DETERMINANTES ECOLÓGICOS, EVOLUTIVOS E HISTÓRICO ... · gradiente latitudinal de diversidade, bem como a formação da biota de diferentes habitats. A conservação de tolerâncias

UNIVERSIDADE FEDERAL DE GOIÁS

PRÓ-REITORIA DE PESQUISA E PÓS-GRADUAÇÃO

INSTITUTO DE CIÊNCIAS BIOLÓGICAS

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA & EVOLUÇÃO

Franciele Parreira Peixoto

DETERMINANTES ECOLÓGICOS, EVOLUTIVOS E

HISTÓRICO-BIOGEOGRÁFICOS DOS PADRÕES DE

DIVERSIDADE DE MAMÍFEROS TERRESTRES EM

DIFERENTES ESCALAS

Orientador: Dr. Marcus Vinícius Cianciaruso

Coorientador: Dr. Crisóforo Fabrício Villalobos

GOIÂNIA - GO

MARÇO – 2017

UNIVERSIDADE FEDERAL DE GOIÁS

PRÓ-REITORIA DE PESQUISA E PÓS-GRADUAÇÃO

INSTITUTO DE CIÊNCIAS BIOLÓGICAS

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA & EVOLUÇÃO

Franciele Parreira Peixoto

DETERMINANTES ECOLÓGICOS, EVOLUTIVOS E

HISTÓRICO-BIOGEOGRÁFICOS DOS PADRÕES DE

DIVERSIDADE DE MAMÍFEROS TERRESTRES EM

DIFERENTES ESCALAS

Orientador: Dr. Marcus Vinícius Cianciaruso

Coorientador: Dr. Crisóforo Fabrício Villalobos

Tese apresentada à Universidade Federal de

Goiás, como parte das exigências do Programa de

Pós-graduação em Ecologia e Evolução para

obtenção do título de Doutora.

GOIÂNIA - GO

MARÇO – 2017

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“It is not the mountain we conquer, but ourselves”.

Edmund Hillary

Dedico ao que representa a escalada em rocha na minha vida.

É mais que um esporte, é um estilo de vida. Fez ressurgir no meu ser

o instinto primata e minha conexão com a natureza. Ao longo do

doutorado, foi um modo de obter autoconfiança e força

(principalmente para minhas costas e braços, que sofriam com horas

na frente do computador) para enfrentar os obstáculos. Aprendi que

tudo é possível, desde que se deseje profundamente, e que a subida é

mais importante que o pico, apesar da vista ser recompensadora.

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AGRADECIMENTOS

Em quatro anos de doutorado, muitas pessoas passaram pela minha vida e

contribuíram para a concretização desse projeto. Muitas dessas pessoas não serão citadas

aqui, por falta de espaço ou falha de memória, mas mesmo assim agradeço por fazerem

parte do meu crescimento pessoal e profissional.

Agradeço:

A minha família querida, sem a qual não teria tido a chance de estudar e realizar

meus projetos. Um especial agradecimento aos meus pais, Celso Alves Peixoto e Maria

Aparecida Parreira, e meus avós maternos, Maria Euripa de Assunção Salgado e

Hermógenes Parreira de Assunção.

Ao meu companheiro, Tiago Segantini Zaterka, por ter estado ao meu lado durante

os últimos três anos, me apoiando com muita compreensão e amor, mesmo nos piores

momentos. Por ter ficado comigo em casa por vários finais de semana e ter se privado de

várias oportunidades de viagens e escaladas.

Aos meus mais que amigos, Elisa Barreto, José Hidasi Neto, Pedro Henrique

Pereira Braga, Vinícios Macedo e Viviane Neves, que estiveram do meu lado nesses anos

em todos os tipos de situação, me ajudando a superar os obstáculos impostos pela vida.

Aos amigos Advaldo Carlos de Souza-Neto, José Hidasi Neto, Pedro Henrique

Pereira Braga e Poliana Mendes, que também foram um pouco orientadores e sempre me

ajudaram com análises e discussões.

Aos integrantes da minha família Hogar, por terem me acolhido nesses últimos

três anos com tanta amizade e carinho. Por cederem uma mesinha na sala para que eu

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pudesse estudar e por aliviarem a carga de vários finais de semana, que passei estudando

o dia todo, com cafés, conversas, bolos, jogos e filmes.

A fundação de amparo a pesquisa do estado de Goiás (FAPEG) pela bolsa de

estudos concedida.

Ao órgão onde trabalho desde o mestrado, SECIMA, que me concedeu dispensa

de horários para que eu pudesse finalizar a pós-graduação. Um agradecimento especial a

superintendente Gabriela de Val pela compreensão em todos os momentos e aos meus

companheiros do GCP pelo carinho e por tornarem o ambiente de trabalho mais

aconchegante.

Ao meu orientador, Marcus Cianciaruso, por ter me aguentado durante quase 6

anos de parceria e por ter contribuido muito no meu crescimento pessoal e profissional.

Ao meu coorientador Fabrício Villalobos por estar sempre disposto a ajudar e me

ensinar. Aprendi muito com você e só tenho a agradecer pela parceria

Aos meus co-autores que contribuíram para a qualidade do presente trabalho,

principalmente os professores Joaquin Hortal e Adriano Melo.

A todos os integrantes do Laboratório de Ecologia de Comunidades, professores

e alunos, que tornaram o ambiente de trabalho agradável e prazeroso.

A Universidade Federal de Goiás, em especial ao programa de pós-graduação em

Ecologia e Evolução que me propiciou anos maravilhosos, pessoas incríveis e muito

aprendizado.

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RESUMO GERAL

O conceito de nicho ecológico descreve o conjunto de condições bióticas e

abióticas nas quais as espécies podem manter populações e define a área de distribuição

das espécies. O nicho ecológico é multidimensional e, desse modo, é função de várias

características das espécies. Essas características podem mudar rapidamente ou podem

variar de forma muito lenta, permanecendo conservadas ao longo do tempo evolutivo, o

que caracteriza a conservação de nicho (i.e., a tendência de as espécies manterem o nicho

ancestral ao longo do tempo evolutivo). Reter o nicho ancestral implica em menor

capacidade do grupo em se adaptar fora dos limites de distribuição definidos pelo nicho

ecológico. Portanto, o modo como as características ligadas ao nicho evoluíram ao longo

do tempo é determinante para gerar padrões diferenciais de diversidade entre táxons. De

fato, a conservação de nicho tem sido levantada como fator relevante para explicar o

gradiente latitudinal de diversidade, bem como a formação da biota de diferentes habitats.

A conservação de tolerâncias ecológicas em algumas linhagens implica na herança de

uma capacidade limitada de colonizar e se estabelecer em diferentes habitats. Por outro

lado, alguns grupos podem ser ecologicamente mais flexíveis e colonizar novos habitats

através da evolução de nicho, provavelmente relacionada a adaptações funcionais

específicas que possibilitam o estabelecimento das espécies. Este trabalho teve como

objetivo geral avaliar padrões de diversidade de mamíferos terrestres para inferir acerca

dos principais processos ecológicos, evolutivos e histórico-biogeográficos atuantes.

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SUMÁRIO

INTRODUÇÃO GERAL ............................................................................................... 1

REFERÊNCIAS ............................................................................................................ 4

CAPÍTULO 01: PHYLOGENETIC CONSERVATISM OF CLIMATIC NICHE

IN BATS .......................................................................................................................... 7

ABSTRACT .................................................................................................................. 8

INTRODUCTION ........................................................................................................ 9

METHODS ................................................................................................................. 13

RESULTS ................................................................................................................... 21

DISCUSSION ............................................................................................................. 25

CONCLUSION ........................................................................................................... 30

REFERENCES ........................................................................................................... 31

SUPPORTING INFORMATION ............................................................................... 37

CAPÍTULO 02: GEOGRAPHICAL PATTERNS OF PHYLOGENETIC BETA

DIVERSITY COMPONENTS IN TERRESTRIAL MAMMALS .......................... 45

ABSTRACT ................................................................................................................ 46

INTRODUCTION ...................................................................................................... 47

METHODS ................................................................................................................. 52

RESULTS ................................................................................................................... 55

DISCUSSION ............................................................................................................. 60

CONCLUSION ........................................................................................................... 66

REFERENCES ........................................................................................................... 66

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SUPPORTING INFORMATION ............................................................................... 71

CAPÍTULO 03: PHYLOGENETIC AND FUNCTIONAL STRUCTURE OF

AFRICAN MAMMAL ASSEMBLAGES: THE IMPRINT OF HISTORICAL

CLIMATIC CHANGES ON HABITAT FORMATION .......................................... 85

ABSTRACT ................................................................................................................ 86

INTRODUCTION ...................................................................................................... 87

METHODS ................................................................................................................. 92

RESULTS ................................................................................................................... 96

DISCUSSION ........................................................................................................... 100

CONCLUSION ......................................................................................................... 107

REFERENCES ......................................................................................................... 108

SUPPORTING INFORMATION ............................................................................. 114

CONCLUSÃO GERAL ............................................................................................. 123

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INTRODUÇÃO GERAL

O conceito de nicho ecológico (sensu Hutchinson; Holt, 2009) é central em

ecologia e evolução e descreve o conjunto de condições bióticas e abióticas nas quais as

espécies podem persistir e manter populações. Nesse contexto, o nicho das espécies pode

ser classificado em nicho Grinnelliano e nicho Eltoniano (sensu Soberón, 2007). O nicho

Grinnelliano, também chamado não interativo, diz respeito às variáveis ambientais (e.g.

temperatura, precipitação, pH, etc.) que geralmente definem os limites de distribuição das

espécies em escalas amplas. Por outro lado, o nicho Eltoniano está relacionado a

interações bióticas em escalas locais (e.g. competição, predação, parasitismo, etc.) e à

dinâmica de consumo de recursos (Soberón, 2007). O nicho ecológico é o que,

primariamente, define a área de distribuição das espécies, mas os fatores abióticos (nicho

Grinnelliano) parecem ser mais importantes para definir os limites de distribuição das

espécies em grandes escalas e determinar padrões biogeográficos (Pearson & Dawson,

2003; Soberón & Peterson, 2005; Wiens, 2011). Entretanto, é possível que as variáveis

climáticas possam atuar indiretamente em combinação com fatores bióticos, por exemplo,

determinando a distribuição de recursos no espaço (Wiens, 2011).

O nicho ecológico é multidimensional e, desse modo, é função de várias

características das espécies, como tolerância de temperatura, adaptações de

forrageamento, hábito e outras (sensu Hutchinson; Holt, 2009). Essas características

podem mudar rapidamente ou podem variar de forma muito lenta, permanecendo

conservadas ao longo do tempo evolutivo (Hansen, 1997; Losos, 2008; Kozak & Wiens,

2010; Wiens et al., 2010; Peterson, 2011). Quando características ligadas ao nicho

permanecem semelhantes podemos dizer que há “conservação de nicho”, que pode ser

definida como a tendência de as espécies manterem o nicho ancestral ao longo do tempo

evolutivo (Wiens & Graham, 2005). A conservação de nicho pode ocorrer por meio de

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um processo de estase evolutiva, no qual a evolução da característica é limitada a um

ótimo adaptativo (Hansen, 1997; Kozak & Wiens, 2010). O modo e a velocidade com que

essas características relacionadas ao nicho evoluem varia entre clados e até mesmo dentro

dos clados, como por exemplo entre famílias dentro da mesma ordem (Diniz-Filho et al.,

2010).

As espécies não podem dispersar fora dos limites do seu nicho ecológico, pois o

mesmo define as condições nas quais elas podem sobreviver. Desse modo, as barreiras à

dispersão (e.g. entre regiões biogeográficas) representam áreas fora dos limites do nicho

ecológico, que são espécie-específico (Wiens, 2011). Reter o nicho ancestral implicará

em menor capacidade do grupo em se adaptar fora desses limites (Wiens, 2011). Portanto,

o modo como as características ligadas ao nicho evoluíram ao longo do tempo é

determinante para gerar padrões diferenciais de diversidade entre táxons (Wiens &

Donoghue, 2004). De fato, a conservação do nicho climático (Grinneliano) tem sido

levantada como fator importante para explicar o gradiente latitudinal de diversidade

(conservação de nicho tropical; Wiens & Donoghue, 2004). Essa hipótese prediz que os

grupos com maior diversidade em menores latitudes teriam se originado em regiões

tropicais e que, devido a conservação de nicho, poucas linhagens teriam evoluído

características que as permitissem conquistar climas mais frios ou áridos em zonas

extratropicais (Wiens & Donoghue, 2004). A prevalência de maior diversidade de

espécies em regiões tropicais se daria pelo fato de que a maioria dos grupos se originaram

nesse clima, que prevaleceu por um longo período em grande parte da superfície terrestre

(Behrensmeyer et al., 1992). Pensando na mesma lógica, a conservação de nicho

climático também poderia gerar os padrões de diminuição de diversidade com o aumento

das altitudes (e.g. Kozak & Wiens, 2010). Desse modo, a conservação/evolução de nicho

climático entre diferentes grupos taxonômicos causaria padrões de dissimilaridade de

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biota nos limites entre zonas climáticas, bem como entre áreas de diferentes elevações

(Wiens & Donoghue, 2004).

O modo de evolução de nicho também pode ter grande influência na formação da

biota de diferentes habitats. A conservação de tolerâncias ecológicas em algumas

linhagens implica na herança de uma capacidade limitada de colonizar e se estabelecer

em diferentes habitats (conservação de habitat), como foi demonstrado para plantas (Crisp

et al., 2009). Por outro lado, alguns grupos podem ser ecologicamente mais flexíveis e

colonizar novos habitats através da evolução de nicho (Wiens & Donoghue, 2004). A

capacidade diferencial dos clados em se estabelecer em outros habitats, somado com a

história climática das regiões, podem influenciar na formação do pool de linhagens entre

biomas e determinar a estrutura de assembleias no presente (Wiens & Donoghue, 2004;

Gerhold et al., 2015). Conservar o nicho ancestral pode comprometer o potencial

evolutivo do grupo se houver uma contração do habitat, por exemplo, em decorrência de

mudanças climáticas (Crisp et al., 2009). A perda de área do habitat ancestral implicaria

em extinção de espécies ou intensa redução das populações, como parece ter ocorrido

para muitas linhagens de florestas tropicais da África (Janis, 1993; Plana, 2004; Lawes et

al., 2007). Algumas linhagens permaneceram confinadas a pequenas áreas de floresta,

comumente chamadas de refúgios (e.g. Anthony et al., 2007; Johnston & Anthony, 2012;

Jacquet et al., 2014). Por outro lado, algumas linhagens podem ter se adaptado aos novos

habitats formados (Kamilar et al., 2009; Johnston & Anthony, 2012; Cantalapiedra et al.,

2014). O continente Africano teve a maior taxa de perda de área de floresta tropical e

manteve a maior área de savana ao longo do Cenozoico devido a mudanças climáticas e

à prevalência de um clima mais árido ao longo da África equatorial (Maley, 1996;

Kissling et al., 2012; Willis et al., 2013). Os limites entre floresta tropical e ambientes

savânicos africanos estão associados a um gradiente ambiental de aridez que,

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provavelmente, exigiu adaptações funcionais específicas que possibilitassem a

colonização e estabelecimento das espécies (e.g. Cantalapiedra et al., 2014).

Este trabalho teve como objetivo geral avaliar padrões globais, regionais e locais

de diversidade de mamíferos terrestres para inferir acerca dos principais processos

ecológicos, evolutivos e histórico-biogeográficos atuantes. No primeiro capítulo testei a

hipótese de que a distribuição atual das espécies é resultado de um processo de estase na

evolução de nicho ambiental (Grinnelliano) do grupo. Este capítulo está em revisão no

periódico Global Ecology and Biogeography. No segundo capítulo investiguei o

gradiente global de dissimilaridade filogenética e testei hipóteses relacionando esses

padrões (dissimilaridade real e dissimilaridade devido à diferença de diversidade

filogenética) com processos históricos que originaram o padrão latitudinal de diversidade.

Além disso, testei se a variação desses padrões para três ordens de mamíferos com

diferentes histórias evolutivas está relacionada com a capacidades de dispersão. Este

capítulo foi aceito para publicação no periódico Global Ecology and Biogeography. O

último capítulo está mais centrado em padrões locais de diversidade filogenética e

funcional de assembleias de mamíferos no continente africano. Nesse capítulo investiguei

como esses padrões locais refletem a história climática do continente e a formação do

conjunto de linhagens dos biomas nos quais as assembleias estão inseridas, além dos

processos ecológicos determinantes na coexistência das espécies no nível de assembleia.

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through time : evolutionary paleoecology of terrestrial plants and animals,

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B., Morales, J. & Mooers, A.Ø. (2014) Dietary innovations spurred the

diversification of ruminants during the Caenozoic. Proceedings of the Royal Society

B: Biological Sciences, 281, 20132746.

Crisp, M.D., Arroyo, M.T.K., Cook, L.G., Gandolfo, M. a, Jordan, G.J., McGlone, M.S.,

Weston, P.H., Westoby, M., Wilf, P. & Linder, H.P. (2009) Phylogenetic biome

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Diniz-Filho, J.A.F., Terribile, L.C., Da Cruz, M.J.R. & Vieira, L.C.G. (2010) Hidden

patterns of phylogenetic non-stationarity overwhelm comparative analyses of niche

conservatism and divergence. Global Ecology and Biogeography, 19, 916–926.

Gerhold, P., Cahill, J.F., Winter, M., Bartish, I. V & Prinzing, A. (2015) Phylogenetic

patterns are not proxies of community assembly mechanisms (they are far better).

Functional Ecology, 29, 600–614.

Hansen, T.F. (1997) Stabilizing selection and the comparative analysis of adaptetion.

Evolution, 51, 1341–1351.

Holt, R.D. (2009) Bringing the Hutchinsonian niche into the 21st century : ecological and

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Jacquet, F., Nicolas, V., Colyn, M., Kadjo, B., Hutterer, R., Decher, J., Akpatou, B.,

Cruaud, C. & Denys, C. (2014) Forest refugia and riverine barriers promote

diversification in the West African pygmy shrew (Crocidura obscurior complex,

Soricomorpha). Zoologica Scripta, 43, 131–148.

Janis, C.M. (1993) Tertiary mammal evolution in the context of changing climates,

vegetation, and tectonic events. Annual Review of Ecology and Systematics, 24, 467–

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Johnston, A.R. & Anthony, N.M. (2012) A multi-locus species phylogeny of African

forest duikers in the subfamily Cephalophinae: evidence for a recent radiation in the

Pleistocene. BMC Evolutionary Biology, 12, 120.

