Nicho abiótico e efeitos do aquecimento global em Riorajini
(Rajiformes, Arhynchobatidae), raias do Atlântico Sudoeste
JÉSSICA FERNANDA RAMOS COELHO________________________________________________
Dissertação de Mestrado
Natal/RN, Fevereiro de 2020
JÉSSICA FERNANDA RAMOS COELHO
Nicho abiótico e efeitos do aquecimento global em Riorajini
(Rajiformes, Arhynchobatidae), raias do Atlântico Sudoeste
Dissertação de Mestrado apresentada ao Programa de Pós-
Graduação em Sistemática e Evolução da Universidade
Federal do Rio Grande do Norte como requisito parcial
para obtenção de título de mestre.
Orientador: Dr. Sergio Maia Queiroz Lima
Coorientadora: Dra. Flávia de Figueiredo Petean
Fevereiro, 2020
Natal–RN
Nicho abiótico e efeitos do aquecimento global em Riorajini
(Rajiformes, Arhynchobatidae), raias do Atlântico Sudoeste
BANCA EXAMINADORA:
______________________________
Dra. Françoise Dantas de Lima
Secretaria da Educação da Paraíba
Examinadora externa à instituição
______________________________
Dra. Maria Cristina Oddone
Universidade Federal do Rio Grande
Examinadora externa à instituição
______________________________
Dr. Sergio Maia Queiroz Lima
Universidade Federal do Rio Grande do Norte
Orientador/Presidente
Fevereiro, 2020
Natal–RN
Universidade Federal do Rio Grande do Norte - UFRN
Sistema de Bibliotecas - SISBI
Catalogação de Publicação na Fonte. UFRN - Biblioteca Setorial Prof. Leopoldo Nelson - Centro de Biociências - CB
Coelho, Jéssica Fernanda Ramos.
Nicho abiótico e efeitos do aquecimento global em Riorajini
(Rajiformes, Arhynchobatidae), raias do Atlântico Sudoeste /
Jéssica Fernanda Ramos Coelho. - Natal, 2020. 73 f.: il.
Dissertação (Mestrado) - Universidade Federal do Rio Grande do
Norte. Centro de Biociências. Programa de Pós-Graduação em
Sistemática e Evolução. Orientador: Prof. Dr. Sergio Maia Queiroz Lima.
1. Conservatismo filogenético de nicho - Dissertação. 2.
Modelagem de nicho ecológico - Dissertação. 3. Mudanças
climáticas - Dissertação. 4. Simpatria - Dissertação. 5.
Sobreposição de nicho - Dissertação. I. Lima, Sergio Maia Queiroz. II. Universidade Federal do Rio Grande do Norte. III.
Título.
RN/UF/BSE-CB CDU 575.8
Elaborado por KATIA REJANE DA SILVA - CRB-15/351
AGRADECIMENTOS
Primeiramente agradeço a UFRN, onde realizei graduação e agora o mestrado;
instituição pública da mais alta qualidade, sinônimo de resistência em meio ao caos atual; me
orgulho de ser daqui e de ter alguns dos meus maiores sonhos sendo realizados aqui. Também
agradeço ao Programa de Pós-Graduação em Sistemática e Evolução, em especial ao Bruno
Bellini e a Gilmara, pela atenção e competência; e a Capes, pela bolsa de estudos, sem a qual
esse trabalho não seria possível.
Agradeço demais ao meu orientador Sergio, por ter me aceitado de braços abertos no
laboratório; pelo espaço, apoio e confiança na minha independência; espero poder carregar essa
parceria científica pro resto da vida.
Um obrigada muito especial à minha coorientadora Flávia, pela oportunidade (lá atrás)
de me deixar ajudar no doutorado e me apoiar no mestrado. Foi um grande privilégio ser sua
primeira orientanda; obrigada pela confiança, pela ajuda e por me lembrar de “respirar” nos
(incontáveis) momentos de surto. No mesmo balaio agradeço a parceria da sereia de água doce
do Nerds, Yasmin, por ser a fofa que é, minha parceira de forró e cachaça. Vocês duas são
exemplos inspiradores do que é ser cientista, embora, às vezes, talvez nem percebam que são.
Eu tenho muita sorte em chamá-las amigas.
A toda a galera do LISE e do GEEFAA – Thais, Germano, Valéria, Sávio, Luciano,
Carol, Aninha, Matheus Arthur, Salu, Lai, Geni, Ori, Diego, Lucas, Roney, demais
componentes e agregados do laboratório mais zuero do DBZ. Vocês são profissionais em fazer
qualquer um rir, em quebrar qualquer clima que ouse ficar sério; assim fica fácil (ou impossível)
trabalhar. Um obrigada especial a Sávio e Salu, pela ajuda estatística e com os mapas desse
trabalho. Vocês (todos) são massa demais!
A alguns amigos de vida que fizeram muita diferença nesses dois anos, em especial
Mari, Higo e Viktor; às esposas que a Austrália me deu, Marina e Luiza; and Jamie, for being
‘so close, no matter how far; it couldn’t be much more from the heart’. Meus amores, a cabeça
não teria aguentado sem a leveza, a amizade e o amor de vocês.
Um grandíssimo obrigada ao Filipe Serrano (Giro), pela ajuda na discussão dos
resultados, mas também pelas músicas e os papos sincronizados.
Aos queridos com os quais dividi moradia nesse período de perrengues e cheio de
mudanças, em especial a Ellen, amiga-irmã de longa data. E o Camurugi, por ser o melhor
parceiro de casa que eu poderia ter; obrigada demais pelas conversas regadas a muito café (e
cerveja) e pelo carinho de sempre; “um brinde à porr* da amizade”.
Por fim, sou muito grata à minha família: meu pai, minha mãe, minhas duas irmãs e
Nina; obrigada pela liberdade, pela educação de base, pela confiança e pela força. Dedico esses
anos todos de estudo, e os próximos que virão, a vocês, meus maiores exemplos de perseverança
e amor.
Não sei se um dia conseguirei retribuir o apoio que recebi ao longo dessa jornada.
Obrigada demais a todos que cruzaram meu caminho, que me ajudaram nesse período e a tantos
outros que me permitiram ajudar de algum modo. Foram dois anos de intenso aprendizado e da
certeza de que ainda há muito a ser aprendido.
I was hoping we’d make real progress
But it seems we have lost the power
Any tiny step of advancement
Is like a raindrop falling into the ocean
We’re running on the spot; always have, always will?
We’re just the next generation of the emotionally crippled
Though we keep piling up the building blocks
The structure never seems to get any higher
Because we keep kicking out the foundations
And stand useless while our lives fall down
I believe in life and I believe in love
But the world in which I live in keeps trying to prove wrong
Out the pastures we call society
You can’t see further than the bottom of your glass
Only you but easily shocked
You get all violent when the boat gets rocked
Just like sheep little lambs into the slaughter
Don't fully grasp what exactly is wrong
Truth is you never cared still
You get all violent when the boat gets rocked
Intelligence should be our first weapon
And stop reveling in rejection
Follow yourselves, not some ageing drain brain
Whose quite content to go on feeding your garbage
We’re running on the spot; always have, always will?
We’re just the next generation of the emotionally crippled
Running on the spot Paul Weller
Paralamas do Sucesso
RESUMO
O nicho abiótico de espécies conta parte de sua história ecológica e evolutiva, bem como seu
estudo pode ajudar a identificar grupos mais susceptíveis à extinção em um contexto de rápidas
mudanças climáticas. Espécies marinhas de ambientes temperados estão entre as mais
vulneráveis, pois o estresse térmico e demais impactos em cascata do aquecimento global
podem resultar em perda de habitat e deslocamento de distribuição geográfica para maiores
latitudes. A tribo Riorajini é composta por quatro espécies de raias marinhas – classificadas
pela IUCN como vulneráveis ou em perigo de extinção – que coocorrem no sudoeste
Neotropical do Atlântico: Atlantoraja castelnaui, A. cyclophora, A. platana e Rioraja agassizii.
A presente dissertação, dividida em dois capítulos, usa esse agrupamento como modelo de
estudos ecológico-evolutivos. No primeiro capítulo, questiona-se o conservatismo filogenético
de nicho para um clado de espécies potencialmente competidoras em simpatria. Tratando-se de
espécies filogeneticamente próximas, espera-se que uma baixa sobreposição de nicho reduza
competição interespecífica. No segundo capítulo, estimaram-se os impactos das mudanças
climáticas sobre a atual distribuição geográfica da tribo Riorajini. Primeiro, reconstruiu-se a
filogenia desse grupo. Posteriormente, modelos de nicho ecológico para cada espécie do grupo
foram desenvolvidos sob condições geofísicas e climáticas atuais e futuras (2100, sob cenário
climático extremo) do ambiente marinho. Dados ambientais e de ocorrência das espécies foram
compilados de bancos de dados públicos e literatura. Análises de sobreposição e deslocamento
de nicho foram conduzidas a níveis inter- e intraespecíficos. Os resultados indicam
conservatismo filogenético de nicho no qual águas rasas, proximidade da costa e baixa
concentração de nitrato são as variáveis mais importantes para a ocorrência das espécies. Em
um cenário climático futuro projetado, as áreas de maior adequabilidade ambiental à ocorrência
de cada espécie analisada aumentam em até 20% em direção a áreas de maior profundidade,
sugerindo que esse clado resistirá ao estresse térmico decorrente do aquecimento global. Apesar
disso, estudos futuros devem considerar efeitos combinados do aumento da temperatura a
aspectos biológicos desses animais, como o tempo de eclosão das cápsulas ovígeras e o
desenvolvimento dos juvenis, bem como o impacto a outros fatores potencialmente
determinantes à coexistência dessas espécies, como a disponibilidade de presas.
Palavras-chave: Conservatismo filogenético de nicho; modelagem de nicho ecológico;
mudanças climáticas; simpatria; sobreposição de nicho.
ABSTRACT
The abiotic niche of species tells part of their ecological and evolutionary history, as well as
helps to identify groups that are more susceptible to extinction in a context of a rapidly changing
climate. Marine species from temperate regions are among the most vulnerable taxa because
habitat loss as a consequence of thermal stress and other cascading impacts can constrain the
availability of suitable area of occurrence, or result in distribution shift towards higher latitudes.
The tribe Riorajini comprises four species of neotropical skates that are evaluated by IUCN as
vulnerable or endangered, and cooccur in the subtropical Atlantic Ocean: Atlantoraja
castelnaui, A. cyclophora, A. platana and Rioraja agassizii. The present dissertation is divided
into two chapters and uses this group as a model for eco-evolutionary studies. In the first
chapter, phylogenetic niche conservatism is questioned for a clade of sympatric and competitive
sister-species. Low niche overlap was expected to reduce interspecific competition between
closely-related species. The second chapter assessed the impacts of climate change on the
current geographical distribution of the tribe Riorajini. First, the phylogeny of the tribe was
reconstructed. Then, ecological niche models for each species of the group were developed
under current and future (2100, for the most extreme scenario) geophysical and climatic
conditions of the marine environment. Environmental data and species occurrence data were
compiled from public databases and literature. Niche shift and overlap were measured within
and between species. Results indicate phylogenetic niche conservatism in which shallow
waters, proximity to the coast, and low nitrate concentration are the most important variables
for the occurrence of these species. Under the future climatic scenario projected, the areas of
higher environmental suitability for the occurrence of each species analysed increases up to
20% towards deeper areas, suggesting that this clade will resist the thermal stress resulting from
global warming. Nevertheless, future studies should consider the combined effects of an
increase in temperature in the time of hatching of egg-capsules and the early development of
juveniles, as well as the impact of other factors potentially determining the coexistence of these
species, such as prey availability.
Key-words: Phylogenetic niche conservatism; ecological niche modelling; climate change;
sympatry; niche overlap.
Sumário
RESUMO ......................................................................................................................................
ABSTRACT ....................................................................................................................................
CAPÍTULO 1 – LISTA DE FIGURAS ....................................................................................................
CAPÍTULO 1 – LISTA DE TABELAS .....................................................................................................
CAPÍTULO 2 – LISTA DE FIGURAS ....................................................................................................
CAPÍTULO 2 – LISTA DE TABELAS .....................................................................................................
INTRODUÇÃO GERAL ......................................................................................................... 14
Dividindo o ambiente .............................................................................................................. 14
Grupo de estudo ..................................................................................................................... 14
O futuro não demora – o problema do aquecimento global .................................................. 15
OBJETIVOS ........................................................................................................................ 17
Geral ........................................................................................................................................ 18
Específicos ............................................................................................................................... 18
CAPÍTULO 1: Phylogenetic conservatism of abiotic niche in neotropical skates .................... 19
ABSTRACT ................................................................................................................................ 19
INTRODUCTION ....................................................................................................................... 20
MATERIALS AND METHODS .................................................................................................... 23
Phylogenetic analysis .......................................................................................................... 23
Ecological Niche Models (ENMs) ......................................................................................... 24
Comparing niches – testing niche conservatism ................................................................. 26
RESULTS ................................................................................................................................... 27
DISCUSSION ............................................................................................................................. 33
Evolutionary perspective ..................................................................................................... 33
Environmental drivers of occurrence .................................................................................. 35
FINAL CONSIDERATIONS ......................................................................................................... 38
REFERENCES ............................................................................................................................ 39
CAPÍTULO 2: Temperate skates’ shift ranges as an outcome of global warming ................... 47
ABSTRACT ................................................................................................................................ 47
INTRODUCTION ....................................................................................................................... 48
MATERIALS AND METHODS .................................................................................................... 50
Models of present and future climatic scenarios ................................................................ 50
Statistical Analysis – Measuring differences ....................................................................... 51
RESULTS ................................................................................................................................... 51
DISCUSSION ............................................................................................................................. 58
Minding the caveats ............................................................................................................ 58
What explains the modelled increase in environmental suitability? .................................. 58
Beyond distribution ............................................................................................................. 59
CONCLUSION AND FUTURE PERSPECTIVES ............................................................................. 61
REFERENCES ............................................................................................................................ 62
CONCLUSÃO GERAL ................................................................................................................. 68
REFERÊNCIAS BIBLIOGRÁFICAS ............................................................................................... 70
CAPÍTULO 1 – LISTA DE FIGURAS
Figure 1: Phylogeny of Riorajini based on a Bayesian inference using gene NADH
dehydrogenase 2 (ND2), including Sympterygia acuta as outgroup. Node values as posterior
probabilities. Photos of A. cyclophora and A. castelnaui by Bianca de Sousa Rangel ©. Photo
of A. platana by Pablo D. Meneses. Photo of R. agassizii by Itamar A. Martins. Photo of S.
acuta by Marcelo Vianna. .............................................................................................. 27
Figure 2: Occurrence records of preserved specimens used for the ecological niche models of
Atlantoraja castelnaui, A. cyclophora, A. platana, and Rioraja agassizii along the marine
provinces (Spalding et al., 2007) in Southwestern Atlantic Ocean. ............................... 28
Figure 3: Ecological niche models of Atlantoraja castelnaui (A), A. cyclophora (B), A. platana
(C), and Rioraja agassizii (D). ....................................................................................... 30
Figure 4: Principal Component Analysis (PCA) illustrating the influence of environmental
variables for Atlantoraja castelnaui (Acas), Atlantoraja cyclophora (Acyc), Atlantoraja
platana (Apla), and Rioraja agassizii (Raga). Largest circles are the centroids of distribution of
the scattered points. Contrib: contribution of environmental variables (vectors), with darker
shades indicating stronger contribution. ......................................................................... 31
Figure 5: PCA-env results of niche overlap and niche similarity tests for each pair of Riorajini
species. The gridded niche of the first species in a pair is green; the second, in red; overlap
between the two niches is in blue. Arrows point to the direction of shift for the centroids of
distribution. Acas: Atlantoraja castelnaui; Acyc: Atlantoraja cyclophora; Apla: Atlantoraja
platana; and Raga: Rioraja agassizii. ............................................................................ 32
Figure 6: Scheme illustrating phylogenetic relationship in Riorajini and the main abiotic niche
features shared. ............................................................................................................... 34
Figure 7: Nitrate and phosphate concentrations along the coast of South America. Images
retrieved from Bio-ORACLE (available at: bio-oracle.org). Black arrows point to San Matías
Gulf in Argentina, where nitrate and phosphate concentrations are higher than surrounding
areas. ............................................................................................................................... 37
CAPÍTULO 1 – LISTA DE TABELAS
Table 1: List of environmental variables selected after Pearson’s Correlation Test (|r| ≥ 0.8) for
running ecological niche models of Atlantoraja castelnaui, A. cyclophora, A. platana, and
Rioraja agassizii. ............................................................................................................ 29
Table 2: Summary of the best combination (ΔAICc = 0) of parameters established by ENMeval
package (Muscarella et al., 2014), and performance evaluation. n – number of occurrence points
in the dataset; FC – Feature Classes allowed in the model (L – linear; Q – quadratic; H – hinge);
RM – Regularization multiplier; AUC – Area Under ROC curve; sd – AUC standard deviation.
