COMO A PAISAGEM MOLDA O PADRÃO ESPACIAL DE VARIAÇÃO GENÉTICA DOS QUELÔNIOS AMAZÔNICOS PODOCNEMIS ERYTHROCEPHALA E P.
SEXTUBERCULATA (TESTUDINES, PODOCNEMIDIDAE)?
JESSICA DOS ANJOS OLIVEIRA
Manaus, Amazonas Abril, 2017
JESSICA DOS ANJOS OLIVEIRA
COMO A PAISAGEM MOLDA O PADRÃO ESPACIAL DE VARIAÇÃO GENÉTICA DOS QUELÔNIOS AMAZÔNICOS PODOCNEMIS ERYTHROCEPHALA E P. SEXTUBERCULATA
(TESTUDINES, PODOCNEMIDIDAE)?
Orientadora: Dra. FERNANDA DE PINHO WERNECK Co-orientadores: Dra. Izeni Pires Farias
Dr. Gabriel Corrêa Costa
Manaus, Amazonas Abril, 2017
Dissertação apresentada ao
Instituto Nacional de Pesquisas
da Amazônia como parte dos
requerimentos para obtenção do
título de Mestre em Biologia
Ficha catalográfica
O48 Oliveira , Jessica dos Anjos
Como a paisagem molda o padrão espacial de variação genética dos quelônios amazônicos Podocnemis erythrocephala e P. sextuberculata (Testudines, Podocnemididae)? / Jessica dos Anjos Oliveira . --- Manaus: [s.n.], 2017.
112 f.: il., color.
Dissertação (Mestrado) --- INPA, Manaus, 2017. Orientador: Fernanda de Pinho Werneck
Coorientador: Gabriel Corrêa Costa; Izeni Pires Farias Área de concentração: Biologia (Ecologia)
1. Quelônios . 2.Genética da paisagem . 3. Isolamento por resistência. I. Título.
CDD 597.92
Sinopse
Estudou-se a influência de fatores locais e de conectividade da paisagem nos padrões espaciais de diversidade e diferenciação genéticas de duas espécies de quelônios aquáticos amazônicos.
Palavras-Chave: Genética da Paisagem, Isolamento por Resistência, diversidade genética, diferenciação
AGRADECIMENTOS
Tenho muitas pessoas a agradecer por contribuições diretas e indiretas ao sucesso desta dissertação. De fato, na Ciência não se faz nada sozinha, e eu não poderia deixar de reconhecer a todos que me ajudaram ao longo do processo de fazer Ciência!
Primeiramente, agradeço à minha orientadora Fernanda Werneck por me aceitar como aluna e apoiar o desenvolvimento deste projeto. Graças ao seu suporte fui aos poucos moldando as questões e abordagens deste trabalho. Muito obrigada também pela amizade, pelas conversas, pela compreensão, pela disposição em ajudar e por acompanhar todas as etapas do trabalho. Agradeço à minha co-orientadora Izeni Farias, que é uma mãezona e sempre se preocupou em me ajudar a ter sucesso na obtenção dos dados genéticos. Obrigada pelo constante apoio no processo de coleta dos dados genéticos, bem como ao longo da concepção do projeto, da minha aula de qualificação e escrita final. Também sou grata ao meu co-orientador Gabriel Costa, cuja disponibilidade no período que passei em Natal foi fundamental para a análise dos dados. Agradeço por me ensinar modelagem espacial, gastar dias debatendo as análises espaciais adequadas, seja por Skype ou pessoalmente, e por todos os inputs na concepção e desenvolvimento do projeto e no texto.
Este projeto foi possível graças a um extenso banco de dados genéticos e de amostras biológicas provenientes do esforço coletivo de diversos pesquisadores. Agradeço primeiramente à Maria das Neves Viana por disponibilizar o banco de dados de Podocnemis sextuberculata antes de estar disponível no GenBank e pelo apoio durante meu trabalho em laboratório com as amostras adicionais de ambas espécies. Agradeço à Izeni por também disponibilizar o banco de dados de
Podocnemis erythrocephala antes de estar disponível no GenBank. Sou grata a todos os que
coletaram amostras de quelônios deste banco de dados e que me ajudaram na busca pelas coordenadas de cada localidade, especialmente: Richard Vogt, Paulo Andrade, Rafael Bernhard, Daniely Félix, Francivane Fernandes, José Erickson e Cleiton Fantin. Também agradeço ao Richard Vogt e à Fernanda Werneck pelas oportunidades de ida a expedições de campo no Parque Nacional do Jaú e ao longo do Rio Negro, onde coletei amostras adicionais para o trabalho.
A coleta de dados genéticos rendeu alguns meses no LEGAL (Laboratório de Evolução e Genética Animal/UFAM). Obrigada Maria Augusta por ter me acompanhado e ensinado tudo no lab e por ser minha companheira de genética de quelônios! Agradeço ao Giovanni, que aprendeu
rapidamente os protocolos e me auxiliou bastante no laboratório. E obrigada ao pessoal do LEGAL pela convivência no período de lab e por toda ajuda que me deram quando precisei.
A coleta de dados da paisagem foi a parte do meu trabalho na qual contei com mais ajuda,
insight e dados de diversas pessoas. Agradeço ao Urbano Silva Jr. por disponibilizar os dados de
variação interanual nas cotas dos rios. Sou eternamente grata pela ajuda que tive da Camila Fagundes e da Camila Ferrara, pensando nas variáveis de paisagem e na interpretação dos resultados. Conversar com vocês duas sobre o aspecto biológico e espacial do trabalho foi essencial para eu saber se estava indo no caminho certo e ter novas ideias. À Camila Fagundes agradeço também pela contribuição na minha aula de qualificação, pela disponibilização dos pontos de ocorrência das espécies, além de shapefiles e ideias sobre variáveis. Também sou grata ao André Antunes e à Thaís Morcatty por me ajudarem a pensar na variável de pressão de caça. Agradeço aos especialistas de quelônios que contribuíram com o questionário de resistência à cor da água: Richard Vogt, Paulo Andrade, Rafael Bernhard, Augusto Fachín-Terán, Camila Ferrara e Camila Fagundes. As variáveis de conectividade só puderam ser desenvolvidas graças ao apoio de Felipe Martello e seus scripts. Sou também extremamente grata à professora Marina Côrtes da UNESP Rio Claro, pelos ensinamentos na disciplina de Genética da Paisagem ministrada, além das importantes contribuições dadas pessoalmente e via Skype sobre as variáveis de paisagem e análises espaciais.
Agradeço aos amigos e colegas do grupo Evolução e Biogeografia da Biota Amazônica (EBBA) e à Ariane pelas discussões de artigos, reuniões, confraternizações e amizade. Também agradeço as sugestões de Igor Kaefer, Waleska Gravena e Camila Fagundes, membros da banca da minha aula de qualificação.
Muito obrigada às maravilhosas pessoas da turma da Ecologia de 2015 por estes dois anos! Sou grata também aos moradores da república Viracopos e moradores das kitnets rosinhas pela convivência. Um obrigada especial à Sulamita, minha irmã de Amazônia. E nada disso teria sido possível sem o apoio incondicional da minha família e amigos de Brasília, vocês foram fundamentais!
Finalmente, agradeço ao CNPq por conceder a bolsa de Mestrado. E ao CNPq e à FAPEAM pelo financiamento dos campos e laboratório.