Kamilar, J.M., Martin, S.K. & Tosi, A.J. (2009) Combining biogeographic and

phylogenetic data to examine primate speciation: an example using cercopithecin

monkeys. Biotropica, 41, 514–519.

Kissling, W.D., Eiserhardt, W.L., Baker, W.J., Borchsenius, F., Couvreur, T.L.P.,

Balslev, H. & Svenning, J.-C. (2012) Cenozoic imprints on the phylogenetic

structure of palm species assemblages worldwide. Proceedings of the National

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Kozak, K.H. & Wiens, J.J. (2010) Niche conservatism drives elevational diversity

patterns in Appalachian salamanders. The American naturalist, 176, 40–54.

Lawes, M.J., Eeley, H.A.C., Findlay, N.J. & Forbes, D. (2007) Resilient forest faunal

communities in South Africa: a legacy of palaeoclimatic change and extinction

filtering? Journal of Biogeography, 34, 1246–1264.

Losos, J.B. (2008) Phylogenetic niche conservatism, phylogenetic signal and the

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relationship between phylogenetic relatedness and ecological similarity among

species. Ecology Letters, 11, 995–1003.

Maley, J. (1996) The African rain forest - main characteristics of changes in vegetation

and and climate from the Upper Cretaceous to the Quaternary. Proceedings of The

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distribution of species: are bioclimate envelope models useful? Global Ecology and

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Peterson, A.T. (2011) Ecological niche conservatism: a time-structured review of

evidence. Journal of Biogeography, 38, 817–827.

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Damschen, E.I., Jonathan Davies, T., Grytnes, J.-A., Harrison, S.P., Hawkins, B. a,

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CAPÍTULO 01: PHYLOGENETIC

CONSERVATISM OF CLIMATIC NICHE

IN BATS

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PHYLOGENETIC CONSERVATISM OF CLIMATIC NICHE IN BATS

Franciele P. Peixoto, Fabricio Villalobos, Marcus V. Cianciaruso

Em revisão no periódico Global Ecology and Biogeography

ABSTRACT

Aim: to evaluate the patterns of climatic niche conservatism in bats. Bats have been

regarded as showing strong niche conservatism, mainly by testing predictions on the

effect of niche conservatism in diversity gradients. However, no specific tests have been

conducted to investigate the extent to which bat species niches are evolutionary

conserved. We address this questions at different phylogenetic scales and using

phylogenetic and geographical approaches.

Location: worldwide

Methods: we used nine climatic variables to describe a multivariate representation of bat

climatic niches. We measured niche position, niche breadth and niche overlap between

sister species pairs (using Schoener’s D). We performed a Mantel test to verify if niche

overlap was related to phylogenetic proximity. At the order and family level, we tested

for phylogenetic signal using the K statistic and phylogenetic signal representation curve.

We also compared the fit of four evolutionary models.

Results: 42.8% of sister species pairs comparison showed that niche similarity was higher

than expected. However, we did not find a significant phylogenetic signal for niche

overlap. At deeper evolutionary scales, K values were smaller than one for NP and NB,

thus different than expected under Brownian motion. PSR and the fit of the OU model

supported a slower evolution than expected under BM, suggesting phylogenetic

conservatism, particularly for niche position. Vespertilionidae and Molossidae presented

similar results.

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Main conclusions: our findings show that climatic niche position of bats presents

evidence for phylogenetic niche conservatism. In addition, we verified that the evolution

of climatic niches is non-stationary across the order Chiroptera, consistent with the

different histories of clades. We stress the importance of taking into account the method

of choice, the niche feature and the phylogenetic scale being evaluated when testing for

phylogenetic niche conservatism at higher taxonomic levels and its influence on

biodiversity gradients.

Keywords: Chiroptera, evolutionary stasis, Grinneallian niche, niche overlap,

phylogenetic signal, PSR, trait evolution models.

INTRODUCTION

Chiroptera is the second most species-rich mammalian order and the main taxon driving

the latitudinal diversity gradient of the whole mammalian class (Kaufman, 1995; Buckley

et al., 2010). Given the importance of bats in determining mammalian diversity gradients,

they have been considered as an ideal group for testing explanatory hypotheses for such

gradients (Willig et al., 2003a; Stevens, 2006; Buckley et al., 2010). One explanation for

the strong diversity gradient exhibited by bats is phylogenetic niche conservatism (PNC)

–the tendency of species to retain their ancestral niches over time (Wiens & Graham,

2005)–particularly in relation to the tropical niche conservatism hypothesis (Wiens &

Donoghue, 2004). For instance, Buckley et al. (2010) argued that the positive

temperature-richness relationship found for Chiroptera supported PNC as higher richness

accumulates in environments resembling the clades’ tropical ancestral niche. In the same

vein, Stevens (2006, 2011) found that bats inhabiting temperate species-poor regions are

more recently originated (i.e. younger) whereas bats in species-rich tropical regions are

more early diverged (i.e. older), thus supporting PNC in driving the bat diversity gradient.

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Conversely, Pereira & Palmeirim (2013) recently refuted Stevens’ results, finding that

more early diverged species at higher latitudes and more recently derived species at lower

latitudes. All of these studies, however, have evaluated bats PNC by testing predictions

on its effect in diversity gradients and not specifically testing for niche similarity among

related species. Therefore, an important unanswered question is to what extent do bat

species resemble each other niches.

The ‘niche’ concept is a complex term referring to several aspects of species

ecologies, mainly to the factors allowing their existence and their impacts on those factors

(Chase & Leibold, 2003; Peterson, 2011). Based on these two broad aspects, Soberón

(2007) defined two niche classes: Grinnellian and Eltonian niches. The Grinnellian niche

refers to the non-interactive environmental variables determining species distributions at

the geographical scale, whereas the Eltonian niche refers to the resource-consumer

dynamics and biotic interactions determining distributions at the local scale (Soberón,

2007). Given the coarse spatial structure of the environmental variables defining the

Grinnellian niche compared to the local and complex dynamics of Eltonian niches

(Soberón, 2007), the former is often more tractable and have thus being more widely

studied (Peterson, 2011; Pyron et al., 2015; Olalla-Tárraga et al., 2017). For example,

climatic variables have been extensively used to describe Grinnellian niche, mainly

because of their relevance in setting distributional limits of species or the resources they

rely upon (e.g. fruits for frugivore bats) (Fleming, 1973; Wiens, 2011). Indeed, most PNC

studies have focused on Climatic niches as characterized by the geographical distribution

of species (Hutchinson’s duality; Colwell & Rangel, 2009) and suggested that PNC may

be a widespread biogeographical pattern (Wiens & Graham, 2005; Wiens et al., 2010;

Peterson, 2011). Whether PNC prevails or not, the appropriate methods for testing it and

11

even its meaning are still subject to strong debates (Losos, 2008; Wiens, 2008; Cooper et

al., 2010; Münkemüller et al., 2015; Pyron et al., 2015).

Given the relevance of PNC for addressing many important issues in ecology and

evolution, from species distributions and adaptation to diversity gradients (Pyron et al.,

2015), the application of a particular method for testing it depends on the question of

interest (Wiens et al., 2010). In addition, such a question should also explicitly consider

the temporal or phylogenetic scale as the degree of PNC can vary according to the extent

of phylogenetic inclusiveness of a study (Losos, 2008; Peterson, 2011). For example,

studies at shallow phylogenetic scales (e.g. at species level) can either reveal similar,

stronger or weaker PNC patterns compared to more inclusive studies (e.g. at family level)

as a result of variation in rates of niche evolution (i.e. phylogenetic non-stationarity)

(Diniz-Filho et al., 2010; Diniz-filho et al., 2015). In the case of niche similarity among

related species, two different approaches have been mostly applied: those based on niche

variation across a phylogenetic tree and those based on comparing species distributional

patterns (Wiens et al., 2010). Importantly, both of these approaches should be conducted

on an explicit phylogenetic framework (Diniz-Filho et al., 2010).

Phylogenetic tree approaches include tests of ‘phylogenetic signal’ – “tendency

for related species to resemble each other more than they resemble species drawn at

random from the tree” (Blomberg & Garland, 2002)— and the comparison of the relative

fit of evolutionary models to the data (Wiens et al., 2010; Münkemüller et al., 2015).

Although tests of phylogenetic signal have been widely applied to study PNC (Buckley

et al., 2010; Cooper et al., 2010; Hof et al., 2010; Olalla-Tárraga et al., 2011), it has been

shown that such test can be misleading since no signal could either mean random variation

(no PNC) or stasis (strong PNC) (Revell et al., 2008; Münkemüller et al., 2015).

Alternatively, the relative fit of evolutionary models seems to be a more appropriate way

12

to study PNC (Kozak & Wiens, 2010; Münkemüller et al., 2015). The most common

models are the Brownian motion (BM), a stasis model such as the Ornstein-Uhlenbeck

(OU), and a random variation model (white noise; WN) (Wiens et al., 2010). Under a BM

model, in which tests of phylogenetic signal are based, traits evolve under a random walk

with differences accumulating over time (Felsenstein, 1985). The OU model describes

constrained evolution whereby traits are pulled toward an optimal value, thus evolving

slower than in a Brownian motion model. Accordingly, the fit of an OU model can be

interpreted as stronger evidence for PNC compared to the BM model (Kozak & Wiens,

2010).

Geographical approaches for testing PNC comprise several applications of species

distribution models (SDMs) for which the reciprocity between Grinnellian niche and

geographical distribution of species (Hutchinson’s duality; Colwell & Rangel, 2009) is

the basic assumption. Using SDMs, PNC can be tested under a scenario of invasion where

the prediction is that an invasive species must occupy regions that are environmentally

similar to its native range (Pearman et al., 2008; Guisan et al., 2014). Alternatively, SDMs

can be used to evaluate the potential of one species SDM to predict another species

distribution, where a positive finding would support PNC if conducted between sister

species (Peterson et al., 1999; Wiens et al., 2010). Finally, PNC could also be tested by

measuring the degree of overlap in environmental space (e.g. climatic) among pairs of

sister species, expecting a higher overlap than expected by their environmental

backgrounds under PNC (Warren et al., 2008; Broennimann et al., 2012). This latter

approach can be complemented by a correlation test between the degree of environmental

overlap and species phylogenetic distance (i.e. niche vs phylogenetic similarity) (Knouft

et al., 2006; Cardillo & Warren, 2016). Note that these geographical approaches represent

PNC tests at the species level, thus at a shallower phylogenetic scale than the phylogenetic

13

tree approaches mentioned above that are based on entire phylogenies and thus covering

deeper phylogenetic scales. Therefore, the combination of both phylogenetic and

geographical approaches can provide a more robust way of testing PNC (Wiens et al.,

2010) covering different phylogenetic scales within entire clades and thus evaluating

phylogenetic non-stationarity (Diniz-Filho et al., 2010).

Here, we evaluate for the first time the patterns of climatic niche evolution across

the order Chiroptera. We explicitly focus on climatic niches, using a multivariate

representation of a species niche and two of its features (position and breadth), to

investigate the extent to which bat species niches are evolutionary conserved and the

degree of niche similarity among related bat species. In particular, we are interested in

assessing the prevalence of phylogenetic niche conservatism at different phylogenetic

scales. To do so, we apply phylogenetic approaches at the order level and on different bat

families as well as a geographical approach at the species level by correlating niche and

phylogenetic similarity on species pairs. For the purpose of this study, we follow the

‘PNC as a pattern’ perspective (Pyron et al., 2015) and Losos' (2008) view that niches of

closely related species should be more similar than expected under neutral drift to qualify

as PNC. Based on the predictions of PNC over time and space, we expect that (i) bats will

exhibit a higher phylogenetic signal than expected under Brownian motion, (ii) the

distribution of niche values over the bat phylogeny will correspond to an OU model, and

that (iii) sister species will show greater niche overlap than less related species.

METHODS

Phylogenetic information and geographical data

We obtained phylogenetic information on bats relationships from a species-level

supertree of mammals (Fritz et al. (2009), which is an update of Bininda-Emonds et al.

14

(2007) supertree and based on the bats’ supertree originally proposed by Jones et al.

(2002). To perform the analyses, we extracted all bat species from the supertree and

obtained range maps (i.e., extents of occurrence polygons) for these species available

from the IUCN online database (IUCN 2013). We extracted the information of occurrence

for each species from a global raster of 1° × 1° cell resolution. Species were considered

as present in a grid cell if they occurred in at least 50% of it. We recognize that range

maps overestimate species distributions at fine scales but, at the global extent of our

analysis, they are congruent with survey-based data (Hawkins et al., 2008) and provide a

less biased view of distributions than occurrence records (Hurlbert & Jetz, 2007). In

addition, our choice of resolution (1º) may underestimate climatic variability within grid

cells but given the relatively large ranges of bat species (Willig et al., 2003b), this effect

can be negligible as suggested by several studies of niche conservatism at

macroecological scales using similar or even coarser resolutions (e.g. Hof et al., 2010;

Gouveia et al., 2014). From the 1030 bat species on the supertree, 121 species lack

geographical information. Therefore, we conducted subsequent analyses for a set of 909

species with both phylogenetic and geographical data.

Climatic variables and climatic niche

To characterize the climatic niches of bat species, we used a multivariate representation

based on nine climatic variables that are highly related to the ecological and physiological

tolerances of bats and have been suggested as effective predictors of their geographical

distributions (e.g., Ratrimomanarivo et al., 2009; Monadjem et al., 2010; Dixon, 2011;

Flanders et al., 2011; Stoffberg et al., 2012; Schoeman et al., 2013). These variables,

obtained from the Worldclim online database (Hijmans et al., 2005), were the following:

Annual mean temperature (BIO1), isothermality (BIO3), temperature, Seasonality

15

(BIO4), maximum temperature of the warmest month (BIO5), minimum temperature of

the coldest month (BIO6), annual precipitation (BIO12), precipitation of the wettest

month (BIO13), precipitation of the driest month (BIO14) and precipitation seasonality

(BIO15).

Species-level analyses

We were interested in testing PNC at different phylogenetic scales, from species to

families and the whole order. At the level of species (or ‘tips’ in the phylogeny), we

applied an explicitly geographical and environmental test of niche conservatism by

comparing the degree of overlap, similarity and equivalency of climatic niches between

pairs of species (Peterson et al., 1999; Warren et al., 2008). Focusing on species pairs

allowed us a closer inspection of the phylogenetic structure of niche conservatism without

focusing on the entire depth of the phylogeny (Cardillo, 2015). We selected from the

phylogeny those species representing a resolved cherry pair. A cherry is a pair of adjacent

tips on a tree representing two sister species. We identified 161 pairs (322 species), from

which 126 (252 species) had sufficient information for the analyses (i.e. at least five

occurrences for the overlap test).

We follow Broennimann et al. (2012) method that applies kernel smoothers to

species occurrence densities and climatic factors in a gridded space. Such method allows

correcting for niche similarity expected from spatial autocorrelation in the environment,

providing biologically meaningful inferences that are independent of sampling effort and

resolution (Broennimann et al., 2012; Warren et al., 2014). In this case, species climatic

niches were characterized as the envelope of climatic conditions (described by the

selected climatic variables) occupied by its occurrences and their accessible areas. By

considering species accessible areas and their complete occurrences, niche overlap can

16

reliably be evaluated (Barve et al., 2011; Broennimann et al., 2012). We defined

accessible areas as the set of ecoregions where a species occurs (Soberón, 2010; Barve et

al., 2011). We used those ecoregions proposed by Olson et al. (2001). For each species

pair, a PCA ordination test is calibrated on the occupied environments of both species and

use for testing niche overlap, similarity and equivalency along axes of such ordination

(Broennimann et al., 2012). Niche overlap was estimated using Schoener’s D metric,

which varies from 0 (no overlap) to 1 (complete overlap).

To evaluate if the observed patterns of niche overlap were statistically significant,

we conducted tests of niche equivalency and similarity between species pairs. These tests

entertain the possibility of niche overlap being simply determined by the spatial structure

of the climatic spaces occupied by the species (i.e. null expectation). More specifically,

the niche equivalency test evaluates if two niches in two geographical ranges are

indistinguishable in comparison with random occurrences of both species within the two

ranges. In this case, a null distribution of niche overlaps (D metric) is obtained by creating

pseudo-replicates where occurrences from the two ranges are pooled together and

randomly divided into two groups while keeping the original number of occurrences.

Complementarily, the niche similarity test evaluates the more relaxed possibility of the

climatic space occupied in one range being more similar (or different) to the one occupied

in the other range compared to what would be expected by chance. In this case, the null

distribution of D values is obtained from pseudo-replicates generated by randomly

assigning each occurrence in one range to a new location in the other range (Broennimann

et al., 2012). Significant niche equivalency and similarity was determined when the

observed values were outside the 95% confidence limit of a null distribution created by

100 iterations. To determine the existence of niche conservatism, we compared the

percentage of significant niche overlap patterns (similarity and equivalency) between true

17

species pairs and randomly formed species pairs (see below).

Finally, to integrate our niche overlap analysis with the phylogeny of Chiroptera we

performed a Mantel test to evaluate if niche overlap (Schoener’s D) was correlated to

phylogenetic (patristic) distance among species pairs, (e.g. Knouft et al., 2006; Warren et

al., 2008; Diniz-Filho et al., 2010) with significance estimated by 1000 matrix

permutations. A significant negative correlation would imply niche conservatism between

closely related species and niche divergence between distantly related species (Knouft et

al., 2006). During the selection of species pairs to perform the overlap analyses, we

excluded 12 pairs for having less than five occurrences, and 29 pairs for which one species

did not have enough occurrences. Thus, our final set included 281 species from 126

complete pairs plus 29 species who lost their pair but were kept to form random pairs.

Accordingly, such 126 pairs were considered as our true species pairs and random pairs

were formed from obtaining all possible pairwise combinations of those 281 species

(totaling 39340 pairs).

