yes Phylogeography of sharks and rays: a global review based on life history traits and biogeographic partitions [1/1, 1 ed.]

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Phylogeography of sharks and rays: a global review based on life history traits and biogeographic partitions Sudha Kottillil1 ,2 , Chetan Rao3 , Brian W. Bowen4 and Kartik Shanker1 ,3 1

Centre for Ecological Sciences, Indian Institute of Science, Bengaluru, Karnataka, India Department of Energy and Environment, TERI School of Advanced Studies, New Delhi, India 3 Dakshin Foundation, Bengaluru, Karnataka, India 4 Hawai‘i Institute of Marine Biology, University of Hawaii, Kaneohe, Hawai‘i, United States of America 2

ABSTRACT

Submitted 13 June 2022 Accepted 20 April 2023 Published 1 June 2023 Corresponding author Sudha Kottillil, [email protected]

Considerable research exists on the life history traits, evolutionary history, and environmental factors that shape the population genetic structure of marine organisms, including sharks and rays. Conservation concerns are particularly strong for this group as they are highly susceptible to anthropogenic stressors due to a combination of life history traits including late maturity and low fecundity. Here, we provide a review and synthesis of the global phylogeography of sharks and rays. We examined existing data for 40 species of sharks belonging to 17 genera and 19 species of rays belonging to 11 genera. Median joining haplotype networks were constructed for each species for the mtDNA cytochrome C oxidase subunit I (COI), and an Analysis of Molecular Variance (AMOVA) was conducted to understand patterns of genetic diversity and structure across the three major ocean basins—the Indian, Atlantic and Pacific Oceans. Haplotype networks showed very shallow coalescence in most species, a finding previously reported for marine teleosts. Star topologies were predominant among sharks while complex mutational topologies predominated among rays, a finding we attribute to extremely limited dispersal in the early life history of rays. Population structuring varied amongst species groups, apparently due to differences in life history traits including reproductive philopatry, site fidelity, pelagic habitat, migratory habits, and dispersal ability. In comparison to reef-associated and demersal species, pelagic and semi pelagic species showed lower levels of structure between and within ocean basins. As expected, there is variation between taxa and groups, but there are also some broad patterns that can guide management and conservation strategies.

Academic editor Khor Waiho

Subjects Aquaculture, Fisheries and Fish Science, Biogeography, Bioinformatics, Genetics,

Additional Information and Declarations can be found on page 28

Keywords Conservation genetics, Elasmobranchs, Genetic diversity, Mitochondrial DNA,

DOI 10.7717/peerj.15396 Copyright 2023 Kottillil et al. Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS

Zoology Population structure, Sharks, Rays, Phylogeography, Life history

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).

How to cite this article Kottillil S, Rao C, Bowen BW, Shanker K. 2023. Phylogeography of sharks and rays: a global review based on life history traits and biogeographic partitions. PeerJ 11:e15396 http://doi.org/10.7717/peerj.15396

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

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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

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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.

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

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Distribution Indo–Pacific

Global

Global

Global Indo–west Pacific Global

Global Indo–west Pacific Global Global

Global

Global Indo–west Pacific Indo–Pacific

Common name Pelagic thresher shark Bigeye thresher shark Common thresher shark Bignose shark Graceful shark Pigeye shark

Spinner shark Whitecheek shark Silky shark Bull shark

Common blacktip shark

Oceanic whitetip shark Hardnose shark Blacktip reef shark

Species name

Alopias pelagicus

Alopias superciliosus

Alopias vulpinus

Carcharhinus altimus

Carcharhinus amblyrhynchoides

Carcharhinus amboinensis

Carcharhinus brevipinna

Carcharhinus dussumieri

Carcharhinus falciformis

Carcharhinus leucas

Carcharhinus limbatus

Carcharhinus longimanus

Carcharhinus macloti

Carcharhinus melanopterus

Coastal and reefassociated

Reef-associated and demersal

Oceanic and pelagic

Coastal and reefassociated

Coastal & estuarine and reef-associated

Coastal, oceanic and pelagic

Coastal and demersal

Coastal and pelagic

Coastal, oceanic and demersal

Coastal and pelagic

Benthopelagic

Coastal, oceanic and pelagic

Coastal, oceanic and pelagic

Oceanic and pelagic

Habitat

Table 1 Life history attributes and distribution of shark species assessed in the present study.

160 ≤ 200

100–110

350–395

258–297

340–360

330–350

100–110

280–283

280

178–190

300

575–635

470–484

259–376

Body sizea (cm)

Vulnerable

Near threatened

Critically endangered

Near threatened

Near threatened

Vulnerable

Endangered

Near threatened

Data deficient

Near threatened

Near threatened

Vulnerable

Vulnerable

Endangered

IUCN category

(continued on next page)