........................................................................................................................................ 29
Table 3: Permutation importance (%) per variable (lines) per species (columns). Acas:
Atlantoraja castelnaui; Acyc: A. cyclophora; Apla: A. platana; Raga: Rioraja agassizii. Bold
highlights the variables of higher contribution (Σ > 70%) to the models of each species.31
CAPÍTULO 2 – LISTA DE FIGURAS
Figure 1: Ecological niche models of present (left) and future (right) climatic scenarios,
showing degrees of environmental suitability for the occurrence of Atlantoraja castelnaui
(Acas). ............................................................................................................................ 54
Figure 2: Niche dynamics of Atlantoraja castelnaui. Red arrow in the bottom left graph
indicates direction of shift of the distribution’ centroid between the two climatic scenarios.
Green: present climatic conditions; red: projected future climatic scenario (2100, RCP 8.5);
purple: overlap between scenarios. ................................................................................. 54
Figure 3: Ecological niche models of present (left) and future (right) climatic scenarios,
showing degrees of environmental suitability for the occurrence of Atlantoraja cyclophora
(Acyc). ............................................................................................................................ 55
Figure 4: Niche dynamics of Atlantoraja cyclophora. Red arrow in the bottom left graph
indicates direction of shift of the distribution’ centroid between the two climatic scenarios.
Green: present climatic conditions; red: projected future climatic scenario (2100, RCP 8.5);
purple: overlap between scenarios. ................................................................................. 55
Figure 5: Ecological niche models of present (left) and future (right) climatic scenarios,
showing degrees of environmental suitability for the occurrence of Atlantoraja platana (Apla).
........................................................................................................................................ 56
Figure 6: Niche dynamics of Atlantoraja platana. Red arrow in the bottom left graph indicates
direction of shift of the distribution’ centroid between the two climatic scenarios. Green: present
climatic conditions; red: projected future climatic scenario (2100, RCP 8.5); purple: overlap
between scenarios. .......................................................................................................... 56
Figure 7: Ecological niche models of present (left) and future (right) climatic scenarios,
showing degrees of environmental suitability for the occurrence of Rioraja agassizii (Raga).
........................................................................................................................................ 57
Figure 8: Niche dynamics of Rioraja agassizii. Red arrow in the bottom left graph indicates
direction of shift of the distribution’ centroid between the two climatic scenarios. Green: present
climatic conditions; red: projected future climatic scenario (2100, RCP 8.5); purple: overlap
between scenarios. .......................................................................................................... 57
CAPÍTULO 2 – LISTA DE TABELAS
Table 1: Summary of the best combination (ΔAICc = 0) of parameters established by ENMeval
package (Muscarella et al., 2014) per species and climatic scenario: P – present; F – future. n:
number of occurrence points in the dataset; FC: Feature Classes allowed in the model (L –
linear; Q – quadratic; H – hinge); RM: Regularization Multiplier; AUC: Area Under ROC curve
per model; sd: standard deviation of AUC. .................................................................... 52
Table 2: Permutation importance (%) per variable per species for present (P) and future (F)
climatic scenarios. Acas – Atlantoraja castelnaui; Acyc – A. cyclophora; Apla – A. platana;
Raga – Rioraja agassizii. Bold highlights the variables of higher contribution (Σ > 80%) to
models. ............................................................................................................................ 53
Table 3: Minimum and maximum values of the three main variables to the ENMs of Riorajini
species in present (P, grey shaded) and future (F, white) climatic scenarios modelled. Acas:
Atlantoraja castelnaui; Acyc: A. cyclophora; Apla: A. platana; Raga: Rioraja agassizii.53
Table 4: Niche overlap, expansion, stability, and unfilling measured between present and future
climatic scenarios for each Riorajini' species. All values range from 0 (none) to 1 (identical).
Acas – Atlantoraja castelnaui; Acyc – A. cyclophora; Apla – A. platana; Raga – Rioraja
agassizii. ......................................................................................................................... 58
14
INTRODUÇÃO GERAL
Dividindo o ambiente
Desde os trabalhos clássicos de Darwin (1859), Wallace (1876) e Grinnell (1917)
naturalistas buscam compreender como uma alta biodiversidade compartilha o ambiente. O
princípio da exclusão competitiva, atribuído a Gause (1934), é uma maneira de explicar tal
padrão. Esse princípio estipula que, quando em simpatria, duas espécies competidoras
particionam os recursos do ambiente de modo que ambas possam coexistir sem tendência à
exclusão de uma por competição. Tal particionamento ocorre em algum nível do nicho das
espécies, que, de modo geral, pode ser biótico (trófico ou reprodutivo, por exemplo; também
chamado de nicho Eltoniano (Elton, 1927)) ou abiótico (ambiental; ou nicho Grinnelliano
(Grinnell, 1917)).
A tendência de linhagens em manter características de nicho no decorrer do tempo é
denominada conservatismo filogenético de nicho (Harvey & Pagel, 1991; Wiens et al., 2010).
Como uma possível consequência desse processo, espécies mais próximas filogeneticamente
tendem a compartilhar mais aspectos de nicho entre si do que o esperado ao acaso, reflexo de
sinal filogenético (Losos, 2008). Esse padrão, porém, é pouco provável entre espécies-irmãs
simpátricas, dado que uma alta sobreposição de nicho pode significar maior competição
interespecífica. Ainda assim, contrariando parte da lógica ecológica teórica, é possível
encontrar exemplos de espécies-irmãs ocorrendo em simpatria na natureza (Kocher, 2004). A
tribo de raias neotropicais marinhas Riorajini, grupo de estudo do presente trabalho, é um desses
exemplos.
Grupo de estudo
Riorajini (sensu McEachran & Dunn, 1998) é um clado formado por quatro espécies de
raias marinhas da família Arhynchobatidae (Chondricthyes: Rajiformes): Atlantoraja
castelnaui (Miranda Ribeiro, 1907), A. cyclophora (Regan, 1903), A. platana (Gunther, 1880),
e Rioraja agassizii (Müller & Henle, 1841). Atualmente, a avaliação global da União
Internacional pela Conservação da Natureza (UICN) classifica A. cyclophora, A. platana e R.
agassizii como ‘Vulneráveis’ (VU), e A. castelnaui como ‘Em perigo’ de extinção (EN)
(Hozbor et al., 2004; Massa et al., 2006; Kyne et al., 2007; San Martín et al., 2007). A
sobrepesca é um dos principais contribuintes ao alarmante status de ameaça dessas espécies,
que no Brasil são alvo da pesca, bem como são frequentemente capturadas acidentalmente (by-
15
catch) (Lessa et al., 1999). Decorrente desta pressão, uma estimativa para A. castelnaui aponta
para um declínio de 75% na biomassa dessa espécie entre 1994 e 1999 na Argentina e Uruguai
(Hozbor & Massa, dados não publicados; Hozbor et al., 2004).
Endêmicas da região subtropical do Oceano Atlântico, essas quatro espécies coocorrem
do litoral do sudeste brasileiro, a partir do Espírito Santo (20°S), ao litoral da Argentina (45°S)
(Figueiredo, 1977). Essa faixa latitudinal apresenta a maior concentração de elasmobrânquios
classificados sob algum nível de ameaça de extinção da região Neotropical (vulnerável,
ameaçada ou criticamente ameaçada – VU, EN e CR) (Field et al., 2009). Dados recentes da
literatura expandem a área de distribuição de algumas dessas espécies em até cinco graus ao sul
(Menni et al., 2010; Bovcon et al; 2011), indicando que os mapas de distribuição atualmente
disponíveis estão desatualizados (e.g. mapas da UICN e FishBase) – um típico déficit
Wallacean (incertezas acerca da distribuição geográfica de espécies) para o grupo (Hortal et
al., 2015). Mapas defasados limitam o entendimento de aspectos ecológicos desse grupo, como:
até onde esses táxons coexistem? Existem diferenças latitudinais e/ou longitudinais
significativas entre eles? Quais características ambientais limitam a distribuição geográfica de
uma espécie em relação à outra? Além disso, conhecer a distribuição geográfica de uma espécie
é importante para avaliar seu estado de conservação. Assim, atualizar os mapas de distribuição
dessas espécies com dados mais recentes da literatura é essencial para compreender não
somente implicações ecológicas desses padrões de distribuição, mas para planejar medidas
efetivas de conservação do grupo e propor estudos futuros.
O futuro não demora1 – o problema do aquecimento global
O ritmo acelerado de mudanças ambientais desafia a capacidade adaptativa dos
organismos. Alterações fenológicas, metabólicas e de distribuição geográfica de espécies já têm
sido atribuídas às mudanças climáticas (Edwards & Richardson, 2004; Pistevos et al., 2015).
No tocante às mudanças de distribuição geográfica, de modo geral, o padrão observado é um
deslocamento para altas latitudes e maior elevação de altitude para táxons terrestres (Hickling
et al., 2006; Chen et al., 2011). No ambiente aquático, além do latitudinal, há o deslocamento
batimétrico, em direção a áreas de maior profundidade (Perry et al., 2005; Nicolas et al., 2011).
Em ambos os ambientes, as mudanças latitudinais ocorrem em direção oposta à linha do
Equador, região de maior incidência solar do planeta e, consequentemente, mais exposta aos
efeitos do estresse térmico imediato do aquecimento global.
1 Álbum da banda BaianaSystem (2019).
16
O ambiente marinho provê bens e serviços cruciais à manutenção da vida na Terra. Mais
da metade do oxigênio do planeta é produzido nos oceanos, bem como a maior parte do dióxido
de carbono – um dos gases estufa mais abundantes – é absorvido por sistemas marinhos (Field
et al., 1998; Falkowski, 2012). Além disso, correntes marítimas transportam calor do Equador
aos polos, regulando padrões climáticos (Chahine, 1992). Em escala local, zonas costeiras são
uma área econômica importante, contribuindo com quase 80% do valor dos serviços
ecossistêmicos globais (Costanza et al., 1997), os quais incluem armazenamento e ciclagem de
nutrientes, disponibilidade de água e comida (Martínez et al., 2007). Por este motivo,
certamente, há alta densidade populacional humana nas zonas costeiras, embora essas regiões
estejam também mais vulneráveis a desastres naturais (Nicholls & Small, 2002). Portanto, não
somente o bom funcionamento de ecossistemas terrestres, mas também o estilo de vida humano
atual, dependem intimamente de oceanos saudáveis.
O problema
Algumas características biológicas e ecológicas tornam algumas espécies mais
susceptíveis às consequências negativas do aquecimento global. Por exemplo, para raias da
família Arhynchobatidae, o hábito bentônico e a filopatria tornam esse grupo particularmente
vulnerável às mudanças climáticas se comparado a outros elasmobrânquios de hábitos pelágicos
(Dulvy & Reynolds, 2002). Isso acontece porque uma área de ocorrência restrita e dependência
de habitats específicos limitam a capacidade dispersiva das espécies do grupo (Di Santo, 2015).
Ademais, crescimento lento e maturidade sexual tardia, além da deposição de cápsulas ovígeras
sésseis, também podem limitar a capacidade dispersiva e adaptativa do grupo frente às rápidas
mudanças climáticas, bem como dificultar a reposição de indivíduos em uma população
(population replenishment), o que aumenta a vulnerabilidade das espécies aos impactos da
sobrepesca, por exemplo (Stevens et al., 2000; Iglésias et al., 2009).
As diferenças de nicho influenciando a simpatria desse clado ainda são pouco
exploradas. Tratando-se de uma tribo de espécies potencialmente competidoras simpátricas, de
dieta generalista e reprodução anual (Barbini & Lucifora, 2011; 2012; 2016; Viana & Vianna,
2014; Viana et al., 2017), o primeiro capítulo dessa dissertação testa a hipótese de que espécies
mais aparentadas filogeneticamente apresentarão nichos mais dissimilares. Ou seja, espera-se
que diferenças de nicho abiótico, potencialmente refletidas em diferenças ecológicas, explicam
como tais espécies coexistem temporal e espacialmente. O segundo capítulo usa cenários
climáticos projetados (até 2100) para testar a hipótese de que, em decorrência das mudanças
climáticas, haverá diminuição e/ou deslocamento ao sul das áreas de maior adequabilidade
17
ambiental para distribuição das espécies de raias da tribo Riorajini. Um contexto de mudanças
climáticas globais destaca a importância de avaliar diferenças de nicho ecológico entre espécies,
pois tais características podem indicar grupos mais tolerantes ou vulneráveis à mudança termal,
auxiliando a tomada de decisões relativas aos planos de manejo e conservação (Gallagher et al.,
2012).
Resumo gráfico dos dados da literatura sobre tamanho corpóreo máximo, amplitude batimétrica
de ocorrência e principais itens de dieta das quatro espécies da tribo Riorajini (do topo a baixo):
Rioraja agassizii (vermelho), Atlantoraja castelnaui (azul), A. cyclophora (verde) e A. platana
(amarelo).
18
OBJETIVOS
Geral
Os objetivos principais desta dissertação são (i) identificar o padrão de particionamento
de nicho abiótico da tribo Riorajini: Rioraja agassizii (Müller & Henle, 1841), Atlantoraja
platana (Günther, 1880), A. cyclophora (Regan, 1903) e A. castelnaui (Miranda Ribeiro, 1907),
raias neotropicais simpátricas, endêmicas do Atlântico subtropical ocidental (Capítulo 1), e (ii)
estimar os impactos das mudanças climáticas sobre a atual distribuição geográfica dessas
espécies (Capítulo 2).
Específicos
• Atualizar os mapas de distribuição geográfica das quatro espécies da tribo Riorajini,
indicando as províncias biogeográficas segundo Spalding et al. (2007) nas quais cada
uma ocorre;
• Modelar o nicho ecológico dessas espécies sob o cenário climático atual e futuro (2100)
de maior concentração de gases estufa (Representative Concentration Pathway – RCP
8.5);
• Identificar as variáveis abióticas de maior influência ao nicho ecológico atual de cada
uma dessas espécies;
• Relacionar o grau de sobreposição de nicho ecológico par-a-par entre essas espécies ao
grau de parentesco (proximidade filogenética);
• Medir as diferenças entre os modelos de nicho ecológico atual e futuro para cada espécie
da tribo Riorajini, identificando as espécies mais e menos vulneráveis quanto à potencial
disponibilidade de habitat em um cenário climático futuro.
19
CAPÍTULO 1: Phylogenetic conservatism of abiotic niche in neotropical skates
Jéssica Fernanda Ramos Coelho¹, Sergio Maia Queiroz Lima¹, Flávia de Figueiredo Petean¹
1Departamento de Botânica e Zoologia, Centro de Biociências, Universidade Federal do Rio
Grande do Norte, Campus Universitário, BR 101 s/n, 59078-900, Lagoa Nova, Natal, RN,
Brazil.
E-mail: [email protected]
ABSTRACT
From the perspective of phylogenetic niche conservatism (PNC), we expect that closely related
species share more aspects of niche among them than expected randomly. However,
considering the competitive exclusion principle, PNC is questionable for closely related species
occurring in sympatry. The present research aims to test niche conservatism in Riorajini, a tribe
of four Neotropical sympatric skates endemic to the subtropical western Atlantic Ocean:
Atlantoraja castelnaui, A. cyclophora, A. platana and Rioraja agassizii. We hypothesized that
an abiotic niche differentiation supports the coexistence of this clade, questioning niche
conservation in a sympatric clade of potentially competitive species. We used R as an interface
to conduct Ecological Niche Models (ENMs) to map the set of conditions that characterize the
abiotic niche for each species under current marine geophysical and climatic conditions. We
compiled presence records for each species from public online databases and literature, and
nine uncorrelated (Pearson Correlation Test < 0.8) environmental variables from MARSPEC
and Bio-ORACLE databases considering biological and ecological relevance for the group. We
calculated niche overlap, equivalency, and similarity using a variation of a principal component
analysis (PCA-env) for all pairwise combination of Riorajini’ species. Results indicate niche
conservatism in this tribe, suggesting that a differentiation in an aspect of niche, other than the
abiotic niche, allows the coexistence of these species.
Key-words: Competitive Exclusion principle; Ecological Niche Model; Grinnellian Niche.
20
INTRODUCTION
The term “niche” can assume multiple meanings. Grinnell (1917) was the first to denote
the idea of niche when referring to the set of abiotic/climatic conditions in a species habitat
allowing survival and reproduction. Later, Elton (1927) and Gause (1934) introduced the
functional role of organisms and other biotic interactions to the niche concept, such as
competition. A further thorough concept presented by Hutchinson (1957) refers to niche as an
n-dimensional hypervolume of characteristics required for a species to exist in an area, called
fundamental niche. Where the species in fact occur, however, must consider biotic interactions
and other limiting factors (e.g.: the ability to reach an area), called realized niche (Soberón &
Nakamura, 2009). Although the realized niche is logically smaller than the fundamental niche,
mathematical proof was only recently presented (Soberón & Arroyo-Peña, 2017).
