Phylogeography of sharks and rays: a global review based on life history traits and biogeographic partitions

Sudha Kottillil; Chetan Rao; Brian W. Bowen; Kartik Shanker

doi:10.7717/peerj.15396

Introduction

Many marine organisms are characterized by very large distribution ranges, a finding often attributed to the lack of physical barriers. Allen (2008) estimated that the average range of a teleost (bony) reef fish in the Indo–Pacific is 9 million km², roughly the size of China, compared to 350,000 km² for a typical freshwater fish range (Albert & Carvalho, 2011). The spatial scales of population structure and dispersal in marine ecosystems are also much larger than in terrestrial and freshwater environments (Avise, 2000). Long-distance colonisation and range expansion, both facilitated and constrained by oceanographic and geographic processes, have shaped the distribution and genetic architecture of marine fishes (Hellberg, 2009).

The patterns of genetic variation within species are linked to geographical processes that give rise to sub-divided populations, as indicated by quantifiable factors such as genetic connectivity and demography. The study of population connectivity is especially pertinent to management practices for commercial exploitation and conservation (Allendorf, Luikart & Aitken, 2013). Genetic diversity is also an important axis for species assessment in a conservation context, especially for wide-ranging marine species like sharks (Domingues, Hilsdorf & Gadig, 2018).

Sharks and rays play a crucial role in the sea by maintaining coastal and oceanic ecosystem structure and function. Large sharks function as top predators while smaller sharks are mesopredators and prey of larger sharks and other oceanic predators (Dulvy et al., 2008). Unlike teleosts, most elasmobranchs (sharks, rays, and skates) show late sexual maturity, long gestation period, low fecundity, slow growth rate, high level of maternal investment and long-life spans (Dulvy et al., 2014). These extreme life histories result in elasmobranchs being among the slowest reproducing vertebrates in the ocean and make their populations extremely vulnerable to anthropogenic pressures such as overfishing, habitat modification, pollution and climate change (Heist, 2008; Snelson, Burgess & Roman, 2008). The primary cause of declining shark and ray populations is overfishing, as harvest rates exceed their capacity to replenish, and their life history traits render them vulnerable to rapid declines (Bräutigam et al., 2015; Dulvy et al., 2021; Pacoureau et al., 2021). More than half of this fishing mortality is due to bycatch (Hall, Alverson & Metuzals, 2000; Gupta et al., 2020; Dulvy et al., 2021). The expansion of the shark and ray fishing industry is an outcome of declining commercial fish populations (teleosts) and/or stringent restrictions on their capture (Bräutigam et al., 2015).

Sharks are found in coastal, demersal, and pelagic habitats that are largely limited to continental shelves, although there are a few completely oceanic species like Carcharhinus longimanus (oceanic whitetip shark). Several species in the family Sphyrnidae (hammerheads), Carcharhinus falciformis (silky shark), Galeocerdo cuvier (tiger shark), and Carcharodon carcharias (white shark) migrate between coastal and oceanic waters (Ferretti et al., 2010). Rays are mostly marine except for a few species in the family Dasyatidae capable of living in low salinity habitats, and members of Potamotrygonidae completely adapted to a life cycle in freshwater (Last et al., 2016). Like sharks, they occupy a variety of niches with pelagic rays capable of undertaking long migrations (Last et al., 2016). However, population subdivisions may be more common in rays because of limited dispersal and greater susceptibility to geographical impediments (Heist, 2005; Heupel, Carlson & Simpfendorfer, 2007). Sharks that inhabit coastal waters aggregate for mating and parturition at specific discrete locations which provide protection for juveniles (Heupel, Carlson & Simpfendorfer, 2007). The extent of population subdivision and genetic divergence between populations in different geographic regions is directly influenced by such segregation and philopatry (Hueter et al., 2005) and the dispersal ability of individuals.

For example, philopatry to natal sites in blacktip reef sharks (Carcharhinus melanopterus) is a major contributor to genetic structuring within the Indo-Pacific and between islands in French Polynesia, by reducing dispersal (Mourier & Planes, 2013; Vignaud et al., 2014). Bull shark juveniles from nurseries in the Gulf of Mexico and Atlantic showed significant genetic variation in the mitochondrial control region (mtDNA-CR) but were homogenous with nuclear microsatellites indicating male biased dispersal (Laurrabaquio et al., 2019). Similarly, population structure (using mtNADH sequences) among juvenile bull sharks from 13 nurseries located in rivers around Northern Australia also indicated a strong influence of female reproductive philopatry (Tillett et al., 2012). These observed genetic differences support philopatry and indicate a strong role in shaping population separations in bull sharks (Tillett et al., 2012; Laurrabaquio et al., 2019). Site fidelity and long-term residency also resulted in fine-scale genetic structuring within reef manta rays (Mobula alfredi) in New Caledonia (Lassauce et al., 2022).

