universidade federal de goiÁs instituto de ciÊncias ... · palavras, essas espécies são...

UNIVERSIDADE FEDERAL DE GOIÁS INSTITUTO DE CIÊNCIAS BIOLÓGICAS

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E EVOLUÇÃO

José Hidasi Neto

HOMOGENEIZAÇÃO TAXONÔMICA, FILOGENÉTICA E FUNCIONAL DE

COMUNIDADES: CAUSAS, CONSEQUÊNCIAS E IMPLICAÇÕES PARA A

CONSERVAÇÃO

Orientador: Dr. Marcus Vinicius Cianciaruso

Goiânia-GO

Agosto de 2018

UNIVERSIDADE FEDERAL DE GOIÁS INSTITUTO DE CIÊNCIAS BIOLÓGICAS

PROGRAMA DE PÓS-GRADUAÇÃO EM ECOLOGIA E EVOLUÇÃO

José Hidasi Neto



CONSERVAÇÃO

Orientador: Dr. Marcus Vinicius Cianciaruso

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 Doutor.

Goiânia-GO Agosto de 2018

2

Dados Internacionais de Catalogação na Publicação na (CIP)

GPT/BC/UFG

INSERIR NA VERSÂO FINAL

3

José Hidasi Neto



CONSERVAÇÃO

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 Doutor.

Banca avaliadora: Membros Internos: Dr. José Alexandre Diniz-Filho, Dr. Mário Almeida Neto Membro Externos: Dr. Leandro da Silva Duarte, Dr. Thiago Gonçalves-Souza Orientador: Dr. Marcus Vinicius Cianciaruso

Goiânia-GO Agosto de 2018

4

AGRADECIMENTOS

Agradeço aos familiares, amigos, professores, carregadores do Santo Graal, e

agências financiadoras que me ajudaram ao longo dos anos em que estive fazendo

Doutorado.

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SUMÁRIO

RESUMO GERAL............................................................................................06

INTRODUÇÃO GERAL...................................................................................07

CAPÍTULO 1....................................................................................................12

Resumo................................................................................................13

Introdução.............................................................................................14

Material e Métodos...............................................................................16

Resultados............................................................................................20

Discussão.............................................................................................22

Referências...........................................................................................25

Material Suplementar............................................................................28

CAPÍTULO 2......................................................................................................35

Resumo.................................................................................................36

Introdução.............................................................................................38


Resultados............................................................................................48

Discussão.............................................................................................50

Referências...........................................................................................56


CAPÍTULO 3......................................................................................................75

Resumo........ ...... ................................................................................76

Introdução.............................................................................................77


Resultados............................................................................................86

Discussão.............................................................................................89

Referências...........................................................................................94


CONCLUSÃO GERAL.......................................................................................115

6

RESUMO GERAL

A homogeneização biótica pode ser definida como a substituição de organismos

especialistas (normalmente nativos) por generalistas (normalmente exóticos) por meio de

invasões biológicas e extinções locais. Esse processo é capaz de homogeneizar vários

componentes da biodiversidade, reduzindo a diversidade taxonômica, funcional, genética e

filogenética de populações e comunidades. Sabe-se ainda que a homogeneidade desses

componentes pode sofrer alterações de modo natural ou induzido (pelo homem). Além

disso, em face à perda de diversidade (ou heterogeneidade) dos componentes da

biodiversidade, estudos são feitos para reduzir os efeitos não naturais causados pelo

homem. Investigaremos as seguintes questões: “onde se encontram as espécies ou

comunidades mais similares entre si?”, “quais são as causas naturais e humanas dessa

alta semelhança?”, “quais são as consequências da alta similaridade?”, “como podemos

evitar a alta homogeneização da biodiversidade?”. No primeiro capítulo testaremos se

espécies menos abundantes são aquelas filogenética e funcionalmente mais distintas

(“mais especialistas”). No segundo capítulo testaremos se ilhas vivas (hospedeiros)

filogeneticamente isoladas apresentam comunidades de insetos (colonizadores) mais

taxonomicamente homogêneas do que ilhas filogeneticamente não isoladas. No terceiro

capítulo testaremos se mudanças climáticas homogeneizarão taxonômica, funcional e

filogeneticamente as comunidades de mamíferos no Cerrado. Esperamos contribuir para o

conhecimento das causas e das consequências da alta homogeneidade no meio ambiente.

Adicionalmente, esperamos que mais medidas de conservação sejam planejadas global e

regionalmente em face aos processos naturais e antropogênicos responsáveis pela

homogeneização biótica.

Palavras-chave: homogeneização biótica, especialistas, generalistas, nativas, exóticas,

abundância, originalidade funcional, originalidade filogenética, diversidade de habitats,

conservação.

7

INTRODUÇÃO GERAL

Os efeitos antrópicos na biodiversidade aumentam a cada dia devido às expansões

urbana e agrícola. Essas expansões modificam ambientes naturais, impossibilitando a

permanência de espécies que previamente usufruíam das condições e recursos no local.

Como resultado, espécies nativas de certas regiões deixam de ser encontradas em

alguns locais onde, sem o efeito antrópico, poderiam ocorrer naturalmente. Em outras

palavras, essas espécies são localmente extintas devido a ações antrópicas (como a

expansão de um município ou a construção de uma fazenda). A expansão humana não

só pode causar a extinção local de espécies, mas também permite que certas espécies

passem a ocorrer em locais onde previamente não ocorriam. Esse processo de invasão

local por espécies exóticas pode ocorrer devido às modificações antrópicas no ambiente

e/ou ao transporte proposital ou não feito pelo homem. Espécies invasoras podem causar

a extinção de espécies nativas, pois normalmente são competitivamente superiores e

carregam doenças de outras regiões. Ao passar dos anos, cientistas notaram que

extinções e invasões locais ocorrem ao mesmo tempo, fazendo com que comunidades

ecológicas distantes passem a ter faunas e floras similares. Por exemplo, as faunas de

duas cidades devem ser mais parecidas do que as faunas de duas florestas na mesma

região. O processo de mistura de espécies nativas resistentes à extinção com espécies

invasoras tem sido chamado de homogeneização biótica (McKinney & Lockwood 1999).

Extinção: quem sai?

De acordo com Primack & Rodrigues (2008) as causas mais comuns para a extinção de

espécies são: perda de habitat (por exemplo, devido a queimadas não naturais),

fragmentação de habitat (por exemplo, devido à construção de estradas), degradação de

habitat (por exemplo, devido à poluição), superexploração dos recursos naturais (como

pesca ou caça excessiva), invasão de espécies exóticas e a propagação de doenças (por

exemplo, carregadas por invasores). Todavia, algumas espécies são mais resistentes do

que outras em face a perturbações ambientais. Ao passar dos anos, estudos nos

permitiram entender quais características biológicas das espécies estão relacionadas a

um alto risco de extinção (Tabela 1).

8

Tabela 1. Características biológicas de espécies e motivos pelos quais são ligadas ao risco de extinção.

Característica biológica

Motivo

Tamanho da distribuição

Espécies com pequena distribuição geográfica (que ocorrem em áreas restritas) utilizam apenas uma pequena variedade de condições e recursos do ambiente, geralmente bastante específicos.

Área de vida Indivíduos de espécies que precisam de grandes áreas para viver são mais vulneráveis à perda, degradação e fragmentação de habitats.

Densidade populacional

Espécies com baixa densidade populacional normalmente possuem baixa diversidade genética, aumentando a vulnerabilidade em relação a distúrbios no ambiente.

Nível trófico Espécies que estão no topo da cadeia alimentar (como grandes predadores) são normalmente mais vulneráveis à extinção, pois sofrem efeitos cumulativos de extinções de espécies de níveis tróficos inferiores.

Especialização na dieta

Espécies que se alimentam de uma pequena diversidade de alimentos normalmente possuem maior vulnerabilidade à extinção, pois se esses poucos alimentos desaparecerem do meio ambiente, as espécies se extinguirão.

Capacidade de dispersão

Espécies que não se dispersam bem (indivíduos próximos de seus progenitores) normalmente não conseguem colonizar novos habitats com facilidade.

Especialização no habitat

Espécies que só vivem em alguns poucos habitats normalmente apresentam alta vulnerabilidade à extinção, pois se esses poucos habitats desaparecerem do meio ambiente, as espécies se extinguirão.

Tempo de geração

Espécies com longo tempo de geração possuem maior dificuldade para que as taxas de natalidade compensem as de mortalidade, aumentando a probabilidade de extinção com o tempo.

Tamanho do corpo

Espécies grandes normalmente são mais especializadas na dieta e no habitat, e possuem poucos indivíduos, longo tempo de geração, alta idade para a maturidade e grande área de vida. Espécies grandes também são normalmente mais caçadas por humanos.

Idade da maturidade

Espécies em que os indivíduos demoram para atingir a maturidade possuem maior dificuldade para que as taxas de natalidade compensem as de mortalidade.

Tamanho da ninhada

Espécies com pequeno tamanho de ninhada normalmente possuem baixa capacidade reprodutiva e longo tempo de geração, resultando em alta vulnerabilidade à extinção.

Invasão: quem entra?

Enquanto populações de espécies não resistentes a perturbações antrópicas são extintas

em locais onde há expansão urbana ou rural, populações de espécies exóticas podem

invadir essas mesmas áreas, mudando a estrutura e o funcionamento das comunidades

ecológicas. Algumas espécies possuem maior facilidade de invadir comunidades do que

outras, pois apresentam características biológicas que permitem rapidamente formar

populações estáveis (Figura 1). Quando uma área natural é modificada devido a ações

humanas, as condições ambientais (como a temperatura e a umidade) e os recursos

(como os possíveis alimentos para uma determinada espécie) disponíveis também são

modificados, desfavorecendo espécies nativas, mas possivelmente favorecendo

invasoras. Ainda, espécies exóticas podem ser transportadas propositalmente, ou não,

por seres humanos. Por exemplo, samambaias do gênero Pteridium são originalmente

nativas da Europa, mas já possuem ampla distribuição no Brasil. Essas samambaias

ocorrem frequentemente em áreas com alta ocorrência de queimadas e em bordas de

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florestas. Notavelmente, ações antrópicas no ambiente aumentam a ocorrência desses

organismos (Matos & Pivello 2009). Temos conhecimento, portanto, para prever quais

espécies podem invadir outras comunidades ecológicas e para identificar as possíveis

causas dessas invasões.

Tamanho grande

Alto nível trófico

Poucos indivíduos

Alta especialização

...

Tamanho pequeno

Baixo nível trófico

Muitos indivíduos

Baixa especialização

...

Quem sai? Quem entra?

Figura 1. Representação das características biológicas que favorecem a saída (extinção local) ou entrada (invasão local) nas comunidades ecológicas.

Quais são as consequências e como evita-las?

A homogeneização biótica é o resultado de processos locais de extinção e invasão.

Como resultado, comunidades ecológicas próximas de centros urbanos e rurais

apresentam uma mistura de algumas poucas espécies resistentes à extinção e de

espécies invasoras provenientes de outras comunidades. Devido às atividades humanas

no ambiente, várias espécies estão atualmente ameaçadas de extinção. Mesmo que a

extinção seja um processo normal na natureza (99,9% das espécies que já existiram

estão extintas), os níveis atuais de extinção estão tão altos que se assemelham a

grandes eventos de extinção em massa, como o que ocorreu ao final do período

Cretáceo dizimando a maioria dos dinossauros (Barnosky et al. 2011; Figura 2). Além

disso, atividades humanas possuem impactos tão grandes nos ecossistemas que

cientistas começaram a denominar o período em que vivemos de Antropoceno.

10

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ordovician Devonian Permian Triassic Pliensbachian Tithonian Cenomanian Cretaceous Priabonian Quaternary –Scenario 1

Quaternary –Scenario 2

% species lost % genera lost

(in ~1,580 yr)

Ordoviciano Devoniano Permiano Triássico Pliensbachiano Tithoniano Cenomaniano Cretáceo Priaboniano Quaternário

Espécies extintas Gêneros extintos% %

443 359 251 200 187 145 90 65 35 em 390 anos

Milhões de anos antes do presente Figura 2. Intensidades de extinção (percentagens de espécies e gêneros extintos) estimadas para vários eventos de extinção. Informações para os eventos do Ordoviciano, Devoniano, Permiano, Triássico e Cretáceo foram retiradas da Tabela 1 em Barnosky et al. (2011), e informações para os eventos do Pliensbaquiano, Tithoniano, Cenomaniano e Priaboniano foram retiradas da Tabela 1 em Jablonski (1991). O cenário do Quaternário (que ocorrerá em aproximadamente 390 anos) é baseado em resultados de Barnosky et al. (2011). Esse cenário refere-se ao tempo necessário para atingirmos uma perda de 75% das espécies de vertebrados caso todas as espécies ameaçadas sejam extintas em 100 anos e as taxas de perda de espécies permaneçam constantes ao longo do tempo.

A substituição de espécies nativas por espécies invasoras pode causar diversos efeitos

negativos no funcionamento dos ecossistemas e nos serviços que eles nos providenciam.

Por exemplo, as samambaias do gênero Pteridium não só substituem espécies nativas de

plantas, mas também são tóxicas para o gado, trazendo prejuízos a pecuaristas (Matos &

Pivello 2009). Além disso, já foi demonstrado, no estado de Goiás, que a perda de

abelhas nativas reduz a produção de tomates (Neto et al. 2013). Esses exemplos

demonstram os riscos ecológicos e socioeconômicos ligados à substituição de espécies

nativas por algumas poucas invasoras. Como podemos evitar a homogeneização biótica

e seus respectivos efeitos? Uma maneira é usar métodos de priorização de espécies,

conservando espécies ameaçadas de extinção que possuam papeis ecológicos

importantes para o funcionamento dos ecossistemas (Hidasi-Neto et al. 2015). Podemos

também criar parques e corredores ecológicos, maximizando a conectividade entre

populações de espécies nativas. Por último, devemos controlar de maneira mais eficiente

populações de potenciais espécies invasoras.

Conclusão

A homogeneização biótica é um processo que pode ocorrer naturalmente no meio

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ambiente, mas que está ocorrendo mais efetivamente ao redor do mundo devido aos

impactos humanos globais. Como indicado ao longo do texto, temos o conhecimento para

identificar quais espécies serão as primeiras a saírem ou entrarem nas comunidades

ecológicas e as possíveis causas dessas saídas ou entradas. Temos também métodos

para a conservação de espécie prioritárias para o funcionamento das comunidades.

Sabemos, portanto, como evitar que a diversidade biológica seja resumida a algumas

poucas espécies resistentes a perturbações antrópicas ao longo de regiões inteiras.

Entretanto, isso só será possível caso recursos continuem sendo direcionados para

estudos em Ecologia e para a conservação da biodiversidade.

Referências Bibliográficas

1. MCKINNEY, M. L.; LOCKWOOD, J. L. Biotic homogenization: a few winners replacing

many losers in the next mass extinction. Trends in Ecology and Evolution, v. 14, n. 11, p.

450–453, 1999.

2. PRIMACK, R. B.; Rodrigues, E. Biologia da Conservação. Londrina: Efraim Rodrigues,

2001. 328 p.

3. MATOS, D. M. S.; PIVELLO, V. R. O impacto das plantas invasoras nos recursos naturais

de ambientes terrestres: alguns casos brasileiros. Ciência e Cultura, v. 61, n. 1, p. 27–30,

2009.

4. BARNOSKY, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature, v.

471, n. 7336, p. 51–57, 2011.

5. JABLONSKI, D. Extinctions: a paleontological perspective. Science, v. 253, n. 5021, p.

754–757, 1991.

6. NETO, C. M. S. et al. Native bees pollinate tomato flowers and increase fruit production.

Journal of Pollination Ecology, v. 11, n. 6, p. 41–45, 2013.

7. HIDASI-NETO, J.; CIANCIARUSO, M. V.; LOYOLA, R. D. Global and local evolutionary

and ecological distinctiveness of terrestrial mammals: identifying priorities across scales.

Diversity and Distributions, v. 21, n. 5, p. 548–559, 2015.

Fonte Financiadora

O autor foi bolsista da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) durante a realização deste trabalho.

12

CAPÍTULO 1

Ecological Similarity Explains Relative Abundances of Small Mammals

José Hidasi-Neto*1, Luís Mauricio Bini1, Tadeu Siqueira2, Marcus Vinicius Cianciaruso1

1Departamento de Ecologia, Universidade Federal de Goiás (UFG), Goiânia, Brazil.

2Universidade Estadual Paulista (Unesp), Instituto de Biociências, Departamento de

Ecologia, Câmpus Rio Claro, Brazil.

* Corresponding author: José Hidasi Neto. Email: [email protected]. ORCID ID:

orcid.org/0000-0003-4097-7353.

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ABSTRACT

Ecologists have for long been trying to explain how species abundance distributions (SAD)

are formed within communities. From pure mathematical models we have arrived at niche

models, where species habitat requirements and life-history traits have a great impact in

SADs. Here, based on predictions from a well-known niche-based SAD (Sugihara’s) model

to test if, in general, abundant species are functionally more similar to most abundant

species than less abundant ones, and if this relationship is mitigated in high-productivity

zones (Potential Evapo-Transpiration). For that we used 88 mammalian communities

around the world with both abundance and trait data. Using mixed-effects models,

controlling for the effects within communities, as expected we found that most abundant

species (which commonly have small body sizes) are similar to other highly abundant ones,

and that this relationship is mitigated in high-productivity zones. These results may indicate

that there is a hierarchy of habitat conditions which consequently models species

abundances, and that high-energy habitats have less ecospace for specialist species. We

finally suggest that the order of species arrivals during the assembly of communities will be

necessary to uncover biological processes underlying the formation of SADs.

Key-words: functional diversity, SAD, species abundance, meta-analysis, niche space.

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INTRODUCTION

There have been several attempts to understand how ecological and evolutionary factors

shape species abundance distributions (the so-called SADs) (McGill et al. 2007, Ferreira

and Petrere 2008, Ulrich et al. 2010). The first SAD models, such as the geometric

(Motomura 1932) and logarithmic (Fisher et al. 1943) series, were more focused on

explaining mathematical patterns of SADs (a few very abundant and various less abundant

species) rather than the biological processes underlying them. SAD models based on

biological processes appeared later and can be generally divided into two classes. Neutral

models consider parameters related to rates of birth, death, migration, and speciation

(McGill et al. 2007, Ferreira and Petrere 2008). This class of models does not consider the

role of trait differences in producing SADs. Models based on niche partitioning (McGill et al.

2007, Ferreira and Petrere 2008) were proposed to explain SADs as a result of biotic

interactions. For example, in Sugihara’s niche-hierarchy model (Sugihara et al. 2003),

species relative abundances are the result of a sequential breakdown of the niche space

related to how species share the available resource pool. As in Sugihara et al. (2003), this

can be illustrated with a bird community in which the first birds to arrive are those that

forage on the ground and in trees. This would represent a first split in a dendrogram of

niche similarity. Then, a second split could happen if those birds that forage in trees were

divided into those foraging along the trunk and those foraging in branches. This process

would follow with the proportional abundance of each species corresponding to the

proportion of the broken niche space related to the species. Thus, species with few similar

co-occurring species would have higher abundance than those with many similar ones

(MacArthur 1957, Sugihara et al. 2003, Ferreira and Petrere 2008).

15

Many traits are related to species abundances (McKinney 1997, Purvis et al. 2000). For

example, large-bodied species tend to have low population density, long generation time,

high age at maturity, and large home range (McKinney 1997, Purvis et al. 2000). Thus,

body size is usually expected to influence SADs (McGill et al. 2007, White et al. 2007). Diet

and habitat specialization are also usually associated with species relative abundances

(McKinney 1997). Habitat- and diet-specialized species commonly have narrow niches, with

low local abundances and small geographic ranges. So, diet and habitat traits should also

be associated with SADs. When analyzing differences in species traits, one may also test

whether using an individual trait, rather than multiple traits, is better for explaining general

functional differences among species (Lefcheck et al. 2015). Thus, differences in a single

trait (such as body mass) could more efficiently explain SADs than multiple traits, as the

former alone may show a stronger and more explicit association with how species differ in

their abundances.

One idea that is still present in the literature is that biotic interactions are stronger in the

tropics (Coley and Barone 1996, Coley and Kursar 2014). Doing a parallel with the last

topic, it has been suggested that there might be more k-strategist species (with high

parental investment and competitiveness) in tropical than in temperate regions due to the

less variable climate and high energy availability (Pianka 1970). Also, it is thought that

communities in high-productivity areas (such as with high temperature and precipitation)

could support a large number of specialist species, which would unlikely compete with each

other due to their different habitat requirements (Srivastava and Lawton 1998). Therefore,

one could expect more equitable relative abundances from communities in areas with high

energy availability, as a result of high competitiveness (new species do not easily arrive)

and specialization (species have different resource requirements).

16

Here we examined the relationship between species ecological differences and their

relative abundances. We first observed whether species with high abundances are

ecologically more similar to the most abundant species in a community, following the

predictions from Sugihara’s niche-hierarchy model. Then, we tested whether this

relationship is mitigated in high-productivity zones. We investigated these relationships by

using a single (body mass) and multiple traits as measures of functional differences among

species and observed if using multiple traits is better for detecting the SAD pattern

predicted by Sugihara’s model.

MATERIAL AND METHODS

Data collection

We used data published by Thibault et al. (2011) consisting in communities of small

mammals sampled in various regions of the world. Some were sampled for more than one

year. For those, we averaged species abundances across all sampled years. Then, we

filtered the data to use only communities with at least 10 species, and in homogeneous and

not human-modified habitats, so removing communities in “mixed habitats”, “mixed

temperate forests”, and “agricultural, cropland” habitats ("MX", "MF", and "AG" habitat

codes; Thibault et al. 2011). We ended up with the relative abundances of species across

88 communities worldwide (Appendix 1). For each location we also extracted values of

potential evapotranspiration, which represents the amount of water that can evaporate in a

region through evapotranspiration processes (Global-PET geospatial dataset; Zomer et al.

2007, 2008). We used this variable as a surrogate for productivity because it is generally

thought to correlate with plant productivity due to its close relationships with sites’ climate

17

variables such as temperature and precipitation (Hawkins et al. 2003, Ferguson and

Larivière 2006).

For all the 521 species sampled in all locations, we collected information on traits related to

the flow of energy through communities (Wilman et al. 2014). Specifically, we used

information on: body mass, diet [(1) invertebrates, (2) mammals and birds, (3) reptiles,

snakes, amphibians and salamanders, (5) fish, (6) general vertebrates, (7) scavenge, (8)

nectar, (9) seeds, (10) plants; percent use], foraging stratum [(1) ground level, (2) arboreal,

(3) scansorial; categorical) and activity period [(1) diurnal, (2) nocturnal, (3) crepuscular;

presence/absence] (Wilman et al. 2014).

Calculating correlations

First, for each community we calculated the absolute value of the difference between the

body mass of the most abundant species in the community and the body mass of each

other species in the same community. This variable represents the ”size difference”

between each species and the most abundant species, and is related to the functional

differences among species. Then, for each community we calculated a Pearson correlation

between the log of size differences to the most abundant species and the square root of the

species relative abundance. We ended up with 88 Pearson correlation values. Values were

expected to be negative, indicating that as species are more different from the most

abundant one, they will have lower abundances. Values of Pearson’s r were transformed to

Fisher’s z correlation coefficient (Fisher 1921) in order to be used as effect sizes (and

related sampling variances) (Viechtbauer 2010).