Residency, site fidelity, natal philopatry; viviparous

Viviparous

Seasonal residency and site fidelity; viviparous

Seasonal residency, regional philopatry and site fidelity; viviparous

Residency, regional philopatry and site fidelity; viviparous

Site fidelity; viviparous

Viviparous

Regional philopatry, site fidelity; viviparous

Residency, regional philopatry; viviparous

Viviparous

Viviparous

No evidence of philopatry; ovoviviparous

No evidence of philopatry; ovoviviparous

No evidence of philopatry; ovoviviparous

Traitsb

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Distribution Global Indo–west Pacific Indo–west Pacific Global Atlantic, Indo– west Pacific Atlantic, Pacific and Artic Indo–west Pacific Indo–west Pacific Indo–west Pacific Global Global Global

Indo–Pacific Atlantic, East Pacific Global Global Indo–west Pacific Indo–west Pacific Indo–west Pacific Global

Common name Sandbar shark Blackspotted shark Spot-tail shark Great white shark Sand tiger Basking shark Grey bamboo shark Slender bamboo shark Brown banded bamboo shark Tiger shark Short mako shark Longfin mako

Sicklefin lemon shark Lemon shark

Blue shark Whale shark Grey sharpnose shark Milk shark Spadenose shark Scalloped hammerhead shark

Species name

Carcharhinus plumbeus

Carcharhinus sealei

Carcharhinus sorrah

Carcharodon carcharias

Carcharias taurus

Cetorhinus maximus

Chiloscyllium griseum

Chiloscyllium indicum

Chiloscyllium punctatum

Galeocerdo cuvier

Isurus oxyrinchus

Isurus paucus

Negaprion acutidens

Negaprion brevirostris

Prionace glauca

Rhincodon typus

Rhizoprionodon oligolinx

Rhizoprionodon acutus

Scoliodon laticaudus

Sphyrna lewini

Table 1 (continued)

Semi oceanic and semi pelagic

Coastal and demersal

Benthopelagic

Coastal, reef-associated

Coastal, oceanic and semi pelagic

Oceanic and pelagic

Coastal, reef-associated and demersal

Coastal and demersal

Oceanic, pelagic

Coastal, oceanic and pelagic

Coastal, oceanic and semi pelagic

Reef-associated and demersal

Coastal and demersal

Reef-associated and demersal

Coastal, oceanic and pelagic

Coastal, reef-associated and demersal

Oceanic and pelagic

Coastal and reefassociated

Coastal and benthopelagic Reef-associated

Habitat

370–420

74–75

178–180

70

1,600–2,100

383–385

250–300

310–380

417–430

200–400

400 ≥ 550

104

65

77

1,200–1,220

320

400–600

160–180

100

200–300

Body sizea (cm)

Critically endangered

Vulnerable

Vulnerable

Least concern

Endangered

Near threatened

Near threatened

Vulnerable

Endangered

Endangered

Near threatened

Near threatened

Vulnerable

Vulnerable

Endangered

Vulnerable

Vulnerable

Near threatened

Near threatened

Near threatened

IUCN category

(continued on next page)

Seasonal residency, regional philopatry and site fidelity; viviparous

Viviparous

Viviparous

Viviparous

Site fidelity; ovoviviparous

Viviparous

Residency, site fidelity, natal philopatry; viviparous

Residency, site fidelity; viviparous

Site fidelity; ovoviviparous (oviphagy)

Site fidelity; ovoviviparous

Residency, site fidelity; ovoviviparous

Oviparous

Oviparous

Oviparous

Ovoviviparous (oviphagy)

Seasonal residency, site fidelity; ovoviviparous

Ovoviviparous (oophagus)

Residency; viviparous

Seasonal residency, site fidelity; viviparous Viviparous

Traitsb

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Not mapped Indo–Pacific North Pacific

North Atlantic and Southern hemisphere

Zebra shark

Whitetip reef shark

Salmon shark

Porbeagle

Stegostoma fasciatum

Triaenodon obesus

Lamna ditropis

Lamna nasus

Oceanic, coastal and pelagic

Coastal, oceanic and pelagic

Coastal and reefassociated

Coastal and demersal

Semi oceanic and semi pelagic Semi oceanic and semi pelagic

Habitat

350–365

221–305

170–213

235–246

370–400

550–610

Body sizea (cm)

Vulnerable

Least concern

Vulnerable

Endangered

Vulnerable

Critically endangered

IUCN category

Notes. a Body 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. b Traits refer to the type of philopatry and reproduction shown by the particular shark species.

Global

Global

Great hammerhead shark Smooth hammerhead shark

Sphyrna mokarran

Sphyrna zygaena

Distribution

Common name

Species name

Table 1 (continued)

Ovoviviparous (oophagus)

Residency, seasonal residency, site fidelity, regional philopatry; ovoviviparous (oophagus)

Residency; viviparous

Seasonal residency and site fidelity; oviparous

Site affinity; viviparous Regional philopatry; viviparous

Traitsb

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Atlantic Ocean Indo–west Pacific Northern Indian Ocean Western Indian Ocean Atlantic Ocean Indo–Pacific Indo–west Pacific Indo–west Pacific Lessepsian migrant found in the Mediterranean Sea. Indo–west Pacific Indo-Pacific

Indo–west Pacific Global Global Global Western Indian Ocean Southwest Pacific Ocean