Resumo
Associações entre fatores da paisagem e processos ecológicos como dispersão, reprodução e sobrevivência de organismos podem afetar processos microevolutivos como fluxo gênico, deriva e seleção. A Genética da Paisagem surgiu como um campo de pesquisa que combina genética populacional, ecologia de paisagens e análises espaciais para quantificar explicitamente os efeitos da qualidade da matriz, da composição e configuração da paisagem nos processos microevolutivos. Em sistemas fluviais, como a bacia Amazônica, o processo de isolamento por distância é normalmente forte e pode mascarar a importância de barreiras, resistência da paisagem e fatores ambientais locais em moldar padrões genéticos. Portanto, nesta dissertação eu avaliei a importância de variáveis locais e de conectividade em moldar os padrões espaciais de variação genética de dois quelônios aquáticos Amazônicos com diferentes capacidades dispersoras. Meus objetivos foram: 1) avaliar se a espécie com maior capacidade de dispersão (Podocnemis sextuberculata) possui menor estrutura genética especial que a espécie com baixa capacidade dispersora (P.
erythrocephala); 2) testar se fatores de conectividade estão relacionados à diferenciação genética
para a espécie de baixa capacidade dispersora (P. erythrocephala) mas não para a de alta capacidade dispersora (P. sextuberculata); e 3) testar se fatores locais estão mais fortemente associados à diversidade genética intrapopulacional da espécie de baixa capacidade de dispersão (P. erythrocephala). Com ampla amostragem pela distribuição geográfica das espécies na bacia Amazônica, eu estimei os parâmetros genéticos para P. erythrocephala em 14 localidades (273 amostras) e para P. sextuberculata em 20 localidades (336 amostras). Apliquei seleção de modelos em modelos associando a diversidade genética a variáveis locais representando hipóteses de clima e produtividade, instabilidade interanual de níveis da água dos rios, pressão de caça e aumento de diversidade genética a jusante dos rios. Usei General Dissimilarity Modelling (GDM) para modelar a relação entre diferenciação genética e variáveis de conectividade representando hipóteses de isolamento por distância (IBD), isolamento por resistência (IBR) e isolamento por barreira (IBB). Diferentemente do esperado, variáveis locais foram mais importantes em explicar a diversidade genética intrapopulacional da espécie com maior capacidade de dispersão (P. sextuberculata) que de P. erythrocephala, com melhores modelos incluindo produtividade, distância da localidade mais a jusante, densidade de vilas humanas e adequabilidade climática histórica. Fatores de conectividade em geral não foram importantes em explicar a diferenciação genética para nenhuma das espécies, entretanto, como esperando, os modelos GDM explicaram uma maior parte da variação para a espécie de menor capacidade dispersora, P. erythrocephala. Além disso, modelos de IBB e IBR explicaram mais diferenciação genética que IBD, revelando a importância em incluir a complexidade ambiental e da paisagem quando estudar padrões genéticos espaciais. Nessa dissertação mostro que, apesar de variáveis locais serem frequentemente desconsideradas em estudos de Genética da Paisagem, elas podem influenciar a diversidade genética intrapopulacional de espécies aquáticas, inclusive daquelas com alta capacidade dispersora. Ao usar um método inédito de modelos de resistência no contexto de Genética da Paisagem de Rios (Riverscape
Genetics) e ao usar fatores da paisagem relevantes no contexto Amazônico, forneço uma
abordagem para o estudo dos papéis de variáveis locais e de conectividade em moldar os padrões genético-espaciais de vertebrados aquáticos em sistemas fluviais.
Abstract
How does the riverscape shapes the spatial pattern of genetic variation of Amazonian river turtles Podocnemis erythrocephala and P. sextuberculata (Testudines, Podocnemididae)? Associations between landscape factors and ecological processes such as dispersal, reproduction and survival of organisms can ultimately affect microevolutionary processes such as gene flow, drift and selection. Landscape Genetics emerged as a research field that combines population genetics, landscape ecology, and spatial analyses to explicitly quantify the effects of landscape composition, configuration, and matrix quality on microevolutionary processes. In fluvial systems, such as Amazon basin, the process of isolation by distance is often strong and can mask the importance of barriers, landscape resistance and local environmental factors on shaping genetic patterns. Therefore, in this dissertation I assessed the importance of local and connectivity variables in shaping the spatial genetic variation patterns of two Amazonian river turtle species with distinct dispersal abilities. My objectives were: 1) assess whether the high-dispersal species (Podocnemis
sextuberculata) has less spatial genetic structure than the low-dispersal species (P. erythrocephala); 2) test whether connectivity factors are related to genetic differentiation for the
low-dispersal (P. erythrocephala) species but not for the high-dispersal species (P. sextuberculata); and 3) test whether local factors are more strongly associated to intrapopulational genetic diversity for the low dispersal species (P. erythrocephala). With broad sampling throughout their distribution in Amazon basin, I estimated genetic diversity and differentiation for 14 localities totaling 273 samples of P. erythrocephala and for 20 localities totaling 336 samples of P.
sextuberculata. I applied model selection on models associating genetic diversity to local variables
representing hypothesis of climate and productivity, instability of inter-annual water levels, hunting pressure and downstream increase in genetic diversity. I used General Dissimilarity Modelling (GDM) to model the relationship of genetic differentiation with connectivity variables representing hypothesis of isolation by distance (IBD), isolation by resistance (IBR) and isolation by barrier (IBB). Differently from the expected, local variables were more important in explaining genetic diversity of the high-dispersal species (P. sextuberculata) than of P. erythrocephala, with best models including productivity, distance from downstream locality, density of human villages and historical climatic suitability. Connectivity factors in general were not important in explaining genetic differentiation turnover for either species, but as expected, the GDM models explained a larger amount of deviance for the low-dispersal species, P. erythrocephala. Also, IBB and IBR models explained more genetic differentiation turnover than IBD, revealing the importance of including the environmental and landscape complexity when studying spatial genetic patterns. I showed that, although local variables are often overlooked in Landscape Genetics studies, they can influence intrapopulacional genetic diversity of aquatic species, even those with high dispersal ability. By applying a novel resistance-model framework in Riverscape Genetics and by using riverscape factors relevant in Amazonian context, I provide an approach to study the roles of local and connectivity variables in shaping genetic patterns of aquatic vertebrates in fluvial systems.
Sumário
Introdução geral ... 1
Objetivos ... 7
Capítulo I. – Model-based riverscape genetics: disentangling the roles of local and connectivity factors in shaping spatial genetic patterns of two Amazon River turtles with different dispersal abilities ... 9
INTRODUCTION ... 11
METHODS ... 15
RESULTS ... 21
DISCUSSION ... 23
CONCLUSIONS AND PERSPECTIVES ... 32
REFERENCES ... 33 TABLES ... 46 FIGURE LEGENDS ... 51 FIGURES ... 53 Conclusões ... 59 Apêndices ... 60
Introdução geral
Associações entre fatores da paisagem e processos ecológicos como dispersão, reprodução e sobrevivência dos organismos podem afetar processos microevolutivos como fluxo gênico, deriva genética e seleção (Sork e Waits, 2010). Compreender estas associações e seus efeitos é essencial para a conservação das espécies, uma vez que impactos na variação genética de populações é um dos principais fatores que podem levar à extinção de espécies (Spielman et al., 2004). A Genética da Paisagem surgiu como um campo de pesquisa que combina genética populacional, ecologia da paisagem e análises espaciais para quantificar explicitamente os efeitos da composição e configuração da paisagem e qualidade da matriz nos processos microevolutivos (Balkenhol et al., 2016). Desde que o termo foi cunhado (Manel et al., 2003), o campo evoluiu de métodos descritivos para abordagens que testam hipóteses explícitas e modelam respostas genéticas em relação às variáveis de paisagem (Cushman et al., 2006; Storfer et al., 2010). Apesar de apenas 15% das pesquisas de genética da paisagem serem conduzidas em ambientes de água doce (Storfer et al., 2010), há diversas evidências de estrutura genética em espécies associadas a estes hábitats (Hughes
et al., 2009; Ozerov et al., 2012; Hand et al., 2015). Entretanto, em ambientes de água doce,
especialmente em sistemas fluviais, o processo de isolamento por distância-IBD (Wright, 1943) é frequentemente forte e pode mascarar a importância de outras variáveis da paisagem em moldar padrões genéticos (Selkoe et al., 2015). Portanto, em sistemas de rios é essencial implementar abordagens que separem os efeitos da distância geográfica de outros fatores ambientais.