Then, for each community we generated a species distance matrix, using their traits, with a

modification of Gower’s distance that deals with distinct variable types (qualitative and

18

quantitative traits) and missing data (Pavoine et al. 2009). We used the functional distance

between each species in the community in relation to the most abundant species as a

second variable of ”functional difference”. For each community we then calculated a

Pearson correlation coefficient between the log of functional differences to the most

abundant species and the square root of the relative abundances of species. We ended up

with 88 Pearson correlation values between functional differences and abundances of

species. Values were again expected to be negative, indicating that as species are more

different from the most abundant one, they will have lower abundances (see above). Also,

Pearson’s r values were transformed to Fisher’s z correlation coefficient as described

above.

Additionally, we tested for spatial autocorrelation in correlation values based both on mass

and multivariate functional distances using two-sided Moran’s tests. The test was not

significant for correlations based both on mass (observed=-0.03, expected=-0.011, standard

deviation=0.054, p-value=0.733) and multivariate functional distances (observed=0.079,

expected=-0.011, standard deviation=0.053, p-value=0.088). For completeness, we

additionally extracted data for our samples from the 19 Wordlclim’s bioclimatic variables

(Hijmans et al. 2005, Fick and Hijmans 2017), did a Principal Component Analysis (PCA)

with them, and fitted a multiple regression model to observe the relationship between PCA

axes and correlation values based on mass and multivariate functional distances (Appendix

2 for complementary analysis), expecting variables usually found in high-productivity

locations (e.g. with high temperature and precipitation) to explain correlations.

Analyses

We ran two separated analyses using r-to-z transformed values; one with correlations using

species body size differences and their relative abundances and another with correlations

19

using species functional differences (multivariate) and their relative abundances. To do that

we used the ‘rma’ function from ‘metafor’ R package (Viechtbauer 2010) to calculate a

mixed-effects model for each case using effect sizes and their related sampling variances.

For each analysis we also added log of potential evapotranspiration as an explanatory

variable to observe if high productivity is related to low correlation between species

functional distances to most abundant species and relative abundances. All analyses were

done in R Software (R Development Core Team 2015). The procedures we used were

developed in the field of meta-analysis. However, we refrain to classify our study as a meta-

analysis due to the lack of several components that would be necessary to correctly do so

(Koricheva and Gurevitch 2014).

As we found a weak but significant negative relationship between evapotranspiration and

correlations based on multivariate functional distances, we performed two correlation tests

to observe if correlations were weaker in high-productivity zones (high potential

evapotranspiration) due to (1) more equitable abundances or (2) more functional

redundancy among species. So, we ran a Pearson correlation test between (1) correlation

values based on multivariate functional distances and the variance of the square root

values of species relative abundances, and another between (2) the same correlation

values and the variance of values of functional distance divided by the maximum functional

distance in the functional distance matrix. We also performed an ANOVA using habitat

categories (”habitat codes”) from Thibault et al. (2011) as a predictor variable to observe if

high-productivity habitats (such as tropical forests) had weaker correlations than low-

productivity ones (Appendix 3 for complementary analysis). In addition, we fitted a mixed-

effects model using mean mass difference to the mass of the most abundant species as

response variable, and each community as random effects.

20

RESULTS

A total of 88 assemblages of small mammals were used in the analysis (Figure 1). On

average, communities had a species richness of 13.25 ± 4.136, total abundance of 656.891

± 1013.76, and species mean abundance of 46.485 ± 62.429. Most abundant species had,

in general, lower body masses than other species in their respective communities

(estimate=57.112; standard error=10.56; t-value=5.408; p-value<0.001).

Figure 1. Locations of the 88 studied communities.

For the analysis using correlations between body size differences in relation to most

abundant species and species relative abundances, effect sizes were homogeneous

[Q(df=87)=51.346; p-value=0.999]. We found a significant cumulative effect size [estimate=-

0.558; standard error=0.033; z-value=-16.76; p-value<0.001] (Figure 2), indicating that,

within communities, species with highest abundances have similar body mass in relation to

the most abundant species. This result goes in line with the prediction by Sugihara et al.

(2003) that high-abundant species will functionally resemble the most abundant species in

communities. Moreover, when adding potential evapotranspiration as an explanatory

variable, it did not significantly influence results [QM(df=1)=0.454; p-value=0.501],

indicating that effect sizes are not weaker in high-productivity sites.

21

Precision

0 1 2 3 4 5 6 7

-2

0

2

zi

yi

vi2

-1.13

-0.96

-0.78

-0.61

-0.44

-0.27

-0.10

Fisher's z Transformed Correlation Coefficient

Sta

nd

ard

Err

or

0.3

78

0.2

83

0.1

89

0.0

94

0

-1.5 -1 -0.5 0

Sta

nd

ard

ize

d e

sti

mate

(b)

Precision Fisher’s z transformed Correlation Coefficient

(a)

Sta

nd

ard

err

or

Figure 2. (a) Radial plot indicating that, in general, correlations are negative between body

size differences in relation to most abundant species and species relative abundances. (b)

Funnel plot indicating low bias in data used in the meta-analysis.

For the second meta-analysis, using correlations between multivariate functional

differences and relative abundances of species, in general, the hypothesis of homogeneity

among effect sizes cannot be discarded [Q(df=87)=79.741; p-value=0.697]. Again, we

found a significant cumulative effect size (estimate=-0.671; standard error=0.033; z-value=-

20.163; p-value<0.001), indicating that within communities, high-abundant species have

similar ecological traits in relation to the most abundant species. Again, this result goes in

line with the prediction by Sugihara et al. (2003) that highly abundant species will

functionally resemble the most abundant species in communities. Moreover, when adding

potential evapo-transpiration as an exploratory variable, it significantly influenced results

[QM(df=1)=7.227; p-value=0.007], although it had a weak relationship with correlation

values (estimate=-2.673; standard error=0.104; z-value=2.688; p-value=0.007). This

indicates that the correlation tends to be weaker in high-productivity sites. Notably, our

Pearson correlation tests showed that potential evapotranspiration was weakly related to

variance of relative abundances (r=-0.253; p-value=0.018), and not related to variance of

functional dissimilarities (r=0.206; p-value=0.054). So, communities in high-productivity

22

sites tend to have low correlation values because species have more equitable

abundances, and not because species are ecologically more similar. Also, our multiple

regression analysis with PCA axes using Worldclim’s bioclimatic variables showed a result

similar to that of evapotranspiration. Variables related to high productivity (e.g. high

temperature and precipitation) had a weak but significant relationship with correlation

values (Appendix 2). Our ANOVA using habitat categories also indicated that there is no

strong relationship between productivity and the correlation values, but note that tropical

forests had lower correlation values than decidious forests (Appendix 3).

Fisher's z Transformed Correlation Coefficient

Sta

nd

ard

Err

or

0.3

78

0.2

83

0.1

89

0.0

94

0

-1.5 -1 -0.5 0

Precision

0 1 2 3 4 5 6 7

-2

0

2

zi

yi

vi2

-1.38

-1.13

-0.88

-0.63

-0.39

-0.14

0.11

Sta

nd

ard

ized

esti

ma

te

(b)

Precision Fisher’s z transformed Correlation Coefficient

(a)

Sta

nd

ard

err

or

Figure 3. (a) Radial plot indicating that, in general, correlations are negative between

functional differences in relation to most abundant species and species relative

abundances. (b) Funnel plot indicating low bias in data used in the meta-analysis.

DISCUSSION

Our analyses indicated that, in general, predictions made by Sugihara’s niche-hierarchy

model are corroborated across communities. This suggests that as species are more

functionally different from the most abundant one, they are also less abundant in the

community. When we used various traits (multivariate functional differences), potential

evapotranspiration was a weak, albeit significant explanatory variable. In that sense, the

23

relationship expected under Sugihara’s model tends to be mitigated in high-productivity

communities, but the expected functional pattern among species’ ecological differences and

relative abundances seems to be general regardless of the studied habitat type or

productivity.

We found that species with highest abundances in communities were those more

functionally similar to the most abundant species. This might have happened because when

species arrive in communities they accommodate themselves in relation to their habitat and

resource requirements and to how they share them with pre-existing species. If a species is

not very similar to co-occurring organisms, it will compete less with those, allowing for a

high abundance. Indeed, species arrival order and asymmetric competition have been

shown to affect both species richness and abundance (Shulman et al. 1983, Ejrnæs et al.

2006, Dickie et al. 2012). Moreover, Godet et al. (2015) found that the loss of high-

abundant bird species was strongly related to the functional simplification of communities.

This indicates that high-abundant species have resource requirements and, consequently,

ecological traits that are very unique in communities, as predicted by Sugihara et al. (2003).

In general, species that are functionally similar to low-abundant species tend to also have

low abundances due to competition with other species from the same community, which

already have higher abundances and/or share the same habitat and resource requirements.

The strength of the relationship between species abundances and functional similarity to

the most abundant species was significantly but weakly decreased with energy availability

in communities (here proxied by potential evapotranspiration). There is evidence that high-

productivity communities present high richness of specialist species (Srivastava and Lawton

1998), which would not compete due to their different habitat requirements. Moreover,

although it is generally expected to find a higher number of individuals and/or species in

24

sites with higher energy availability (More Individuals and Species-energy hypotheses)

(Evans et al. 2008), species richness can increase independently of total abundance (see

Evans et al. 2005, 2008). This indicates that energy availability can shape species relative

abundances in several ways, such as allowing a higher number of specialists with equitable

abundances as high productivity increases the amount of rare resources, consequently

allowing specialists to arrive and maintain viable populations in communities (Evans et al.

2005). For example, Kaspari (2001) found higher numbers of specialist ant species in

areas with high energy availability. Therefore, it could be expected to find a higher number

of species with equitable abundances in communities from high-productivity areas, as

observed in this present work. It is also notable (and as commonly expected) that most

abundant species had very low body mass.

Our results indicate that species abundances are strongly related to ecological

dissimilarities among organisms. They therefore support niche-partitioning SAD models

(McGill et al. 2007, Ferreira and Petrere 2008), such as Sugihara’s niche-hierarchy model

(Sugihara et al. 2003), which explain species relative abundances as a result of biotic

interactions among species that are not functionally equivalent (contrary to purely neutral

models). Moreover, we showed that energy availability can indirectly shape the SADs of

communities by probably acting upon how species compete. We suggest that new studies

should explore how ecological traits are related to the order of species arrivals during the

assembly of communities so that one could better identify the biological processes

underlying the formation of SADs.

ACKNOWLEDGEMENTS

25

JHN thanks. JHN received a PhD scholarship by Coordernação de Aperfeiçoamento de

Pessoal de Nível Superior (CAPES).

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Supporting Information

Appendix 1. Information on “Site_ID”, “Country”, “Latitude”, “Longitude”, “Habitat_code” (all

the former variables from Thibault et al. 2011), Fisher's r-to-z transformed correlation

coefficients and respective variances for both correlations using log of mass (“zcor mass”,

“zcor mass variance”) or multivariate functional differences (“zcor_func”,

“zcor_func_variance”) and square root of species relative abundances.

Appendix 2. Complementary analysis on the relationship between bioclimatic variables and

the correlation between species functional differences to the most abundant species and

the relative abundances of species.

Appendix 3. Complementary analysis on the relationship between habitats and the

correlations between species functional differences to the most abundant species and

species relative abundances.

29

Appendix 1.

Site_ID Country Latitude Longitude Habitat_ code

zcor_ mass

zcor_mass_ variance

zcor_ func

zcor_func_ variance

1008 USA 36.462 -116.867 D -0.401 0.083 -0.476 0.083

1009 USA 33.837 -115.953 D -0.394 0.077 -0.497 0.077

1021 USA 34.252 -112.523 D -0.227 0.125 -0.192 0.125

1022 USA 34.252 -112.523 D -0.338 0.125 -0.817 0.125

1180 USA 41.641 -114.968 DF -0.657 0.125 -0.905 0.125

1208 Central African Republic

3.9 17.033 TF -0.464 0.067 -0.175 0.067

1210 Central African Republic

4.013 17.067 TF -0.515 0.143 -0.217 0.143

1230 Democratic Republic of Congo

-1.9 23.45 TF -0.319 0.1 0.111 0.1

1232 Gabon -2.526 9.998 TF -0.441 0.125 -0.389 0.125

1240 USA 40.086 -79.979 GL -0.732 0.111 -0.736 0.111

1249 USA 32.513 -106.821 D -0.851 0.111 -0.704 0.111

1250 Brazil -7.771 -51.962 TF -0.702 0.091 -0.88 0.091

1251 USA 47.114 -122.565 DF -0.294 0.111 -0.496 0.111

1262 USA 33.076 -105.713 D -0.804 0.1 -0.823 0.1

1263 USA 35.987 -115.37 D -0.982 0.125 -1.163 0.125

1264 USA 32.109 -110.884 D -0.221 0.1 -0.742 0.1

1265 Mongolia 43 101 D -0.879 0.143 -1.036 0.143

1266 Mongolia 44 99 D -0.289 0.1 -0.281 0.1

1267 Mongolia 43 107 D -1.084 0.125 -1.114 0.125

1268 Canada 49.667 -119.883 CF -0.669 0.143 -0.723 0.143

1269 Canada 49.667 -119.883 CF -0.615 0.143 -0.594 0.143

1271 USA 46.887 -122.677 CF -0.6 0.077 -0.488 0.077

1272 USA 46.887 -122.677 CF -0.682 0.077 -0.359 0.077

1274 USA 41.667 -104.333 GL -0.551 0.125 -0.593 0.125

1275 USA 41.667 -104.333 GL -0.655 0.143 -0.869 0.143

1278 USA 41.667 -104.333 GL -0.923 0.143 -1.163 0.143

1282 USA 40.175 -91.8 DF -0.543 0.111 -1.082 0.111

1283 USA 40.175 -91.8 DF -0.725 0.111 -1.283 0.111

1288 USA 42.078 -122.461 CF -0.461 0.091 -0.992 0.091

1289 USA 42.078 -122.461 CF -0.566 0.125 -0.988 0.125

1291 USA 42.078 -122.461 DF -0.511 0.143 -1.28 0.143

1294 USA 38.63 -79.818 DF -0.794 0.125 -0.859 0.125

1299 USA 36.492 -121.183 SH -0.557 0.067 -0.568 0.067

1307 Canada 50.717 -120.417 DF -0.36 0.125 -0.272 0.125

1320 Malaysia 4.198 114.043 TF -0.277 0.063 -0.834 0.063

1341 Canada 46.643 -71.129 GL -0.595 0.125 -1.031 0.125

1342 Canada 46.643 -71.129 SH -0.293 0.111 -0.649 0.111

1343 Canada 46.643 -71.129 DF -0.624 0.091 -0.713 0.091

1344 Malaysia 6.026 116.323 TF -1.011 0.059 -0.236 0.059

1347 Tanzania -6.997 31.102 DF -0.527 0.071 -0.728 0.071

1348 Swaziland -26.133 31.133 DF -0.238 0.091 -0.678 0.091

1353 Swaziland -25.967 31.717 DF -0.559 0.143 -0.737 0.143

30

1356 French Guiana

3.617 -53.2 TF -0.097 0.1 -0.356 0.1

1358 USA 43.938 -73.088 DF -0.86 0.1 -1.165 0.1

1359 USA 43.938 -73.088 DF -0.687 0.091 -1.381 0.091

1377 Malaysia 3.108 101.817 TF -0.715 0.111 -0.826 0.111

1378 Selangor 3.108 101.817 TF -0.841 0.111 -0.94 0.111

1379 Selangor 3.108 101.817 TF -0.959 0.091 -0.414 0.091

1397 USA 40.877 -77.837 DF -0.562 0.111 -1.119 0.111

1417 Vietnam 11.44 107.415 TF -0.63 0.1 -0.556 0.1

1419 Bolivia -14.664 -66.29 TF -0.892 0.143 -1.067 0.143

1465 USA 47.831 -123.677 CF -0.45 0.091 -0.756 0.091

1466 USA 47.831 -123.677 CF -0.384 0.143 -0.704 0.143

1467 USA 47.831 -123.677 CF -0.422 0.091 -0.724 0.091

1486 USA 33.933 -111.592 SH -0.881 0.091 -0.687 0.091

1550 USA 32.404 -110.454 D -0.291 0.143 -0.502 0.143

1581 USA 31.986 -109.141 D -0.561 0.111 -0.802 0.111

1611 USA 36.98 -117.02 D -0.327 0.143 -0.66 0.143

1614 USA 36.514 -117.175 D -0.595 0.143 -0.589 0.143

1620 USA 43.266 -118.844 D -0.882 0.111 -0.471 0.111

1730 USA 37.309 -88.966 DF -0.489 0.143 -1.326 0.143

1731 Peru -11.9 -71.367 TF -0.262 0.056 -0.398 0.056

1732 Peru -11.9 -71.367 TF -0.308 0.091 -0.581 0.091

1733 Peru -11.9 -71.367 TF -0.315 0.091 -0.43 0.091

1734 Peru -11.9 -71.367 TF -0.405 0.091 -0.677 0.091

1742 Mexico 23.062 -99.205 TF -0.607 0.125 -0.099 0.125

1746 USA 38.048 -81.678 GL -0.568 0.1 -1.273 0.1

1747 USA 38.048 -81.678 SH -0.533 0.143 -1.057 0.143

1748 USA 38.048 -81.678 DF -0.709 0.125 -1.376 0.125

1751 USA 39.446 -87.24 GL -0.37 0.143 -0.382 0.143

1791 Brazil -7.771 -51.962 TF -0.392 0.048 -0.564 0.048

1812 Peru -13.136 -69.611 TF -0.524 0.143 -0.919 0.143

1814 USA 31.585 -110.344 D -0.32 0.05 -0.518 0.05

1820 USA 39.4 -79.65 DF -0.597 0.067 -0.511 0.067

1821 USA 39.433 -79.667 DF -0.819 0.05 -0.58 0.05

1822 USA 39.4 -79.7 DF -0.781 0.05 -0.544 0.05

1823 USA 38.441 -112.63 D -0.907 0.143 -0.816 0.143

1835 Canada 49.116 -81.364 CF -0.466 0.091 -0.852 0.091

1870 USA 45.074 -84.14 CF -0.616 0.143 -0.825 0.143

1904 Peru -3.967 -73.417 TF -0.198 0.032 -0.491 0.032

1944 USA 31.939 -109.083 SH -0.477 0.056 -0.35 0.056

1945 Mauritania 16.6 -16.43 D -0.983 0.143 -0.822 0.143

1956 Guinea 10 -10.97 DF -1.126 0.125 -0.84 0.125

1980 Benin 9.06 2.06 DF -0.42 0.143 -0.776 0.143

1985 Benin 8.2 1.41 DF -0.658 0.143 -0.726 0.143

1986 Benin 8.2 1.41 DF -0.359 0.143 -0.576 0.143

1987 Benin 8.17 1.45 SH -1.128 0.111 -0.493 0.111

1992 Australia -37.474 149.592 DF -0.329 0.143 -0.618 0.143

31

Appendix 2. Complementary analysis on the relationship between bioclimatic variables and the correlation between species functional differences to the most abundant species and the relative abundances of species.

First, we extracted data from Wordlclim’s bioclimatic variables (Table A1) (Fick and

Hijmans 2017) using coordinates from studied samples (Appendix 1). Then, we did a

Principal Component Analysis (PCA) with all bioclimatic variables to reduce

multidimensionality and find correlations between them. We selected the first three PCA

axes based on Kaiser-Guttman criterion (Tables A2 and A3). We fitted two multiple

regressions to observe the relationship between selected PCA axes (predictor variables)

and Pearson correlation coefficients between the log of (1) mass differences and (2)

multivariate functional differences to the most abundant species and the square root of

relative species abundances (response variables) (the former was not significant, p-

value=0.362; Table A4 for the latter). The second multiple regression was significant but had

extremely low explanatory power (R²=0.087). Notably, PC1, which was related to variables

such as high temperature and precipitation, was weakly, but positively related to correlation

values.

Table A1. Abbreviations for bioclimatic variables from Worldclim.

Abbreviation Bioclimatic variable

BIO1 Annual mean temperature

BIO2 Mean diurnal range

BIO3 Isothermality

BIO4 Temperature seasonality

BIO5 Max temperature of warmest month

BIO6 Min temperature of coldest month

BIO7 Temperature annual range

BIO8 Mean temperature of wettest quarter

BIO9 Mean temperature of driest quarter

BIO10 Mean temperature of warmest quarter

BIO11 Mean temperature of coldest quarter

BIO12 Annual precipitation

BIO13 Precipitation of wettest month

BIO14 Precipitation of driest month

BIO15 Precipitation seasonality

BIO16 Precipitation of wettest quarter

BIO17 Precipitation of driest quarter

BIO18 Precipitation of warmest quarter

BIO19 Precipitation of coldest quarter

Table A2. Standard deviation, proportion of explained variance, and cumulative proportion

of explained variance for each PCA axis (selected axes are in bold).

32

PCA axis Standard deviation Proportion of variance Cumulative proportion

PC1 3.081 0.500 0.500

PC2 2.233 0.262 0.762

PC3 1.409 0.105 0.867

PC4 0.940 0.047 0.913

PC5 0.755 0.030 0.943

PC6 0.657 0.023 0.966

PC7 0.527 0.015 0.981

PC8 0.454 0.011 0.991

PC9 0.254 0.003 0.995

PC10 0.216 0.002 0.997

PC11 0.197 0.002 0.999

PC12 0.069 0.000 1.000

PC13 0.059 0.000 1.000

PC14 0.044 0.000 1.000

PC15 0.034 0.000 1.000

PC16 0.029 0.000 1.000

PC17 0.013 0.000 1.000

PC18 0.006 0.000 1.000

PC19 0.000 0.000 1.000

Table A3. Correlations between PCA scores of each selected PCA axis and raw variables

used in analysis.

Variable PC1 PC2 PC3

BIO1 -0.836 0.530 -0.088

BIO2 0.427 0.668 -0.157

BIO3 -0.903 0.287 0.039

BIO4 0.928 -0.117 -0.244

BIO5 -0.282 0.853 -0.351

BIO6 -0.940 0.297 0.083

BIO7 0.931 0.079 -0.266

BIO8 -0.480 0.489 -0.487

BIO9 -0.751 0.437 0.236

BIO10 -0.487 0.750 -0.330

BIO11 -0.908 0.395 0.059

BIO12 -0.809 -0.551 -0.018

BIO13 -0.817 -0.435 0.200

BIO14 -0.480 -0.620 -0.563

BIO15 -0.073 0.508 0.614

BIO16 -0.805 -0.459 0.206

BIO17 -0.502 -0.651 -0.515

BIO18 -0.742 -0.288 -0.397

BIO19 -0.492 -0.583 0.296

Table A4. Summary of multiple regression (p-value=0.014; adjusted R²=0.087) fitted using

PCA axes as predictor variables, and the correlations between the square root of relative

33

abundances and log of multivariate functional distances to the most abundant species as

the response variable. Significant p-values are in bold.