Family- and order-level analyses

To perform analyses at deeper phylogenetic scales, such as families and order, we first

defined two features of the species climatic niches as characterized by the multivariate

climatic space: niche position and niche breadth. To do so, we conducted an ordination

technique known as the outlying mean index (OMI; Dolédec et al., 2000) analysis that

produces uncorrelated niche axes, giving equal weights to sites with high and low richness

and making no assumption on species response curves (Thuiller et al., 2005). OMI results

allow to describe species niches based on niche marginality and niche breadth. Niche

marginality measures how much the mean conditions of a species niche deviates from the

theoretical niche (i.e. an average niche describing the mean conditions within the

18

distribution of the whole clade). Because species having similar distances but in opposite

directions from the average theoretical niche will have similar values of niche

marginality, we used the species scores along the first niche axis of the OMI ordination

as descriptor of niche position (NP) (Gouveia et al., 2014). Niche breadth (NB), or

tolerance, is measured as the dispersion of species occurrences over the multivariate

climatic space, thus, describing the variation of climatic conditions used by the species

(Dolédec et al., 2000; Thuiller et al., 2005). Although niche breadth can be confounded

by range size (Slatyer et al., 2013), our NB metric is based on the variance of species

occurrences along the niche axes. Therefore, species recorded in broad areas with similar

climatic conditions do not necessarily have broader niche breadth/tolerance (Thuiller et

al., 2005). Both NP and NB for each species were later mapped onto the complete

phylogeny for the order-level analysis, which was later pruned to include only species for

each of the studied families (see below).

We considered both niche features (NP and NB) as species traits and tested for their

phylogenetic signal across the family- and order -level phylogenetic trees using the K

statistic (Blomberg et al., 2003). When K = 1, trait evolution follows the expectation

under the Brownian motion model (i.e. trait divergence proportional to time). K > 1

indicates higher trait similarity than expected under Brownian motion, whereas K < 1

indicates lower trait similarity than expected under Brownian motion (Blomberg et al.,

2003). Both K = 1 and > 1 have been used as evidence of PNC (Losos, 2008; Cooper et

al., 2010), whereas K < 1 could mean either stasis (thus PNC) or no phylogenetic structure

(e.g. random variation; Wiens et al., 2010). As stated before, this test can be problematic

but given its ample application, we performed it for the sake of comparison.

Besides evaluating phylogenetic signal, we also applied a graphical method that

provides a means to explore phylogenetic patterns of trait evolution that can be interpreted

19

both in terms of phylogenetic signal and evolutionary models (Bini et al., 2014; Diniz-

Filho et al., 2015). Such method is called Phylogenetic Signal-Representation (PSR)

curves (Diniz-Filho et al., 2012) and it is based on Phylogenetic Eigenvector Regression

(PVR) models where trait variation is modeled by eigenvectors extracted and selected

from a pairwise phylogenetic distance matrix (Diniz-Filho et al., 1998). PSR curves are

built by plotting the R2 from sequential PVR models fitted by successively increasing the

number of eigenvectors against their accumulated eigenvalues (Diniz-Filho et al., 2012).

The shape of the PSR curve allows interpreting trait variation in terms of evolutionary

models such as Brownian motion or an OU process (Diniz-Filho et al., 2012; Bini et al.,

2014). For instance, a 45º line provides evidence that trait evolution follows Brownian

motion (as in K = 1), whereas a PSR curve above the 45º reference line denotes

accelerated trait divergence (i.e. trait evolving faster, less conserved, than expected under

Brownian motion). Conversely, when the PSR curve is below the 45º reference line but

above that of a null model (i.e. absence of phylogenetic signal), there is evidence for

decelerated trait evolution (i.e. trait evolving slower, more conserved, than expected

under Brownian motion like in an OU process) (Diniz Filho et al., 2012). We used the

area between the observed PSR curve and a line with slope = 1 to measure the deviation

from a Brownian motion model. Using permutations, we tested if the PSR area of both

niche features (NP and NB) deviated significantly from Brownian motion (a neutral

model) and null models of trait evolution (Bini et al., 2014; Gouveia et al., 2014; Diniz-

filho et al., 2015).

Given the criticisms on using phylogenetic signal tests for testing PNC, we

followed more recent recommendations (Kozak & Wiens, 2010; Münkemüller et al.,

2015) and compared the relative fit of four evolutionary models to the observed variation

of niche features (NP and NB). The models were the white noise (WN) model of random

20

variation where species trait similarity is phylogenetically independent, a Brownian

motion model of gradual drift, an OU model representing stasis or stabilizing selection

(Butler & King, 2004), and an Early Burst (EB) model describing a slowdown in trait

evolution over time (Harmon et al., 2010). Comparison and selection of best-fitting model

among the four evaluated models was done using the Akaike information criterion

corrected for sample size (Kozak & Wiens, 2010). According to theoretical and more

recent simulation studies, the fit of an OU model can be interpreted as evidence of niche

conservatism (Butler & King, 2004; Münkemüller et al., 2015). This is based on the OU

model basic assumption of trait (niche) evolution under adaptive constraints (Butler &

King, 2004) that, in this case, implies evolutionary stasis associated with climatic regimes

(Kozak & Wiens, 2010). Some of these studies have also suggested fitting more flexible

models of niche evolution, such as OU models considering different optima at different

parts of a phylogeny (Butler & King, 2004; Beaulieu et al., 2012; Münkemüller et al.,

2015). Such models would capture different PNC patterns (i.e. niche conservatism vs

evolution) of particular groups within a larger clade, testing PNC scale-dependency

(Losos, 2008) and thus phylogenetic stationarity (i.e. constant changes throughout the

phylogeny; Diniz Filho et al., 2011). However, such flexible models are computationally

intensive and can produce ill-fitting models resulting in erroneous parameter estimates

(Ho & Ané, 2014; Cooper et al., 2016; J. Beaulieu pers. comm.). Instead, we evaluated

phylogenetic stationarity of niche evolution within Chiroptera by conducting analyses at

different phylogenetic levels: Chiroptera as a whole and families. Considering bat

families separately, which are phylogenetically well-established (Teeling et al., 2005),

we aimed to identify if different bat subclades showed different niche conservatism

patterns, thus accounting for potential non-stationarity. Accordingly, we performed all of

the above analyses separately for each bat family with more than 45 species available for

21

analyses. This approach is akin to fitting OU evolutionary models with multiple optima,

where each group (e.g. family) would have its own optimal niche value (e.g. Kozak &

Wiens, 2010; Wüest et al., 2015). For these analyses, we considered seven bat families:

Emballunoridae (47 species), Hipposideridae (62), Molossidae (88), Phyllostomidae

(141), Pteropodidae (133), Rhinolophidae (64) and Vespertilionidae (330).

RESULTS

Species level

Niche equivalency tests showed that 0.66% of all possible species pairs (39340) exhibited

significant equivalency, from which only 2.3% (six pairs) corresponded to sister species

pairs. Niche similarity tests demonstrated almost the same proportion of higher than

expected niche similarity among both sister and random pairs (sister pairs: 42.8%;

random pairs: 30.82%). The proportion of pairs with lower than expected niche similarity

was 1.04% for random pairs and zero for sister pairs. Schoener’s D values (niche overlap)

among sister species pairs were slightly higher but not significantly different from those

found for random pairs (Appendix S1). Accordingly, the Mantel test ruled out the

hypothesis that niche overlap decreases with phylogenetic distance (Z = 1003780; P =

0.31).

Family and order levels

In general, phylogenetic signal for Chiroptera as a whole and for the individual

families showed Blomberg’s K values smaller than one for both niche features (breadth:

NB; and position: NP). For niche breadth, only the Emballorunidae and Vespertilionidae

families showed significant K values. For niche position (NP), Chiroptera as a whole and

the families Emballorunidae, Molossidae, Pteropodidae and Vespertilionidae showed

22

significant values (Table 1). We found just one case of K value significantly higher than

one, corresponding to the niche position (NP) of the Emballonuridae family (Table 1).

Table 1. The results of K statistic with the respective P values for niche breadth (NB) and

niche position (NP). The results are given for Chiroptera and separately for seven

families.

CLADE NB NP

K value P value K value P value

Chiroptera 0.118 0.277 0.209 0.001

Emballonuridae 0.839 0.001 1.237 0.001

Hipposideridae 0.470 0.626 0.562 0.387

Molossidae 0.460 0.126 0.498 0.019

Phyllostomidae 0.322 0.335 0.322 0.335

Pteropodidae 0.237 0.223 0.353 0.001

Rhinolophidae 0.086 0.679 0.129 0.100

Vespertilionidae 0.302 0.001 0.425 0.001

Conversely, results from phylogenetic signal-representation (PSR) curves showed

that both niche features for most groups (Chiroptera and families) displayed negative PSR

areas, which imply a slower evolution than expected under Brownian motion and thus

higher similarity among closely related species. However, most of these PSR areas were

not significantly different from expected under both null and Brownian motion evolution

models varying greatly among families (Table 2; Appendix S2-S8 in Supporting

Information). For instance, PSR areas of both niche features for Emballorunidae showed

significant differences from a null model but not from Brownian motion, whereas

Rhinolophidae and Hipposideridae showed the opposite with both niche features being

significantly different from Brownian motion but not from the null model.

Vespertilionidae was the only case in which the negative PSR area for both niche features

was significantly different from the null and Brownian motion models (i.e. slower

23

evolution than expected under BM, corresponding to an OU process of evolutionary

stasis). Similarly, Chiroptera and Phyllostomidae showed a significantly negative PSR

area from both the null and Brownian motion models but only for niche position (NP),

thus supporting evolutionary stasis under a OU process. Molossidae niche position

showed significant difference from null but not from Brownian motion. Pteropodidae

PSR area of niche breadth was negative and significantly different from null and

Brownian motion models whereas its niche position differed only from null model (Table

2).

Table 2. Phylogenetic signal representation (PSR) results and their respective P values,

regarding null and Brownian model, for climatic niche features: niche breadth (NB) and

niche position (NP). The results are given for Chiroptera and separately for seven

families.

Clade/variable PSR area P value (null model ) P value (Brownian model)

Chiroptera

NB -0.413 0.111 < 0.001

NP -0.352 < 0.001 < 0.001

Emballonuridae

NB -0.056 < 0.001 0.485

NP 0.089 < 0.001 0.899

Hipposideridae

NB -0.192 0.526 < 0.001

NP -0.109 0.070 0.061

Molossidae

NB -0.214 0.050 < 0.001

NP -0.149 < 0.001 0.031

Phyllostomidae

NB -0.322 0.536 < 0.001

NP -0.268 < 0.001 < 0.001

Pteropodidae

NB -0.295 0.010 < 0.001

NP -0.137 < 0.001 0.091

Rhinolophidae

NB -0.258 0.222 < 0.001

24

NP -0.260 0.292 < 0.001

Vespertilionidae

NB -0.265 < 0.001 < 0.001

NP -0.1803 < 0.001 < 0.001

Results from fitting different evolutionary models were similar to those of the PSR

curves. In general, we found support for an OU evolutionary model in niche position (NP)

for Chiroptera, Molossidae, Pteropodidae and in both niche breath and position for

Vespertilionidae (Table 3). There was no support for the early burst (EB) model in any

of the groups, but there was one case supporting white noise (WN) (Hipposideridae; NB),

and one case supporting Brownian model (BM) (Emballunoridae; NP). Note that the

cases in which we found more support for OU models also presented significantly

negative areas in PSR analysis (Tables 2 and 3), thus supporting phylogenetic niche

conservatism as a result of evolutionary stasis.

Table 3. Fit of alternative evolutionary models for climatic niche features of Chiroptera

and separately for seven families: niche breadth (NB) and niche position (NP). Sample

size–corrected AIC (AICC) and AIC weights (AICW) are reported for each model and for

each variable: WN (white noise); BM (Brownian motion); OU (Ornstein-Uhlenbeck

model) e EB (early burst). Results are highlighted for ∆AIC values ≤ 2.

Clade/variable WN BM OU EB

Chiroptera AICC AICW AICC AICW AICC AICW AICC AICW

NB 3663.7 0.47 4085.83 0.00 3663.49 0.53 4087.85 0.00

NP 3638.3 0.00 3566.38 0.00 3366.82 1.00 3568.39 0.00

Emballonuridae

NB 179.13 0.00 167.50 0.58 169.37 0.23 169.79 0.19

NP 179.51 0.00 148.98 0.61 151.26 0.19 151.21 0.20

Hipposideridae

NB 250.47 0.75 279.48 0.00 242.11 0.25 281.69 0.00

NP 240.37 0.71 262.57 0.00 242.11 0.30 264.78 0.00

25

Molossidae

NB 347.38 0.42 355.07 0.01 346.75 0.57 357.22 0.00

NP 304.96 0.26 316.91 0.00 302.85 0.74 319.06 0.00

Phyllostomidae

NB 676.62 0.55 717.20 0.00 677.01 0.45 719.29 0.00

NP 406.45 0.42 441.75 0.00 405.83 0.58 443.84 0.00

Pteropodidae

NB 540.19 0.60 603.86 0.00 541.01 0.40 605.96 0.00

NP 493.09 0.00 507.66 0.00 471.62 1.00 509.75 0.00

Rhinolophidae

NB 275.07 0.72 309.44 0.00 276.93 0.28 311.65 0.00

NP 251.06 0.44 268.01 0.00 250.61 0.56 270.21 0.00

Vespertilionidae

NB 1146.03 0.00 1183.15 0.00 1125.42 1.00 1185.19 0.00

NP 1364.53 0.00 1288.80 0.00 1269.45 1.00 1290.84 0.00

DISCUSSION

Phylogenetic niche conservatism has been advanced as a major explanation for the

contemporary latitudinal diversity gradient exhibited by bats (Stevens, 2006, 2011;

Buckley et al., 2010; Villalobos et al., 2013). However, no specific evidence for niche

similarity among related bat species, a basic tenet of PNC, has been provided. We aimed

to fill this gap by evaluating patterns of climatic niche conservatism in bats across space

and time. Our findings at different phylogenetic scales support PNC at the order and

family levels but not at the species level. These findings, as well as the variation of PNC

patterns among bat families, imply that the evolution of bat climatic niches has not been

constant over time or similar among different subclades, showing phylogenetic non-

stationarity. Accordingly, our findings highlight the obvious, but usually neglected, role

of the temporal or phylogenetic scale in the consideration of PNC (Losos, 2008; Peterson,

2011). Explicitly considering phylogenetic scales and based on the specific aims of each

of our analyses, we argue that our findings broadly support phylogenetic conservatism of

bat climatic niches.

26

Under PNC, retaining ancestral niches over time means that closely related species

should show higher niche similarity than distantly related species (Losos, 2008; Holt,

2009). Considering the Grinnellian aspect of the niche (Soberón, 2007), such similarity

can be tested by measuring the overlap in the climatic conditions linked to species

geographical distributions (Broennimann et al., 2012). This approach for testing PNC is

common in applications of species distribution modelling, particularly those related to

biological invasions (Guisan et al., 2014) but it has, to the best of our knowledge, never

been applied for testing PNC across species within higher taxa. Our species-level findings

using this niche overlap approach showed that in almost half of the compared species

pairs, niche similarity was higher than expected given their environmental backgrounds.

Taken at face value and for each species pair, these results could be interpreted as

evidence for PNC (Peterson, 2011). However, considering all species pairs, we found no

relationship between niche and phylogenetic similarity, suggesting that closely as well as

distantly related bat species have similar climatic niches. This, in turn, could be

interpreted as absence of PNC particularly in terms of phylogenetic signal since closely

related species do not resemble each other more than distantly related species.

Nevertheless, as pointed out by Wiens et al. (2010), the actual pattern expected

under PNC would be that of no change, meaning that all species within a clade can indeed

show niche similarity (or considerable overlap) regardless of their phylogenetic distance.

In the case of bats, this finding is congruent with the fact that species richness of most bat

families is highest in tropical regions (Procheş, 2005), hence bat species from different

lineages do share their climatic preferences to some extent. Indeed, this has been used as

evidence supporting the tropical niche conservatism hypothesis in bats (Stevens, 2006,

2011; Buckley et al., 2010). In this context our species-level results could be interpreted

as consistent with PNC even though we found no significant phylogenetic signal.

27

Moreover, our order- and family-level results showing phylogenetic non-stationarity and

supporting a stasis evolutionary model could also explain the absence of phylogenetic

signal at the species-level while more strongly supporting PNC at these more inclusive

levels.

Focusing on different phylogenetic scales under different approaches allowed us to

more strongly infer the prevalence of PNC in bat climatic niches. In accordance with the

species-level results, phylogenetic signal analysis at the order and family level did not

support PNC, regardless of its interpretation (i.e., equal or higher than expected under

BM; except for niche position in Emballorunidae) and niche feature (NP and NB),

showing values of K < 1. Nevertheless, as stated before, this finding could actually be

interpreted as evidence for PNC resulting from a stasis evolutionary process as suggested

by recent simulation studies (Revell et al., 2008; Münkemüller et al., 2015). In fact, our

PSR and model-based analyses, supported this interpretation. For Chiroptera as a whole

and regarding niche position (NP), the area of the PSR curve was significantly different

from the null and BM models, just as expected under a stasis or stabilizing selection

model (Diniz-Filho et al., 2012). In agreement, the best fitted model was the OU model,

consistent with a mechanism for PNC (Hansen, 1997; Münkemüller et al., 2015). Taken

together, these findings give evidence that the climatic niches of Chiroptera, at least niche

position, have evolved more slowly than expected under neutral drift (Brownian motion)

thus suggesting PNC at the order level.

We found similar results at the family level but with variation among the studied

families. This phylogenetic non-stationarity shows that PNC patterns are not constant

throughout the Chiropteran phylogeny. For instance, we found support for PNC in the

Molossidae, Vespertilionidae and Emballorunidae but not for the other families. For the

first two families, PSR curves and model fit supported an OU model for niche position,

28

indicating slower evolution than expected under BM (Tables 2,3). For the

Vespertilionidae, we also found support for PNC in niche breadth (i.e., tolerance). These

two families are among the most speciose bat families (representing ~ 46% of all bat

species used in our analyses). Therefore, it is likely that they could have driven the result

found for the whole Chiroptera order. As stated above, Chiroptera has being claimed as

presenting PNC, particularly tropical niche conservatism (TNC) (Buckley et al., 2010;

Stevens, 2011) but also as not presenting it (Pereira & Palmeirim, 2013). Given that most

of bat species are tropical (see Buckley et al., 2010) it seems reasonable to hypothesize

that TNC may have contributed to the latitudinal diversity gradient of bats. However, this

may be true for a couple of families (Phyllostomidae and, perhaps, Emballonuridae) but

not for the Vespertilionidae and Molossidae. The success of molossids and vespertilionids

in the temperate zones is possibly a result of their particular adaptations such as low

metabolic rates and hibernation capacity, that are needful to overcome cold seasons

(McNab, 1969; Stevens, 2004). Therefore, these families could be seen as cases of niche

evolution under the perspective of TNC (Pereira & Palmeirim, 2013). However, this is

not necessarily against the existence of PNC in these families, as suggested by our

findings, especially given their potential origin in the temperate region (Teeling et al.,

2005) and their converse latitudinal gradient (Stevens, et al. 2005), particularly for

Vespertilionidae.