Understanding elasmobranch biology and life history is therefore important for evolving species-specific management plans. Their governance poses a challenge as many species of sharks and rays migrate across national boundaries and international waters, and there is little knowledge/information about the migratory habits of transboundary species in international waters (Bräutigam et al., 2015; Kizhakudan et al., 2015). Presently, conservation measures for sharks and rays are influenced by political boundaries, oceanic expanses, and centres of high demand (Bräutigam et al., 2015). Conservation efforts are under-resourced due to lack of adequate funds, technical capacity and political will to efficiently monitor, control and manage elasmobranch fisheries/trade (Bräutigam et al., 2015).

Hence, we examined patterns of phylogeography and population structure within and across multiple families of sharks (Carcharhinidae, Cetorhinidae, Hemiscyllium, Odontaspididae, Stegostomatidae, Alopiidae, Rhincodontidae, Sphyrnidae and Lamnidae) and rays (Dasyatidae, Mobulidae, Myliobatidae and Gymnuridae) in relation to their habitat and life history. We explored these patterns by (a) compiling data from a variety of published and unpublished sources, (b) constructing haplotype networks and examining network topology, (c) estimating nucleotide and haplotype diversity, and (d) assessing population genetic structure using AMOVA. In a few species, we report data from a single study, but for many species, our meta-analysis combines data from multiple sources, both published and unpublished, and provides insights from comparisons across genera, and between sharks and rays.

Methods

A literature review was carried out on the phylogeography of shark and ray species to check for availability of sequence data. The accession numbers provided in publications were used to identify sequences from GenBank (Benson et al., 2013), a National Centre for Biological Sciences (National Center for Biotechnology Information, 1988) and US National Institute of Health (NIH) sequence database, as well as the geographical locations of specimens. Additional unpublished sequences were downloaded from GenBank and included in the study. We then narrowed the species list to those with sufficient sample sizes (>10). Cytochrome C oxidase subunit I (COI) was selected for this analysis because it was the most common sequence across species. Other markers may provide greater resolution for the detection of intraspecific (population level) divergences but it is a pragmatic choice based on data availability. Our final set of target species included 40 shark species from 17 genera (Table 1) and 19 ray species from 11 genera (Table 2). All mitochondrial DNA sequences (mtDNA) were obtained/downloaded from GenBank (Tables S1 and S2). The sample collection locations provided in the published research article and/or on GenBank were used for population analysis.

Table 1:

Life history attributes and distribution of shark species assessed in the present study.