Variable Estimate Standard error t-value p-value

(Intercept) -0.577 0.020 -29.047 0.000

PC1 -0.018 0.006 -2.726 0.008

PC2 0.015 0.009 1.725 0.088

PC3 0.013 0.014 0.930 0.355

References Fick, S. E. and Hijmans, R. J. 2017. WorldClim 2: New 1-km spatial resolution climate

surfaces for global land areas. - Int. J. Climatol. in press.

34

Appendix 3. Complementary analysis on the relationship between habitats and the correlations between species functional differences to the most abundant species and species relative abundances.

First, we categorized our samples’ habitats (Appendix 1) according to ”habitat codes”

from Thibault et al. (2011) to observe if high-productivity habitats (such as tropical forests)

had weaker correlations between species functional distances to the most abundant

species and species relative abundances than low-productivity ones. Notably, in order to

avoid categories with only one sample (and considering habitat similarity) we changed the

habitat codes from sites 1814 and 1945 respectively from DG and SD to D. To do that we

used two ANOVAs using “habitat” as a predictor variable and correlation values as the

response variable: one using correlations between (1) log of species mass differences to

the most abundant species and species relative abundances, and another using

correlations between (2) log of species multivariate functional distances to the most

abundant species and species relative abundances. The former was not significant (p-

value=0.728), while the latter was significant (Table A1). A post-hoc Tukey’s test indicated

that only tropical and deciduous forests significantly differed in relation to their correlation

values (mean difference=0.214; p-value=0.002).

Table A1. Summary ANOVA using habitat as predictor variable and correlations between

the log of multivariate functional distances to the most abundant species as the response

variable.

Variable Degrees of freedom Sum of squares Mean square F-value p-value

Habitat 5 0.601 0.120 3.642 0.005

Residuals 82 2.705 0.033

References Thibault, K. M. et al. 2011. Species composition and abundance of mammalian

communities. - Ecology 92: 2316.

35

CAPÍTULO 2

A Forest Canopy as a Living Archipelago:

Why Phylogenetic Isolation May Increase and Age Decrease Diversity

Short title: Trees as living islands

José Hidasi-Neto*1, Richard I. Bailey2, Chloé Vasseur3, Steffen Woas4, Werner Ulrich5,

Olivier Jambon6, Ana M.C. Santos7, Marcus V. Cianciaruso1, Andreas Prinzing6

1Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, Brazil.

2Department of Zoology, Charles University, Prague, Czech Republic.

3Département Sciences pour l’action et le développement, French National Institute for

Agricultural Research, Paris, France.

4Staatliches Museum für Naturkunde Karlsruhe, Karlsruhe, Germany.

5Chair of Ecology and Biogeography, Nicolaus Copernicus University Toruń, Toruń, Poland.

6Ecosystèmes Biodiversité Evolution (ECOBIO), Université de Rennes 1 / CNRS, Rennes,

France.

7Departament of Biogeography & Global Change, Museo Nacional de Ciencias Naturales

(CSIC), Madrid, Spain.

*Corresponding author: José Hidasi Neto, Programa de Pós-Graduação em Ecologia e

Evolução, Universidade Federal de Goiás, 74001-970, Goiânia, Goiás, Brazil. Email:

[email protected]. ORCID ID: orcid.org/0000-0003-4097-7353.

mailto:[email protected]

36

ABSTRACT

Aim

An individual tree can be broadly understood as a living island upon which colonizers are

able to survive and maintain populations, but several differences exist in relation to oceanic

islands: A tree is relatively young, is composed of numerous differently aged branches, may

be phylogenetically isolated from neighbours, and its colonizers may be specific to

particular tree lineages. We suggest that these specificities strongly affect not only alpha

but also beta diversity within trees, including positive effects of isolation on diversity of

generalists and reinforcement of isolation with tree age.

Location

Western France

Taxon

Oribatid mites (Acari: Oribatida) and gall wasps (Hymenoptera: Cynipidae).

Methods

We tested the effects of tree and branch age, tree and branch habitat diversity, and tree

phylogenetic isolation on per-branch and per-tree alpha diversity, and on within-tree beta

diversity of two colonizer groups: little-dispersive, generalist oribatid mites and highly-

dispersive, specialist gall wasps.

Results

For gall wasps, no variable explained diversity patterns at any level. In contrast, for oribatid

mites we found that increasing tree phylogenetic isolation and branch age increased alpha

diversity per tree and per branch (in young trees) as well as turnover among branches.

37

Increasing tree age decreased alpha diversity per branch (in phylogenetically isolated trees)

and increased turnover among branches. Increasing habitat diversity increased alpha

diversity per tree, but decreased alpha diversity per branch (in young trees).

Main conclusions

The results suggest that contrary to common expectation (i) phylogenetically distant

neighbours are a source of immigration of distinct mite species; and (ii) with increasing tree

age, species-sorting results in a few species colonizing and dominating their preferred

patches. In galls, in contrast, strict specialization on, and efficient dispersal among, oaks

may render oak age or isolation unimportant. Finally, the positive relationship between

isolation and within-island turnover might be a new contribution to biogeography in general.

Keywords

alpha and beta diversity; community assembly; gall wasps; island biogeography; living

island; oribatid mites; species turnover.

38

INTRODUCTION

Studies on oceanic islands have provided key insights into the assembly and structuring of

ecological communities (Whittaker et al., 2008). Also, it has been seen that host organisms

can be understood as living islands upon which entire communities or even meta-

communities of colonizers can live or feed (e.g. Gossner et al., 2009; Vialatte et al., 2010),

surrounded by an unsuitable matrix of non-host organisms (Gripenberg & Roslin, 2005).

However, contrary to oceanic islands, hosts as living islands are relatively young, so

communities have less time to be assembled through local speciation. Further,

‘archipelagos’ of hosts (individuals) may be phylogenetically structured, with hosts differing

not only in spatial but also phylogenetic distance from neighbours. Finally, most plant hosts

are composed of numerous small habitat patches, such as branches, each being much

younger than the host itself.

Properties of islands have major effects on species diversity. Islands with higher habitat

diversity typically harbour larger numbers of species because they can accommodate

species with different habitat requirements (Fattorini et al., 2015; Hortal et al., 2009),

particularly habitat specialists. Larger islands also tend to have higher species diversity,

probably because the rate of extinction relative to colonization is lower (Whittaker et al.,

2008). There is indication that youngest islands are occupied by smallest numbers of

species due to little time available for their arrival, although species richness may decline if

islands shrink with age (Simberloff & Wilson, 1969; Cornell & Harrison, 2014). Finally,

spatially isolated islands typically have lower species diversity, primarily because they can

only be reached by few dispersers (Simberloff & Wilson, 1969; Hendrickx et al., 2009).

Considering hosts as living islands would lead to similar expectations: habitat diversity, host

size and age should increase and spatial isolation decrease the diversity of the fauna

39

inhabiting those hosts (e.g. Gripenberg & Roslin, 2005; Lie et al., 2009). These hypotheses

are largely application of existing hypotheses for oceanic islands to hosts as islands.

Unlike oceanic islands also phylogenetic relationships among hosts may influence the

composition and diversity of colonizer species. Hosts’ physical or physiological

characteristics control habitat conditions available to colonizers and are often more different

among distantly than among closely related host species (Revell et al., 2008). Colonizers of

an individual host may perceive neighbouring hosts from distantly related species as

different and unsuitable habitats. Therefore, from the point of view of colonizers specialized

on that host species, the phylogenetically isolated host individual may be surrounded by an

unsuitable matrix (and may be unsuitable for specialist colonizers living on surrounding

hosts) (Yguel et al., 2011, 2014). As a consequence, phylogenetically isolated hosts have

been shown to harbour relatively depauperate and homogenized colonizer communities

(Vialatte et al., 2010; Yguel et al., 2014; Grandez-Rios et al., 2015). Alternatively, we

hypothesize that if habitat characteristics are only moderately different among

phylogenetically distant species (Revell et al., 2008; Gossner et al., 2009), or colonizers are

only moderately specialized on these characteristics, phylogenetic isolation of a host may

have opposite effects: exchange among distantly related hosts remains possible and

increases local species diversity due to mass effects, i.e. due to strong immigration from

adjacent patches including such of different quality occupied by different species (Brown &

Kodric-Brown, 1977; Mouquet & Loreau, 2003; Leibold et al., 2004; Gossner et al. 2009;

Table 1). To our knowledge this alternative hypothesis has not been tested so far.

Unlike oceanic islands, plant hosts are composed of numerous young patches. We

hypothesize that diversity within these patches reflects that of the entire hosts, i.e.

increases with host age or habitat diversity or change with the host’s phylogenetic isolation.

Alternatively, we hypothesize that the same factors may increase rather turn-over among

40

patches than diversity within. Finally, diversity within local patches may depend on patch

characteristics rather than host characteristics. Diversity might increase with increasing

patch age due to more time available for species arrival (Whittaker et al., 2008; Cornell &

Harrison, 2014), and with the availability of more diverse habitats for more diverse

specialists (Hortal et al., 2009). However, according to the area–heterogeneity trade-off

hypothesis (Kadmon & Allouche, 2007; Allouche et al., 2012; but see Hortal et al., 2013 for

criticisms), species diversity within patches may decrease with habitat diversity due to a

decreased area available per habitat, constraining in particular habitat specialists. To our

knowledge the importance of patch-level characters and host-level characters for path-level

diversity have not been compared so far. More generally, beta diversity due to species-

turnover among patches on islands or hosts has to our knowledge not been related to

island or host characters.

Contrary to oceanic islands, the effect of host characteristics on assembly of colonizer

communities might increase with the strength of the co-evolutionary relationships between

hosts and colonizers (Gossner et al., 2009; Yguel et al., 2014) and with the colonizers’ lack

of dispersal capacities or high degree of specialization (above and Castagneyrol et al.,

2014). If phylogenetic isolation operates in a similar way to spatial isolation (Yguel et al.,

2014) then, for a given degree of specialization, phylogenetic isolation should limit the

assembly of poor dispersers rather than good dispersers (consistent with e.g. Hendrickx et

al., 2009). On trees as hosts, for instance, flightless organisms such as oribatid mites

(Acari: Oribatida) depend on passive dispersal (e.g. by wind; Lehmitz et al., 2011; Lehmitz

et al., 2012) leading to low capacity to disperse to new hosts (Jung and Croft 2001), while

winged organisms such as gall wasps (Hymenoptera: Cynipidae) can disperse both

passively and actively across large distances (Gilioli et al., 2013). However, in the specific

case of oribatid mites and gall wasps on trees, the two groups also differ considerably in

degree of specialization. Oribatid mites (contrary to many other mites) usually live and feed

41

on detritus or cryptogam upon their hosts and hence only indirectly depend on tree traits

(e.g. bark structure) controlling the accumulation of detritus or growth of cryptogams (e.g.

Prinzing, 2003; Nash, 2008; for a review Walter & Proctor, 2013). It is known that such traits

may show moderate, albeit significant, phylogenetic signal, resulting in a tendency of

different cryptogam covers among tree lineages (Rosell et al., 2014). Gall wasp larvae, in

contrast, directly depend on host anatomical and physiological traits that affect larval

development (such as sclerotization), and many of these traits show strong phylogenetic

signal (Stone et al., 2002; Hayward & Stone, 2005). Therefore, gall wasps have a higher

degree of host specialization than oribatids (e.g. Ambrus, 1974 vs Behan-Pelletier & Walter,

2000), being restricted to e.g. a single host genus or even host species.

We examine the above hypotheses focusing on oak trees (Quercus spp.) as living-island

hosts growing in closed forest canopy where spatial distances between focal oaks and their

direct neighbours are almost zero (foliage in contact) but phylogenetic distances may be

very large. We studied the two aforementioned colonizer groups with contrasting biology:

oribatid mites and oak gall wasps. We calculate diversity measures that integrate

abundances per species as abundances provide more fine-grained information and are

affected by dispersal limitation (Simberloff 2009; Boulangeat et al. 2012). We first examine

relationships between alpha diversity of mites and gall wasps on the one hand and

microhabitat diversity, tree age, and phylogenetic isolation of the host tree on the other

hand. Second, we test whether these relationships also occur at the within-tree scale of

individual branches. Finally, we test whether among-branch species turnover of mite and

gall wasp communities within single trees is linked to microhabitats on and ages of

branches and to age and phylogenetic isolation of trees. We aim to determine the relative

importance of different host and colonizer characteristics on the diversity and turnover of

species within individual living islands. We summarize our hypotheses and predictions in

Table 1.

42

MATERIALS AND METHODS

Study system

We sampled a temperate mixed forest located close to Rennes, Bretagne, France (48.11 N,

-1.34 W) in which oaks (Quercus petreae and robur) grow in neighbourhoods of Ilex

aquifolium, Fagus sylvatica, Castanea sativa, Ulmus minor, Alnus glutinosa, Sorbus

torminalis, Corylus avellana, Carpinus betulus, Populus tremula, Salix caprea, Abies alba,

Rhamnus frangula, Tilia cordata, Betula pendula, Prunus avium, Malus sylvestris and Pyrus

pyraster. We sampled from mid-August to mid-September 2006 (Vialatte et al., 2010; Yguel

et al., 2011 for details). We studied nine triplets - sets of three nearby (<150m) Quercus sp.

trees - with each triplet composed of either Q. petreae or Q. robur; two closely related

species that can hybridize (Yguel et al., 2014) albeit individual trees had to be dropped from

further analyses as explained below. Within each triplet, trees were selected in order to

maximize differences in tree circumference at breast height and phylogenetic isolation to

neighbours. Such an approach of “blocking” and maximizing variation of independent

variables of interest within blocks has been recommended to partial out spatially varying

environmental impacts (Legendre et al., 2004). Tree circumferences were a proxy of age

(as in Vialatte et al., 2010), and ranged from 60 to 277cm, corresponding to 80 to 180 years

old according to local forestry authorities (see Yguel et al., 2011). We did not consider

younger, understory trees as such trees are often characterized by a different fauna from

adult trees (Gossner et al., 2009). For each focal tree, phylogenetic isolation was

calculated, according to Vialatte et al., (2010), as [∑(Ntree sp. x ttree sp.)/Ntotal trees].

Ntree sp. is the number of neighbours of a particular tree species directly in contact with a

focal tree’s crown, ttree sp. is the phylogenetic distance (in MYBP) between the

establishment of the clade of the neighbouring species and the oaks, and Ntotal trees is the

number of trees (all species) in contact with the focal tree’s crown (see Vialatte et al., 2010

43

for more information; Yguel et al., 2011, 2014; see Appendix S1 in Supporting Information).

A tree was considered to be in contact with the focal tree when they had their leaves in

contact (at least during wind), albeit barks remained separate. Phylogenetic distances were

continuous (Appendix S1, Tables S1-S6). For these trees in contact we also quantified the

Simpson’s species diversity (as 1-D; D = ∑(abundance of a species/total abundance)²]). We

finally quantified the distance to the closest oak (either Q. petraea or robur, or their hybrids).

Species and habitat sampling

In each crown, six (for mites) and ten (for gall wasps) branches between 1.5 and 2m in

length were sampled from the canopy, using single-rope climbing and 6m branch cutters, in

each of the three following strata: upper crown, lower-shaded crown, lower sun-exposed

crown. Branches were aged by counting back shoot growth branching points (identified by

narrow winter growth marks) from the tip and were grouped into the branch tips (6 years old

and younger) and the older part of the branches (the age range of which was recorded).

Each branch subsection was placed over a plastic tray and washed under high water

pressure over its entire length with the help of a pressure washer. The solution obtained for

each branch was filtered using a coffee filter, which was dipped in alcohol. Using slide-

mounted material, species (juvenile and adult individuals) were identified using Weigmann

(2006). For gall wasps, we measured branch length and recorded leaf galls, excluding bud

galls, identifying species based on gall morphology (Ambrus, 1974).

We quantified alpha diversity of gall wasps and mites per branch by the unit equivalent of

Simpson’s diversity [calculated as 1-D; D = ∑(abundance of a species/total abundance)²]

using abundances per branch (Simpson’s index is largely independent of sample size). To

avoid potential under-sampling and zero-inflated data we only considered branches with

Simpson’s diversity>0. Alpha diversity per entire tree was calculated as the Simpson’s

diversity (1-D) of the averaged abundances of each species on the entire tree (“per-tree

44

alpha diversity” could also be called “gamma diversity across branches”). We found that the

mite communities are strongly dominated by two species. High alpha diversity might hence

simply reflect that these two species are of similar abundance: We hence verified whether

high alpha diversity corresponds to a small (close to one) ratio of the most abundant to the

second most abundant species. We found that the opposite is the case and we found

relationships low compared to the relationships we found when relating diversity to the

independent variables outlined in the Introduction (r=0.46).

We also calculated within-tree turnover and, for completeness, nestedness partitions of

beta diversity (Baselga, 2010) as the averaged value of turnover and nestedness between

each pair of branches of a tree based on a Bray-Curtis distance of the occurrences of mites

(using 'bray.part' function from 'betapart' package; Baselga & Orme, 2012). Our measure of

turnover is particularly unbiased by unequal sampling efforts resulting from unequal

numbers of animals per sample, and particularly independent of nestedness (Barwell et al.,

2015). Turnover is related to balanced variation, where the abundance of a species is

replaced by a similar abundance of another species in another site (indicating

compositional differences), while nestedness is related to unidirectional variation, where the

abundances of species differ from one site to another (indicating differences in relative

abundances) (Baselga, 2013). We used pairwise turnover and nestedness values to

calculate average turnover and nestedness for each tree.

Oribatid mites are usually associated with algae, fungi, moss or lichens, commonly feeding

on these organisms (Walter & Proctor, 2013, on older branches and stems variation in bark

structure may be important, but our samples consist of young branches; Prinzing, 2003).

Consequently, the distribution of oribatids should depend on the presence of these

microhabitats. Therefore, we measured per branch the coverage (%) of: algae, mosses,

crustose lichens, foliose lichens, and “mixed” (intermingled cryptogams). Again we used the

45

Simpson’s metric to assess habitat diversity per branch and per tree. Habitat diversity per

branch was calculated as the Simpson’s diversity of habitat measurements on the branch.

Habitat diversity per tree was calculated as the Simpson’s diversity of the averaged per-

branch measurements of each habitat variable. To measure habitat composition and reduce

data dimensionality, we also used the scaled and centred percentages of each habitat

variable to perform a Principal Components Analysis (PCA), which works by producing a

reduced number of orthogonal variables based on previously inputted variables (Kaiser-

Guttman criterion was used for axis selection).

Gall wasp larvae feed on plant tissue. Consequently, the distribution of gall wasps should

depend on the composition of plant tissues. To identify habitat conditions of gall wasp

larvae, we sampled ten leaves from each stratum (upper crown, lower shaded crown, and

lower exposed crown) of each tree (always the third leaf from base of the branch) and

placed them into freezing bags for chemical analyses. We measured leaf C/N and Dry mass

contents according to standard protocols as detailed in Appendix S1. Note that such

diversity of leaves as habitats was available at the tree scale.

Explaining alpha diversity per tree

We did a preliminary analysis to identify if alpha diversity depends on an independent

variable not related to our hypotheses, i.e. spatial proximity among the oaks studied (testing

for spatial autocorrelation using Moran’s tests), and spatial proximity to any other oak or

Simpson’s diversity of neighbourhoods (using Pearson’s correlation tests). We found that

alpha diversity was unrelated to these variables: spatial autocorrelation was non-significant

(for oribatids, I=0.131, expected=0.042, SD=0.109, p=0.113; for gall wasps, I=-0.03,

expected=-0.05, SD=0.086, p=0.815), relationship to distance to nearest oak (for oribatids,

r=0.317, p=0.123; for gall wasps, r=0.104, p=0.653) or neighbourhood diversity (for

oribatids, r=-0.118, p=0.574; for gall wasps, r=-0.036, p=0.876) were non-significant. We

46

hence continued from here on accounting only for the independent variables related to our

hypotheses. We note that these variables are correlated among each other, but only weakly

(unsigned relationships mostly < 0.4; Appendix S1, Tables S3 and S6), indicating that

multicollinearity is likely not a problem, consistent with the high adjusted R² of our analyses.

We tested effects of a tree crown’s (1) age, (2) habitat diversity, and (3) phylogenetic

isolation on the tree crown’s alpha diversity. For both mites and gall wasps, we fitted

multiple regressions using log(Simpson’s diversity per tree) as a response variable, and the

logs of tree circumference, tree phylogenetic isolation, and habitat diversity per tree as

predictor variables. To account for possible changes in the effects of each predictor variable

at different values of the other predictors, we fitted three further models, each including one

of the following interactions: log(tree age):log(habitat diversity per tree), log(tree

phylogenetic isolation):log(habitat diversity per tree), and log(phylogenetic isolation):log(tree

age) (including all interaction terms together would lead to excessive multicollinearity). We

then chose the model with the lowest value of sample-size corrected Akaike’s Information

Criteria (AICc) (Bunnefeld & Phillimore, 2012). In the mite dataset, after evaluating the

residuals (using probability and predicted-vs-residual plots) we removed a maximum of

three outliers in order to provide a better fit to our model. This procedure did not

qualitatively change results, but adjusted R² increased from 0.294 to 0.689.

Explaining alpha diversity per branch

We fitted mixed-effects regressions (Bunnefeld & Phillimore, 2012) to analyze how

characteristics of trees and branches explain species diversity at the within-tree scale. We

used log(colonizer species Simpson’s diversity per branch) as the response variable, and

remind here that this variable is different from tree-level alpha diversity analyzed above as

tree-level diversity reflects the combined effect of branch-level diversity and turnover among

branches. As predictor variables we used log(tree age), log(tree phylogenetic isolation), and

47

either log(branch age) (for mites) or log(branch length) (for gall wasps). The tree where the

branch was collected was the random effect. As above we also fitted three other models

with the same variables, but including either of the following interactions: log(tree

age):log(branch age or length), log(tree phylogenetic isolation):log(branch age or length),

and log(tree phylogenetic isolation):log(tree age). We chose the models with the lowest

values of AICc. To account for the uncertainty in the selection of sets of variables in mixed-

effects models we then conducted a model-averaging procedure. For both mites and gall

wasps, we permuted the fixed-effect variables found in the best mixed-effects model, fitting

a new mixed-effects model for each subset ('dredge' function; Bunnefeld & Phillimore, 2012;

Bartoń, 2015). Next, we generated averaged models for mites and gall wasps using subset

models with ΔAICc<2 ('model.avg' function from 'MuMIn' package; Burnham & Anderson,

2002; Bartoń, 2015). Values of “importance” were calculated for each predictor variable as

the sum of Akaike weights of all models in which the variable appeared (Burnham &

Anderson, 2002). After this procedure, mites had only one model with delta AICc<2, so we

interpreted results from this mixed-model. Also, for mites we repeated this approach

including as explanatory variables the log of habitat diversity per branch and three axes of

the PCA performed with the habitat variables (related to, respectively, presence of

uncovered branch and lack of algae; the lack of crustose lichens and of mosses; and the

presence of foliose lichen (Appendix S1, Tables S7-S9); equivalent per-branch data were

not available for galls). In this analysis we also included two additional interactions: log(tree

age):log(habitat diversity per branch) and log(tree phylogenetic isolation):log(habitat

diversity per branch). We calculated averaged models again using the threshold of delta

AICc<2. We only discuss results from this latter analysis for mites as it is the most

complete. In all model-averaging procedures, we included the marginal and conditional R²

of subset models. These values represent, respectively, the variance explained by only

48

fixed and by fixed and random variables (calculated with 'r.squaredGLMM' function;

Nakagawa & Schielzeth, 2013).