Apesar do IBD ser responsável por parte da estrutura genética encontrada em populações de diversos táxons (Jenkins et al., 2010), a heterogeneidade ambiental de paisagens pode afetar a sincronização dos processos de migração e reprodução entre populações, modificando padrões de fluxo gênico e assim aumentando a diferenciação genética entre elas (Sexton et al., 2014; Wang e Bradburd, 2014). Estudos de genética da paisagem de rios (em inglês “Riverscape genetics”) frequentemente testam barreiras discretas (isolamento por barreira, IBB) como cachoeiras e represas (Wofford et al., 2005; Deiner et al., 2007; Leclerc et al., 2008; Kanno et al., 2011), mas fatores menos conspícuos também podem agir como barreiras ao fluxo gênico e causar diferenciação detectável. Por exemplo, paisagens dendríticas estão hierarquicamente estruturadas por elevação e portanto o fluxo gênico é tipicamente assimétrico (Selkoe et al., 2015), com a declividade muitas vezes determinando a variação genética espacial de espécies de água doce
(Hughes et al., 2009; Cook et al., 2011; Kanno et al., 2011). Além disso, estudos com diversas espécies de peixes com diferentes histórias de vida ilustram que dissimilaridades físico-químicas de massas d’água causam divergência genética (Leclerc et al., 2008; Cooke et al., 2014; Beheregaray et al., 2015). Entretanto, essas dissimilaridades ambientais são raramente – ou não são – avaliadas em termos de resistência oferecida à migração entre populações, resultando em uma falta de estudos empíricos com modelos de resistência da paisagem de rios. Para espécies terrestres, o uso de caminhos de menor custo (LCPs) e superfícies de resistência tem se mostrado mais eficaz para prever padrões de fluxo gênico entre localidades do que medidas diretas de dissimilaridades ou distâncias (Cushman et al., 2006; McRae, 2006; Spear et al., 2010; Wang et
al., 2013). Ainda, a integração de modelos de adequabilidade climática em análises de LCPs pode
melhorar o entendimento sobre conectividade da paisagem, rotas potenciais para dispersão e distribuição de hábitats adequados às espécies (Wang et al., 2008; Ortego et al., 2015).
Variáveis de conectividade sozinhas normalmente não explicam os padrões espaciais de estruturação genética observados em populações de água doce, sendo que processos locais podem também influenciar padrões genéticos neutros (Murphy et al., 2010; Ozerov et al., 2012; Kovach
et al., 2015). Enquanto fatores de conectividade moldam taxas de migração e fluxo gênico, fatores
locais podem determinar tamanhos populacionais efetivos (Ne) e, por deriva genética, deixar um sinal mais forte na diversidade genética intrapopulacional (Wright, 1931; Frankham, 1996; Wagner e Fortin, 2013; DiLeo e Wagner, 2016). Apesar da importância da diversidade genética em manter o fitness de populações e reduzir os riscos de extinção, poucos estudos de genética da paisagem consideram os efeitos de variáveis locais, em nível de pontos amostrais, na diversidade genética intrapopulacional (DiLeo e Wagner, 2016). Em redes de rios, diversos fatores contribuem para a manutenção de padrões genéticos locais por meio de processos neutros (Thomaz et al., 2016). Por exemplo, muitos táxons de rios apresentam um padrão de acúmulo local de alelos (e, portanto, de diversidade genética) em regiões a jusante, devido a um fluxo gênico enviesado a jusante (Downstream Increase in Intraspecific Genetic Diversity – DIGD, Paz‐Vinas et al., 2015). Em sistemas fluviais sujeitos à dinâmica de áreas alagáveis, outro fator local que pode ter efeitos sobre a diversidade genética é a instabilidade interanual nos níveis do rio, que impactam a qualidade anual e localização de hábitats para desova e nidificação de animais (Ouellet‐Cauchon et al., 2014; Bermudez-Romero et al., 2015). Flutuações interanuais do nível da água resultam em migração
forçada por longas distâncias para desovar ou eventos frequentes de extinção/recolonização, causando reduzida estrutura genética e aumento de deriva genética (Østergaard et al., 2003; Ouellet‐Cauchon et al., 2014). Além disso, variáveis climáticas e de produtividade também estão associadas à variação genética neutra e adaptativa da fauna de água doce, provavelmente porque condições locais adequadas aumentam a persistência populational e oferecem resiliência frente às mudanças climáticas (Murphy et al., 2010; Vincent et al., 2013; Hand et al., 2015; Kovach et al., 2015). Adicionalmente, a persistência populacional de animais silvestres pode ser afetada pela pressão antrópica de caça, por meio de mudanças genéticas como alterações na subdivisão populacional, perda de variação genética e mudanças genéticas seletivas (Allendorf et al., 2008).
Estudos comparativos elucidam como diferenças ecológicas intrínsecas entre espécies podem resultar em efeitos distintos de fatores locais e de conectividade da paisagem em padrões genéticos, além de sugerir efeitos consistentes de uma dada paisagem em mais de uma espécie (Storfer, 2013). Por exemplo, ao examinarmos espécies proximamente relacionadas com distribuições geográficas parcial ou totalmente sobrepostas, podemos investigar se diferentes capacidades dispersivas correlacionam com padrões genéticos distintos exibidos por cada espécie (Steele et al., 2009). Espécies animais de baixa capacidade de dispersão, comparadas às de maior capacidade, frequentemente exibem maior divergência genética, menor diversidade genética e estrutura genética espacial mais acentuada (Gomez‐Uchida et al., 2009; Steele et al., 2009; Richardson, 2012). Isto pode levar a uma maior resposta genética aos fatores locais e de conectividade para espécies de baixa capacidade dispersora devido à alta deriva genética e menor fluxo gênico entre localidades (Gomez‐Uchida et al., 2009). Comparações interespecíficas são particularmente úteis para guiar estratégias de manejo para organismos ameaçados habitando paisagens heterogêneas, como quelônios (Reid et al., 2017). Apesar de viverem na interface entre terra e água e possuírem diversos traços de história de vida influenciados por fatores da paisagem, quelônios têm sido amplamente ignorados em estudos de genética da paisagem em geral. Existem poucos estudos de genética da paisagem com jabutis terrestres de desertos (Hagerty et al., 2011, Latch et al., 2011), e apenas um com espécies aquáticas e semi-aquáticas (Reid et al., 2017), todos em regiões temperadas. Tal lacuna de conhecimento é preocupante, considerando que quelônios estão entre os grupos de vertebrados mais ameaçados no mundo (Rhodin et al., 2008) e que a
maioria de espécies de quelônios de água doce ocorrem em zonas tropicais e subtropicais megadiversas (Bour, 2008), como a bacia Amazônica.