Species name	Common name	Distribution	Habitat	Body size^a(cm)	IUCN category	Traits^b
Alopias pelagicus	Pelagic thresher shark	Indo–Pacific	Oceanic and pelagic	259–376	Endangered	No evidence of philopatry; ovoviviparous
Alopias superciliosus	Bigeye thresher shark	Global	Coastal, oceanic and pelagic	470–484	Vulnerable	No evidence of philopatry; ovoviviparous
Alopias vulpinus	Common thresher shark	Global	Coastal, oceanic and pelagic	575–635	Vulnerable	No evidence of philopatry; ovoviviparous
Carcharhinus altimus	Bignose shark	Global	Benthopelagic	300	Near threatened	Viviparous
Carcharhinus amblyrhynchoides	Graceful shark	Indo–west Pacific	Coastal and pelagic	178–190	Near threatened	Viviparous
Carcharhinus amboinensis	Pigeye shark	Global	Coastal, oceanic and demersal	280	Data deficient	Residency, regional philopatry; viviparous
Carcharhinus brevipinna	Spinner shark	Global	Coastal and pelagic	280–283	Near threatened	Regional philopatry, site fidelity; viviparous
Carcharhinus dussumieri	Whitecheek shark	Indo–west Pacific	Coastal and demersal	100–110	Endangered	Viviparous
Carcharhinus falciformis	Silky shark	Global	Coastal, oceanic and pelagic	330–350	Vulnerable	Site fidelity; viviparous
Carcharhinus leucas	Bull shark	Global	Coastal & estuarine and reef-associated	340–360	Near threatened	Residency, regional philopatry and site fidelity; viviparous
Carcharhinus limbatus	Common blacktip shark	Global	Coastal and reef-associated	258–297	Near threatened	Seasonal residency, regional philopatry and site fidelity; viviparous
Carcharhinus longimanus	Oceanic whitetip shark	Global	Oceanic and pelagic	350–395	Critically endangered	Seasonal residency and site fidelity; viviparous
Carcharhinus macloti	Hardnose shark	Indo–west Pacific	Reef-associated and demersal	100–110	Near threatened	Viviparous
Carcharhinus melanopterus	Blacktip reef shark	Indo–Pacific	Coastal and reef-associated	160 ≤ 200	Vulnerable	Residency, site fidelity, natal philopatry; viviparous
Carcharhinus plumbeus	Sandbar shark	Global	Coastal and benthopelagic	200–300	Near threatened	Seasonal residency, site fidelity; viviparous
Carcharhinus sealei	Blackspotted shark	Indo–west Pacific	Reef-associated	100	Near threatened	Viviparous
Carcharhinus sorrah	Spot-tail shark	Indo–west Pacific	Coastal and reef-associated	160–180	Near threatened	Residency; viviparous
Carcharodon carcharias	Great white shark	Global	Oceanic and pelagic	400–600	Vulnerable	Ovoviviparous (oophagus)
Carcharias taurus	Sand tiger	Atlantic, Indo–west Pacific	Coastal, reef-associated and demersal	320	Vulnerable	Seasonal residency, site fidelity; ovoviviparous
Cetorhinus maximus	Basking shark	Atlantic, Pacific and Artic	Coastal, oceanic and pelagic	1,200–1,220	Endangered	Ovoviviparous (oviphagy)
Chiloscyllium griseum	Grey bamboo shark	Indo–west Pacific	Reef-associated and demersal	77	Vulnerable	Oviparous
Chiloscyllium indicum	Slender bamboo shark	Indo–west Pacific	Coastal and demersal	65	Vulnerable	Oviparous
Chiloscyllium punctatum	Brown banded bamboo shark	Indo–west Pacific	Reef-associated and demersal	104	Near threatened	Oviparous
Galeocerdo cuvier	Tiger shark	Global	Coastal, oceanic and semi pelagic	400 ≥ 550	Near threatened	Residency, site fidelity; ovoviviparous
Isurus oxyrinchus	Short mako shark	Global	Coastal, oceanic and pelagic	200–400	Endangered	Site fidelity; ovoviviparous
Isurus paucus	Longfin mako	Global	Oceanic, pelagic	417–430	Endangered	Site fidelity; ovoviviparous (oviphagy)
Negaprion acutidens	Sicklefin lemon shark	Indo–Pacific	Coastal and demersal	310–380	Vulnerable	Residency, site fidelity; viviparous
Negaprion brevirostris	Lemon shark	Atlantic, East Pacific	Coastal, reef-associated and demersal	250–300	Near threatened	Residency, site fidelity, natal philopatry; viviparous
Prionace glauca	Blue shark	Global	Oceanic and pelagic	383–385	Near threatened	Viviparous
Rhincodon typus	Whale shark	Global	Coastal, oceanic and semi pelagic	1,600–2,100	Endangered	Site fidelity; ovoviviparous
Rhizoprionodon oligolinx	Grey sharpnose shark	Indo–west Pacific	Coastal, reef-associated	70	Least concern	Viviparous
Rhizoprionodon acutus	Milk shark	Indo–west Pacific	Benthopelagic	178–180	Vulnerable	Viviparous
Scoliodon laticaudus	Spadenose shark	Indo–west Pacific	Coastal and demersal	74–75	Vulnerable	Viviparous
Sphyrna lewini	Scalloped hammerhead shark	Global	Semi oceanic and semi pelagic	370–420	Critically endangered	Seasonal residency, regional philopatry and site fidelity; viviparous
Sphyrna mokarran	Great hammerhead shark	Global	Semi oceanic and semi pelagic	550–610	Critically endangered	Site affinity; viviparous
Sphyrna zygaena	Smooth hammerhead shark	Global	Semi oceanic and semi pelagic	370–400	Vulnerable	Regional philopatry; viviparous
Stegostoma fasciatum	Zebra shark	Not mapped	Coastal and demersal	235–246	Endangered	Seasonal residency and site fidelity; oviparous
Triaenodon obesus	Whitetip reef shark	Indo–Pacific	Coastal and reef-associated	170–213	Vulnerable	Residency; viviparous
Lamna ditropis	Salmon shark	North Pacific	Coastal, oceanic and pelagic	221–305	Least concern	Residency, seasonal residency, site fidelity, regional philopatry; ovoviviparous (oophagus)
Lamna nasus	Porbeagle	North Atlantic and Southern hemisphere	Oceanic, coastal and pelagic	350–365	Vulnerable	Ovoviviparous (oophagus)

DOI: 10.7717/peerj.15396/table-1

Notes:

aBody size refers to the maximum total length of the adult shark. Total length is the length measured from the tip of the snout to the tip of the longer lobe of the caudal fin.

bTraits refer to the type of philopatry and reproduction shown by the particular shark species.