Phylogenetic niche conservatism is a lineage’s likelihood to maintain ancestral niche
features through time (Harvey & Pagel, 1991). Since this first definition, numerous studies
exploring this idea have been published typically testing whether species closer in a phylogeny
share more aspects of niche than expected randomly (Prinzing et al., 2001; Ahmadzadeh et al.,
2013; Peixoto et al., 2017). Pyron et al. (2015) argued phylogenetic niche conservatism as a
process from which three patterns of niche may arise: niche conservation, niche divergence,
and niche constrain. The first is intuitive. The second, although contradictory at first glance,
states that niches are considered divergent when they are less similar than expected given
phylogenetic proximity of lineages (Pyron et al., 2015). When such pattern is attributed to
ecological speciation (Wiens, 2004; Gorel et al., 2019), then selective forces other than stasis
in current niche are acting, and phylogenetic niche conservatism does not occur (Pyron et al.,
2014). Finally, niches of species are considered constrained when they vary within a limited
subset of the niches available in the environment (Pyron et al., 2015).
Understanding species abiotic preferences is an important aspect to comprehend why
these occur in some areas and not in others, despite geographic closeness. Besides,
characterizing the abiotic niche (also called Grinnellian niche) of species is of paramount
importance to understand physiological aspects and tolerances, as well as to predict the role of
a lineage in an ecosystem (Dumbrell et al., 2010). For example, higher biomasses of
phytoplankton are present in areas of high concentration of chlorophyll-a and nutrients (Panda
et al., 2012), which reflects the photosynthetic role of this group in the ecosystem. Such
characterizations are important to understand evolutionary dynamics, interactions among
21
groups of organisms, and the impacts of environmental changes on species (Harmon et al.,
2009; Rinnan & Lawler, 2019).
Competitive species in sympatry must agree on a co-occurrence strategy. Even though
usually attributed to Gause (1934), the competitive exclusion principle has its roots in works as
early as Grinnell (1917) and Darwin’s (1859) (Kneitel, 2008). This principle states that two
species cannot coexist if they occupy the exact same niche. Therefore, without a tendency to
competitive exclusion as a consequence of high niche overlap, we expect niche conservatism
to be unlikely between sympatric sister-species (Pigot & Tobias, 2012; Scriven et al., 2016). In
other words, between two closely related species coexisting in space and time, niche divergence
is probable to be the rule for the stable occurrence of both lineages. Yet, contrary to theoretical
ecological expectation, sister-species co-occur in nature (Kocher, 2004). The tribe of
neotropical skates Riorajini is one of these examples (Last et al., 2016).
The tribe Riorajini (sensu McEachran & Dunn, 1998) is a clade of four skates: Rioraja
agassizii (Muller & Henle, 1841), Atlantoraja platana (Günther, 1880), A. cyclophora (Regan,
1903), and A. castelnaui (Miranda Ribeiro, 1907). Originally, Menni (1972) described
Atlantoraja as a subgenus in Raja Linnaeus, 1768 based on the shape of the dorsal terminal 1
cartilage. Later, McEachran and Dunn (1998) elevated Atlantoraja and Rioraja to the genus
level based on morphological characteristics. Using sequences of NADH dehydrogenase
subunit 2 (ND2), a mitochondrial gene, Naylor et al. (2012) presented a distance analysis
depicting the genetic similarity of the tribe as R. agassizii(A. castelnaui (A. platana A.
cyclophora))2. However, no phylogenetic analysis has been conducted to the moment, which
limits evolutionary discussions on the group.
As other elasmobranchs, skates are oviparous and egg-laying occurs all year (Oddone
& Vooren, 2005; Oddone et al., 2007; Oddone & Capapé, 2011), however these are species of
slow growth, slow metabolism, late maturity age, and high investment of energy in offspring
(Stevens et al., 2000; Helfman et al., 2009). The latter particularly increases new-borns survival
rate. The downside of slow growth and late maturation is a decrease in overall population
resilience, making species vulnerable to immediate anthropogenic impacts, such as commercial
exploitation (Shepherd & Myers, 2005; Helfman et al., 2009). Additionally, skates are the most
diverse group within batoids, yet presents highly conserved morphological and ecological
characters (Ebert & Compagno, 2007; Ball et al., 2016). Some morphological characters in
2 Newick format.
22
Riorajini, such as reduction of rostral cartilage and extension of pectoral radials – the former
considered a paedomorphism – reflects adaptation to benthic habitats (McEachran & Dunn,
1998).
Despite differences in mean body size and ontogenetic diet shift, Riorajini species
converge to the consumption of similar prey items, mostly crustaceans (amphipods, shrimps,
brachyurans), teleosts, and to a lesser extent, A. castelnaui also feeds on cephalopods and other
elasmobranchs (Paesch, 2000; Viana & Vianna, 2014; Barbini & Lucifora, 2011; 2012; 2016;
Viana et al., 2017). Changes in diet have been noticed seasonally, although this is more likely
to be a consequence of prey availability and behaviour rather than a change in preferences by
these skates (Barbini & Lucifora, 2012). High dietary overlap between these species suggests
they compete for prey.
Abiotic conditions rapidly change with increasing depth of seafloor, influencing
community composition and population dynamics along the environmental gradient (Smith &
Brown, 2002). Temperature, salinity, and bathymetry likewise affect elasmobranchs’
distribution, and shifts from a species’ optimum set of environmental conditions can impact
behaviour, physiology, and metabolic functioning (Green & Jutfelt, 2014; Pistevos et al., 2015).
Understanding species-specific requirements of environmental conditions tells part of the
evolutionary’ history of a group, as well as helps identifying taxa more vulnerable to extinction
in face of climatic changes. The influence of the environmental heterogeneity on Riorajini’
distribution, however, remains poorly explored.
The southwestern portion of the Atlantic Ocean (SWA) hosts the highest number of
threatened chondrichthyan species in the Neotropical region (Field et al., 2009). Due to its high
richness, endemism, and number of threatened species, Stein et al. (2018) classified the SWA
as a priority area for conservation of Chondrichthyes. The four Riorajini species are endemic
to this area and present occurrence records, as for the IUCN maps, from Espírito Santo in Brazil,
to Patagonia in Argentina (Hozbor et al., 2004; Massa et al., 2006; Kyne et al., 2007; San
Martín et al., 2007; Moreira et al., 2017), although some of this maps do not consider data from
more recent literature (e.g. Bovcon et al., 2011) and are, therefore, outdated. Such obsolete
maps can over- or underestimate the area of occurrence of these species, making it more
difficult to conduct management. Besides, failing to include new data of species occurrence
into maps of distribution limits our ability to visualize the degree of sympatric occurrence in
this tribe, and, consequently, to understand the dynamics of the coastal community they occupy.
23
Anthropogenic activities are one of the main sources of disturbance to the dynamics of
coastal communities. For example, overfishing has led skates to local extinction, such as
Dipturus batis in the Irish sea (Brander, 1981). This is of particular concern given that as a
consequence of a high fishing pressure, all Riorajini species are threatened with extinction,
according to the classification of the International Union for Conservation of Nature (IUCN):
Atlantoraja cyclophora, A. platana, and Rioraja agassizii are classified as vulnerable (Massa
et al., 2006; Kyne et al., 2007; San Martín et al., 2007), and A. castelnaui as endangered
(Hozbor et al., 2004). This, combined with a limited geographic distribution and life-history
traits previously exposed, makes skates one of the most vulnerable taxa of all marine species
(Stevens et al., 2000; Dulvy et al., 2014). The problem of outdated maps of geographic
distribution also jeopardize identification of conservation statuses. Thus, one objective of the
present research is to incorporate data available in the literature to provide updated maps of
geographic distribution for these species, which can aid future evaluation of their degree of
threat.
Biological characteristics suggest Riorajini species explore resources similarly, thus
they are likely to play similar ecologic roles in the environment (Rosenfeld et al., 2002). These
same characteristics, however, combined with sympatry in a limited geographic range raises
the question on which aspect of their niche allows co-occurrence. We hypothesize species-
specific responses to environmental factors. In other words, abiotic niche differentiation might
play an important role in species sympatry, guiding species to different strata in the environment
(Scriven et al., 2016). We first built the phylogeny of the tribe to then test niche conservatism,
as the former is a must to understand and discuss results. For the purposes of the present
research, we consider the Grinnellian niche concept, which focus on abiotic and climatic
conditions necessary for a species to survive (Soberón, 2007).
MATERIALS AND METHODS
Phylogenetic analysis
We used sequences of NADH dehydrogenase 2 (ND2) available on GenBank to infer
the phylogenetic relationships in Riorajini. ND2 is a mitochondrial gene considered barcode for
chondrichthyans for its bigger length and faster evolution rates (more variation) in comparison
with cytochrome oxidase 1 (CO1), commonly used in other taxa (Moore et al., 2011; Naylor et
al., 2012). We chose Sympterygia acuta Garman, 1877 as an outgroup for the phylogenetic
24
analysis, a skate of the same family as the tribe Riorajini occurring in sympatry with these
species, for which ND2 sequence is also available on GenBank (Massa et al., 2004; Naylor et
al., 2012). Sequences were retrieved under the following accession numbers: A. castelnaui:
JQ519082.1; A. cyclophora: JQ519084.1; A. platana: JQ519083.1; R. agassizii: JQ519080.1;
and S. acuta: JQ519081.1.
We used Mega version 7.0.26 (Tamura et al., 2007) to align the five sequences using
the ClustalW method (Larkin et al., 2007). The same software was used to select the molecular
evolution model under the Bayesian Inference Criteria (BIC), which indicated Hasegawa-
Kishino-Yano+Gamma (HKY+G) as the best model. To infer phylogenetic relationship under
a Bayesian analysis, we used BEAST version 1.10.4 (Suchard et al., 2018), set the molecular
clock to a relaxed log normal distribution, and ran 107 generations sampled every 1000, with a
burn-in of 10%.
Ecological Niche Models (ENMs)
The R program version 3.5.1 (R Core Team, 2018) was used as an interface to perform
a machine-learning algorithm of maximum entropy (maxent) models. ENMs were conducted
using species’ records of occurrence (presence) and data characterizing the environment it
occupies, following a correlative approach (Pearson, 2007).
Following Muscarella et al. (2014), we tested six combinations of maxent’s feature
classes (FC): L, H, LQ, LQH, LQHP, LQHPT (L: linear; H: hinge; Q: quadratic; P: product; T:
threshold). Feature classes represent raw or modified values of environmental variables. For
each FC combination, we tested eight values of regularization multiplier (varying from 0.5 to
4.0, with a 0.5 increment). Regularization multiplier (RM or β) decreases overfitting of models
(Merow et al., 2013). ENMeval package was used to choose the best combination of parameters
(FC and RM) per model (Muscarella et al., 2014); the combination to generate the most
parsimonious model (deltaAICc = 0) was considered the best. Models’ training and testing
points were partitioned applying the ‘block’ method. Models were run with a 10-5 convergence
threshold, 10,000 maximum iterations, and 10,000 maximum background points. Each model
is a mean of 15 bootstrap replicates. Maps were edited with QGIS 2.8.9 software (QGIS
Development Team, 2019).
Occurrence records
25
Occurrence data for each species derived from online databases, such as Global
Biodiversity Information Facility (GBIF, 2019; http://gbif.org), speciesLink (CRIA, 2019;
http://splink.cria.org.br/) and FishNet2 (http://www.fishnet2.net/) (full set of compiled records:
Supplementary Material – Table 1). We conducted an exploratory analysis to remove discrepant
values (outliers) using vegan package version 2.5.2 in R (Oksanen et al., 2013). In an attempt
to increase data accuracy, only georeferenced preserved specimens in each species’ known
occurrence area were accounted (Brazilian, Uruguayan and Argentinean coasts; coordinates
from other regions – e.g. one specimen at coast of Panama – were considered misidentifications
and therefore eliminated from the analysis). Remarkable morphological differences between
the species of this group – evidenced by well-defined species diagnoses, as well as notably
different patterns of dorsal coloration (e.g. Figueiredo, 1977; Gomes et al., 2010) – aggregates
trustworthiness to the identification of the specimens used as reference in this research.
Duplicates and redundant points (i.e.: points in the same grid cell) were removed to
increase data uniformity of distribution and avoid spatial autocorrelation (Shcheglovitova &
Anderson, 2013). Additionally, we used spThin package version 0.1 in R to return the best
dataset of occurrence records per species (Aiello-Lammens et al., 2015). These procedures
avoid biasing the model towards areas of easier access and higher sampling effort by removing
aggregations of one species’ occurrence records. These data per species were then plotted in
the marine biogeographic provinces as in Spalding et al. (2007).
Environmental data
The environmental layers used in ENMs are variables, also called predictors, that
characterize the abiotic conditions of the region to be modelled. Each layer is a raster file
derived from satellite data. Bio-ORACLE (Tyberghein et al., 2012; Assis et al., 2017) and
MARSPEC (Sbrocco & Barber, 2013) offer high resolution (5-arc-min and 30-arc-sec,
respectively) environmental layers for the present climatic and geophysical marine conditions.
There is no consensual guideline regarding the ideal number of predictors for ENMs. However,
the selection of environmental layers must consider aspects of the species’ biology and ecology
(Fourcade et al., 2017), and the question to be answered (Merow et al., 2013). Besides, from a
model-performance point-of-view, the selection of predictors must be conducted in a way to
avoid model overfitting and multicollinearity – which can happen when the number of
predictors is much higher than the number of occurrence points in a dataset (Parolo et al., 2008)
or when variables are correlated (Warren et al., 2014), respectively. Thus, a Pearson correlation
26
test was performed with 36 layers available (18 from MARSPEC and 18 from Bio-ORACLE)
for current geo-climatic conditions to remove highly correlated layers (|r| ≥ 0.8).
Even without strong correlation with remaining variables, layers with immediate
appearance of no relevance for the clade (e.g. plan curvature) were manually removed. The
removal of one from a pair of highly correlated variables considered ecological and biological
knowledge of the clade. Likewise, chosen environmental layers correspond to benthic
maximum depth. Before running the models, predictors were scaled to equal dimension and
resolution (0.833°, ~9 km). We used boxplots to visualize and remove occurrence records
outliers for each environmental variable selected after the Pearson correlation test.
Comparing niches – testing niche conservatism
We conducted a principal component analysis (PCA) to reduce dataset dimensions
(Jollife & Cadima, 2016), and visualize the degree of divergence between centroids of
distribution of each species and the set of environmental layers selected. The first principal
components explaining more than 70% of the proportion of variance (PV) of the data were kept
in the analysis (Zuur et al., 2010). Eigenvectors showing |PV| ≥ 0.4 in at least one principal
component were kept. Data homoscedasticity was confirmed using biotools package version
3.1 in R (da Silva et al., 2017), thus we used a permutational multivariate analysis of variance
(PERMANOVA) to test multivariate significance of niche overlap.
The Schoener’s D index was calculated to measure the degree of niche overlap between
models of pairs of species (Warren et al., 2008). Schoener’s D values vary from 0 (no overlap)
to 1 (identical models). Then, following Boennimann’s et al. (2012) framework, we conducted
a variation of a principal component analysis (PCA-env) to compare niches of Riorajini’
species. This approach allows to test niche equivalency and niche similarity between pairs of
species. The first, tests the null hypothesis of niche equivalency (the two niches are
identical/equivalent) by comparing the true equivalency calculated to a null distribution of niche
equivalency scores based on the pooling of occurrence records of the two species. The second
tests if the niche occupied by one species in its range is more similar than what would be
expected at random to the niche occupied in the other range. A null distribution to which the
true overlap is compared is created by measuring niche overlap between one species and the
background space of the other species. Both niche equivalency and similarity tests are based on
100 repetitions and null hypotheses cannot be rejected if the measured value falls within 95%
27
of simulated values (Broennimann et al., 2012). Tests were conducted using ecospat package
version 3.0 in R (Broennimann et al., 2018).
RESULTS
The Bayesian phylogenetic inference recovered the same topology of relationships
among Riorajini species (Figure 1) as the neighbour-joining analysis by Naylor et al. (2012).
Besides, all nodes have high posterior probabilities (> 0.97). Coupling data from online
biodiversity databases and literature (Supplementary Material – Table 1), occurrence records
for Riorajini species expanded in up to five degrees southward (Figure 2) in comparison with
distribution maps currently available (e.g. IUCN maps).
3
3 Images available at: A. cyclophora: shark-references.com/species/view/Atlantoraja-cyclophora
A. castelnaui: https://shark-references.com/species/view/Atlantoraja-castelnaui
A. platana: https://www.fishbase.se/summary/Atlantoraja-platana.html
R. agassizii: https://www.fishbase.se/summary/50857
S. acuta: https://www.fishbase.se/summary/Sympterygia-acuta.html
Figure 1: Phylogeny of Riorajini based on a Bayesian inference using gene NADH dehydrogenase 2 (ND2),
including Sympterygia acuta as outgroup. Node values as posterior probabilities. Photos of A. cyclophora
and A. castelnaui by Bianca de Sousa Rangel ©. Photo of A. platana by Pablo D. Meneses. Photo of R.
agassizii1 by Itamar A. Martins. Photo of S. acuta by Marcelo Vianna.