A bacia Amazônica é a maior bacia hidrográfica do mundo, composta por um sistema complexo e ambientalmente heterogêneo formado por rios, riachos e florestas alagáveis com ampla variação na geomorfologia, dinamismo do pulso de inundação e propriedades físico-químicas da água (Sioli, 1984). Esta complexidade influencia a movimentação, reprodução e sobrevivência dos organismos, moldando padrões genéticos populacionais de diversos vertebrados aquáticos (Farias
et al., 2004; De Thoisy et al., 2006; Escalona et al., 2009; Farias et al., 2010; Beheregaray et al.,
2015; Gravena et al., 2015), incluindo tartarugas de rios (Pearse et al., 2006; Santos et al., 2016). Entretanto, ao meu conhecimento, até o momento nenhum estudo usou uma abordagem espacialmente explícita baseada em modelos para testar quais fatores de paisagem de rios da bacia Amazônica podem estar por trás dos padrões genéticos observados. No sistema de estudo,
Podocnemis erythrocephala (conhecida como “irapuca”) é a menor espécie do gênero Podocnemis
ocorrendo na bacia Amazônica. É também a espécie com distribuição geográfica mais restrita, ocorrendo no Brasil, na Colômbia e na Venezuela, principalmente em rios de águas pretas e seus tributários (Mittermeier e Wilson, 1974; Pritchard, 1979; Ernst e Barbour, 1989), mas também em rios e lagos de águas claras (Pritchard, 1979; Hoogmoed e de Avila-Pires, 1990; Vogt et al., 1991; Iverson, 1992). A segunda menor espécie, Podocnemis sextuberculata (conhecida como “iaçá”), é amplamente distribuída na drenagem do Rio Amazonas no Peru, na Colômbia e no Brasil (Ernst e Barbour, 1989; Iverson, 1992), principalmente em grandes rios de águas brancas e claras (Pezzuti e Vogt, 1999; Pezzuti et al., 2000; Bernhard, 2001; Fachín-Terán et al., 2003). A distribuição geográfica das duas espécies sobrepõe em algumas regiões de tributários do rio Amazonas (Figura 1). P. sextuberculata é uma espécie de alta capacidade dispersora cujas fêmeas migram longas distâncias em grupo para nidificar em grandes praias arenosas (Pezzuti e Vogt, 1999), com registro de até 60 km percorridos por uma fêmea em um ano (Fachín-Terán et al., 2006). Por outro lado, fêmeas de P. erythrocephala nidificam sozinhas ou em pequenos grupos em regiões de solos arenosos e vegetação arbustiva (campinas e campinaranas) e praias (Rueda-Almonacid et al., 2007; Batistella e Vogt, 2009). P. erythrocephala possui movimentos mais curtos (Bernhard, 2010 dados não publicados) e portanto parece ter menor capacidade dispersora, sendo comumente encontrada em pequenos riachos e lagos em vez de no canal principal dos grandes rios (Rhodin et al., 2015).
Nessa dissertação busquei avaliar a importância de variáveis locais e de conectividade da paisagem para moldar a variação genética espacial de duas espécies de tartarugas de rio Amazônicas com diferentes capacidades dispersoras e preferências de hábitat (tipos de água e substratos de nidificação). Para isso, usei variáveis locais biologicamente relevantes representando hipóteses de produtividade e clima, instabilidade interanual de níveis de água, pressão de caça e aumento de diversidade genética a jusante do rio (DIGD). A expectativa é de que essas variáveis locais reduzam ou aumentem os tamanhos populacionais efetivos (Ne), consequentemente afetando a taxa de deriva genética e diversidade genética intrapopulacional. Usei também variáveis de conectividade ambiental representando hipóteses de isolamento por distância (IBD), isolamento por resistência (IBR) e isolamento por barreira (IBB). Os modelos de IBR incluem resistência oferecida pelo tipo de rio (custo de águas brancas, pretas e claras para a movimentação de cada espécie), por hábitats climaticamente inadequados (atual e histórico) e por declividade. Estas variáveis de conectividade devem restringir a dispersão e padrões reprodutivos entre localidades, reduzindo o fluxo gênico e aumentando a diferenciação genética entre populações.
Figura 1. Distribuição geográfica potencial de Podocnemis erythrocephala (máscara vermelho escura) e P. sextuberculata (máscara amarela) estimada por Fagundes et al., 2015. A sobreposição na distribuição das duas espécies está destacada pela máscara laranja. Fotos: P. erythrocephala - Jessica dos Anjos Oliveira; P. sextuberculata - Claudia Keller.
Objetivos
Objetivo geral
Investigar a importância de variáveis ambientais locais e de conectividade para determinar a variação genética espacial de duas espécies de tartarugas de rio Amazônicas (Podocnemis
erythrocephala e P. sextuberculata) com diferentes capacidades dispersoras.
Objetivos específicos
1. Avaliar se a espécie com maior capacidade dispersora (P. sextuberculata) possui menor estruturação genética espacial que a espécie que dispersa menos (P. erythrocephala). 2. Testar se fatores de conectividade que reduzem o fluxo gênico estão relacionados à
diferenciação genética para a espécie com menor capacidade dispersora (P. erythrocephala) mas não para P. sextuberculata (alta dispersão).
3. Testar se fatores locais estão relacionados à diversidade genética intrapopulacional de ambas espécies, mas com efeito mais forte na diversidade genética da espécie de baixa dispersão (P. erythrocephala).
Capítulo I.
Oliveira, J. A., Farias, I. P., Costa, G. C. & Werneck, F. P. Model-based riverscape
genetics: disentangling the roles of local and connectivity factors in shaping
spatial genetic patterns of two Amazonian turtles with different dispersal
abilities. Manuscrito submetido para Ecography.
ORIGINAL RESEARCH
Model-based riverscape genetics: disentangling the roles of local and connectivity factors in shaping spatial genetic patterns of two Amazonian turtles with different dispersal abilities Jessica dos Anjos Oliveira1,2*, Izeni Pires Farias2, Gabriel C. Costa3 and Fernanda P. Werneck4
1 Programa de Pós-Graduação em Ecologia, Instituto Nacional de Pesquisas da Amazônia,
69080-971 Manaus, Amazonas, Brazil
2 Laboratório de Evolução e Genética Animal, Departamento de Genética, Universidade Federal
do Amazonas, 69077-000 Manaus, Amazonas, Brazil
3 Department of Biology, Auburn University at Montgomery, Montgomery AL 36124 4 Programa de Coleções Científicas Biológicas, Coordenação de Biodiversidade, Instituto
Nacional de Pesquisas da Amazônia, 69060-000 Manaus, Amazonas, Brazil
ABSTRACT
In fluvial systems, the process of isolation by distance is often strong and can mask the
importance of barriers, landscape resistance and local environmental factors on shaping genetic patterns. By comparing two Amazonian river turtle species with distinct dispersal abilities, we assessed how differently and which local and connectivity variables influence, respectively, their genetic diversity and differentiation. With broad sampling throughout their distribution in the Amazon basin, we estimated genetic diversity and differentiation for 14 localities totaling 273 samples of Podocnemis erythrocephala and for 20 localities totaling 336 samples of P.
sextuberculata. We applied model selection on models associating genetic diversity to local
variables representing hypothesis of climate and productivity, instability of inter-annual water levels, hunting pressure and downstream increase in genetic diversity. We used General Dissimilarity Modelling to model the relationship of genetic differentiation with connectivity variables representing hypothesis of isolation by distance (IBD), isolation by resistance (IBR) and isolation by barrier (IBB). Local variables were more important in explaining genetic diversity of the high-dispersal species (P. sextuberculata) than of P. erythrocephala, with best models
including productivity, distance from downstream locality, density of human villages and historical climatic suitability. Connectivity factors in general were not important in explaining genetic differentiation turnover for either species, but GDM models explained a larger amount of deviance for the low-dispersal species, P. erythrocephala. Also, IBB and IBR models explained more genetic differentiation turnover than IBD. We showed that, although local variables are often overlooked in Landscape/Riverscape Genetics studies, they can influence intrapopulacional genetic diversity of aquatic species, even those with high dispersal ability. By applying a novel resistance-model framework in Riverscape Genetics and by using riverscape factors relevant in Amazonian context, we provide an approach to study the roles of local and connectivity variables in shaping genetic patterns of aquatic vertebrates in fluvial systems.
Key words: landscape genetics, resistance model, genetic differentiation, genetic diversity, Amazon basin, Podocnemis erythrocephala, Podocnemis sextuberculata.