In contrast with previous studies, we did not find strong support for PNC in the

tropical family Phyllostomidae (see Stevens, 2011; Villalobos et al., 2013).

Phyllostomids have been one of the most studied chiropteran families and have served as

a model system given their strong influence in driving the latitudinal gradient of bat

diversity (Willig et al., 2003a; Stevens, 2004). Actually, the strong latitudinal gradient of

phyllostomids, their successful radiation in the tropics and limited distribution in high

29

latitudes are considered a good example of TNC (Stevens, 2004; 2006; 2011). Aside from

the PSR results supporting conservatism of niche position in this family, we rejected the

effect of an OU process (i.e. stabilizing selection) in the evolution of the phyllostomid

climatic niches. In fact, we could not distinguish between the fit of an OU and a White

Noise (WN) evolutionary model for niche position in this family. Importantly, a recent

simulation study showed that a single optimum OU model (like the one applied here) with

strong selection strength can be readily misidentified as a WN model (Münkemüller et

al., 2015). This, in turn, could either mean that phyllostomid climatic niche may have

evolved either under strong selection or randomly (Diniz-Filho et al., 2010). Looking at

the phyllostomid richness and distributional patterns, it is plausible to suppose that their

climatic niches may have evolved under a strong evolutionary stasis (Stevens, 2004;

2011). In fact, recent phylogenetic studies suggest that phyllostomid evolution has been

characterized by ecological and phenotypic stasis following early species’ diversification

(Dumont et al., 2011; Monteiro & Nogueira, 2011; Rojas et al., 2011).

Emballonuridae was the only family where results supported a Brownian-like

evolution of both niche features. Species richness of this family shows a weaker increase

towards the equator compared to the Phyllostomidae, suggesting higher independence of

latitudinally varying resources (Stevens, 2004). Depending on the reference evolutionary

model used to assume PNC, we could consider the support for Brownian evolution of the

emballorunid climatic niche as consistent with PNC (Cooper et al., 2010). In fact, the

emballorunid pattern was the only situation in which Blomberg’s K statistic was higher

than one, providing evidence for PNC in this family also under this debated test (Losos,

2008).

Overall, we found more support for PNC regarding niche position (NP) instead of

niche breadth (NB). This is interesting given that NP provides information about the mean

30

position of the species niche (Thuiller et al., 2005), whereas NB informs about the

variation of climatic conditions that describe species niches (Dolédec et al., 2000). Thus,

PNC in bats seems to be acting through species conserving their niche position instead of

conserving their climatic tolerances. The lack of support for PNC in niche breadth is

consistent with previous findings showing that geographical range size (usually related

with niche breadth; Slatyer et al., 2013) of New World bats is not phylogenetically

structured (Arita, 1993; Villalobos et al., 2013), presenting considerable variation within

and among bat families (Willig et al., 2003b). Finally, recent findings considering the

Eltonian aspect of bat niches (i.e., diet) (Olalla-Tárraga et al., 2017) seem to agree with

our findings on their climatic niches, with support for PNC and scale-dependency of such

pattern. More detailed investigations on the connection between the evolution of different

aspects of species ecological niches and their role in determining diversity patterns are

surely needed. Such endeavor can bring us closer to an evolutionary theory of the niche

(Holt, 2009).

CONCLUSION

Several studies have suggested the presence of phylogenetic niche conservatism in bats

(Stevens, 2006, 2011; Buckley et al., 2010; Villalobos et al., 2013) but none have directly

tested for species niche similarity and across the whole order. Here we have provided

evidence that at least one Climatic niche feature (niche position) of Chiroptera shows

phylogenetic conservatism. Under a ‘PNC as a pattern’ perspective, our findings support

the claim that bat climatic niches have evolved more slowly than expected under neutral

drift (Losos, 2008; Pyron et al., 2015). Moreover, they can also serve as a basis for the

‘PNC as a process’ perspective if, as suggested by the abovementioned studies,

conservation of climatic niches drive bat diversity patterns. Testing for PNC can be a

31

complex endeavor, but comparing among different methodologies and phylogenetic

scales can help us get a clearer picture on the existence of PNC and its role in determining

the observed biodiversity patterns.

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37

SUPPORTING INFORMATION

Appendix S1. Boxplot showing the average of niche overlap values (D) between

species pairs formed by random and pairs in cladogenesis (phylogenetically close).

38

Appendix S2. PSR (phylogenetic signal representation) areas for two climatic niche

features of Emballonuridae family: (NP) niche position and (NB) niche breath. Orange

and yellow bands are the confidence intervals for the neutral (Brownian motion) and null

(random) expectations, respectively.

39


features of Hipposideridae family: (NP) niche position and (NB) niche breath. Orange



40


features of Molossidae family: (NP) niche position and (NB) niche breath. Orange and

yellow bands are the confidence intervals for the neutral (Brownian motion) and null


41


features of Phyllostomidae family: (NP) niche position and (NB) niche breath. Orange



42


features of Pteropodidae family: (NP) niche position and (NB) niche breath. Orange and



43


features of Rhinolophidae family: (NP) niche position and (NB) niche breath. Orange and



44


features of Vespertilionidae family: (NP) niche position and (NB) niche breath. Orange



45

CAPÍTULO 02: GEOGRAPHICAL

PATTERNS OF PHYLOGENETIC BETA

DIVERSITY COMPONENTS IN

TERRESTRIAL MAMMALS

46

GEOGRAPHICAL PATTERNS OF PHYLOGENETIC BETA DIVERSITY

COMPONENTS IN TERRESTRIAL MAMMALS

Aceito para publicação no periódico Global Ecology and Biogeography

F. P. Peixoto, F. Villalobos, A. S. Melo, J. A. F. Diniz-Filho, R. Loyola, T. F. Rangel &

M. V. Cianciaruso

ABSTRACT

Aim: to investigate geographical patterns of phylogenetic beta diversity (PBD) and its

turnover and nestedness-resultant components for terrestrial mammals. We expect an

increase on the contribution of the nestedness-resultant component towards temperate

regions given the historical loss of lineages caused by environmental and spatial

constraints. Analogously, we expect to find a similar increase of the nestedness-resultant

component contribution towards higher altitudes. We expect these patterns to be stronger

for Rodentia because they have poor dispersal ability and may have been less efficient in

recolonizing areas after glaciations.

Location: worldwide.

Methods: we generated terrestrial Mammalia species composition for 200 x 200 km cells

to calculate PBD and its turnover and nestedness-resultant components. All measures

were computed for each cell and the cells in the surrounding radius of one, two or three

adjacent layer cells. We calculated the relative importance of the nestedness-resultant

component as the proportion of the total PBD (PBDratio) and also PBD deviation given

taxonomic beta diversity (PBDdev). PBDdev measures the importance of phylogenetic beta

diversity after factoring out taxonomical beta diversity. We ran simple linear regressions

http://rdloyola.tk/

47

and piecewise regressions to investigate relationships between PBDratio and mean annual

temperature and altitude.

Results: we found a major contribution of nestedness-resultant component linked to

temperate climate, especially for groups with better dispersal capacity. Higher altitudes

were associated with major contribution of turnover-resultant component, particularly for

Rodentia.

Main conclusions: we provide the first global representation of phylogenetic beta

diversity in terrestrial mammals and demonstrated that at higher latitudes PBD is mostly

a result of lineage loss, whereas in highlands it is linked to lineage turnover. By analyzing

global patterns of PBD component contribution, we show demonstrate that dispersal

capacity is essential in determining the response of different lineages to geographical and

environmental barriers.

Keywords: phylobetadiversity, Mammalia, nestedness-resultant component, turnover,

phylogenetic diversity, Carnivora, Chiroptera, Rodentia.

INTRODUCTION

One of the main research goals of both ecologists and biogeographers alike is to

understand the ecological and evolutionary processes determining biodiversity patterns

(Ricklefs, 1987; Gaston, 2000; Wiens & Donoghue, 2004). Among the most ubiquitous

and extensively studied geographical pattern in biodiversity is the latitudinal diversity

gradient (LDG), in which tropical regions hold greater diversity than temperate regions

(Hillebrand, 2004; Mittelbach et al., 2007). Although the main drivers of this pattern are

still in dispute (Brown, 2014), dominant biogeographical theories support historical

processes as responsible for LDG (Mittelbach et al., 2007; Rolland et al., 2014). For

48

example, the Tropical Niche Conservatism hypothesis (TNC, Wiens & Donoghue, 2004)

states that most lineages originated and accumulated in the tropics, given that they have

had more time to speciate. Moreover, dispersal and adaptation to temperate regions have

been constrained for most groups under niche conservatism, thus producing higher

species richness in the tropics. Alternatively, the Out of the Tropics hypothesis (OTT,

Jablonski et al., 2006) predicts that high rates of diversification (i.e. more speciation than

extinction) generated high clade diversity in the tropics. Additionally, low diversity in

temperate zone is hypothesized to be caused by high extinction rates added to abiotic

environmental constraints, hindering colonization of these regions (Jablonski et al.,

2006).

The LDG pattern has been related to an analogous gradient in species beta

diversity, with higher dissimilarity between sites at lower latitudes (Qian & Ricklefs,

2012). Consequently, similar explanations have been put forward to explain both

gradients (Qian & Xiao, 2012). High levels of beta diversity were found to be related to

high energy availability, which indicate an increase in speciation and may have

contributed to LDG pattern. Exploring species dissimilarity patterns across space may

help us to elucidate historical and ecological causes of taxa distribution in a given gradient

(Whittaker, 1972). Beta diversity can be related to a range of processes regarding species

ecology (e.g. traits and niche) and environmental characteristics (e.g. isolation,

topography) (Tuomisto et al., 2003; Potts et al., 2004; Graham et al., 2006; Mcknight et

al., 2007). At global level, the best predictor for beta diversity is the difference in altitude

instead of biotic transitions (Mcknight et al., 2007; Melo et al., 2009), as evidence of

species adaptation to particular environmental conditions (Melo et al., 2009).

Recent developments in estimation and interpretation of beta diversity have

emphasized the need for decomposing between distinct aspects of dissimilarity across

49

space. Lennon et al. (2001) showed that common beta diversity indices are affected not

only by species turnover (or replacements) but also by differences in species richness and

suggested the use of turnover-only indices. This suggestion was fully developed by

Baselga (2010, 2012) who formally proposed a framework for partitioning beta diversity

into two components: dissimilarity due to species turnover and dissimilarity due to

richness differences. Global patterns of beta diversity components revealed a differential

importance of each component in determining the latitudinal diversity gradient,

highlighting the role of distinct processes (Baselga et al., 2012). For example, high

latitudes are generally related to dissimilarity owing to species richness differences

(Baselga et al., 2012), which is interpreted as an imprint of past glaciations and is more

evident for groups with less dispersal capacity (Dobrovolski et al., 2012).

Although beta diversity and its components may provide information about

ecological and evolutionary processes, species composition alone does not consider the

shared ancestry among species (Purvis & Hector, 2000). Even two regions with

completely different species compositions can share much of their evolutionary histories

(Graham & Fine, 2008). Thus, including evolutionary information into the analysis of

compositional and turnover patterns allows for a deeper investigation of the evolutionary

processes responsible for generating biodiversity gradients (Graham & Fine, 2008;

Kubota et al., 2011; Peixoto et al., 2014). Lozupone & Knight (2005), Bryant et al. (2008)

and Graham & Fine (2008) proposed the incorporation of phylogenetic information to

understand beta diversity (BD), which they referred as phylogenetic beta diversity (PBD).

PBD explicitly adds phylogenetic information to describe the difference among species

assemblages, that is, it measures to what extent these assemblages differ in terms of the

evolutionary relationships of its members (Graham & Fine, 2008). Leprieur et al. (2012)

extended Baselga’s partitioning of beta diversity to PBD and proposed its decomposition

50

in components due to lineage turnover (turnover-resultant component; PBDTURN) and due

to differences in phylogenetic diversity (PD) between assemblages (the nestedness-

resultant component; PBDPD) (Leprieur et al., 2012).

Here, we explored for the first time the global patterns of phylogenetic turnover

and nestedness-resultant components for terrestrial mammalians and tested some

biogeographical hypotheses that could explain these patterns (Table 1). In particular, we

are interested in inferring potential historical mechanisms driving PBD geographical

gradient for terrestrial mammalians by extending predictions of current biogeographical

hypotheses for LDG pattern. We hypothesized that historical processes, which have shaped

biodiversity latitudinal patterns, would produce different contributions of PBD components

between tropical and temperate regions. In accordance with TNC and OTT hypotheses, tropics

are both cradles and museums. Therefore, we expect higher lineage replacement (PBDTURN)

towards the equator. On the other hand, we expect higher influence of PBDPD in temperate

regions resulting from lineage loss towards the poles (Baselga, 2012). In the same way as

for climatic zones, we expect that colonization of higher altitudes will be followed by loss

of lineages, displaying major contribution of PBDPD component in mountainous areas.

We also investigated if Carnivora, Chiroptera, and Rodentia have differential

contributions of PBD components due to their efficiency in recolonizing areas that were

affected by climatic changes (Smith & Green, 2005; Dobrovolski et al., 2012).

Considering geographical range size as a proxy for dispersal ability (Gaston, 1996) we

expect that PBDPD contribution in high latitudes will be higher for Rodentia.

51

Table 1 Two hypotheses and their predictions for the phylogenetic beta diversity (PBD)

patterns in Mammalia and its components: the turnover-resultant component (PBDTURN)

and the phylogenetic diversity component (PBDPD).

Hypotheses Predictions References

H1: Historical processes that

shaped biodiversity latitudinal

patterns will produce different

contributions of PBD

components between tropical

and temperate regions. In

accordance with “out of the

tropics” and “tropical niche

conservatism” hypotheses,

tropics are both cradles and

museums. Therefore,

dissimilarity among

communities will be driven

mainly by PBDTURN

component. In opposite,

PBDPD component would be

more important among toward

temperate regions, given loss

of lineages towards the poles.

P1. We expect a negative

relationship between PBDratio

and the annual mean

temperature. Therefore,

regions with temperate climate

will be related to high values

of PBDratio, which means great

contribution of PBDPD.

Otherwise, tropical climate

will be related to lower values

of PBDratio that mean major

contribution of PBDTURN.

Wiens & Donoghue 2004

Jablonski et al., 2006

Baselga, 2010

Baselga et al., 2012

Dobrovolski et al., 2012

H2: In the same way that

dispersal and adaptation to

temperate regions have been

prevented by environmental

constrains, colonization of

higher altitudes will also be

followed by loss of lineages.

P2. We expect high values of

PBDratio being related to high

altitudes, displaying major

contribution of PBDPD.

Wiens & Donoghue 2004

Jablonski et al., 2006

Baselga, 2010

Baselga et al., 2012


H3: Differences among the

dispersal capacities among

mammalian orders will result

in distinct contributions of

PBD components in

accordance with their

efficiency in recolonizing

areas that were affected by

climatic changes.

P3. Among the mammalian

orders analysed, we expect

that the contribution of PBDPD

in temperate regions

(prediction 1) will be stronger

for Rodentia, which has

greater frequency of small

range sizes.

Smith & Green, 2005

Harrison et al., 1992

Böhning-Gaese et al., 1998


Gaston, 1996

52

METHODS

Data origin

We gathered information on the geographical distribution of 4536 terrestrial

mammalian species (IUCN, 2015). To derive species composition for cells, we

overlapped range maps in a global raster of 200 X 200 km cell size (see Safi et al., 2011).

A grid cell was considered occupied by those species where the centre of the grid cell

intersected with the species ranges. In cases where a species’ range (or isolated sections

of the range distribution) did not intersect with the centre of any grid cell, we considered

the grid cell (or grid cells) intersected by that range as occupied by that species. We used

a dated phylogenetic tree to obtain phylogenetic relationship information among the

species we were studying (4536 mammalian species) (Bininda-Emonds et al., 2007; Fritz

et al., 2009). We collected climate (annual mean temperature) and altitudinal data for

these species from Worldclim online database (Hijmans et al., 2005).

Phylogenetic beta diversity partitioning

We calculated both taxonomic beta diversity (BD) and phylogenetic beta diversity

(PBD) among cells using two families of dissimilarity indices, Sørensen (Sørensen, 1948)

and Jaccard (Jaccard, 1912). PBD differs from BD simply by the replacement of

exclusive/shared ‘species’ by ‘branch lengths’. Thus, PBD captures the proportion of

shared and exclusive branch lengths among assemblages. PBD varies from 0 (when the

assemblage composition and, thus, shared branch lengths is identical) to near 1 (when the

assemblages are composed by distinct species that share no branch in the rooted

phylogenetic tree). We evaluated the deviations of PBD patterns given the taxonomic beta

diversity (BD) in order to identify places where the exchange of lineages was higher or

lower than expected given the species exchange (Graham & Fine, 2008). Such deviations

53

(hereafter PBDdev) were calculated as PBDdev = BD-PBD / BD. When PBD is the same as

the taxonomic beta diversity (i.e. in the case of a star phylogeny, see Melo et al., 2014)

this ratio will be zero. Conversely, if PBD among places is low in relation to BD this ratio

will be close to 1, indicating that there is no lineage exchange. On the other hand, negative

values indicate that lineage exchange is more important than taxonomic beta diversity.

To understand the relative contribution of PBD components in our analysis, we

used an additive partitioning framework (Leprieur et al., 2012) built upon the original

approach of Baselga (2010, 2012). This framework partitions PBD index into one

component representing the turnover-resultant dissimilarity (PBDTURN) and another

component that corresponds to the dissimilarity due to PD differences between regions,

the nestedness-resultant component (PBDPD). In this framework the richness-difference

dissimilarity (PBDPD) accounts for phylogenetic diversity differences derived only from

nestedness (Baselga & Leprieur, 2015).

Data analysis

All measures were computed within a window of cells, using the same procedure of Melo

et al. (2009). To explicitly consider the scale dependence of results, we did the analyses

using three window sizes, defined as radii of one, two, and three cell layers adjacent to

the focal cell. We used both pairwise and multiple-site approaches to calculate PBD. For

the pairwise approach, we obtained dissimilarity values between the focal cell and all

adjacent cells and assigned the average value for the focal cell. In the multiple-site

approach, a single value was obtained from all samples compared (all cells in the window)

following Baselga et al. (2007). A multiple-site index avoids repetition of each site in

several pairs (i.e. lack of independence) and prevents information loss regarding the

number of species shared among the sites that are being compared (Diserud & Ødegaard,

54

2007). The same partitions cited above for the pair-wise indices were calculated for the

multi-site indices. We used functions of the betapart (Baselga et al., 2013) and the

CommEcol (Melo, 2013) packages implemented in R version 3.3.0.

