibians are a particularly suitable study system to test
this hypothesis because they represent an ancient radiation (~7700 species,
www.amphibiaweb.org), with widespread latitudinal and altitudinal distribution
across the globe [41] and a growing availability of phylogenetic information [42,43].
In addition, their high philopatry [44], restricted dispersal capabilities [45], limited
osmotic tolerance [46], high sensitivity to temperature in early developmental stages
[47], and adaptations to particular elevations [48,49] bond their evolutionary fate
strongly to their geographic settings, providing a valuable opportunity to investigate
the forces shaping speciation patterns in montane regions.
METHODS
Amphibian Phylogeny
When inferring diversification dynamics through time, inclusion of all lineages in
focal clades or regions has been proven to be of special importance [50,51].
Considering the known sampling bias towards particular clades and specific
geographic areas as well as the global character of our approach, we attempted to
improve the performance of our analysis by using a tree containing as many species
as possible, even those lacking molecular data. Recent practice enables the
incorporation of lineages lacking genetic data on tree inference using a given set of
priors on branching times [52]. Then, we based our macroevolutionary analyses on
31
recently published trees which to date represent the most complete amphibian
phylogenetic inference [53]. These trees were constructed using the Phylogenetic
Assembly with Soft Taxonomic Inferences (PASTIS) approach [52] updating an
existing molecular supermatrix [43] that contains sequence data (5 mitochondrial
and 10 nuclear genes) for ~56% of extant amphibian species. A Maximum-
Likelihood (ML) topology for these species then served as backbone for a set of 10,
000 trees containing 7,238 species, which represent ~94% of the known extant
amphibian diversity and includes most families, subfamilies and genera. For detailed
description on dating and tree construction, see [53].
Amphibian Evolutionary Dynamics
In order to estimate evolutionary rates, we modelled macroevolutionary dynamics
across the amphibian phylogeny using Bayesian Analysis of Macroevolutionary
Mixtures (BAMM) [54]. BAMM models complex dynamics of speciation,
extinction and trait evolution on phylogenetic trees, by detecting and quantifying
heterogeneity on those rates while exploring a vast parameter-space of
diversification models via reversible Markov Chain Monte Carlo (MCMC) [55].
This approach is useful since it does not assume that rates of speciation and
extinction are constant, and can account for rate variation through time and among
lineages [56]. The performance and theoretical foundations of BAMM has recently
32
received criticism, mainly dealing with the algorithm’s likelihood function, the
posterior distribution on the number of rate shifts and the reliability of its
diversification rate estimates [57]. However, BAMM’s authors have provided
detailed evidence to clarify those concerns and demonstrated satisfactory and
consistent performance of the method [58]. BAMM analysis provide speciation and
extinction rates per species as direct output, and is possible to estimate net
diversification rates by subtracting extinction rates from speciation rates [59].
However, we decided to focused our analyses on speciation rates, because they can
be estimated with much more confidence than extinction rates, for which confidence
intervals tend to be large, even when all assumptions of the inference model are
satisfied [60]. Details of this analysis are provided in the supplementary material
(electronic supplementary material, text S1).
Spatial patterns of Amphibian speciation
We used geographical range maps for 6311 amphibian species obtained from the
IUCN (www.iucnredlist.org). These maps represent approximately 85% of the
known extant amphibian species (~7500 species, www.amphibiaweb.org). Although
we estimated macro evolutionary dynamics using ~94% of amphibian diversity
represented in our phylogenetic tree, available range maps limited our analyses to a
smaller number of species projected in the geographical space. We overlaid species
33
range maps in a 1x1 degree global grid and extracted species presence-absence
within each grid cell, creating a presence-absence matrix for the 6311 species in the
phylogeny that had range maps available. These analyses were conducted in the R
package LetsR [61]. We further estimated speciation rates based on species
composition within each grid cell.
Some authors have argued that species ranges may be too dynamic and this
would mask any potential relationship between current distributions and the
geography of speciation [62]. However, strong evidence supporting range stasis is
available in the literature for a variety of organisms, from fossil molluscs to living
insects and mammals [63–65]. We considered that it is unlikely that all species have
altered their ranges enough to remove geographical signal from their past
distribution. Most amphibian species have low dispersal ability [66] and are highly
sensitivity to environmental conditions, resulting in a high proportion of species of
small range sizes [67,68]. Therefore, the effects of range dynamics on the
geographical signal we are investigating should be a minor concern in this study,
especially at the scales we are working.
Topographic complexity
In order to have an informative proxy of geomorphologic heterogeneity, we
generated a global index of topographic complexity (TC). Using a global layer of
34
elevation at 30-second resolution (~1km at the equator, http://www.worldclim.org/)
we calculated the standard deviation of differences between 100x100 adjacent
elevations. This procedure has been demonstrated to more accurately represent
topographic roughness than elevation range, which only indicates the strength of a
gradient within a cell [69]. We projected our TC layer to match the 1x1 degree
resolution of our species distribution dataset.
Amphibian speciation in topographic complex regions
TC is not evenly distributed around the world [70]. This pattern reflects in our metric
of TC, for which the number of cells with low values widely exceeds the number of
cells with high values across the globe (Fig. 1c). To account for this, we created two
categories: low topographic complexity (LTC) and high topographic complexity
(HTC). We considered as HTC cells that have a complexity index value higher than
300. Our complexity index is correlated with altitude and a value of 300 assures that
we are selecting regions that are at least 600 meters elevation. This approach is
conservative considering that Körner et al. (2017) defined montains as those areas
above 200m elevation.
To test the montane pump hypothesis, we compared speciation rates between
LTC and HTC regions. According to this hypothesis, HTC areas should show higher
speciation rates than LTC areas. HTC cells represent only a small fraction of the
35
total number of cells across the globe (2196 cells or 14.5% of all cells analysed).
Therefore, to test for significant differences between LTC and HTC speciation rates
we used a rarefaction procedure [71]. We calculated the average speciation rate for
all HTC cells and next, randomly sampled 2196 cells from the LTC and calculate
the average speciation rate. Finally, we repeated this procedure 10,000 times,
generating a distribution of average speciation rates for LTC. The observed average
for HTC was then compared with the LTC generated distribution to assess
significance. In order to test how speciation rates vary between LTC and HTC
regions at different latitudes, we conducted this same analysis within the updated
zoogeographic realms, a classification that defines robust biogeographic units based
on global distributions and phylogenetic relations from over 20,000 world´s
vertebrate species [71]. Therefore, using this delimitation also allows us to consider
the evolutionary histories of the different zoological Realms.
Finally, we provided some examples to illustrate general patterns, where we
compared mean speciation trajectories between predominantly montane groups and
groups mainly distributed in adjacent lowlands. For this, we gathered species-
specific information on elevation ranges for species belonging to several montane or
lowland genera available at http://www.iucnredlist.org. We used this information to
plot elevational distribution pattern for each genus and extract their respective
36
speciation rates to visualize how they vary through time and how different they are
between lowland and montane clades.
RESULTS
Evolutionary Dynamics
When checking for convergence of BAMM runs, we obtained values of 210.66 and
418.99 for the effective sample sizes of the log-likelihood and the number of shift
events present in each sample respectively. These values have been shown to be
reasonable for very large datasets confirming convergence of our analyses [72]. We
found strong evidence for heterogeneous diversification dynamics in amphibians.
Based on the values of posterior quasi-probability across all bootstrap replicates
from post burn-in BAMM, we found support for 45 evolutionary rate shifts (mean =
48.35; median = 48) (electronic supplementary material, Fig S1). We focus our
discussion on speciation rates dynamics, however since we found a high positive
linear correlation between speciation and net diversification rates (Pearson's r = 0.97,
p < 0.001) we consider that speciation might provide good insights on the
diversification of amphibians.
37
Figure 1. Species richness based on the distribution of 6311 species (A); mean speciation rate (B) and topographic complexity (C) per 1° grid cell in a global scale.
38
Geographic patterns of Amphibian speciation
Mean amphibian speciation-rates are unevenly distributed across the world.
Speciation rates show an inverse latitudinal gradient in the New World, with faster
speciation rates towards the poles. In the Old World speciation rates increase only
towards northern latitudes, while regions such as Africa, Madagascar and Western
Australia are characterized by low speciation rates. A major portion of Southeast
Asia and the Neotropical Region show low to intermediate mean speciation rates
(Fig. 1).
We detected that speciation rates vary widely within regions. Such variability
peaks in Mesoamerica, Patagonia and North America (Fig. 2), where there is a
mixture of groups with both fast and slow speciation rates (Fig. 2). Rapidly
diversifying groups are concentrated in the Neotropical, Panamanian, Nearctic and
Australian regions. In contrast, we found, lowest values of speciation rates in
western Africa and most of the Palearctic region (Fig. 2).
39
Figure 2. Variability on speciation rates across the world
40
Topographic complexity as driver of speciation
At the global scale, we found faster speciation rates in HTC regions than in LTC
regions (HTCmean=0.0679, LTCmean=0.0651, p-value <0.0001). Considering
independent evolutionary histories, we applied the same approach across global
biogeographic realms. At this regional scale, we found the same pattern of faster
speciation in HTC in eight out 11 realms (Fig. 3, Table 1). In addition, speciation
rates also tended to be higher in HTC in the realms were statistical difference were
not significant (i.e., Afrotropical, Madagascan and Nearctic realms) (Table 1, Fig 3).
Table 1. Differences in mean speciation rates between LTC and HTC areas in global scale and within the 11 Zoogeographical regions of the world.
Region Mean Speciation Rate in HTC
Mean Speciation Rate in LTC
SD of Speciation Rates in LTC
P-value
Global 0.0679 0.0651 0.0004 <0.001
Neotropical 0.0605 0.0551 0.0002 <0.001
Afrotropical 0.0532 0.0523 0.0005 0.969
Madagascan 0.0500 0.0483 0.0008 <0.001
Australian 0.0648 0.0546 0.0005 <0.001
Nearctic 0.0724 0.0717 0.0002 0.996
Oceania 0.0605 0.0560 0.0002 <0.001
Oriental 0.0639 0.0592 0.0002 <0.001
Panamanian 0.0642 0.0604 0.0004 <0.001
Saharo-Arabian 0.0685 0.0639 0.0006 <0.001
Sinojapanese 0.0746 0.0713 0.0004 <0.001
Palearctic 0.0682 0.0651 0.0002 <0.001
41
Figure 3. Mean speciation rates for LTC and HTC areas in a global scale and within the different zoogeographic realms. Histograms represent the distribution of values obtained after resampling 10 000 times the number of cells in HTC from the pool of LTC cells. Dashed lines represent the mean values of speciation rate for LTC (blue) and HTC (red) regions.
42
Faster speciation rates are generally associated with clades that predominantly
inhabit HTC areas. In the New World for example, those clades occur in several
Andean, Mesoamerican and North American mountain chains, and in a series of
islands dominated by steeped topographies such as Jamaica and Dominican
Republic. In the Old World, speciation rates peak at the Himalayans and other major
mountainous systems in China, Philippines and Papua New Guinea. In Australia, we
found speciation rates maxima similar to those of the other regions although they
were not exclusively associated to mountainous areas.
When comparing speciation rates across the phylogeny, we found faster rates
in salamanders (Caudata = 0.0781±0.034; Anura = 0.053±0.016;
Gymnophiona=0.028±0.001; p = 0.0054, Df = 2). Differences are also significant
among Amphibian families (p<0.0001, Df = 75) as well as among families within
the orders Anura (p<0.0001, Df = 57) and Caudata (p<0.0001, Df = 8). Mean
speciation rates among Gymnophiona families did not differ significantly (p = 0.077,
Df = 8). At the genus level, the fastest speciation rates occur in the Patagonian spiny
frogs Alsodes (mean = 0.1934±0.006, n = 18). Other anuran genera showing high
rates of speciation are the bufonid genera Rhinella and Atelopus, ranids of the genera
Rana, Odorrana, Babina and Amolops, as well as the New World direct developing
frogs of the genus Brachycephalus. Salamanders of the family Plethodonthidae,
which includes genera such as Bolitoglossa, Eurycea, Pseudoeurycea,
43
Batrachoseps, Thorius, Nototriton and Oedipina (electronic supplementary material,
Fig S2) showed the highest speciation rates at the family level (mean =
0.0982±0.0383, n = 450).
Figure 4. Comparative patterns of altitudinal distributions and speciation trajectories for contrasting montane and lowland anuran genera. A-B. The bufonid highland genus Atelopus from Northern Andes, a closely related genus (Rhaebo)
44
and a highly diverse hylid genus (Scinax), both distributed in lower elevations mainly the adjacent Amazon Basin; C-D. Three centrolenid genera from the Andean and Mesoamerican Region presenting different altitudinal distributions: Hyalinobatrachium with the lower elevation range and most species occurring below 1000 m.a.s.l and Nymphargus and Centrolene with mean altitudes around 2000 m.a.s.l; E-F. Three Ranid genera from the Old World with different patterns of altitudinal distribution: Pelophylax and Meristogenys with most of their representatives occurring below 500 m.a.sl and Odorrana with a peak of diversity above 1000 m.a.s.l and several species reaching 3000 m.a.s.l
As predicted, most of the rapidly diversifying clades showed predominantly
montane distributions. To exemplify this trend, we compared speciation through
time plots between some of these montane genera and lowland genera. To make it
comparable, we contrast genera with similar richness. In all cases, speciation rates
were higher in clades that are mostly montane, and the differences were constant
through the evolutionary history of these groups, depicting historical differences in
their speciation trajectories (Fig 4).
DISCUSSION
We found that speciation rates are generally higher in HTC regions than in LTC
regions at a global scale, in concordance with the montane-pump hypothesis. In
addition, our results provide evidence showing that maximum speciation rates are
generally associated with clades that predominantly inhabit HTC regions. These
includes several Andean ranges, Mesoamerican mountain chains, various Sierras in
45
North America, and a series of islands dominated by steeped topographies such as
Jamaica and Hispaniola in the Western Hemisphere. In the Old World, the
Himalayans and other major mountainous systems in China, Philippines and Papua
New Guinea exhibit similar dynamics. This suggests that highly complex reliefs
around the globe, independently of their latitude, have an important role as engines
of speciation. It also suggests that these dynamics are specific to the geographical
setting of montane regions generally, and not specific geographic areas or traits
possessed by specific lineages that confer increased diversification.
A growing body of literature provide evidence supporting the role of
mountains as species cradles for numerous taxa. A few examples are the
Australasian Sky Islands [73,74], the Hendguan Mountains [75,76], and the
Anatolian Mountains in the eastern hemisphere [75,76]. In the New World, evidence
of such tendency has been documented in regions such as the Andes [32,75–80] and
the North American Sky Islands [81,82]. Such studies have often focused on few
clades and specific geographic regions that exhibit high diversity. Our study is the
first to our knowledge to contrast speciation rates between HTC and LTC regions at
global scale. We provide evidence of the general importance of mountain ranges as
speciation pumps. Importantly, our results suggest that mountains affect speciation
rates independently of region, diversity, or specific lineage in amphibians.