Explaining beta diversity among branches within tree crowns

We performed multiple linear regressions using as response variables the log of the

average turnover and nestedness partitions of beta diversity among branches of trees. We

used log(tree age), log(tree phylogenetic isolation), and log(habitat diversity per tree) as

predictor variables. We again tested for interactions by fitting models including the following

interactions: log(tree age):log(habitat diversity per tree), log(tree phylogenetic

isolation):log(habitat diversity per tree), and log(tree phylogenetic isolation):log(tree age).

We chose models with the lowest AICc values. For the mites dataset, after evaluating the

residuals, we decided to remove three outliers (not the same as mentioned previously) for

the model using turnover as a response variable. This procedure did not qualitatively

change results, but adjusted R² increased from 0.534 to 0.66. Statistical representations of

interaction effects were visualized using “visreg” R package (Breheny & Burchett, 2015).

We added one to all variables for which we used log transformation.

RESULTS

A total of 24 mite (and one undetermined) and 10 gall wasp species were recorded on 181

branches from 25 trees and 153 branches from 21 trees, respectively (Appendix S2, Tables

S10 and S11). Mite abundance varied from 40 to 1028 individuals per tree, Micreremus

brevipes (Michael, 1888) being most abundant. Gall wasp abundance varied from 6 to 414

individuals per tree, Neuroterus anthracinus (Curtis, 1838) being most abundant.

Alpha diversity per tree

49

For both mites and gall wasps, the best AICc model included no interactions (Table 2

throughout; Appendix S3). For mites, alpha diversity increased with both increasing

phylogenetic isolation (Figure 1a) and tree-level habitat diversity (Figure 1b). Tree age did

not explain mite alpha diversity. In the case of gall wasps, the best AICc model did not

explain variation in alpha diversity [F(3, 17)=1.098; adjusted R²=0.014; p=0.377].

Alpha diversity per branch

For gall wasps, the best AICc model was the one with no interaction (Appendix S3), but no

predictor was significant and explained variance was extremely low and none of these

relationships was significant (Table 3). For mites, the best AICc model included the

interaction between phylogenetic isolation and tree age. In the additional model including

information on branch habitat composition (PCA scores) the best model included the

interaction between tree age and branch habitat diversity (Appendix S3). Without per-

branch habitat composition variables (PCA scores), mite diversity increased with

phylogenetic isolation and tree age on single branches. The significant interaction term

between phylogenetic isolation and age indicated that branches of more phylogenetically

isolated trees had higher mite diversity when the tree was young, but lower diversity when

the tree was old (Table 1; Appendix S1, Figure S1). When including habitat composition

variables (Table 2), phylogenetic isolation increased mite diversity while tree age and

habitat diversity per branch decreased mite diversity. The significant interaction between

habitat diversity per branch and tree age indicated that high habitat diversity decreased mite

diversity in young and increased diversity in old trees (Appendix S1, Figure S2). Branch age

was in all models positively related to mite diversity (Table 3; Figure 1c).

Beta diversity among branches

For gall wasps, no interaction was included (Appendix S3), no predictor was significant and

explained variance was extremely low (both adjusted R² < 0.05, p > 0.5; Appendix S3). The

50

best AICc model with average turnover as a response variable was the one that included

the interaction between phylogenetic isolation and tree age for mites (Table 2). Specifically,

for mites, turnover was higher in trees with high habitat diversity and phylogenetic isolation

(Table 2; Figure 1d). The significant interaction between tree age and tree phylogenetic

isolation indicated that mite turnover increased with tree age in phylogenetically non-

isolated trees, while decreased in isolated trees (Table 2; Appendix S1, Figure S3). The

best AICc model with average nestedness as a response variable did not contain this

interaction between tree age and tree habitat diversity for mites (Table 2; Appendix S3).

Specifically, mite nestedness was higher in old than in young trees (Table 2). Also, mite

nestedness increased with tree age when trees provided low habitat diversity, but

decreased when trees were habitat diverse (Table 2; Appendix S1, Figure S4).

DISCUSSION

We characterized host trees as particular, living islands; they are isolated from spatially

adjacent neighbours by evolutionary distance, are composed of numerous young to very

young patches (branches), and are colonized by lineages that are specialized on the

studied oaks and others that seem to be less specialized. We found that island age

(measured here as tree circumference), phylogenetic isolation, and habitat diversity control

alpha diversity of colonizers on the entire living islands as well as on and among their

individual branches (while other neighbourhood characteristics were not significant; see

Methods). Notably, such patterns only occurred for oribatid mites, which have poor

dispersal ability and are less specialized.

We found higher mite diversity on phylogenetically isolated host trees, across the entire

trees and on each of their branches, contradicting what has been observed before

(reviewed by Grandez-Rios et al., 2015 for species richness as a measure of alpha

51

diversity). We suggest that mite populations studied here represent an evolutionary

situation different from those in taxa studied before. Mites do not feed on the tree itself and

hence do not directly depend on tree traits nor on their phylogenetic signal. However, mites

depend indirectly on some tree traits, such as bark structure and pH, that control the

cryptogam cover (Nash, 2008) and thus substrate and food of oribatids. Such bark traits

appear to show moderate but significant phylogenetic signal (e.g. Rosell et al., 2014)

resulting in differences in cryptogam covers among tree lineages. Consequently, distantly

related trees should be preferred by different mite species, while most mites could be able

to survive on most tree species, even if they present lower habitat quality for some of them

(Behan-Pelletier & Walter, 2000 for a review). Arboreal mites are frequently dispersed

passively through wind (Lehmitz et al., 2011; Lehmitz et al., 2012) and a host surrounded by

distant relatives might hence be colonized by the mites preferring neighbouring hosts

(Mouquet & Loreau, 2003). Consequently, species diversity should increase on

evolutionarily isolated trees due to mass effects. The idea that mass effects may contribute

to local species richness under particular conditions is not new itself (Mouquet & Loreau,

2003). Nevertheless, here we found evidence that for colonizers of hosts such mass effects

might be particularly prominent in phylogenetically diverse host communities and under

intermediate (or indirectly operating) phylogenetic signal of traits controlling the habitat

quality of such hosts.

Mite alpha-diversity per branch decreased with host-tree age (at least when branches had

low habitat diversity), but increased with branch age. Older trees might increase the chance

that the species that are more efficient in colonizing a specific tree will dominate all

branches of the crown sometime after arrival (Badano et al., 2005; Lekevicius, 2009, for

“classical” islands). In contrast, at any point in the life of a tree, a branch with high age gives

both space and time for more species to accommodate themselves into different habitats

within patches (Whittaker et al., 2008). We may then speculate that colonizers of living

52

islands hence appear to go through two types of nested successions, consistent with

repetitive short-term filling of vacant niches within patches (branch age increasing mite

diversity) and parallel long-term sorting of dominant species into patches (tree age

decreasing mite diversity).

We found a positive relationship between habitat and mite alpha diversity per tree, but a

negative relationship per branch. An increase of species alpha diversity with habitat

diversity is intuitive as habitat specialists can better accommodate themselves at islands

with a high variation of resources and conditions available (Fattorini et al., 2015; Hortal et

al., 2009). A decrease of species diversity in patches with high habitat diversity, in contrast,

might reflect the decreased area available for a given suitable habitat that in turn may

increase the risk of stochastic extinctions. Such a pattern is predicted by the area–

heterogeneity trade-off hypothesis (Allouche et al., 2012). In this respect, we found that the

negative effect of habitat diversity was mitigated in old trees, possibly because local

extinctions on individual branches were more rapidly compensated by recolonizations.

Overall, the local communities within, and the species pool across, a given host individual

appear to be driven by opposing effects of habitat diversity, in part perhaps because the

local communities are confined to very small modules (branches) where habitat surface

might become a limiting factor.

Tree characteristics influenced the spatial turnover of mites among branches within trees.

Precisely, mite turnover increased with phylogenetic isolation. A possible explanation

invokes again mass effects from phylogenetically distant neighbours that increase the pool

of species that colonize different branches. (Badano et al., 2005; Lekevicius, 2009, for

“classical” islands). Further, tree age gives time for habitat filtering by sorting specialists into

their most suitable microhabitats (tree branches). We are not aware of any study that has

identified the effect of isolation and age of hosts on the assembly of communities among

53

patches within these islands. There are, however, studies that describe the assembly within

and among communities based on the properties of the landscape mosaics in which these

communities were embedded (Hendrickx et al., 2009; Chisholm et al., 2011). Assembly

processes on individual hosts may hence be captured by concepts of landscape ecology,

albeit landscape ecologists have so far not accounted for the effect of the age of an entire

landscape or the degree of its isolation from other landscapes.

Contrary to the findings regarding oribatids, the assembly of gall wasps does not seem to

be driven by the characteristics of hosts we have evaluated here. Contrary to oribatids, gall

wasps are good dispersers that present high host-specialization and that can even alter

their habitats by inducing the growth of plant tissue (Stone et al., 2002; Hayward & Stone,

2005). These contrasting ecological characters might at least partly explain why gall wasps

depend less on characters of their living islands and their neighbourhoods than mites

(Figure 2). The fact that species from both ecological groups were sampled on the same

trees and with sample sizes in similar orders of magnitude strengthens our confidence that

the observed differences do not stem from methodological biases. Based on the same

experiment as the present study, Yguel et al. (2011, 2014) found that the alpha diversity of

chewing phytophages (mainly Lepidoptera) was lower on phylogenetically isolated host

trees. Notably, lepidopterans have intermediate (between oribatids and gall wasps) degrees

of host-specialization and dispersal capacities (for lepidoptera in the present study system:

Yguel et al., 2011). Therefore, lepidopterans on phylogenetically distant neighbours may be

less capable of colonizing a focal tree than are oribatid mites, and lepidopterans from

spatially distant but phylogenetically proximate host trees might be less capable of finding

focal trees than are gall wasps (Figure 2). Similar effects of host phylogenetic isolation that

was found for gall wasps might also occur in chalcidoid wasps (Yguel et al., 2014). Overall,

a given character of a host – being surrounded by distantly related neighbours – may have

opposite effects on colonizer organisms that differ in host specialization and dispersal

54

ability. We stress however that this conclusion resides on a single taxon per type of

colonizer biology. Further confirmations for other taxa are needed.

We are aware that our study may present limitations. Sampling was done during the end of

summer, so identified patterns might not fully reflect the effects of tree characteristics on the

assembly of colonizers. This is especially important for gall wasps, which might produce

smaller generations and smaller galls during spring than in summer (Hayward & Stone,

2005). Also, sampling was restricted to peripheral branches, leaving out major branches,

deadwood and trunks, with their deeply fissured bark and thick, three-dimensional

cryptogam cover harbouring different species of oribatid mites in high abundances. We

hence do not pretend to characterise the entire oribatid fauna of a tree, but only that of one

relatively young structure, comparable among trees of all ages. We also stress that

phylogenetic isolation as such does not directly affect colonizer assembly. It indirectly

reflects increasing dissimilarity in traits of trees that might have co-evolved with colonizers,

and these traits would in turn control colonizer assembly. One could also consider possible

limited statistical power due to our limited sample sizes of 21 to 25 trees, albeit this would

not explain any observed significant effects. Despite these limitations, our major results are

relatively solid (e.g. with non-significant effects with p>>0.05, adjusted R² up to 0.689, and

partly based on averaging across numerous models; see Table 2), what increases our

confidence in conclusions taken from them.

Our results suggest that, unless colonizers can easily reach hosts and manipulate habitat

quality, forest trees can function as living islands. However, these living islands present

specificities, such as phylogenetic isolation from neighbours and rapid growth of individual

patches (in comparison to oceanic islands), which can in turn determine the assembly of

their colonizers. Phylogenetically isolated trees may, for the studied mites, have increased

diversity consistent with mass effects from distantly related neighbour hosts, while old trees

55

have decreased colonizer diversity within patches, consistent with sorting of organisms into

their preferable patches through time. These results also show that distinct metacommunity

perspectives (such as mass effect and species sorting) are necessary for understanding

how biological variation of hosts as living islands influence the assembly of their colonizers.

Overall, the results suggest that assembly is driven by isolation, age and habitat diversity,

although the processes invoked may be different from those on true islands. Specifically, we

suggest that the level of insularity is controlled also by the biology of the colonizers

themselves, insularity being highest for colonizers that disperse little and depend directly on

host traits that show strong phylogenetic signal.

ACKNOWLEDGEMENTS

JHN thanks Paulo De Marco Jr. and Luis M. Bini for discussing the ideas presented here.

JHN received a PhD scholarship by Coordernação de Aperfeiçoamento de Pessoal de

Nível Superior (CAPES) and a PhD mobility scholarship by Rennes Metropole. AMCS was

supported by a Marie Curie Intra-European Fellowship (IEF 331623 ‘COMMSTRUCT’).

MVC has a productivity grant awarded by CNPq (307796/2015-9). AP received a SAD

fellowship from Région Bretagne. WU was supported by the institutional fund of UMK.

56

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61

BIOSKETCH

José Hidasi-Neto is a PhD candidate studying the ecology and evolution of communities

under different environmental and biotic conditions. This article is a product of Hidasi-Neto’s

PhD thesis under the supervision of Andreas Prinzing and Marcus Cianciaruso.

Author contributions: A.P. and J.H.N. designed the study and performed initial analyses.

A.P., R.I.B., C.V., and S.W. collected the data. All authors contributed significantly to the

writing of the manuscript.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article:

Appendix S1. Details on methodology.

Appendix S2. Species abundances and richness.

Appendix S3. Details on fitted models.

62

Table 1. Summary of studied alternative hypotheses. Hypotheses corroborated for oribatids are in bold. No hypothesis was corroborated 1

for gall wasps. 2

Response variable Predictor variable Alternative Hypothesis 1 Alternative Hypothesis 2

Alpha diversity per host

Age Positive relationship because of more time available for the arrival of species on older hosts.

Negative relationship because of increasing dominance by particular species.

Habitat diversity Positive relationship because more habitats will be available for species with different niches.

Island phylogenetic isolation

Negative relationship if relevant host characteristics for colonizers are phylogenetically strongly conserved or colonizers are highly specialized. Then isolated hosts will be hard to reach for the species that can live there (ecological sorting, impeded by isolation).

Positive relationship if relevant host characteristics for colonizers are phylogenetically moderately conserved or colonizers are only moderately specialized. Then, isolated hosts will receive species from neighbouring, phylogenetically distant hosts (mass effect, facilitated by isolation).

Alpha diversity per patch within host

Age and phylogenetic isolation of hosts

Positive relationship because age and phylogenetic isolation may increase species richness in the hosts (explained above) and hence in its constituent patches.

Negative relationship because phylogenetic isolation may decrease species diversity in the host (explained above) and hence in its constituent patches.

Patch age and its interaction with host characteristics

Positive relationship because of more time available for the arrival of species in older patches within hosts - provided that host age and phylogenetic isolation ensure a sufficiently large species pool.

Habitat diversity per patch and its interaction with island characteristics

Positive relationship because more habitats will be available for species with different niches - provided that host age and phylogenetic isolation ensure a sufficiently large species pool.

Negative relationship because the area available per habitat will also decrease, constraining in particular habitat specialists.

Within-host beta diversity

Age Positive relationship because environmental heterogeneity within living islands increases or because species differently occupying these environments arrive through time.

Habitat diversity Positive relationship because more habitats will be available for different species with different niches.

Island phylogenetic isolation

If host lineages strongly sort colonizer species and phylogenetic isolation impedes sorting, the few remaining colonizer species may spread across the host and reduce turnover among patches

If host lineages moderately sort colonizer species and phylogenetic isolation permits spill over from neighbouring host lineages, the numerous species might separate among patches of different environments

Effects of phylogenetic isolation

Biological group Colonizers with low dispersal capacity (and low host-specialization) will show stronger effects of host phylogenetic isolation because these colonizers will have difficulties reaching their suitable hosts.

Colonizers with high host-specialization (and high dispersal capacities) will show stronger effects of host phylogenetic isolation because they will not find their most suitable hosts.

63

Table 2. Effects of age and phylogenetic isolation on alpha diversity, turnover and

nestedness of mites in crowns of host trees (using multiple regression analysis with best

subset search based on delta AICc<2). Alpha diversity response variables were (1) tree-

level species Simpson’s diversity [F(3,20)=16.54; adjusted R²=0.689; p-value<0.001],

and (2) branch-level species Simpson’s diversity (observations=181; groups=25;

Marginal R²=0.152; Conditional R²=0.201; see Table 2 for analyses including habitat

covariables). Beta diversity response variables were: (3) average mite turnover [F(4,

17)=11.19; adjusted R²=0.66; p-value<0.001], and (4) average mite nestedness [F(4,

20)=3.619; adjusted R²=0.304; p-value=0.022] among branches of each tree. Significant

p-values are in bold. Equivalent analyses for gall wasps were all non-significant with

R²<0.015.

Predictor variables Estimate SE t value p-value

PER-TREE MITE ALPHA DIVERSITY

(Intercept) -0.362 0.120 -3.025 0.007

log(phylogenetic isolation + 1) 0.093 0.015 6.322 <0.001

log(tree age + 1) -0.005 0.042 -0.123 0.904

log(tree habitat diversity + 1) 1.055 0.211 4.999 <0.001

PER-BRANCH MITE ALPHA DIVERSITY

(Intercept) -0.587 0.236 -2.487 0.014

log(tree age + 1) 0.877 0.315 2.78 0.011

log(phylogenetic isolation + 1) 0.211 0.063 3.324 0.003

log(branch age + 1) 0.076 0.025 3.055 0.003

log(phylogenetic isolation + 1):log(tree age + 1) -0.241 0.091 -2.648 0.015

WITHIN-TREE MITE TURNOVER AMONG BRANCHES

(Intercept) -1.326 0.222 -5.963 <0.001

log(phylogenetic isolation + 1) 0.366 0.063 5.811 <0.001

log(tree age + 1) 1.645 0.300 5.483 <0.001

log(tree habitat diversity + 1) 0.577 0.181 3.182 0.005

log(phylogenetic isolation + 1):log(tree age + 1) -0.497 0.090 -5.498 <0.001

WITHIN-TREE MITE NESTEDNESS AMONG BRANCHES

(Intercept) -0.260 0.386 -0.673 0.509

log(phylogenetic isolation +1) 0.010 0.017 0.603 0.554

log(tree age + 1) 0.871 0.383 2.275 0.034

log(tree habitat diversity + 1) 1.301 0.817 1.593 0.127

log(tree age + 1):log(tree habitat diversity + 1) -2.055 0.863 -2.381 0.027

64

Table 3. Effects of habitat variables, and (as in Table 2) age and phylogenetic isolation on

Simpson’s alpha diversity of mites and gall wasps in crowns of host trees. Averaged

models of parameter estimates, each based on five models (averaged marginal

R²=0.166; averaged conditional R²=0.220), and three (averaged marginal R²=0.002;

averaged conditional R²=0.131) subset models.

Predictor variable Importance Estimate SE

Adjusted

SE z value p-value

PER-BRANCH MITE ALPHA DIVERSITY WITH PER-BRANCH HABITAT VARIABLES

(Intercept) 0.256 0.13 0.131 1.957 0.05

log(phylogenetic isolation + 1) 1 0.042 0.014 0.015 2.808 0.005

log(branch age + 1) 1 0.067 0.026 0.026 2.57 0.01

log(tree age + 1) 1 -0.251 0.114 0.12 2.089 0.037

log(branch habitat diversity + 1) 1 -0.733 0.25 0.252 2.91 0.004

log(tree age + 1):log(branch

habitat diversity + 1) 1 0.9 0.314 0.317 2.841 0.005

PC2 0.59 -0.007 0.008 0.008 0.833 0.405

PC3 0.29 0.003 0.007 0.007 0.423 0.673

PC1 0.12 <0.001 0.003 0.003 0.136 0.892

PER-BRANCH GALL WASP ALPHA DIVERSITY

(Intercept) 0.322 0.041 0.042 7.712 <0.001

log(tree age + 1) 0.248 0.008 0.026 0.028 0.296 0.767

log(branch length + 1) 0.203 <0.001 0.006 0.006 0.156 0.876

65

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-0.05 0.050.001.00.50.0-0.5-1.0

2.0 2.2 2.4 2.6 2.8 3.0

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66

Oribatid mite species Gall wasp species Oak species Pine species

< Migration rate> Migration rateNo migration

< Phylogenetic Isolation > Phylogenetic Isolation

Lepidopteran species

Figure 2.

67

FIGURE LEGENDS

Figure 1. Partial residual plots showing the effects of (a) log(tree phylogenetic

isolation + 1) and (b) log(habitat diversity per tree + 1) on log(mite alpha diversity per

tree + 1), (c) of log(branch age + 1) on log(mite species diversity per branch + 1),

and (d) of log(tree phylogenetic isolation + 1) on log(within-tree mite turnover + 1).

Partial residual presents the response of a given dependent variable to a given

predictor variable while accounting for the effects of other predictor variables in the

multiple regression models (see Table 2).

Figure 2. Observed effect of host phylogenetic isolation on the assembly of different

groups of colonizers. A phylogenetically isolated host (right) may present higher

species diversity of oribatid mites due to immigration, even at low rate, of new

species (grey) from distantly related host neighbours, while there is no long-distance

immigration from distant closely related host trees (long-distance immigration is

represented on top of figure). On the other hand, host phylogenetic isolation may

have no effects on gall wasps due to their high dispersal and habitat manipulation

capacities, which allow them to colonize across large distances any tree individual to

which they are specialized. Finally, the diversity of lepidopterans may be negatively

affected by phylogenetic isolation of a focal tree because lepidopterans on

neighbouring trees cannot use the focal tree nor can they manipulate its nutritional

quality (Yguel et al., 2011, 2014). Lack of colonization from distantly related

neighbours is not compensated by increased immigration across large distances

from closely related host trees. Silhouettes of oribatids (by B. Lang) and gall wasps

(by M. Broussard), and lepidopterans (uncredited image) and pines (uncredited

image) from http://phylopic.org, respectively under licenses of

http://creativecommons.org/licenses/by/3.0/, and

http://creativecommons.org/publicdomain/mark/1.0/.

http://phylopic.org/

http://creativecommons.org/licenses/by/3.0/

http://creativecommons.org/publicdomain/mark/1.0/

68

Appendix 1. Evolutionary distances between a focal oak seedling and its closest

adult tree (1). Details on how we measured leaf C/N and dry mass contents (2).

Details on studied variables for oribatid mites and gall wasps (3). Details on the PCA

analysis (4). Representations of interaction effects (5) found for: per-branch mite

alpha diversity per branch analysis (Figure S1); per-branch mite alpha diversity

analysis including per-branch habitat variables (Figure S2); within-tree mite turnover

among branches (Figure S3); and within-tree mite nestedness among branches

(Figure S4).