Table 2:

Life history attributes and distribution of ray species assessed in the present study.

Species name	Common name	Distribution	Habitat	Disc width^a(cm)	IUCN category	Traits^b
Aetobatus narinari	Spotted eagle ray	Atlantic Ocean	Oceanic, coastal and benthopelagic	330	Near threatened	Site fidelity; ovoviviparous
Aetobatus ocellatus	Ocellated eagle ray	Indo–west Pacific	Oceanic, coastal and benthopelagic	300	Vulnerable	Ovoviviparous
Brevitrygon imbricata	Bengal whipray	Northern Indian Ocean	Coastal and demersal	23	Data deficient	Ovoviviparous
Brevitrygon walga	Scaly whipray/ Dwarf whipray	Western Indian Ocean	Coastal and demersal	32	Near threatened	Ovoviviparous
Gymnura micrura	Smooth butterfly ray	Atlantic Ocean	Coastal and demersal	110	Data deficient	Ovoviviparous
Gymnura poecilura	Longtail butterfly ray	Indo–Pacific	Coastal and demersal	104	Near threatened	Ovoviviparity
Himantura leoparda	Leopard whipray	Indo–west Pacific	Coastal and demersal	140	Vulnerable	Ovoviviparous
Himantura uarnak	Honeycomb stingray/ Reticulate whipray	Indo–west Pacific Lessepsian migrant found in the Mediterranean Sea.	Coastal and intertidal	160	Vulnerable	Site affinity; ovoviviparous
Maculabatis gerrardi	Sharpnose stingray	Indo–west Pacific	Coastal and demersal	116	Endangered	Ovoviviparous
Mobula birostris	Giant oceanic manta ray	Indo-Pacific	Oceanic, coastal and demersal	670–900	Vulnerable	Site fidelity, seasonal residency; ovoviviparous
Mobula kuhlii	Shortfin devil ray	Indo–west Pacific	Coastal, oceanic and pelagic	135	Endangered	Ovoviviparous
Mobula mobular	Devil Fish	Global	Oceanic, coastal and pelagic	520	Endangered	Ovoviviparous
Mobula tarapacana	Sicklefin devil ray	Global	Coastal, oceanic and pelagic	370	Endangered	Ovoviviparous
Mobula thurstoni	Bentfin devil ray	Global	Coastal and pelagic	189	Endangered	Ovoviviparous
Neotrygon indica	Indian ocean blue spotted maskray	Western Indian Ocean	Coastal and reef-associated	31.4	Newly described species
Neotrygon kuhlii	Bluespotted maskray	Southwest Pacific Ocean	Reef-associated and demersal	30	Data deficient	Site affinity, regional philopatry: ovoviviparous
Pateobatis jenkinsii	Jenskin’s whipray	Indo–Pacific	Coastal and demersal	150	Vulnerable	Ovoviviparous
Pteroplatytrygon violacea	Pelagic stingray	Global	Oceanic and pelagic	96	Least concern	Ovoviviparous
Taeniura lymma	Blue spotted ribbon ray	Indo–west Pacific	Coastal, reef-associated and benthic	30–35	Near threatened	Ovoviviparous

DOI: 10.7717/peerj.15396/table-2

Notes:

aDisc Width refers to the maximum measured wing-span of the ray species.

bTraits refer to the type of philopatry and reproduction shown by the particular ray species.

Alignment of sequences

The sequences were downloaded and aligned using ‘Clustal W’ in MEGA 5.05 (MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms; Kumar et al., 2018). The length of shark sequences ranged from 648 bp to 687 bp and the sequence length for rays ranged from 651 bp to 693 bp. Stop codons, if present in the aligned DNA segment, were deleted and sequences re-aligned before constructing the haplotype networks.

Data analysis

DNA Sequence Polymorphism 6.12.03 (DnaSP 6; Rozas et al., 2017) was used to generate the haplotype data file and calculate haplotype and nucleotide diversities (Nei, 1987; Nei & Miller, 1990) with corresponding standard deviations. The sample sites were categorised as Western, Eastern and Central Indian Ocean within the Indian Ocean group; Northern & Southern Atlantic within the Atlantic Ocean group; and Northern & Southern Pacific within the Pacific Ocean group. A Median Joining Haplotype Network (Bandelt, Forster & Röhl, 1999) was constructed using PopART 1.7 (Population Analysis with Reticulate Trees; Leigh & Bryant, 2015) for sharks (Table 1) and rays (Table 2). In population genetics, haplotype networks are used to understand biogeography and genealogical relationships at intraspecific levels (Leigh & Bryant, 2015). Median joining networks can handle large data sets (Posada & Crandall, 2001) and combines the features of minimum spanning trees (Kruskal’s algorithm) and Farris’s maximum-parsimony heuristic algorithm (Bandelt, Forster & Röhl, 1999).