46
Across the phylogeny, we found that salamanders have the highest mean
speciation rates among Amphibians, followed by anurans and caecilians.
Salamanders are abundant in North temperate regions where seven of the 10 families
in the order are distributed (www.amphibiaweb.org). Within the order, we found the
highest speciation rates in Plethodontidae, a family whose representatives reach the
tropics of the western hemisphere [83]. The major radiation of this family is the
Neotropical tribe Bolitoglossini, which occurs throughout complex topographies
within Mesoamerica, and contains nearly 300 species, that accounts for over 65% of
the species in the family and 43% of the diversity in the order [84].
Among anurans, speciation rates also peak in montane-associate clades.
Fastest speciation rates occur in the genera Alsodes [85] and Eupsophus from the
Patagonian Andes (despite the low diversity of this region) and bufonids such as the
Harlequin toads of the genus Atelopus which have mainly radiated in the highlands
of the northern Andes [17]. In mountain ranges of south eastern and eastern Asia,
ranids of the genus Odorrana [86–88] also rank among the anuran clades with the
highest means of speciation rate. As examples of these evolutionary contrasts, we
compared altitudinal distributions and speciation trajectories within these genera
with those of closely relatives or similarly diverse clades occurring in adjacent
lowlands. In all cases, it is evident that montane clades have higher speciation rates
and these differences have been constant through time (Fig 4).
47
Rates of speciation can be influenced by both intrinsic biological attributes
and extrinsic environmental factors [13,89]. Some of the latter factors may be
magnified in topographically complex landscapes. For example, characteristic
rugged reliefs in mountainous regions are more likely to impose physical barriers,
fragmenting species ranges and promoting geographical isolation [14,16,17,21,90].
Furthermore, altitudinal gradients in these complex landscapes, provide
heterogeneous environmental conditions that could promote ecological
specialization and niche divergence based on trait differences [91,92]. Both
scenarios restrict gene flow, augmenting founder effects and driving speciation
whether in allopatric or parapatric conditions [43,85]. For groups with low dispersal
rates such as amphibians, these conditions appear to have a major impact on the
processes of incipient population differentiation, and ultimately, speciation [43,92].
Our results also provide insights on the latitudinal and zoogeographic patterns
of amphibian speciation. We found high latitudinal variance in amphibian speciation
rates. Such variability is strikingly decoupled from the well-documented latitudinal
diversity gradient (LDG) present in amphibians and many other groups [94] . For
example, mean speciation rates for all amphibians are higher in temperate zones of
both the New and the Old world, while lower mean rates were concentrated in more
speciose regions such as Africa, Madagascar, and Western Australia. Other hotspots
of diversity, including a major portion of Southeast Asia and the Amazon Basin [60],
48
showed intermediate mean speciation rates. We suggest that the great variability of
speciation rates in speciose areas with heterogeneous species compositions may
obscure the latitudinal patterns of speciation. However, future studies should explore
the relation between latitude and speciation rates more deeply, in order to understand
the main evolutionary forces shaping the LDG in amphibians.
CONCLUSIONS
Our findings bolster the general importance of mountains as engines of
speciation at different geographical scales and independently of latitude. However,
due to their remote conditions, many mountain ranges remain unexplored and their
real contribution to the origin and maintenance of global biodiversity is still
underestimated. For these reasons and the risk these regions face during ongoing
global changes [71], mountains around the world must be considered conservation
priorities in local and regional agendas. The evidence presented here highlights the
role of such areas in the evolutionary history of modern patterns of diversity; further
efforts must be oriented to increase the knowledge of these areas to inform future
decisions for the conservation of their particular biotas.
49
DATA ACCESSIBILITY
The phylogeny used here is a random tree extracted from the topologies made
available by Jetz & Pyron [53] at https://vertlife.org/files_20170703/#amphibians.
TCI was calculated using an elevation layer available at www.worldclim.org at 30
secs resolution. R code and associated files are available as electronic supplementary
material
AUTHORS' CONTRIBUTIONS
AGR conceived of the study, discussed design, conducted analyses and drafted the
manuscript. PAM conceived of the study and participated in data analyses. BFO
participated in data analyses. RAP participated in data analyses. GCC conceived of
the study, discussed design of analyses and drafted the manuscript. All authors
improved the draft of the manuscript and gave final approval for publication.
COMPETING INTERESTS
We have no competing interests.
FUNDING
AGR was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior, Brazil. GCC thanks CNPq produtivity grant 302297/2015-4. RAP was
50
supported by US NSF grant DEB-1441719 and DEB-1655737. BFO thanks
University of Florida for providing generous support.
ACKNOWLEDGEMENTS
We thank Marcelo Araya and Juan Pablo Zurano for valuable discussion and
suggestions during the development of this study. To our colleagues Jodi Rowley,
Luis Coloma, Santiago Ron, Alexander Haas, Andreas Nöllert and Brian Gratwicke
that kindly provide permission to use the photos included in figure 3, and Paula
Acosta who helped with the edition and improving of one of those images.
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-Electronic Supplementary Material-
Faster amphibian speciation in mountains support their role as biodiversity pumps Supplementary material, text S1.
BAMM analysis
To perform the BAMM analysis we first used the 'setBAMMpriors' function
to generate a prior block in accordance with the scale of our tree. We ran 60,000,000
generations of reversible-jump MCMC sampling on our phylogeny with samples
drawn from the posterior every 5,000 generations. Considering the massive number
of tips in our tree we used a poisson prior of 0.033, which correspond to 30 expected
shifts of diversification. The selection of low values for this parameter allows the
algorithm to explore a broader range of shift configurations, facilitating
convergence. We used the BAMMtools R package -version 2.0-[1] in R, to analyze
consistency among BAMM outputs. We visually checked for convergence of the
MCMC algorithm by inspecting the relation between likelihood scores and sampled
generations. Then, we reviewed adequate mixing of chains, examined for effective
sample sizes above at least 10% of our sampled generations, discarded the first 20%
of samples as burn-in and used the 80% left to estimate evolutionary rate values for
each tip and resulting shifts of diversification across the tree. We used the R package
‘coda’ [2] to diagnose convergence, considering as satisfactory values of effective
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sample size of the log-likelihood and the number of shift events present in each
sample above 200. BAMM analyses provide speciation and extinction rates per
species as direct output and is possible to estimate net diversification rates by
subtracting extinction rates from speciation rates [3]. However, we focused our
analyses on speciation rates, because they can be estimated with much more
confidence than extinction rates, for which confidence intervals tend to be large,
even when all assumptions of the inference model are satisfied [4]
Supplementary References
1. Rabosky DL, Grundler M, Anderson C, Title P, Shi JJ, Brown JW, Huang H,
Larson JG. 2014 BAMMtools: An R package for the analysis of evolutionary
dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707.
(doi:10.1111/2041-210X.12199)
2. Plummer M, Best N, Cowles K, Vines K. 2006 CODA: Convergence
Diagnostics and Output Analysis for MCMC. R News 6, 7–10.
3. Morlon H. 2014 Phylogenetic approaches for studying diversification. Ecol.
Lett. 17, 508–525. (doi:10.1111/ele.12251)
4. Rabosky DL, Title PO, Huang H. 2015 Minimal effects of latitude on present-
day speciation rates in New World birds. Proc. R. Soc. B 282, 20142889.
(doi:10.1098/rspb.2014.2889)
61
SUPPLEMENTARY FIGURES
Figure S1. Tree showing best configuration of speciation rate shifts among groups in extant Amphibians. Colours in branches represent speciation rate dynamics, blue represent slower while red represent faster rates. Red dots highlights the regions of the phylogeny where major shifts were detected
.
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1
Figure S2. Clade-specific speciation trajectories for some of the groups with the highest values of speciation rates detected. A. Patagonian Spiny frogs (Alsodes, 18 species); B. South American Harlequin toads (Atelopus,97 spp); C. Neotropical true toads (Rhinella, 35 spp); D, E and F. Bolitoglossine salamanders (Bolitoglossa, 137 spp; Oedipina, 36 spp and Pseudoeurycea, 50 spp).
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CAPÍTULO II**
Idiosyncratic responses to drivers of genetic differentiation in the complex landscapes of Isthmian Central America
64
Idiosyncratic responses to drivers of genetic differentiation in the complex
landscapes of Isthmian Central America
Adrián García-Rodríguez1,2, Carlos E. Guarnizo3, Andrew J. Crawford3,4, Adrian A.
Garda1 & Gabriel C. Costa5
1Departamento de Ecologia, Universidade Federal do Rio Grande do Norte, Natal,
59078-900 RN, Brasil.
2Escuela de Biología, Universidad de Costa Rica, San Pedro, 11501-2060 San José,
Costa Rica.
3Departamento de Ciencias Biológicas, Universidad de los Andes, A.A. 4976,
Bogotá, Colombia.
3Smithsonian Tropical Research Institute, Apartado, 0843–03092, Panamá,
Republic of Panama.
5Department of Biology, Auburn University at Montgomery, Montgomery AL
36124.
Corresponding author: Adrián García-Rodríguez
65
ABSTRACT
The isthmian portion of Central America (ICA) is one of the most biodiverse regions
in the world, hosting the highest number of species per unit area, for many taxa. Due
to the geological history of this region, the assembly of this biota is relatively recent
and results from dispersal events and in situ diversification processes across a
complex landscape. Here, we combined information on mitochondrial DNA
sequence variation with climatic and physical environmental features to understand
abiotic forces that might have promoted diversification. To this end, we evaluated
the role of isolation by distance, topography, habitat suitability, and environment in
shaping patterns of genetic differentiation in eleven amphibian species with
disparate life histories co-distributed in the region. In seven of the species studied,
we found that at least one the factors tested, significantly explains genetic divergence
patterns. Instead of finding a major force responsible for the intraspecific genetic
divergence in ICA, our results reveal idiosyncratic responses of each species,
suggesting that intrinsic characteristics of each species play an important role in
determining responses to different drivers of isolation. We show that confluence of
several determinants of isolation with a heterogeneous biota having different life
histories, geographic origins, and arrival times to ICA maximizes the chances of
genetic differentiation. We conclude that evolutionary dynamics of ICA’s biota is
far more complex than simply vicariance between Caribbean and Pacific clades as a
main form of speciation in the region. Drivers of diversification likely act even in
short distances in complex landscapes, contributing to high levels of endemism as is
the case LCA. More research is needed, not only to understand the causal relation
between environment and genetic differentiation, but also to better document a
diversity that is still remains underestimated.
66
Keywords: Gene flow, Isolation by distance, Isolation by resistance, Isolation by
environment, Generalized dissimilarity Modeling, Multiple matrix regression with
randomization
67
INTRODUCTION
Isthmian Central America (ICA), centered at Costa Rica and Panama, is one of the
most diverse regions in the globe (Myers et al. 2000). Inserted in the Mesoamerican
hotspot of biodiversity, this region roughly covers 0.1% of earth's land surface,
nonetheless harbors an immense number of species; reaching estimates for some
taxonomic groups of 4-10% of the global biodiversity residing in Costa Rica alone
and Panama may be more diverse (Bagley and Johnson, 2014). Some examples of
highly diverse taxa in the region includes birds (>1,000 spp), amphibians (~300 spp),
reptiles (~500 spp), insects (>300,000 spp) and vascular plants (> 20,000 spp)
(Anger and Dean, 2010; Garrigues and Dean, 2014; Frost, 2017).
ICA has a relatively recent geological origin, nevertheless, the precise timing
of the formation of the region´s major landscape features is still under debate
(Montes et al., 2012; O’Dea et al., 2016). Historically, the most accepted hypothesis
was that the Panama Isthmus closed relatively recently, around 3-4 million years ago
(Ma) (Coates et al., 1992). Multiple independent sources of evidence supported this
hypothesis, including divergence times in marine organisms separated by the
Isthmus, and the fossil record found at the Panama Canal area (Keigwin, 1978;
O’Dea et al., 2016). However, recent studies using sources of evidence such as
petrographic, geochronological, and termo-chronological data suggests that the
Panama Isthmus closed much earlier, around 15 Ma (Montes et al., 2012). This
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earlier date is also supported by recent molecular-based studies showing early
dispersal between North and South America around 10 Ma (Pinto-Sánchez et al.,
2012; Bacon et al., 2015).
Independently of the debate regarding the precise date of the Isthmus closing,
this geological formation precipitated one of the greatest biogeographic events of
the Cenozoic, the Great American Biotic Interchange (GABI), a bidirectional
dispersal of terrestrial mammals between the previously isolated North and South
American landmasses, around 2 Ma (Marshall, 1988; Webb, 2006). Evidence
available for other groups such as frogs, birds and invertebrates (Webb, 2006; Weir
et al., 2009; Pinto-Sánchez et al., 2012; Wilson et al., 2014) also highlights the
important influence of the rising of the isthmus in the conformation of the regional
biota. In consequence, biodiversity in ICA is largely constituted by northern and
southern lineages that arrived after the completion of the land bridge (Janzen, 1991;
Savage, 2002). Surprisingly, the Isthmian fauna also has a striking high number of
endemics (e.g. Rodriguez-Herrera et al. 2004; Kluge & Kessler 2006; Savage &
Bolaños 2009; Bogarín et al. 2013; Garrigues & Dean 2014), including lineages of
considerably old age (Wang et al., 2008) . This high prevalence of endemism
highlights the importance of in situ processes of diversification contributing to this
region´s biota.
69
The complex tectonic and geological history of LCA resulted in a steep
topography where a variety of habitats and climatic regimes converges in a small
surface (Weyl, 1980; Gabb et al., 2007). For example, in less than 1000 km along
the Pacific coast of Costa Rica, mean annual precipitation varies from ~1800 mm in
some areas of the northern dry forest of Guanacaste to nearly 5000 mm in the very
humid rainforests of Peninsula de Osa (Savage, 1966; Coen, 1991). The
physiography of the region is characterized by NW-trending volcanic cordilleras that
can reach altitudes over 3000 m in several peaks (Luteyn, 1999). Such cordilleras
are bisected by valleys which result in a mosaic of sky-islands along with steep
altitudinal gradients, microclimates, and diverse vegetation zones (Marshall, 2007;
Bagley and Johnson, 2014). In addition, climatic and sea level fluctuations during
the Quaternary (Horn, 1990; Islebe et al., 1995, 1996) likely promoted species range
contractions and expansions throughout this complex landscape, imposing or
removing barriers to gene flow and playing a central role in diversification processes
(Hewitt, 2004). Given all these conditions, allopatric speciation (Mayr, 1942)
through vicariant events promoted by physical barriers and parapatric speciation
(Doebeli and Dieckmann, 2003) across environmental gradients may have occurred
in the region, although strong evidence of the latter is still scarce around the globe.