Indo–Pacific Global Indo–west Pacific

Spotted eagle ray Ocellated eagle ray Bengal whipray Scaly whipray/ Dwarf whipray Smooth butterfly ray Longtail butterfly ray Leopard whipray Honeycomb stingray/ Reticulate whipray

Sharpnose stingray Giant oceanic manta ray

Shortfin devil ray Devil Fish Sicklefin devil ray Bentfin devil ray Indian ocean blue spotted maskray Bluespotted maskray

Jenskin’s whipray Pelagic stingray Blue spotted ribbon ray

Aetobatus narinari

Aetobatus ocellatus

Brevitrygon imbricata

Brevitrygon walga

Gymnura micrura

Gymnura poecilura

Himantura leoparda

Himantura uarnak

Maculabatis gerrardi

Mobula birostris

Mobula kuhlii

Mobula mobular

Mobula tarapacana

Mobula thurstoni

Neotrygon indica

Neotrygon kuhlii

Pateobatis jenkinsii

Pteroplatytrygon violacea

Taeniura lymma

Notes. a Disc Width refers to the maximum measured wing-span of the ray species. b Traits refer to the type of philopatry and reproduction shown by the particular ray species.

Distribution

Common name

Species name

Coastal, reef-associated and benthic

Oceanic and pelagic

Coastal and demersal

Reef-associated and demersal

Coastal and reef-associated

Coastal and pelagic

Coastal, oceanic and pelagic

Oceanic, coastal and pelagic

Coastal, oceanic and pelagic

Oceanic, coastal and demersal

Coastal and demersal

Coastal and intertidal

Coastal and demersal

Coastal and demersal

Coastal and demersal

Coastal and demersal

Coastal and demersal

Oceanic, coastal and benthopelagic

Oceanic, coastal and benthopelagic

Habitat

Table 2 Life history attributes and distribution of ray species assessed in the present study.

30–35

96

150

30

31.4

189

370

520

135

670–900

116

160

140

104

110

32

23

300

330

Disc widtha (cm)

Ovoviviparous

Ovoviviparous

Ovoviviparous

Ovoviviparous

Site fidelity, seasonal residency; ovoviviparous

Ovoviviparous

Site affinity; ovoviviparous

Ovoviviparous

Ovoviviparity

Ovoviviparous

Ovoviviparous

Ovoviviparous

Ovoviviparous

Site fidelity; ovoviviparous

Traitsb

Near threatened

Least concern

Vulnerable

Data deficient

Ovoviviparous

Ovoviviparous

Ovoviviparous

Site affinity, regional philopatry: ovoviviparous

Newly described species

Endangered

Endangered

Endangered

Endangered

Vulnerable

Endangered

Vulnerable

Vulnerable

Near threatened

Data deficient

Near threatened

Data deficient

Vulnerable

Near threatened

IUCN category

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 (8ST , 8SC , 8CT ) 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 8ST values, while population structuring within individual ocean basins is represented by 8SC ; population structuring across ocean basins is given by 8CT 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

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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).

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 χ 2 = 738, p = 0.49). 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 χ 2 = 6.55, p = 0.59). There was no difference in diversity values between species occupying different habitats—h (χ 2 = 4.04, p = 0.40) and π (χ 2 = 2.78, p = 0.59). Genetic structure The coastal-demersal Ca. dussumieri had the highest overall 8ST 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 8ST values. Most species that exhibited structuring within Carcharhinidae were reef-associated and/or demersal. Isurus oxyrinchus had the lowest 8ST 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

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A

B

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. Full-size DOI: 10.7717/peerj.15396/fig-1

species with Indo–Pacific/global distribution showed negative or 8ST = 0 values, indicating a lack of structure among oceans. Overall, there was no significant difference between habitats in 8ST (Fisher’s exact test, p = 0.796) (Fig. 3A; See Table 3 for a summary).

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A

B

Figure 2 Haplotype (A) and nucleotide (B) diversity values of sharks and rays represented as boxplots. Full-size DOI: 10.7717/peerj.15396/fig-2

Nested AMOVA results show significant structuring of population samples among oceans (8CT ) 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 8ST , 8CT was significant

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(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 8CT 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 8CT 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 (8SC ) 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 8SC 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.

RAYS: patterns of genetic structure and diversity Network topology Complex mutational topologies were observed in 10 out of 19 ray species (Fig. 1B, and Fig. S2 and Table S4), including one pelagic, two benthopelagic, two reef-associated & demersal, four demersal, one reef-associated species. Long branches between two clusters of haplotypes were observed in three demersal species while complex star topology was observed in one pelagic species. Star shaped haplotype networks were observed in only four species of rays (Fig. S2, Table S4) inhabiting pelagic (three) and benthopelagic (one) habitats. Haplotype and nucleotide diversity The mean haplotype diversity value was 0.74 ± 0.217 (Fig. 2A). B. walga, a coastal-benthic species, had the highest haplotype diversity (h = 0.963 ± 0.028) among rays while the pelagic Mob. tarapacana had the lowest (h = 0.17 ± 0.021; Table S4). All the species belonging to family Dasyatidae had high h values (0.67 to 0.91) while the diversity values for family Mobulidae ranged from 0.17 to 0.85. Haplotype diversity was high (h ≥ 0.50) for eighteen species representing all habitats, while only one pelagic species—Mob. tarpacana had a low value (h = 0.17 ± 0.102). The mean nucleotide diversity value for rays was 0.0184 ± 0.0134 (Fig. 2B). Nucleotide diversity was highest in the coastal-demersal G.