Evolutionary distances between a focal oak seedling and its closest adult tree

We utilized evolutionary distances established by Vialatte et al. (2010) based

on phylogenetic classification (THE ANGIOSPERM PHYLOGENY GROUP, 2009),

and also used by Yguel et al. (2011), Yguel, Bailey, et al. (2014), and Yguel, Courty,

et al. (2014). These evolutionary distances correspond to the approximate time, in

Million Years Before Present (MYBP), since the evolutionary establishment of the

clades of oaks and of a given neighboring tree species. For instance, we ranked the

comparison between oak and pine species as a comparison between two classes,

Gymnosperms and Angiosperms, between which the younger is approximately 140

million years old (the crown age of angiosperms), and the evolutionary distance is

hence 140 million years. Thus, the younger of the two crown ages represents

biologically the time when the oak lineage and the other lineage started to be

physically and physiologically distinct from a point of view of enemies and mutualists

of the tree. Moreover, this age also avoids giving overly weight to Gymnosperms, in

contrast to stem-age distance which would in many cases simply be a descriptor of

the presence of Gymnosperms in the neighborhood given the extreme age of the

common ancestor of Gymnosperms and Angiosperms (Savard et al., 1994).

References:

THE ANGIOSPERM PHYLOGENY GROUP (2009) An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III. Botanical Journal of the Linnean Society, 161, 105–121.

Savard, L., Li, P., Strauss, S.H., Chase, M.W., Michaud, M., & Bousquet, J. (1994) Chloroplast and nuclear gene sequences indicate late Pennsylvanian time for the last common ancestor of extant

69

seed plants. Proceedings of the National Academy of Sciences of the United States of America, 91, 5163–5167.

Vialatte, A., Bailey, R.I., Vasseur, C., Matocq, A., Gossner, M.M., Everhart, D., Vitrac, X., Belhadj, A., Ernoult, A., & Prinzing, A. (2010) Phylogenetic isolation of host trees affects assembly of local Heteroptera communities. Proceedings of the Royal Society B, 277, 2227–2236.

Yguel, B., Bailey, R., Tosh, N.D., Vialatte, A., Vasseur, C., Vitrac, X., Jean, F., & Prinzing, A. (2011) Phytophagy on phylogenetically isolated trees: Why hosts should escape their relatives. Ecology Letters, 14, 1117–1124.

Yguel, B., Bailey, R.I., Villemant, C., Brault, A., Jactel, H., & Prinzing, A. (2014) Insect herbivores should follow plants escaping their relatives. Oecologia, 176, 521–532.

Yguel, B., Courty, P.E., Jactel, H., Pan, X., Butenschoen, O., Murray, P.J., & Prinzing, A. (2014) Mycorrhizae support oaks growing in a phylogenetically distant neighbourhood. Soil Biology and Biochemistry, 78, 204–212.

Details on how we measured leaf C/N and dry mass contents

The percentages of leaf C and N, and leaf dry matter (DM) content and

polyphenols were measured. Specifically, for polyphenols we sampled half (including

the main central vein) of one leaf of each stratum, lyophilized it (36h) and measured

concentration of polyphenols (analyzes done by Polyphenol Biotech, Bordeaux,

France) following the Folin-Ciocalteu method. Concentrations were calculated as the

percentage of dry mass gallic acid equivalent (Singleton et al., 1999). The other half

of each leaf (lacking the main central vein) was used for measuring nitrogen (N) and

carbon/nitrogen (C/N) ration concentrations, and leaf dry matter content. C and N

concentrations were measured by the “flash combustion” method and were done by

Christa Schafellner and collaborators (Vienna, Austria). Leaf dry matter content was

measured following the protocol used in Cornelissen et al. (2003). The frozen leaves

were rehydrated and placed into plastic bags inside a refrigerator for 12h.

Afterwards, leaves were weighed (Balance Sartorius, 0.1mg) for obtaining the water-

saturated fresh mass values (FM). After 48h drying at 60ºC, DM was weighed.

Percentage of dry mass content was estimated as [(DM))/FM]*100. Then, we

calculated a standard deviation value for each of these four compositional habitat

variables for each tree. Overall habitat diversity per tree was calculated as the

average across the values of standard deviation of the four habitat variables.

References:

Cornelissen, J.H.C., Lavorel, S., Garnier, E., Díaz, S., Buchmann, N., Gurvich, D.E., Reich, P.B., Steege, H., Morgan, H.D., Heijden, M.G.A., Pausas, J.G., & Poorter, H. (2003) Handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian Journal of Botany, 51, 335–380.

70

Singleton, V.L., Orthofer, R., & Lamuela-Raventós, R.M. (1999) Analysis of total phenols and other oxidation substrates and antioxidants by means of Folin-Ciocalteu reagent. Methods in Enzymology, 99, 152–178.

Details on studied variables

Table S1. Mean, standard deviation and range across trees of studied variables for oribatid mites.

Variable Mean SD Range

Tree age 1.271 0.567 2.092

Phylogenetic isolation 48.495 35.149 134.29

Tree habitat diversity 0.524 0.087 0.403

Table S2. Mean, standard deviation and range across tree branches of studied variables for oribatid

mites.


Branch age 9.356 3.566 14.5

Branch habitat diversity 0.351 0.167 0.674

PC1 -0.009 1.376 4.496

PC2 -0.004 1.179 10.152

PC3 -0.007 1.007 10.278

Table S3. Table of Pearson's correlations among studied variables for oribatid mites.

Variable Tree age

Phylogenetic isolation

Tree habitat

diversity Branch

age

Branch habitat

diversity PC1 PC2 PC3

Tree age 1 -0.346 0.313 -0.051 0.315 -0.139 -0.17 0.349

Phylogenetic isolation -0.346 1 -0.339 0.022 -0.288 0.033 0.226 -0.05

Tree habitat diversity 0.313 -0.339 1 0.091 0.317 0.096 -0.451 0.136

Branch age -0.051 0.022 0.091 1 -0.087 -0.428 -0.151 -0.085 Branch habitat

diversity 0.315 -0.288 0.317 -0.087 1 0.119 -0.307 0.121

PC1 -0.139 0.033 0.096 -0.428 0.119 1 0.003 0.006

PC2 -0.17 0.226 -0.451 -0.151 -0.307 0.003 1 -0.004

PC3 0.349 -0.05 0.136 -0.085 0.121 0.006 -0.004 1

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Table S4. Mean, standard deviation and range across trees of studied variables for gall wasps.


Tree age 1.262 0.528 1.582

Phylogenetic isolation 49.07 32.531 106.67

Tree habitat diversity 2.855 0.685 2.583

Table S5. Mean, standard deviation and range across tree branches of branch lengths studied for gall wasps.


Branch length 388.268 232.446 1360

Table S6. Table of Pearson's correlations among studied variables for gall wasps.

Variable Tree age Phylogenetic

isolation Tree habitat

diversity Branch length

Tree age 1 -0.459 0.318 -0.14

Phylogenetic isolation -0.459 1 -0.54 0.129

Tree habitat diversity 0.318 -0.544 1 -0.245

Branch length -0.14 0.129 -0.25 1

Details on the PCA analysis

Details on the PCA analysis. Results for the PCA using mite microhabitat variables: PCA summary with standard deviation, proportion of variance explained, and cumulative proportion of variance explained for each PCA axis (Table S7); correlations between PCA scores and raw variables (Table S8); and loadings for PCA axes (Table S9).

Table S7. PCA summary: standard diviation, proportion of variance explained, and cumulative proportion of variance explained for each PCA axis.

PCA axis Standard deviation

Proportion of variance explained

Cumulative proportion of variance explained

PC1 1.391 0.323 0.323

PC2 1.174 0.23 0.552

PC3 1.006 0.169 0.721

PC4 0.988 0.163 0.884

PC5 0.836 0.116 1

Table S8. Pearson’s correlations between raw variables and PCA Scores.

PCA axis Algae Crustose lichen Moss Nothing Mixed Foliose lichen

PC1 -0.895 -0.017 -0.092 0.995 -0.326 -0.171

PC2 0.296 -0.778 -0.743 0.073 -0.356 0.025

PC3 -0.073 -0.018 0.175 0.006 -0.32 0.935

PC4 0.322 0.265 0.231 -0.001 -0.815 -0.291

PC5 0.035 -0.57 0.596 0.07 0.036 -0.108

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Table S9. Loadings for PCA axes.

PCA

axis Algae

Crustose

Lichen Moss Nothing Mixed

Foliose

Lichen

PC1 -0.644 -0.012

-

0.066 0.715 -0.234 -0.123

PC2 0.253 -0.662

-

0.633 0.062 -0.303 0.021

PC3 -0.073 -0.018 0.174 0.006 -0.318 0.929

PC4 0.326 0.268 0.234 -0.001 -0.825 -0.295

PC5 0.042 -0.682 0.713 0.084 0.043 -0.129

Representations of interaction effects

Representations of interaction effects found for: per-branch mite alpha diversity per branch analysis (Figure S1); per-branch mite alpha diversity analysis including per-branch habitat variables (Figure S2); within-tree mite turnover among branches (Figure S3); and within-tree mite nestedness among branches (Figure S4).

0.6 0.8 1.0 1.2

0.1

0.2

0.3

0.4

0.5

0.6

log(circumference + 1)

log

(sp

ecie

s d

ive

rsity +

1)

Phylo.Isol : 2.398 Phylo.Isol : 3.564 Phylo.Isol : 4.663

Figure S1. Representation of the interaction effect between log(circumference + 1) and log(phylogenetic isolation + 1) on log(mite Simpson’s diversity + 1) within tree crowns. Regression lines were generated for cross-sections taken at the 10th (red line = 2.398), 50th (green line = 3.564), and 90th (blue line = 4.663) quantiles of log(phylogenetic isolation + 1). Partial residuals are plotted on the graph.

73

0.0 0.1 0.2 0.3 0.4 0.5

0.1

0.2

0.3

0.4

0.5

log(habitat diversity + 1)

log

(sp

ecie

s d

ive

rsity +

1)

circumference : 0.525 circumference : 0.727 circumference : 1.085

Figure S2. Representation of the interaction effect between log(branch habitat diversity + 1) and log(circumference + 1) on log(mite Simpson’s diversity + 1) within tree crowns (among branches). Regression lines were generated for cross-sections taken at the 10th (red line = 0.525), 50th (green line = 0.727), and 90th (blue line = 1.085) quantiles of log(circumference + 1). Partial residuals are plotted on the graph.

0.6 0.8 1.0 1.2

0.0

0.1

0.2

0.3

0.4


log

(tu

rno

ve

r +

1)

phyisol : 2.398 phyisol : 3.564 phyisol : 4.663

Figure S3. Representation of the interaction effect between log(circumference + 1) and log(phylogenetic isolation + 1) on log(mite turnover + 1) within tree crowns. Regression lines were generated for cross-sections taken at the 10th (red line = 2.398), 50th (green line = 3.564), and 90th (blue line = 4.663) quantiles of log(phylogenetic isolation + 1). Partial residuals are plotted on the graph.

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0.6 0.8 1.0 1.2

0.20

0.25

0.30

0.35

0.40

0.45


log

(ne

ste

dn

ess +

1)

treehabdiv : 0.429 treehabdiv : 0.5 treehabdiv : 0.664

Figure S4. Representation of the interaction effect between log(circumference + 1) and log(tree habitat diversity + 1) on log(mite nestedness + 1) within trees. Regression lines were generated for cross-sections taken at the 10th (red line = 0.429), 50th (green line = 0.5), and 90th (blue line = 0.664) quantiles of log(tree habitat diversity + 1). Partial residuals are plotted on the graph.

Appendix S2. Species abundances and richness (arquivo grande de Excel; pedir

para o autor).

Appendix S3. Details on fitted models (arquivo grande de Excel; pedir para o autor).

75

CAPÍTULO 3

Climate Change Will Drive Species Loss and Biotic Homogenization in a

Biodiversity Hotspot

José Hidasi Neto1, Daiany Caroline Joner1, Fernando Resende1, Lara de Macedo

Monteiro1, Frederico Valtuille Faleiro1, Rafael Dias Loyola1, Marcus Vinicius

Cianciaruso1

1Departamento de Ecologia, Universidade Federal de Goiás, Goiânia, Goiás, Brazil.

e-mail: [email protected]

mailto:[email protected]

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Abstract

Anthropogenic climate change has been shown to be one of the most pervasive

threats to biodiversity. However, only a few studies have considered its effects on

communities. Here we use niche models and future climate scenarios to study if and

how climate change will reduce taxonomic, phylogenetic and functional alpha and

beta diversities of mammals, biotically homogenizing the faunas (through the

extinction of native specialists and expansion of exotic generalists) of communities

from a biodiversity hotspot (Cerrado, Brazil). Although species richness will decrease

in most Cerrado, we found a very heterogeneous result for biotic homogenization,

indicating that faunas from various regions can not only become more, but also less

homogenized. Specifically, most Cerrado will become less taxonomically,

phylogenetically and functionally homogenized, but Southern Cerrado may become

biotically homogenized in the near future. This is very problematic as this region is

the one that has received most anthropogenic changes in the past. Niche models are

being used in ecology, but only now new methods can (and should) be applied in

association with them in order to study the effects of anthropogenic or natural

processes at large scales on whole communities.

Keywords: alpha and beta diversity, species turnover and nestedness, spatial and

temporal beta.

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1. Introduction

Climate change has been shown to affect species distribution by determining where

they will or will not occur due to their climatic tolerances [1,2]. In addition, there is a

growing consensus that human activity may have a large contribution to recent

climate changes occurring worldwide [3]. For instance, the Intergovernmental Panel

on Climate Change (IPCC) presents different scenarios of future climate change

depending on possible concentrations of greenhouse gases in the atmosphere due

to human activity [3]. Studying these scenarios, Loarie et al. [4] showed that some

regions will present faster effects of climate change than others, indicating, for

example, that flat biomes (e.g. flooded grasslands and savannas) will have very fast

rates of temperature change (such as increased temperature and reduced

precipitation [3,5]) due to their lack of topographic complexity. Thus, one may focus

on these regions in order to predict how ecological communities will respond to

changes in climatic variables in the near future.

Most studies on climate change usually focus on how populations or species were or

will be affected through time [6]. It is already known that effects of climatic changes

on organisms can vary according to biological groups and habitats [7–10]. There are

evidences that some species might even respond positively (e.g. expanding their

spatial distribution) to the elevated temperatures or increased CO2 concentrations

that are expected in the coming years [7,11]. For example, tree species richness can

increase at places such as Eastern United States [11]. However, it is mostly likely

that future climate change will reduce species richness worldwide by changing

spatial distributions of environmental conditions more quickly than species

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adaptations are able to follow [7,12,13]. A few studies have explicitly addressed the

issue on how climatic changes will affect communities [6,12–18]. Xenopoulos et al.

[12] found that the number of freshwater fishes will decrease worldwide in response

to reductions in water availability due to elevated temperatures. Also, Sommer et al.

[17] found that plant species richness is likely to decline in most tropical regions

because of an excess of species’ drought tolerances. Overall, one could expect

communities in tropical, flat biomes to experience reductions in species richness due

to the inability of species to track rapid changes in the spatial distribution of

environmental conditions.

One of the most pervasive effects of climate change is the decreasing on beta

diversity among communities, leading to a biotic homogenization across entire

regions [19,20]. Often this occurs through the extinction of specialists (usually with

small geographic distributions) and expansion of generalists (usually with large

geographic distributions) [21,22]. This process can end up by having negative

consequences for ecosystem functioning and the goods and services provided by

them [20,23,24]. For example, biotic homogenization can simplify ecological

networks by reducing species connections at a specific trophic level, and

consequently affecting connections at lower and higher levels [23]. It can also reduce

the resistance and resilience of communities to environmental disturbances [23,24],

for example by preventing the colonization of species with locally extirpated traits

[23]. So far there are few evidences that climate change is homogenizing ecological

communities at large scales. Using niche modelling techniques Thuiller et al. [13]

found that changes in temperature and moisture conditions are causing a decline in

both richness and turnover of European plants. Conversely, Albouy et al. [14] found

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that Mediterranean fish assemblages can show highly heterogeneous (not

necessarily negative) changes on species richness, turnover and nestedness due to

climate change in the near future. One could expect communities in tropical, flat

biomes to experience increases in their compositional similarity due to extinctions of

various specialists, narrowly-distributed species, and expansion of a few generalists,

broadly-distributed species, caused by directional changes in climate.

It is known that the diversity of phylogenetic clades (phylogenetic diversity) and

biological traits (functional diversity) are respectively related to species evolutionary

history [25] and to ecosystem functioning and stability [26]. However, only a few

studies have investigated if climate change will not only affect taxonomic diversity

(such as reducing species alpha and beta diversities), but also the phylogenetic and

functional diversities of communities (e.g. [27,28]). One should therefore evaluate

taxonomic, phylogenetic and functional diversities (including temporal and spatial

homogeneity in their compositions) when studying changes in biodiversity.

Nevertheless, some species may be more vulnerable than others to climatic changes

[22,29]. Usually, species with high extinction probability after environmental

disturbances have a small geographic distribution [22,29], a feature also observed in

threatened species ([29]; see [30] for more information). Thus, one could expect

regional extinctions to be commonly found in species that are currently threatened.

There is also an increasing concern on the conservation status of Data Deficient

species (those with insufficient data to asses extinction risk; DD species; [31]). For

example, Bland et al. [32] predicted that most DD species (~64%) can be threatened

with extinction. Therefore, DD species might also be strongly affected by changes in

climate in the coming years.

80

The Brazilian Cerrado is a flat biome classified as a biodiversity hotspot [33,34] that

will probably face fast rates of increasing temperature and decreasing precipitation in

the coming years [3–5]. Here we project distributions of present and late 21st century

Cerrado mammals under distinct scenarios of climate change to observe whether

and where alpha and beta diversity of mammals will decrease in the future. In

addition, we explore the frequency and incidence of future regional extinctions and

immigrations among threatened and non-threatened mammal species. Specifically,

we answer the following questions: (1) Are we going to lose Cerrado mammal

taxonomic, phylogenetic and functional diversities due to the expected changes in

climate?; (2) Are mammalian communities going to become more taxonomically,

phylogenetically and functionally homogenized?; (3) how and where are these

changes going to happen throughout the Cerrado biome?

2. Material and methods

(a) Study area

We studied the impacts of climate change in the mammals of the Brazilian Cerrado.

We selected this region because (1) it is expected to face fast rates of climate

change in the coming years [4], and (2) it is a biodiversity hotspot, with several

endemic and threatened species [33,34]. The Cerrado biome originally covered an

area of approximately 2,000,000km² (about 25% of Brazil’s territory), but it has

already lost at least 50% of its area due to agriculture and urbanization [35]. It

consists of very heterogeneous physiognomies (from grasslands to woodlands) [36],

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and is considered to be one of the most rapidly disappearing biomes of the world

mainly due to habitat destruction and fragmentation [37]. In that sense, it is an

important region to understand and predict changes in diversity patterns of mammals

using climatic data available for the near future.

(b) Ecological niche modeling

First, we downloaded occurrence maps for all Brazilian mammals (available at:

http://www.iucnredlist.org/technical-documents/spatial-data) and overlapped them

with a Neotropic grid of 0.25º x 0.25º latitude and longitude. To reduce

methodological biases in our methods, we only considered the presence of a species

if it was in at least 50% of a grid cell. We also excluded species that were in less

than 10% or more than 90% of all grid cells in order to decrease biases in the

models. We then collected data for three climatic scenarios from the

Intergovernmental Panel on Climate Change (IPCC; [3]) representing different future

concentrations of greenhouse gases (Representative Concentration Pathways;

RCP). The models were: RCP 2.6 (low concentration), RCP 6.0 (intermediate

concentration), and RCP 8.5 (high concentration). We then predicted ecological

niche models (ENMs) of Brazilian mammals using four bioclimatic variables (see

http://worldclim.org/bioclim; [38]): annual mean temperature, annual precipitation,

temperature seasonality, and precipitation seasonality. We did that according to four

global climate models (GCMs): Community Climate System Model (CCSM), Institut

Pierre Simon Laplace (IPSL), Model for Interdisciplinary Research on Climate

(MIROC), and Meteorological Research Institute (MRI).

http://www.iucnredlist.org/technical-documents/spatial-data

http://worldclim.org/bioclim

82

We performed niche modeling for both the present (1950-1999) and the future

(2070) using each GCM under each RCP scenario using the following ecological

niche models (ENMs): Generalized Linear Model (GLM), Generalized Additive Model

(GAM), Multivariate Adaptive Regression Splines (MARS), and Random Forest.

Models were adjusted within all the Neotropic. During calibration and validation of

ENMs, we used 75% of the random data for training and 25% for testing them,

repeating the process 10 times. We evaluated the predictive accuracy of each model

using True Skill Statistics (TSS). We also produced a consensus for each RCP

scenario weighting models by their TSS values. We used each consensus, which

indicated the frequency of projection of each species (i.e. relative number of times

that the occurrence of a species was predicted by an ENM), to generate

presence/absence maps for each species, considering the species present when it

had a frequency of projection > 0.5. All models were done in BioEnsembles software

[39].

Using frequency of projections to generate occurrence maps can make species’

future occurrences more distant from present occurrences than possible considering

their biological dispersal capacities. To avoid this problem, we estimated the home

range of each species based both on its weight and diet according to Kelt and Van

Vuren [40]. Following, we used home ranges to calculate, for each species, its

maximum dispersal distance according to Bowman et al. [41]. We attributed these

values of maximum dispersal distances to the generation length (in days) of each

species [available in 35] and calculated the maximum dispersal distance in 71 years

(1999 to 2070). If a grid cell where a species occurs in the future was more distant

from its nearest grid cell in the present than the species’ value of maximum dispersal

83

distance in 71 years, the presence of the species was replaced by an absence. All

grids and matrices were filtered in order to accommodate only Brazilian Cerrado and

its mammal species. For the present, we only kept 154 species with the occurrence

confirmed in Cerrado [43]. Chiroptera species were not considered in our analyzes

and IUCN taxonomy (Mammal Species of the World [44]) was used in all files. We

ended up with 309 Cerrado mammals, including those species occurring in the

present and those predicted to shift into the Cerrado in the future (see

Supplementary Material, Appendix S1 for TSS values).

(c) Phylogeny, biological traits, and threatened categories

We compiled data for ecological traits [45,46] and phylogeny [47,48] for all mammals

occurring or projected to occur in the Cerrado (309 species in total). The following

traits were considered in the study: body mass (in grams), diet (vertebrates,

invertebrates, foliage, stems and bark, grass, fruits, seed, flowers, nectar and pollen,

roots and tubers; presence/absence), habit (aquatic, fossorial, ground dwelling,

above-ground dwelling, aerial; presence/absence), and activity period (cathemeral,

crepuscular, diurnal, nocturnal; presence/absence). These traits indicate how much,

how, when, and what type of resources mammals use from the environment [45,46],

and are closely related to energy flow through communities [46,49]. In the phylogeny,

we added 63 species that were previously absent as polytomies in their respective

genera (Supplementary Material, Appendix S2). Additionally, we categorized

mammal species according to IUCN categories [31]: DD (“Data Deficient”), LC

(“Least Concern”), NT (“Near Threatened”), VU (“Vulnerable”), EN (“Endangered”),

and CR (“Critically Endangered”) [31].