Considering its recent geologic history and high levels of richness and
endemism, ICA represent an ideal natural laboratory to study the role of landscape
70
factors in driving early phases of genetic differentiation. One way to understand the
role that such factors have had on the in-situ diversification of the ICA is to study
the relationship between geographic or environmental variables and intraspecific
genetic divergence. In the simplest of these relationships, called isolation by
distance (IBD), genetic differentiation is expected to increase with geographic
distance due to restricted gene flow by individuals (Wright, 1943; Slatkin, 1993).
However, in highly heterogeneous regions, dispersal across the landscape is more
restricted due to the effect of barriers imposed by complex topographies and their
associated climatic gradients (Manel and Holderegger, 2013). Consequently, genetic
isolation is expected to be more associated with landscape heterogeneity than with
geographic distances alone. Isolation by resistance (IBR) accounts for the reduced
dispersal among populations caused by the relative unsuitability or ‘friction’
presented by heterogeneous landscape components located in between two
populations (McRae, 2006). In contrast, Isolation by Environment (IBE) has been
recently proposed to describe a pattern in which genetic isolation between two
populations increases with environmental differences at the respective localities,
independently of the resistance imposed by the landscape found between the two
populations (Wang and Bradburd, 2014).
Discerning between IBD, IBR, and IBE can help discriminate among different
forces promoting genetic divergence within species and its relationship with the
71
prevalent endemism of ICA. An ideal study system with which to test this idea are
amphibians: their low dispersal capabilities (Blaustein et al., 1994) prevent them
from moving across landscapes as much as other vertebrates (Beebee 2005),
promoting the accumulation of genetic differences among populations (Funk et al.,
2005; Wollenberg et al., 2011). Because of their permeable skin and poikliothermic
physiology, amphibians are also thought to be relatively sensitive to environmental
variation (Navas & Otani 2007; Cruz-Piedrahita et al. 2018). Thus, amphibians
should show a marked imprint of the potential effects of historical, ecological and
geographic factors in driving genetic divergence among populations.
In this study, we explore how ICA´s landscape features predict patterns of
genetic variation within eleven nominal species of amphibian belonging to 7
taxonomic families. We quantified the relative role of geographic distance (IBD),
topography (IBRt) and climate (IBRsuitability and IBE) in shaping genetic
divergence in each species. Using this information, we tested the following
hypotheses and related predictions applied to amphibian species with diverse life
histories.
1) Linear geographic distance itself is not a good predictor of genetic divergence in
the complex landscapes of ICA. Variation in genetic divergence is better explained
by metrics that acknowledge the topographic and climatic heterogeneity among
populations.
72
2) Prominent mountain chains in the region represent major vicariant barriers for the
local fauna. Due to the massive dimensions of these orogenic formations, we expect
major genetic structure in species occurring on both the Pacific and Caribbean
lowlands, regardless of their biology.
3) Climatic suitability increases the probability of a population to persist in the
environment. We predict that genetic structure may be explain by patterns of
isolation due to patchy distribution of climatically suitable habitats in the complex
landscapes of the region.
4) High climatic heterogeneity, promotes divergence among populations of the same
species occurring in distinct environments. Different climatic regimes within the
region promoted adaptation to local conditions and such process may reflect the
patterns of genetic divergence.
METHODS
Study species
We used as study system a set of 11 amphibian species, including nine anurans and
two salamanders for which we had mitochondrial DNA (mtDNA) sequence data.
Such species represent a variety of reproductive strategies, body sizes, dispersal
capabilities, and distribution ranges (see details in Table 1).
73
Table 1. Amphibian species studied with details on several biological attributes and genetic data analyzed. 1
2 Family Species SVL
(mm)
Reproductive Mode Clutch
Size
Elevation
(m)
Genes Localities
Plethodontidae Bolitoglossa lignicolor 46-81 Direct Development Unknown 0-900 11 (16S) 9
Plethodontidae Oedipina alleni 40-58 Direct Development Unknown 0-900 5 (16S) 4
Eleutherodactylidae Diasporus diastema 16-24 Direct Development
Arboreal eggs
7 to 19 1- 1500 11 (COI) 8
Strabomantidae Pristimantis ridens 16-25 Direct Development
Terrestrial eggs
Unknown 15-1600 14 (16S) 11
Centrolenidae Sachatamia albomaculata 20 -32 Arboreal eggs
tadpoles into streams
Unknown 0-850 14 (16S),
14 (COI)
7,7
Centrolenidae Espadarana prosoblepon 21 -31 Arboreal eggs,
tadpoles into streams
~20 20-1900 26 (16S) 12
Hylidae Dendropsophus ebraccatus 23-35 Arboreal eggs,tadpoles into
puddles, ponds, streams
15 to 296 0-1300 24 (16S) 10
Hylidae Smilisca phaeota 40-78 Eggs and tadpoles in small
ponds or shallow streamlets
1.5K -> 2K 0-1000 9 (16S) 8
Hylidae Agalychnis callidryas 30-71 Arboreal eggs, tadpoles into
puddles, ponds, streams
11 to 104 1 -1000 51 (16S) 14
Ranidae Lithobates warszewitschii 37-52 Eggs /tadpoles in lotic water Unknown 1 - 1750 29 (16S) 11
Bufonidae Rhinella marina 85-175 Eggs /tadpoles in lentic water 5K - 25K 1 - 2100 9 (16S) 8
74
We selected these species from a bigger data set of mtDNA from which we discarded
cryptic species or species with less than 5 sampled localities. The final set analyzed
here included sequences from 350 individual amphibians, corresponding to 95
nominal species sampled from localities across Costa Rica (Fig. 1).
Figure 1. Maps with the distribution of available genetic data for the 11 amphibian species included in this study
75
Sampling and sequencing methods
Most tissues used in this study were obtained from samples deposited at the
Herpetology Collection of the Museo de Zoología de la Universidad de Costa Rica
(UCR). These new data were supplemented with sequence data available in
GenBank obtained from Costa Rica as well as from Panama. The final dataset
analyzed here contained 136 sequences of the cytochrome oxidase I (COI-5’, also
known as the Barcode of Life fragment [Hebert et al. 2003]) and 321 sequences of
a fragment of the 16S ribosomal RNA gene. Species names, field collection
numbers, museum numbers, localities, Barcode of Life Data Systems (BoLD;
Ratnasingham and Hebert 2007) Process ID for each specimen, and GenBank
accession numbers for each sequence used in the present study are provided in Table
S1.
DNA extraction was carried out on a BioSprint 96 (Qiagen) robotic extractor based
on magnetic beads, including digestion with proteinase K (0.4 mg/mL) at 55 °C. We
amplified two mitochondrial gene fragments, the fast-evolving COI, and the more
slowly-evolving 16S gene, following Crawford et al. (2010). The primer pairs used
to amplify and Sanger-sequence the COI-5’ gene were: dgHCO2198 (5′-TAA ACT
TCA GGG TGA CCA AAR AAY CA-3′) and dgLCO1490 (5′-GGT CAA CAA
ATC ATA AAG AYA TYG G-3′) (Folmer et al. 1994; Meyer et al. 2005) and 0.25
µg/µL of bovine serum albumin. The 16S gene fragment was amplified and
76
sequenced with primers 16SB-H (aka, 16Sbr-H) (5′-CCG GTC TGA ACT CAG
ATC ACG T-3′) and 16SA-L (aka, 16Sar-L) (5′-CGC CTG TTT ATC AAA AAC
AT-3′) (Kessing et al. 2004). PCR products were cleaned using ExoI and SAP
enzymes (Werle et al. 1994), with Sanger sequencing reactions run on ABI 3130
automated sequencers. All enzymatic and sequencing reactions were performed in a
high-throughput 96-well format. Both genes were sequenced bi-directionally to
confirm base calls. The sequences of each gene were aligned independently using
default parameters in MUSCLE (Edgar, 2004) -available at
https://www.ebi.ac.uk/Tools/msa/muscle/ - and reviewed by eye.
Potential cryptic species handling
Preliminary genetic analyses showed that some museum voucher specimens were
misidentified, a common mistake in mega-diverse regions such as ICA, where a high
number of sister species maintain morphological stasis after speciation (Padial and
De La Riva, 2009; Funk et al., 2012). To prevent potential errors assigning DNA
sequences to species: First, we used the Automated Barcode Gap Discovery (ABGD)
algorithm (http://wwwabi.snv.jussieu.fr/public/abgd/), which identifies clusters of
sequences that may correspond to more than one species based on the distribution of
pairwise genetic distances between the aligned DNA sequences. This method
statistically infers multiple potential barcode gaps or thresholds, and partitions the
sequences such that the distance between two sequences taken from distinct clusters
77
is larger than the barcode gap (Puillandre et al., 2012). This method performs well
in terms of efficiency and success in species identification compared with other
DNA barcode algorithms (Paz and Crawford, 2012). The COI, 16S, and
concatenated alignments were processed in ABGD assuming a Kimura two-
parameter (K2P) nucleotide substitution model (Kimura, 1980) and the following
settings: prior for the maximum value of intraspecific divergence between 0.001 and
0.1, with 10 recursive steps within the primary partitions defined by the first
estimated gap, and a gap width of 1.0. K2P is the standard model of DNA
substitution for barcode studies, performing as well as other more complex models
in identifying specimens (Collins and Cruickshank, 2012). Even though there are
not many DNA substitution models available in ABGD, a recent study suggests that
species identification success rate is not affected by the model (Collins et al., 2012)
We corroborated the ABGD results by checking if there was evidence of
highly divergent DNA sequences in sympatry, which may suggest potential
misidentifications. To do this, we contrasted geographic versus genetic distances for
each nominal species and searched for cases where genetic divergence at short
geographic distances (in sympatry) were above 5% for COI and 2.5% for 16S. We
selected these thresholds considering those proposed for candidate species in frogs,
10% for COI and 5% (Vences et al., 2005) or 3% (Fouquet et al. 2007) 16S. The
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total number of sequences and nominal species that we ended up after the ABDG
analyses are in Table S
Estimation of genetic distances
Pairwise genetic p-distances were estimated between 16S and COI sequences
available for each species (See Table 1). We chose the best-fit model of nucleotide
substitution for each set of sequences using jModelTest (Posada, 2008) based on the
Akaike information criterion (AIC). Then, we estimated genetic distances with
MEGA 4.0 (Tamura et al., 2013) using the pairwise-deletion option, therefore
excluding inferred gaps in each pair of sequences for the genetic distance
calculations. We used these genetic distances as a proxy for gene flow (Rousset,
1997; Selonen et al., 2010). We estimated separately genetic distances for COI or
16S (we did not concatenated both genes) since in many nominal species there were
no sequences available for both genes. We used the landscape genetics approach,
where the individual sequence is the unit of analysis, to avoid biases in the
identification of populations (step needed to estimate other genetic divergence
measurements, such as Fst).
Estimation of geographic, resistance and environmental distances
To understand the relationship between geography and the intraspecific genetic
divergence we estimated landscape derived distances, including geographic distance
79
(IBD), and resistance distances that account for topography (IBRt) and climatic
suitability (IBRcs) as well as local environmental dissimilarity (IBE) between all
individual pairwise combinations within each species. This method allowed us to
compare the relative importance of different landscape variables in explaining
genetic divergence within each species.
To test for IBD, we estimated linear geographic distances as pairwise
Euclidean distances (in km) between the geographic position of each pair of
individuals of each species using the R package raster (Hijmans and van Etten,
2010). To test for IBR we estimated resistance distances using topography and
environmental suitability as friction layers based on a circuit-theory approach
conducted in Circuitscape V.4.0 (Shah and McRae, 2008). For topography, we
created a layer that quantifies local terrain complexity, using a raster grid of
elevation with a resolution of 30 arcseconds (~0.8 km at the equator) and estimated
the standard deviation of a set of adjacent cells to obtain a value of topographic
complexity for cells of ~5 by 5 km resolution. In the case of environmental
suitability, we used the inverse of species distribution model suitability (SDMs) for
each species, assuming that areas with low suitability have higher resistance for
dispersal (Wang, Yang, et al., 2008).
To construct SDMs, we gathered additional occurrence data from the Global
Biodiversity Information Facility and the Collection of Herpetology of Universidad
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de Costa Rica. We generated SDMs using 11 out of the 19 bioclimatic variables
available at worldclim.org (2.5 arcmin resolution ~ 4.5 km at the equator) for current
conditions. We excluded variables using a Variance Inflation Factor Analysis to
avoid predictor redundancy. We created all models with a Maximum Entropy
algorithm using the R package dismo (Hijmans et al., 2012) after a process of
parameter tuning and evaluation conducted in the R package ENMeval (Muscarella
et al., 2014). We provide details on modeling procedures in the supplementary
material. We also quantified historical isolation by projecting these models to Mid
Holoce (~ 6 Kya) and Last Glacial Maximum -LGM- (~ 22 Kya) conditions and
using the inverse of these projections as friction layers.
To test for IBE, we estimated environmental dissimilarity between locality
pairs. For each sampled locality, we extracted the values for the 19 bioclimatic
variables available in the worldclim data set (worldclim.org) at 30 arcseconds
resolution (~ 1km). Then we estimated Euclidean pairwise distances in the
multidimensional space using the function dist in R which computes specific
distances between the rows of a multivariate matrix.
Quantifying the relative effects of IBD, IBR and IBE
We tested for the independent association between geographic, resistance (i.e.
topographic and climatic suitability), and climatic distances against genetic distances
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using Multiple Matrix Regression with Randomization Analysis (MMRR) and
Generalized Dissimilarity Matrix. MMRR uses randomized permutation to account
for the fact that distances are not independent from each other (Wang et al., 2013).
We preferred this method to partial Mantel tests because of the well-known high
type-I error rate and low power characteristic of these kind of tests (Raufaste and
Rousset 2001; Harmon and Glor 2010; Guillot and Rousset 2013). In contrast to
partial Mantel tests, MMRR provides the independent effect of each variable as beta
coefficients, allowing simultaneous comparisons among them. Because all estimated
distances are at different scales, we normalized (subtracted the mean and divided by
the standard deviation) them to facilitate the interpretation of the beta coefficients.
We performed the MMRR method using the R function provided by Wang (2013)
with 10,000 permutations.
In addition, to further explore how our explanatory variables shape the
patterns of genetic differentiation observed in the study species, we also
implemented a Generalized Dissimilarity Modelling (GDM). This matrix regression
technique can fit nonlinear relationships of environmental variables to biological
variation using I-spline basis functions (more details in Ferrier et al. 2007). The
splines plots are very informative because they provide insights into the total
magnitude of biological change as a function of each gradient and where those
changes are most pronounced along each gradient.