INTRODUCTION
Associations between landscape factors and ecological processes such as dispersal, reproduction and survival of organisms can ultimately affect microevolutionary processes such as gene flow, drift and selection (Sork and Waits 2010). Understanding these associations and their effects is essential for species conservation because factors that negatively impact the genetic diversity of populations can eventually drive species extinction (Spielman et al. 2004). Landscape genetics emerged as a research field that combines population genetics, landscape ecology, and spatial analyses to explicitly quantify the effects of landscape composition, configuration, and matrix quality on microevolutionary processes (Balkenhol et al. 2016). Since the term was coined (Manel et al. 2003), the field evolved from descriptive approaches to explicit hypothesis testing framework and modeling of genetic responses in response to predictive landscape variables (Cushman et al. 2006, Storfer et al. 2010). Although only 15% of landscape genetic studies were conducted in freshwater habitats (Storfer et al. 2010), there is mounting evidence for complex spatial genetic structure in these habitats (Hughes et al. 2009, Ozerov et al. 2012, Hand et al. 2015). However, in freshwater environments, especially in fluvial systems, the process of
isolation by distance-IBD (Wright 1943) can often overwhelm the importance of other processes that might shape genetic patterns (Selkoe et al. 2015). As such, in river systems, it is especially necessary to implement approaches that are able to disentangle the confounding effects of geographical distance and other environmental factors.
While IBD is responsible for part of the populational genetic structure found in several taxa (Jenkins et al. 2010), landscape environmental heterogeneity can affect synchronization of migration and mating processes among populations, modifying gene flow patterns and increasing genetic differentiation (Wang and Bradburd 2014). Riverscape genetics studies usually test for discrete barriers (isolation by barrier, IBB) such as waterfalls and dams (Wofford et al. 2005, Deiner et al. 2007, Kanno et al. 2011), but less conspicuous factors may also act as barriers to gene flow and cause detectable differentiation. For example, dendritic landscapes are structured hierarchically by elevation and therefore gene flow is typically asymmetric (Selkoe et al. 2015), with stream gradient often determining the spatial genetic variation of freshwater species
(Hughes et al. 2009, Cook et al. 2011). In addition, studies with a broad range of fish species with different life-histories showed that physical-chemical dissimilarities of water masses can cause genetic divergence (Leclerc et al. 2008, Beheregaray et al. 2015). However, these environmental dissimilarities are rarely – if at all – assessed in terms of resistance to migration between
populations, resulting in a lack of empirical studies with riverscape resistance models. For terrestrial species, least-cost paths (LCPs) and resistance surfaces have been shown to be better predictors of gene flow patterns among localities than direct measures of dissimilarity or distances (Cushman et al. 2006, McRae 2006, Spear et al. 2010, Wang et al. 2013). Also, integration of climatic suitability models into LCP analyses can improve our understanding on landscape connectivity, potential routes of dispersal and distribution of suitable habitats for the species (Wang et al. 2008, Ortego et al. 2015).
Connectivity variables alone often do not explain the observed spatial genetic structure of freshwater populations, and local processes may also influence neutral genetic patterns (Murphy et al. 2010, Ozerov et al. 2012, Kovach et al. 2015). While connectivity factors shape migration and gene flow rates affecting genetic differentiation among populations, local factors can determine effective population sizes (Ne) and through genetic drift leave a stronger signal in genetic diversity within populations (Wright 1931, Frankham 1996, Wagner and Fortin 2013, DiLeo and Wagner 2016). Regardless of the importance of genetic diversity on maintaining population fitness and reducing extinction risk, very few landscape genetics studies consider the effects of site-based, local variables on intrapopulational genetic diversity (DiLeo and Wagner 2016). In river networks, several factors can contribute to the maintenance of local genetic patterns through neutral processes (Thomaz et al. 2016). For example, a broad variety of riverine taxa show a pattern of local accumulation of genetic diversity in downstream regions due to downstream-biased gene flow (Downstream Increase in Intraspecific Genetic Diversity – DIGD, Paz‐Vinas et al. 2015). In river systems subject to floodplain dynamics, another important local factor is the instability of inter-annual water level, which impacts on yearly quality and
localization of spawning or nesting habitats for animals (Ouellet‐Cauchon et al. 2014, Bermudez-Romero et al. 2015). The inter-annual water level fluctuations result in forced migration over
longer distances to spawn or frequent extinction/recolonization events, causing reduced genetic structure and increased genetic drift (Østergaard et al. 2003, Ouellet‐Cauchon et al. 2014). Also, at-site climate and productivity variables are associated to neutral and adaptive genetic variation for freshwater fauna, likely because suitable conditions can enhance population persistence and offer resiliency in the face of climate change (Murphy et al. 2010, Vincent et al. 2013, Hand et al. 2015, Kovach et al. 2015). In addition, population persistence of wild animals can be affected by local human harvest, through genetic changes such as alteration of population subdivision, loss of genetic variation, and selective genetic changes (Allendorf et al. 2008).
Comparative studies are essential to elucidate how intrinsic ecological differences among species can generate distinct effects of local and connectivity landscape factors on genetic patterns (Storfer 2013). For instance, differences in dispersal ability among closely related species correlates with distinct genetic patterns (Steele et al. 2009). Low-dispersal species,
compared to species with high-dispersal capacities, often exhibit higher genetic divergence, lower genetic diversity and more pronounced spatial genetic structure (Gomez‐Uchida et al. 2009, Steele et al. 2009, Richardson 2012). This may lead to stronger genetic response to local and connectivity factors for poor-dispersers due to increased drift and lower gene flow among localities (Gomez‐Uchida et al. 2009). These comparisons are particularly useful in guiding management strategies for threatened organisms inhabiting heterogeneous landscapes, such as turtles (Reid et al. 2017). Despite living in the land-water interface and having variable life history traits influenced by landscape factors, turtles have been largely ignored in landscape genetics studies in general. There are two studies with terrestrial desert tortoises (Hagerty et al. 2011, Latch et al. 2011) and only one with aquatic and semi-aquatic species (Reid et al. 2017), all in temperate regions. This knowledge gap is concerning, given that turtles are among the most threatened vertebrate species in the world (Rhodin et al. 2008) and the majority of freshwater turtle species occur in megadiverse tropical and subtropical zones (Bour 2008), such as the Amazon basin.
The Amazon basin is the largest hydrographic basin in the world, composed by a complex and environmentally heterogeneous system formed by rivers, streams and floodplain forests with
varying geomorphology, flood pulse dynamism and physical-chemical water properties (Sioli 1984). This complexity influences the movement, mating and survival of organisms, shaping population genetic patterns of several aquatic vertebrates (Farias et al. 2004, De Thoisy et al. 2006, Escalona et al. 2009, Farias et al. 2010, Beheregaray et al. 2015, Gravena et al. 2015), including river turtles (Pearse et al. 2006, Santos et al. 2016). However, to our knowledge, no study attempted to use a spatially explicit model-based framework to test which Amazon basin riverscape factors may be behind the observed genetic patterns. In our study system, Podocnemis
erythrocephala (Red-headed Amazon River turtle) is the smallest Podocnemis species occurring
in the Amazon basin, reaching a maximum of 32.2 cm of carapace length. It is also the least broadly distributed, occurring in Brazil, Colombia and Venezuela, mainly in black water rivers and their tributaries (Mittermeier and Wilson 1974, Pritchard 1979, Ernst and Barbour 1989), but also in clear water lakes and rivers (Pritchard 1979, Hoogmoed and de Avila-Pires 1990, Vogt et al. 1991, Iverson 1992). The second smallest species, reaching a maximum of 34 cm of carapace length, Podocnemis sextuberculata (Six-tubercled Amazon River Turtle), is broadly distributed in the Amazon River drainage in Peru, Colombia and in Brazil (Ernst and Barbour 1989, Iverson 1992), mainly in large white water and clear water rivers (Pezzuti and Vogt 1999, Pezzuti et al. 2000, Fachín-Terán et al. 2003). The geographical distribution of both species overlaps in a few regions in Amazon River tributaries. P. sextuberculata is a high-dispersal species whose females migrate long distances to nest in group in large sandy beaches (Pezzuti and Vogt 1999), with records of up to 60 km moved by a female in a year (Fachín-Terán et al. 2006). On the other hand, females of P. erythrocephala nest alone or in small groups in sandy shrub lands or forests and beaches (Rueda-Almonacid et al. 2007, Batistella and Vogt 2008). Podocnemis
erythrocephala is commonly found in smaller streams and lakes instead of the main river
channels (Mittermeier et al. 2015) and therefore seems to have lower dispersal potential.