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(d) Alpha and Beta diversity analyses

Using the occurrence matrices and grids for each RCP scenario we calculated the

taxonomic (species richness), phylogenetic, and functional alpha diversities, and the

spatial and temporal taxonomic, phylogenetic and functional beta diversities of

mammals. We used the Mean Pairwise Distance index [50,51] to calculate the

phylogenetic (MPD) and functional (MFD) alpha diversities. MPD and MFD are

respectively the mean of phylogenetic and functional distances between all pairs of

species from a community. For each grid cell (e.g. present or a RCP scenario) we

calculated MPD and MFD respectively using the cophenetic distances from the

phylogeny of Cerrado mammal species and a Gower’s functional distance matrix [52]

of mammals occurring in the cell.

We calculated spatial taxonomic beta diversity as the mean turnover partition of

Sorensen’s index between a focal cell and its neighbors (up to eight cells) [53,54].

We also used the phylogeny and traits to calculate the phylogenetic and functional

beta diversities. We calculated spatial phylogenetic beta diversity using the same

process we did for taxonomic beta, but using the turnover partition of PhyloSor index

[54,55]. The same process was also applied to calculate functional beta diversity, but

we used Gower’s distance and UPGMA methods to generate functional

dendrograms for mammals occurring in each focal grid cell or in its neighbor cells.

The functional dendrogram of each “focal cell and its neighbors” was used to

calculate the mean PhyloSor between a focal cell and each of its neighbors. To

calculate temporal beta diversities, we calculated the Sorensen’s index (for

85

taxonomic) and PhyloSor (for phylogenetic and functional beta diversities) indices

between each grid cell from the present and its respective cell in the future. For all

beta diversity calculations, we also calculated the proportion of “turnover/total beta

diversity”, where “total beta diversity” is the sum of turnover and nestedness resultant

component. Turnover indicates how much of the change in beta diversity is related to

the spatial or temporal differences in species compositions, while nestedness

resultant component represents how much is related only to the loss or gain of

species.

Between each future RCP and present scenarios, we calculated the values of

differences between future and present values of taxonomic, phylogenetic and

functional alpha (Δrichness, ΔMPD, and ΔMFD) and spatial beta (Δspatial taxonomic

beta, Δspatial phylogenetic beta, and Δspatial functional beta) diversities. For both

spatial and temporal beta diversities (taxonomic, phylogenetic and functional), we

also calculated the differences between the future and present values of the

proportions of turnover/total beta diversity, indicating how much of total beta diversity

is related to turnover or nestedness resultant component in both the present and the

future. We then generated maps for alpha, spatial beta and temporal beta diversities

for the present and all the future RCP scenarios and for the differences between the

future and present for each diversity metric. Additionally, we also compared the

occurrences of species in the future and present scenarios to observe how regional

extinctions and immigrations of species were related to IUCN categories. Because

all future scenarios presented qualitatively similar results, we only showed and

discussed results for the RCP 6.0 scenario (intermediate concentration of

86

greenhouse gases). The results of the other models are in the Supplementary

Material (Appendix S3). All calculations were done in R software [56].

3. Results

There was a general pattern of future reduction in the richness of mammals (from a

mean of 73.3 ± 10.3 to 61.1 ± 12, or a loss of approximately 20% of species, per

cell), indicating that climatic changes in Cerrado may cause local extinction of

several mammalian species throughout the biome (Figures 1a and 1b). However, we

observed a more heterogeneous pattern of increase or decrease of MPD (Figures 1e

and 1f), and a general pattern of increase of MFD (Figures 1i and 1j). Such patterns

indicate that results for taxonomic, phylogenetic and functional diversities are not

explainable among each other, and therefore deserve individual attention.

Observing results of temporal turnover, we can see how changes in taxonomic,

phylogenetic, and functional alpha diversities are related to changes in the

taxonomic, phylogenetic and functional compositions of communities through time

(turnover). We found that there will be a higher temporal taxonomic turnover in the

Northern Cerrado (Figures 1c and 1d), despite the general decrease in richness.

Notably, although patterns of temporal phylogenetic and functional turnovers were

similar to temporal taxonomic turnover, compositional changes related to

phylogenetic and functional alpha diversities did not follow the same spatial trends.

For example, there was an increase of phylogenetic alpha diversity in Eastern

Cerrado, a region where there was moderate to high temporal phylogenetic turnover.

87

Moreover, there was a decrease of functional alpha diversity in a small region in

Southeastern Cerrado, where there was a high temporal functional turnover.

(b) Future

Taxonomic α (richness)

240

220

200

180

160

140

120

100

80

60

220

200

Temporal functional β

40%

30%

20%

10%

100%

70%

60%

50%

40%

30%

20%

10%

0%

Temporal phylogenetic β

Temporal taxonomic β

0%

10%

20%

30%

(h) Turnover/Total90%

80%

95

90

85

80

75

70

65

60

55

50

45

80%

0%

-20%

-40%

-60%

20%

40%

60%

(a) Present

3.5

4.0

3.0

4.5

0.30(g) Beta

0.50(c) Beta

0%

0.40(k) Beta

(d) Turnover/Total0%90%

80%

70%

60%

50%

(l) Turnover/Total

40%

50%

60%

70%

80%

90%

100%

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.28

0.26

0.24

0.22

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

100%

0.35

0.30

0.25

0.20

0.15

0.10

0.05

3.0

2.5

2.0

1.5

1.0

0.5

0

-0.5

3.4

2.4

2.0

1.6

1.2

0.8

0.4

3.5

2.5

2.0

1.5

0.5

0

-0.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

-0.5

2.8

0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

-0.5

1.0

4.5

(e) Present208

Phylogenetic α (MPD)

206

204

202

200

198

196

194

192

190

188

186

184

(f) Future

Functional α (MFD)

8%

6%

4%

2%

0%

-2%

-4%

-6%

(i) Present0.67

0.66

0.65

0.64

0.63

0.62

0.61

0.60

7%(j) Future

6%

5%

4%

3%

2%

1%

0%

-1%

-2%

-3%

-4%

Figure 1. On the left side, maps indicating the present values and the percentages of increase or decrease (future-present) in values of taxonomic alpha diversity (“a” and “b” for richness), phylogenetic alpha diversity (“e” and “f” for MPD), and functional alpha diversity (“i” and “j” for MFD). On the right side, maps indicating the temporal beta diversity and percentage of turnover (out of total beta diversity) of taxonomic (“c” and “d”), phylogenetic (“g” and “h”), and functional (“k” and “l”) beta diversities between present time and the near future. Future values were calculated for the year of 2070 under a scenario of intermediate concentration of greenhouse gases (RCP 6.0).

We also observed patterns of decrease (indicating biotic homogenization) and

increase in spatial beta diversity of Cerrado mammals. Similarly to richness,

decreases (in percentages) of spatial taxonomic (Figures 2a, 2b, 2c, and 2d),

phylogenetic (Figures 2e, 2f, 2g, and 2h) and functional (Figures 2i, 2j, 2k, and 2l)

88

turnover were concentrated in locations (blue pixels) from Southern Cerrado. This

indicates that mammalian communities living mostly in this region will become more

taxonomically, phylogenetically, and functionally homogenized in the coming years.

On the other hand, several areas (mostly in Northern Cerrado) presented a future

increase in taxonomic, phylogenetic, and functional turnover. So, these areas are

likely to receive species taxonomically, phylogenetically, and functionally different to

those occurring in nearby areas. In this sense, we can expect to observe biotic

heterogenization rather than homogenization in these areas.

Taxonomic turnover

450%

200%

100%

0%

0.040

0.035

(c) Present (d) Future

(i) Present (i) Present100%

80%

(c) Present (d) Future

Taxonomic turnover/Total

500%

400%

250%

300%

150%

50%

-50%

0.025

0.020

0.015

0.010

80%

50%

90%

(b) Future

(e) Present

(k) Present

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0

350%

-100%

(a) Present

Phylogenetic turnover

0.030

0.005

0Functional turnover

500%

450%

400%

350%

300%

250%

200%

150%

100%

50%

0%

-50%

-100%

0.050

0.045

0.040

0.035

0.030

0.025

0.020

0.015

0.010

0.005

0

(j) Future500%

450%

400%

350%

300%

250%

200%

150%

100%

50%

0%

-50%

-100%

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

100%

80%

60%

40%

20%

0%

-20%

-40%

-60%

100%

90%

40%

30%

20%

10%

(g) Present

70%

60%

0%

Phylogenetic turnover/Total

80%

50%

Functional turnover/Total

-60%

-40%

-20%

0%

20%

70%

60%

50%

40%

30%

20%

10%

0%

80%

60%

40%

20%

0%

-20%

-40%

-60%

-80%

(l) Future

(h) Future

40%

60%

80%

Figure 2. Maps indicating the present values and the percentage of turnover (out of total beta), and the increase or decrease in both, of taxonomic (“a”, “b”, “c”, and “d”), phylogenetic (“e”, “f”, “g”, and “h”), and functional (“i”, “j”, “k” and “l”) beta diversities between present time and the year of 2070 under a scenario of intermediate concentration of greenhouse gases (RCP 6.0).

89

We observed how mammals can potentially immigrate or potentially become

regionally extinct in the Cerrado due to climatic changes expected for the year 2070

(see Supplementary Material, Appendix S4 for a list of regional immigrants and

extinctions). We found that nine species may become regionally extinct, from three

IUCN categories: DD, LC, and EN. Also, our models indicated that 138 species can

potentially immigrate to Cerrado in the coming years. Most of these potential

immigrants (~85%) were not endangered. Notably, although our approach resulted in

more regional immigrants than extinctions, we still had a general decrease of

richness, and biotic homogenization of mammals in Southern Cerrado.

Table 1. Numbers and percentages, for each IUCN category, of potential regionally extinct and immigrant species expected in the Brazilian Cerrado for the year of 2070 under a scenario of intermediate concentration of greenhouse gases (RCP 6.0).

Species DD LC NT VU EN CR

Extinctions 3 (33.3%) 2 (22.2%) 0 0 4 (44.4%) 0

Immigrants 17 (12.32%) 96 (69.56%) 4 (2.9%) 9 (6.52%) 7 (5.07%) 5 (3.62%)

4. Discussion

We found that the mammalian communities may, in general, lose species richness,

but have a heterogeneous pattern of increase or decrease of phylogenetic alpha

diversity, and a general increase of functional alpha diversity throughout the Cerrado

biome. Alongside richness reductions, changes in species compositions will occur

both temporally and spatially. Specifically, the highest temporal taxonomic,

phylogenetic and functional turnovers will be mostly found in Northern Cerrado.

Moreover, we observed that several mammalian communities from Southern

Cerrado are going to become more taxonomically, phylogenetically and functionally

90

homogenized, while communities from remaining areas are mostly expected to

become heterogenized (increased spatial beta diversity).

The richness of mammalian communities is expected to decrease in the near-future

Cerrado. This result goes in line with the expected shifts in geographic ranges for

several biological groups, including mammals, birds, amphibians, reptiles [57,58],

and trees [59,60]. These studies predicted a loss in the geographic range of most of

Cerrado’s species due to upcoming climatic changes. Recently, Carvalho et al. [61]

showed that several protected areas are not able to protect phylogenetic or

functional diversities better than expected by chance. They also found that the

Central and Southern Cerrado region would need more conservation attention as it

presents sites with high phylogenetic and functional diversities. At least for Southern

region, our results indicated that, in fact, this part of Cerrado will experience a future

decrease of phylogenetic, but an increase of functional diversity, of mammals.

Nevertheless, more attention should be focused on Northern Cerrado, which is today

the Cerrado’s region with highest amount of native vegetation [62], and also where

agricultural expansion will likely take place in the future [63]. In conjunction with the

expected high rates of land conversion, we found that this region may also

experience a general decrease in both taxonomic and phylogenetic alpha diversities.

To analyse temporal changes in taxonomic, phylogenetic and functional

compositions associated to changes in the alpha diversities of mammalian

communities, one can use an approach similar to Sobral et al. [64]. For example,

Serra da Bodoquena National Park is located at Southwestern Cerrado, a place

where we found a future decrease of both taxonomic and phylogenetic alpha

91

diversities of mammals, and an intermediate to high nestedness between present

and future communities. Therefore, there will be a taxonomic and phylogenetic

“partial compensation” (see Fig. 1 in [64]) of mammals in the area where the national

park is located. Moreover, in the same area, there was an intermediate nestedness

in the functional composition of mammals between the present and the future.

However, functional diversity increased with time, indicating a functional

“compensation with gain” [64]. Another example is Chapada dos Veadeiros National

Park, located in Central Cerrado, which presented a future decrease of mammalian

richness, and also a high temporal turnover rate, indicating a change in the

composition of species (“loss with no compensation”; [64]). Also, phylogenetic and

functional diversities increased with time, but their temporal turnovers were high.

This indicates that the reduced species pool in the area will have a more diverse

composition of clades and biological characteristics, but will be phylogenetically and

functionally different from the species pool in the present (“gain but no

compensation”; [64]). As we can see, alpha diversities and temporal changes in

species compositions have to be investigated together when analysing future

changes in biodiversity in order to avoid losing or changing the composition of

species, clades or biological characteristics in communities.

We found both patterns of decrease (indicating biotic homogenization) and increase

(biotic heterogenization) of spatial beta diversity happening in the Cerrado due to

climatic changes. The lower part of Cerrado presented several mammalian

communities expected to become taxonomically, phylogenetically and functionally

homogenized. On the other hand, in most of Northern Cerrado, communities are

expected to have an increase of spatial beta diversity. Interestingly, Northern

92

Cerrado is the region with the highest amount of native vegetation [62]. So,

homogenization is going to be strongest where human activities have already had a

high impact in the ecosystem and, possibly, in the region’s climate (Southern

Cerrado). Moreover, this homogenization can cause several ecological and

evolutionary impacts [23]. For example, modifying the within and between species

composition could reduce community functioning, stability and resistance to

environmental changes due to the narrowing of possible species specific responses

[23]. This is a warning that conservation actions must be urgently taken to conserve

or regenerate the lower part of Cerrado, and to promote connectivity [65] between

areas where homogenization and heterogenization are expected to occur.

When we identified mammals that could potentially immigrate or become regionally

committed to extinction, we found nine potential extinctions and 138 potential

immigrants. We must note that the higher number of immigrations occurred mainly

due to the step in our approach where we filtered the occurrences of mammals in the

present by only the species with occurrences actually confirmed in published lists

(such as in [43]). Notably, even with this potential bias in immigrations (which we

partially corrected by applying dispersal limitation through time), we observed a

general pattern of decrease in the richness of Cerrado’s mammals. Moreover,

regional extinctions were not only from endangered species, but also from DD and

LC species, indicating that data deficient and common species can also become

regionally extinct by the year 2070 (for more about the conservation of DD and

common species, see [66,67]). Notwithstanding, most (~85%) of potential immigrants

were from non-threatened categories. So it is likely that more widely distributed

species will become more common in the future (see [21,23,30]; but also [68]).

93

There is still a lack of knowledge on the effects of climate change on whole

communities. However, new methods are being developed, helping community

ecologists to study classical and new hypotheses in order to shine light on Ecology

and Conservation Biology’s issues [14,39]. Our results show that we are already able

to predict changes in taxonomic, phylogenetic, and functional alpha, temporal beta,

and spatial beta diversities of communities that are going to happen in the near

future. They also show that conservation has to be differently planned depending on

how the species pool size and its composition are expected to change through time

in a specific region. Climate change is a huge, but only one of the obstacles related

to the investigation of the ecology and evolution of species ranges. One way to

observe its potential effects on biodiversity is using correlative niche models, which

can be used in order to study climate change at large scales, including a large

number of species and communities. Studies on the future effects of human impacts

on biodiversity, such as through habitat destruction or fragmentation, will help us to

have a better understanding on how agriculture and urbanization may interact with

climate changes while modifying ecological communities.

Authors’ contributions

All authors conceived the study. JHN drafted the manuscript. DCJ and JHN carried

out the statistical analyses. All authors contributed to the writing of subsequent

versions and gave final approval for publication.

Competing interests

We have no competing interests.

94

Funding

JHN, DCJ FR, and LMM received MSc (DCJ, LMM) and PhD (JHN, FR) scholarships

from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

ACKNOWLEDGMENTS

We thank Matheus S. L. Ribeiro for his insightful comments on the very first version

of this manuscript.