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We fitted the initial model using as response variable our matrices of genetic
distance and the previously described matrices of isolation (IBD, IBRt, IBRcs,
IBRlgm and IBE) as predictor variables. Then, we plotted the I-splines to assess how
magnitudes and rates of genetic differentiation varied along the gradients
represented in our predictor variables. To confirm the significance obtained in the
GDM, we estimated variable importance and significance using matrix permutation
and backward elimination (as detailed in Ferrier et al. 2007). After summing the
coefficients of the I-splines, we discarded the less contributing predictor, and then
using this reduced set of n-1 predictors, we fitted a second GDM model. We ran 500
random permutations and excluded the variable with the least significant
contribution to explained deviance in a stepwise procedure. At each step of the
procedure, the unique contribution of each variable to total explained deviance was
calculated. We repeated the method until all variables retained in the final model
made significant unique contributions to explained deviance (P ≤ 0.05). We
performed these analyses with the R package ‘gdm’ (Manion et al. 2017).
RESULTS
Intraspecific patterns of genetic differentiation
For the two salamander species in our data set, both of them having distributions in
the Pacific, our ABGD analysis recovered two clades. In Bolitoglossa lignicolor, we
83
found a break between populations in the Central Pacific Region and those extending
from the Southern Pacific of Costa Rica to Peninsula de Azuero in Panama. In the
case of O. alleni the break seems to be altitudinal, since strictly coastal individuals
grouped apart from those individuals in slightly higher elevations.
Among the direct-developing frogs (Terrarana), we found two major clades
for Diasporus diastema; one including Caribbean populations from the slopes of
Cordillera de Tilarán to the Chiriquí Province in Panama and other containing
samples from Coclé Region in Central Panama. For Pristimantis ridens we found
support for a clade represented by Costa Rican populations and a clade containing
all Panamanian populations. Among the glass frogs, we found two clades with high
support for Sachatamia albomaculata, one on the Caribbean and other on the Pacific
versant of Costa Rica. For 16S, for which we have a wider geographic sampling for
this species, we found divergence between populations in the Caribbean and Pacific
of Costa Rica as well as between the central region of Panama and the eastern portion
of the Cordillera de Talamanca, which lays in Panama. In Espadarana prosobleplon,
we found four clades with high support: Darien Region, Eastern Canal Zone,
Cordillera Central de Panama and Pacific versant of Costa Rica.
In our hylid species, we found four genetically differentiated groups in
Agalychnis callidryas, two on the Pacific and two on the Caribbean; in Smilisca
phaeota, samples grouped in two major clusters, although these are not clearly
84
differentiated in the geography. In Dendropsophus ebbraccatus we detected the
major genetic divergence between samples from southern Pacific of Costa Rica and
a group including samples from Central Pacific and all the Caribbean versant
In the ranid Rana (Lithobates) warszewitschii, we found one clade widely
distributed in both the Pacific and the Caribbean versants of Costa Rica, which
differs from two clades occurring in Panama, one in the Eastern Canal Zone and the
other in the Cordillera Central. Finally, in the common cane toad Rhinella horribilis,
we detected two clusters, although their geographic distribution is not clearly
differentiated.
Relative role of IBD, IBRt, IBRcs and IBE on genetic differentiation
From the MMRR analyses, we found that at least one of the factors here tested,
significantly explained the patterns of genetic differentiation in seven out of the 11
species studied (Fig. 2). None of the models tested here explained genetic
differentiation in the salamander species (O. alleni and B. lignicolor), the hylid frog
S. phaeota and the toad R. horribilis.In the species where the full model significantly
explained genetic differentiations among populations, we found idiosyncratic
responses to the predictors tested.
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Figure 2. Contribution of different types of isolation in explaining genetic differentiation within our study species. Black bars in the first colum represent the percentange of variance in the genetic data significantly explained by the full model. Second to fifth colums show beta values for the tested predictors, black bars represent predictors significantly associated with patterns of genetic differentiation
86
Geographic distance played an important role in the genetic differentiation of
P. ridens (βD=0.648, P<0.001), topography (IBRt) explained most genetic
divergence for A. callidryas (βT=0.580, P<0.001) and S. albomaculata, for both
available genes: 16S (βT=0.580, P<0.001) and COI (βT=1.173, P<0.001). While
climatic suitability (IBRcs) represented the main driver of genetic divergence for D.
ebracattus (βCS=0.998, P<0.001), D. diastema (βCS=0.816, P<0.001), and L.
warszewitschii (βCS=0.947, P<0.001), local environmental dissimilarity (IBE) was
the major factor explaining genetic variation within E. prosoblepon (βED=0.486,
P<0.001).
From the GDM we found that, not only the responses of each species are
idiosyncratic to the drivers of genetic differentiation but, also the magnitude and
rates in which each species are affected along the gradient of variation of each
predictor. In figure 3, we show this heterogeneity by presenting the spline plots for
the two variables that contribute more within the species in which the full model was
significant in explaining patterns of genetic differentiation.
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Figure 3. Generalized dissimilarity model-fitted I-splines for variables significantly associated with genetic differentiation in seven of our study species. The maximum height reached by each curve, indicates the total amount of genetic differentiation associated with the respective variable, holding all other variables constant. The shape of each function shows how the rate of genetic differentiation varies along the gradients.
88
DISCUSSION
Despite its young geological history, ICA hosts an immense number of species,
higher than any other region of the globe with similar area (Bagley and Johnson,
2014). Such richness was assembled a combination of dispersal events and in situ
diversification, reflected in high levels of endemism (Marshall 1988; Webb 2006;
Kluge & Kessler 2006; Savage & Bolaños 2009; Bogarín et al. 2013). Here, we
quantified the role that different components of the landscape has had on driving
isolation between populations and restricting gene flow.
In almost all species studied, we found that at least one of the factors tested,
significantly explained patterns of spatial genetic differentiation in ICA. However,
instead of finding a major force that generally explained intraspecific genetic
divergence across the region, our results reveal idiosyncratic responses, so the
drivers we tested differentially affect each species. Previous studies focused on
describing spatial patterns of amphibian genetic divergence in the ICA, have also
showed that phylogeographic histories among species have few patterns in common
(Weigt et al. 2005; Crawford et al. 2007; Wang et al. 2008; Robertson et al. 2009).
In a recent review, Bagley & Johnson (2014), summarized the emerging
phylogeographic patterns in the region by collecting over 50 studies dealing with
more than 90 nominal taxa distributed in LCA. Such work compiles at least 31
phylogeographic breaks - recovered from mitochondrial DNA markers - in a region
89
spanning only ~127 000 km2 (Bagley and Johnson, 2014). Our findings support this
observation and provide strong evidence of intense processes of in situ lineage
diversification promoted not necessarily by a dominant factor but by multiple drivers
in the complex landscapes of ICA. By applying the same analyses over a multi-
species data set containing the same molecular markers, we were able to make robust
comparisons and go a step further, as we aimed to test for causality in the observed
patterns of genetic differentiation and disentangle the relative contribution of its
drivers. By combining MMRR and GDM approaches, we were able not only to make
a partition of the variation explained by each predictor (Wang, 2013; Wang et al.,
2013) but also to quantify the intensity and rate in which genetic divergence is drove
along the gradient of each tested variable (Ferrier et al., 2007).
Even geographic distance (IBD), which was not expected to be a good
predictor in the roughed landscapes of the ICA, was the major factor in explaining
genetic structure in two species, E. prosoblepon and P. ridens. In such cases, we
hypothesized that the major geographic gap between our Costa Rican and
Panamanian samples could influence such result. E. prosoblepon is a small species
distributed from lowlands to almost 2000 altitude (Kubicki, 2007), despite its wide
altitudinal range, reproduction in this species is restricted to streams, a typical feature
of glass frogs (Castroviejo-Fisher et al., 2014). It may promote isolation between
watersheds instead of along altitudinal gradients, explaining the increase in genetic
90
differentiation with geographic distance due to restricted dispersal between more
distant populations as expected by IBD (Wright, 1943; Slatkin, 1993).
Complementarily, previous studies has documented phylogeographic breaks
separating frog lineages from Costa Rica and western Panama from those occurring
in Central Panama, probably as result of a the vicariance generated by a Dry Forest
Barrier (Crawford et al., 2007). In P. ridens for example, phylogeographic
approaches, revealed a Panamanian lineage showing long-term geographic stasis and
another showing rapid geographic expansion occurring from Costa Rica to Honduras
(Wang, Crawford, et al., 2008). These dynamics, although potentially resulting from
climatic factors, could explain the higher divergence between our Costa Rican and
Panamanian samples and the signal we recovered of IBD driving such pattern.
The region’s abundant mountainous reliefs reflect a rich geological history
mainly dominated by orogeny of volcanic and tectonic origin (Gabb et al., 2007)
which results in major physical barriers (IBRt). Within a species, genetic divergence
is expected to be greater in areas of higher topographic complexity (Guarnizo and
Cannatella, 2013). In our case, IBRt significantly explained divergence patterns in
the red-eye tree frog A. callidryas and the cascade glass frog S. albomaculata. For
these species, we analyzed samples from both, the Pacific and the Caribbean versant.
Our results highlight the role of the three main Cordilleras that bisect the region
serving as a vicariant barrier between lowland populations in both slopes.
91
In the case of A. callidryas, the dimension of this effect is evident even at the
phenotypic level, previously documented on the variation in flank coloration among
individuals from Caribbean and Pacific populations (Robertson and Zamudio, 2009).
Genetic variation assessed in that same work among 20 populations, recovered five
well-supported mitochondrial clades, some of which the authors explain by IBD
(Robertson and Zamudio, 2009), the second force in contributing to the patterns of
genetic divergence we found. To our knowledge, our study is the first in evaluating
genetic divergence among populations of S. albomaculata, our geographic sampling
on both sides of the Cordilleras as well as our results are similar to A. callidryas.
Sachatamia albomaculata has been reported to reach the 1500 m elevation (Savage,
2002) but, it is more common below 1000 m (Kubicki, 2007). This fact, in
combination with life history traits of this species such as its small size and high
levels of phylopatry -restricted to forest covered streams- (Solís et al., 2010), may
explain the resistance imposed by topographic barriers not only among versants, but
also between peaks within the same mountainous range.
A similar pattern to the one found at the intra-specific level for these two
species could have led to speciation in many groups in the region for which sister
taxa occur. On each side of major mountain systems, such as several anurans
including Dendropsophus microcephalus and D. phlebodes, Oophaga granulifera
and O. pumilio or Phyllobates lugubris and P.vittatus (Savage, 2002). Among
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reptiles some examples include snakes as Lachesis stenophrys and L. melanocephala
(Solórzano and Cerdas, 2010), the lizards Basiliscus basiliscus and B. vittatus,
Sphaerodactylus graptolaemus and S. homolepis or Leposoma flavimaculatum and
L. reticulatum (Savage, 2002). This pattern occurs even in representatives of groups
with higher dispersal abilities such as the birds Amazilia decorata and A. amabilis,
Cotinga ridwayi and C. amabilis as well as Manacus candei and M. aurantiacus
(Prum et al., 2000; Brumfield and Braun, 2001; Stiles et al., 2017). Divergence
between Pacific and Caribbean groups is expected to be more evident in eastern
Costa Rica and western Panama, where topographic barriers becomes stronger as
mountains reaches their higher elevations (>3000 m) in Cordillera de Talamanca (de
Boer et al., 1995). In western Costa Rica instead, vicariant events may derive from
sky island dynamics, because high elevation habitats in those regions are isolated
and surrounded by lower intervening valleys. In such cases species tend to have
restricted distributions limited to mountain tops, as occur in many microendemic
amphibians including Nototriton Guanacaste and N. gamezi, representatives of
Crepidophryne, Incilius periglenes, I. holdridgei and I. fastidiosus (Savage, 2002;
Vaughan and Mendelson, 2007; Abarca et al., 2010).
We also hypothesized that either current or historical barriers imposed by
climatic conditions that determine niche suitability (IBRsuit) for each species could
promote isolation and influence genetic differentiation in the study region. We found
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that such explanation fits in the case of D. ebraccatus, D. diastema and L.
warszewitschii. In all cases, the resistance imposed by less suitable regions between
sampled localities is the factor that best explains genetic differentiation in these
species. Such trend follows the principles of niche conservatism, the tendency of
species to retain ancestral ecological characteristics (Wiens and Graham, 2005;
Wiens et al., 2010). At the intraspecific level, it could result either from the
fragmentation of continuous distributions due to climatic oscillations that reflect on
range contractions (Hewitt, 2003, 2004) or from disjointed distributions that result
from different routes of colonization. In both cases populations could become
isolated by means of unsuitable conditions in the regions that connect them.
In our fourth hypothesis, we predicted that climatic heterogeneity in the region
might promote adaptation to local conditions (IBE) and influence the patterns of
genetic differentiation. We found that this scenario explains genetic differentiation
in the COI gene of S. albomaculata.
Certainly, one of the most striking characteristics of LCA is the vast variety of
climates that converges in such a small area (Coen, 1991). For example, transitions
between arid and very humid conditions occur in few hundreds of kilometers in the
Pacific of Costa Rica while temperature may change significantly in short distances
along elevation gradients in the mountain systems of the region (Coen, 1991;
Savage, 2002). Variation in species-specific tolerances and local adaptation to such
94
diverse climates seems to influence distribution of several taxa in the region. It
occurs even in groups considered good dispersers, such as birds (Garrigues and
Dean, 2014) and volant mammals like the vespertilionid bats Rhogeessa bickhami
and R. io.
Finally, our full model containing all predictors was not able to significantly
explain genetic divergence in four species: the salamanders B. lignicolor and O.
alleni, the masked tree frog S. phaeota and the toad R. horribilis. These species have
several particularities that may account for such lack of relation between our
predictors and their genetic structure. Both salamanders have distributions restricted
to the Pacific slope of the study region, in elevations below 900 meters (Savage,
2002). This removes the opportunity to test for topographic or climatic barriers along
broad altitudinal gradients. Conversely, it also highlights the role that physical
barriers and climate play as primary constraints of distributions by limiting species
potential for dispersion and establishment, depending on their specific tolerances to
abiotic conditions (Grinnell, 1917; Barve et al., 2011).
The two anurans can be considered more generalists in their preferences and
thus are expected to have wider distributions. Such distributions can be maintained
due to their, broader tolerances and higher dispersal capabilities, probably as result
of their intrinsic biological features. For example, S. phaeota a common species, is
the largest tree frog among the hylids included in this study (Savage, 2002). This
95
species has extended breeding through the year, reproduces in small ponds or
shallow streamlets even in altered habitats, clutch size can reach the 2000 eggs and
tadpoles are resistant enough to survive up to 24 hours out of the water (Valerio,
1971; Savage, 2002). Rhinella horribilis was only recently described as a cryptic
species related to the common cane toad R. marina (Acevedo et al., 2016). Rhinella
mariana, is widely distributed in the Neotropics, has high dispersal potential and is
known for one of the most aggressive invasions in Australia (Phillips et al., 2006,
2010). In their native range these species are common even in altered habitats from
sea level to above 2000 meters elevation where they lay up to 25000 eggs in lentic
water bodies (Savage, 2002).
Morphological features, high dispersal ability, intrinsic physiological
tolerances and behavioral strategies in these large species may explain its
distribution and persistence under heterogeneous environmental pressures (Hilje and
Arévalo-Huezo, 2012; Mccann et al., 2014). For these reasons, we consider that
barriers affecting other anurans do not necessarily represent major drivers of
isolation and gene flow must be higher in this species.