We generated global pattern maps for PBD and their components for terrestrial

mammals and for three mammalian orders separately. We chose Carnivora, Chiroptera,

and Rodentia owing to their global distribution and their dissimilar dispersal capacity.

We tested hypothesis 2 (Table 1) following the rationale that clades with high dispersal

capacity will be more efficient in recolonizing areas that were deglaciated only recently

(e.g. Schloss et al., 2012). As a proxy for dispersal capacity we used the median values

of geographical range size (Gaston, 1996), quantified as the number of occupied cells:

Carnivora (83) > Chiroptera (19) > Rodentia (5). Then, we produced maps showing the

relative importance of the nestedness-resultant PBD component (hereafter PBDratio)

following Dobrovolski et al. (2012): PBDratio = PBDPD / PBD. Values smaller than 0.5

indicate that PBD is mainly determined by turnover-resultant dissimilarity (PBDTURN),

whereas values greater than 0.5 indicate that PBD results from a greater influence of the

dissimilarity component due to PD differences between assemblages (PBDPD). To test

our predictions, we performed simple linear regressions to investigate if PBDratio was

correlated with [H1] annual mean temperature and [H2] altitude. Because Baselga et al.

(2012) demonstrated the existence of a breakpoint in BD geographical pattern for

amphibians, we compared linear regression fits with equivalent piecewise regressions (R

package “segmented” implemented in R version 3.3.1; Muggeo, 2008). We searched for

the combinations of breakpoint values that yielded the lowest global residual sum of

squares and which presented the minimum BIC (Bayesian information criterion) partition

(R package “strucchange” implemented in R version 3.3.0; Zeileis et al., 2002).

55

RESULTS

Jaccard and Sorensen indices produced qualitatively similar results. Also,

although the pairwise approach resulted in absolute values higher than the multiple-site

approach, the overall pattern was the same. Therefore, we present results only for

multiple-site approach with Jaccard index. In general, PBD patterns produced by different

window scale definitions were similar. However, given the higher number of compared

cells and consequently the higher distance regarding the focal cell and its adjacent cells,

well-defined boundaries were easier to identify at larger scale. We present the smallest

scale (1 layer of adjacent cells) map in the main text. Maps for the other scales are

available in Supporting Information (Fig.1; Appendix S1-S2).

We did not detect a clear latitudinal pattern for PBD components. Instead, we

found a complex geographical pattern for all mammalians and for Carnivora, Chiroptera,

and Rodentia (Fig. 1). We did not find support to our expectation that annual mean

temperature would be linearly and negatively related to PBDratio. In general, we found an

interesting non-monotonic relation with U-shaped patterns for almost all groups and

scales. Higher annual mean temperature values were related to wide variation of PBDratio

values. However, at lower temperatures we observed a negative relationship with

PBDratio, denoting a major contribution of PBDPD towards colder climates (Appendix S7-

S10). In all cases, piecewise regressions improved model fit when compared to simple

linear regressions (Appendix S7-S10). The best regression improvement was found to

Carnivora larger scale (from 0.17 to 0.43; p <0.001) and the worst to Carnivora smallest

scale (from 0.05 to 0.08; p <0.001). This result supports the existence of breakpoints on

these relationships, with distinct regions regarding PBD components contribution. Model

fit was higher with the increase of window scale (number of compared cells). The best fit

was found at larger scales for Carnivora (r2=0.43, p <0.001), Rodentia (r2=0.24, p <0.001)

56

and Chiroptera (r2=0.12, p <0.001). Contrary to our prediction, we identified a lower

contribution of PBDPD in colder temperatures for Rodentia rather than for Chiroptera and

Carnivora [Appendix S7-S10 (c)]. Besides, the proportion of nestedness-resultant

component (PBDratio) in overall beta diversity was higher for Carnivora (mean±SD =

0.608±0.285) than for Chiroptera (0.490±0.282) and Rodentia (0.399±0.261).

We observed a tendency towards high PBD values for all groups in mountainous

areas (Fig. 1). That seems to be a common pattern across highlands worldwide, mainly in

regions above 2000 meters (Andes, Nearctic Cordilleran Mountain System, Himalayas,

and Altai Mountains). These regions have a great contribution of lineage turnover in

phylogenetic dissimilarity patterns (PBDTURN) (regions marked in light yellow in Fig. 1).

In addition, turnover-resultant component was more important for Rodentia regarding

other orders (Fig. 1d). Nevertheless, we did not find support to our expectation that

altitude would be positively related to PBDratio. In all cases, both simple linear and

piecewise regressions presented a poor fit (Appendix S11-S14). In general, we observed

a triangular relationship where higher altitudes were related to low PBDratio values (major

contribution of PBDTURN) and a wide PBDratio variation at lower altitudes (Appendix S11-

S14).

57

Figure 1. Maps relating phylogenetic beta diversity (PBD) and relative importance of

PBD components (PBDratio) for (a) 4536 terrestrial mammalian species and, separately,

for (b) Carnivora (c) Chiroptera and (d) Rodentia. Each colour change means a 10%

58

quantile shift in our variables. For example, purple areas present top PBD values with a

great contribution of nestedness resultant component (PBDPD). High values of PBD that

are determined by turnover resultant component (PBDTURN) are represented by light

yellow.

Regions for which PBD were most distinct from the expected based on taxonomic

data (PBDdev) included mountainous and desert regions (blue pattern; Fig. 2). In these

areas, species exchange seems to have less importance in determining PBD patterns than

in other regions. On the other hand, for most of the world’s lowlands species exchange

was similar to lineage exchange (Fig. 2). High PBDTURN values in mountainous areas

were more determined by lineage replacement than by species exchange. In the same way,

major contribution of PBDPD found in transitional and desert areas was more related to

lineage loss than simply to species loss. Moreover, we noticed that the Arabian Peninsula

and the transition surrounding the Sahara Desert presented lower contribution of

taxonomic beta diversity in determining PBD patterns for Chiroptera than for Carnivora

and Rodentia (Fig. 2 c).

59

Figure 2. Maps of phylogenetic beta diversity deviations given the taxonomic beta

diversity (PBDdev) for (a) 4536 terrestrial mammalian species and, separately, for (b)

60

Carnivora (c) Chiroptera and (d) Rodentia. PBDdev identifies places where the exchange

of lineages were higher or lower than expected given the exchange of species. If

phylogenetic beta diversity (PBD) is the same as the taxonomic dissimilarity (BD), this

ratio will be zero. Conversely, if PBD among places is low in relation to BD, this ratio

will be close to 1, indicating that there is no exchange of lineages. On the other hand,

negative values indicate that the exchange of lineages is more important than the

taxonomic beta diversity.

DISCUSSION

Climatic gradient and PBD components

Contrary to what was observed for the taxonomic beta diversity of amphibians,

birds and mammals of the New World (Dobrovolski et al., 2012), at global scale we did

not detect a clear pattern in the relative contribution of PBD components regarding

temperature gradient. On the other hand, we found an interesting non-monotonic relation

with inversion points in the contribution of PBD components regarding global climate

zones, (Appendix S7-S10) in accordance with findings for New World mammal beta

diversity (Castro-Insua et al., 2016). Mechanisms, such as tolerance, are known to be

drivers of non-monotonic responses, since organisms may be deeply affected by severe

environmental changes (Zhang et al., 2015). The U-shaped curve that we found (mainly

for Carnivora) might demonstrate the role of extreme temperatures in extinction events

and, consequently lineage loss, which is supported by high PBDPD values (Appendix S7-

S10).

In some cases, (mainly for Carnivora and Chiroptera) warmer climates were

mostly linked to a wide variation of PBDratio values, while at colder climates the PBDPD

contribution clearly increases towards the poles (Appendix S7-S10). Dobrovolski et al.

61

(2012) demonstrated that high contribution of nestedness-resultant component in

taxonomic beta diversity was related to areas covered by ice during the last glaciations,

as suggested by Baselga (2012). They argued that these areas went through extinction

events and were recolonized since then, missing poor dispersal species. Environmental

constraints caused by low temperatures may have historically influenced spatial lineage

distribution, but we were only able to detect that pattern from a specific breaking point,

which varied for each taxonomic group (Appendix S7-S10). Here, the poorest model fit

was related to Chiroptera, the most temperature-sensitive among the analyzed

mammalian orders. There is a close relationship between temperature and energy costs

for Chiroptera (Stevens, 2006; McCain, 2009) and we did not identify a clear increment

of PBDPD values towards polar climate (glaciation imprints in the lineage dissimilarity

pattern) given that chiropteran distribution do not reach extremely low temperatures.

Dispersal abilities and PBD components

We did not find support to our hypothesis that dispersal abilities of mammalian

orders would determine PBDPD contribution in temperate regions. We expected to find

major PBDPD contribution in colder temperatures for Rodentia, which on average have

small distribution sizes and, therefore, could be less efficient in recolonizing areas that

were affected by climatic changes (e.g. Dobrovolski et al. 2012). Nonetheless, we found

that PBDratio in areas with temperate climate was higher for Carnivora than for Chiroptera

and Rodentia, demonstrating that PBDPD was more important for groups with better

dispersal abilities (Appendix S7-S10). Even not corroborating our hypothesis, dispersal

ability seems to be the main factor determining PBD component contributions to

phylogenetic dissimilarity. We found that, in general, Rodentia presented higher PBD

values [Appendix S4-S6 (a)], which is in accordance with previous findings that less

62

vagile organisms tend to have higher species beta diversity (Buckley et al., 2008; Qian &

Ricklefs, 2012). Moreover, the great contribution of turnover-resultant component for

Rodentia may also be explained by its lower dispersal capacity. Rodentia lineages may

have had more opportunities for in situ speciation, as they tend to have more fragmented

populations that could experience reduced gene flow. On the contrary, high dispersal

abilities should render Carnivora species less sensitivity to barriers, which should inhibit

speciation (Gaston, 1998; Birand et al., 2012).

Dobrovolski et al. (2012) compared taxonomic beta diversity components for

birds, mammals and amphibians, and found that poor dispersal ability increased the

contribution of nestedness-resultant component towards polar regions. In accordance with

our results, dispersal capacity does not act in the same way to generate lineage loss in

colder regions. Nonetheless, Dobrovolski et al. (2012) compared groups with very

distinct evolutionary history whereas here we are working with mammalian orders.

Amphibians are more sensitive to extreme climates because they are ectothermic and they

have very poor dispersal abilities when compared with birds and mammals (Smith &

Green, 2005; Buckley & Jetz, 2007). Therefore, at that taxonomic scale, they were able

to capture distinct imprints of glaciation extinctions among groups. On the other hand,

our findings are in agreement with Castro-Insua et al. (2016) results, which demonstrated

major importance of physiological constraints, compared to dispersal ability, to determine

beta diversity components contribution along latitudinal gradient. However, to infer

deterministic mechanisms it is important to evaluate the contribution of PBD (or BD)

components (PBDratio) taking into account the observed PBD values (Fig. 1). We need to

have in mind that presence or absence of a single species would substantially affect the

contributions of PBD components in areas with extremely low PBD values. For instance,

major contribution of PBDPD in American high latitudes for Carnivora [red cells;

63

Appendix S4 (b)] is also related to low PBD values [blue cells; Fig. 1(b); Appendix S4

(a)].

Altitude and PBD components

The most noticeable pattern we found was high PBD values in mountainous areas,

similar to what has been observed in taxonomic beta diversity (Mcknight et al., 2007;

Melo et al., 2009; Dobrovolski et al., 2012). In the Western Hemisphere, we found the

highest PBD values along the mountainous Pacific edge of the continent, highlighting the

Western Cordilleran System at the Neartic region (Sanmartín et al., 2001) and the Andes

at the Neotropical region (e.g. Mcknight et al., 2007; Melo et al., 2009). In the Eastern

Hemisphere, high PBD values were observed in continental Asia, where a complex of

high mountainous areas are found, including the Altai Mountains (Klinge et al., 2003)

and the Tibetan Plateau (Zheng, 1996). Analogously to temperate zones, we predicted that

highlands represent environmental barriers containing extinction imprints with major

contribution of PBDPD in comparison with areas of low elevation. However, we found

that extreme high altitudes are often related to a major contribution of PBDTURN (low

values of PBDratio; Appendix S11-S14), particularly for Rodentia (light yellow; Fig.1). In

highlands, large differences in altitude and temperature occur over short distances, which

is expected to increase beta diversity (e.g. Mcknight et al., 2007; Melo et al., 2009). High

environmental heterogeneity among short-distance sites may create barriers that

accelerate diversification events (Weir, 2006). In the Andean region, for instance, species

richness is higher than predicted by models of climate and altitude (Weir, 2006). These

barriers could have been even more important to groups with low dispersal capacity, such

as Rodentia, which favored speciation (see Gaston, 1998; Birand et al., 2012) and

contributed to lineage segregation and, consequently, the high PBDTURN values we found.

64

PBDdev patterns

In general, PBDdev patterns highlighted mountainous areas, demonstrating that

lineage exchange in these areas is more important when compared with species exchange.

Indeed, Graham et al. (2009) demonstrated that the strong environmental gradient

produced by elevation differences might produce larger than expected PBD for

hummingbirds, suggesting that members from distinct clades are segregated along these

regions. Additionally, we found that deserts are important areas for chiropteran lineage

exchange, which could be explained for the same reason, since deserts represent a strong

environmental filter for this group (Peixoto et al., 2014).

An interesting pattern arose from negative PBDdev values, which are represented

in dark grey (Fig. 2) and indicate that PBD is more important than taxonomic beta

diversity. It is important to note that our methodological approach restricts comparisons

among neighboring cells, thus, these results reflect local information about phylogenetic

and taxonomic composition. These values resulted from cases where there is low

taxonomic beta diversity among nearby cells and where they did not share particular

species that represent a large branch of phylogeny. In the Carnivora map, for instance,

most dark grey cells are concentrated in extreme north latitudes. In the Eastern

hemisphere, most of these cases were related to the presence of Lynx lynx and Ursus

arctos and in the Western hemisphere to Lynx canadensis.

Transitional areas

Transitional areas found in the South Sahara Desert, Arabian Peninsula, deserts in

Patagonia region, around the Tibetan Plateau, and Panama Isthmus presented high PBD

values with major contribution of nestedness-resultant component (purple patterns;

65

Fig.1). It might be a result of lineage loss produced by extreme environmental conditions

represented by these areas (e.g. Baselga, 2010). Additionally, it is interesting to note how

it phylogenetic beta diversity varies among mammalian orders. The transitions in the

south of Sahara Desert are less pronounced for Rodentia than for Chiroptera and

Carnivora (Fig. 1 c, d). Rodentia is well distributed and adaptable to deserted

environments (Kelt et al., 1996), while the aridity is in fact a great barrier for Chiroptera

(Peixoto et al., 2014). On the other hand, we noticed that Panama Isthmus transitional

area presented low PBD values for Chiroptera, which demonstrates that lineage

composition is more homogeneous for Chiroptera than for Carnivora and Rodentia

(Peixoto et al., 2014). This indicates that the Isthmus was not a strong barrier to several

Chiroptera lineages, probably due to their flight ability as it was to most non-volant

mammal lineages.

An interesting circular-shape pattern appeared in continental Asia on the Qinghai-

Tibetan Plateau, the largest and highest plateau in the world, with an area of 2.5 × 106

km2 and average elevation of ∼4000 m (Zheng, 1996). Around Tibetan Plateau, high PBD

values are mainly determined by differences in phylogenetic diversity (PBDPD) (purple

patterns), particularly for Rodentia, producing a ring circumscribed by areas where

lineage turnover is preponderant (PBDTURN). PBDPD pattern around Tibetan Plateau may

be a result of lineage loss with increasing altitude, which represents a strong

environmental filter (Presley et al., 2012). The pattern on the Qinghai-Tibetan Plateau is

characterized by low PBD values with more contribution of PBDPD for Rodentia,

Carnivora and all mammalian pooled, while Chiroptera is absent. For Chiroptera, the

environmental filter imposed by high altitudes may be stronger owing to energetic costs

linked to low temperatures for this group (Stevens, 2006; McCain, 2009; Presley et al.,

2012). This characteristic contributes to the presence of few lineages in temperate regions

66

and to their absence in colder environments (Lack & Van Den Bussche, 2010), as well as

extreme arid conditions (Fig. 1 c).

CONCLUSION

Previous studies have demonstrated that decoupling beta diversity, or

phylogenetic beta diversity, in its turnover and nestedness-resultant components may be

used to disentangle effects of different processes in shaping diversity distribution

(Baselga, 2012; Dobrovolski et al., 2012; Leprieur et al., 2012; Mouillot et al., 2013;

Peixoto et al., 2014). Here, we identified for the first time global areas for which

mammalian phylogenetic beta diversity was mainly determined by turnover (PBDTURN)

or nestedness-resultant (PBDPD) components. Higher latitudes seem to be mostly

associated with lineage loss, generating stronger PBDPD patterns. On the other hand,

highlands are linked to lineage segregation, highlighted by major contribution of

PBDTURN. Additionally, we found that patterns on PBD components may reveal

peculiarities related to evolutionary history characteristics, such as dispersal capacity,

which is important to understand differential organism responses to geographical or

environmental barriers.

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Appendix S1. Maps relating phylogenetic beta diversity (PBD) (second scale: radii of

two cell layers adjacent to the focal cell) and relative importance of PBD components

(PBDratio) for (a) mammalian species and, separately, for (b) Carnivora (c) Chiroptera and

(d) Rodentia. Each colour change means a 10% quantile shift in our variables.

72

Appendix S2. Maps relating phylogenetic beta diversity (PBD) (third scale: radii of three

cell layers adjacent to the focal cell) and relative importance of PBD components

(PBDratio) for (a) mammalian species and, separately, for (b) Carnivora (c) Chiroptera and

(d) Rodentia. Each colour change means a 10% quantile shift in our variables.

73

Appendix S3. Maps of the (a) phylogenetic beta diversity (PBD) and (b) the relative

importance of the PBD components (PBDratio) for 4536 terrestrial mammalian species.

PBD varies from 0 (when the shared branch lengths is identical) to near 1 (when the

assemblages compared share no branch in the rooted phylogenetic tree). PBDratio values

smaller than 0.5 indicate that PBD is mainly determined by the turnover-resultant

dissimilarity, whereas values greater than 0.5 indicate that PBD results from a greater

influence of the component of dissimilarity due to PD differences between assemblages.