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**

0.99**

0.052 (0.183) 0.99**

Ca. amboinensis (n = 72)

Ca. brevipinna (n = 132)

Ca. dussumieri (n = 24)

*

0.238 (0.02) 0.163 (0.15) 0.915** **

0.244 (0.008) 0.091* (0.058) 0.633** *

Ca. limbatus (n = 78)

Ca. longimanus (n = 30)

Ca. macloti (n = 12)

0.442* (0.026)

−0.013 (0.292) 0.012 (0.521) **

0.997 0.379* (0.041) −0.0104 (0.246)

Ca. sealei (n = 18)

Ca. sorrah (n = 124)

Ca. taurus

Carcharodon carcharias (n = 18)

Ce. maximus (n = 56)

0.787

0.067

0.306* (0.032)

0.22 (0.046)

0.582**

IP

−0.013 (0.3)

0.342 (0.084)

0.998**

0.0707 (0.037)

0.568 0 0 −0.001 (0.935)

P. glauca (n = 534)

Rhin. typus (n = 48)

−0.055 (0.993)

0

0.718

**

**

N. brevirostris (n = 11)

0.427**

0.455**

N. acutidens (n = 31)

L. nasus (n = 81)

L. ditropis (n = 15)

I. paucus (n = 46)

0.519**

0.067 0.401**

I. oxyrinchus (n = 140)

*

0.833

*

0.068

**

**

G. cuvier (n = 228)

Ch. punctatum (n = 20)

0.429 (0.123)

−0.047 (0.698)

0.798* (0.057)

0.0022 (0.482)

0

0.085

0.75 (0.003)

*

0

0.038 (0.297)

−0.086 (0.267)

−0.038 (0.793)

−0.13 (0.527)

IP

0.409**

−0.201 (0.915)

−0.17 (0.477)

−0.109 (0.791)

−0.022 (0.31)

0.501* (0.027)

0.993**

0.076

0.107 (0.213)

*

0.13* (0.009)

0.509**

0.0104

0.86**

0.009 (0.478)

−0.319 (0.879)

0.756* (0.05)

Indo–Pacific distribution

0.649**

Indo–Pacific distribution

IP

−0.175 (0.17)

0.35 (0.016)

*

0.46* (0.029)

Indo–Pacific distribution

0.11 (0.222)

Indo–Pacific distribution

0 (continued on next page)

All sequences from Indo–Pacific

0

Indo–Pacific distribution

All sequences from Atlantic and Pacific Ocean

0.562**

*

0.88**

Atlantic distribution

0.016 (0.325)

−0.621 (0.731)

North Pacific distribution

−0.05 (0.532)

0

0.714

**

0.646**

0.157 (0.163)

0.112 (0.016)

*

−0.078 (0.814) 0.59* (0.019)

All sequences from eastern Indian and south Pacific Ocean 0.174 (0.006)

*

All sequences from eastern Indian Ocean

0.066 (0.389)

0.134 (0.187)

0.667 (0.105)

0.067**

8CT

Indo–west Pacific distribution

0.369* (0.015) *

8SC

Indo–Pacific vs Atlantic Indo–Pacific distribution

0.0018 (0.537)

0.759

**

0.56** (0.001)

−0.12 (0.735)

8ST

All sequences from Indian Ocean

−0.122 (0.087)

0.692

*

0.894**

0.351 (0.096)

**

0.041 (0.279)

0.247* (0.007)

−0.388 (0.665)

0.106 (0.807)

0.162 (0.888)

0.152 (0.728)

−0.245 (0.602)

0.382* (0.025)

0.072 (0.228)

0.116

**

Ch. indicum (n = 14)

−0.065 (0.329)

0.355 (0.361)

0.994

**

0.007 (0.601)

0.645**

0.151 (0.291)

0.191**

−0.289 (0.497)

0.007 (0.22)

0.398 (0.046)

−0.054 (0.483)

0.99**

0.124 (0.06)

0.065 (0.246)

0.303 (0.144)

**

0.374 (0.365)

−0.149 (0.949)

8CT

All sequences from central Indian Ocean

0.0005 (0.2250)

*

0.218 (0.002)

8SC

Ch. griseum (n = 12) 0.206* (0.019)

0.06 (0.345)

0.493*

Ca. plumbeus (n = 48)

0.998

0.601**

**

0.735

0.408 (0.021)

Ca. melanopterus (n = 54)

0.423 (0.003)

*

Ca. leucas (n = 66)

0.37 (0.017)

0.206 (0.081)

−0.086 (0.904)

*

Ca. falciformis (n = 116)

0.036 (0.423)

0.018 (0.601)

Ca. amblyrhynchoides (n = 32)

−0.105 (0.875)

0.714 0.024 (0.296)

Ca. altimus (n = 29) 0.735

0.513** (0.001)

A. vulpinus (n = 44) **

0.613**

−0.07 (0.882) **

−0.066 (0.755)

0.211

0.308

8ST

Global comparison (nested AMOVA)

A. superciliosus (n = 104)

**

Overall 8ST

A. pelagicus (n = 146)

Species name

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.