96
CONCLUSIONS
The convergence of several drivers of isolation and the co-occurrence of such
a heterogeneous biota with different life histories, origins and arrival times to the
complex landscapes of ICA maximize the chances of genetic differentiation in the
region. Different types of isolation such as IBD and IBE (both reviewed in Sexton
et al. 2014) or IBR driven by topographic and climatic barriers (Rodríguez et al.,
2015; Thomas et al., 2015; Oliveira et al., 2017) are proved forces involved in the
genetic differentiation of different taxa around the globe. Our study highlights how
they can act simultaneously and differentially affect co-distributed taxa in a
relatively small area. Certainly, the intrinsic characteristics of each species play an
important role in how a given species respond to different drivers of isolation; and
such interaction between organisms and their environment must be considered when
trying to understand patterns of genetic divergence (Paz et al., 2015; Rodríguez et
al., 2015). The evolutionary dynamic of ICA is far from the simplistic view that
point out vicariance between Caribbean and Pacific clades as the main form of
speciation in the region. In situ diversification plays an important role in shaping
richness patterns in the region and its biota is undoubtedly underestimated. For that
reason, further efforts must be oriented to first document unknown diversity and then
add more groups to this kind of analysis.
97
ACKNOWLEDGEMENTS
Javier Guevara and Sistema Nacional de Areas de Conservación de Costa Rica
provided permits to researchers of Museo de Zoología, Universidad de Costa Rica
(MZUCR). We = thank Federico Bolaños (MZUCR) for permission and access to
the samples sequenced for this study; Sandra Flechas for his patient collaboration
during lab procedures. NAOS Island Laboratories of the Smithsonian Tropical
Research Institute for support during lab work A.G.R acknowledges Coordenação
de Aperfeiçoamento de Pessoal de Nível Superior, Brazil (CAPES) for the financial
support and Gerardo Chaves (MZUCR) for his teachings during innumerous field
trips, as well as for the constant discussion on the herpetology of Central America
and his comments on early versions of this manuscript.
AUTHOR CONTRIBUTIONS
A.G.R, C.E.G and G.C.C conceived the study; A.G.R and A.J.C conducted field
work and collected DNA sequence data; A.G.R and C.E.G analyzed data; A.G.R,
C.E.G and G.C.C wrote the first drafts of the manuscript; All authors provide vital
inputs, discussed, edited, and improved the final version of this study.
98
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-Supplementary Material-
Idiosyncratic responses to drivers of genetic differentiation in the complex
landscapes of lower central america
Ecological Niche Modeling details
Model Evaluation and Tuning
We built a series of candidate models, using MaxEnt algorithm with a variety of
user-defined settings and provided multiple evaluation metrics to aid in selecting
optimal model settings. We built multiple models for each species varying the values
of regularization multipliers from 1 to 5 at 0.5 intervals and testing several feature
classes (linear, quadratic, hinge, linear) in all their possible combinations. We
created the models using a training and test datasets generated using the block
partition method, which divides occurrences into four bins based on the lines of
latitude and longitude that divide occurrence localities as equally as possible
(Radosavljevic & Anderson 2014)
Model Selection
Between candidate models, we choose the optimal settings assessing the values of 4
metrics in the following order of priority: Omission rate of Minimum training
presence threshold, Area under the curve (AUC) for test data (AUCtest), AUC
difference between training and test data (AUCdiff) and Akaike Information Criteria
105
(AICc). The area under the curve based on test data (AUCtest) measures model
ability to discriminate conditions at withheld occurrence localities from those at
background samples (Radosavljevic & Anderson 2014). AUCdiff quantifies model
overfitting by comparing training and test AUC, values for this metric should be
high in the case of overfit models (Warren & Seifert, 2011). AICc provides
information on the relative quality of a model given the data (Burnham & Anderson
2004; Warren & Seifert 2011).
Variables Used
We eliminated variables with VIF values above 10 and kept the following 11
variables: Mean Diurnal Range of Temperature, Isothermality, Temperature
seasonality, Temperature Annual Range, Mean Temperature of Wettest Quarter,
Annual Precipitation, Precipitation of Wettest Month, Precipitation of Driest Month,
Precipitation Seasonality, Precipitation of Warmest Quarter and Precipitation of
Coldest Quarter.
106
Table S1. Details on source, accession, locality and coordinates of each sample included in this study.
Species Source Accession Marker Country Locality Latitude Longitude
Agalychnis callidryas GenBank FJ489260 16s Costa Rica Cabo Blanco 9.581 -85.125
Agalychnis callidryas GenBank FJ489261 16s Costa Rica Cabo Blanco 9.581 -85.125
Agalychnis callidryas GenBank FJ489262 16s Costa Rica Cabo Blanco 9.581 -85.125
Agalychnis callidryas GenBank FJ489263 16s Costa Rica Cabo Blanco 9.581 -85.125
Agalychnis callidryas GenBank FJ489264 16s Costa Rica Cahuita 9.719 -82.814
Agalychnis callidryas GenBank FJ489265 16s Costa Rica Cahuita 9.719 -82.814
Agalychnis callidryas GenBank FJ489266 16s Costa Rica Cahuita 9.719 -82.814
Agalychnis callidryas GenBank FJ489276 16s Panama El Cope 8.630 -80.592
Agalychnis callidryas GenBank FJ489277 16s Panama El Cope 8.630 -80.592
Agalychnis callidryas GenBank FJ489278 16s Panama El Cope 8.630 -80.592
Agalychnis callidryas GenBank FJ489279 16s Panama El Cope 8.630 -80.592
Agalychnis callidryas GenBank FJ489280 16s Costa Rica Guacimo 10.237 -83.567
Agalychnis callidryas GenBank FJ489281 16s Costa Rica Guacimo 10.237 -83.567
Agalychnis callidryas GenBank FJ489282 16s Costa Rica Guacimo 10.237 -83.567
Agalychnis callidryas GenBank FJ489283 16s Costa Rica Guacimo 10.237 -83.567
Agalychnis callidryas GenBank FJ489284 16s Panama El Valle 8.630 -80.116
Agalychnis callidryas GenBank FJ489285 16s Panama El Valle 8.630 -80.116
Agalychnis callidryas GenBank FJ489286 16s Panama El Valle 8.630 -80.116
Agalychnis callidryas GenBank FJ489288 16s Panama Gamboa 9.123 -79.693
Agalychnis callidryas GenBank FJ489289 16s Panama Gamboa 9.123 -79.693
Agalychnis callidryas GenBank FJ489290 16s Panama Gamboa 9.123 -79.693
Agalychnis callidryas GenBank FJ489291 16s Costa Rica La Selva 10.433 -84.008
Agalychnis callidryas GenBank FJ489292 16s Costa Rica La Selva 10.433 -84.008
Agalychnis callidryas GenBank FJ489293 16s Costa Rica La Selva 10.433 -84.008
Agalychnis callidryas GenBank FJ489294 16s Costa Rica La Selva 10.433 -84.008
Agalychnis callidryas GenBank FJ489295 16s Costa Rica La Selva 10.433 -84.008
Agalychnis callidryas GenBank FJ489296 16s Costa Rica La Selva 10.433 -84.008
107
Agalychnis callidryas GenBank FJ489297 16s Costa Rica Manzanillo 9.633 -82.656
Agalychnis callidryas GenBank FJ489298 16s Costa Rica Manzanillo 9.633 -82.656
Agalychnis callidryas GenBank FJ489299 16s Costa Rica Manzanillo 9.633 -82.656
Agalychnis callidryas GenBank FJ489301 16s Costa Rica Manzanillo 9.633 -82.656
Agalychnis callidryas GenBank FJ489302 16s Costa Rica Manzanillo 9.633 -82.656
Agalychnis callidryas GenBank FJ489307 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489308 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489309 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489310 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489311 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489312 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489313 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489314 16s Costa Rica Bandera 9.519 -84.377
Agalychnis callidryas GenBank FJ489315 16s Panama Santa Fe 8.507 -81.114
Agalychnis callidryas GenBank FJ489316 16s Panama Santa Fe 8.507 -81.114
Agalychnis callidryas GenBank FJ489321 16s Costa Rica Siquirres 8.889 -83.477
Agalychnis callidryas GenBank FJ489322 16s Costa Rica San Ramon 10.234 -84.529
Agalychnis callidryas GenBank FJ489323 16s Costa Rica San Ramon 10.234 -84.529
Agalychnis callidryas GenBank FJ489324 16s Costa Rica Tilaran 10.516 -84.960
Agalychnis callidryas GenBank FJ489325 16s Costa Rica Tilaran 10.516 -84.960
Agalychnis callidryas GenBank FJ489327 16s Costa Rica Uvita 9.124 -83.701
Agalychnis callidryas GenBank FJ489328 16s Costa Rica Uvita 9.124 -83.701
Agalychnis callidryas GenBank FJ489331 16s Costa Rica Uvita 9.124 -83.701
Agalychnis callidryas GenBank FJ489333 16s Costa Rica Uvita 9.124 -83.701
Bolitoglossa lignicolor BoLD BSUCR444 16s Costa Rica S. Isidro de Dota
9.677 -84.076
Bolitoglossa lignicolor BoLD BSUCR441 16s Costa Rica Dominical 9.264 -83.872
Bolitoglossa lignicolor GenBank JX434638 16s Panama Santa Clara 8.830 -82.780
Bolitoglossa lignicolor GenBank JX434639 16s Panama Buenos Aires 8.470 -81.510
Bolitoglossa lignicolor BoLD BSUCR442 16s Costa Rica La Gamba 8.675 -83.203
108
Bolitoglossa lignicolor GenBank JX434640 16s Panama Cerro Hoya 7.320 -80.790
Bolitoglossa lignicolor GenBank JX434643 16s Panama Cerro Hoya 7.320 -80.790
Bolitoglossa lignicolor GenBank JX434641 16s Panama Cerro Hoya 7.320 -80.790
Bolitoglossa lignicolor GenBank JX434642 16s Panama Guacá 8.500 -82.430
Bolitoglossa lignicolor GenBank AF218484 16s Costa Rica Osa 9.150 -83.335
Bolitoglossa lignicolor BoLD BSUCR443 16s Costa Rica Potrero Grande 9.098 -83.113
Diasporus diastema GenBank FJ766809 COI Panama Cocle 8.670 -80.590
Diasporus diastema GenBank FJ766810 COI Panama Cocle 8.670 -80.590
Diasporus diastema GenBank KT186558 COI Panama Fortuna 8.719 -82.232
Diasporus diastema GenBank JN991347 COI Costa Rica San Ramon 10.220 -84.540
Diasporus diastema BoLD BSUCR240 COI Costa Rica Sabalito 8.944 -82.753
Diasporus diastema BoLD BSUCR241 COI Costa Rica Sabalito 8.944 -82.753
Diasporus diastema BoLD BSUCR234 COI Costa Rica Balsa 10.186 -84.508
Diasporus diastema BoLD BSUCR097 COI Costa Rica Balsa 10.186 -84.508
Diasporus diastema BoLD BSUCR223 COI Costa Rica Guayacan 10.050 -83.550
Diasporus diastema BoLD BSUCR218 COI Costa Rica Veragua 9.926 -83.188
Diasporus diastema BoLD BSUCR224 COI Costa Rica Tapanti 9.775 -83.797
Dendropsophus ebraccatus GenBank FJ542181 16s Costa Rica Manzanillo 9.633 -82.654
Dendropsophus ebraccatus GenBank FJ542180 16s Costa Rica Manzanillo 9.633 -82.654
Dendropsophus ebraccatus GenBank FJ542179 16s Costa Rica Manzanillo 9.633 -82.654
Dendropsophus ebraccatus GenBank FJ542195 16s Costa Rica Uvita 9.994 -83.032
Dendropsophus ebraccatus GenBank FJ542194 16s Costa Rica Uvita 9.994 -83.032
Dendropsophus ebraccatus GenBank FJ542184 16s Costa Rica Pavones 8.388 -83.140
Dendropsophus ebraccatus GenBank FJ542197 16s Costa Rica Uvita 9.994 -83.032
Dendropsophus ebraccatus BoLD BSUCR267 16s Costa Rica Golfito 8.651 -83.180
Dendropsophus ebraccatus BoLD BSUCR268 16s Costa Rica Piro 8.411 -83.344
Dendropsophus ebraccatus BoLD BSUCR400 16s Costa Rica Altamira 8.784 -83.019
Dendropsophus ebraccatus GenBank FJ542196 16s Costa Rica Uvita 9.994 -83.032
Dendropsophus ebraccatus GenBank FJ542185 16s Costa Rica Pavones 8.387 -83.140
Dendropsophus ebraccatus GenBank FJ542183 16s Costa Rica Pavones 8.387 -83.140
109
Dendropsophus ebraccatus GenBank FJ542182 16s Costa Rica Pavones 8.387 -83.140
Dendropsophus ebraccatus GenBank FJ542193 16s Costa Rica Siquirres 10.092 -83.515
Dendropsophus ebraccatus GenBank FJ542192 16s Costa Rica Siquirres 10.092 -83.515
Dendropsophus ebraccatus GenBank FJ542191 16s Costa Rica Siquirres 10.092 -83.515
Dendropsophus ebraccatus GenBank FJ542189 16s Costa Rica Bandera 9.523 -84.412
Dendropsophus ebraccatus GenBank FJ542188 16s Costa Rica Bandera 9.523 -84.412
Dendropsophus ebraccatus GenBank FJ542186 16s Costa Rica Bandera 9.523 -84.412
Dendropsophus ebraccatus GenBank FJ542190 16s Costa Rica Bandera 9.523 -84.412
Dendropsophus ebraccatus GenBank FJ542187 16s Costa Rica Bandera 9.523 -84.412
Dendropsophus ebraccatus GenBank FJ542178 16s Costa Rica La Selva 10.430 -84.008
Dendropsophus ebraccatus BoLD BSUCR271 16s Costa Rica Veragua 9.926 -83.188
Espadarana prosoblepon GenBank KR863251 16s Panama Cerro Brewster 9.320 -79.289
Espadarana prosoblepon GenBank KR863250 16s Panama Rio Chagres 9.265 -79.508
Espadarana prosoblepon GenBank KR863246 16s Panama Rio Chagres 9.265 -79.508
Espadarana prosoblepon GenBank KR863235 16s Panama Cerro Brewster 9.320 -79.289
Espadarana prosoblepon GenBank KR863241 16s Panama Cerro Brewster 9.320 -79.289
Espadarana prosoblepon GenBank KR863253 16s Panama Cerro Azul 9.231 -79.403
Espadarana prosoblepon GenBank KR863245 16s Panama Cerro Azul 9.231 -79.403