Here we assessed the importance of local and connectivity variables in shaping the spatial genetic variation of two Amazon river turtle species differing in their dispersal abilities and habitat preferences. For this, we used biologically meaningful local variables representing hypothesis of climate and productivity, instability of inter-annual water levels, hunting pressure
and downstream increase in intraspecific genetic diversity (see Table 1). These local variables are hypothesized to reduce or increase the effective population sizes (Ne), consequently affecting the rate of genetic drift and genetic diversity of populations (Figure 1). The connectivity variables we used represent hypothesis of isolation by distance (IBD), isolation by resistance (IBR) and
isolation by barrier (IBB). The IBR models include resistance offered by river type (cost of white, black or clear waters for each species movement), by climatically unsuitable habitats (current and historical) and by slope (Table 1). These connectivity variables are hypothesized to restrict the dispersal and mating patterns among localities, reducing the gene flow and increasing the genetic differentiation between populations (Figure 1). We therefore tested the hypotheses that 1)
connectivity factors that reduce gene flow are related to genetic differentiation for P.
erythrocephala, which has lower dispersal ability, but not for P. sextuberculata (higher dispersal
capacity); and 2) local factors are related to intraspecific genetic diversity of both species, but leave a stronger effect on the diversity of the low-dispersal species, P. erythrocephala. By using this model-based riverscape genetics approach, we can gain insights on broader patterns and processes taking place at the Amazon basin and make use of recently available macro-scale and high-resolution variables biologically relevant for freshwater vertebrates. In addition to being the first landscape/riverscape genetics approach with tropical freshwater turtles, our study is the first with an Amazon aquatic vertebrate that takes in consideration explicit resistance models to test for IBR.
METHODS
Study region and genetic sampling
We used samples from 14 localities for P. erythrocephala and 20 localities for P. sextuberculata (Figures 2 and 3; Appendix A in Supporting Information), covering a large portion of their respective geographic distributions. Since our genetic sampling covers a large portion of the species’ distributions and the predictor variables represent potential historical (rather than contemporary) effects on the populations, we used the mtDNA control region (CR) as molecular marker for both species. We choose this marker because of the high polymorphism in the CR
reported for Podocnemis species (Pearse et al. 2006, Santos et al. 2016, Viana et al. under
review). For P. erythrocephala we had a total of 273 sequences (503 bp), from which 246 were sequenced by Santos et al. (2016, GenBank KY702009–KY702254). Following the same laboratory procedures as Santos et al. (2016) we extracted, amplified and sequenced the CR for additional 27 samples related to four new localities (5-BCL, 8-JAU, 13-JUR and 14-PAR; Figure 2; Appendix A; GenBank KY713319–KY713345). For P. sextuberculata we had a total of 336 sequences (605 bp), from which 319 were sequenced in a recent study by Viana et al. (under
review, GenBank KY702255–KY702573). We extracted, amplified and sequenced the CR of
additional 17 samples from three new localities (6-IPX, 10-PPP and 12-CAP; Figure 3; Appendix A; GenBank KY713302–KY713318), following the same laboratory methods of Viana et al. (under review).
Genetic metrics
We implemented descriptive genetic analyses in our data set to assess genetic structure by
constructing a haplotype network, performing an AMOVA and a Bayesian analysis of population admixture. The details and results of these descriptive analyses are available in Appendix B.
We calculated for each sampled locality two intraspecific genetic diversity indices, haplotypic diversity (Hd, Nei 1987) and nucleotide diversity (π, Nei 1987), in DNASP v.5.10.1 (Librado and Rozas 2009). We also estimated pairwise φST between sampling sites using the software ARLEQUIN v. 3.5.2.2 (Excoffier and Lischer 2010), testing for significance by
randomization with 1,000 permutations. We used the diversity metrics as response variable for node-level analysis and the pairwise φST for the link-level analysis.
Landscape data
We collected several landscape metrics for each analytical approach (nodes and links) in order to represent non-mutually exclusive hypothesis that may explain diversity and differentiation patterns for the species. We describe the hypotheses and mechanisms linking the local (nodes)
and connectivity (links) factors to the expected effects on, respectively, diversity and differentiation indices of populations on Table 1.
Node-level local variables
For energy availability hypothesis, we used the mean Net Primary Productivity (NPP) from 2000 to 2015 (NASA 2016) to represent the availability of food (fruits, seeds and leaves) for turtles in each locality sampled.
To represent the environmental stability hypotheses, we used Ecological Niche Modeling (ENM) to predict the climatic suitability for each species. We used the maximum entropy
machine-learning algorithm, MAXENT, implemented in the R package dismo (Hijmans et al. 2015) to construct the models. The projection to present conditions was the variable for current
environmental stability. In addition, to enable a continuous view of historical climatic suitability,
we projected the models to 62 climatic reconstructions covering the last 120 kyr at small time intervals (1 to 4 kyr) using the Hadley Centre Climate model (HadCM3; Singarayer and Valdes 2010, Fuchs et al. 2013, Carnaval et al. 2014). We calculated the mean value of suitability for the 62 layers of time and used the resulting mean raster layer as the variable of historical
environmental stability.
To represent high variability of extreme water levels, which can potentially decrease predictability of available nesting beaches annually (Bermudez-Romero et al. 2015) or cause nests flooding (Pantoja-Lima et al. 2009), we used two raster maps created by Silva-Junior (2015) representing extremes of river flows. The rasters was generated from the coefficient of variation of high (CVmax) and low river flows (CVmin) for 5 thousand points in Amazon basin for the period of 1998 to 2009.
To represent the subsistence consumption of turtles by rural/riverine human villages, we generated a kernel-density map of human villages occurring along the distribution of samples for both species. We used the kernel values of each sampling locality as a surrogate for subsistence
turtles (Fachín-Terán et al. 2004), we measured for each sampling locality the distance (by river way) to the closest urban center as a mean to characterize illegal commercial hunting.
Finally, to assess if there is a pattern of Downstream Increase in Intraspecific Genetic
Diversity (DIGD), we defined the mouth of Amazon River as the ultimate downstream point and
extracted for each locality the distance by river way to the Amazon River mouth.
Link-level connectivity variables
To test the hypothesis of isolation by distance (IBD) we measured the river distance between localities using the R package gdistance (van Etten 2012).
For the links analytical level, we used resistance models, a novel approach for riverscape genetics that can increase our understanding of gene flow patterns as it tests specifically for migration complexity and resistance between populations. The least-cost paths (LCPs) are calculated by searching for the path that minimizes the total cumulative cost (or resistance) between two points (Wang et al. 2009). A difference on the approach within a riverscape genetics framework (compared to terrestrial habitats) is that, for species using exclusively river ways to move – the case for the species here studied –, the only path possible is the river path. Therefore, the LCPs between two localities will always be the same regardless of the variable under
consideration. However, the cost values of each pixel (and therefore the accumulated-cost of LCP) will be distinct for different variables. To characterize isolation by resistance (IBR) we used slope, river types (colors) and climatic suitability (Table 1).
We calculated LCPs of average upstream slope (Domisch et al. 2015) between localities as a surrogate for the presence of topographic barriers (e.g., rapids or waterfalls) or increased topographic resistance to turtles’ movement.