74


importance of the PBD components (PBDratio) for Carnivora. PBD varies from 0 (when

the shared branch lengths is identical) to near 1 (when the assemblages compared share

no branch in the rooted phylogenetic tree). PBDratio values smaller than 0.5 indicate that

PBD is mainly determined by the turnover-resultant dissimilarity, whereas values greater

than 0.5 indicate that PBD results from a greater influence of the component of

dissimilarity due to PD differences between assemblages.

75


importance of the PBD components (PBDratio) for Chiroptera. PBD varies from 0 (when

the shared branch lengths is identical) to near 1 (when the assemblages compared share

no branch in the rooted phylogenetic tree). PBDratio values smaller than 0.5 indicate that

PBD is mainly determined by the turnover-resultant dissimilarity, whereas values greater

than 0.5 indicate that PBD results from a greater influence of the component of

dissimilarity due to PD differences between assemblages.

76


importance of the PBD components (PBDratio) for Rodentia. PBD varies from 0 (when the

shared branch lengths is identical) to near 1 (when the assemblages compared share no

branch in the rooted phylogenetic tree). PBDratio values smaller than 0.5 indicate that PBD

is mainly determined by the turnover-resultant dissimilarity, whereas values greater than

0.5 indicate that PBD results from a greater influence of the component of dissimilarity

due to PD differences between assemblages.

77

Appendix S7. Relationship between mean annual temperature and relative contribution

of Mammalia PBD components (PBDratio) for the (a) first, second (b) and third scale (c)

analysed (radii of one, two, and three cell layers adjacent to the focal cell). Results for

linear regression model (lm) and piecewise regression (pw) are displayed. Fitted functions

of piecewise regression are shown (black lines).

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of Carnivora PBD components (PBDratio) for the (a) first, second (b) and third scale (c)




79


of Chiroptera PBD components (PBDratio) for the (a) first, second (b) and third scale (c)




80


of Rodentia PBD components (PBDratio) for the (a) first, second (b) and third scale (c)




81

Appendix S11. Relationship between altitude and relative contribution of Mammalia

PBD components (PBDratio) for the (a) first, second (b) and third scale (c) analysed (radii

of one, two, and three cell layers adjacent to the focal cell). Results for linear regression

model (lm) and piecewise regression (pw) are displayed. Fitted functions of piecewise

regression are shown (black lines).

82

Appendix S12. Relationship between altitude and relative contribution of Carnivora PBD

components (PBDratio) for the (a) first, second (b) and third scale (c) analysed (radii of

one, two, and three cell layers adjacent to the focal cell). Results for linear regression



83

Appendix S13. Relationship between altitude and relative contribution of Chiroptera

PBD components (PBDratio) for the (a) first, second (b) and third scale (c) analysed (radii

of one, two, and three cell layers adjacent to the focal cell). Results for linear regression



84

Appendix S14. Relationship between altitude and relative contribution of Rodentia PBD

components (PBDratio) for the (a) first, second (b) and third scale (c) analysed (radii of

one, two, and three cell layers adjacent to the focal cell). Results for linear regression



85

CAPÍTULO 03: PHYLOGENETIC AND

FUNCTIONAL STRUCTURE OF

AFRICAN MAMMAL ASSEMBLAGES:

THE IMPRINT OF HISTORICAL

CLIMATIC CHANGES ON HABITAT

FORMATION

86

PHYLOGENETIC AND FUNCTIONAL STRUCTURE OF AFRICAN MAMMAL

ASSEMBLAGES: THE IMPRINT OF HISTORICAL CLIMATIC CHANGES ON

HABITAT FORMATION

Franciele Parreira Peixoto, Joaquín Hortal, Jesús Rodríguez, Marcus Vinicius

Cianciaruso

ABSTRACT

Aim: to investigate the phylogenetic and functional structure of mammal assemblages

distributed over the major African habitats. We addressed the role of historical and

ecological processes in determining the lineage pool formation and local species co-

occurrence.

Location: Afrotropical region

Methods: we explored the phylogenetic and functional structure of mammal local

assemblages and evaluated the phylogenetic and functional structure by habitat to know

in what extent the assemblage structure is a reflection of the habitat lineage/trait pool. We

also assessed the phylogenetic and functional dissimilarities among habitats, and

investigated the contribution of specific clades to the phylogenetic structure and

dissimilarity of the evaluated habitats.

Reuslts: there was a general tendency for increasing phylogenetic overdispersion and

functional clustering towards drier and less productive environments, an evidence that in

harsher environments species from distinct lineages converged to a set of similar

functional traits. On the other hand, closely related species had high trait diversity towards

forested habitats. Additionally, functional turnover was more important than phylogenetic

turnover among habitats.

87

Main conclusions: overall, it seems that a complex history of colonization among

habitats was responsible for lineage pool formation of African biomes. This history of

lineage colonization and diversification was different for specific clades and related to

ecological flexibility and, consequently, the ability to lead with climatic changes, which

have been the main driver of African mammal diversity along the evolutionary time.

Keywords: Afrotropical, historical processes, rainforests, savannas, phylogenetic

diversity, functional diversity.

INTRODUCTION

The assembly of biotic communities has been by far one of the most explored

subjects in ecology (MacArthur & Wilson, 1967; Cody & Diamond, 1975; Ricklefs &

Schluter, 1993; Graves & Rahbek, 2005; Rodríguez et al., 2006; Adler et al., 2013).

Species co-occurrence patterns are shaped by regional and local processes that operate in

a continuum of scales (Ricklefs, 1987). It is hypothesized that historical large-scale

processes (e.g. speciation, extinction, dispersal) shape regional composition, and species

are then filtered by environmental conditions and ecological processes (e.g. competition,

predation) to form local assemblages (Ricklefs, 1987; Ricklefs & Schluter, 1993; Zobel,

1997). Historical processes, such as the time for speciation and past climatic changes,

may drive diversification (Stephens & Wiens, 2003; Wiens, 2011; Kissling et al., 2012)

and shape the available diversity. Moreover, niche evolution and formation of

geographical barriers over geological time may affect dispersion among regions (Renner,

2004; Webb, 2006). Thereby, local patterns rely on historical processes that have

determined which species are available and apt to be present in a given site (Ricklefs,

1987; Rodríguez et al., 2006).

88

By knowing current patterns of community structure, we can understand about the

determinants of community assembly. The phylogenetic structure of a given community

may be used to infer historical processes that shaped the habitat lineage pool in which a

particular community is placed (Hubert et al., 2011; Kissling et al., 2012; Gerhold et al.,

2015). For example, phylogenetic overdispersion might be found in communities from

habitats formed by ancient lineages, and phylogenetic clustering may be driven by recent

diversification in young habitats (Gerhold et al., 2015; Souza-Neto et al., 2016). On the

other hand, by knowing functional community structure associated to niche-related traits

we can better conclude about the acting ecological processes, since they are niche-based

processes (Gerhold et al., 2015). Competition would drive patterns of co-occurrence of

species with different functional traits (resulting in overdispersion), and habitat filtering

would select species with the same characteristics able to deal with stressful environments

(resulting in clustering) (Webb et al., 2002). Thus, by allying functional and phylogenetic

approaches, we should be able to better understand the role of local processes in

structuring community composition and to discriminate it from historical-evolutionary

processes determining lineage pool formation (Safi et al., 2011; Cianciaruso et al., 2012;

Gerhold et al., 2015).

Besides the structure of assemblages, we can investigate the formation of lineage

pool by knowing the phylogenetic beta dissimilarity among sites (Graham & Fine, 2008;

Peixoto et al., 2014, 2017). For example, if distinct habitats are formed by similar

lineages, the phylogenetic dissimilarity between them would be low (Peixoto et al., 2014;

Souza-Neto et al., 2016). The same reasoning could also be followed for functional

diversity (e.g. Swenson et al., 2012). For example, the lineage pool formation of a more

recent habitat/biome might be driven by colonization of lineages from an older habitat

(e.g. Souza-Neto et al., 2016). The colonization of lineages, which characterizes habitat

89

shift, may be accompanied by the development of specific adaptations if the change of

habitat is related to a harsh environmental gradient, such as the aridity gradient (Souza-

Neto et al., 2016). In this case, the lineage composition between habitats would be similar,

but the composition of functional characteristics would be different. On the other hand,

the prevalence of niche conservatism might reduce the exchange of lineages among

habitats because the lineages will conserve environmental tolerances from their ancestral,

rarely shifting of habitat (Wiens & Donoghue, 2004; Crisp et al., 2009).

In the African continent, a unique combination of large-scale historical-

evolutionary processes, such as, continental drift, past climatic changes, extinction,

speciation, and dispersal events have determined the current composition of regional biota

(Janis, 1993; de Vivo & Carmignotto, 2004; Lawes et al., 2007). Continual cycles of

climatic changes have repeatedly caused events of forest contraction and expansion,

which had a significant impact on the diversity and distribution of African flora and fauna

(Janis, 1993; Plana, 2004). The Afrotropics was the continental region with the highest

rate of rainforest loss since Eocene due to a drier climate along most of equatorial Africa

(Maley, 1996; Kissling et al., 2012; Willis et al., 2013). Reduction of rainforest coverage

during glacial maxima confined forest organisms to small areas promoting local

extinctions and in some cases led to the elimination of entire lineages (Vrba, 1992; Janis,

1993; Plana, 2004; Lawes et al., 2007). Conversely, the Afrotropical region has supported

large and stable areas of suitable dry open habitats even during moister periods, when

savannas would have expanded into previously arid areas (Cristoffer & Peres, 2003; de

Vivo & Carmignotto, 2004). That could have turned savannas into a diversification arena

where forest lineages that evolved adaptations would be able to colonize dry open

environments (Cristoffer & Peres, 2003; de Vivo & Carmignotto, 2004). Thus, African

past climactic events led to distinct historical and evolutionary processes determining

90

different habitat lineage-pool, which is supposed to influence community assembly

although it has not been extensively explored (see Gerhold et al., 2015).

Here, we tested the hypothesis that (H1) historical climatic instability and habitat

area loss led to lineage loss in rainforests, which coupled with speciation in forest refuges

and colonization/adaptation of forest lineages to dry open environments (habitat shift)

may have acted shaping contrasting community structure patterns among habitats in a

dryness gradient (Cristoffer & Peres, 2003; de Vivo & Carmignotto, 2004; deMenocal,

2004; Plana, 2004; Lawes et al., 2007; Kissling et al., 2012). From a functional

perspective, we tested the hypothesis (H2) that dryness would represent a strong

environmental filter, tending to select species with similar functional traits that allow

them to colonize and survive in dry open habitats (Ojeda et al., 1999; Cristoffer & Peres,

2003; deMenocal, 2004; Cantalapiedra et al., 2014). On the other hand, competition

would play a major role in more productive environments (e.g. rainforests), where a

higher variety of traits would be found (Schoener, 1982; Weiher & Keddy, 1995) (see

Table 1 for predictions). We explored the phylogenetic and functional structure of

mammal local assemblages distributed over the major African biomes (from desert and

savanna to rainforests). We also evaluated the phylogenetic and functional structure by

habitat to know in what extent the assemblage structure is a reflection of the habitat

lineage/trait pool. We also assessed the phylogenetic and functional dissimilarities among

habitats, and investigated the contribution of specific clades to the phylogenetic structure

and dissimilarity of the evaluated habitats, to test for our hypothesis of dry open habitat

colonization by rainforest lineages (e.g. Leprieur et al., 2012; Souza-Neto et al., 2016).

91

Table 1. Hypotheses and their predictions about phylogenetic and functional patterns for

African mammal assemblages.

Hypotheses Predictions

H1: Historical climatic instability and loss of

rainforest area led to lineage loss in forested

habitats and, at same time, speciation in

forest refuges. On the other hand, the

vastness and stability of dry open habitats

during glacial cycles would have turned them

into diversification sites for several forest

lineages that have evolved adaptations to

colonize these environments. Thus, the

lineages found in dry open habitats will be

the result of habitat shift of rainforest

lineages (Cristoffer & Peres, 2003; de Vivo

& Carmignotto, 2004; deMenocal, 2004).

P1.1. We expect to find progressive levels

of phylogenetic clustering in Tropical

Rainforest assemblages and/or at habitat

level.


of phylogenetic overdispersion in dry

open habitat assemblages and/or at

habitat level.

P1.3. Phylogenetic beta diversity among

Rainforest and the other habitats will be

lower than expected by chance.

P1.4. Phylogenetic beta diversity among


mainly due to phylogenetic nestedness.

P1.5. We expect to find in savanna habitats

overabundance of the same higher taxa

found in forested habitats.

H2: Adaptations to dry open habitats there

were required to make possible the

colonization by forest lineages. Dryness

would represent a strong environmental filter

where only specialized adaptations can

successfully persist (Ojeda et al., 1999;

Cristoffer & Peres, 2003; deMenocal, 2004).

In opposition, in more productive

environments, where competition tend to

play a major role, a higher variety of traits

would be found (Schoener, 1982; Weiher &

Keddy, 1995)


of functional clustering towards dry open

environments.


of functional overdispersion towards

rainforest habitat.

P2.3. Functional beta diversity among


higher than expected by chance.

P2.4. Functional beta diversity among


mainly due to functional turnover.

92

METHODS

Local assemblages

We selected 16 terrestrial non-volant mammal checklists (Chiroptera, Cetacea and

Pinnipedia were excluded) located in all major continental habitats of sub-Saharan Africa

(see Table S1 for references). These checklists were selected due to their completeness

and, especially, because they are located in protected areas covered mainly by a single

biome-type (for example, rainforest or savanna). These sites can be ordered according to

their biome-type in a dryness gradient: desert, savanna, humid savanna, dry forest and

rainforest (Table 2; Figure 1).

Table 2. Description of the sixteen studied communities regarding their identity (ID) and

site names, habitat from Bailey (1989) provinces, species richness, (S) and geographical

coordinates in decimal degrees (Lat, Long).

ID Site Habitat S Lat Long

1 Basse Casamance National Park (Senegal) Humid/semi humid Savanna 45 12.45 -16

2 Parc National de la Pendjari (Benin) Humid/semi humid Savanna 42 11.00 1.50

3 Taï National Park (Ivory Coast) Humid/Semi-humid forests 64 5.46 -6.65

4 Omo Biosphere Reserve (Nigeria) Humid/Semi-humid forests 32 6.50 4.25

5 Dja Faunal Reserve (Cameroon) Humid/Semi-humid forests 95 3.06 13

6 Parc National d'Odzala (Rep. of Congo) Humid/Semi-humid forests 62 0.80 14.88

7 Kibale Forest Corridor National Park (Uganda) Dry Forests/shrub 65 0.5 30.2

8 Amboseli National Park (Kenya) Humid/semi humid Savanna 67 -1.61 37.15

9 West Caprivi Game Reserve (Namibia) Savanna/Shrubland 89 -16.09 22.62

10 Kalahari Gemsbok National Park (South Africa) Savanna/Shrubland 56 -25.68 20.37

11 Naute Dam (Namibia) Desert/Semi-arid 66 -26.97 17.96

12 Malolotjia Nature Reserve (Swaziland) Savanna/Shrubland 68 -26 31.03

13 Mlawula Nature Reserve (Swaziland) Savanna/Shrubland 66 -26 32

14 Augrabies Falls National Park (South Africa) Desert/Semi-arid 46 -27.42 20.35

15 Weenen Game Reserve (South Africa) Humid/semi humid Savanna 39 -28.7 30.4

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16 Kogelberg Nature Reserve (South Africa) Dry Forests/shrub 55 -33.77 19

Figure 1. Map of Afrotropical region showing the sixteen studied assemblages. The

habitat in which the assemblages are placed is displayed by different symbols (see the

legend). The numbers showed next to the symbols represent the assemblage ID, which

are listed in table 2.

Phylogenetic and functional information

To perform the phylogenetic analyses, we used a dated mammalian phylogenetic

tree (Bininda-Emonds et al., 2007; Fritz et al., 2009). When a given species was not

present in the original phylogeny, we placed it as a polytomy within a sister taxon. After

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all taxonomic revisions and polytomies insertions (seven species) we obtained a

phylogenetic tree with 750 species (around 80% of all terrestrial non-volant mammals

registered; Galster et al., 2007). To implement functional analyses, we built a dendrogram

using an updated version of the PanTHERIA functional data (Jones et al., 2009; Hidasi-

Neto et al., 2015). We used quantitative data of body mass and binary data of dietary,

habit and activity and missing data were extrapolated by genus (408 species had at least

one of these traits extrapolated by genus).

Assemblage level analyses

One of the greatest challenges in investigating community structure is the species

pool definition (Connor & Simberloff, 1979; Gotelli & McCabe, 2002; Webb et al., 2002;

Lessard et al., 2012a). Results largely depend on how species pools are defined, which

reflects historical processes that shaped species composition at regional scale (see

Carstensen et al., 2013a). We built eleven source pools for each of the 16 studied

assemblages using a geographic database on the distribution of all studied species across

mainland sub-Saharan Africa (South of 20 º N) at the resolution of 1º x 1º grid cells

(Galster et al., 2007; held at the Zoological Museum, University of Copenhagen). The

source pool definitions were: (1) continental, (2) circle unweighted, (3) circle weighted,

(4) biome unweighted, (5) biome weighted, (6) dispersion field unweighted, (7)

dispersion field weighted, (8) cluster unweighted, (9) cluster weighted, (10) module

unweighted and (11) module weighted (see appendix S1 for details).

We calculated standardized effect sizes (SES) of phylogenetic diversity (PD;

Faith, 1992) and functional diversity (FD; Petchey & Gaston, 2002) for each mammal

assemblage. We tested for deviance of community structure values in regards to null

expectation. In all null models species richness was fixed and only species identity was

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randomized (1000 thousand times) to obtain a standardized effect size (SES) (Gotelli &

Entsminger, 2003). We performed these analyses to each source pool definition (SI

methods). To run these analyses we used the function ses.pd implemented in Picante

package (Kembel et al., 2010).

We calculated Pearson correlation coefficients among SES values (phylogenetic

and functional) obtained from the eleven source pool definitions to test the robustness of

our results. Because we did not find qualitative differences among source pool definitions

(Table S5) we only present the results for “dispersion field weighted”, which is a more

realistic approach in biological terms (Graves & Rahbek, 2005). We performed a linear

regression analysis to test for predictions of our hypotheses H1 and H2 regarding the

relation between phylogenetic and functional SES values and the dryness gradient.