Espadarana prosoblepon BoLD BSUCR056 16s Costa Rica Balsa 10.189 -84.504
Espadarana prosoblepon BoLD BSUCR055 16s Costa Rica Balsa 10.186 -84.508
Espadarana prosoblepon BoLD BSUCR148 16s Costa Rica Londres de Quepos
9.462 -84.063
Espadarana prosoblepon BoLD BSUCR147 16s Costa Rica Potrero Grande 9.098 -83.113
Espadarana prosoblepon GenBank FJ784362 16s Panama El Cope 8.667 -80.592
Espadarana prosoblepon GenBank FJ784363 16s Panama El Cope 8.667 -80.592
Espadarana prosoblepon BoLD BSUCR054 16s Costa Rica Rodeo 9.904 -84.280
Espadarana prosoblepon GenBank KR863252 16s Panama Cana Station 7.756 -77.684
Espadarana prosoblepon GenBank KR863249 16s Panama Cana Station 7.756 -77.684
Espadarana prosoblepon GenBank KR863248 16s Panama Cana Station 7.756 -77.684
Espadarana prosoblepon GenBank KR863243 16s Panama Rio Cana 7.762 -77.724
110
Espadarana prosoblepon GenBank KR863242 16s Panama Rio Cana 7.762 -77.724
Espadarana prosoblepon GenBank KR863238 16s Panama Rio Cana 7.762 -77.724
Espadarana prosoblepon GenBank KR863237 16s Panama Rio Cana 7.762 -77.724
Espadarana prosoblepon GenBank KR863247 16s Panama Cana Station 7.756 -77.684
Espadarana prosoblepon GenBank KR863244 16s Panama Rio Cana 7.762 -77.724
Espadarana prosoblepon GenBank KR863240 16s Panama Rio Cana 7.762 -77.724
Espadarana prosoblepon GenBank KR863239 16s Panama Rio Cana 7.762 -77.724
Espadarana prosoblepon GenBank KR863234 16s Panama Rio Cana 7.762 -77.724
Lithobates warscewitschii GenBank FJ784384 16s Panama El Cope 8.667 -80.592
Lithobates warscewitschii GenBank KR863272 16s Panama Cerro Brewster 9.320 -79.289
Lithobates warscewitschii GenBank KR863282 16s Panama Cerro Brewster 9.320 -79.289
Lithobates warscewitschii GenBank KR863281 16s Panama Rio Chagres 9.265 -79.508
Lithobates warscewitschii GenBank KR863280 16s Panama Cerro Brewster 9.320 -79.289
Lithobates warscewitschii GenBank KR863279 16s Panama Cerro Brewster 9.320 -79.289
Lithobates warscewitschii GenBank KR863276 16s Panama Rio Chagres 9.265 -79.508
Lithobates warscewitschii GenBank KR863275 16s Panama Rio Chagres 9.265 -79.508
Lithobates warscewitschii GenBank KR863274 16s Panama Cerro Azul 9.231 -79.403
Lithobates warscewitschii GenBank KR863271 16s Panama Cerro Brewster 9.320 -79.289
Lithobates warscewitschii GenBank KR863284 16s Panama Cerro Azul 9.231 -79.403
Lithobates warscewitschii GenBank KR863283 16s Panama Cerro Azul 9.231 -79.403
Lithobates warscewitschii GenBank KR863278 16s Panama Cerro Azul 9.231 -79.403
Lithobates warscewitschii GenBank KR863277 16s Panama Cerro Azul 9.231 -79.403
Lithobates warscewitschii GenBank KR863273 16s Panama Cerro Brewster 9.320 -79.289
Lithobates warscewitschii GenBank FJ784454 16s Panama El Cope 8.667 -80.592
Lithobates warscewitschii GenBank KR911917 16s Panama El Cope 8.667 -80.592
Lithobates warscewitschii GenBank KR911916 16s Panama El Cope 8.667 -80.592
Lithobates warscewitschii GenBank KR911918 16s Panama El Cope 8.667 -80.592
Lithobates warscewitschii BoLD BSUCR366 16s Costa Rica Fila Matama 9.618 -83.283
Lithobates warscewitschii BoLD BSUCR364 16s Costa Rica Fila Matama 9.618 -83.268
Lithobates warscewitschii BoLD BSUCR365 16s Costa Rica Fila Matama 9.618 -83.283
111
Lithobates warscewitschii BoLD BSUCR367 16s Costa Rica Fila Matama 9.605 -83.280
Lithobates warscewitschii BoLD BSUCR129 16s Costa Rica Talamanca 9.357 -83.229
Lithobates warscewitschii BoLD BSUCR382 16s Costa Rica Talamanca 9.357 -83.229
Lithobates warscewitschii BoLD BSUCR372 16s Costa Rica Balsa 10.183 -84.510
Lithobates warscewitschii BoLD BSUCR373 16s Costa Rica Veragua 9.926 -83.188
Lithobates warscewitschii BoLD BSUCR370 16s Costa Rica Potrero Grande 9.102 -83.114
Lithobates warscewitschii BoLD BSUCR378 16s Costa Rica La Gamba 8.679 -83.202
Oedipina alleni BoLD BSUCR464 16s Costa Rica S. Isidro de Dota
9.677 -84.076
Oedipina alleni BoLD BSUCR465 16s Costa Rica Londres 9.462 -84.063
Oedipina alleni GenBank AF199209 16s Costa Rica Cerro Zapote 8.750 -82.983
Oedipina alleni GenBank AF199208 16s Costa Rica Damas 9.462 -84.224
Oedipina alleni GenBank AF199207 16s Costa Rica Sirena 8.481 -83.594
Pristimantis ridens GenBank FJ784399 16s Panama El Cope 8.667 -80.592
Pristimantis ridens GenBank FJ784398 16s Panama El Cope 8.667 -80.592
Pristimantis ridens GenBank JN991466 16s Costa Rica Rio Claro 8.740 -82.960
Pristimantis ridens BoLD BSUCR415 16s Costa Rica Veragua 9.926 -83.188
Pristimantis ridens BoLD BSUCR414 16s Costa Rica Balsa 10.186 -84.508
Pristimantis ridens BoLD BSUCR416 16s Costa Rica S. Isidro de Dota
9.677 -84.076
Pristimantis ridens BoLD BSUCR420 16s Costa Rica Potrero Grande 9.117 -83.097
Pristimantis ridens GenBank KR863320 16s Panama Cerro Azul 9.217 -79.422
Pristimantis ridens GenBank KR863318 16s Panama Cerro Azul 9.231 -79.403
Pristimantis ridens GenBank KR863319 16s Panama Cerro Azul 9.231 -79.403
Pristimantis ridens GenBank JN991465 16s Panama Nusgandi 9.317 -78.983
Pristimantis ridens GenBank KR863317 16s Panama Cerro Brewster 9.290 -79.300
Pristimantis ridens GenBank FJ784389 16s Panama El Cope 8.667 -80.592
Pristimantis ridens GenBank FJ784388 16s Panama El Cope 8.667 -80.592
Rhinella horribilis BoLD BSUCR042 16s Costa Rica Veragua 10.877 -84.329
Rhinella horribilis GenBank DQ415563 16s Costa Rica - 10.453 -84.081
112
Rhinella horribilis BoLD BSUCR123 16s Costa Rica Los Chiles 10.947 -84.725
Rhinella horribilis BoLD BSUCR124 16s Costa Rica Medio Queso 11.032 -84.691
Rhinella horribilis BoLD BSUCR041 16s Costa Rica Veragua 10.871 -84.350
Rhinella horribilis BoLD BSUCR127 16s Costa Rica Crucitas 10.877 -84.329
Rhinella horribilis BoLD BSUCR125 16s Costa Rica Golfito 8.604 -83.170
Rhinella horribilis GenBank FJ784357 16s Panama El Cope 8.667 -80.592
Rhinella horribilis BoLD BSUCR126 16s Costa Rica Veragua 9.926 -83.188
Sachatamia albomaculata BoLD BSUCR159 16S Costa Rica Veragua 9.926 -83.188
Sachatamia albomaculata BoLD BSUCR157 16S Costa Rica Talamanca 9.618 -83.268
Sachatamia albomaculata BoLD BSUCR156 16S Costa Rica La Tirimbina 10.402 -84.108
Sachatamia albomaculata BoLD BSUCR158 16S Costa Rica Londres 9.462 -84.063
Sachatamia albomaculata BoLD BSUCR059 16S Costa Rica Rodeo 9.904 -84.280
Sachatamia albomaculata GenBank FJ784392 16S Panama El Cope 8.667 -80.592
Sachatamia albomaculata GenBank FJ784550 16S Panama El Cope 8.667 -80.592
Sachatamia albomaculata GenBank FJ784441 16S Panama El Cope 8.667 -80.592
Sachatamia albomaculata GenBank FJ784474 16S Panama El Cope 8.667 -80.592
Sachatamia albomaculata GenBank FJ784468 16S Panama El Cope 8.667 -80.592
Sachatamia albomaculata GenBank KR863349 16S Panama Cerro Azul 9.231 -79.403
Sachatamia albomaculata GenBank KR863347 16S Panama Cerro Azul 9.231 -79.403
Sachatamia albomaculata GenBank KR863346 16S Panama Cerro Azul 9.231 -79.403
Sachatamia albomaculata GenBank KR863348 16S Panama Cerro Azul 9.231 -79.403
Sachatamia albomaculata BoLD BSUCR158 COI Costa Rica Londres 9.462 -84.063
Sachatamia albomaculata BoLD BSUCR160 COI Costa Rica Potrero Grande 9.098 -83.113
Sachatamia albomaculata BoLD BSUCR156 COI Costa Rica La Tirimbina 10.402 -84.108
Sachatamia albomaculata BoLD BSUCR157 COI Costa Rica Talamanca 9.618 -83.268
Sachatamia albomaculata BoLD BSUCR159 COI Costa Rica Veragua 9.926 -83.188
Sachatamia albomaculata GenBank KR863092 COI Panama Cerro Azul 9.231 -79.403
Sachatamia albomaculata GenBank KR863091 COI Panama Cerro Azul 9.231 -79.403
Sachatamia albomaculata GenBank KR863090 COI Panama Cerro Azul 9.231 -79.403
Sachatamia albomaculata GenBank FJ766595 COI Panama Cocle 8.670 -80.590
113
Sachatamia albomaculata GenBank FJ766594 COI Panama Cocle 8.670 -80.590
Sachatamia albomaculata GenBank FJ766598 COI Panama Cocle 8.670 -80.590
Sachatamia albomaculata GenBank FJ766596 COI Panama Cocle 8.670 -80.590
Sachatamia albomaculata GenBank FJ766599 COI Panama Cocle 8.670 -80.590
Sachatamia albomaculata GenBank KR863089 COI Panama Cerro Azul 9.231 -79.403
Smilisca phaeota GenBank AY326040 16s Costa Rica La Lola 10.100 -83.383
Smilisca phaeota BoLD BSUCR321 16s Costa Rica Veragua 9.926 -83.188
Smilisca phaeota BoLD BSUCR318 16s Costa Rica Balsa 10.189 -84.509
Smilisca phaeota GenBank FJ784433 16s Panama El Cope 8.667 -80.592
Smilisca phaeota GenBank FJ784413 16s Panama El Cope 8.667 -80.592
Smilisca phaeota BoLD BSUCR317 16s Costa Rica Piro 8.411 -83.344
Smilisca phaeota BoLD BSUCR320 16s Costa Rica S. Isidro de Dota
9.677 -84.076
Smilisca phaeota BoLD BSUCR311 16s Costa Rica Crucitas 10.868 -84.345
Smilisca phaeota BoLD BSUCR319 16s Costa Rica Londres 9.462 -84.063
114
CAPÍTULO III***
The role of geography, topography and climate in the acoustic divergence of Neotropical Diasporus frogs
115
The role of geography, topography and climate in the acoustic divergence of Neotropical Diasporus frogs
Adrián García-Rodríguez1,2, Marcelo Araya-Salas3, & Gabriel C. Costa4
1Departamento de Ecologia, Universidade Federal do Rio Grande do Norte,
Natal - RN, Brasil, 59078-900
2Escuela de Biología, Universidad de Costa Rica, San Pedro, 11501-2060 San
José, Costa Rica.
3Laboratory of Ornithology, Cornell University, 159 Sapsucker Woods Road,
Ithaca, New York 14850, USA
4Department of Biology, Auburn University at Montgomery, Montgomery AL
36124.
Correspondence: [email protected]
116
Abstract
Background. Acoustic communication is central in the biology of most anuran
species. Geographic variation in acoustic signals can led to failure in
conspecific recognition, becoming an important mechanism of reproductive
isolation and a primer of speciation. Here we documented patterns of acoustic
variation among 21 populations of two species of Diasporus frogs. Then, we
assessed whether geographic distance, topography, connectivity of suitable
habitats and local climate have a role in shaping those patterns.
Results. We found deep acoustic divergence in both species. Pacific
populations of D. diastema vocalize at lower frequencies than Caribbean
populations. Males of D. hylaeformis from Tapantí, showed striking differences
in call duration. Topography explained ~30% of the deviance in the acoustic
divergence of D.diastema. In D. hylaeformis our model was not able to explain
acoustic variation, although, we found a signature of association with isolation
by environment and isolation by topography.
Conclusions. Most abiotic factors tested here clearly promote isolation among
populations due to the complexity of the Costa Rican landscapes. However,
only topography –significantly- and climatic dissimilarity –marginally
explained patterns of acoustic divergence in these species. Considering the high
levels of acoustic variation detected, we conclude that signal evolution in this
case is likely determined by a combination of mechanisms operating
independently in local scales on isolated populations such as sexual selection,
character displacement or genetic drift.
Keywords: Advertisement calls, isolation by distance, isolation by resistance, isolation by environment, generalized dissimilarity modeling
117
Background
In a broad range of taxonomic groups, from insects to mammals,
communication is essentially mediated by acoustic signals [1]. These signals
have evolved to efficiently transfer information that is encoded by a sender and
then is decoded by a receiver [2,3]. However, for this process to be efficient
signals must be first recognized and then interpreted on the basis of their
spectro-temporal features [4,5]. For organisms depending on acoustic
communication for mating purposes, these signals are crucial to discriminate
between conspecifics and individuals of other species [6]. This process is
possible due to the evolution of highly stereotyped call characteristics that
assures receivers the recognition at species level [7,8]. Acoustic signals in this
context are even more relevant for organisms that attract mates over relatively
long distances [9–11] or interact in complex environments with limited
visibility [12,13]. This is the case of many anurans, a group where the vast
majority of species are active at night and many reproduces in noisy settings
[14].
Anuran advertisement calls, produced by males in reproductive contexts,
must be highly stereotype to minimize energetic costs associated to non-viable
crosses [15,16]. Since failure in recognition during mating affects reproductive
success, divergence on advertisement calls has been proposed as a pre-mating
118
isolation mechanism [17,18]. Hence, call divergence, has evolutionary
implications on population differentiation and may ultimately play a relevant
role on speciation process [19–21]. Although several studies have documented
intraspecific divergence in anuran advertisement calls, fewer efforts have been
oriented to understand what factors promote such patterns of variation [22–25].