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0.654**

0.467**

T. obesus (n = 53)

Notes. *p < 0.05. ** p < 0.001.

0 0.28* (0.071)

0 0.306* (0.013)

St. fasciatum (n = 26)

0.80**

Sp. zygaena (n = 91)

0.075 (0.153)

0.084 (0.129) 0.726**

**

Sp. lewini (n = 323)

−0.0105 (0.622)

S. laticaudus (n = 27)

0.818

8ST **

0.843* (0.05)

0.466* (0.017)

0

0.76**

0.361 (0.0156)

0.744

8SC

8ST

8SC

8CT

Indo–Pacific vs Atlantic

−1.2 (0.767)

−0.35 (0.495)

0

0.207 (0.344)

−0.447 (0.728)

0

0.734**

0.24 (0.202)

−0.188 (0.704) 0

−0.375 (0.678) Indo–Pacific distribution

0

0.81**

All sequences from Indo–Pacific

0.095 (0.139)

All sequences from Indian Ocean

0.286 (0.263) Indo–west Pacific distribution All sequences from Atlantic Ocean

8CT

Global comparison (nested AMOVA)

Sp. mokarran (n = 59)

0.678 0.026 (0.37)

**

Overall 8ST

Rhiz. acutus (n = 78) Rhiz. oligolinx (n = 15)

Species name

Table 3 (continued)

A

B

Figure 3 Proportion of shark (A) and ray (B) species exhibiting population structuring/no structuring in relation to their habitats across different 8 statistics (grey bars represent significant structuring; black bars represent no structure). (i) Population structuring among all populations (overall 8ST ), (ii) population structuring within ocean basins (8SC ), (iii) population structuring across ocean basins (8CT ). 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. Full-size DOI: 10.7717/peerj.15396/fig-3

poecilura (π = 0.042 ± 0.0087) while Mob. birostris and Mob. tarapacana had the lowest value (π = 0.002; Table S4). In rays, all the nucleotide diversity values were lower than 0.005 (Table S4). There were no differences in the diversity values between ten families of rays, h (Kruskal–Wallis χ 2 = 12.19, p = 0.25) and π (χ 2 = 15.86, p = 0.069). However, demersal habitats were significantly higher in h and π relative to pelagic habitats (for h, p = 0.03 and for π, p = 0.002). The mean of both the diversity values was significantly higher for rays than for sharks—h (Mann Whitney U test, p < 0.0000096) and π (Mann Whitney U test, p < 0.00016).

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HAPLOTYPE NETWORK

SPECIES

HABITAT

a

b

c

d

e

f

Coastal

Demersal

Reef - associated

Semi pelagic

Pelagic

Oceanic

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. Full-size DOI: 10.7717/peerj.15396/fig-4

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Genetic structure Gymnura micrura (demersal) showed very high structuring (8ST = 0.989, p < 0.001) between North and South Atlantic samples with no sharing of haplotypes (simple exclusive topology; Table 3). A. narinari showed weak but significant structuring between the North and South Atlantic Ocean. The Western and Central Indian Ocean samples of N. indica (reef-associated) differed significantly with no haplotype sharing (8ST = 0.357, p < 0.001). Himantura leoparda and G. poecilura showed highly significant population structure (8ST = 0.918, p < 0.001 and 0.989, p < 0.001 respectively). In addition to these cases, three globally distributed species and four Indo–Pacific species had significant overall 8ST values. No significant structuring was observed in ten species, including T. lymma, B. imbricata, N. kuhlii, Pa. jenkinsii, Pt. violacea, Mob. kuhlii, Mob. tarapacana, Mob. thurstoni, Mob. birostris, and A. ocellatus (Fisher’s exact test, p = 0.13). Of these, five are pelagic, two are reef-associated & demersal, one is benthopelagic and two are demersal species (Fig. 3B). (See Table 4 for a summary). A nested AMOVA revealed that none of the four globally distributed species showed structuring across the three major ocean basins. Two of nine Indo–Pacific species (H. leoparda and Mac. gerrardi) showed structuring (Table 4). Structuring at the level of ocean basins was only detected in benthopelagic and demersal rays (Fig. 3B). There was no significant difference in the proportion of structured population samples among oceans (Fisher’s exact test, p = 0.25). Variation among populations within an ocean basin (8SC ) was significant for nine species of rays–six demersal, one benthopelagic and two pelagic (Fig. 3B). The highest variation within oceans was observed in G. micrura (8SC = 0.989, p = 0.001, demersal) followed by an intertidal species–H. uarnak (8SC = 0.913, p < 0.001; Lessepsian migrant), found in Indian Ocean and Mediterranean Sea (Table 4). There was no significant difference in the proportion of structured and non-structured population samples within ocean basins (Fisher’s exact test, p = 0.13). None of the four globally distributed ray species showed Indo–Pacific vs Atlantic structuring. Seven species of rays having distribution ranges in either all three or two ocean basins lacked structuring at all levels–five pelagic (four belong to genus Mobula), one reef-associated (T. lymma) and one demersal (M. birostris) species. See Fig. 5 for an overview of the network topologies and life history characteristics of five ray species.

