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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
HOMOGENEIZAÇÃO TAXONÔMICA, FILOGENÉTICA E FUNCIONAL DE
COMUNIDADES: CAUSAS, CONSEQUÊNCIAS E IMPLICAÇÕES PARA A
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
HOMOGENEIZAÇÃO TAXONÔMICA, FILOGENÉTICA E FUNCIONAL DE
COMUNIDADES: CAUSAS, CONSEQUÊNCIAS E IMPLICAÇÕES PARA A
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.
5
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
Material e Métodos...............................................................................42
Resultados............................................................................................48
Discussão.............................................................................................50
Referências...........................................................................................56
Material Suplementar............................................................................61
CAPÍTULO 3......................................................................................................75
Resumo........ ...... ................................................................................76
Introdução.............................................................................................77
Material e Métodos...............................................................................80
Resultados............................................................................................86
Discussão.............................................................................................89
Referências...........................................................................................94
Material Suplementar............................................................................101
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
9
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
11
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.
13
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.
14
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.
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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Figure 1.
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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/.
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.
Variable Mean SD Range
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.
Variable Mean SD Range
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.
Variable Mean SD Range
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(circumference + 1)
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(circumference + 1)
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]
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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.
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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).
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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].
84
(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:
Taxonomic α (richness)
240
220
200
180
160
140
120
100
80
60
220
200
Temporal functional β
40%
30%
20%
10%
70%
60%
50%
40%
30%
20%
10%
0%
Temporal phylogenetic β
Temporal taxonomic 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%
Phylogenetic α (MPD)
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
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Figure A1. 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 low concentration of greenhouse gases (RCP 2.6).
111
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Figure A2. 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 low concentration of greenhouse gases (RCP 2.6). Table A1. 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 low concentration of greenhouse gases (RCP 2.6).
Species DD LC NT VU EN CR
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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High concentration of greenhouse gases:
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Figure A3. 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) alpha diversities. 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 low concentration of greenhouse gases (RCP 8.5).
113
Taxonomic turnover
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Figure A4. 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 low concentration of greenhouse gases (RCP 8.5). Table A3. 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 high concentration of greenhouse gases (RCP 8.5).
Species DD LC NT VU EN CR
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)
122
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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