The rivers in Amazon basin are classified in three types (black, white and clear waters) based on different origins and physical and chemical properties of their waters (Sioli 1984). Since there is a lack of biological data on movement preference related to water types for turtles, we used expert parameterization of resistance values (Zeller et al. 2012). We sent a questionnaire (Appendix C) to six Amazon turtle experts, asking them to assign different costs to each water
type representing how costly they are to the movement of each species. The cost values would range from 1 (low or no cost to animal movement) to 5 (high cost or barrier to movement). Because the responses varied among experts (Appendix C), we used the mean cost value of their opinions to calculate the LCPs between localities.ir
To assess resistance to movement offered from present and past climatic unsuitable habitats for turtles we clipped the historical and current ENM maps generated (see node-level
local variables section) to the river courses. We used the reverse of suitability values (1 –
suitability) to assign resistances to each pixel in the river network for each species, because places with lower suitability should represent higher resistance to the movement (Wang et al. 2013). The resulting raster maps have resistance values ranging from 0 (no resistance to species movement) to 1 (complete resistance to species movement). We then calculated LCPs between localities for historical and current resistance imposed by unsuitable habitats for each species.
Additionally, the Amazon River was proposed as a potential barrier to the dispersal of P.
erythrocephala (Santos et al. 2016) because the large extension and whitewaters of the Amazon
River may represent a barrier to this species. With samples from two additional locations on the right-margin of Amazon River, we tested for isolation by barrier (IBB), only for P.
erythrocephala. For this, we attributed binary codes for localities from the same (0) or opposite
(1) sides of Amazon River.
In Appendix D we: describe in further details how we obtained local landscape variables; show the results for model performance of ENM; and display maps for variables NPP, CVmax, CVmin, distCITY, villages, distMOUTH, resistance from current and historical suitability, and river type categories and upstream slope used to generate the LCPs.
Landscape genetic analyses
Because the genetic diversity and differentiation metrics used here can be affected by sampling sizes (Goodall-Copestake et al. 2012), for both node and link-level analyses we only used
localities for which we had at least 10 individuals sampled (N ≥ 10). This reduced our number of sites from 14 to 11 for P. erythrocephala and from 20 to 17 for P. sextuberculata.
For node-level analysis, we modeled the genetic response variables (Hd and π) in relation to the predictor landscape variables using generalized linear models (GLMs). To avoid
multicollinearity we only included non-correlated predictor variables in mixed models (r < 0.6; Appendix E). We also tested for the presence of spatial autocorrelation in the response variables to ensure the relationships between genetic and landscape are not an artefact of spatial structure (Wagner and Fortin 2015). We therefore constructed a Moran’s I correlograms but since no autocorrelation was detected on the response variables or model residuals, we did not built spatial models (Appendix E). We built GLMs comprising all combinations of one to two predictors (except when they were collinear). We used a maximum of two predictor covariates per model because of the limited sample size (N=11 for P. erythrocephala and N=17 for P. sextuberculata). We also included a null model without predictors to compete with the set of models. To perform model selection we calculated AIC corrected for small sample sizes (AICc) and Akaike’s weight of evidence (wAICc) as the relative contribution of models (Burnham and Anderson 2003). We considered models with ΔAIC (the difference between each model and the best model) ≤ 2 as equally plausible to explain the observed pattern. To run the AIC-based analyses, we used the R package AICcmodavg (Mazerolle and Mazerolle 2016).
To assess the importance of each landscape factor in link-level analysis, we controlled for the geographic distance in the LCPs (IBR models) by dividing the accumulated-costs of LCPs by the riverway distance among pairs of localities. We believe that by doing so we are representing in each hypothesis solely the environmental dissimilarity of resistance among localities, despite longer or shorter geographic distances (see Figure 4). After this control, all correlations between predictor matrices (IBD, IBR/distance and IBB) were < 0.7 (Appendix F), enabling the test of non-mutually exclusive hypotheses in multiple regression models (Wagner and Fortin 2015). To model genetic differentiation (φST) in relation to the geographic and environmental
dissimilarities we applied a generalized dissimilarity modelling (GDM). GDM is a nonlinear extension of permutational matrix regression that models pairwise biological (in this case
genetic) dissimilarity between sites (Ferrier et al. 2007). GDM accounts for the two types of non-linearity often encountered in ecological modelling of biological traits: (1) since φST values are
scaled between 0–1, the population divergence cannot extend beyond φST = 1, even if habitat differentiation or geographic distance keep increasing; (2) the rate of change in response variables along environmental gradients is often not constant (Thomassen et al. 2010). The two main advantages of using GDM in a landscape genetics approach are its particular suitability for genetic data (pairwise differentiation) and the possibility of using resistance/LCP models along with true measures of geographic distances (Thomassen et al. 2010). We therefore applied GDM, including five predictor variables for P. sextuberculata and six for P. erythrocephala (Table 3), using the R package gdm (Manion et al. 2014). We assessed the relationship among φST and each predictor by examining the response curves generated for variables for which I-spline basis functions could be calculated (i.e., presented non-zero coefficients). In these response curves, the maximum height represents the relative importance of the variables retained in the model and the slopes indicate the rate of change in the response variable along the environmental gradient concerned (Ferrier et al. 2007). We also performed a test of variable importance using an iterative process that adds and removes the variables to determine the significance by computing the difference in deviance explained by a model with and a model without the variable concerned (Fitzpatrick et al. 2013). Although model selection would be the best approach to compare node and link-level analyses, because the residuals of matrix regressions are not independent of each other, information-theoretic indices commonly used for model selection (AIC, AICc, or BIC) are not applicable to distance matrices (Wagner and Fortin 2015). In fact, model selection on link-level analyses remains to be developed, thus currently the influential observations are best identified with leave-one-out jackknife methods (Wagner and Fortin 2015), as the one implemented here.
RESULTS Genetic metrics
Detailed results for descriptive genetic analyses are available in Appendix B. We recovered overall moderate haplotype diversity for both species (P. erythrocephala: Hd = 0.627; P.
studies using control region of mtDNA for Podocnemis species (see Pearse et al. 2006), 0.00234 for P. erythrocephala and 0.00458 for P. sextuberculata. The localities were significantly differentiated for both species, with pairwise φST between localities ranging from 0 to 0.898 for
P. erythrocephala and from 0 to 0.937 for P. sextuberculata (see tables in Appendix B).
Landscape genetic analyses Node-level analysis
For P. erythrocephala, two of our competing models explained haplotype and nucleotide diversity (Table 2), but with the null model being equally plausible to explain the observed patterns (ΔAICc < 2). Although not differentiated from the null model, the two best models explaining the genetic diversity of P. erythrocephala are the distance to the nearest urban center (distCITY) and a combined effect of this distance and the coefficient of variation of high river flow (CVmax). The relationships among these predictor variables and the response variables are according to our expectations: increased genetic diversity on localities farther from cities
(positive relationship with distCITY) and on localities with lower variability in maximum flows (negative relationship with CVmax). For P. sextuberculata, six competing models explained the two diversity metrics (Table 2): the site productivity (NPP) alone, the distance to Amazon River mouth (distMOUTH) alone, and the combined effects of each of these variables with density of rural human communities (NPP+villages and distMOUTH+villages) and historical climatic suitability (NPP+suit_past and distMOUTH+suit_past). The two most important variables, NPP and distMOUTH, are highly correlated (r = 0.93; p < 0.001), being difficult to determine which of the two are influencing genetic diversity. In addition, relationships between distMOUTH, villages and suit_past with genetic diversity are opposed to the expected: increased genetic diversity on upstream localities (positive relationship with distMOUTH; Figure 6), on localities near higher density of human settlements (positive relationship with villages; Figure 6), and on localities with lower climatic suitability (negative relationship with suit_past; Figure 6). The relationship for NPP was as expected: higher genetic diversity at more productive sites (higher NPP; Figure 6). The cumulative contribution (wAICc) of the models to the observed pattern were
moderate, 0.53 for Hd and 0.52 for π (Table 2). Full tables of AIC models are available at Appendix E.