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101

Supplementary material

Appendix S1 {Results of True Skill Statistics (TSS) for studied species.}

Species Ensembled_TSS

Akodon_cursor 0.7011

Akodon_lindberghi 0.5827

Akodon_montensis 0.7817

Akodon_sanctipaulensis 0.5623

Akodon_serrensis 0.751

Alouatta_belzebul 0.6956

Alouatta_caraya 0.9133

Alouatta_discolor 0.5771

Alouatta_guariba 0.7475

Alouatta_macconnelli 0.8521

Alouatta_puruensis 0.703

Alouatta_sara 0.8432

Alouatta_ululata 0.8491

Aotus_azarae 0.8715

Aotus_nigriceps 0.7414

Aotus_trivirgatus 0.8567

Ateles_chamek 0.8806

Ateles_marginatus 0.6129

Ateles_paniscus 0.8148

Bassaricyon_beddardi 0.7615

Bibimys_labiosus 0.77

Blarinomys_breviceps 0.6988

Blastocerus_dichotomus 0.8543

Brachyteles_hypoxanthus 0.7815

Bradypus_torquatus 0.641

Bradypus_tridactylus 0.6912

Bradypus_variegatus 0.9427

Brucepattersonius_igniventris 0.7101

Brucepattersonius_soricinus 0.7285

Cabassous_tatouay 0.6847

Cabassous_unicinctus 0.9337

Callicebus_brunneus 0.7496

Callicebus_coimbrai 0.7886

Callicebus_donacophilus 0.8583

Callicebus_dubius 0.7769

Callicebus_hoffmannsi 0.6303

Callicebus_moloch 0.6285

Callicebus_nigrifrons 0.7937

102

Callicebus_pallescens 0.9495

Callicebus_personatus 0.738

Callithrix_aurita 0.6848

Callithrix_flaviceps 0.581

Callithrix_geoffroyi 0.8157

Callithrix_jacchus 0.8832

Callithrix_penicillata 0.8109

Calomys_callosus 0.8637

Calomys_expulsus 0.8605

Calomys_hummelincki 0.6778

Calomys_laucha 0.9252

Calomys_tener 0.7662

Calomys_tocantinsi 0.7792

Caluromys_lanatus 0.8266

Caluromys_philander 0.894

Caluromysiops_irrupta 0.6869

Carterodon_sulcidens 0.8185

Cavia_aperea 0.8522

Cavia_fulgida 0.5834

Cebus_apella 0.8794

Cebus_cay 0.9089

Cebus_flavius 0.7586

Cebus_kaapori 0.5685

Cebus_libidinosus 0.9239

Cebus_nigritus 0.8417

Cebus_olivaceus 0.816

Cebus_robustus 0.8166

Cebus_xanthosternos 0.8265

Cerdocyon_thous 0.9287

Cerradomys_maracajuensis 0.8991

Cerradomys_marinhus 0.4996

Cerradomys_scotti 0.6907

Cerradomys_subflavus 0.892

Chironectes_minimus 0.8962

Chiropotes_albinasus 0.6607

Chiropotes_chiropotes 0.8606

Chiropotes_satanas 0.6082

Chiropotes_utahickae 0.5789

Choloepus_hoffmanni 0.7845

Chrysocyon_brachyurus 0.8285

Clyomys_bishopi 0.652

Clyomys_laticeps 0.7399

Coendou_bicolor 0.9296

Coendou_nycthemera 0.6141

Coendou_prehensilis 0.9275

Conepatus_chinga 0.8506

103

Conepatus_semistriatus 0.908

Cryptonanus_agricolai 0.896

Cryptonanus_chacoensis 0.9701

Ctenomys_boliviensis 0.8148

Ctenomys_brasiliensis 0.6777

Ctenomys_minutus 0.91

Cuniculus_paca 0.9531

Cyclopes_didactylus 0.9433

Dactylomys_boliviensis 0.7986

Dactylomys_dactylinus 0.7679

Dasyprocta_azarae 0.7086

Dasyprocta_cristata 0.6953

Dasyprocta_leporina 0.8754

Dasyprocta_prymnolopha 0.8926

Dasyprocta_punctata 0.7235

Dasypus_kappleri 0.8961

Dasypus_novemcinctus 0.9549

Dasypus_septemcinctus 0.7774

Delomys_collinus 0.6479

Delomys_dorsalis 0.8138

Delomys_sublineatus 0.6054

Didelphis_albiventris 0.9327

Didelphis_aurita 0.7949

Didelphis_marsupialis 0.9697

Echimys_chrysurus 0.6888

Eira_barbara 0.9444

Euphractus_sexcinctus 0.8256

Euryoryzomys_emmonsae 0.5267

Euryoryzomys_lamia 0.7021

Euryoryzomys_macconnelli 0.8036

Euryoryzomys_nitidus 0.7125

Euryoryzomys_russatus 0.7592

Euryzygomatomys_spinosus 0.7886

Galea_flavidens 0.8394

Galea_spixii 0.9183

Galictis_cuja 0.921

Galictis_vittata 0.9768

Gracilinanus_agilis 0.9095

Gracilinanus_emiliae 0.7252

Gracilinanus_microtarsus 0.8263

Graomys_griseoflavus 0.9628

Holochilus_brasiliensis 0.8035

Holochilus_sciureus 0.9699

Hydrochoerus_hydrochaeris 0.9351

Hylaeamys_acritus 0.75

Hylaeamys_laticeps 0.6231

104

Hylaeamys_megacephalus 0.8519

Hylaeamys_oniscus 0.6865

Hylaeamys_yunganus 0.8223

Isothrix_bistriata 0.7004

Juliomys_pictipes 0.7952

Juliomys_rimofrons 0.5588

Kannabateomys_amblyonyx 0.8182

Kerodon_acrobata 0.6191

Kerodon_rupestris 0.8554

Kunsia_fronto 0.5

Kunsia_tomentosus 0.7566

Lagothrix_cana 0.7128

Leontopithecus_chrysopygus 0.781

Leontopithecus_rosalia 0.7027

Leopardus_colocolo 0.8886

Leopardus_pardalis 0.9494

Leopardus_tigrinus 0.9348

Leopardus_wiedii 0.9593

Lonchothrix_emiliae 0.5596

Lontra_longicaudis 0.9649

Lutreolina_crassicaudata 0.8889

Makalata_didelphoides 0.8674

Marmosa_constantiae 0.9056

Marmosa_demerarae 0.9353

Marmosa_murina 0.9318

Marmosa_paraguayanus 0.8642

Marmosops_bishopi 0.8288

Marmosops_incanus 0.7494

Marmosops_parvidens 0.8047

Marmosops_paulensis 0.5595

Mazama_americana 0.9164

Mazama_bororo 0.7576

Mazama_gouazoubira 0.9405

Mazama_nana 0.8959

Mazama_nemorivaga 0.9562

Mesomys_hispidus 0.8449

Mesomys_stimulax 0.5315

Metachirus_nudicaudatus 0.884

Mico_argentatus 0.6758

Mico_emiliae 0.5141

Mico_melanurus 0.8528

Mico_nigriceps 0.694

Mico_rondoni 0.5836

Microakodontomys_transitorius 0.5

Monodelphis_americana 0.8919

Monodelphis_brevicaudata 0.8933

105

Monodelphis_dimidiata 0.8305

Monodelphis_domestica 0.891

Monodelphis_emiliae 0.7966

Monodelphis_glirina 0.7913

Monodelphis_iheringi 0.7025

Monodelphis_kunsi 0.8135

Monodelphis_rubida 0.8693

Monodelphis_scalops 0.7827

Monodelphis_theresa 0.7359

Monodelphis_umbristriata 0.8387

Mus_musculus 0.8228

Mustela_africana 0.8912

Myocastor_coypus 0.9576

Myoprocta_acouchy 0.7398

Myrmecophaga_tridactyla 0.921

Nasua_nasua 0.9275

Neacomys_guianae 0.6722

Neacomys_paracou 0.7296

Neacomys_spinosus 0.7268

Necromys_lasiurus 0.7813

Necromys_lenguarum 0.9046

Nectomys_rattus 0.9625

Nectomys_squamipes 0.7947

Odocoileus_virginianus 0.7522

Oecomys_auyantepui 0.7134

Oecomys_bicolor 0.9377

Oecomys_catherinae 0.9001

Oecomys_cleberi 0.5

Oecomys_concolor 0.8462

Oecomys_mamorae 0.9423

Oecomys_paricola 0.5459

Oecomys_rex 0.7403

Oecomys_roberti 0.8834

Oecomys_rutilus 0.7535

Oecomys_trinitatis 0.8281

Oligoryzomys_chacoensis 0.8549

Oligoryzomys_eliurus 0.8694

Oligoryzomys_flavescens 0.8335

Oligoryzomys_fornesi 0.6768

Oligoryzomys_fulvescens 0.9143

Oligoryzomys_microtis 0.871

Oligoryzomys_moojeni 0.6154

Oligoryzomys_nigripes 0.797

Oligoryzomys_rupestris 0.5438

Oligoryzomys_stramineus 0.8031

Oxymycterus_amazonicus 0.5298

106

Oxymycterus_angularis 0.874

Oxymycterus_caparoae 0.5998

Oxymycterus_dasytrichus 0.598

Oxymycterus_delator 0.514

Oxymycterus_hispidus 0.6195

Oxymycterus_inca 0.761

Oxymycterus_nasutus 0.8097

Oxymycterus_quaestor 0.7753

Oxymycterus_roberti 0.7518

Ozotoceros_bezoarticus 0.7886

Panthera_onca 0.9107

Pecari_tajacu 0.9489

Phaenomys_ferrugineus 0.636

Philander_frenatus 0.8924

Philander_opossum 0.9226

Phyllomys_blainvillii 0.8535

Phyllomys_brasiliensis 0.5

Phyllomys_lamarum 0.6735

Phyllomys_medius 0.8006

Phyllomys_nigrispinus 0.7334

Phyllomys_pattoni 0.6906

Pithecia_irrorata 0.7307

Pithecia_pithecia 0.8321

Potos_flavus 0.9697

Priodontes_maximus 0.9392

Procyon_cancrivorus 0.8821

Proechimys_gardneri 0.6533

Proechimys_goeldii 0.5515

Proechimys_guyannensis 0.7469

Proechimys_longicaudatus 0.6545

Proechimys_roberti 0.6832

Pseudalopex_gymnocercus 0.9347

Pseudalopex_vetulus 0.8609

Pseudoryzomys_simplex 0.924

Pteronura_brasiliensis 0.935

Puma_concolor 0.8947

Puma_yagouaroundi 0.9366

Rhagomys_rufescens 0.5383

Rhipidomys_cariri 0.5685

Rhipidomys_emiliae 0.5286

Rhipidomys_macrurus 0.7968

Rhipidomys_mastacalis 0.6824

Rhipidomys_nitela 0.7767

Saguinus_imperator 0.7962

Saguinus_labiatus 0.849

Saguinus_midas 0.85

107

Saguinus_niger 0.7042

Saimiri_sciureus 0.9211

Saimiri_ustus 0.6031

Sciurus_aestuans 0.8773

Sciurus_gilvigularis 0.7099

Sciurus_ignitus 0.7511

Sciurus_spadiceus 0.8077

Sigmodon_alstoni 0.5967

Sooretamys_angouya 0.754

Speothos_venaticus 0.9318

Sphiggurus_insidiosus 0.7486

Sphiggurus_melanurus 0.711

Sphiggurus_spinosus 0.8059

Sphiggurus_villosus 0.7187

Sylvilagus_brasiliensis 0.6808

Tamandua_tetradactyla 0.9106

Tapirus_terrestris 0.8827

Tayassu_pecari 0.8829

Thalpomys_cerradensis 0.6664

Thalpomys_lasiotis 0.5747

Thaptomys_nigrita 0.7354

Thrichomys_apereoides 0.8668

Thrichomys_inermis 0.7434

Thrichomys_pachyurus 0.9196

Thylamys_karimii 0.7794

Thylamys_macrurus 0.865

Thylamys_velutinus 0.9249

Tolypeutes_matacus 0.9576

Tolypeutes_tricinctus 0.7516

Toromys_grandis 0.7196

Trinomys_dimidiatus 0.602

Trinomys_eliasi 0.5594

Trinomys_gratiosus 0.7683

Trinomys_iheringi 0.7841

Trinomys_moojeni 0.5147

Trinomys_myosuros 0.7184

Trinomys_paratus 0.7109

Trinomys_setosus 0.8133

Wiedomys_cerradensis 0.5

Wiedomys_pyrrhorhinos 0.891

Zygodontomys_brevicauda 0.8477

108

Appendix S2 {Species added as polytomies in the phylogeny.}

Species_Added_As_Polytomies

Akodon_paranaensis

Alouatta_discolor

Alouatta_puruensis

Alouatta_ululata

Artibeus_planirostris

Brucepattersonius_griserufescens

Brucepattersonius_igniventris

Brucepattersonius_soricinus

Callibella_humilis

Callicebus_bernhardi

Callicebus_coimbrai

Callicebus_stephennashi

Calomys_tocantinsi

Cebus_cay

Cebus_flavius

Cebus_kaapori

Cebus_robustus

Cerradomys_maracajuensis

Cerradomys_marinhus

Cerradomys_scotti

Cryptonanus_chacoensis

Echimys_vieirai

Euryoryzomys_emmonsae

Hyladelphys_kalinowskii

Hylaeamys_acritus

Hylaeamys_oniscus

Juliomys_rimofrons

Kerodon_acrobata

Lonchorhina_inusitata

Mazama_bororo

Mazama_nemorivaga

Mico_acariensis

Mico_argentatus

Mico_emiliae

Mico_humeralifer

Mico_mauesi

Mico_melanurus

Mico_nigriceps

Mico_rondoni

Mico_saterei

Microakodontomys_transitorius

Micronycteris_sanborni

Natalus_espiritosantensis

109

Neacomys_paracou

Neusticomys_ferreirai

Oligoryzomys_moojeni

Oligoryzomys_rupestris

Oligoryzomys_stramineus

Oxymycterus_amazonicus

Oxymycterus_caparoae

Phyllomys_lundi

Phyllomys_pattoni

Proechimys_gardneri

Rhipidomys_cariri

Rhogeessa_hussoni

Thrichomys_pachyurus

Thylamys_velutinus

Thyroptera_devivoi

Trinomys_eliasi

Trinomys_moojeni

Trinomys_yonenagae

Wiedomys_cerradensis

Xeronycteris_vieirai

110

Appendix S3 {Supplementary results for low (RCP 2.6) and high (RCP 8.5)

concentrations of greenhouse gases.}

Low concentration of greenhouse gases:


240

220

200

180

160

140

120

100

80

60

220

200


40%

30%

20%

10%

70%

60%

50%

40%

30%

20%

10%

0%

Temporal phylogenetic β


0%

10%

20%

30%

95

90

85

80

75

70

65

60

55

50

45

(a) Present

0.30(g) Beta

0.50(c) Beta

0%

0.40(k) Beta

0%90%

80%

70%

60%

50%

40%

50%

60%

70%

80%

90%

0.45

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.28

0.26

0.24

0.22

0.20

0.18

0.16

0.14

0.12

0.10

0.08

0.06

0.04

100%

0.35

0.30

0.25

0.20

0.15

0.10

0.05

2.0

1.5

0.5

0

-0.5

1.0

80%

0%

-20%

20%

40%

60%

(b) Future

-40%


3.5

3.0

2.5

1.5

1.0

0.5

0

-0.5

2.0

0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0

-0.5

3.5

2.5

3.0

2.0

4.0

1.5

1.0

0.5

0

-0.5

-1.0

2.0

1.5

0.5

0

-0.5

1.0

3.5

2.5

3.0

2.0

4.0

1.5

1.0

0.5

0

-0.5

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Extinctions 2 (50%) 0 0 0 2 (50%) 0

Immigrants 18 (12.16%) 101 (68.24%) 3 (2.03%) 11 (7.43%) 10 (0.07%) 5 (0.03%)

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Extinctions 8 (44.44%) 5 (27.78%) 1 (5.56%) 0 4 (22.22%) 0

Immigrants 9 (13.43%) 48 (71.64%) 2 (2.98%) 2 (2.98%) 3 (4.48%) 3 (4.48%)

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Appendix S4 {Regional immigrants and extinctions of Cerrado mammals under

future scenarios of low (RCP 2.6), medium (RCP 6.0) and high (RCP 8.5)

concentrations of greenhouse gases (Arquivo grande de Excel; Pedir ao autor).}

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Conclusão Geral

BIOLOGICAL CORRELATES OF EXTINCTION: WHAT DOES DIFFERENTIATE

SURVIVORS FROM VICTIMS?

José Hidasi-Neto

PhD candidate in Ecology and Evolution, Department of Ecology, Federal University of Goiás

– UFG, Goiânia, Goiás, Brasil. ([email protected])

Recebido em: 02/10/2017 – Aprovado em: 21/11/2017 – Publicado em: 05/12/2017

DOI: 10.18677/EnciBio_2017B72

ABSTRACT

Since Georges Cuvier introduced the concept of extinction to the scientific community

at the end of the 18th century, researchers further studied how, where, and when it

tended to occur in nature. Some of these researchers, such as JeanBaptiste Lamarck

and Charles Lyell, speculated whether species with certain biological traits were more

prone to extinction than other species lacking these traits (i.e. extinction selectivity).

Following the definition of “mass extinction”, first studies on the Cretaceous mass

extinction, and the development Conservation Biology field, there has been an

increasingly amount of studies on extinction. In addition, several of these studies have

been able to identify various biological traits usually found in organisms with high

extinction risk (i.e. biological correlates of extinction). In this work, extinction

correlates usually found in both past and recent extinctions for several biological

groups are reviewed. Also, it is discussed how extinction selectivity can have distinct

macroevolutionary effects on biodiversity depending on the extent of environmental

disturbances in a specific time period. Moreover, evidences are presented indicating

that a new period of mass extinction can begin if conservation actions are not taken

properly in the near future. After so many years of development of biological theory on

extinction, advantage must be taken of its advances in an objective way in

prioritization methods to improve conservation actions while dealing with the

reduction of resources for humans and wildlife. This is not only necessary to decrease

biodiversity loss, but also to prevent humans from extinction.

KEYWORDS: Extinction selectivity, Mass extinction, Conservation.

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EXTINÇÃO SELETIVA: O QUE DIFERENCIA SOBREVIVENTES DE

VÍTIMAS?

RESUMO

Georges Cuvier introduziu o conceito de extinção para a comunidade científica ao final

do século 18. Desde então, pesquisadores estudam como, onde e quando a extinção

tende a ocorrer na natureza. Alguns desses pesquisadores, como JeanBaptiste Lamarck

e Charles Lyell, já especulavam que algumas espécies com determinadas características

biológicas eram mais propensas à extinção do que outras que não as possuíam (i.e.

extinção seletiva). Após (1) a definição do conceito de "extinção em massa", (2) os

primeiros estudos sobre a extinção em massa do Cretáceo, e (3) o desenvolvimento do

campo da Biologia da Conservação, tem havido uma quantidade cada vez maior de

estudos sobre extinção. Vários destes estudos identificaram características biológicas

normalmente encontradas em organismos com alto risco de extinção (i.e. correlatos

biológicos de extinção). Neste trabalho são revisadas características biológicas

normalmente encontradas em espécies extintas no passado e recentemente para vários

grupos biológicos. Também é discutido como a extinção seletiva pode ter distintos

efeitos macroevolutivos sobre a biodiversidade dependendo da extensão das

perturbações ambientais em períodos específicos de tempo. Ainda, são apresentadas

evidências que indicam que um novo período de extinção em massa pode começar caso

ações conservacionistas não sejam tomadas corretamente no futuro próximo. Após

tantos anos de desenvolvimento da teoria biológica sobre extinção, seus avanços

devem ser incluídos de maneira objetiva em métodos de priorização para que a

efetividade de ações conservacionistas aumente, mesmo que recursos disponíveis para

a conservação biológica sejam reduzidos.

PALAVRAS-CHAVE: Correlatos de extinção, Extinção em massa, Conservação.

INTRODUCTION

At the end of the 18th century, Georges Cuvier introduced the concept of species

extinction to the scientific community (RUDWICK, 1998; LOCKWOOD, 2008). Since

then, geologists, paleontologists and biologists have widely improved the

understanding of how and why the individuals of a species can be terminated locally,

regionally or globally (i.e. completely extinct) (PURVIS et al., 2000a, 2000b;

BAGUETTE et al., 2013). Paleontological records indicate that all species of whole

genera, families and orders became extinct throughout geological time (e.g. FEIST &

MCNAMARA, 2013). There is also an increasing body of evidence that several species

are rapidly going extinct in the following years (WAKE & VREDENBURG, 2008;

BARNOSKY et al., 2011). Despite the development of the research on extinction, there

is still a debate whether past and recent extinctions are random or not in respect to

which species are more prone to extinction (MCKINNEY, 1997; PURVIS et al., 2000a;

HEARD et al., 2007; LOCKWOOD, 2008).

How frequent is extinction in nature? In terms of geological time, it happens all

the time (LOCKWOOD, 2008; PIMM et al., 2014). Indeed, most species (about

99.9%) that have ever existed are already extinct (BARNOSKY et al., 2011).

Moreover, researchers noted that the amount of extinctions in some specific periods of

Earth’s history was higher than what would be commonly expected over geological

time (i.e. higher than the “background extinction rate”; JABLONSKI, 1991;

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PROENÇA & PEREIRA, 2013). As a consequence, paleontologists defined five events

of “mass extinction” (Ordovician, Devonian, Permian, Triassic, and Cretaceous events;

a.k.a. the “Big Five”) as those in which more than 75% of species went extinct in a

short time interval (JABLONSKI & CHALONER, 1994; BARNOSKY et al., 2011).

Studies indicate that about 10% of all past species extinctions happened at the “Big

Five” (JABLONSKI & CHALONER, 1994). Therefore, these periods have been

targeted as good time intervals to study how and why species go extinct.

The characterization of mass extinction events by NEWELL (1952), the study

on the Cretaceous mass extinction event by ALVAREZ et al. (1980), and, probably, the

emergence (1980s) and further development (1990s) of the field of Conservation

Biology (see MEINE et al., 2006), paved the way to hundreds of studies on species

extinctions (BAMBACH, 2006; LOCKWOOD, 2008). This can be observed in the

scientific literature with the rapid growth of the percentage of studies with the topic

“extinction” since the 1950s (FIGURE 1; also see Figure 1 in LOCKWOOD, 2008).

Recently, various of these studies began to indicate that there is a sixth mass extinction

event currently happening due to human activity (BARNOSKY et al., 2011; PIMM et

al., 2014). This period is usually referred to as Anthropocene, and it is more precisely

defined as the period when human activities started to have impacts as significant or

greater than natural processes on ecosystems (DIRZO et al., 2014; CORLETT, 2015).

Although the starting date of the Anthropocene is still not defined in the scientific

literature, it usually ranges from the Late Pleistocene to the periods of Industrial

Revolutions (DIRZO et al., 2014; CORLETT, 2015).

FIGURE 1 – Columns indicate numbers of papers with the topic “extinction” published since 1945

within research areas of Paleontology, Environmental Sciences & Ecology and

Evolutionary Biology from the Web of Science repository. The line shows percentages of

all articles published within these research areas containing the topic “extinction”. The

search was conducted on May 3, 2016.

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One must not stick only to extinction patterns, but also to their respective causal

processes. In a recent study, FEULNER (2008) proposed a few major extinction drivers

related to mass extinction events: (1) comet or asteroid impact, (2) large-scale

volcanism, (3) climate forcings (e.g. determined by plate tectonics, solar radiation, and

chemical composition of ocean water), (4) gamma-ray burst. However, it is notable that

these are highly unpredictable but influential drivers ("grey and black swans";

LOGARES & NUÑEZ, 2012) associated to some more basic and widely mentioned

extinction drivers in conservation studies: (1) habitat loss, (2) habitat fragmentation, (3)

habitat degradation, (4) overexploitation, (5) invasive species, and (6) spread of

diseases (PRIMACK & RODRIGUES, 2008). For example, SZABO et al. (2012)

found that the introduction of invasive species (mainly on oceanic islands),

overexploitation (mainly on continental islands), and agricultural-related habitat loss

and degradation (mainly on continents) were responsible for 141 human-related bird

species extinctions since the 1500s. They also noted that most of these extinctions

(78.7%) happened on oceanic islands, indicating that the restricted range of island

species contributed to the high number of historical bird extinctions. Moreover, in an

attempt to compare past and recent extinction drivers of the marine biodiversity,

HARNIK et al. (2012) found a higher variety of threats for biodiversity in recent time

in comparison to past periods of high extinction rate due to human activity (see Table 1

in HARNIK et al., 2012).

Soon after Cuvier introduced the concept of extinction to the scientific

community, researchers such as Jean-Baptiste Lamarck, Charles Lyell, and Charles

Darwin, began to speculate whether some taxa were more prone to extinction than

others (MCKINNEY, 1997). On the first half of the 19th century, Charles Lyell wrote

about the extinction proneness of cold-intolerant plants in colder periods, and about the

high extinction proneness of small-ranged species (LYELL, 1832; see MCKINNEY,

1997). Lyell even noted that human activities related to habitat loss and

overexploitation were responsible for the extinction of small-ranged endemic species

and to the introduction of domesticated species throughout the anthropogenic world

(LYELL, 1832; WILKINSON, 2004). Several recent studies have pointed out that

species presenting certain biological traits can be more prone to extinction than species

lacking such traits. For instance, large body mass, small range size and high habitat and

dietary specialization have been widely recognized as important positive correlates of

extinction proneness (MCKINNEY, 1997; PURVIS et al., 2000b; DULVY et al., 2003).

Despite advances in the subject, it is still necessary to investigate whether these

biological correlates of extinction differ among taxa and whether they explain both past

and current extinctions (TABLE 1; MCKINNEY, 1997; PURVIS et al., 2000b).

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TABLE 1 – Summary of biological correlates of extinction risk and reasons why they are usually

thought to predict species’ extinction risk. Extinction correlate Reason Geographic range Species with small geographic range may have narrower niches, being able to use a smaller variety of conditions

and resources from the environment (MCKINNEY, 1997). In addition, species with small geographic range

commonly have low local abundance at multiple scales, and more fragmented ranges (MCKINNEY, 1997).

Home range Species with large home range are more vulnerable to extinction drivers such as habitat loss, degradation, and

fragmentation (PURVIS et al., 2000b). Population density Populations with less individuals are expected to suffer stronger negative effects from demographic stochasticity,

slow evolutionary rates, and inbreeding (PURVIS et al., 2000b). Also, species with low population density

commonly have small geographic ranges, high trophic position, high habitat specialization and large body size

(MCKINNEY, 1997; SHAHABUDDIN & PONTE, 2005). Trophic level High-trophic level species are very vulnerable to cumulative negative effects on species at lower trophic levels

(PURVIS et al., 2000b). Dietary

specialization Dietary specialized species are expected to have narrow niches, usually presenting low local abundance and small

geographic range (MCKINNEY, 1997). Dispersal ability Species with low dispersal ability may have traits related to low capacity to colonize new habitats and to low

reproductive ability (SODHI et al., 2008). In addition, dispersal ability is commonly positively correlated to

geographic range size (NOGUÉS-BRAVO et al., 2014). Species with low dispersal ability also tend to have

highly fragmented, small populations (BAGUETTE et al., 2013). Habitat

specialization Similarly to dietary specialization, habitat-specialized species are expected to have narrow niches, presenting low

local abundance and small geographic range (MCKINNEY, 1997). Generation time Long generation time is related to decreased probability to which populations' birth rates will be able to

compensate increases in mortality rates (PURVIS et al., 2000b). Body size Large species tend to be habitat- and dietary-specialized, and to have low population density, long generation

time, high age at maturity, and large home range (MCKINNEY, 1997; PURVIS et al., 2000b). Large species are

usually more affected by hunting than smaller ones (PURVIS et al., 2000b). Island living Island species tend to have small geographic ranges and small populations. They also tend to be very vulnerable

to extinction drivers such as invasive species and overexploitation (MCKINNEY, 1997; PURVIS et al., 2000b;

SZABO et al., 2012). Age at maturity Similarly to generation time, high age at maturity is related to decreased probability to which populations' birth

rate will be able to compensate increases in mortality rates (PURVIS et al., 2000b). Clutch size Large clutch size indicates high reproductive ability, and it is found in species with fast life histories (HUANG et

al., 2013). Such species can more easily colonize new patches (BROMHAM et al., 2012).

Here are presented differences and similarities between past and recent

extinctions in relation to how biological extinction selectivity acts on species with

different biological traits. For this, relationships commonly found between several

biological extinction correlates and extinction risk in both past and recent extinctions

are listed. It is also discussed how distinct environmental scenarios can have different

types of extinction selectivity, and how they can promote or not evolutionary changes

within clades. Then, evidences suggesting that urgent actions need to be taken against

the loss of currently threatened biodiversity are presented. Finally, it is shown the need

for studies on background extinction rates, and discussed how studies on extinction

(especially extinction selectivity) can enhance our knowledge on the outcomes of

conservation planning.

PAST EXTINCTIONS

Pioneer studies on extinction relied on comparisons between fossils and extant

organisms. For instance, when “dinosaurs” were described by Richard Owen in the

nineteenth century (SPOTILA et al., 1991), it was clear that those giant reptiles were

no longer living. However, paleontologists at the 19th and early 20th centuries were

mainly focused on taxonomic and phylogenetic relationships rather than on biological

traits of dinosaurs (SPOTILA et al., 1991). Moreover, considering that absolute dating

120

techniques were not available by that time, not much could be told about the

mechanisms related to the enigmatic extinction of dinosaurs. Much later, Alvarez et al.

