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: jess.fernandd@gmail.com

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: jess.fernandd@gmail.com

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

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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).

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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.

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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.

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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.

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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.