DISCUSSION Genetic structure in sharks and rays There are at least three tiers of population structure in marine fishes. The low-dispersal damselfishes and anemonefishes (family Pomacentridae) can have population structure at the scale of individual bays and archipelagos (e.g., Dohna et al., 2015; Tenggardjaja, Bowen & Bernardi, 2016). This is often attributed, at least in part, to a greatly attenuated pelagic larval duration (PLD), but larval duration can only provide part of the answer. In a survey of 35 reef-associated species across the Hawaiian Archipelago, Selkoe et al. (2014) could attribute only 50% of the variance in population structure to PLD. The second tier is coastal fishes with broad ranges in the Indo–Pacific and Atlantic. These species typically

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0.567* (0.002)

B. walga (n = 20) 0.339** (0.001)

G. poecilura (n = 39)

0.05 (0.364) −0.278 (0.998)

Mob. birostris (n = 16)

Mob. kuhlii (n = 18) −0.062 (0.659) 0.099 (0.116)

Mob. tarapacana (n = 23)

Mob. thurstoni (n = 32) 0 0.074 (0.272) −0.812 (0.782) −0.046 (0.671)

N. kuhlii (n = 59)

P. jenkinsii (n = 22)

P. violacea (n = 25)

T. lymma (n = 20)

Notes. *p < 0.05. ** p < 0.001.

0.357

N. indica (n = 15)

**

0.1587 (0.044)

Mob. mobular (n = 15)

*

0.595* (0.015)

0.318** (0.001)

Mac. gerrardi (n = 37)

−0.233 (0.628)

0.201 (0.085)

−0.134 (0.905)

0.228 (0.052)

*

−0.462 (0.96)

−1.09 (0.543)

0.915** (0.001)

0.636**

0.974

0.918

H. uarnak (n = 49)

**

0.438** (0.001)

0.688**

−0.12 (0.704)

8ST

Indo–Pacific distribution

0.274 (0.28)

−0.124 (0.682)

0.774 (0.145)

−0.0032 (0.826)

−1.502 (0.751)

0.344**

0.019 (0.359)

0.931

**

0.618 (0.247)

0.341 (0.059)

−1.057 (0.642)

0.277 (0.135)

0.053 (0.328)

−0.0267 (0.432)

0.208**

Southwest Pacific Ocean distribution −0.291 (0.887)

−0.204 (0.666) Indo–Pacific distribution

0.045 (0.683)

−0.332 (0.859)

All sequences from central or eastern Indian Ocean 0.096 (0.31)S

0.304 (0.301)

−1.0043 (0.772)

0.087 (0.213)

Indo–Pacific distribution

Indo–Pacific distribution

Indo–Pacific distribution

Indo–Mediterranean distribution

Indo–Pacific distribution

Indo–Pacific distribution

All sequences from north or south Atlantic

0.092 (0.327)

8CT

Indo–Pacific distribution

8SC

All sequences from western or central Indian Ocean

−0.1014 (0.409)

−0.0086 (0.534)

0.165 (0.013)

*

−0.457 (0.999)

0.165 (0.116)

0.384* (0.004)

0.913**

0.626 (0.025)

*

8ST Atlantic Ocean distribution

0.27 (0.674)

8CT

Indo–Pacific vs Atlantic

All sequences from western or central Indian Ocean

0.618* (0.026)

0.714**

0.117 (0.218)

8SC

Global comparison (nested AMOVA)

H. leoparda (n = 23)

**

0.989

G. micrura (n = 15)

**

-0.028 (0.522) 0.011 (0.317)

B. imbricata (n = 23)

0.021* (0.007)

A. narinari (n = 29)

A. ocellatus (n = 18)

Simple 8ST

Species name

Table 4 AMOVA analysis carried out using cytochrome C oxidase subunit I for rays. The numerals in bold indicate significant structuring, p values are given in brackets and n is the sample size.

HAPLOTYPE NETWORK

SPECIES

HABITAT

a

b

c

d

e

f

Coastal

Demersal

Reef - associated

Benthopelagic

Pelagic

Oceanic

Figure 5 Haplotype network structures of six ray species representing different habitats. All images have been taken from Wikimedia Commons. Picture credits: (A) Taeniura lymma, Jon Hanson CC BY-SA 2.0; (B) Himantura leoparda, Julie Lawson ; (C) Gymnura poecilura, Hamid Badar CC BY 3.0; (D) Aetobatus narinari, Nicholas Lindell Reynolds CC BY-SA 4.0; (E) Mobula mobular, Julien Renoult ; (F) Mobula birostris, Jon Hanson CC BY-SA 2.0. The icons representing habitat type are available at https://icons8.com/ (Icons8 LLC., 2023). Full-size DOI: 10.7717/peerj.15396/fig-5