(1980) published evidences that a large asteroid collided with the Earth at the end of

the Cretaceous period (~65 million years ago). Notably, knowledge on the biology (e.g.

trophic level) of extinct animals (including dinosaurs) were in accordance with their

predictions. For example, the suppression of plant photosynthesis due to the reduction

of sunlight might have caused the reduction of populations of several plant species that

were essential for herbivorous dinosaurs, which would have caused negative effects on

populations of their related predators (ALVAREZ et al., 1980).

Despite the limitations of studying fossils, there are several studies indicating

biological correlates of extinction for organisms that lived before the global effects of

human activity (TABLE 2; TABLE 3). To study biological correlates in past

extinctions, researchers have to make use of various proxies for assessing biological

traits of species. For example, using dental morphology, Wilson (2013) found an

extinction selectivity against dietary specialist mammals during the late Cretaceous

mass extinction event. Also, using information on the distribution of fossils of

organisms that lived over the past 500 million years, and information on water depth,

substrate and latitude to define habitats of organisms, Harnik et al. (2012) found an

extinction selectivity against small-ranged and habitat specialized marine invertebrates.

The use of proxies to study biological traits of already extinct species is essential in

paleobiological studies. However, researchers always need to be certain of what these

proxies really tell them about species, so that wrong conclusions because of poorly

defined assumptions are avoided.

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TABLE 2 – Summary of biological correlates of extinction risk (based on Table 2 in Purvis et al.,

2000b) in past (before 1500 AD) extinctions for several biological groups. The superscript

g and l letters indicate if the studies used global (species) or local (population) measures of

extinction. Reference numbers (see TABLE 3) and studied time periods of the respective

studies are in parentheses.

Extinction correlate Positive Negative No relationship

Geographic range

Terebratulide brachiopodsg

(20, Devonian), Marine

invertebratesg&l (14, 23,

Phanerozoic), Scleractinian coralsg&l (28,

K/Pg boundary)

Population density

Marine gastropodsg (17, Permian-Early Jurassic), Coralsg&l (30, Neogene-

Quaternary)

Marine invertebratesg&l

(23, Phanerozoic), Scleractinian coralsg&l (28,

K/Pg boundary), Bivalvesg&l (18, 29, K/Pg

boundary)

Trophic level

Marine teleostsg (16, K/Pg boundary),

Neoselachian sharksg (19, K/Pg boundary)

Dietary

specialization

Mammalsl (15, K/Pg

boundary), Marine teleostsg (16, K/Pg

boundary)

Habitat

specialization Marine invertebratesg&l (23,

Phanerozoic)

Body size

Mammalsl (15, K/Pg

boundary), Marine teleostsg (16, K/Pg

boundary), Neoselachian

sharksg (19, K/Pg boundary), Gastropodsg&l

(21, P/Tr boundary), Eutheriodont therapsidsg&l

(24, P/Tr

boundary), Sea urchinsg (27, K/Pg boundary)

Scallopsg&l (26, Late

Pliocene-Middle Pleistocene)

Foraminiferansg&l (22, Late

Permian-Late Triasic), Bivalvesg (29, K/Pg

boundary)

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TABLE 3 – Reference list or tables 2 and 4 (reference numbers

according to the tables). N Reference 1 PURVIS et al. (2000b) 2 ANDERSON, et al. (2011) 3 DULVY & REYNOLDS (2002) 4 KOH, et al. (2004) 5 ANGERMEIER (1995) 6 COOPER et al. (2008) 7 CARDILLO et al. (2008) 8 GAGE et al. (2004) 9 JONES et al. (2003) 10 COMONT et al. (2014) 11 HUTCHINGS et al. (2012) 12 SODHI et al. (2008) 13 LEHTINEN & RAMANAMANJATO (2006) 14 PAYNE & FINNEGAN (2007) 15 WILSON (2013) 16 FRIEDMAN (2009) 17 PAYNE et al. (2011) 18 LOCKWOOD (2003) 19 KRIWET & BENTON (2004) 20 HARNIK et al. (2014) 21 PAYNE (2005) 22 REGO et al. (2012) 23 HARNIK et al. (2012) 24 HUTTENLOCKER (2014) 25 HERO et al. (2005) 26 SMITH & ROY (2006) 27 SMITH & JEFFERY (1998) 28 KIESSLING & BARON-SZABO (2004) 29 MCCLURE & BOHONAK (1995) 30 JOHNSON et al. (1995)

Body size and geographic range size are two relatively well-studied traits on

past species extinctions. These traits are usually based on two pieces of information

commonly collected in paleontological studies: the morphology of fossils and the

location where they are found. Large organisms are usually seen as highly specialized

and with low reproductive rates (MCKINNEY, 1997; CARDILLO et al., 2005), while

organisms with narrow distributions are usually considered to have low dispersal

ability and to be more susceptible to extinction drivers such as habitat loss,

fragmentation and degradation (MCKINNEY, 1997; FRITZ et al., 2009). For instance,

PAYNE (2005) found a global extinction selectivity against large gastropods during the

Late Permian mass extinction. On the other hand, MCCLURE & BOHONAK (1995)

found no size selectivity for bivalves during the end-Cretaceous mass extinction.

Notably, size selectivity should be dependent on both the taxon and timeperiod studied.

Moreover, there is much yet to learn about the life history of organisms and the

environmental conditions where they lived before we define general patterns of size

selectivity (see MCKINNEY, 1997). Using global data on marine invertebrates,

PAYNE & FINNEGAN (2007) found a selectivity against narrow ranged organisms

over most of the Phanerozoic eon. It is also notable that they found a weaker

geographic range selectivity during mass extinctions, which shows that widespread

environmental disturbances can reduce our perception of biological correlates of

extinction.

123

RECENT EXTINCTIONS

There are several species reported to have become extinct since the 1500s.

According to the International Union for Conservation of Nature (IUCN), there are at

least 861 species of animals and plants documented as extinct and 68 species

documented as extinct in the wild (IUCN, 2017). Humans have a major role in

promoting these extinctions. For instance, Szabo et al. (2012) found that human related

extinction drivers caused most bird extinctions since 1500. Moreover, recent amphibian

extinctions have been caused by the widespread of a pathogenic fungus driven by

climate change (ALAN POUNDS et al., 2006), which is acknowledged to have a major

anthropogenic component (IPCC, 2014).

The scenario is even more problematic when local extinctions are considered.

Charles Lyell once noted that some domesticated species were becoming increasingly

more common throughout the urbanized world in areas where they were not found

before (LYELL, 1832; WILKINSON, 2004). More than a century later, Charles Elton

studied how invasions of non-native species could cause the local extinction of native

species by modifying both the habitat and the biological interactions within

communities (ELTON, 1958). Following this line, McKinney & Lockwood (1999)

suggested that native, usually specialist species are being replaced by non-native,

usually generalist species at multiple scales, producing a worldwide biotic

homogenization. The authors suggested that some traits such as large size, low

fecundity, slow dispersal, and high habitat specialization, are typically found in species

that are locally replaced by others with opposing traits.

Biological correlates of recent extinctions are abundant in the scientific

literature. For instance, studying the occurrence and the number of generations per year

of British ladybird beetles, Comont et al. (2014) found an extinction selectivity against

ladybirds with small geographic range and long generation time. Similarly, Hero et al.

(2005) found an extinction selectivity against species with small geographic range and

small clutch size in their study on the occurrence and number of eggs laid per female of

amphibians in Eastern Australia. Dulvy & Reynolds (2002) identified body size as the

only biological correlate of local extinctions of the skates (Rajidae, Rajiformes) of the

world. Some traits identified as good correlates of past extinctions (some both in mass

and background extinctions), such as small geographic range, big body size, and high

habitat and dietary specialization, are also commonly identified in recent extinctions

(TABLE 3; TABLE 4). These traits are closely related to the probability that

demographic changes could lead species to extinction due to a single or multiple

extinction drivers.

124

TABLE 4 – Summary of biological correlates of extinction risk (based on Table 2 in Purvis et al.,

2000b) in recent (since 1500 AD) extinctions for several biological groups. The superscript g and l

letters indicate if the studies used global (species) or local (population) measures of extinction.

Reference numbers (see TABLE 3) of studies are in parentheses.

Extinction correlate Positive Negative No relationship

Geographic range

Primatesg, Carnivoresg (1),

Birdsg&l (1, 2, 8),

Freshwater fishesl (2, 5),

Tropical butterfliesl (4), Mammalsg (7), Batsg (9),

Ladybird beetlesl (10),

Frogsg&l (6, 25)

Skatesl (3)

Home range Carnivoresl, Primatesl (1),

Mammalsg (7)

Population density

Primatesg, Carnivoresg (1),

Terrestrial mammalsg, Birdsg (2), Mammalsg (7),

Reptilesl (1, 13), Frogsl

(13)

Trophic level Primatesg, Carnivoresg

(1) Freshwater fishesl (5)

Dietary

specialization Hoverfliesg (1), Freshwater

fishesl (5)

Dispersal ability Insectsg (1), Birdsl (2), Batsg

(9)

Habitat

specialization

Reptilesl, Primatesg (1),

Tropical butterfliesl (4), Freshwater fishesl (5),

Frogsg (6), Birdsg (1, 8),

Angiospermsl (12)

Insectsg, Birdsg (1), Reptilesl,

Frogsl (13)

Generation time Carnivoresg, Birdsg (1),

Ladybird beetlesl (10) Birdsg (1)

Primatesg, Reptilesl,

Hoverfliesg (1)

Body size

Primatesg, Hoverfliesg

(1), Terrestrial mammalsg&l,

Marine mammalsg&l, Marine

fishesg&l, Freshwater fishesl (2), Birdsg&l (1, 2, l

g 8), Skates (3), Frogs

(6), Mammalsl, Chondrichthyansl (11)

Birdsg&l, Mammalsg (1),

Freshwater fishesg (2)

Mammalsg, Carnivoresg

(1), Tropical butterfliesl (4),

Freshwater fishesl (5), Batsg (9), Teleostsl (11), Reptilesl (1, 13), Frogsl

(13)

Island living Mammalsg (1), Batsg (9) Birdsg (1) Primatesg, Carnivoresg (1)

Age at maturity

Terrestrial mammalsg, Freshwater fishesl, Marine fishesl (2),

Mammalsl, Teleostsl,

Chondrichthyansl (11)

Batsg (9)

Clutch size

Terrestrial mammalsg, Birdsg (2), Batsg (9),

Mammalsl (11), Frogsg&l (6,

25)

Teleostsl, Chondrichthyansl

(11)

125

PATTERNS OF EXTINCTION SELECTIVITY: RANDOM OR NOT?

Extinction drivers in background and current extinctions are relatively more

diverse than those in past mass extinction events (DULVY et al., 2003; PAYNE &

FINNEGAN, 2007; JACKSON, 2008; TURVEY & FRITZ, 2011; PROENÇA &

PEREIRA, 2013). Mass extinction events presented broad and, in some extent,

homogeneous and rapid effects on whole populations of species (PAYNE &

FINNEGAN, 2007; PAYNE & CLAPHAM, 2012). On the other hand, more local

extinction drivers seem to have stronger effects in periods of background extinction,

especially when considering the lack of evidence for broad environmental disturbances

during these intervals (PAYNE & FINNEGAN, 2007). In addition, although it seems

species are going through a new event of mass extinction (see next section), human

activity is not only responsible for regional but also for local processes that can lead

species to extinction (PRIMACK & RODRIGUES, 2008). Therefore, distinct drivers

of extinction can act at multiple scales throughout human modified regions.

Notwithstanding, biological correlates of extinction should always be studied in light

of which extinction drivers were, are, or probably will be acting at specific spatial (e.g.

local, regional or global) and temporal (e.g. the past 500 or the next 100 years) scales

being studied.

A way of investigating whether extinctions are random or not at distinct time

periods is studying patterns of extinction selectivity in species with certain biological

traits. It is commonly accepted that the extinction selectivity that happens in both

background and current-time extinctions is not only non-random but also affect

evolutionary patterns within clades (JABLONSKI, 2005). In that sense, this selectivity,

usually referred to as “constructive extinction selectivity”, would be able to promote or

reinforce adaptations, gradually changing species traits through space and time. For

instance, Smith & Roy (2006) found a non-random species-level extinction selectivity

against small scallops of Plio-Pleistocene California. They also found that surviving

species tended to be larger than the median size for their respective genera, which

indicates a trend in biological trait evolution of the scallops, revealing the occurrence

of a constructive extinction selectivity.

As previously mentioned, environmental disturbances during past mass

extinctions were broad and rapid, causing impactful demographic changes in species

populations. In addition, only a few species had adaptations or exaptations that allowed

them to survive these events (JABLONSKI, 2005). These characteristics of mass

extinction events usually make the identification of extinction selectivity patterns

harder. Although examples of biological correlates of extinction can be found for mass

extinctions (KITCHELL et al., 1986; PAYNE & FINNEGAN, 2007; WILSON, 2013),

some authors claim that the kind of selectivity during these events with broad and rapid

environmental impacts was not able to promote or reinforce species adaptations ("not

constructive"; RAUP, 1984; JABLONSKI, 2005). This type of nonrandom selectivity,

where species with specific biological traits are more prone to extinction but no

adaptation is promoted, is usually referred to as “nonconstructive extinction

selectivity” (RAUP, 1984; JABLONSKI, 2005). Furthermore, the most accepted

evidence for the occurrence of nonconstructive selectivity during mass extinction

events is the commonly observed selectivity against narrow geographic range in taxa

higher than the species level (see Table 1 in JABLONSKI, 2005). Moreover, contrarily

to periods of background and current extinction, selectivity against traits at several

levels (individual, species, and clade levels), such as body size, dietary specialization

126

and species-level geographic range, are not commonly identified during mass

extinction events (JABLONSKI, 2005). However, there is still much to learn about

macroevolutionary processes and patterns in periods of mass extinction, as examples of

constructive selectivity during these events can still be found in the scientific literature

(e.g. KITCHELL et al., 1986; WILSON, 2013; HUTTENLOCKER, 2014).

Mass extinctions are also thought to promote ecological restructurings of the

biota (DINEEN et al., 2014; ABERHAN & KIESSLING, 2015). Extinctions in these

periods would open up vacant niches (LEKEVICIUS, 2009), or ecospace (BENTON,

1995; LOCKWOOD, 2008), allowing dominant species to be replaced by others, and

also allowing the diversification of marginal taxa (JABLONSKI, 2005). For example,

Silvestro et al. (2015) found that shifts in the origination and extinction rates of plants

occurred during mass extinction events. The authors found that the currently dominant

seed-bearing plants and angiosperms respectively had decreased extinction and

increased origination rates after the Cretaceous mass extinction event (SILVESTRO et

al., 2015). Another example from the same mass extinction event is the rise of present-

day mammals. Some authors claim that the Cretaceous mass extinction opened up

ecospace, allowing the increase of inter- and intradiversification rates of mammals

(MEREDITH et al., 2011). Curiously, other authors argue that the relationship between

the Cretaceous event and the rise of mammals was not so direct, presenting evidences

that the diversification of mammals gradually increased until reaching a peak much

later of the mass extinction event (BININDAEMONDS et al., 2007). Notably, the

occasional extinction of dominant species from geographically broadly distributed taxa

in periods of mass extinction appear to be more probable than in periods of background

and recent extinctions (PAYNE & FINNEGAN, 2007). Although more studies should

clarify this topic, these latter periods seem to present a disproportional survivorship of

dominant species (PAYNE & FINNEGAN, 2007).

THE SIXTH MASS EXTINCTION: “DAYS OF FUTURE PAST”

Studies are revealing an increasing number of species that will probably face

extinction in a few years. According to the IUCN, there are already 24,431 eukaryotes

(animals, plants, fungi and chromists) documented as threatened (Vulnerable,

Endangered or Critically Endangered category) with extinction (IUCN, 2017). In

addition, it is a consensus that human activity has a great contribution to the

biodiversity loss that the planet is currently facing (CORLETT, 2015; DIRZO et al.,

2014; PIMM et al., 2014). For example, human-induced habitat loss and

overexploitation were responsible for the recent decline of several marine species with

large body mass, small range size, or high habitat specialization (DULVY et al., 2003).

Moreover, it has been shown that the potential loss of threatened species to extinction

is not spatially, taxonomically, phylogenetically or ecologically random (PURVIS et

al., 2000a; HIDASI-NETO et al., 2013, 2015; PIMM et al., 2014). For instance, Hidasi-

Neto et al. (2013) found several cases where threatened birds at multiple spatial scales

were phylogenetically and ecologically more similar than expected by chance. This

indicates the occurrence of an extinction selectivity against biological traits shared by

phylogenetically and ecologically similar species.

Some researchers claim that the extinction intensity we are currently facing may

be even higher than commonly accepted. These authors agree that we are in the middle

of a big extinction event due to the effects of widespread extinction drivers caused by

human activity on terrestrial and marine species populations (BARNOSKY et al., 2011;

CORLETT, 2015; DIRZO et al., 2014; HARNIK et al., 2012; PROENÇA &

127

PEREIRA, 2013; WAKE & VREDENBURG, 2008). This event may come to be the

sixth mass extinction if conservation measures are not urgently taken (WAKE &

VREDENBURG, 2008; BARNOSKY et al., 2011). For example, over one-third of

amphibians are threatened, and this number can increase because most amphibians

have small geographic ranges (WAKE & VREDENBURG, 2008). BARNOSKY et al.

(2011) found that if threatened species go extinct in 100 years, and the rate of

extinction keeps constant through time, it would take 240 to 540 years for a worldwide

loss of 75% species. Therefore, the extinction intensity in the Quaternary can reach a

similar level found during the Big Five, and a higher level than those found during

other “small” extinction events (FIGURE 2). Furthermore, as it is known that the

Anthropocene presents a high species extinction intensity and that this intensity is

mostly directed to species sharing similar biological traits, ways should be found to

predict and conserve clades that are more prone to go extinct.

FIGURE 2 – Estimated extinction intensity for several extinction events. Information for the Big Five

(Ordovician, Devonian, Permian, Triassic, Cretaceous events) was taken from Table 1 in BARNOSKY et al. (2011), while information for the Pliensbachian, Tithonian,

Cenomanian and Priabonian events was taken from Table 1 in JABLONSKI (1991). The

two Quaternary scenarios are based on results from BARNOSKY et al. (2011). The first

scenario refers to the time needed to reach a loss of 75% of vertebrate species if all

threatened species become extinct in 100 years and the rate of species loss remain constant

through time. The second scenario is similar to the first one, but only Critically

Endangered species become extinct in 100 years.

EXTINCTION SELECTIVITY AND CONSERVATION

PRIORITIZATION: “THE AGONY OF CHOICE”

It is a consensus among researchers that conservation resources (e.g. money,

scientists, time) are not enough to conserve the world’s threatened biodiversity (VANE-

WRIGHT et al., 1991). Thus, discussions about where these resources should be

directed have vastly increased in the past years (ISAAC et al., 2007; BECKER et al.,

2010; HIDASI-NETO et al., 2015). Currently threatened species commonly share traits

related to high vulnerability to extinction (e.g. small geographic range, low abundance,

and long generation time). For instance, less abundant species have proved to be more

128

prone to extinction independently of their geographic ranges (PIMM & JENKINS,

2010; PROENÇA & PEREIRA, 2013). Therefore, widespread but scarce species such

as top predators are usually found to be at high risks of extinction (PIMM & JENKINS,

2010; PROENÇA & PEREIRA, 2013; PIMM et al., 2014). In that sense, conservation

resources can be directed to species or groups of species (e.g. top predators) sharing

traits that increase their risks of extinction. Alternatively, conservation resources can be

directed to species or groups of species with traits ecologically relevant (e.g.

scavenging) to the communities they live in. Nevertheless, a strategy that increasingly

gains attention is to focus on both the endangerment level and the ecological and/or

evolutionary relevance of organisms (for information on charismatic and flagship

species in conservation practice see Smith et al., 2012).

The concern on prioritizing the conservation of species based on their

endangerment level and their ecological, socio-economic, scientific, and existence

(among other) values is not new. In 1869, the “Act for the Preservation of Sea-Birds”

of the United Kingdom was one of the first modern legislations against the extinction

of ecologically important organisms that are disproportionally vulnerable to extinction

and have great socio-economic value for humans (BARCLAY-SMITH, 1959). The idea

was to prohibit the hunting of seabirds during their breeding season, preventing from

the loss of, for example, their valuable scavenging function and their ecological service

of pointing out good localities for fishing (BARCLAY-SMITH, 1959). However, it was

only after the second half of the 20th century that approaches have been developed and

used for prioritizing the conservation of species in an objective manner (ISAAC et al.,

2007; BECKER et al., 2010; HIDASI-NETO et al., 2015; IUCN, 2017). For instance,

the IUCN uses demographic variables (e.g. population size and generation time) to

measure the endangerment level of species (IUCN, 2017). In addition, the EDGE

(“Evolutionarily Distinct and Globally Endangered”) initiative, launched by the

Zoological Society of London, was developed to allow the conservation prioritization

of threatened species that are also very distinct in relation to their evolutionary history

(ISAAC et al., 2007). In this approach, a very evolutionarily distinct species can be

prioritized over a less evolutionarily distinct one even if it has higher extinction risk.

Other approaches, like the EcoEDGE (“Ecologically and Evolutionarily Distinct and

Globally Endangered”), which is an expanded version of EDGE, include species traits

that are related to valuable ecological functions and services on the prioritization

process (HIDASI-NETO et al., 2015). This latter type of approach can be used to help

on the conservation of ecological community functioning and the benefits it provides to

human well-being.

CONCLUDING REMARKS: LOOKING FOR BACKGROUND

ACTORS

Although in the present work it was mostly discussed about extinctions during

periods with unusually high rates of extinction (e.g. mass extinctions), there is still

much to learn about usual background extinction rates. For example, it is still needed to

be able to accurately understand and estimate these rates (see DE VOS et al., 2014) so

that more precise answers can be found about whether or not current extinction rates

are higher than expected without the effects of human activity on biodiversity (e.g.

PIMM et al., 2014). Van Valen proposed that the probability of extinction of any given

taxon is independent on how long it has already existed ("Law of Constant Extinction";

see LIOW et al., 2011). Van Valen also proposed the Red Queen hypothesis, stating

129

that, for a given clade, organisms must face a deteriorating environment while

constantly evolving to survive in response to cooccurring organisms that are also

constantly evolving (LIOW et al., 2011; QUENTAL & MARSHALL, 2013). In

accordance with this hypothesis, Quental & Marshall (2013) found that high extinction

rates can be as important as low origination rates to cause declines of diversity within

clades. Although mechanisms responsible for these declines are still unclear

(QUENTAL & MARSHALL, 2013), they are probably hidden within biological traits

of species and, thus, at least in part in biological extinction selectivity. In conclusion,

researchers should be able to differentiate species extinctions that are naturally

expected from those that are driven by humans. Improving the understanding on how

extinction works in nature will enable conservation attention to be directed mostly to

species directly affected by human activity.

ACKNOWLEDGMENTS

Thanks to Thiago F. L. V. B. Rangel and Luis M. Bini for giving directions on

where to go in the study. Also to Marcos B. Carlucci for kindly reviewing and

providing insightful comments, and to Marcus V. Cianciaruso for the discussion

concerning topics presented here. Finally, to Coordenação de Aperfeiçoamento de

Pessoal de Nível Superior (CAPES) for providing a PhD scholarship to the author.

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