All the observed haplotype networks were classified into seven types of topologies. Based on Jenkins, Castilho & Stevens (2018), we classified networks as star (a single dominant haplotype with many haplotypes related to it), complex mutational (a few haplotypes differing by one or two mutations and some by a very large number) and complex star (many dominant haplotypes and high frequency connections). When species from different geographic ranges did not share any haplotypes and differed by several mutations, we termed it as ‘simple exclusive’ (referred to as reciprocally monophyletic by some authors). We classified networks as ‘single’ when species from different geographic regions were represented by a single haplotype. The term ‘simple linear’ was used to refer to networks having three or more haplotypes arranged linearly with no branches. Networks that did not fall into any of the above mentioned six categories and had two or more haplotypes arranged in no particular pattern were categorized as ‘simple’.

Statistical analyses were carried out using PopART. To understand the extent of geographic structuring of evolutionary lineages, nested/hierarchical AMOVA (Excoffier, Smouse & Quattro, 1992) was conducted after defining the groups. The groups defined were Indian Ocean, Pacific Ocean, and Atlantic Ocean with subgroups as defined above. Fixation indices (Φ_ST, Φ_SC, Φ_CT) and percentage variation among groups, among populations and within populations were reported from AMOVA. Differentiation among all populations i.e., all subgroups is represented by overall Φ_ST values, while population structuring within individual ocean basins is represented by Φ_SC; population structuring across ocean basins is given by Φ_CT values. Genetic structuring of populations between Indo–Pacific and Atlantic species was also carried out, wherein the samples from Indian and Pacific Oceans were combined (Indo–Pacific) and samples from Atlantic Ocean formed the second group. The extent of genetic structuring among shark and ray species from different regions was analysed using the haplotype networks, AMOVA, and diversity values.

Kruskal–Wallis (Siegel & Castellan, 1988) tests were carried out to statistically test if the haplotype (h) and nucleotide ( π) diversity values differed among different families of sharks and rays. The test was also carried out to determine if the diversity values differed significantly between species based on their habitat. A Mann–Whitney U test (Siegel & Castellan, 1988) was used to determine whether diversity values differed significantly between sharks and rays. Fisher’s exact test was used to assess the effect of structuring across habitats for both sharks and rays. The habitats occupied by species of both sharks and rays were classified into three broad categories–pelagic (includes semi pelagic species as well), demersal (includes benthopelagic species) and reef-associated (includes those that are bottom dwelling among coral reefs, i.e., reef-associated and demersal).

Results

Before presenting results, we reiterate that data was drawn primarily from published studies (for 11 shark and three ray species) but also include unpublished data (for 28 shark and 17 ray species; Tables S1 and S2). We regard the published data as highly reliable. The unpublished data was given a higher level of scrutiny, including contribution source, chronology, and volume of sequence data. In cases where unpublished data corroborated published data, we considered the results to be supported. In instances where observed divergent lineages were entirely based on unpublished data, we regarded the results as provisional. In particular, Gymnura micrura had highly divergent lineages and the results should be treated as provisional. The careful use of GenBank data, while provisional, is unlikely to introduce a systemic bias that would alter the interpretation of results.

SHARKS: patterns of genetic structure and diversity

Network topology

Star networks were predominant among shark species, including eight pelagic, two reef-associated, two demersal and reef-associated, two demersal and one semi-pelagic species (Fig. 1A, Fig. S1 and Table S3). Complex mutational networks were observed in three reef-associated, one demersal & reef-associated, two semi-pelagic, one pelagic and one benthopelagic species. Many species exhibiting complex mutational networks also had a dominant (presumably) ancestral haplotype at the centre of a star topology. Complex star topologies were observed in four species inhabiting pelagic waters and one reef-associated species. The coastal-demersal Ca. dussumieri had two small clusters of haplotypes, corresponding to the Western Indian Ocean and Eastern Indian/South Pacific, separated by a long branch with 33 mutations (Fig. S1). Two globally distributed pelagic species (P. glauca and Sp. zygaena) and, one demersal (Ch. Griseum) and one Atlantic species (N. brevirostris) were represented by a single haplotype (Fig. S1 and Table S3).

Figure 1: Proportion of shark (A) and ray (B) species exhibiting different haplotype network topologies.
The total number of shark species in the present study is forty and number of ray species is nineteen.