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have population structures at the scale of biogeographic provinces such as the Caribbean vs Brazil (Rocha et al., 2002), Red Sea vs Western Indian Ocean (Coleman et al., 2016), or Hawaiian Archipelago vs West Pacific (Leray et al., 2010). The third tier is pelagic wanderers or those that have very long PLD, including tunas (family Scombridae, Pecoraro et al., 2018; Puncher et al., 2018; Rodríguez-Ezpeleta et al., 2019) and billfishes (family Istiophoridae, Graves & McDowell, 2015). These species show population structure at the scale of ocean basins. Some moray eels (family Muraenidae), with PLDs exceeding 100 days, show no population structure across the entire Indo–Pacific basin (Reece, Bowen & Larson, 2011). Deepwater species, those below 200 m depth, seem to fall into this category as well (Andrews et al., 2020; McDowell & Brightman, 2018). Based on our analysis of existing data sets, sharks and rays (lacking a PLD) fall primarily in the second tier (biogeographic provinces) and third tier (ocean basins) because a majority of the genetic structuring was observed within or across ocean basins. Examples of the second tier include pelagic sand tiger shark (Carcharias taurus; Ahonen, Harcourt & Stow, 2009), and blacktip reef shark (Ca. melanopterus; Vignaud et al., 2014) where structuring was observed within the Atlantic and Pacific oceans respectively. The genetic structure within the Atlantic basin (Northwest Atlantic and Brazil samples) for sand tiger sharks has been attributed to the warm equatorial currents acting as a barrier along with high breeding site fidelity (Dicken et al., 2007; Ahonen, Harcourt & Stow, 2009; Bansemer & Bennett, 2009), while for blacktip reef sharks, philopatry and deep oceanic waters influence genetic structuring (Vignaud et al., 2014). Shortfin mako shark (I. oxyrhynchus; Heist, Musick & Graves, 1996) and zebra shark (St. fasciatum; Dudgeon, Broderick & Ovenden, 2009) exhibited structuring at the level of biogeographic provinces. The porbeagle shark (L. nasus) has an anti-tropical distribution (absent in tropical waters) because it prefers regions where the mean water temperature is 7–18 ◦ C (González et al., 2021). In the present study, South Atlantic and South Pacific samples comprise a southern lineage, and samples from the Mediterranean Sea and North Atlantic are united in a northern lineage. Isurus oxyrinchus, a highly migratory species also shows anti-tropical structuring. It is known to exhibit extended periods of residency and has an affinity to coastal waters and therefore does not frequently undertake trans-equatorial migrations (Corrigan et al., 2018). Apart from the ones mentioned above, bignose shark (Ca. altimus), sandbar shark (Ca. plumbeus), lemon shark (N. acutidens), milk shark (Rhiz. acutus), scalloped hammerhead shark (S. lewini) and whitetip reef shark (T. obesus) showed structuring within basins. Among rays, the spotted eagle ray (A. narinari), scaly whipray (B. walga), reticulate whipray (H. uarnak), blue spotted stingray (N. indica), giant devil ray (Mob. mobular), longtail butterfly ray (G. poecilura) and smooth butterfly ray (G. micrura) showed within basin structuring. In the third tier, several oceanic and pelagic sharks exhibit population structure between the Atlantic and Indo–Pacific basins. These include the whale shark (Rhin. typus) (8ST = 0.107; Castro et al., 2007), longfin mako (I. paucus; present study) and oceanic whitetip shark (Ca. longimanus; present study). This is a common pattern in pelagic teleost fishes where there is genetic structuring between the Atlantic and Indo–Pacific with little to no genetic structuring within ocean basins (Graves & McDowell, 2015; Bowen et al., 2016). At least two pelagic sharks show low-to-no genetic structure worldwide: basking shark

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(Ce. maximus; Hoelzel et al., 2006), and blue shark (P. glauca; Veríssimo et al., 2017). Other pelagic sharks with more limited distribution–bigeye thresher shark (A. superciliosus), great hammerhead shark (Sp. mokarran), smooth hammerhead shark (Sp. zygaena) and spinner shark (Ca. brevipinna)–showed no structuring (panmictic population) across their entire range. Silky shark (Ca. falciformis; pelagic) and spot-tail shark (Ca. sorrah) showed structured lineages only between three and two (Indo–Pacific) basins respectively. In rays, such clear structuring was not observed wherein the species showed genetic separations only between ocean basins. This difference can be attributed to the habitat preference of target species, with the majority of the ray species being demersal and exhibiting genetic structuring within ocean basins. While pelagic rays lacked genetic structuring at all levels, two demersal species (H. leoparda and Mac. gerrardi) had structuring both within and between ocean basins. Pelagic stingray (P. violacea), and five pelagic species—Mob. kuhlii, Mob. tarapcana, Mob. thurstoni, Mob. birostris and A. ocellatus showed no structuring, possibly indicating greater gene flow between and within ocean basins. The mean haplotype and nucleotide diversity (h and π) of rays were significantly higher than that of sharks. While sharks did not show any significant differences in diversity among species occupying different habitats, rays showed significantly higher diversity in species occupying demersal (rather than pelagic) habitats. This is in keeping with the observation that pelagic organisms are more dispersive and have geographically larger populations. However, neither sharks nor rays showed significant differences in diversity values among families.