Link-level analysis
The full GDM model explained 20.44% of the deviance in φST turnover for P. erythrocephala and derived I-spline basis functions for four out of the six variables (Table 3; see Appendix G for GDM response curves). Summing the coefficients of I-spline basis functions as a measure of relative variable importance (i.e., the height of each curve; Fitzpatrick and Keller 2015), the main predictor for genetic differentiation of P. erythrocephala was the Amazon River (0.387),
followed by resistance from current climatic suitability (0.189), resistance from historical climatic suitability (0.136) and riverway distance (0.110) (Appendix G). For P. sextuberculata the full GDM model explained only 6.49% of the deviance in φST turnover and derived I-splines for three out of five variables (Table 3; Appendix G). The most important variable to predict genetic differentiation of P. sextuberculata was the resistance from river color (0.953), followed by resistance from current climatic suitability (0.226) and riverway distance (0.187) (Appendix G).
Although the response curves and I-splines coefficients can elucidate the most important variables to φST turnover, we detected no significance for models or variables in terms of variable importance testing by permutations (P. erythrocephala: Full model-2, p = 0.12; P.
sextuberculata: Full model-2, p = 0.11; Table 3). The correction of IBR models by geographic
distance allowed us to disentangle the effects of riverway distance and environmental resistance, being IBD only retained as a potential predictor after this correction (results not shown).
DISCUSSION
Here we investigated which local and connectivity factors from the Amazon basin riverscape influenced genetic diversity and differentiation of two Amazonian River turtle species with different dispersal abilities. We found a relationship between genetic patterns and biologically meaningful local variables, relevant in the Amazonia context, such as hunting pressure,
productivity, and river flow variation. We also found, but in a minor extent, influence of connectivity variables on genetic differentiation of the turtles: barrier, riverway distance, and resistance offered by river types and climatically unsuitable habitats. Opposed to our initial expectations, our results show a stronger influence of local factors on the intrapopulational genetic diversity of the high-dispersal species, Podocnemis sextuberculata, than on the genetic diversity of P. erythrocephala (lower dispersal potential). Although in general connectivity factors are less important in shaping genetic structure of both species, connectivity factors as expected explain a higher percentage of genetic differentiation of P. erythrocephala than of P.
sextuberculata. Our results therefore demonstrate the importance of assessing the effects of local
variables in riverscape genetics studies, even when dealing with high-dispersal species without apparent discrete genetic structure.
Influence of local factors on genetic diversity
For both species, the best-fit models associated to genetic diversity patterns contained the proxies for hunting pressure. For P. erythrocephala, although not differentiated from the null model, the distance to nearest urban center (distCITY) was included in the best model along with variability of high river flow (CVmax), and ranked alone as second best model to explain nucleotide
diversity. This may be an evidence of the potential impacts of illegal commercial hunting, or urban centers per se, on turtles. Illegal harvest by professional fishermen is common and
widespread, being characterized by removal of large quantities of adult turtles to be sold in urban centers (Pantoja-Lima et al. 2014). For example, a study in the Xingu River found a decrease in abundance and density of P. unifilis with increasing proximity to urban centers (Alcântara et al. 2013). Human rural communities may also pose a threat to Podocnemis species if exploitation occurs in an unsustainable manner, causing population declines (Conway-Gómez 2007, Bernardes et al. 2014) and ultimately affecting genetic patterns (Allendorf et al. 2008). For P.
sextuberculata, the density of rural human settlements (villages) was included in the second best
models for nucleotide and haplotype diversity, combined with primary productivity (NPP) and distance from the Amazon River mouth (distMOUTH), respectively. However, the relationship is
opposed to the predicted, as the intraspecific genetic diversity was higher in places with higher density of human communities. This is unexpected given the historical use of turtles since the 18th century and high rates of consumption of P. sextuberculata by villagers reported along the
Amazon basin (Smith 1979, Fachín-Terán et al. 2004, Kemenes and Pezzuti 2007, Pantoja-Lima et al. 2014). The case may be that density of villages per se do not represent turtle consumption, since feeding habits and consumption rates vary among places (Pezzuti et al. 2010). Although human settlements also often represent habitat loss for species (Turtle Conservation Fund 2002), the higher genetic diversity of P. sextuberculata where there is more human villages may be a consequence of human settlements often establishing in productive sites offering protein
resources, where people hunt in the proximities (Peres 2000). The model NPP + villages supports the hypothesis that human villages may be established in more productive sites, which in turn harbor larger population sizes of P. sextuberculata, therefore maintaining higher nucleotide diversity where productivity and number of villages are higher. Yet, we need to be cautious when interpreting effects of recent events on mtDNA genetic diversity (Wang 2010) because while population declines due to harvesting in turtles occurs over years, genetic variation is lost over generations (Marsack and Swanson 2009). Nonetheless, we do recommend localized efforts to assess current consumption rate in villages in relation to availability/abundance of turtles as resource, along with a local genetic study, to measure the direct impacts of preference and harvesting on these species.
The positive influence of NPP on genetic diversity of P. sextuberculata but not in P.
erythrocephala could be due to their different geographical distributions. Because P.
sextuberculata occurs mainly in white water rivers, known to be very productive compared to the
black waters of the Negro River (main occurrence of P. erythrocephala; Sioli 1984) this factor may be more relevant to the establishment and growth of populations of P. sextuberculata. While NPP is the most relevant variable explaining nucleotide diversity of P. sextuberculata, the best model determining haplotype diversity was distance from Amazon River mouth (distMOUTH). NPP and distMOUTH are highly correlated, and their correlation is probably due to a west-east gradient of decreasing primary productivity (Malhi et al. 2004) and distance to Amazon River
mouth. For distMOUTH we found a downstream decrease in genetic diversity of P.
sextuberculata, as opposed to the expected pattern of Downstream Increase in Genetic Diversity
(DIGD). This reverse pattern may occur because floodplains and wetlands, which serve as feeding and movement habitat for P. sextuberculata (Fachin-Terán and Vogt 2014), are more abundant in western compared to the eastern portion of Amazon basin (Junk et al. 2011). Also, upstream sites (i.e., mostly western localities in our sampling) are less affected by deforestation, urbanization, and other anthropogenic alterations of habitats widespread on eastern localities closer to Amazon River mouth (Laurance et al. 2001). Hence, these conditions, along with productivity of upstream sites, could harbor larger effective population sizes and larger genetic diversity in P. sextuberculata across the basin. DIGD is often modelled in dendritic-like river systems and is more widespread across species with exclusive aquatic dispersal (Paz‐Vinas et al. 2015). We believe DIGD does not describe broad genetic patterns for Amazon River turtles because 1) turtles also use land environments to nest and bask; 2) Amazon basin is not a true dendritic network (as large portions are floodplains and wetlands); 3) the scale of Amazon basin is too coarse to capture the processes underlying the pattern. Further investigation within a sub-basin (engaging intensive sampling) is necessary to determine whether processes of downstream-biased dispersal, increase in habitat availability downstream, and upstream-directed colonization generate a pattern of DIGD for turtles at smaller spatial scales.
The best model for nucleotide diversity of P. erythrocephala, although not differentiated from the null model, included variability of inter-annual highest water levels (CVmax) in
combination with distance to the nearest urban center (distCITY). Under this model, populations of P. erythrocephala which are further from urban centers (higherdistCITY) and in places with more stable high river flows (lowerCVmax) would maintain larger effective population sizes and be less affected by genetic drift. Since extremes of flood pulse in the Amazon basin influence composition of fish assemblages (Sousa and Freitas 2008, Correia et al. 2015, Röpke et al. 2017) and the dynamics of flooded/non-flooded areas (Junk 1997), it probably also affects populations of species directly depending on these factors. During falling water periods, while P.