28
Figure 2: Occurrence records of preserved specimens used for the ecological niche models of Atlantoraja castelnaui, A.
cyclophora, A. platana, and Rioraja agassizii along the marine provinces (Spalding et al., 2007) in Southwestern Atlantic
Ocean.
29
Nine uncorrelated (|r| ≤ 0.8) environmental layers were selected for modelling the
ecological niche of Atlantoraja castelnaui, A. cyclophora, A. platana and Rioraja agassizii
(Table 1; Figure 3). The best model (ΔAICc = 0) of each species presented a different
combination of parameters (Table 2). When models are created with presence-only data, AUC
reflects the model ability to differentiate occurrence from background points (Phillips et al.,
2006). The ENM of each species (Figure 3) presented a different set of environmental variables
with higher contribution to the model (> 70%, measured as the permutation importance) (Table
3), although the niches in bidimensional, gridded space showed high overlap and similarity
(Figures 4 and 5).
Table 1: List of environmental variables selected after Pearson’s Correlation Test (|r| ≥ 0.8) for running ecological
niche models of Atlantoraja castelnaui, A. cyclophora, A. platana, and Rioraja agassizii.
Variable Code Unit Scaling Reference
Temperature mean SST_mean °C 100x MARSPEC
Temperature range SST_range °C 100x MARSPEC
Salinity mean SSS_mean psu 100x MARSPEC
Salinity range SSS_range psu 100x MARSPEC
Distance to shore dist_shore km 1x MARSPEC
Depth of seafloor bathy_5m m 1x MARSPEC
Nitrate concentration nitrate_mean mol.m-3 1x Bio-ORACLE
Iron concentration iron_mean mol.m-3 1x Bio-ORACLE
Currents velocity current_vel m-1 1x Bio-ORACLE
Table 2: Summary of the best combination (ΔAICc = 0) of parameters established by ENMeval package
(Muscarella et al., 2014), and performance evaluation. n – number of occurrence points in the dataset; FC – Feature
Classes allowed in the model (L – linear; Q – quadratic; H – hinge); RM – Regularization multiplier; AUC – Area
Under ROC curve; sd – AUC standard deviation.
Species Code n FC RM AUC sd
Atlantoraja castelnaui Acas 31 LQ 2.5 0.984 0.003
Atlantoraja cyclophora Acyc 60 LQH 1.0 0.994 0.001
Atlantoraja platana Apla 30 LQH 2.0 0.988 0.002
Rioraja agassizii Raga 43 LQ 0.5 0.989 0.002
30
Figure 3: Ecological niche models of Atlantoraja castelnaui (A), A. cyclophora (B), A. platana (C), and Rioraja agassizii
(D).
31
Table 3: Permutation importance (%) per variable (lines) per species (columns). Acas: Atlantoraja castelnaui;
Acyc: A. cyclophora; Apla: A. platana; Raga: Rioraja agassizii. Bold highlights the variables of higher
contribution (Σ > 70%) to the models of each species.
Acas Acyc Apla Raga
Temperature mean 1.6 4.5 8.9 2.6
Temperature range 18.2 3.2 1.7 0.1
Salinity mean 0.2 0.5 0.4 0.3
Salinity range 0.1 1.4 1.2 11.7
Distance to shore 12.2 2 26.5 30.6
Depth of seafloor 19.6 3.8 53.6 33.7
Nitrate mean 37.1 80.1 0.1 20.2
Iron mean 10.9 3.1 7.5 0.5
Currents velocity 0.1 1.4 0.1 0.2
Figure 4: Principal Component Analysis (PCA) illustrating the influence of environmental variables for
Atlantoraja castelnaui (Acas), Atlantoraja cyclophora (Acyc), Atlantoraja platana (Apla), and Rioraja agassizii
(Raga). Largest circles are the centroids of distribution of the scattered points. Contrib: contribution of
environmental variables (vectors), with darker shades indicating stronger contribution.
32
Figure 5: PCA-env results of niche overlap and niche similarity tests for each pair of Riorajini species. The gridded
niche of the first species in a pair is green; the second, in red; overlap between the two niches is in blue. Arrows
point to the direction of shift for the centroids of distribution. Acas: Atlantoraja castelnaui; Acyc: Atlantoraja
cyclophora; Apla: Atlantoraja platana; and Raga: Rioraja agassizii.
33
Niche equivalency results indicate Riorajini species present equivalent niches, and niche
similarity results indicate they are more similar than expected by chance, although the former
did not always find a significant value for the results (Supplementary material – PCA-env
results). Statistically non-significant results (p > 0.05) will not be discussed despite being
available in all graphs/figures depicting PCA-env results. Phylogeny results coupled with PCA
analysis suggest niche conservatism in this tribe.
DISCUSSION
As the term “niche” can assume multiple meanings, when testing for conservatism it is
important to highlight the concept and the taxonomic level at which it is discussed (Peixoto et
al., 2017). In the present research we applied a test of niche conservatism coupled with a
phylogenetic reconstruction to test the hypothesis that a differentiation in abiotic niche at the
tribe level allows the co-occurrence of four species of skates. Our results, however, show that
niche conservatism is the overall pattern within Riorajini, though no linear relationship between
phylogenetic proximity and niche similarity is clear, as some pairs of species more
phylogenetically distant show highly similar niches, whereas congeners display more divergent
niches.
Evolutionary perspective
The highest value of niche similarity was found between A. castelnaui and R. agassizii
(~72%) and the lowest value was found between A. castelnaui and A. platana (~43%). From a
phylogenetic perspective, it is more parsimonious to assume that an ancestral lineage had a
niche “A”, which could be a feature at the node of the tribe shared by R. agassizii and its sister-
clade. Within this clade, the niche “A” is also present in A. castelnaui as a conserved
characteristic. The sister-clade to A. castelnaui evolved a distinct feature, “B”, which is shared
by A. cyclophora and A. platana (Figure 5). These two groups of species differ in preferable
habitats, with R. agassizii and A. castelnaui occurring in shallower waters, closer to the
shoreline in comparison to A. cyclophora and A. platana, which explore the continental shelf
farther.
34
Figure 6: Scheme illustrating phylogenetic relationship in Riorajini and the main abiotic niche features shared in
the southwest Atlantic Ocean.
In each group separated by depth, species partition food resources. Atlantoraja
castelnaui and R. agassizii feed mainly on teleosts, however, the former attains up to 1470 mm
in total length (TL) (Weigmann, 2016), being the largest body-sized species of Riorajini, which
allows it to also explore larger preys, such as cephalopods and other chondrichthyans (Barbini
& Lucifora, 2012). Rioraja agassizii, the smallest species in the group, feeds on smaller preys
such as crustaceans and polychaetes (Barbini & Lucifora, 2011). Atlantoraja platana and A.
cyclophora have diets based on crustaceans decapods, although teleosts are also an important
item for the latter (Schwingel & Assunção, 2009; Barbini & Lucifora, 2016). Differences in
depth of occurrence have been mentioned for these four species in the literature (Menni et al.,
2010) and, as a general pattern for elasmobranchs, Smith and Brown (2002) found a negative
relationship between bathymetry and body size with larger elasmobranchs’ species occurring
in shallower waters – a pattern to some extent present in our results.
The events that resulted in or contributed to the speciation of the tribe Riorajini are still
unknown. The southwestern Atlantic region where these species occur is under the influence of
the freshwater outflow of the La Plata river, between Uruguay and Argentina. This impacts
environmental heterogeneity and provides a plethora of new niches to explore, which is
reflected in species’ niches when they present different tolerances to environmental
characteristics. We would expect, however, a stronger signal of niche divergence to argue that
35
these differences played an important role triggering cladogenesis within the group in a scenario
of sympatric speciation – in fact, Riorajini species co-occurring nowadays do not necessarily
imply the clade underwent sympatric speciation since secondary contact of previously isolated
lineages is commonly seen in nature (Petit et al., 2003; Chevolot et al., 2006). As the species
in this tribe show niches that are more similar than expected by chance, however still differing
within a subset of conditions, it is likely that an ancestral lineage of this monophyletic group
accumulated differences along an environmental gradient. Reviews on the topic [of speciation]
highlight this scenario of parapatry as an important driver of diversification in the marine
environment, preventing the need of strong vicariant barriers and large geographic scales to
occur (Rocha & Bowen, 2008; Bowen et al., 2013).
Environmental drivers of occurrence
For the nine variables included in the ENMs, five were the most significant for all
species: nitrate concentration, temperature range, salinity range, depth of seafloor (bathymetry),
and distance to shore. Our study indicates these variables characterize the fundamental abiotic
niche of the group. It is worth noting that the two latter variables are likely to experience drastic
changes in a global warming scenario, as the rise of sea level is expected to be one of the main
consequences of higher temperatures in the near future (Zhang et al., 2004). However, how
these changes will translate into an impact to the niche of this group remains to be tested
(Chapter 2). The importance of each of these predictors varied between species and up to three
variables were necessary to characterize the niche of each species in more than 70 per cent,
which we consider to be the abiotic conditions to exert higher influence in the realized abiotic
niche of each species.
Species differed in response to nitrate concentrations. Response curves for this variable
shows that A. cyclophora presents a peak in probability of occurrence when the concentration
reached approximately 5 mol.m-3 (Supplementary Material – Maxent figures, Figure 2) and the
jackknife test indicates nitrate as the variable that, alone, is the most useful as well as has the
most unique information (i.e.: information that is not present in other variables) for developing
the model (Supplementary Material – Maxent figures, Figure 1). Therefore, nitrate mean is the
environmental variable to better characterize the ecological niche of this species. Rioraja
agassizii shows a peak in probability of occurrence for even lower concentrations of nitrate (~
1.0 mol.m-3). Only for A. platana, nitrate was not an important predictor for the model.
36
The Pearson’s correlation test revealed that the concentrations of phosphate, silicate,
and nitrate are highly correlated (|r| ≥ 0.8), indicating these variables represent similar
environmental information. To avoid biasing the model towards this redundancy, we excluded
one from a pair of highly-correlated variables, leading the removal of phosphate and silicate
from the analyses. Other studies with Chondrichthyes have disregarded information of these
variables (Lucifora et al., 2012), so there is a gap in literature on models revealing and exploring
the link between nitrate concentration and the distribution of skates in general.
Low concentrations of nitrate in Southwestern Atlantic near the shoreline reflects the
influence of the Brazilian current flowing southward along a shallow continental shelf. Tropical
waters in the Brazilian current are oligotrophic and present low concentrations of suspended
particles, which also indicate that the influx of organic matter from land does not affect the
water in this current (Seeliger et al., 1998). On the other hand, the Falkland current, reaching
the south of Warm Temperate Southwestern Atlantic province and flowing northward, is rich
in dissolved nutrients and, therefore, sustain primary productivity and a vast food chain in the
region (Seeliger et al., 1998). As illustrated in Figure 2, Riorajini species occurrence is
constrained to the continental shelf, where nitrate concentration is low. For A. platana, for
example, it is important to notice the isolated population at the San Matías Gulf in Argentina.
In this region, nitrate and phosphate concentrations tend to be higher in comparison with its
surroundings (Figure 5). Mean nitrate concentration is probably not influencing the ENM of A.
platana because this species occurs in areas of either low (e.g. south coast of Brazil) or high
values of this variable. This can translate into an ecological resilience of this species, or, at least,
of the population at San Matías Gulf.
37
Oddone and Vooren (2004) did not find a correlation between the frequency of
occurrence and abundance of A. cyclophora with temperature, salinity, or depth for specimens
collected off the coast of Rio Grande do Sul, Brazil. Accordingly, these variables were not
significantly relevant for modelling the ecological niche of this species. Combined, mean
temperature, salinity, and depth of seafloor contributed with less than 10% to the model of A.
cyclophora (Table 3). Similarly, nitrate concentration is the variable of higher influence
(37.1%) to the niche model (Table 3; Figure 3) of A. castelnaui. However, depth of seafloor
and temperature range also played important roles in increasing model gain for this species
(19.6 and 18.2% permutation importance, respectively). The graph showing the response curve
for temperature range suggests this species tolerates high variations in this variable
(Supplementary Material – Maxent figures, Figure 4), which could be considered an advantage
in a global warming scenario.
Figure 7: Nitrate and phosphate concentrations along the coast of South America. Images retrieved from
Bio-ORACLE (available at: bio-oracle.org). Black arrows point to San Matías Gulf in Argentina, where
nitrate and phosphate concentrations are higher than surrounding areas.
38
FINAL CONSIDERATIONS
Phylogenetic niche conservatism is the overall pattern in Riorajini. However, no linear
relationship between phylogenetic proximity and niche similarity is clear since some pairs of
species that are more phylogenetically distant show highly similar niches. The environmental
features to characterize the abiotic niche of the group are bathymetry, distance to shore, nitrate
concentration, temperature range, and salinity range. Species-specific responses to these
variables characterize the abiotic niche of each species.
Despite imperative importance to understand ecosystems’ dynamics and species’
biology, data registering the occurrence of elasmobranchs are often a by-product of other
researches, such as studies of species diet, or fisheries statistics. Perhaps for this reason, the
process of mapping the distribution of species is yet not entirely automated, evidenced by
outdated maps for some species in renowned, popular websites of biological information (e.g.
IUCN and FishBase). Previous maps of distribution mostly failed to illustrate the southern limit
of distribution of all Riorajini species, either underestimating distribution, which was the case
for A. castelnaui (Hozbor et al., 2004), or disregarding distribution gaps, such as for A. platana
(San Martín et al., 2007). The maps of geographic distribution here presented considered data
of georeferenced preserved specimens and literature, and the niche models offer a simple
bidimensional overview of the probability of occurrence of each species as for the abiotic
conditions. Additionally, these maps can aid future assessment of conservation status for this
tribe as they comprise the data available so far on the occurrence of the four species and indicate
a general set of ecological tolerances for each of them.
An extensive body of research is available on diet, morphology, and reproductive
biology of Riorajini species (e.g., Oddone & Vooren, 2004, 2005, 2008; Colonello et al., 2007;
Oddone & Amorim, 2007; Oddone et al., 2007, 2008; Barbini & Lucifora, 2011, 2012, 2016;
Oddone & Capapé, 2011) although studies focusing on other physiological and ecological
aspects such as behaviour, for example, are lacking in the literature, perhaps for the high cost
and logistic difficulties to conduct such. Low cost, non-invasive methodologies are an
alternative way to explore some of these areas. Our research is the first to apply a modelling
approach to identify and discuss signals of abiotic niche conservatism in Riorajini skates. These
provide insights into possible triggers to the isolation of ancestral lineages, telling part of the
evolutionary history of the clade, even though numerous strands, as abovementioned, remain
to be explored in the group.
39
REFERENCES
Ahmadzadeh, F., Flecks, M., Rödder, D., Böhme, W., Ilgaz, Ç., Harris, D. J., ... & Carretero,
M. A. (2013). Multiple dispersal out of Anatolia: biogeography and evolution of oriental
green lizards. Biological Journal of the Linnean Society, 110(2), 398-408.
Aiello‐Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B., & Anderson, R. P. (2015).
spThin: an R package for spatial thinning of species occurrence records for use in
ecological niche models. Ecography, 38(5), 541-545.
Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017).
Bio‐ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global
Ecology and Biogeography, 27(3), 277-284.
Ball, R. E., Serra-Pereira, B., Ellis, J., Genner, M. J., Iglésias, S., Johnson, A. F., ... & Menezes,
G. (2016). Resolving taxonomic uncertainty in vulnerable elasmobranchs: are the
Madeira skate (Raja maderensis) and the thornback ray (Raja clavata) distinct
species? Conservation Genetics, 17(3), 565-576.
Barbini, S. A., & Lucifora, L. O. (2011). Feeding habits of the Rio skate, Rioraja agassizi
(Chondrichthyes: Rajidae), from off Uruguay and north Argentina. Journal of the
Marine Biological Association of the United Kingdom, 91(6), 1175-1184.
Barbini, S. A., & Lucifora, L. O. (2012). Feeding habits of a large endangered skate from the
south-west Atlantic: the spotback skate, Atlantoraja castelnaui. Marine and Freshwater
Research, 63(2), 180-188.
Barbini, S. A., & Lucifora, L. O. (2016). Diet composition and feeding habits of the eyespot
skate, Atlantoraja cyclophora (Elasmobranchii: Arhynchobatidae), off Uruguay and
northern Argentina. Neotropical Ichthyology, 14(3).
Bovcon, N. D., Cochia, P. D., Góngora, M. E., & Gosztonyi, A. E. (2011). New records of
warm‐temperate water fishes in central Patagonian coastal waters (Southwestern South
Atlantic Ocean). Journal of Applied Ichthyology, 27(3), 832-839.