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DOI: 10.7717/peerj.15396/fig-1

Haplotype and nucleotide diversity

The mean haplotype diversity value of sharks was 0.422 ±0.260 (Fig. 2A) with the highest diversity (h = 0.91 ± 0.047) observed in L. ditropis, a coastal-oceanic-pelagic species. The lowest (non-zero) diversity (h = 0.004 ± 0.066) was observed in Carcharias carcharias (Table S3). Within genus Carcharhinus, the haplotype diversity value was lowest for a coastal-demersal species Ca. amboinensis (h = 0.108 ± 0.049) and highest for Ca. plumbeus, a coastal-benthopelagic species (h = 0.671 ± 0.083). Haplotype diversity was high (h ≥ 0.50) for eighteen species, of which twelve were coastal. Haplotype diversity was low (h ≤ 0.50) for twenty-two species, with the majority (12) inhabiting oceanic/semi-oceanic waters (Table S3). There was no difference in h values between the nine families of sharks (Kruskal–Wallis χ² = 738, p = 0.49).

Figure 2: Haplotype (A) and nucleotide (B) diversity values of sharks and rays represented as boxplots.

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DOI: 10.7717/peerj.15396/fig-2

The highest nucleotide diversity was observed in the coastal, reef associated Rhiz. oligolinx (π = 0.119 ± 0.00787) while the coastal-oceanic Rhin. typus had the lowest (non-zero) diversity (0.0002 ± 0.0001; Table S3). The mean nucleotide diversity value for sharks was 0.0086 ± 0.02 (Fig. 2B). Within genus Carcharhinus, coastal, reef associated Ca. dussumieri had the highest nucleotide diversity (π = 0.027 ± 0.0039) while oceanic/pelagic Ca. longimanus had the lowest (π = 0.0003 ± 0.0001). The differences in nucleotide diversity for the remaining eight species within the genus Carcharhinus were minimal. High nucleotide diversity values (π ≥ 0.005) were observed in twelve species with the most common habitat being pelagic waters (five). Twenty-eight species had low nucleotide diversity values (π ≥ 0.005) with the most common habitat being pelagic (10) followed by reef-associated (six) and demersal (five). There was no difference in π values between the nine families of sharks (Kruskal–Wallis χ² = 6.55, p = 0.59). There was no difference in diversity values between species occupying different habitats—h (χ² = 4.04, p = 0.40) and π (χ² = 2.78, p = 0.59).

Genetic structure

The coastal-demersal Ca. dussumieri had the highest overall Φ_ST value (0.99, p < 0.001; Table 3). Eleven of 22 species (50%) belonging to the family Carcharhinidae and four pelagic species of family Lamnidae (80%) had significant overall Φ_ST values. Most species that exhibited structuring within Carcharhinidae were reef-associated and/or demersal. Isurus oxyrinchus had the lowest Φ_ST value (0.067, p = 0.018) indicating weak but significant structuring. Very weak structuring was observed in Ca. longimanus, I. oxyrhincus and G. cuvier, all inhabiting pelagic and semi pelagic waters. Four pelagic and one semi pelagic species with Indo–Pacific/global distribution showed negative or Φ_ST = 0 values, indicating a lack of structure among oceans. Overall, there was no significant difference between habitats in Φ_ST (Fisher’s exact test, p = 0.796) (Fig. 3A; See Table 3 for a summary).

Nested AMOVA results show significant structuring of population samples among oceans (Φ_CT) in five reef-associated, one semi pelagic and three pelagic species (Fig. 3A; Table 3). Among these, four reef-associated, one semi-pelagic and two pelagic species had distribution ranges in the Indo–Pacific, indicating that structuring was primarily between the Indian and Pacific Ocean. In contrast to the findings above for Φ_ST, Φ_CT was significant (Fisher’s exact test p = 0.018) for species occupying different habitats. Pairwise comparison revealed that the proportion of structured populations was significantly lower in demersal species than reef-associated species (p = 0.026).