Habitat level analyses

We calculated standardized effect sizes of phylogenetic diversity (PD) and

functional diversity (FD) for each African habitat recorded (i.e. all assemblages of a given

habitat pooled together) with the same null model approach used to assemblage level (see

above). To assess phylogenetic (PBD) and functional beta diversity (FBD) among

habitats, we used an additive partitioning framework (Leprieur et al., 2012) based on the

original approach of Baselga (2010, 2012). This framework allows us to assess the

amount of phylogenetic and functional dissimilarity between assemblages (PhyloSor

index; PBD or FBD) that is owing to real lineages or functional traits turnover, (turnover-

resultant dissimilarity PBDTURN or FBDTURN) or determined by phylogenetic or functional

diversity differences between nested assemblages (PD/FD-resultant dissimilarity

component; PBDPD or FBDPD). PD/FD-resultant component is simply the difference

between PBD/FBD and PBDTURN /FBDTURN i.e. (i.e. PBDPD = PBD – PBDTURN and

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FBDPD = FBD – FBDTURN). A standardized effect size (SES) was calculated for PBD and

PBDTURN. SES values greater than 1.96 indicate a higher PBD than expected by beta

diversity while SES values below -1.96 indicate lower PBD than expected by beta

diversity (Leprieur et al., 2012). To investigate the taxa responsible for phylogenetic

structure and dissimilarity among habitats, we tested each phylogenetic node for an

overabundance of terminal taxa using a nodesig analysis available in Phylocom 4.2

(Webb et al., 2008). The observed patterns for each sample were compared to those for

random samples (fixing the number of terminal taxa) to find the nodes significantly

overabundant (see Souza-Neto et al., 2016 for a similar approach).

RESULTS

At the assemblage level (using specific species pool definition for each

community), savannas and desert habitats were phylogenetically overdispersed (i.e.

formed by distantly related species) while the phylogenetic diversity of rainforest

assemblages was equal to what one may expect by chance (Table S2). We found a

tendency for decreasing SES values (phylogenetic structure) towards rainforest

assemblages (supporting predictions P1.1. and P1.2; Figure 2a). At habitat scale we found

that rainforest lineage pool was composed by closest related lineages (phylogenetic

clustering) and the phylogenetic structure of the other habitats showed a random pattern

(Table S4), with SES values increasing towards more arid environments (supporting

predictions P1.1. and P1.2; Figure 2c). In general, phylogenetic beta diversity (PBD) among

habitats was low, equal to the expected by chance and mainly determined by turnover

component (contrarily to predictions P1.3. and P1.4.). The highest turnover value was found

between rainforest and savanna (Table 3). Rainforest was mainly related to

overabundance of terminal taxa related to the orders Primate, Cetartiodactyla and

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Carnivora. The nestedness pattern that we predicted in (P1.5) was only supported for

Cetartiodactyla and Carnivora. The carnivoran taxa overabundance was generally related

to different families for rainforest and the other habitats, while Cetartiodactyla was always

related to Bovidae, but with different subfamilies being represented in rainforest and for

humid savanna (Table 4).

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Figure 2. Linear regression results relating the SES values and the habitat type, for

assemblage (using Dispersion Field unweighted pool) and habitat level. Results for

phylogenetic structure using (a, c) PD and for functional structure, using (b, d) FD.

Rainforest = Humid/Semi-humid forests; Dry Forest = Dry Forests/shrub; H. Savanna =

Humid/semi humid Savanna; Savanna = Savanna/Shrubland and Desert = Desert/Semi-

arid.

Table 3. Pairwise phylogenetic beta diversity (PBD) values among African habitats are

shown below diagonal and turnover-resultant component of PBD (PBDTURN) is above

diagonal. Values in bold indicate smaller standardized effect size (SES) values than

expected by chance. Rainforest = Humid/Semi-humid forests; Dry Forest = Dry

Forests/shrub; H. Savanna = Humid/semi humid Savanna; Savanna = Savanna/Shrubland

and Desert = Desert/Semi-arid.

Habitat Rainforest Dry forest H. Savanna Savanna Desert

Rainforest --- 0.353 0.367 0.456 0.455

Dry forest 0.379 --- 0.373 0.250 0.348

H. Savanna 0.379 0.386 --- 0.194 0.204

Savanna 0.482 0.316 0.248 --- 0.068

Desert 0.549 0.434 0.326 0.271 ---

Table 4. Clades overrepresented on nodesig analysis by habitat. Rainforest =

Humid/Semi-humid forests; Dry Forest = Dry Forests/shrub; H. Savanna = Humid/semi

humid Savanna; Savanna = Savanna/Shrubland and Desert = Desert/Semi-arid

Habitats Clades overrepresented on nodesig analysis

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The functional structure of the mammal assemblages showed functional clustering

for dry open environments and increasing SES values (functional structure) towards

rainforest assemblages (corroborating P2.1 and P2.2; Table S3; Figure 2b). Functional

structure at the habitat scale showed the same pattern found to assemblages (Table S4;

Figure 2d). Likewise, supporting predictions P2.3 and P2.4., functional beta diversity (FBD)

Rainforest

Carnivora: families Herpestidae and Nandiniidae, subfamily Viverrinae

(Viverridae) and Genetta genus (Viverridae);

Cetartiodactyla: family Bovidae, subfamily Cephalophinae (Bovidae),

Cephalophus and Tragelaphus genera (Bovidae);

Hyracoidea: Dendrohyrax genus (Procaviidae);

Pholidota: Family Manidae;

Primate: Primate order, Families Cercopithecidae and Galagonidae, subfamily

Perodicticinae and Cercopithecus and Colobus genera;

Rodentia: Family Anomaluridae, Anomalurus and Idiurus genera

(Anomaluridae).

Dry Forest Carnivora: families Mustelidae and Felidae, Herpestes genus (Herpestidae);

H. Savanna Carnivora: families Mustelidae, Felidae, Canidae, Hyaenidae and Canis

genus (Canidae);

Cetartiodactyla: family Bovidae, subfamily Reduncinae (Bovidae), Redunca

and Tragelaphus genera (Bovidae);

Hyracoidea: Dendrohyrax genus (Procaviidae).

Savanna Carnivora: families Herpestidae, Mustelidae, Felidae, Canidae, Hyaenidae

and Felis genus (Felidae);

Cetartiodactyla: family Bovidae;

Desert

Carnivora: families Mustelidae, Felidae, Canidae, Hyaenidae, Panthera

(Felidae) and Galerella (Herpestidae) genus;

Lagomorpha: family Leporidae;

Perissodactyla: Equus genus (Equidae);

Rodentia: Paratomys (Muridae) and Gerbillurus (Muridae) genus.

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among rainforest and the other habitats were always higher than expected by chance and

mainly determined by turnover (PBDTURN), with functional dissimilarities being higher

when rainforest was compared with savanna and desert (Table 5).

Table 5. Pairwise functional beta diversity (FBD) values among African habitats are

shown below diagonal and turnover-resultant component of FBD (FBDTURN) is above

diagonal. Values in bold indicate higher standardized effect size (SES) values than

expected by chance. Rainforest = Humid/Semi-humid forests; Dry Forest = Dry


and Desert = Desert/Semi-arid

Habitat Rainforest Dry forest H. Savanna Savanna Desert

Rainforest --- 0.385 0.423 0.544 0.503

Dry forest 0.420 --- 0.372 0.270 0.372

H. Savanna 0.446 0.383 --- 0.269 0.296

Savanna 0.548 0.317 0.304 --- 0.095

Desert 0.623 0.490 0.440 0.320 ---

DISCUSSION

Phylogenetic structure of habitats and assemblages

In general, results at assemblage and habitat level were congruent, supporting a

major role of African biome formation history in determining lineage co-occurrence

(Gerhold et al., 2015). While the significant phylogenetic structure patterns varied

between the analyzed scales, the same tendency of increasing phylogenetic

overdispersion in a dryness gradient was keep in both, assemblage and habitat level

(Figure 2a, c). It seems that historical processes have determined current lineage pool

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across African habitats (structured along a dryness gradient) leading to contrasting

phylogenetic structures (Cavender-Bares et al., 2009; Lessard et al., 2012b; Gerhold et

al., 2015). The lineage pool of these habitats have been formed along million years,

reflecting the role of large-scale processes (e.g. climatic changes) in diversification and

extinction patterns (Plana, 2004; Hernández Fernández & Vrba, 2005). Low values of

phylogenetic beta diversity between habitats denotes that even very different habitats

share a great amount of evolutionary history (Leprieur et al., 2012). It is in accordance

with our prediction regarding the supposed colonization history of dry open environments

by African forest lineages (deMenocal, 2004). However, we found that most of

dissimilarity recorded was mainly determined by real lineage turnover. Thus, African

habitats share great amount of mammal lineages but have also keep exclusive lineages

(Table 3; see also Table 4).

It has been suggested that habitat phylogenetic structure could be an imprint of

habitat macroevolutionary diversification, which would result in phylogenetic

overdispersion in ancient habitat and clustering in young habitats (Gerhold et al., 2015;

Souza-Neto et al., 2016). Our results are contrary to this statement, since we found

phylogenetic clustering related to rainforest habitats and a tendency towards

overdispersion for savanna assemblages (Figure 2a, c). Rainforests are much older than

current savanna habitats and prevailed in Afrotropical region during the period when

Mammals passed through a great radiation (i.e. the early Tertiary) (Janis, 1993; Maley,

1996). During climatic cycles, cooling of global temperatures has driven aridification and

replacement of rainforest areas by savanna-type woodlands, followed by grassland

savannas (Cerling, 1992; Cerling et al., 1997; Plana, 2004). In accordance with fossil

evidences, current grassland savanna habitats probably did not exist until the late Miocene

(Janis, 1993). Thus, we could expect that appearance of young clades would coincide

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with the expansion of new environments, in this case the savannas (e.g. Buckley et al.,

2010). However, it is probably that the patterns we have found may represent the imprints

of a complex history of lineage colonization, speciation and extinction that African

habitats have suffered along the evolutionary time.

In the analysis of overabundant taxa, we found a mixture of shared/exclusive and

ancient/derived clades between forested and dry open habitats (Table 4). In fact, in

accordance with the expected given the rainforest history, the lineage pool was related

with overabundance of ancient taxa, such as Manidae (Pangolins), the only extant family

of the Pholidota order (Janis, 1993; Du Toit et al., 2014). We also found overabundance

related to anomalurid rodents (Anomaluridae), for which the diversification is supposed

to have occurred during the Middle Eocene (Janis, 1993). At this time, vegetation was

primarily tropical forest, which explain why they are forest specific (Maley, 1996).

Overabundance was also found to the order Primates, related to both ancient (e.g. lorisids,

with a Late Eocene in situ evolution) and derived families, such as Cercopithecidae (Janis,

1993; Kamilar et al., 2009; Pozzi et al., 2014). On the other hand, lineage pool structure

of desert, savanna, humid savannas and dry forest were associated with overabundance

of Late Oligocene (around 34 MYA) immigrant taxa (Leporidae family), but mainly

related to Felidae, Hyaenidae and Mustelidae families, which are recognized as

immigrants that only arrived in Africa after the Late Miocene (11 to 5 MYA).

Furthermore, Canidae and Equus genus were also overabundant, which fossil records

recognize as Pliocene (from 5 to 1.9 MYA) immigrants (Vrba, 1992; Janis, 1993).

We predicted that forested and dry open habitats would be related with

overabundance of the same higher taxa, evidencing the lineage colonization history of

African biomes. The overabundance patterns for Carnivora showed different families

being represented in rainforest regarding other habitats. However, Bovidae

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(Cetartiodactyla) was an overabundant family among almost all African habitats. It has

been proposed that in situ evolution accounted for the appearance of bovids during the

Oligocene-Miocene transition (Janis, 1993; Hernández Fernández & Vrba, 2005;

Cantalapiedra et al., 2014). The Tragelaphus genus (Bovidae) was overabundant in

rainforest and humid savanna and belongs to the most basal among the bovid subfamilies

(Bovinae), which have diversified around 22 MYA as consequence of climatic change

events (Janis, 1993; Hernández Fernández & Vrba, 2005). Specific Bovidae clades were

also related to different habitats, such as Cephalophinae subfamily and its Cephalophus

genus (around 10.8 MYA), which are overabundant in rainforest. This subfamily is

composed by species in general adapted to forest habitats (Pérez-Barbería et al., 2001).

Reduncinae and its Redunca genus (around 6.7 MYA) were overabundant in humid

savanna. Reduncinae is a derived subfamily, which diversified in the Middle Miocene, a

period marked by a global cooling event. Interesting, the diversification of Bovidae and

the distribution of its subfamilies among African habitats help us to understand the

influence of climatic changes in the history of habitat formation (Janis, 1993; Hernández

Fernández & Vrba, 2005; Cantalapiedra et al., 2014).

Most hypotheses on the evolution of the African fauna invoke adaptations to arid

environments, following the emergence of grassland savannas after the late Miocene

(Cerling, 1992; Cerling et al., 1997; deMenocal, 2004). Paleoclimatic records suggest that

shifts in African climatic conditions were accompanied by some changes in African

faunal assemblages towards arid-adapted and grazing species (Cristoffer & Peres, 2003;

deMenocal, 2004). In the latest Miocene, bovids became increasingly predominant and

during the Pliocene the bovid fauna was formed by 50% grazer lineages (Janis, 1993;

Cantalapiedra et al., 2014). Small mammals also experienced a shift to more arid-adapted

forms during the Pliocene, with for example, an increase in abundance of gerbilline

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rodents (overabundant lineage in desert; Table 4) (Janis, 1993). Although the directional

view of bovid dietary evolution mode (browsing to mixed feeding to grazing), it has been

seen that reversions were common along the clade evolution. It means that dry open

habitat adapted lineages could have recolonized forested environments (Pérez-Barbería

et al., 2001; Cantalapiedra et al., 2014). Another example of the complex history of

lineage pool formation among African habitats is the Genetta genus (Viverridae), which

is a transitional group between rainforest and savanna habitats (Janis, 1993; Gaubert et

al., 2004). Such habitat transition was not directional, with basal and derived species both

adapted to savanna and rainforest habitats (Gaubert et al., 2004).

A lot has been discussed about the influence of climatic events in the evolution of

the African fauna, especially regarding the “Pleistocene forest refuge hypothesis”, which

may have kept ancient lineages and contributed to allopatric speciation during periods of

intense rainforest retraction (e.g. Anthony et al., 2007; Johnston & Anthony, 2012;

Jacquet et al., 2014). On the other hand, hypotheses focused in savannas, such as the

“turnover pulses”, claim that changes in average climate were responsible for periods of

increased speciation and species extinction related to the dynamic of expansion and

retraction/disappearance of habitats (see deMenocal, 2004). In fact, there is evidence that

supports that rainforest refuges were determinant in the diversification of forest

dependent groups (e.g. Cercopithecidae and Cephalophinae; see Table 4), while savannas

were also a cradle for some groups, with intense diversification during expansion periods

(Kamilar et al., 2009; Johnston & Anthony, 2012; Cantalapiedra et al., 2014). Moreover,

African mammal community structure (phylogenetic and functional) was found to be

influenced by modern climate, for primate assemblages, and by paleoclimate (mid-

Holocene and LGM) for Carnivora and ungulate assemblages (Rowan et al., 2016). These

differences were attributed to differential ecological flexibility among mammal groups,

105

corroborated by primate dependence of forested habitats and by the ability to transit

among diets and habitats that has been seen for bovid evolutionary history (Cantalapiedra

et al., 2014; Gouveia et al., 2014; Rowan et al., 2016).

Functional structure of habitats and assemblages

Results at assemblage and habitat level were congruent regarding the tendency of

increasing functional clustering towards dry open habitats. However, at the habitat level,

it seems that a strong filter is acting under desert and savanna habitats causing functional

clustering (Figure 2b, d). We corroborated our hypothesis that different ecological

processes have determined current functional trait pool across major African habitats

(structured along a dryness gradient). It is in accordance with findings that variance in

trait composition is highly explained by climate gradients (Lawing et al., 2016). It is

interesting to notice that at assemblage level, the rainforest communities were related with

the highest values of functional structure, while at habitat level the rainforest SES value

was intermediary between desert/savanna and humid savanna/dry forest values (the

highest SES values). Therefore, it demonstrates that species co-occurrence, at assemblage

level, drives the increasing of functional overdispersion regarding the functional pool

available for rainforest habitat, probably owing to competition (see below). Although

rainforest functional structure was not so overdispersed as found to humid savanna and

dry forest, we corroborated our expectation that rainforest functional space would be more

dissimilar from functional space of other habitats than expected by chance and mainly

determined by real trait turnover. It demonstrates the importance of lineage adaptation

process to gain dry open environments (deMenocal, 2004; Cantalapiedra et al., 2014).

We found functional clustering for assemblages recorded in dry open

environments, despite the coexistence of distantly related lineages. Therefore, even

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different lineages seem to have converged to required traits for these environments (e.g.

Louys et al., 2011). Our results suggest that ecological processes have played a major role

to select species traits in African habitats instead of historical contingency and

evolutionary history (Lawing et al., 2016). Environmental conditions might result in trait

convergence towards common adaptive forms, even among lineages with different

evolutionary origins and histories (Schluter, 1986; Schluter & Ricklefs, 1993; Moretti &

Legg, 2009). That would be even more likely for extreme environments, (Rodríguez et

al., 2006) which are subject to disturbance, such as fire regimes (Moretti & Legg, 2009).

Our results are consistent with findings that arid conditions are strong filters that require

specific adaptations both to plants and animals (Ojeda et al., 1999). Moreover,

environmental stability hypothesis posits that in variable environments, such as deserts

and savannas, species are likely to converge in their use of abundant and stable resources,

thereby leading to a functional clustering in assemblage scale (Wiens, 1977). It might be

illustrated by the evolution of adaptations to grazing dietary habits in bovid, which

allowed them to gain dry open habitats (Cantalapiedra et al., 2014).