Bowen, B. W., Rocha, L. A., Toonen, R. J., & Karl, S. A. (2013). The origins of tropical marine
biodiversity. Trends in ecology & evolution, 28(6), 359-366.
Brander, K. (1981). Disappearance of common skate Raia batis from Irish
Sea. Nature, 290(5801), 48-49.
Broennimann, O., Di Cola, V., & Guisan, A. (2018). ecospat: Spatial Ecology Miscellaneous
Methods. R package version 3.0. https://CRAN.R-project.org/package=ecospat
40
Broennimann, O., Fitzpatrick, M. C., Pearman, P. B., Petitpierre, B., Pellissier, L., Yoccoz, N.
G., ... & Graham, C. H. (2012). Measuring ecological niche overlap from occurrence
and spatial environmental data. Global ecology and biogeography, 21(4), 481-497.
Chevolot, M., Hoarau, G., Rijnsdorp, A. D., Stam, W. T., & Olsen, J. L. (2006).
Phylogeography and population structure of thornback rays (Raja clavata L.,
Rajidae). Molecular Ecology, 15(12), 3693-3705.
Colonello, J. H., García, M. L., & Lasta, C. A. (2007). Reproductive biology of Rioraja
agassizii from the coastal southwestern Atlantic ecosystem between northern Uruguay
(34 S) and northern Argentina (42 S). In Biology of Skates (pp. 171-178). Springer,
Dordrecht.
CRIA (Centro de Referência e Informação Ambiental) (2019). speciesLink. Available at:
www.splink.org.br/index.
da Silva, A. R., Malafaia, G., & Menezes, I. P. P. (2017). biotools: an R function to predict
spatial gene diversity via an individual-based approach. Genet. Mol. Res, 16,
gmr16029655.
Darwin, C. R. (1859). On the origin of species by means of natural selection, or the preservation
of favoured races in the struggle for life. London: John Murray. [1st edition]
Dulvy, N. K., Fowler, S. L., Musick, J. A., Cavanagh, R. D., Kyne, P. M., Harrison, L. R., ... &
Pollock, C. M. (2014). Extinction risk and conservation of the world’s sharks and
rays. elife, 3, e00590.
Dumbrell, A. J., Nelson, M., Helgason, T., Dytham, C., & Fitter, A. H. (2010). Relative roles
of niche and neutral processes in structuring a soil microbial community. The ISME
journal, 4(3), 337-345.
Ebert, D. A., & Compagno, L. J. (2007). Biodiversity and systematics of skates
(Chondrichthyes: Rajiformes: Rajoidei). In Biology of Skates (pp. 5-18). Springer,
Dordrecht.
Elton, C. S. (1927). The animal community. Animal ecology, 239-256.
Field, I. C., Meekan, M. G., Buckworth, R. C., & Bradshaw, C. J. (2009). Susceptibility of
sharks, rays and chimaeras to global extinction. Advances in marine biology, 56, 275-
363.
Figueiredo, J. L. (1977). Manual de peixes marinhos do sudeste e sul do Brasil. I. Introdução,
tubarões, raias e quimeras. São Paulo: Museu de Zoologia da Universidade de São
Paulo. 104p.
41
Fourcade, Y., Besnard, A. G., & Secondi, J. (2017). Paintings predict the distribution of species,
or the challenge of selecting environmental predictors and evaluation statistics. Global
Ecology and Biogeography, 27(2), 245-256.
Gause, G. F. (1934). The struggle for existence. Baltimore: Williams and Wilkins. 163 p.
GBIF: The Global Biodiversity Information Facility. (2019). What is GBIF? Available
from https://www.gbif.org/what-is-gbif [13 August 2018].
Gomes, U. L., Signori, C. N., Gadig, O. B. F., & Santos, H. R. S. (2010). Guia para identificação
de tubarões e raias do Rio de Janeiro. Technical Books, Rio de Janeiro.
Gorel, A. P., Duminil, J., Doucet, J. L., & Fayolle, A. (2019). Ecological niche divergence
associated with species and populations differentiation in Erythrophleum (Fabaceae,
Caesalpinioideae). Plant Ecology and Evolution, 152(1), 41-52.
Green, L., & Jutfelt, F. (2014). Elevated carbon dioxide alters the plasma composition and
behaviour of a shark. Biology letters, 10(9), 20140538.
Grinnell, J. (1917). Field tests of theories concerning distributional control. The American
Naturalist, 51(602), 115-128.
Harmon, J. P., Moran, N. A., & Ives, A. R. (2009). Species response to environmental change:
impacts of food web interactions and evolution. Science, 323(5919), 1347-1350.
Harvey, P. H., & Pagel, M. D. (1991). The comparative method in evolutionary biology (Vol.
239). Oxford: Oxford University Press.
Helfman, G., Collette, B. B., Facey, D. E., & Bowen, B. W. (2009). The diversity of fishes:
biology, evolution, and ecology. John Wiley & Sons.
Hozbor, N., Massa, A. M., & Vooren, C. M. (2004). Atlantoraja castelnaui. IUCN Red List of
Threatened Species. Version 2012.
Hutchinson, G. E. (1957). Cold spring harbor symposium on quantitative biology. Concluding
remarks, 22, 415-427.
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent
developments. Philosophical Transactions of the Royal Society A: Mathematical,
Physical and Engineering Sciences, 374(2065), 20150202.
Kneitel, J. M. (2008). Gauses’s Competitive Exclusion Principle In S. E. Jørgensen & B. D.
Fath (Eds.), Encyclopedia of Ecology (1731 – 1734).
Kocher, T. D. (2004). Adaptive evolution and explosive speciation: the cichlid fish
model. Nature Reviews Genetics, 5(4), 288.
Kyne, P.M., San Martín, J. & Stehmann, M.F.W. (2007). Rioraja agassizii. The IUCN Red List
of Threatened Species. Version 2007.
42
Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam,
H., ... & Thompson, J. D. (2007). Clustal W and Clustal X version
2.0. bioinformatics, 23(21), 2947-2948.
Last, P., Naylor, G., Séret, B., White, W., de Carvalho, M., & Stehmann, M. (Eds.).
(2016). Rays of the World. CSIRO publishing.
Lucifora, L. O., García, V. B., Menni, R. C., & Worm, B. (2012). Spatial patterns in the
diversity of sharks, rays, and chimaeras (Chondrichthyes) in the Southwest
Atlantic. Biodiversity and Conservation, 21(2), 407-419.
Massa, A., Hozbor, N. M., & IUCN. (2004). Sympterygia acuta. The IUCN Red List of
Threatened Species. Version 2013.1.
Massa, A., Hozbor, N. & Vooren, C.M. (2006). Atlantoraja cyclophora. The IUCN Red List of
Threatened Species. Version 2006.
McEachran, J. D. & Dunn, K. A. (1998). Phylogenetic Analysis of Skates, a Morphologically
Conservative Clade of Elasmobranchs (Chondrichthyes: Rajidae). Copeia, 2, 271-290.
Menni, R. C. (1972). Raja (Atlantoraja) subgen. nov. y lista crítica de los Rajidae Argentinos
(Chondricthyes, Rajiformes). Zoología 103:165-173.
Menni, R. C., Jaureguizar, A. J., Stehmann, M. F. & Lucifora, L. O. (2010). Marine biodiversity
at the community level: zoogeography of sharks, skates, rays and chimaeras in the
southwestern Atlantic. Biodiversity Conservation, 19:775-796.
Merow, C., Smith, M. J., & Silander Jr, J. A. (2013). A practical guide to MaxEnt for modeling
species’ distributions: what it does, and why inputs and settings
matter. Ecography, 36(10), 1058-1069.
Moore, A. B., White, W. T., Ward, R. D., Naylor, G. J., & Peirce, R. (2011). Rediscovery and
redescription of the smoothtooth blacktip shark, Carcharhinus leiodon
(Carcharhinidae), from Kuwait, with notes on its possible conservation status. Marine
and Freshwater Research, 62(6), 528-539.
Moreira, R. A., Gomes, U. L., & de Carvalho, M. R. (2017). Clasper morphology of skates of
the tribe Riorajini (Chondrichthyes: Rajiformes: Arhynchobatidae) and its systematic
significance. Journal of morphology, 278(9), 1185-1196.
Muscarella, R., Galante, P. J., Soley‐Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., &
Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent
evaluations and estimating optimal model complexity for Maxent ecological niche
models. Methods in Ecology and Evolution, 5(11), 1198-1205.
43
Naylor, G. J. P., Caira, J. N., Jensen, K., Rosana, K. A. M., White, W. T., Last, P. R. (2012). A
DNA sequence-based approach to the identification of shark and ray species and its
implications for global elasmobranch diversity and parasitology. (Bulletin of the
American Museum of Natural History, no. 367).
Oddone, M. C., & Amorim, A. F. D. (2007). Length-weight relationships, condition and
population structure of the genus Atlantoraja (Elasmobranchii, Rajidae,
Arhynchobatinae) in Southeastern Brazilian waters, SW Atlantic Ocean.
Oddone, M. C., & Capapé, C. (2011). Annual fecundity assessment for the Rio skate Rioraja
agassizi (chondrichthyes: arhynchobatidae) endemic to a neotropical area (Southeastern
Brazil). Brazilian Journal of Oceanography, 59(3), 277-279.
Oddone, M. C., & Vooren, C. M. (2004). Distribution, abundance and morphometry of
Atlantoraja cyclophora (Regan, 1903) (Elasmobranchii: Rajidae) in southern Brazil,
southwestern Atlantic. Neotropical Ichthyology, 2(3), 137-144.
Oddone, M. C., & Vooren, C. M. (2005). Reproductive biology of Atlantoraja cyclophora
(Regan 1903) (Elasmobranchii: Rajidae) off southern Brazil. ICES Journal of Marine
Science, 62(6), 1095-1103.
Oddone, M. C., & Vooren, C. M. (2008). Comparative morphology and identification of egg
capsules of skate species of the genera Atlantoraja Menni, 1972, Rioraja Whitley, 1939
and Sympterygia Müller & Henle, 1837. Arquivos de Ciências do Mar, 41(2), 5-13.
Oddone, M. C., Amorim, A. F., & Mancini, P. L. (2008). Reproductive biology of the spotback
skate, Atlantoraja castelnaui (Ribeiro, 1907) (Chondrichthyes, Rajidae), in southeastern
Brazilian waters. Revista de Biología Marina y Oceanografía, 43(2), 327-334.
Oddone, M. C., Amorim, A. F., Mancini, P. L., Norbis, W., & Velasco, G. (2007). The
reproductive biology and cycle of Rioraja agassizi (Müller and Henle, 1841)
(Chondrichthyes: Rajidae) in southeastern Brazil, SW Atlantic Ocean. Scientia
Marina, 71(3), 593-604.
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O’hara, R. B., ... &
Oksanen, M. J. (2013). Package ‘vegan’. Community ecology package, version, 2(9), 1-
295.
Paesch, L. (2000). Habitos alimentarios de algunas especies de elasmobranquios en el frente
oceanico del Rio de La Plata. Frente Maritimo, 18, 71-90.
Panda, S. S., Dhal, N. K., & Panda, C. R. (2012). Phytoplankton diversity in response to abiotic
factors along Orissa coast, Bay of Bengal. International journal of environmental
sciences, 2(3), 1818-1832.
44
Parolo, G., Rossi, G., & Ferrarini, A. (2008). Toward improved species niche modelling: Arnica
montana in the Alps as a case study. Journal of Applied Ecology, 45(5), 1410-1418.
Pearson, R. G. (2007). Species’ distribution modeling for conservation educators and
practitioners. Synthesis. American Museum of Natural History, 50, 54-89.
Peixoto, F. P., Villalobos, F., & Cianciaruso, M. V. (2017). Phylogenetic conservatism of
climatic niche in bats. Global Ecology and Biogeography, 26(9), 1055-1065.
Petit, R. J., Aguinagalde, I., de Beaulieu, J. L., Bittkau, C., Brewer, S., Cheddadi, R., ... &
Mohanty, A. (2003). Glacial refugia: hotspots but not melting pots of genetic
diversity. Science, 300(5625), 1563-1565.
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of
species geographic distributions. Ecological modelling, 190(3-4), 231-259.
Pigot, A. L., & Tobias, J. A. (2013). Species interactions constrain geographic range expansion
over evolutionary time. Ecology letters, 16(3), 330-338.
Pistevos, J. C., Nagelkerken, I., Rossi, T., Olmos, M., & Connell, S. D. (2015). Ocean
acidification and global warming impair shark hunting behaviour and growth. Scientific
reports, 5, 16293.
Prinzing, A., Durka, W., Klotz, S., & Brandl, R. (2001). The niche of higher plants: evidence
for phylogenetic conservatism. Proceedings of the Royal Society of London. Series B:
Biological Sciences, 268(1483), 2383-2389.
Pyron, R. A., Costa, G. C., Patten, M. A., & Burbrink, F. T. (2014). Phylogenetic niche
conservatism and the evolutionary basis of ecological speciation. Biological
Reviews, 90(4), 1248-1262.
QGIS Development Team (2019). QGIS Geographic Information System. Open Source
Geospatial Foundation Project. http://qgis.osgeo.org
R Core Team (2018). R: A language and environment for statistical computing. R Foundation
for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Rinnan, D. S., & Lawler, J. (2019). Climate‐niche factor analysis: a spatial approach to
quantifying species vulnerability to climate change. Ecography, 42(9), 1494-1503.
Ripa, J. (2009). When is sympatric speciation truly adaptive? An analysis of the joint evolution
of resource utilization and assortative mating. Evolutionary Ecology, 23(1), 31-52.
Rocha, L. A., & Bowen, B. W. (2008). Speciation in coral‐reef fishes. Journal of Fish
Biology, 72(5), 1101-1121.
Rosenfeld, J. S. (2002). Functional redundancy in ecology and conservation. Oikos, 98(1), 156-
162.
45
San Martín, J.M., Stehmann, M.F.W. & Kyne, P.M. (2007). Atlantoraja platana. The IUCN
Red List of Threatened Species. Version 2007.
Sbrocco, E. J., & Barber, P. H. (2013). MARSPEC: ocean climate layers for marine spatial
ecology: Ecological Archives E094‐086. Ecology, 94(4), 979-979.
Schwingel, P. R., & Assunção, R. E. N. A. T. A. (2009). Hábitos alimentares da raia Atlantoraja
platana (Günther, 1880) (Elasmobranchii, Rajidae) no litoral norte de Santa Catarina,
Brasil. Pan-American Journal of Aquatic Sciences, 4(4), 446-455.
Scriven, J. J., Whitehorn, P. R., Goulson, D., & Tinsley, M. C. (2016). Niche partitioning in a
sympatric cryptic species complex. Ecology and evolution, 6(5), 1328-1339.
Seeliger, U. (1998). Os ecossistemas costeiro e marinho do extremo sul do Brasil (No. 504.42
ECO).
Shcheglovitova, M., & Anderson, R. P. (2013). Estimating optimal complexity for ecological
niche models: a jackknife approach for species with small sample sizes. Ecological
Modelling, 269, 9-17.
Shepherd, T. D., & Myers, R. A. (2005). Direct and indirect fishery effects on small coastal
elasmobranchs in the northern Gulf of Mexico. Ecology Letters, 8(10), 1095-1104.
Smith, K. F., & Brown, J. H. (2002). Patterns of diversity, depth range and body size among
pelagic fishes along a gradient of depth. Global Ecology and Biogeography, 11(4), 313-
322.
Soberón, J. (2007). Grinnellian and Eltonian niches and geographic distributions of species.
Ecology Letters 10:1115-1123.
Soberón, J., & Arroyo-Peña, B. (2017). Are fundamental niches larger than the realized?
Testing a 50-year-old prediction by Hutchinson. PloS one, 12(4), e0175138.
Soberón, J., & Nakamura, M. (2009). Niches and distributional areas: concepts, methods, and
assumptions. Proceedings of the National Academy of Sciences, 106(Supplement 2),
19644-19650.
Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. A., Finlayson, M. A. X., ...
& Martin, K. D. (2007). Marine ecoregions of the world: a bioregionalization of coastal
and shelf areas. BioScience, 57(7), 573-583.
Stein, R. W., Mull, C. G., Kuhn, T. S., Aschliman, N. C., Davidson, L. N., Joy, J. B., ... &
Mooers, A. O. (2018). Global priorities for conserving the evolutionary history of
sharks, rays and chimaeras. Nature ecology & evolution, 2(2), 288.
46
Stevens, J. D., Bonfil, R., Dulvy, N. K., & Walker, P. A. (2000). The effects of fishing on
sharks, rays, and chimaeras (chondrichthyans), and the implications for marine
ecosystems. ICES Journal of Marine Science, 57(3), 476-494.
Suchard, M. A., Lemey, P., Baele, G., Ayres, D. L., Drummond, A. J., & Rambaut, A. (2018).
Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus
evolution, 4(1), vey016.