Sexual selection, reproductive character displacement, genetic drift and
ecological selection, haven been hypothesized as the most important
mechanisms driving divergence in the evolution of anuran calls [21,26–28]. In
the case of sexual selection, evidence suggest female preferences can select for
acoustic parameters that have impact on mating success [29]. Under a scenario
of reproductive character displacement, acoustic signals are expected to evolve
in order to minimize costs of interspecific competition or maladaptive
hybridization when in sympatry with closely related taxa [30]. Under a scenario
of genetic drift, is expected that populations breeding further apart have more
distinct genomes and therefore more distinct phenotypes that could influence
acoustic traits [31]. It has been also proposed that pressures set by ecological
factors are responsible of shaping patterns of geographic call divergence [32] as
predicted by the Acoustic Adaptation hypothesis (AAH), which expects that
structural differences among habitat influences signal evolution through the
constraints of signal transmission [33]. A way to understand whether ecologica
119
factors have promoted acoustic divergence, is by evaluating the relationship
between geographic and/or environmental variables and geographic patterns of
call variation.
Several hypotheses has been proposed in other fields to evaluate causal
relation between environmental variables and a given response variable, as is
molecular divergence in landscape genetics studies [34,35]. For example,
isolation by distance (IBD), states that genetic differentiation is expected to
increase with geographic distance due to restricted dispersal among populations
[36,37]. Isolation by resistance (IBR), goes a step further and evaluates how the
friction imposed by landscape components such as topography or habitat
suitability influence genetic variation [38]. Isolation by Environment (IBE), in
contrast, has been recently proposed to describe a pattern in which genetic
differences increase with the environmental differences between sampling
localities, independently of geographic distance or the resistance imposed by
the landscape that connects populations [39].
In this study, we used such reasoning to test whether isolation promoted
by different landscape features can explain observed patterns of divergence in
acoustic traits within species. To this end, we first documented geographic call
variation in two direct-developing frog species of the genus Diasporus,
occurring across the complex landscapes of Isthmian Central America. Then,
120
we explored the potential causes of such variation patterns by assessing the role
of distance, topography, connectivity of suitable habitats and influence of local
climate for each species. To quantify the effect of these factors in shaping
acoustic divergence we tested hypotheses and predictions diagramed in figure
1. By assessing acoustic variation within species and its potential drivers we
aim to reach a better understanding of the mechanisms promoting divergence
and early phases of speciation.
Figure 1. Schematic representation of hypotheses tested in this study and their respective predictions
121
Methods Study Species. Five of the eleven species in the genus Diasporus occur in Costa
Rica [40]. D. diastema is distributed in both slopes of the main cordilleras of
the country, from sea level to above 1500 meters elevation, with exception of
the dry forest, D. hylaeformis, can be found above 1300 meters in humid lower
montane forests in the cordilleras of Talamanca, Volcanica Central, Tilarán and
Guanacaste [41] and D. vocator has been documented on the southwestern
portion of the country from 2 to 1600 m (Savage, 2002; A. García-Rodríguez,
pers. observ.). The other two species have restricted distributions: D. tigrillo is
known only from the valley of the Río Lari, Limón, Province (250-440) [41,42]
and D. ventrimaculatus is endemic to a highland valley (2500) in Cordillera de
Talamanca known as Valle del Silencio [43]. All species are abundant and
highly vocal, call from perches hidden into dense vegetation from where
produce calls characterized by short duration and high frequencies, especially
during the rainy season [41,42,44]. We focused our analyses on D. diastema
and D. hylaeformis, the two species with wider distributions in Costa Rica. The
wider geographic spread of these species allow us to document the acoustic
characteristics of several populations in order to assess geographic variation and
test for potential factors (Fig.1) promoting those patterns across complex
landscapes.
122
Field Work. We documented advertisement calls of our study species all over
Costa Rica. We recorded calling males at 23 different localities across all the
regions covered by the distribution of the genus in the country, at elevations
from the sea level to near 2800 m altitude (Fig. 2). It includes the Caribbean and
the Pacific lowlands as well as the four main NW-trending volcanic cordilleras
that bisects the country. Calls were recorded with a Sennheiser ME66 shotgun
microphone and a Marantz PMD-660 digital recorder (.wav file format; 44.1
Khz sampling rate and 16 bit accuracy), a SONY TCM-500EV analogic
recorder (La Selva and La Guaria sites) or a SONY TCM-150 analogic recorder
(Volcán Tenorio and Fortuna sites). We digitalized these analogic recordings
using the software Adobe Audition at a 44.1 kHz sample rate and sample size
of 16 bits.
We recorded all males by positioning the microphone at less than 2
meters of distance. In order to avoid replicated recordings of the same male, at
each locality we walked linearly while searching for calling individuals. When
collected, voucher specimens were deposited at the collection of Herpetology
of the Museo de Zoología, Universidad de Costa Rica (MZUCR).
123
Figure 2. Localities sampled for the 2 species of Diasporus analyzed in this study.
Acoustic Analyses. For each species, we identified individual calls in
recordings through an automatic detection procedure, based on amplitude and
duration thresholds applied within the frequency range of the species. From the
calls detected in each recording we selected those with the highest signal-to-
noise ratio, for a maximum of 8 calls per recording. We visually inspected the
spectrograms of the detected calls, removed undesired sounds or calls that
overlapped in time with other sounds and manually adjusted time and frequency
of selections when necessary using.
124
We analyzed calls from audio recordings belonging to 170 males (D.
diastema = 98, D. hylaeformis = 72). For each male we analyzed a mean of
6.7±1.3 calls, for a total of 1241 calls evaluated. We measured 26 acoustic
parameters (detailed in table S1). Acoustic parameters were BoxCox-
transformed to improved normality. We also removed collinear variables,
identified as those with a mean-absolute Pearson correlation coefficient higher
than 0.95. We used unsupervised random forest (RF) [45] on the acoustic
parameters to identify underlying acoustic structure within species and to
evaluate the relative contribution of the parameters to overall acoustic
differences. Briefly, this method randomizes the data to create a new synthetic
data set [46]. These two data sets (“original” and “synthetic”) are then used in
a two-class classification model using supervised RF, which creates multiple
decision trees using different parameter subsets to discriminate between the
classes [45]. This predictive model generates a proximity matrix, which
contains counts of times in which each pair of items are found in the same tree
node. Hence, this matrix represents the similarity between calls as more similar
calls are expected to be together more often across trees. Thus, we converted it
to a distance matrix by subtracting it from 1 and used it in subsequent analysis
as a measure of pairwise acoustic dissimilarity. Individual models were built for
each species. Distance matrices were calculated between all calls, between
125
individuals and between sites (based on average pairwise similarities between
individuals and sites, respectively). This procedure was run separately for each
species. All acoustic analyses were done using the packages tuneR [47],
Seewave [48], and warbler [49].
Potential Abiotic Drivers of Acoustic Variation. We tested whether patterns
of IBD, IBR or IBE, are able to explain variation in acoustic structure at the
intra-specific level in our study species. To this end we used coordinates
recorded in almost all the study sites using a GPS Garmin 63st. In few cases,
sites were georeferenced a posteriori using the software QGIS 2.16.3. We used
such geographic information to create pairwise distance matrices among
localities. To estimate IBD, we calculated euclidean distances in kilometers
among localities of the same species using the R package raster [50].
To estimate IBR, we considered two factors acting as potential barriers
between localities: topography (IBRt) and poor climatic suitability (IBRs). We
estimated resistance distances using such factors as independent friction layers
based on a circuit-theory approach conducted in the program Circuitscape V.4.0
[51]. To calculate topographic cost distances among localities, we first
generated a friction layer that accounts for topographic complexity. To this, we
used a layer of elevation at 30-second resolution (~1km at the equator,
http://www.worldclim.org/) and calculated the standard deviation of differences
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between 5x5 adjacent elevations using the R package raster [50]. This procedure
has been demonstrated to more accurately represent topographic roughness than
elevation range, which only indicates the strength of a gradient within a cell
[52].
To estimate distances mediated by climatic suitability (IBRs) among
localities we first generated distribution models for each species. To create
them, we gathered occurrence points, for each species, from the Global
Biodiversity Information Facility and the Herpetology Collection held at Museo
de Zoología at the University of Costa Rica. Then we projected and clean those
occurences for georreferencing errors by visualizing geographic outliers and
excluding points outside the known altitudinal range for each species. We used
as predictors the 19 bioclimatic variables available for current conditions at
www.worldclim.org at a resolution of 30 arc seconds (~1km at the Equator). For
each species we ran 52 candidate models using a Maximum Entropy algorithm
[53,54] with different parametrizations, varying regularization multipliers and
features classes [55]. All these procedures were conducted in R using the
packages ENMeval, dismo and raster [50,56,57]. We determined the best
models as those having a combination of the highest mean AUC values and the
lowest mean omission rate of the 10th percentile. Finally, we used as friction
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surface the inverse of suitability derived from the selected ENM’s for each
species.
To test for IBE, we estimated environmental dissimilarity among locality
pairs, independently of the climatic conditions in the areas that separate them.
For each sampled locality, we extracted the values for the 19 bioclimatic
variables available in the Worldclim data set (worldclim.org) at 30 arcseconds
resolution (~1km at the equator). Then, we estimated Euclidean pairwise
distances in the multidimensional space using the function dist in R, which
computes specific distances between the rows of a multivariate matrix. All data
used in this calculation is derived from temperature and precipitation means
[58], which are variables known to have a direct or indirect influence (i.e
humidity) on sound transmission [59]. Then, we consider this metric a proxy to
test the hypothesis of acoustic adaptation.
Generalized Dissimilarity Modelling. Using the acoustic distances between
localities as response matrix and the IBD, IBRt, IBRs and IBE matrices as
predictors we conducted a Generalized Dissimilarity Modelling (GDM). GDM,
is a distance-based statistical approach that uses regression techniques to relate
geographic or environmental distances and dissimilarities in a biological trait
between sites [60]. Contrary to other distance-based approaches, such as
Mantel, Partial Mantel and Multiple Matrix of Regression with Randomization
128
(MMRR), GDM is able to fit the nonlinear responses, commonly encountered
in ecological datasets [61]. This technique deals with nonlinear relationships of
environmental variables to biological variation by using I-spline basis functions
(more details in [60]). Such splines plots provide insights into the total
magnitude of biological change as a function of each gradient and where those
changes are most pronounced along each gradient [62].
Results
Acoustic variation. From the supervised Random Forest analysis on the data
set containing both species we obtained a test data error rate of 0.1650
(e.g.83.5% of the calls correctly classified, ntree=10000; mtry=5, accuracy p-
value<0.0001). The most important variables contributing to this classification
were: spectral flatness, mean frequency, duration and standard deviation of
frequency.
At the intra-specific level for D. diastema we obtained a classification
accuracy of 88.04% on the test data set (ntree=10000; mtry=5; accuracy p-
value<0.0001). In this case, the most important variables were mean frequency,
spectral flatness, standard deviation of frequency and the interquartile
frequency range (Fig 3). In D. hylaeformis, the classification accuracy of the RF
analyses was of 94.65% on the test data and the most important variables for
129
this classification were mean frequency, duration, spectral flatness and
minimum dominant frequency (Fig 4).
Isolation levels among populations. After estimating IBD matrices, we
found geographic linear distances, among localities, ranging between ~1 (La
Selva- La Guaria) and 314 km (Piro-Volcan Tenorio) for D. diastema and
between seven (Queverí-Tapantí) and 113 km (Tapantí-Monteverde) for the
highland frog D. hylaeformis. In the case of IBR based on topographic features,
we confirmed from the resulting connectivity voltage maps, that among studied
populations of D. diastema the most evident geographic barriers are the NW-
trending volcanic cordilleras separating the Caribbean and Pacific lowlands.
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Figure 3. Variation in the four acoustic variables that most contributed in the discrimination among of calls of Diasporus diastema among sites: mean frequency; spectral flatness, standard deviation of frequency, interquartile of frequency range .
131
Figure 4. Variation in the four acoustic variables that most contributed in the discrimination among of calls of Diasporus hylaeformis among sites: mean frequency, call duration, spectral flatness and minimum dominant frequency.
132
For D. hylaeformis instead, the intermontane valleys, below 1300 m represent
a major barrier that limits connectivity, especially among the sites sampled for
this species. The most evident barrier separates the locality we sampled at
Cordillera de Tilaran and the rest of localities sampled within the Cordillera de
Talamanca-Cordillera Central complex. In terms of bioclimatic suitability
(IBRs), this separation is not evident, and all areas between localities of D.
hylaeformis seems to be accessible when accounting for this variable as a
resistance factor (Fig 5).
Figure 5. IBR among sites based on topographic barriers and bioclimatically unsuitable regions. A and B correspond to isolation by habitat suitability and topography, for D. diastema, respectively. C and D represent the same variables for D. hylaeformis.
133
Climatic dissimilarity among studied sites (IBE) for D. diastema was higher
between Agua Buena in the southwestern (South Pacific) and Cahuita in the
northwestern (South Caribbean) portion of the country. As expected due to their
geographic proximity, the most similar pair of sites for this species was La
Selva-Guaria. In D. hylaeformis, local climatic conditions are more different
between Dantas and Tapantí; and more similar between Montserrat and Volcán
Poás, although this two localities are not the closer ones.
Drivers of acoustic divergence. We fitted a generalized dissimilarity model
(GDM) of the acoustic divergence as a function of the different types of
isolation. The GDM explained 30.68% of the deviance the in acoustic
divergence of D.diastema (full model p-value< 0.001). In terms of variable
importance, for D. diastema isolation by topography and isolation by distance
were the two variables that contributed in explaining acoustic divergence (Fig.
6a). For D. hylaeformis the full model was not able to significantly explain
acoustic variation (full model p-value=0.162), although, we obtained a
signature of association with isolation by environment and isolation by
topography (Fig. 6d). The shape of the I-splines of all these variables were all
similar, depicting increases in divergence with the increase in the degree of
isolation (Fig. 6). However, in D. diastema the rate of change in acoustic traits
varies exponentially as topographic isolation increases, while the rate of change
134
associated to IBD in this species and to IBE in D. hylaeformis showed a
logarithmic growth.
135
Figure 6. Variable importance and generalized dissimilarity model-fitted I-splines for variables associated with acoustic divergence in D. diastema (A-C) and D. hylaeformis (D- F). The maximum height reached by each curve, indicates the total amount of divergence associated with the respective variable, holding all other variables constant. The shape of each function shows how the rate of acoustic divergence varies along the tested gradients.
136
Discussion
Our study provide the first quantitative evidence of intra-specific acoustic variation
in the direct-developing frogs Diasporus diastema and D. hylaeformis. High
classificatory power of our RF algorithm (>86% in D. diastema and ~95% in D.
hylaeformis) reveals deep acoustic intra-specific divergences among populations of
each species. Lower discrimination power in D. diastema could be affected by the
potential inclusion of meta-populations from near localities that may obscured
optimal discrimination. Higher discrimination power in D. hylaeformis results from
deeper mean divergences among localities.