Ca. macloti a reef-associated & demersal species had the highest Φ_CT value (0.894, p < 0.001) while the lowest was observed in two reef-associated species (Ca. sorrah and Ca. limbatus). Pelagic and semi-pelagic species in five families had non-significant Φ_CT values, thereby lacking structuring across the three (or two Indian & Pacific) ocean basins. A few reef-associated, demersal and three benthopelagic sharks also lacked structuring (Fig. 3A). Differentiation among population samples within ocean (Φ_SC) was significant for 13 species. These are five reef-associated and/or demersal, three benthopelagic, three pelagic and two semi-pelagic species (Fig. 3A; Table 3). Carcharias taurus had the highest Φ_SC value (0.994, p < 0.001; reef-associated & demersal) and the lowest was for I. oxyrinchus (0.112, p = 0.016; oceanic & semi pelagic). There was no significant difference in the proportion of structured populations within ocean basins (Fisher’s exact test, p = 0.37) across the three habitats. In comparisons of Indo–Pacific vs Atlantic groupings, three of 17 globally distributed species (Ca. longimanus, G. cuvier and I. paucus) showed structuring. A. superciliosus, Ca. brevipinna, Ca. amblyrhynchoides, Ca. amboinensis, P. glauca, Ce. maximus, Rhin. typus, Sp. mokarran and Sp. zygaena lacked detectable structuring at all levels (Table 3). A few haplotypes of Ca. brevipinna are separated by more mutations from the central haplotype compared to Ca. amboinensis, in the same region (Indo-Pacific). This could be due to differences in the number of sequences used covering a broader range for Ca. brevipinna (132 sequences), while Ca. amboinensis was represented by 72 sequences. See Fig. 4A for an overview of the network topologies and life history characteristics of five shark species.

Table 3:

AMOVA analysis carried out using cytochrome C oxidase subunit I for sharks.

The numerals in bold indicate significant structuring, p values are given in brackets and n is the sample size.

Species name	Overall Φ_ST	Global comparison (nested AMOVA)			Indo–Pacific vs Atlantic
		Φ_ST	Φ_SC	Φ_CT	Φ_ST	Φ_SC	Φ_CT
A. pelagicus (n = 146)	0.211^**	0.308^**	0.218^*(0.002)	0.116^**	Indo–Pacific distribution
A. superciliosus (n = 104)	−0.07 (0.882)	−0.066 (0.755)	−0.149 (0.949)	0.072 (0.228)	−0.12 (0.735)	−0.078 (0.814)	−0.038 (0.793)
A. vulpinus (n = 44)	0.513^**(0.001)	0.613^**	0.374 (0.365)	0.382^*(0.025)	0.56^**(0.001)	0.59^*(0.019)	−0.086 (0.267)
Ca. altimus (n = 29)	0.714^**	0.735^**	0.787^**	−0.245 (0.602)	0.759^**	0.75^*(0.003)	0.038 (0.297)
Ca. amblyrhynchoides (n = 32)	0.024 (0.296)	−0.105 (0.875)	0.303 (0.144)	0.152 (0.728)	Indo–west Pacific distribution
Ca. amboinensis (n = 72)	0.018 (0.601)	−0.086 (0.904)	0.065 (0.246)	0.162 (0.888)	Indo–Pacific distribution
Ca. brevipinna (n = 132)	0.052 (0.183)	0.036 (0.423)	0.124 (0.06)	0.106 (0.807)	0.0018 (0.537)	0.11 (0.222)	−0.13 (0.527)
Ca. dussumieri (n = 24)	0.99^**	0.99^**	0.99^**	−0.388 (0.665)	Indo–Pacific distribution
Ca. falciformis (n = 116)		0.206 (0.081)	−0.054 (0.483)	0.247^*(0.007)
Ca. leucas (n = 66)	0.37 (0.017)	0.423^*(0.003)	0.398 (0.046)	0.041 (0.279)	0.369^*(0.015)	0.46^*(0.029)	−0.17 (0.477)
Ca. limbatus (n = 78)	0.244^*(0.008)	0.238^*(0.02)	0.007 (0.22)	0.067^**	0.22^*(0.046)	0.35^*(0.016)	−0.201 (0.915)
Ca. longimanus (n = 30)	0.091^*(0.058)	0.163 (0.15)	−0.289 (0.497)	0.351 (0.096)	*0.306^ (0.032)**	−0.175 (0.17)	0.409^**
Ca. macloti (n = 12)	0.633^**	0.915^**	0.191^**	0.894^**	IP	IP	IP
Ca. melanopterus (n = 54)	*0.408^ (0.021)**	0.735^**	0.151 (0.291)	0.692^*	Indo–Pacific distribution
Ca. plumbeus (n = 48)	0.493^*	0.601^**	0.645^**	−0.122 (0.087)	0.582^**	0.649^**	−0.109 (0.791)
Ca. sealei (n = 18)	−0.013 (0.292)	All sequences from Indian Ocean
Ca. sorrah (n = 124)	0.012 (0.521)	0.06 (0.345)	0.007 (0.601)	0.067^**	Indo–Pacific distribution
Ca. taurus	0.997^**	0.998^**	0.994^**	0.667 (0.105)	0.998^**	0.993^**	*0.756^ (0.05)**
Carcharodon carcharias (n = 18)	*0.379^ (0.041)**	*0.442^ (0.026)**	0.355 (0.361)	0.134 (0.187)	0.342 (0.084)	*0.501^ (0.027)**	−0.319 (0.879)
Ce. maximus (n = 56)	−0.0104 (0.246)	0.0005 (0.2250)	−0.065 (0.329)	0.066 (0.389)	−0.013 (0.3)	−0.022 (0.31)	0.009 (0.478)
Ch. griseum (n = 12)	All sequences from central Indian Ocean
Ch. indicum (n = 14)	All sequences from eastern Indian Ocean
Ch. punctatum (n = 20)	*0.206^ (0.019)**	All sequences from eastern Indian and south Pacific Ocean
G. cuvier (n = 228)	0.068^**	0.833^**	*0.174^ (0.006)**	*0.798^ (0.057)**	0.88^**	*0.13^ (0.009)**	0.86^**
I. oxyrinchus (n = 140)	0.067^*	*0.0707^ (0.037)**	*0.112^ (0.016)**	−0.047 (0.698)	0.085^*	0.076^*	0.0104
I. paucus (n = 46)	0.401^**	0.519^**	0.157 (0.163)	0.429 (0.123)	0.562^**	0.107 (0.213)	0.509^**
L. ditropis (n = 15)	North Pacific distribution
L. nasus (n = 81)	0.455^**	0.427^**	0.646^**	−0.621 (0.731)	All sequences from Atlantic and Pacific Ocean
N. acutidens (n = 31)	0.568^**	0.718^**	0.714^**	0.016 (0.325)	Indo–Pacific distribution
N. brevirostris (n = 11)	0	Atlantic distribution
P. glauca (n = 534)	0	0	0	0	0	0	0
Rhin. typus (n = 48)	−0.001 (0.935)	−0.055 (0.993)	−0.05 (0.532)	0.0022 (0.482)	All sequences from Indo–Pacific
Rhiz. acutus (n = 78)	0.678^**	0.818^**	0.744^**	0.286 (0.263)	Indo–west Pacific distribution
Rhiz. oligolinx (n = 15)	0.026 (0.37)	All sequences from Atlantic Ocean
S. laticaudus (n = 27)	−0.0105 (0.622)	All sequences from Indian Ocean
Sp. mokarran (n = 59)	0.084 (0.129)	0.075 (0.153)	0.361 (0.0156)	−0.447 (0.728)	0.095 (0.139)	0.24 (0.202)	−0.188 (0.704)
Sp. lewini (n = 323)	0.726^**	0.80^**	0.76^**	0.207 (0.344)	0.734^**	0.81^**	−0.375 (0.678)
Sp. zygaena (n = 91)	0	0	0	0	0	0	0
St. fasciatum (n = 26)	*0.306^ (0.013)**	*0.28^ (0.071)**	*0.466^ (0.017)**	−0.35 (0.495)	Indo–Pacific distribution
T. obesus (n = 53)	0.467^**	0.654^**	*0.843^ (0.05)**	−1.2 (0.767)	All sequences from Indo–Pacific