Tamura, K., Dudley, J., Nei, M., & Kumar, S. (2007). MEGA4: molecular evolutionary genetics
analysis (MEGA) software version 4.0. Molecular biology and evolution, 24(8), 1596-
1599.
Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., & De Clerck, O. (2012).
Bio‐ORACLE: a global environmental dataset for marine species distribution
modelling. Global ecology and biogeography, 21(2), 272-281.
Viana, A. D. F., & Vianna, M. (2014). The feeding habits of the eyespot skate Atlantoraja
cyclophora (Elasmobranchii: Rajiformes) in southeastern Brazil. Zoologia
(Curitiba), 31(2), 119-125.
Viana, A. F., Valentin, J. L., & Vianna, M. (2017). Feeding ecology of elasmobranch species
in southeastern Brazil. Neotropical Ichthyology, 15(2).
Warren, D. L., Glor, R. E., & Turelli, M. (2008). Environmental niche equivalency versus
conservatism: quantitative approaches to niche evolution. Evolution: International
Journal of Organic Evolution, 62(11), 2868-2883.
Warren, D. L., Wright, A. N., Seifert, S. N., & Shaffer, H. B. (2014). Incorporating model
complexity and spatial sampling bias into ecological niche models of climate change
risks faced by 90 C alifornia vertebrate species of concern. Diversity and
distributions, 20(3), 334-343.
Weigmann, S. (2016). Annotated checklist of the living sharks, batoids and chimaeras
(Chondrichthyes) of the world, with a focus on biogeographical diversity. Journal of
Fish Biology, 88(3), 837-1037.
Wiens, J. J. (2004). Speciation and ecology revisited: phylogenetic niche conservatism and the
origin of species. Evolution, 58(1), 193-197.
Zhang, K., Douglas, B. C., & Leatherman, S. P. (2004). Global warming and coastal
erosion. Climatic change, 64(1-2), 41.
Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid
common statistical problems. Methods in ecology and evolution, 1(1), 3-14.
47
CAPÍTULO 2: Temperate skates’ shift ranges as an outcome of global warming
Jéssica Fernanda Ramos Coelho¹, Sergio Maia Queiroz Lima¹, Flávia de Figueiredo Petean¹
1Departamento de Botânica e Zoologia, Centro de Biociências, Universidade Federal do Rio
Grande do Norte, Campus Universitário, BR 101 s/n, 59078-900, Lagoa Nova, Natal, RN,
Brazil.
E-mail: [email protected]
ABSTRACT
Climate change is a growing global-scale issue with an increasing body of evidence revealing
its potential to impact patterns of distribution of living organisms. Some biological and
ecological traits make some organisms more vulnerable than others to climate-related abiotic
stress, with species of slow growth, late maturation, and limited geographic distribution being
of particular concern. Riorajini is a tribe of four sympatric skates’ species occurring in the
temperate southwestern Atlantic falling into this category. Considering skates’ life-history
traits, coupled with species’ ecological requirements, we hypothesize a poleward geographic
shift with potential shrink in overall distribution range of these species as a response to climate
change impacts at the coastal zones this clade occupies. We compiled satellite-derived raster
imagery and data on species occurrence from public online databases to model the ecological
niche of Riorajini species under present and future (2100, RCP 8.5) climatic scenarios. Between
the two climatic scenarios modelled per species, we calculated metrics of niche overlap,
stability, expansion, and unfilling, as well as niche similarity and equivalency. All analyses
were conducted in R. Our results show high overlap between the two climatic scenarios and
reveal an expansion in up to 20% in future environmental suitability for the occurrence of the
tribe. The expansion occurred to deeper zones (longitudinal shift), however still within the
bathymetric limit of the continental shelf. Although positive at first glance, future research
focusing on ontogenetically different responses (adults versus eggs capsules) to cascade events
resulting from global warming are needed to address the physiological resilience of this group.
Also, consequences of such shift can be detrimental to the local biota in newly invaded areas,
as the introduction of new predatory species can affect negatively the dynamics of the
community.
Key-words: climate change; distribution shift; Riorajini; RCP 8.5.
48
INTRODUCTION
Evidence for both terrestrial and marine biotas shifting ranges as a response to impacts
of climate change are growing in the literature. Terrestrial organisms are changing distributions
in latitude and elevation to cope with thermal stress (Root et al., 2003; Hickling et al., 2006;
Chen et al., 2011). In the oceans, these differences are in latitudinal range and depth (Perry et
al., 2005; Nicolas et al., 2011). Perry et al. (2005) showed that two-thirds of the fish species
analysed from the North Sea respond to warming waters shifting in mean latitude, depth, or
both, as well as a distribution boundary shift poleward. These changes are a reflection of a
climate-related disequilibrium.
Long-term analyses show a global scale warming resulting from the continuous increase
in the emission of greenhouse gases, likely an effect of anthropogenic activities (Houghton,
1996; Mann et al., 1999; Barnett et al., 2001; Rosenzweig et al., 2008). Such changes are
occurring faster than most organisms can adapt to (Quintero & Wiens, 2013), and, besides the
difficulty to attribute an impact as a consequence of anthropic global warming, studies are
consistently finding rather compelling evidence of theoretical predictions for climate change-
related impacts on biodiversity distribution (Hughes, 2000; Walther et al., 2002; Parmesan &
Yohe, 2003). The accumulation and synergistic effect of these changes on ecosystems can alter
abiotic conditions of the planet to the extent that living organisms will be pressured towards the
maxim “move, adapt or die”.
Climate change is not likely to impact species or habitats in the same way. For example,
for elasmobranchs, animals with long life cycles, slow growth, and late maturation, the “move”
option seems more feasible in face of environmental stress (Stevens et al., 2000; Helfman et
al., 2009). Fewer physical barriers in comparison with terrestrial habitats makes it easier to
move and disperse in the marine environment, however, some biological and ecological
characteristics, along with a lack of connectivity between some populations, makes this group
more susceptible than others to adverse events (Somero, 2010). In skates, for example, sessile
eggs capsules and philopatry imply in a restricted area of occurrence and strong reliance on
particular habitats, which adds to the vulnerability of this group to climatic changes in
comparison with other pelagic elasmobranchs (Dulvy & Reynolds, 2002; Parmesan, 2006;
Dulvy et al., 2014; Di Santo, 2015).
Riorajini (sensu McEachran & Dunn, 1998) is a tribe of four skates of the
Arhynchobatidae family occurring in sympatry in the southwest Atlantic Ocean, a region that
49
harbours the highest number of threatened chondrichthyan species in the Neotropics (Field et
al., 2009). According to the IUCN latest global assessment, the species in this group are
evaluated as “endangered” (EN): Atlantoraja castelnaui (Hozbor et al., 2004), and “vulnerable”
(VU): A. cyclophora, A. platana, and Rioraja agassizii (Massa et al., 2006; Kyne et al., 2007;
San Martín et al., 2007). These species occur mainly at the Warm Temperate Province, as
defined by Spalding et al. (2007), influenced at north by the Cabo Frio upwelling system, and
at south by the effects of cold-water masses from Falkland current (Peterson & Stramma, 1990;
Coelho-Souza et al., 2012). A range of occurrence limited to the shore makes these taxa more
vulnerable as coastal zones are considered to be more exposed to natural climate-related hazards
(Nicholls & Small, 2002; Nicholls & Cazenave, 2010).
Frameworks on how to answer climate change-related questions (e.g.: Broennimann et
al., 2012, and Guisan et al., 2014) coupled with the improvement of computational models able
to address biological issues boosted our capacity to test eco-biological-based hypothesis, and
to visualize theoretical scenarios more efficiently. For example, Representative Concentration
Pathways (RCPs) represents data from the literature on possible paths for the main driving
agents of climate change. There are currently four RCPs available, from mild to more extreme
scenarios, varying from 2.6 to 8.5 W/m² (ranging from ~490 to ~1370 ppm CO2, respectively)
by the end of the century, predicted as for the trends in emission of greenhouse gases and land
use (Van Vuuren et al., 2011). These emissions translate into an increase of up to 1.7 °C in
mean temperature in the 2.6 RCP scenario, and up to 4.8°C in the 8.5 RCP scenario, both
compared to pre-industrial levels (Stocker et al., 2013). Studies on the distribution of species
under different geographic and temporal scenarios benefited from these advances and became
more popular (Guisan & Zimmermann, 2000), however, studies focusing on the impacts of
climate change in distribution patterns in the marine environment are yet scarce in comparison
with terrestrial ones (Dambach & Rödder, 2011).
In a context of rapid climatic changes in a global scale, these are of particular interest
given that the patterns of biodiversity distribution may interfere in ecosystems’ goods for
human populations, such as fisheries (Blanchet et al., 2019). Besides, with an increasing
number of species threatened with extinction detrimental to such changes (Thomas et al., 2004;
Maclean & Wilson, 2011), the use of non-invasive methodologies to aid addressing such urgent
matters has been necessary. There is an increasing body of literature in this regard, using models
to identify ecological barriers to the distribution of species (Costa et al., 2017). Yet, perhaps
because of sampling difficulties and costly logistics, studies focusing on elasmobranchs are to
50
some degree neglected compared to bony fish, molluscs, and marine mammals, for example,
for which studies applying ENMs correspond to over half of the studies published so far
considering marine taxa (Melo-Merino et al., 2020). Such studies are of paramount importance
to provide the basis from which conservation efforts can be planned and implemented.
In this study, we compiled information from public biodiversity databases and RCP data
to model current and projected (2100) distributions of Riorajini, a Neotropical tribe of four
skates’ species. Considering life-history traits (e.g. slow growth, late maturation, and
philopatry), ecological features (benthic, sedentary habit, coastal habitats, temperate waters)
and putative future climate change impacts on coastal zones, we hypothesize Riorajini species
will present a southward shift in their current geographic distribution and a reduction in the
range of areas where these species are likely to occur in a future scenario of climate change
(considering the extreme scenario of global warming impacts, RCP 8.5).
MATERIALS AND METHODS
Models of present and future climatic scenarios
We ran maximum entropy (maxent) ecological niche models (ENMs) for each one of
the four species in the tribe following a correlative approach, which requires georeferenced sites
of occurrence of a given species, and data characterizing abiotic, climatic conditions where such
species is present (also called abiotic predictors, or layers) (Phillips et al., 2006; Robinson et
al., 2011).
Occurrence sites were derived from published literature and public online databases
filtered by preserved specimens georeferenced in the area of known occurrence, to increase data
reliability. Localities were partitioned into model training and testing points applying the
‘block’ method, suitable when spatial and temporal transferability is required (Muscarella et
al., 2014). Layers for future (2100) environmental conditions considered the worst-climatic-
scenario (RCP 8.5) for which 18 variations (e.g. minimum, maximum, mean) of three variables
(salinity, temperature and currents velocity) for benthic maximum depth were available in Bio-
ORACLE (Tyberghein et al., 2012; Assis et al., 2017). A Pearson’s correlation test was
conducted to remove highly-correlated variables (|r| ≥ 0.8) from the analysis aiming to avoid
multicollinearity (Warren et al., 2014) and model overfitting (Parolo et al., 2008). For
comparative purposes, the ENM for the current climatic scenario, including only the variables
51
selected after the Pearson correlation test for future modelling, was also performed for each
species, following the same procedure.
Environmental variables were scaled to equal dimension and resolution (0.833°, ~9km).
ENMeval package was used to select the combination of maxent parameters to output the
parsimonious model (ΔAICc = 0), which are considered the best models (Muscarella et al.,
2014). ENMs were conducted in R program version 3.5.1 (R Core Team, 2018).
Statistical Analysis – Measuring differences
We followed the methodological framework proposed by Guisan et al. (2014) to map
and measure the degree of change between niches of present and future climatic scenarios for
each species. Niche expansion, stability and unfilling were measured by comparing intra-
specific models for both climatic scenarios. Niche expansion refers to the portion of niche
available in a future climatic scenario, but not occupied in the current scenario; niche stability
reflects the proportion of climatic conditions available in both temporal scenarios; and niche
unfilling refers to conditions of current climatic scenario that are not available in the projected
future climatic scenario (Guisan et al., 2014). The Schoener’s D index was calculated to
measure niche overlap between present and future models per species (Warren et al., 2008).
Finally, niche similarity and equivalency were measured to test if present and future niches will
be more similar or equivalent than expected at random (Broennimann et al., 2012). All niche
metrics were calculated using ade4 package version 1.7.13 and ecospat package version 3.0 in
R (Chessel et al., 2004; Dray & Dufour, 2007; Dray et al., 2007; Bougeard & Dray, 2018;
Broennimann et al., 2018).
RESULTS
Six uncorrelated environmental variables were selected for the ENMs of current and
future climatic scenarios: temperature mean (°C), salinity mean and range (psu), current
velocity mean, minimum, and maximum (m-1). The most parsimonious models included
different feature classes and values of regularization multiplier compared with default maxent
models (Table 1).
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Table 1: Summary of the best combination (ΔAICc = 0) of parameters established by ENMeval package
(Muscarella et al., 2014) per species and climatic scenario: P – present; F – future. n: number of occurrence points
in the dataset; FC: Feature Classes allowed in the model (L – linear; Q – quadratic; H – hinge); RM: Regularization
Multiplier; AUC: Area Under ROC curve per model; sd: standard deviation of AUC.
Species Code n FC RM AUC sd
Atlantoraja castelnaui Acas 31 P LQ 0.5 0.986 0.003
F LQ 1.5 0.982 0.003
Atlantoraja cyclophora Acyc 60 P LQ 0.5 0.991 0.001
F H 2.5 0.991 0.001
Atlantoraja platana Apla 30 P H 2.5 0.977 0.004
F H 4.0 0.977 0.005
Rioraja agassizii Raga 36 P LQH 2.5 0.978 0.003
F H 2.0 0.981 0.003
The importance of each of the six abiotic predictors included in the models varied in
each climatic scenario modelled per species (Table 2), as well as the range of values of the three
main environmental variables for the models (Table 3). Within species, niche overlap and
stability were overall high (> 80%), and values of niche expansion and unfilling were low (<
22%), suggesting the abiotic conditions currently required for the existence of these species in
that area will be available in a future of warmer climatic conditions (Table 4). Both climatic
scenarios are also significantly more similar than expected by chance (Figures 2, 4, 6, and 8).
53
Table 2: Permutation importance (%) per variable per species for present (P) and future (F) climatic scenarios.
Acas – Atlantoraja castelnaui; Acyc – A. cyclophora; Apla – A. platana; Raga – Rioraja agassizii. Bold highlights
the variables of higher contribution (Σ > 80%) to models.
Acas Acyc Apla Raga
P F P F P F P F
Temperature mean 94.7 89.1 96.9 51.1 17.1 2.4 48.3 73
Salinity range 0.5 5.5 0.1 23.4 39.8 72 44.6 20.7
Salinity mean 0.7 0.9 1.3 17.7 2.4 0.3 2 2.7
Current velocity minimum 0.1 0.2 0.5 5.8 36.5 18.2 0.8 2
Current velocity mean 1.5 2.5 0.3 1.9 3.3 6.2 1.5 0.1
Current velocity maximum 2.6 1.7 0.9 0.1 0.9 0.9 2.8 1.4
Table 3: Minimum and maximum values of the three main variables to the ENMs of Riorajini species in present
(P, grey shaded) and future (F, white) climatic scenarios modelled. Acas: Atlantoraja castelnaui; Acyc: A.
cyclophora; Apla: A. platana; Raga: Rioraja agassizii.
Salinity
mean (psu)
Salinity
range (psu)
Temperature
mean (°C)
Acas P 33.20–36.72 0.35–1.25 9.34–21.84 F 33.26–37.21 0.38–1.42 11.53–23.99
Acyc P 33.60–36.61 0.43–1.20 10.59–19.67
F 33.83–37.04 0.38–1.34 12.36–21.77
Apla P 34.05–37.12 0.13–1.40 3.53–25.47 F 33.94–37.79 0.18–1.53 4.37–28.09
Raga P 33.64–37.11 0.47–1.59 10.59–25.63
F 33.85–37.77 0.37–1.81 12.36–28.34
ENMs show an increase in habitat suitability for the occurrence of A. castelnaui (Figure
1) and R. agassizii (Figure 7) along the latitudinal gradient they occupy, and in La Plata river
mouth. For A. cyclophora, such increase occurs more expressively at the Brazilian coast (Figure
3). There is a slight loss in environmental adequacy for the occurrence of A. platana near the
coastline of Rio de Janeiro (23°S) but an overall increase in habitat suitability in deeper areas,
still constrained to the continental shelf (Figure 5); besides, occurrence sites plotted into the
future modelled climatic scenario fall into areas of up to 2°C warmer than the present scenario.
54
Figure 1: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of
environmental suitability for the occurrence of Atlantoraja castelnaui (Acas).
Figure 2: Niche dynamics of Atlantoraja castelnaui. Red arrow in the bottom left graph indicates direction of shift
of the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red: projected
future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
55
Figure 3: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of
environmental suitability for the occurrence of Atlantoraja cyclophora (Acyc).