We found that rainforest assemblages support more functionally distinct species

compared to assemblages at the end of the aridity gradient spectrum. In stable habitats,

such as rainforests, it is likely that competition would regulate assemblage structure by

increasing functional diversity through niche segregation (Schoener, 1982). Competition

would be more intense in later-successional communities ( i.e. more productive habitats;

Taylor et al., 1990) leading to divergence of competitive traits (Weiher & Keddy, 1995;

Weiher et al., 1998). This agrees with limiting similarity theory, which posits that

coexisting species need to be sufficiently different to avoid competition (Stubbs &

Wilson, 2004). For example, African rainforest ungulate assemblages can stand very

similar number of co-occurring species with savannas, though the African savannas hold

107

many more species than rainforest, respectively 43 and 29 species (McNaughton &

Georgiadis, 1986). Functional overdispersion may also arise from an increase in

environmental heterogeneity and resource diversification. Increased niche availability

will contribute to the coexistence of higher diversity of functional traits when compared

with homogeneous habitats (Adler et al., 2013). In this context, it has been postulated that

a wider range of foraging methods in ancestral bovids (e.g. Cephalophus genus), that

were forest dwellers, was crucial to their occupancy and diversification in grassy habitats

(Pérez-Barbería et al., 2001).

CONCLUSION

We verified imprints of historical and ecological processes determining the

segregation of mammal lineages and functional traits in the major African habitats. We

found opposite results for phylogenetic and functional structure, which highlights the

need to consider the historical processes in assemblage structure analyses (e.g. Parmentier

et al. 2014). We identified the role of strong environmental filters related to arid systems

promoting trait convergence even among distant related lineages. Rainforests seem to

encompass both recent and ancient lineages and we found a mixture of ancient immigrant

lineages and derived taxa contributing to phylogenetic overdispersion patterns in dry open

habitats (Janis, 1993; Moritz et al., 2000; Plana, 2004). Overall, it seems that a complex

history of lineage colonization among habitats, which was different for specific clades,

was responsible for lineage and functional pool formation of African biomes

(Cantalapiedra et al., 2014; Rowan et al., 2016). The differential diversification history

among African mammal groups is related with their ecological flexibility and

consequently ability to lead with climatic changes that have been the main driver of

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African mammal diversity along the evolutionary time (deMenocal, 2004; Rowan et al.,

2016).

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Table S1. References for each mammal the checklists used in the analyses. MAB; Stands

for the Biological Inventories of the World's Protected Areas Database (UNESCO, Man

and the Biosphere Programme, Information Centre for the Environment, University of

California, Davis; available at http://ice.ucdavis.edu/mab/, Accessed 29 August 2005).

ID Site Ref.

1 Basse Casamance National Park (Senegal) Dupuy (1973)

2 Parc National de la Pendjari (Benin) MAB

3 Taï National Park (Ivory Coast) MAB

4 Omo Biosphere Reserve (Nigeria) MAB

5 Dja Faunal Reserve (Cameroon) MAB

6 Parc National d'Odzala (Rep. of Congo) MAB

7 Kibale Forest Corridor National Park (Uganda) MAB

8 Amboseli National Park (Kenya) MAB

9 West Caprivi Game Reserve (Namibia) MAB

10 Kalahari Gemsbok National Park (South Africa) MAB

11 Naute Dam (Namibia) MAB

12 Malolotjia Nature Reserve (Swaziland) MAB

13 Mlawula Nature Reserve (Swaziland) MAB

14 Augrabies Falls National Park (South Africa) Rautenbach et al. (1979)

15 Weenen Game Reserve (South Africa) Bourquin & Matthias (1995)

16 Kogelberg Nature Reserve (South Africa) MAB

Appendix S1 Methods

Several geographic definitions have been used to determine species membership to a

source pool. The simplest definition includes all species recorded in the biogeographic

region where focal assemblage is located (Hortal et al., 2008). Source pool membership

also has been delimited by distances, including only species recorded in a limited area

size (commonly a circle) around focal assemblage (Graves & Gotelli, 1983; Gotelli &

Graves, 1990). Other definitions have added an additional constraint, including only

species related to specific habitats present in the locality (Gotelli & Graves, 1990). In

addition, Carstensen et al. (2013) have proposed three analytical approaches that can be

used to define source pool, what they called distance-based clustering analysis, network

115

modularity analysis, and assemblage dispersion fields. Indeed, a decay of membership

degree with either increasing geographic distance or decreasing biogeographic affinity

should be used (Graves & Rahbek, 2005).

We used eleven definitions of species pool to perform the analyses, based in these

methodologies cited above: the (1) continental, (2) circle (uw), (3) circle (wd), (4) biome

(uw), (5) biome (wd), (6) dispersion field (uw), (7) dispersion field (ws), (8) cluster (uw),

(9) cluster (wd), (10) module (uw) and (11) module (wd).

Continental pool (AP), defined as all the species present in the Paleotropical

biogeographic region (we avoid using the term ‘regional pool’ to prevent possible

mistakes due to its wide usage in the literature). This assumes that all species

inhabiting the region share a common evolutionary history, and therefore provide a

raw estimate of the species that could have colonized any locality within the region.

Circle pool (CP), defined as all the species present in a circle surrounding the locality,

of 24.49º radius, which is the median size of the area inhabited by at least 10% of the

species present in the locality (see Dispersion field pool below); this assumes that the

assembly of the local biota is made from the species occurring nearby.

Biome pool (BP), defined as all the species present in the continuous area of the same

kind of biome where the assemblage is located, according to an ecoregion

classification followed by Rodríguez et al. (2006). This assumes that the local

assembly is made from species inhabiting ecologically similar habitats.

Dispersion field pool (DP), defined as all species present in the area with overlapping

geographic ranges of, at least, 10% of the species that are present in the locality.

Assemblage dispersion fields are supposed to illustrate the geometry of the source

pool of the local assemblages (Graves & Rahbek, 2005). Therefore, this definition

assumes that there is a strong biogeographical component in the local assembly of the

116

species, and that the geometry of the pool can be assessed from the geographic ranges

of the species present in the locality.

Cluster pool (CLP), defined as the species present in each group formed by the

clumping of the cells in relation to their species composition (Carstensen et al., 2013).

We construct a dissimilarity matrix of the cells in relation to their composition, using

the Sørensen index. The chosen cluster algorithm was the UPGMA method

(unweighted pair-group method using arithmetic averages) (Legendre & Legendre,

2012). We explored visually the k values 2 to 10 to know the number of relevant

groups that were formed inside the cluster. We also performed a K-Means Partitioning

that was coincident with the number of six groups that we chose visually.

Modules pool (MP), defined as the species present in the modules formed by the

network modularity analysis of the cells species composition (Carstensen et al.,

2013). The species and sites are arranged as a two-mode (or bipartite) network, with

sites and species sharing a link if the species is present at the site. Sites and species

that are strongly interconnected are then grouped using a modularity analysis. We

used the Louvain algorithm and obtained four groups.

We evaluate two ways of determining the degree of membership of each species to the

source pool:

Unweighted pool (uw), where all species present in the geographic area defined by

the pool have the same probability of being part of the local assemblage.

Weighted pool (wd or ws), where all species have different degrees of membership to

the source pool according to the geographic location of their ranges, and therefore

have different probabilities of establishing populations in the locality during the

process of assembly. We applied one kinds of weight applied to circle, biome, cluster

and module pools. This was based on the minimum geographic distance between the

http://rfunctions.blogspot.com/2012/12/multivariate-analysis-k-means.html

117

range of each species and the locality (wd). We applied also another weight kind the

dispersion field pool, based in the maximum number of species present in the locality

that can be found in any of the grid cells of the occupied by the species in question

(ws). Probabilities of pertenence to the pool for each species present in the cell where

then assigned following a sigmoid distribution, giving comparatively higher

probability values to the cells placed near to (or sharing more species with) the

locality.

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Graves, G.R. & Rahbek, C. (2005) Source pool geometry and the assembly of continental

avifaunas. Proceedings of the National Academy of Sciences USA, 102, 7871–7876.

Hortal, J., Rodríguez, J., Nieto-Díaz, M. & Lobo, J.M. (2008) Regional and

environmental effects on the species richness of mammal assemblages. Journal of


Legendre, P. & Legendre, L. (2012) Numerical ecology, Elsevier Science, Amsterdam.

Rautenbach, I.L., Schlitter, D.A. & De Graff, G. (1979) Notes on the mammal fauna of

the Augrabies Falls National Park and surrounding areas with special reference to

regional zoogeographical implications. Koedoe, 22, 157–175.

Rodríguez, J., Hortal, J. & Nieto, M. (2006) An evaluation of the influence of

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mammals. Journal of Biogeography, 33, 291–303.

118

Tabela S2. Results of the phylogenetic structure analyzes using PD. We demonstrate the

number of communities that the results were random (Rand), overdispersed (Over) or

clustered (Clust) and their kind of habitats. See the species pool names in methods. The

numbers displayed close to the habitats name corresponds to the ID number of each

community (see Fig.1 and Table 1)

Phylogenetic

Geographic pools Rand. Over. Habitat type Clust. Habitat type

Continental (uw) 15 1 Desert/Semi-arid (11) 0 -

Biome (uw) 15 1 Savanna/Shrubland (12) 0 -

Biome (wd) 14 2 Desert/Semi-arid (11) and

Savanna/Shrubland (12) 0 -

Circle (uw) 15 1 Desert/Semi-arid (11) 0 -

Circle (wd) 15 1 Desert/Semi-arid (11) 0 -

Biogeographic pools

Disp. Field (uw) 14 2 Desert/Semi-arid (11) and


Disp. Field (ws) 14 2 Desert/Semi-arid (11) and


Cluster (uw) 15 1 Desert/Semi-arid (11) 0 -

Cluster (wd) 13 3 Desert/Semi-arid and (11)

Savanna/Shrubland (9,12) 0 -

Module (uw) 14 2 Desert/Semi-arid and (11) and Humid/semi

humid Savanna (2) 0 -

Module (wd) 12 4 Desert/Semi-arid (11), Humid/semi humid

Savanna and (2) Savanna/Shrubland (9,12) 0 -

119

Tabela S3. Results of the functional structure analyzes using FD. We demonstrate the

number of communities that the results were random (Rand), overdispersed (Over) or

clustered (Clust) and their kind of habitats. See the species pool names in methods. The

numbers displayed close to the habitats name corresponds to the ID number of each

community (see Fig.1 and Table 1).

Functional

Geographic pools Rand. Over. Habitat type Clust. Habitat type

Continental (uw) 16 0 - 0 -

Biome (uw) 15 0 - 1 Humid/semi humid Savanna (15)

Biome (wd) 14 0 - 2 Humid/semi humid Savanna (15) and

Savanna/Shrubland (13)

Circle (uw) 13 0 - 3 Humid/semi humid Savanna (15) and

Savanna/Shrubland (10, 13)

Circle (wd) 14 0 - 2 Humid/semi humid Savanna (15) and

Savanna/Shrubland (13)

Biogeographic pools

Disp. Field (uw) 15 1 Humid/Semi-

humid forests (5) 0 -

Disp. Field (ws) 15 0 - 1 Savanna/Shrubland (13)

Cluster (uw) 15 0 - 3 Humid/semi humid Savanna (15)

and Savanna/Shrubland (10, 13)

Cluster (wd) 13 0 - 3 Humid/semi humid Savanna (15) and


Module (uw) 14 0 - 2 Savanna/Shrubland (10, 13)

Module (wd) 13 0 - 3 Humid/semi humid Savanna (15) and


120

Table S4. Standardized effect size for phylogenetic (SES. PD) and functional diversity

(SES.FD) for each habitat. Rainforest = Humid/Semi-humid forests; Dry Forest = Dry


and Desert = Desert/Semi-arid.

Habitat SES.PD P SES.FD P

Rainforest -3.175 0.002* -1.450 0.066

Dry Forest -0.805 0.225 -0.333 0.358

H. Savanna -1.104 0.136 -0.295 0.355

Savanna -0.295 0.381 -2.146 0.016*

Desert -0.329 0.375 -2.125 0.014*

*P < 0.025

Table S5. Pairwise correlations between phylogenetic and functional SES values

obtained for all species pool definitions.

Phylogenetic Functional

Species pool comparisons (SES) r2 P r2 P

Continental Biome (uw) 0.819*** <0.001 0.850*** <0.001

Continental Biome (wd) 0.870* 0.011 0.756*** <0.001

Continental Circle (uw) 0.795*** <0.001 0.747*** <0.001

Continental Circle (wd) 0.805*** <0.001 0.721** 0.001

Continental Disp. Field (uw) 0.996*** <0.001 0.852*** <0.001

Continental Disp. Field (ws) 0.947*** <0.001 0.828*** <0.001

Continental Cluster (uw) 0.790*** <0.001 0.801*** <0.001

Continental Cluster (wd) 0.799*** <0.001 0.734** 0.001

Continental Module (uw) 0.800*** <0.001 0.781*** <0.001

Continental Module (wd) 0.822 0.093 0.705** 0.002

Biome (uw) Biome (wd) 0.922*** <0.001 0.831*** <0.001

Biome (uw) Circle (uw) 0.875*** <0.001 0.798*** <0.001

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Biome (uw) Circle (wd) 0.823*** <0.001 0.791*** <0.001

Biome (uw) Disp. Field (uw) 0.844*** <0.001 0.944*** <0.001

Biome (uw) Disp. Field (ws) 0.880*** <0.001 0.915*** <0.001

Biome (uw) Cluster (uw) 0.837 0.050 0.703** 0.002

Biome (uw) Cluster (wd) 0.799*** <0.001 0.635** 0.008

Biome (uw) Module (uw) 0.853* 0.025 0.669** 0.004

Biome (uw) Module (wd) 0.792*** <0.001 0.607* 0.012

Biome (wd) Circle (uw) 0.947*** <0.001 0.893*** <0.001

Biome (wd) Circle (wd) 0.956*** <0.001 0.910*** <0.001

Biome (wd) Disp. Field (uw) 0.881** 0.006 0.919*** <0.001

Biome (wd) Disp. Field (ws) 0.946*** <0.001 0.944*** <0.001

Biome (wd) Cluster (uw) 0.930*** <0.001 0.751*** <0.001

Biome (wd) Cluster (wd) 0.871* 0.011 0.703** 0.002

Biome (wd) Module (uw) 0.948*** <0.001 0.739** 0.001

Biome (wd) Module (wd) 0.882*** <0.001 0.702** 0.002

Circle (uw) Circle (wd) 0.975*** <0.001 0.976*** <0.001

Circle (uw) Disp. Field (uw) 0.820 0.097 0.872*** <0.001

Circle (uw) Disp. Field (ws) 0.937*** <0.001 0.942*** <0.001

Circle (uw) Cluster (uw) 0.979*** <0.001 0.893*** <0.001

Circle (uw) Cluster (wd) 0.919*** <0.001 0.864*** <0.001

Circle (uw) Module (uw) 0.991*** <0.001 0.888*** <0.001

Circle (uw) Module (wd) 0.917*** <0.001 0.857*** <0.001

Circle (wd) Disp. Field (uw) 0.818*** <0.001 0.838*** <0.001

Circle (wd) Disp. Field (ws) 0.933*** <0.001 0.921*** <0.001

Circle (wd) Cluster (uw) 0.961*** <0.001 0.892*** <0.001

Circle (wd) Cluster (wd) 0.884** 0.005 0.860*** <0.001

Circle (wd) Module (uw) 0.989*** <0.001 0.898*** <0.001

Circle (wd) Module (wd) 0.907** 0.001 0.872*** <0.001

Disp. Field (uw) Disp. Field (ws) 0.960*** <0.001 0.977*** <0.001

Disp. Field (uw) Cluster (uw) 0.815*** <0.001 0.718** 0.001

Disp. Field (uw) Cluster (wd) 0.82*** 0.095 0.662** 0.005

Disp. Field (uw) Module (uw) 0.822*** 0.091 0.688** 0.003

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Disp. Field (uw) Module (wd) 0.836*** 0.053 0.633** 0.008

Disp. Field (uw) Cluster (uw) 0.916*** <0.001 0.809*** <0.001

Disp. Field (uw) Cluster (wd) 0.876** 0.008 0.764*** <0.001

Disp. Field (uw) Module (uw) 0.938*** <0.001 0.787*** <0.001

Disp. Field (uw) Module (wd) 0.895*** <0.001 0.749*** <0.001

Cluster (uw) Cluster (wd) 0.952*** <0.001 0.989*** <0.001

Cluster (uw) Module (uw) 0.979*** <0.001 0.988*** <0.001

Cluster (uw) Module (wd) 0.915*** <0.001 0.971*** <0.001

Cluster (wd) Module (uw) 0.916*** <0.001 0.977*** <0.001

Cluster (wd) Module (wd) 0.966*** <0.001 0.982*** <0.001

Module (uw) Module (wd) 0.927*** <0.001 0.983*** <0.001

*P < 0.05; **P < 0.01; ***P < 0.001

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CONCLUSÃO GERAL

No presente trabalho, avaliei a atuação de processos ecológicos, evolutivos e

histórico–biogeográficos na determinação de padrões de diversidade de mamíferos

terrestres. A partir da observação de padrões de diversidade de Chiroptera, em uma escala

global, investiguei sobre o processo pelo qual se deu a evolução de nicho ambiental.

Concluí que estase evolutiva pode ter sido importante para algumas famílias do grupo, o

que chama a atenção para a importância da não-estacionariedade na evolução de nicho

ambiental e da avaliação de diferentes escalas filogenéticas para concluir sobre

conservação de nicho. Em uma escala ainda global, investiguei o gradiente de diversidade

beta filogenética. Foram identificadas regiões onde a perda de linhagens (diferença na

diversidade filogenética entre locais) é o que determina o gradiente de dissimilaridade

(transições com desertos e limites entre regiões biogeográficas). Foram também

identificadas regiões onde a troca (dissimilaridade real) de linhagens foi mais importante

para gerar esses padrões de diversidade beta filogenética (elevadas altitudes). Esses

padrões foram diferentes entre três ordens de mamíferos analisadas, demonstrando a

importância da capacidade de dispersão e da capacidade de estabelecimento,

possivelmente condicionada à evolução de nicho. Em escala regional, demonstrei que a

história climática do continente Africano foi determinante na formação do pool de

linhagens e características funcionais entre os biomas, o que se refletiu na estrutura

filogenética e funcional de assembleias. A história de colonização e diversificação

diferencial entre grupos de mamíferos parece estar relacionada com a flexibilidade

ecológica e a habilidade de lidar com mudanças climáticas severas que marcaram esse

continente ao longo do Cenozoico. De forma geral, os resultados dos três capítulos

convergiram no sentido de evidenciar a importância da história evolutiva de diferentes

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grupos taxonômicos (desde ordem até gênero e espécies) para compreender os padrões

de diversidade atuais.

DETERMINANTES ECOLÓGICOS, EVOLUTIVOS E HISTÓRICO ... · gradiente latitudinal de diversidade, bem como a formação da biota de diferentes habitats. A conservação de tolerâncias

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