DOI: 10.7717/peerj.15396/table-3

Notes:

*p < 0.05.

**p < 0.001.

Figure 3: Proportion of shark (A) and ray (B) species exhibiting population structuring/no structuring in relation to their habitats across different Φ statistics (grey bars represent significant structuring; black bars represent no structure).
(i) Population structuring among all populations (overall Φ_ST), (ii) population structuring within ocean basins (Φ_SC), (iii) population structuring across ocean basins (Φ_CT). Semi pelagic species have been grouped with pelagic species and benthopelagic with demersal species. Demersal & reef-associated species have been grouped with reef-associated species. Asterisk (*) indicates the p-value (p = 0.026) of test of proportions. Caret (^∧) indicates the p-value (p = 0.13) for the test of proportions.

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DOI: 10.7717/peerj.15396/fig-3

Figure 4: Haplotype network structures of six shark species representing different habitats.
All images have been taken from Wikimedia Commons. Picture credits: (A) *Carcharhinus sorrah*, Tassapon Krajangdara, CC BY 3.0; (B) *Chiloscyllium punctatum*, Coughdrop12, CC BY-SA 4.0; (C) *Carcharhinus altimus*, NOAA; (D) *Prionace glauca,* Diego Delso, CC BY-SA 4.0; (E) *Isurus oxyrinchus*, Patrick Doll, CC BY-SA 3.0; (F) *Galeocerdo cuvier*, Albert Kok, CC BY-SA 3.0. The icons representing habitat type are available at https://icons8.com.

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DOI: 10.7717/peerj.15396/fig-4