Figure 4: Niche dynamics of Atlantoraja cyclophora. Red arrow in the bottom left graph indicates direction of
shift of the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red:
projected future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
56
Figure 5: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of
environmental suitability for the occurrence of Atlantoraja platana (Apla).
Figure 6: Niche dynamics of Atlantoraja platana. Red arrow in the bottom left graph indicates direction of shift
of the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red: projected
future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
57
Figure 7: Ecological niche models of present (left) and future (right) climatic scenarios, showing degrees of
environmental suitability for the occurrence of Rioraja agassizii (Raga).
Figure 8: Niche dynamics of Rioraja agassizii. Red arrow in the bottom left graph indicates direction of shift of
the distribution’ centroid between the two climatic scenarios. Green: present climatic conditions; red: projected
future climatic scenario (2100, RCP 8.5); purple: overlap between scenarios.
58
Table 4: Niche overlap, expansion, stability, and unfilling measured between present and future climatic scenarios
for each Riorajini' species. All values range from 0 (none) to 1 (identical). Acas – Atlantoraja castelnaui; Acyc –
A. cyclophora; Apla – A. platana; Raga – Rioraja agassizii.
Acas Acyc Apla Raga
Niche overlap 0.8953927 0.9241363 0.9520403 0.8097972
Niche expansion 0.1947291 0.10116894 0.05615735 0.10056594
Niche stability 0.8052709 0.89883106 0.94384265 0.89943406
Niche unfilling 0.2116576 0.07294093 0 0.1013457
DISCUSSION
Minding the caveats
Models for current climatic conditions developed in Chapter 1, including nine
environmental predictors, showed temperature mean, salinity mean and range, and current
velocity as variables of low importance for characterizing the abiotic niche of Riorajini
(permutation importance < 12% for these variables, for all species). However, as explained in
the Methods’ section, these are the only predictors available in Bio-ORACLE for modelling
benthic habitats in an RCP 8.5 scenario. Nevertheless, we assume that this lack of data does not
constrain our understanding on the shifts of the realised niche of these species. Bearing in mind
this limitation, a variety of questions worth of debate arise from these results, and some of
which will be briefly discussed in the next sections.
What explains the modelled increase in environmental suitability?
The abiotic niche of Riorajini did not markedly expand southward as hypothesized, but
in longitude, towards deeper areas; however, still within the limits of the continental shelf,
reinforcing the barrier that depth poses to the distribution of this group (Chapter 1). The
environment is expected to change as climate changes. Tracking suitable environmental
conditions leads Riorajini species to occupy deeper zones, and such range expansion in response
to global warming is seemingly the pattern for many other marine organisms, from invertebrates
to teleosts, elasmobranchs, and mammals, for example (Parmesan & Yohe, 2003; Perry et al.,
2005; Parmesan, 2006; Molinos et al., 2015). Marine taxa exhibiting niche conservatism are
even faster tracking these conditions, as for their tendency to maintain ancestral lineages’
characteristics of niche (Chivers et al., 2017), and fewer physical barriers constraining
distribution (Pinsky et al., 2013). Such eastward shift in abiotic conditions necessary for the
occurrence of these species is likely to push their distribution towards the limits of the
59
continental shelf. However, these species are not likely to surpass the barrier imposed by the
continental shelf, since the abiotic conditions beyond this boundary are not suitable for their
occurrence – high depth and high concentration of nitrate, for example (Chapter 1). Besides, a
geographic expansion beyond the shelf requires physiological adaptations, for example,
changes in osmoregulatory strategies (Treberg & Speers-Roesch, 2016), not feasible in 100-
years scale.
Niches of A. castelnaui and R. agassizii are more at risk because these species presented
the lowest values of niche overlap and highest values of niche unfilling between climatic
scenarios modelled. This result was expected considering the shallower waters these species
occur are likely to be more exposed to climate change impacts (Nicholls & Cazenave, 2010).
Abiotic niches of these species are also likely to expand more drastically towards deeper areas,
in ~20 and ~10%, respectively. Atlantoraja cyclophora and A. platana, on the other hand,
presented the highest values of niche overlap between the two climatic scenarios (> 90% for
both species), suggesting that the areas where these species currently occur will not face severe
changes.
Overall for the group, low values of niche unfilling indicate that most of the current
abiotic conditions required by the species will be available in the future. Similarly, high values
of niche overlap and stability (>80% in both metrics) suggest stasis between the two modelled
scenarios. Nevertheless, it is important to consider that an increase in environmental adequacy
does not necessarily translates into the organisms’ ability to occupy new climatically available
areas, as other local forces might interact compromising dispersion (Vaz & Nabout, 2016).
VanDerWal et al. (2013) draw attention to the complexity of the combined climate change
impacts and other uncountable factors influencing species distribution in a way so that simply
looking at the expected poleward shift in biodiversity geographic distribution detrimental to
climate change underestimates the real effects of this phenomenon. To shed light on an often
overlooked pattern arising from a phenomenon it is important to assess issues from different
angles and be able to discuss beyond the obvious (or expected).
Beyond distribution
Temperature mean was the variable of higher contribution to the models of present and
future climatic conditions for all analysed species except A. platana, which showed salinity
range as the variable of higher contribution for both climatic scenarios (Table 2). While higher
temperatures might be tolerable for adults, it is likely to be harmful for young and eggs (Pörtner
60
& Peck, 2010), as seen for skates Leucoraja erinacea (Di Santo et al., 2015) and Raja
microocelata Montagu, 1818 (Hume, 2019), for example.
Changes in biological and physiological aspects of elasmobranchs have been
documented with probable link to global warming. Laboratory experiments simulating future
concentrations of atmospheric carbon dioxide indicate behavioural alterations in sharks
detrimental to water acidification (Green & Jutfelt, 2014), as well as a decrease in metabolic
and hunting efficiency of these predators (Pistevos et al., 2015). Negative impacts in young
bony fishes have also been documented, with Pistevos et al. (2017) study’s showing that ocean
acidification modifies the perception of physiochemical cues by fish larvae with potential to
jeopardize dispersal of young and, later, population replenishment. In embryos of Leucoraja
erinacea, there is evidence of decrease in metabolic efficiency caused by both thermal stress
and ocean acidification (Di Santo et al., 2015). The role of temperature on the timing of hatching
egg capsules of elasmobranchs is well documented in the literature (e.g., Clark, 1922) and
recent lab experiments have illustrated such effect. For example, embryos of Raja microocellata
showed that increasing temperatures leads eggs capsules to hatch faster and produce young of
smaller body size (Hume, 2019). In such study, a temperature increase of 2 °C produced skates
3.5% smaller (Hume, 2019). Such metabolic impacts in early developmental stages can reduce
an organisms’ fitness and later compromise survival, development and reproduction. More
empirical tests and estimates of climate change effects on benthic predators such as skates are
clearly lacking in the literature.
The problem of going locally extinct in some areas, and/or expanding distribution to
others not occupied before, is exacerbated by the short time period in which these changes occur
and has potential to affect the dynamics of the community. Elasmobranchs are typical predators
and, as such, play a crucial role in structuring marine communities, either directly through
predation and influences in prey-behaviour (e.g. changes in preys’ response to the presence of
predators) (Creel & Christianson, 2008; Heithaus et al., 2008), or indirectly, by keeping other
predators out of the local community system (Cailliet et al., 2005). Current abiotic conditions
act like filters delimiting boundaries to the distribution of these skates in their native area.
Climate change effects weaken such filters for aquatic invasive species (Rahel & Olden, 2008).
For Riorajini species, it is the case of expansion of habitat suitability that, if translated into real
occupation by one or more of these species, can alter the dynamics of the new occupied area.
61
CONCLUSION AND FUTURE PERSPECTIVES
Our study shows mainly a longitudinal increase in environmental suitability for the
occurrence of four neotropical skates’ species in a scenario of global warming. In favourable
biotic conditions, these species are, therefore, likely to explore deeper areas. The shift in niche
centroid for these species will push them towards the limits of ecological tolerances and
geographic space. Consequences of such shift can be detrimental both (i) to local biota, as the
introduction of a new predatory species has potential to negatively affect the dynamics of this
community, as well as (ii) to the species themselves, likely to face a reduction of geographic
range considering a longer period of time.
Future studies should take advantage of the increasing amount of biodiversity data
available online (e.g. GBIF) and the numerous modelling and ordination approaches (e.g.
Broennimann et al., 2012; Guisan et al., 2014) to assess aspects of species’ biology and ecology
in a relatively easy-to-follow, low-cost framework.
62
REFERENCES
Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017).
Bio‐ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global
Ecology and Biogeography, 27(3), 277-284.
Barnett, T. P., Pierce, D. W., & Schnur, R. (2001). Detection of anthropogenic climate change
in the world's oceans. Science, 292(5515), 270-274.
Blanchet, M. A., Primicerio, R., Smalås, A., Arias-Hansen, J., & Aschan, M. (2019). How
vulnerable is the European seafood production to climate warming? Fisheries
Research, 209, 251-258.
Bougeard, S., & Dray, S. (2018). Supervised multiblock analysis in R with the ade4
package. Journal of statistical software, 86(1), 1-17.
Broennimann, O., Fitzpatrick, M. C., Pearman, P. B., Petitpierre, B., Pellissier, L., Yoccoz, N.
G., ... & Graham, C. H. (2012). Measuring ecological niche overlap from occurrence
and spatial environmental data. Global ecology and biogeography, 21(4), 481-497.
Cailliet, G. M., Musick, J. A., Simpfendorfer, C. A., & Stevens, J. D. (2005). Ecology and life
history characteristics of chondrichthyan fish. Sharks, rays and chimaeras: the status of
the chondrichthyan fishes. IUCN SSC Shark Specialist Group. IUCN, Gland,
Switzerland and Cambridge, UK.
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B., & Thomas, C. D. (2011). Rapid range shifts
of species associated with high levels of climate warming. Science, 333(6045), 1024-
1026.
Chessel, D., Dufour, A. B., & Thioulouse, J. (2004). The ade4 package-I-One-table methods. R
news, 4(1), 5-10.
Chivers, W. J., Walne, A. W., & Hays, G. C. (2017). Mismatch between marine plankton range
movements and the velocity of climate change. Nature communications, 8(1), 1-8.
Clark, R. S. (1922). Rays and Skates (Raiœ) No. 1. – Egg-Capsules and Young. Journal of the
Marine Biological Association of the United Kingdom, 12(4), 578-643.
Coelho-Souza, S. A., López, M. S., Guimarães, J. R. D., Coutinho, R., & Candella, R. N. (2012).
Biophysical interactions in the Cabo Frio upwelling system, Southeastern Brazil.
Brazilian Journal of Oceanography, 60(3), 353-365.
Costa, T. L., Pennino, M. G., & Mendes, L. F. (2017). Identifying ecological barriers in marine
environment: The case study of Dasyatis marianae. Marine environmental
research, 125, 1-9.
63
Creel, S., & Christianson, D. (2008). Relationships between direct predation and risk
effects. Trends in Ecology & Evolution, 23(4), 194-201.
Dambach, J., & Rödder, D. (2011). Applications and future challenges in marine species
distribution modeling. Aquatic Conservation: Marine and Freshwater
Ecosystems, 21(1), 92-100.
Di Santo, V. (2015). Ocean acidification exacerbates the impacts of global warming on
embryonic little skate, Leucoraja erinacea (Mitchill). Journal of experimental marine
biology and ecology, 463, 72-78.
Dray, S., & Dufour, A. B. (2007). The ade4 package: implementing the duality diagram for
ecologists. Journal of statistical software, 22(4), 1-20.
Dray, S., Dufour, A. B., & Chessel, D. (2007). The ade4 package-II: Two-table and K-table
methods. R news, 7(2), 47-52.
Dulvy, N. K., & Reynolds, J. D. (2002). Predicting extinction vulnerability in
skates. Conservation Biology, 16(2), 440-450.
Dulvy, N. K., Fowler, S. L., Musick, J. A., Cavanagh, R. D., Kyne, P. M., Harrison, L. R., ... &
Pollock, C. M. (2014). Extinction risk and conservation of the world’s sharks and
rays. elife, 3, e00590.
Field, C. B., Behrenfeld, M. J., Randerson, J. T., & Falkowski, P. (1998). Primary production
of the biosphere: integrating terrestrial and oceanic components. Science, 281(5374),
237-240.
Green, L., & Jutfelt, F. (2014). Elevated carbon dioxide alters the plasma composition and
behaviour of a shark. Biology letters, 10(9), 20140538.
Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in
ecology. Ecological modelling, 135(2-3), 147-186.
Guisan, A., Petitpierre, B., Broennimann, O., Daehler, C., & Kueffer, C. (2014). Unifying niche
shift studies: insights from biological invasions. Trends in ecology & evolution, 29(5),
260-269.
Heithaus, M. R., Frid, A., Wirsing, A. J., & Worm, B. (2008). Predicting ecological
consequences of marine top predator declines. Trends in ecology & evolution, 23(4),
202-210.
Helfman, G., Collette, B. B., Facey, D. E., & Bowen, B. W. (2009). The diversity of fishes:
biology, evolution, and ecology. John Wiley & Sons.
64
Hickling, R., Roy, D. B., Hill, J. K., Fox, R., & Thomas, C. D. (2006). The distributions of a
wide range of taxonomic groups are expanding polewards. Global change
biology, 12(3), 450-455.
Houghton, E. (1996). Climate change 1995: The science of climate change: contribution of
working group I to the second assessment report of the Intergovernmental Panel on
Climate Change (Vol. 2). Cambridge University Press.
Hozbor, N., Massa, A. M., & Vooren, C. M. (2004). Atlantoraja castelnaui. IUCN Red List of
Threatened Species. Version 2012.
Hughes, L. (2000). Biological consequences of global warming: is the signal already
apparent? Trends in ecology & evolution, 15(2), 56-61.
Hume, J. B. (2019). Higher temperatures increase developmental rate & reduce body size at
hatching in the small‐eyed skate Raja microocellata: implications for exploitation of an
elasmobranch in warming seas. Journal of fish biology, 95(2), 655-658.
Kyne, P.M., San Martín, J. & Stehmann, M.F.W. (2007). Rioraja agassizii. The IUCN Red List
of Threatened Species. Version 2007.
Maclean, I. M., & Wilson, R. J. (2011). Recent ecological responses to climate change support
predictions of high extinction risk. Proceedings of the National Academy of
Sciences, 108(30), 12337-12342.
Mann, M. E., Bradley, R. S., & Hughes, M. K. (1999). Northern hemisphere temperatures
during the past millennium: Inferences, uncertainties, and limitations. Geophysical
research letters, 26(6), 759-762.
Massa, A., Hozbor, N. & Vooren, C.M. (2006). Atlantoraja cyclophora. The IUCN Red List of
Threatened Species. Version 2006.
Massa, A., Hozbor, N. & Vooren, C.M. (2006). Atlantoraja cyclophora. The IUCN Red List of
Threatened Species. Version 2006.
McEachran, J. D. & Dunn, K. A. (1998). Phylogenetic Analysis of Skates, a Morphologically
Conservative Clade of Elasmobranchs (Chondrichthyes: Rajidae). Copeia, 2, 271-290.
Molinos, J. G., Halpern, B. S., Schoeman, D. S., Brown, C. J., Kiessling, W., Moore, P. J., ...
& Burrows, M. T. (2015). Climate velocity and the future global redistribution of marine
biodiversity. Nature Climate Change, 6(1), 83-88.
Muscarella, R., Galante, P. J., Soley‐Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., &
Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent
evaluations and estimating optimal model complexity for Maxent ecological niche
models. Methods in Ecology and Evolution, 5(11), 1198-1205.
65
Nicholls, R. J., & Cazenave, A. (2010). Sea-level rise and its impact on coastal
zones. Science, 328(5985), 1517-1520.
Nicholls, R. J., & Small, C. (2002). Improved estimates of coastal population and exposure to
hazards released. Eos, Transactions American Geophysical Union, 83(28), 301-305.
Nicolas, D., Chaalali, A., Drouineau, H., Lobry, J., Uriarte, A., Borja, A., & Boët, P. (2011).
Impact of global warming on European tidal estuaries: some evidence of northward
migration of estuarine fish species. Regional Environmental Change, 11(3), 639-649.
Parmesan, C. (2006). Ecological and evolutionary responses to recent climate change. Annual
Review of Ecology, Evolution, and Systematics, 37, 637-669.
Parmesan, C., & Yohe, G. (2003). A globally coherent fingerprint of climate change impacts
across natural systems. Nature, 421(6918), 37.
Parolo, G., Rossi, G., & Ferrarini, A. (2008). Toward improved species niche modelling: Arnica
montana in the Alps as a case study. Journal of Applied Ecology, 45(5), 1410-1418.
Perry, A. L., Low, P. J., Ellis, J. R., & Reynolds, J. D. (2005). Climate change and distribution
shifts in marine fishes. Science, 308(5730), 1912-1915.