For D. diastema we found that calls tend to be more similar among localities
within the Pacific and within the Caribbean, than between versants. The most
variable feature in the calls of this species was mean frequency, with a trend of
Pacific populations to vocalize at lower frequencies. In most anurans, frequency is
constrained by body size, larger males, are expected to have vocal cords of larger
mass, making them able to produce lower frequencies. Although out of the scope of
our study, we have evidence that males from Pacific tend to be bigger than those
from the Caribbean (García-Rodríguez, unpublished data). In the case of D.
hylaeformis, we did not found a clear geographic pattern of variation, nevertheless,
calls from Tapantí were notably different among all sampled sites. Males from this
locality produces remarkably long calls, with more than twice the duration of all
137
other populations. In several species, longer call duration has been proposed as an
indicator of genetic quality [63,64], such evidence support the idea that females are
enabled to choose males of higher quality thorough their mating preferences (i.e
good genes hypothesis [65]) and highlights the importance of biotic pressures on the
evolution of calls. Certainly, is not easy to explain why only females from that
locality may specifically select for that trait. Then, habitat particularities affecting
signal transmission such as vegetation structure, noise sources or even the
composition of the local soundscape may have influenced this striking variation.
Intra-specific geographic variation in acoustic signals as documented here, has
been long reported in anurans (reviewed by [66]). Through time it also has become
a topic of interest in evolutionary research due to its potential as a mechanism of
reproductive isolation and incipient speciation [67]. Ever since such patterns of
variation in communication systems can provide insights on drivers of evolutionary
processes, we evaluated the potential role of several abiotic forces (e.g. distance,
topography, habitat suitability and climatic dissimilarity) in shaping them. Our
results showed that irregular topographies likely influenced divergence patterns in
the advertisement calls of D. diastema. Contrary, none of the factors tested
significantly explained acoustic variation within D. hylaeformis, although climatic
dissimilarity among sites showed an association with acoustic divergence.
138
In D. diastema, detected differences between versants highlights the role of
topographic barriers. This pattern is expectable, considering that this species rarely
occur above 1500 m altitude [41], the cordilleras of the country may represent a
major vicariant barrier. For D. diastema and other lowland species distributed in
both Pacific and Caribbean versants, IBRt should be more evident in the eastern
portion of the country, where Cordillera de Talamanca reaches elevations above
3500 meters [68]. Talamanca has a dynamic geological history with an uplift rate of
approximately 1km/1Ma [69] that has turned into the most evident physical barrier
for lowland organisms in Costa Rica, affecting for example, phylogeographic
patterns in several taxa [reviewed in 72]. In the eastern portion of the country
(Cordillera de Tilaran and Guanacaste), mountain passes are lower, however
influenced by dry conditions from the Northern Pacific [68]. It may be the factor
limiting dispersal between versants in this region, maintaining between versant
isolation and indirectly increasing the effect of the mountainous barriers.
In D. hylaeformis, patterns of acoustic variation could be better explained by
local biotic pressures. For example, local female choices based on call duration may
act as a directional pressure selecting and fixing longer calls in D.hyalaeformis from
Tapantí. Interestingly, in our dataset, Tapantí is the only locality where we have
found sympatric populations of D. diastema and D.hylaeformis, with males calling
at less than 1km from the other species (García-Rodríguez, unpublished data).
139
Hence, another possibility is that substantial differences in the call of D. hylaeformis
there, could be driven by the syntopic occurrence of both species as predicted by
pre-reproductive character displacement: mate attracting signals differ more in
sympatric than in allopatric areas of closely related species [71]. Such shifts are
hypothesized to arise in order to minimize agonistic hetero-specific interactions or
the production of costly hybrids, however evidence of this phenomena associated
with acoustic traits are still scarce [31]. While the focus of our study was to assess
the potential role of abiotic factors drivers of isolation in explaining acoustic
divergence, the above cited factors could be understand as biotic mechanisms driven
by interactions among individuals and should be addressed more deeply in future
studies with these species. Finally although not statistically significant, we recovered
a signal of correlation between climatic dissimilarity and acoustic divergence. It also
could give insights of local pressures such as climate or vegetation structure shaping
call structure according to the acoustic adaptation hypothesis [33].
Previous studies testing the influence of IBD on acoustic divergence,
including some conducted in the same landscapes of Costa Rica, have found
correlations between linear distance and acoustic dissimilarity in birds [72–74],
frogs [22,75,76] and rodents [59]. In some cases as in the Andean frog Colostethus
palmatus [77] or the singing mice Scotinomys teguina [59], these patterns are also
explained by genetic distances, suggesting an important contribution of genetic drift
140
in shaping patterns of acoustic variation. Contrary, in the strawberry poison frog
Dendrobates pumilio and the tree frog Hyla leucophyllata, among population call
variation follows a geographic cline without a strict acoustic separation between
genetic groups [22,78]. It reflects that acoustic divergence is not necessarily
predicted by genetic divergence or at least, that genes used in those studies evolved
slower genes underlying sexual signals [22]. We found a small contribution of IBD
in the GDM of D. diastema, in our opinion, IBR based on topographic complexity
which better explained acoustic variation, is a more robust metric to describe
patterns of isolation in the irregular landscapes of Costa Rica. In this case it also
likely reflects genetic drift as the mechanism leading to major acustic differentiation
between Pacific and Caribbean populations.
Conclusions
Most abiotic factors tested here, failed in significantly explain patterns of acoustic
divergence in our study species, even though they clearly promoted patterns of
isolation among populations due to the topographic and climatic heterogeneity of
the Costa Rican landscapes. Then, given the high levels of acoustic variation
detected, we conclude that signal evolution in this case is likely determined by a
combination of mechanisms like sexual selection [e.g. 22,81,82], character
displacement [e.g 73,83–85] or genetic drift [e.g 23,29,76,77] operating
independently in local scales on isolated populations. Data presented here comes
141
from a genus with a complex taxonomy due to the potential existence of cryptic
diversity [41,44,84]. A recently published molecular analysis for this group
supported the hypothesis of masked cryptic diversity within the genus and
highlighted the potential use of bioacoustics as a powerful approach to solve this
issue [44]. Information on acoustic divergence have been successfully incorporated
as a complementary source of evidence in the field of integrative taxonomy
[23,42,85,86]. In many anuran groups, the definition of species boundaries based
merely on morphological approaches becomes a hard task due to their highly
conserved morphology [87,88]. In these cases, bioacoustic approaches has proved
to be useful and has helped disentangling the taxonomy of cryptic complexes
where many species were masked under one name [88–90]. From that perspective,
our study adds valuable information for future studies concerned with addressing
such questions.
Acknowledgements
We are profoundly grateful to all the people involved in the fieldwork conducted to
obtain the recordings analyzed in this study: Víctor Acosta, Kathia Alfaro,
Esmeralda Arévalo, Erick Arias, Gilbert Barrantes, Eliana Faria de Oliveira,
Francisco Fonseca, Sofía Granados, Castiele Holanda Bezerra, Brian Kubicki,
142
Daniela Masís, Francesca Protti, Sofía Rodríguez, Luis Sandoval, Vinicius São
Pedro, Rodolfo Vargas, Beatriz Willink and Héctor Zumbado. Branko Hilje and
Mark Wainwright provided recordings from La Selva and La Guaria, and
Monteverde, respectively. AGR thanks Federico Bolaños, Gilbert Barrantes and
Gerardo Chaves for initial discussions on this topic.
Funding
This research was partially funded by the National Geographic Society [grant
number W-346-14]. AGR was supported by Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior, Brazil.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
AGR and GCC designed the study; AGR collected data; AGR and MAS analyzed
data; AGR draft the manuscript, subsequently improved by all co-authors.
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-Supplementary Material-
The role of geography, topography and climate in the acoustic divergence of Neotropical Diasporus frogs
Table S1. Acoustic variables measured for each recording analyzed in this study
Acoustic Variable Definition
Mean frequency Weighted average of frequency by amplitude (in kHz)
Minimum dominant frequency Lower frequency bound of the call
Start dominant frequency Dominant frequency measurement at the start of the signal
Frequency median The frequency at which the signal is divided in two frequency intervals of equal energy (in kHz)
Mean dominant frequency Average of dominant frequency measured across the acoustic signal
Mean peak frequency Frequency with highest energy from the mean spectrum
First quartile of frequency The frequency at which the signal is divided in two frequency intervals of 25% and 75% energy respectively (in kHz)
End dominant frequency Dominant frequency measurement at the end of the signal
Maximum dominant frequency Higher frequency bound of the call
Dominant frequency slope Slope of the change in dominant frequency through time ((enddom-startdom)/duration).Units are kHz/s.
Dominant frequency Range Range of dominant frequency measured across the acoustic signal
Modulation index Cumulative absolute difference between adjacent measurements of dominant frequencies divided by the dominant frequency range. 1 means the signals is not modulated.
Standard deviation frequency SD of frequency weighted by amplitude
Time entropy Energy distribution on the time envelope. Pure tone ~ 0; noisy ~ 1.
Duration Length of signal (in s)
First quartile time The time at which the signal is divided in two time intervals of 25% and 75% energy respectively (in s).
Third quartile time The time at which the signal is divided in two time intervals of 75% and 25% energy respectively (in s).
Time median The time at which the signal is divided in two time intervals of equal energy (in s)
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Interquartile time range Time range between 'time.Q25' and 'time.Q75' (in s).
Interquartile frequency range Frequency range between 'freq.Q25' and 'freq.Q75' (in kHz)
Third quartile frequency The frequency at which the signal is divided in two frequency intervals of 75% and 25% energy respectively (in kHz)
Spectral flatness Similar to sp.ent (Pure tone ~ 0; noisy ~ 1).
Entropy Product of time and spectral entropy sp.ent * time.ent.
Spectral entropy Energy distribution; pure tone ~ 0; noisy ~ 1
Skewness Asymmetry of the spectrum
Kurtosis Peakedness of the spectrum
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CONSIDERAÇÕES FINAIS
Nesta dissertação pretendi avaliar a influência da heterogeneidade ambiental
contida em regiões complexas, na geração de padrões de variação em diversas
dimensões biológicas. Meu interesse foi abordar essa questão de maneira
complementar, explorando tanto processos macroevolutivos, como por
exemplo a diversificação de grupos taxonômicos superiores quanto processos
microevolutivos, como a diferenciação de linhagens e divergência de
características comportamentais na escala intraespecífica.
O estudo traz valiosas informações que revelam a importância das regiões
montanhosas como motores evolutivos que sustentam ricas biotas numa escala
global. Mostra também como múltiplas particularidades das paisagens
complexas como a heterogeneidade climática e a irregularidade topográfica são
capazes de promover fases incipientes da especiação a nível regional,
funcionando como “combustível desses motores”. Demostrei que as taxas de
especiação de anfíbios são mais rápidas em áreas montanhosas numa escala
global. O padrão encontrado é robusto e se mantem, na maioria dos casos,
quando é desconstruído e testado a nível de regiões zoogeográficas.
Além disso, apresento evidência comparativa e quantitativa de que a
evolução numa região complexa específica pode ser promovida por múltiplas
forças ao invés de ser explicada por uma pressão exclusiva. Por exemplo,
159
encontramos que a variação genética em 11 espécies que co-ocorrem no Istmo
da América Central é induzida por diversos fatores abióticos da região, como
são a heterogeneidade climática e a topografia montanhosa. O efeito desses
fatores varia em intensidade dependendo da espécie, provavelmente devido as
histórias de vida particulares de cada organismo. Também encontrei que em
fases de especiação ainda mais incipientes, como a divergência em sinais
acústicos envolvidas na reprodução, os padrões de variação não
necessariamente são explicados pelas forças que geram isolamento entre
populações. Neste caso, processos estocásticos que atuam a nível local sob essas
populações isoladas são os prováveis responsáveis da evolução acústica. Porém,
o efeito indireto das paisagens complexas gerando altos níveis de isolamento
certamente acelera esse processo de evolução independente.
Como todo estudo, o meu trabalhalho possui algumas limitações. A
espacialização de padrões evolutivos é certamente uma tarefa complexa que só
agora está começando a ser abordada. Porém, achamos válida e robusta a forma
em que procedemos com este objetivo no primeiro capítulo, mais ainda
considerando a escala geográfica e a quantidade de espécies envolvidas no
estudo. Não duvidamos que no futuro serão desenvolvidos métodos para lidar
melhor com a projeção de taxas evolutivas no espaço. No segundo capítulo, o
uso exclusivo de sequências mitocondriais tem suas fraquezas, mas a natureza
160
de um estudo comparativo incluindo múltiplas espécies, limita a incorporação
de mais marcadores quando eles não estão disponíveis para todas as espécies.
A incorporação de mais marcadores e mais grupos taxonômicos dever ser o
objetivo em futuro estudos. No terceiro capítulo, consideramos que os objetivos
foram atingidos, porém a incorporação de mais populações especificamente na
espécie de altas altitudes poderia ajudar a ter evidências mais conclusivas com
relação ao peso das barreiras testadas como drivers de evolução dos cantos.
Em definitiva as montanhas do mundo têm um papel muito importante na
evolução, montagem e manutenção da biodiversidade. Proporcionam um
fascinante modelo de estudo, com réplicas distribuídas pelo globo, com
diferentes particularidades como idades, configurações, regimes climáticos e
rugosidade de terreno que permitem testar sua influência nos processos
evolutivos. Pessoalmente tenho interesse de continuar explorando essas
questões de maneira integrativa desde as perspectivas macro e microevolutivas.
Por exemplo pretendo continuar essa linha de macroevolução para mapear na
escala global quais regiões do mundo funcionam como bombas e quais como
museus de espécies. Também para testar se as altas taxas de especiação achadas
nessa dissertação podem explicar padrões biogeográficos gerais como os
gradientes latitudinais de diversidade. A nível micro meus esforços serão
orientados a incorporar informação genômica que permita estudar associações
161
entre genótipos e variantes climáticas especificas ao longo de gradientes
climáticos comuns em regiões montanhosas, para entender melhor o processo
de seleção adaptativa e incrementar nosso ainda incompleto conhecimento do
mecanismo de evolução parapátrica.
Finalmente, gostaria de complementar meu programa de pesquisa com
estudos que incorporem particularidades da configuração geomorfológica das
montanhas na avaliação de dinâmicas de contração e expansão de amplitudes
de distribuição de espécies em cenário futuros de mudança climática. Uma
caracterização, não só de quais são os grupos biológicos mais ameaçados, mas
também de quais são os sistemas montanhosos que por suas características
físicas são mais vulneráveis nesse contexto. Essa informação é estritamente
necessária para definir prioridades e informar as ações de conservação dessas
regiões, que temos demostrado são muito importantes para o origem e
manutenção da biodiversidade.