Peterson, R.G. & Stramma, L. 1991. Upper-level circulation in the South Atlantic Ocean.
Progress in Oceanography, 26(1): 1-73.
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of
species geographic distributions. Ecological modelling, 190(3-4), 231-259.
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L., & Levin, S. A. (2013). Marine taxa
track local climate velocities. Science, 341(6151), 1239-1242.
Pistevos, J. C., Nagelkerken, I., Rossi, T., & Connell, S. D. (2017). Ocean acidification alters
temperature and salinity preferences in larval fish. Oecologia, 183(2), 545-553.
Pistevos, J. C., Nagelkerken, I., Rossi, T., Olmos, M., & Connell, S. D. (2015). Ocean
acidification and global warming impair shark hunting behaviour and growth. Scientific
reports, 5, 16293.
Pörtner, H. O., & Peck, M. A. (2010). Climate change effects on fishes and fisheries: towards
a cause‐and‐effect understanding. Journal of fish biology, 77(8), 1745-1779.
Quintero, I., & Wiens, J. J. (2013). Rates of projected climate change dramatically exceed past
rates of climatic niche evolution among vertebrate species. Ecology letters, 16(8), 1095-
1103.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation
for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
66
Rahel, F. J., & Olden, J. D. (2008). Assessing the effects of climate change on aquatic invasive
species. Conservation biology, 22(3), 521-533.
Robinson, L. M., Elith, J., Hobday, A. J., Pearson, R. G., Kendall, B. E., Possingham, H. P., &
Richardson, A. J. (2011). Pushing the limits in marine species distribution modelling:
lessons from the land present challenges and opportunities. Global Ecology and
Biogeography, 20(6), 789-802.Root, T. L., Price, J. T., Hall, K. R., Schneider, S. H.,
Rosenzweig, C., & Pounds, J. A. (2003). Fingerprints of global warming on wild
animals and plants. Nature, 421(6918), 57.
Rosenzweig, C., Karoly, D., Vicarelli, M., Neofotis, P., Wu, Q., Casassa, G., ... & Tryjanowski,
P. (2008). Attributing physical and biological impacts to anthropogenic climate
change. Nature, 453(7193), 353-357.
San Martín, J.M., Stehmann, M.F.W. & Kyne, P.M. (2007). Atlantoraja platana. The IUCN
Red List of Threatened Species. Version 2007.
Somero, G. N. (2010). The physiology of climate change: how potentials for acclimatization
and genetic adaptation will determine ‘winners’ and ‘losers’. Journal of Experimental
Biology, 213(6), 912-920.
Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. A., Finlayson, M. A. X., ...
& Martin, K. D. (2007). Marine ecoregions of the world: a bioregionalization of coastal
and shelf areas. BioScience, 57(7), 573-583.
Stevens, J. D., Bonfil, R., Dulvy, N. K., & Walker, P. A. (2000). The effects of fishing on
sharks, rays, and chimaeras (chondrichthyans), and the implications for marine
ecosystems. ICES Journal of Marine Science, 57(3), 476-494.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., ... & Midgley,
P. M. (2013). Climate change 2013: The physical science basis.
Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y.
C., ... & Hughes, L. (2004). Extinction risk from climate change. Nature, 427(6970),
145-148.
Treberg, J. R., & Speers-Roesch, B. (2016). Does the physiology of chondrichthyan fishes
constrain their distribution in the deep sea? Journal of Experimental Biology, 219(5),
615-625.
Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., & De Clerck, O. (2012).
Bio‐ORACLE: a global environmental dataset for marine species distribution
modelling. Global ecology and biogeography, 21(2), 272-281.
67
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., ... &
Masui, T. (2011). The representative concentration pathways: an overview. Climatic
change, 109(1-2), 5.
VanDerWal, J., Murphy, H. T., Kutt, A. S., Perkins, G. C., Bateman, B. L., Perry, J. J., &
Reside, A. E. (2013). Focus on poleward shifts in species' distribution underestimates
the fingerprint of climate change. Nature Climate Change, 3(3), 239.
Vaz, Ú. L., & Nabout, J. C. (2016). Using ecological niche models to predict the impact of
global climate change on the geographical distribution and productivity of Euterpe
oleracea Mart. (Arecaceae) in the Amazon. Acta Botanica Brasilica, 30(2), 290-295.
Wallace, A. R. (1876). The geographical distribution of animals. Vol. I & II. Harper and
Brothers, New York, 576, 650.
Walther, G. R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J., ... & Bairlein, F.
(2002). Ecological responses to recent climate change. Nature, 416(6879), 389.
Warren, D. L., Glor, R. E., & Turelli, M. (2008). Environmental niche equivalency versus
conservatism: quantitative approaches to niche evolution. Evolution: International
Journal of Organic Evolution, 62(11), 2868-2883.
Warren, D. L., Wright, A. N., Seifert, S. N., & Shaffer, H. B. (2014). Incorporating model
complexity and spatial sampling bias into ecological niche models of climate change
risks faced by 90 California vertebrate species of concern. Diversity and
distributions, 20(3), 334-343.
68
CONCLUSÃO GERAL
O modo como uma alta biodiversidade particiona recursos limitados e compartilha o
ambiente depende de inúmeros fatores e, portanto, nem sempre é refletido em diferenças claras
em algum aspecto do nicho das espécies. Da faixa latitudinal do litoral sudeste do Brasil à
Patagônia, dentro dos limites longitudinais da plataforma continental, as quatro espécies de
raias da tribo Riorajini coexistem com alto grau de similaridade de nicho abiótico. Esse
conservatismo filogenético de nicho está refletido no monofiletismo do grupo, cuja topologia
filogenética é concordante tanto com dados morfológicos (McEachran & Dunn, 1998) quanto
genéticos (Capítulo 1). No entanto, uma sutil diferença existe entre essas espécies quanto à
probabilidade de ocorrência em função da batimetria, da distância da costa e da baixa
concentração de nitrato. Essas três variáveis podem desempenhar um papel crucial na ocupação
do ambiente por esse grupo, sendo que Atlantoraja castelnaui e Rioraja agassizii apresentam
maior probabilidade de ocorrência em águas rasas mais próximas à costa, e A. platana e A.
cyclophora ocorrem em maior profundidade e distância da costa.
Através de modelos computacionais, dados de biodiversidade e informações de satélites,
é possível simular diferentes cenários climáticos e estimar a probabilidade de ocorrência de
uma espécie em determinada área, permitindo o teste de hipóteses eco-evolutivas em direção
ao passado ou ao futuro. Em um contexto climático projetado ao ano de 2100, considerando o
cenário mais drástico de mudanças climáticas, há um aumento de áreas adequadas à ocorrência
das quatro espécies da tribo Riorajini. Na prática, a ocupação dessas novas áreas por essas
espécies pode não ser possível considerando (i) interações com outras espécies, (ii) efeitos
combinados decorrentes das mudanças climáticas, como mudanças na composição iônica e pH
da água, e (iii) impactos na época de reprodução e de eclosão dos ovos. Estudos futuros devem,
em especial, considerar este último ponto, já que a temperatura é um fator importante à eclosão
do ovo em raias (Salinas-de-León et al., 2018). O aumento da temperatura atrelado a outros
efeitos do aquecimento global pode ser prejudicial ao desenvolvimento de juvenis em águas
rasas de zonas costeiras, já que essa variável acelera o desenvolvimento embrionário,
encurtando períodos de incubação; tal estresse térmico pode se refletir numa diminuição de
fitness ao longo das gerações (Di Santo et al., 2015). Ainda, a disponibilidade de presas pode
ser um fator determinante à coocorrência dessas espécies no ambiente, e esta também pode
mudar em um cenário de aquecimento global.
Por fim, os resultados dos dois capítulos contribuem ao entendimento de aspectos
ecológicos e evolutivos dessa tribo, bem como apresenta caminhos a explorar aspectos práticos,
69
como exemplo em estudos aplicados à fisiologia (ex.: efeitos do aumento da temperatura da
água nesses animais em diferentes estágios ontogenéticos). Além disso, através da atualização
dos mapas de distribuição geográfica apresentados, pode-se apontar áreas de possível falha
amostral com informações para subsidiar mais estudos, planos de manejo e conservação das
espécies desse grupo.
70
REFERÊNCIAS BIBLIOGRÁFICAS
Barbini, S. A., & Lucifora, L. O. (2011). Feeding habits of the Rio skate, Rioraja agassizi
(Chondrichthyes: Rajidae), from off Uruguay and north Argentina. Journal of the
Marine Biological Association of the United Kingdom, 91(6), 1175-1184.
Barbini, S. A., & Lucifora, L. O. (2012). Feeding habits of a large endangered skate from the
south-west Atlantic: the spotback skate, Atlantoraja castelnaui. Marine and Freshwater
Research, 63(2), 180-188.
Barbini, S. A., & Lucifora, L. O. (2016). Diet composition and feeding habits of the eyespot
skate, Atlantoraja cyclophora (Elasmobranchii: Arhynchobatidae), off Uruguay and
northern Argentina. Neotropical Ichthyology, 14(3).
Bovcon, N. D., Cochia, P. D., Góngora, M. E., & Gosztonyi, A. E. (2011). New records of
warm‐temperate water fishes in central Patagonian coastal waters (Southwestern South
Atlantic Ocean). Journal of Applied Ichthyology, 27(3), 832-839.
Chahine, M. T. (1992). The hydrological cycle and its influence on climate. Nature, 359(6394),
373-380.
Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B., & Thomas, C. D. (2011). Rapid range shifts
of species associated with high levels of climate warming. Science, 333(6045), 1024-
1026.
Costanza, R., d'Arge, R., De Groot, R., Farber, S., Grasso, M., Hannon, B., ... & Raskin, R. G.
(1997). The value of the world's ecosystem services and natural
capital. Nature, 387(6630), 253-260.
Darwin, C. R. (1859). On the origin of species by means of natural selection, or the preservation
of favoured races in the struggle for life. London: John Murray. [1st edition]
Di Santo, V. (2015). Ocean acidification exacerbates the impacts of global warming on
embryonic little skate, Leucoraja erinacea (Mitchill). Journal of experimental marine
biology and ecology, 463, 72-78.
Dulvy, N. K., & Reynolds, J. D. (2002). Predicting extinction vulnerability in
skates. Conservation Biology, 16(2), 440-450.
Edwards, M., & Richardson, A. J. (2004). Impact of climate change on marine pelagic
phenology and trophic mismatch. Nature, 430(7002), 881-884.
Elton, C. S. (1927). The animal community. Animal ecology, 239-256.
Falkowski, P. (2012). Ocean science: the power of plankton. Nature, 483(7387), S17-S20.
71
Field, C. B., Behrenfeld, M. J., Randerson, J. T., & Falkowski, P. (1998). Primary production
of the biosphere: integrating terrestrial and oceanic components. Science, 281(5374),
237-240.
Field, I. C., Meekan, M. G., Buckworth, R. C., & Bradshaw, C. J. (2009). Susceptibility of
sharks, rays and chimaeras to global extinction. Advances in marine biology, 56, 275-
363.
Figueiredo, J. L. (1977). Manual de peixes marinhos do sudeste e sul do Brasil. I. Introdução,
tubarões, raias e quimeras. São Paulo: Museu de Zoologia da Universidade de São
Paulo. 104p.
Gallagher, A. J., Kyne, P. M., & Hammerschlag, N. (2012). Ecological risk assessment and its
application to elasmobranch conservation and management. Journal of Fish
Biology, 80(5), 1727-1748.
Gause, G. F. (1934). The struggle for existence. Baltimore: Williams and Wilkins. 163 p.
Grinnell, J. (1917). Field tests of theories concerning distributional control. The American
Naturalist, 51(602), 115-128.
Harvey, P. H., & Pagel, M. D. (1991). The comparative method in evolutionary biology (Vol.
239). Oxford: Oxford University Press.
Hickling, R., Roy, D. B., Hill, J. K., Fox, R., & Thomas, C. D. (2006). The distributions of a
wide range of taxonomic groups are expanding polewards. Global change
biology, 12(3), 450-455.
Hortal, J., de Bello, F., Diniz-Filho, J. A. F., Lewinsohn, T. M., Lobo, J. M., & Ladle, R. J.
(2015). Seven shortfalls that beset large-scale knowledge of biodiversity. Annual
Review of Ecology, Evolution, and Systematics, 46, 523-549.
Hozbor, N., Massa, A. M., & Vooren, C. M. (2004). Atlantoraja castelnaui. IUCN Red List of
Threatened Species. Version 2012.
Iglésias, S. P., Toulhoat, L., & Sellos, D. Y. (2010). Taxonomic confusion and market
mislabelling of threatened skates: important consequences for their conservation
status. Aquatic Conservation: Marine and Freshwater Ecosystems, 20(3), 319-333.
Kocher, T. D. (2004). Adaptive evolution and explosive speciation: the cichlid fish
model. Nature Reviews Genetics, 5(4), 288.
Kyne, P.M., San Martín, J. & Stehmann, M.F.W. (2007). Rioraja agassizii. The IUCN Red List
of Threatened Species. Version 2007.
72
Lessa, R., Santana, F. M., Rincón, G., Gadig, O. B. F., & El-Deir, A. C. A. (1999).
Biodiversidade de elasmobrânquios do Brasil (MMA). Necton–Elasmobrânquios,
Recife, 154.
Losos, J. B. (2008). Phylogenetic niche conservatism, phylogenetic signal and the relationship
between phylogenetic relatedness and ecological similarity among species. Ecology
letters, 11(10), 995-1003.
Martínez, M. L., Intralawan, A., Vázquez, G., Pérez-Maqueo, O., Sutton, P., & Landgrave, R.
(2007). The coasts of our world: Ecological, economic and social
importance. Ecological economics, 63(2-3), 254-272.
Massa, A., Hozbor, N. & Vooren, C.M. (2006). Atlantoraja cyclophora. The IUCN Red List of
Threatened Species. Version 2006.
McEachran, J. D. & Dunn, K. A. (1998). Phylogenetic Analysis of Skates, a Morphologically
Conservative Clade of Elasmobranchs (Chondrichthyes: Rajidae). Copeia, 2, 271-290.
Menni, R. C., Jaureguizar, A. J., Stehmann, M. F. & Lucifora, L. O. (2010). Marine biodiversity
at the community level: zoogeography of sharks, skates, rays and chimaeras in the
southwestern Atlantic. Biodiversity Conservation, 19:775-796.
Nicholls, R. J., & Small, C. (2002). Improved estimates of coastal population and exposure to
hazards released. Eos, Transactions American Geophysical Union, 83(28), 301-305.
Nicolas, D., Chaalali, A., Drouineau, H., Lobry, J., Uriarte, A., Borja, A., & Boët, P. (2011).
Impact of global warming on European tidal estuaries: some evidence of northward
migration of estuarine fish species. Regional Environmental Change, 11(3), 639-649.
Perry, A. L., Low, P. J., Ellis, J. R., & Reynolds, J. D. (2005). Climate change and distribution
shifts in marine fishes. Science, 308(5730), 1912-1915.
Pistevos, J. C., Nagelkerken, I., Rossi, T., Olmos, M., & Connell, S. D. (2015). Ocean
acidification and global warming impair shark hunting behaviour and growth. Scientific
reports, 5, 16293.
Salinas-de-León, P., Phillips, B., Ebert, D., Shivji, M., Cerutti-Pereyra, F., Ruck, C., ... &
Marsh, L. (2018). Deep-sea hydrothermal vents as natural egg-case incubators at the
Galapagos Rift. Scientific reports, 8(1), 1788.
San Martín, J.M., Stehmann, M.F.W. & Kyne, P.M. (2007). Atlantoraja platana. The IUCN
Red List of Threatened Species. Version 2007.
Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. A., Finlayson, M. A. X., ...
& Martin, K. D. (2007). Marine ecoregions of the world: a bioregionalization of coastal
and shelf areas. BioScience, 57(7), 573-583.
73
Stevens, J. D., Bonfil, R., Dulvy, N. K., & Walker, P. A. (2000). The effects of fishing on
sharks, rays, and chimaeras (chondrichthyans), and the implications for marine
ecosystems. ICES Journal of Marine Science, 57(3), 476-494.
Viana, A. D. F., & Vianna, M. (2014). The feeding habits of the eyespot skate Atlantoraja
cyclophora (Elasmobranchii: Rajiformes) in southeastern Brazil. Zoologia
(Curitiba), 31(2), 119-125.
Viana, A. F., Valentin, J. L., & Vianna, M. (2017). Feeding ecology of elasmobranch species
in southeastern Brazil. Neotropical Ichthyology, 15(2).
Wiens, J. J., Ackerly, D. D., Allen, A. P., Anacker, B. L., Buckley, L. B., Cornell, H. V., ... &
Hawkins, B. A. (2010). Niche conservatism as an emerging principle in ecology and
conservation biology. Ecology letters, 13(10), 1310-1324.
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