Drivers of geographical genetic structure Habitat and depth preference clearly shapes the geographical genetic structure of sharks and rays (Hirschfeld et al., 2021; Canfield, Galván-Magaña & Bowen, 2022). Among sharks, genetic structure between ocean basins was observed predominantly in the shallow reefassociated species, but four pelagic (one of which is semi pelagic) sharks also showed this type of structuring. In both sharks and rays, within basin structuring was observed primarily in benthopelagic and reef-associated and/or demersal species, although exceptions did exist where pelagic species also showed structuring. In rays, structure of lineages was observed primarily in demersal and benthopelagic species (categorised as demersal in Fig. 3B). A few reef-associated and pelagic sharks and rays also showed within ocean basin structuring. There was a significant difference in the proportion of structured species between demersal and reef-associated sharks across ocean basins but not within basins. While one semi-pelagic and three pelagic sharks showed genetic structure at the scale of Indo–Pacific vs Atlantic, none of the ray species exhibited this structuring. However, this comparison has limited utility as most rays had distributions limited to the Indo–Pacific (8), with one species (H. uarnak) found in Indian Ocean and Mediterranean Sea (Table 2). H. uarnak, with a natural distributional range in the Indo–Pacific, is the largest Lessepsian elasmobranch species reported from the Mediterranean Sea (Golani, 1998; Ali et al., 2010; Amor et al., 2016). This species showed within basin structuring. A significant difference in the proportion of structured species at the ocean basin level (8CT ), was observed between

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demersal and reef-associated shark species but not in rays. The test of proportions for structuring between demersal and pelagic rays for overall 8ST and 8SC was not significant when all three habitats were considered. However, there appeared to be a stronger trend when reef-associated species were excluded from the analysis - 8ST and 8SC (Fisher’s exact test, p = 0.13). Therefore, the inability to observe significant differences between species of these two habitats in the present study may be due to small sample sizes. The dispersal of shark and ray species is entirely mediated by the active movement of juveniles and adults, unlike teleosts whose dispersal depends on planktonic larval stage as well as oceanic circulation (Taguchi et al., 2015). As expected, large-bodied oceanic sharks tend to have a lower population structure (Hirschfeld et al., 2021). Adult mediated population connectivity (AMPC) may result in different population structuring because the ability to overcome physical-biological barriers will be different across ontogenic stages (Frisk, Jordaan & Miller, 2014). Greater genetic connectivity may be observed in AMPC when compared to the classical larval-mediated geneflow as genetic exchange occurs over large distances—100s to 1,000s km (Frisk et al., 2010; Frisk, Jordaan & Miller, 2014). In winter skates (Leucoraja ocellata), adult migration strongly influenced connectivity and was responsible for increased abundance of the species along George’s Bank (Frisk et al., 2010). Apart from causing an increase in the abundance of species during a particular season, adult migrations also result in open populations where emigration and immigration play important roles in maintaining connectivity among locations (Frisk et al., 2010). Just like in any landscape, physical barriers in the marine environment affect the movement of individuals. The three major ocean basins are separated by the Isthmus of Panama, Old World Barrier and the Sunda Shelf Barrier (also referred to as Indo–Pacific barrier). Ocean basins also have mid-oceanic barriers like the East Pacific Barrier, Indian Ocean Barrier and Mid-Atlantic Barrier. Thermal barriers (equatorial warm-water barrier and Aghulas-Benguela), ocean currents, hyaline barriers, straits and depth also hinder the mobility of marine organisms (Toonen et al., 2016; Hirschfeld et al., 2021; Canfield, Galván-Magaña & Bowen, 2022). These physical and environmental barriers pose different constraints on species with varying life histories and would therefore influence the genetic structuring of sharks and rays differently. Apart from the limitations imposed by geophysical barriers, wide-ranging pelagic species may exhibit population structuring due to philopatry. Philopatric behaviour has been documented in a variety of marine taxa including at least 31 sharks (Chapman et al., 2015). Every species has unique migrational tendencies and reproductive strategies which guide their movement; therefore, it is not possible to find a general pattern of stock structure or gene flow that would apply to all species, even those occupying similar habitats (Heist, 2008). For example, tiger sharks (G. cuvier) have a regional population structure even though they undertake trans-oceanic migrations (Ferreira et al., 2015). This population genetic structure, detected in the maternallyinherited mtDNA, is attributed to female site fidelity (philopatry) to reproductive areas (Bernard et al., 2016), resulting in more structured populations (Chapman et al., 2004). Tiger sharks are monandrous and polyandry has not been detected in this species (Pirog et al., 2020). On the other hand, population structuring in pelagic thresher sharks and silky sharks may be shaped by oceanic currents and geography (Cardeñosa, Hyde & Caballero,

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2014; Clarke et al., 2015; Domingues et al., 2018; Kraft et al., 2020). Juvenile sharks have been observed to remain in their natal sites for a long time before moving to habitats used by older juveniles and then to those used by adults (Springer, 1967). Hueter et al. (2005) reported that the traits such as residency, site fidelity and philopatry, either in combination or alone, influence population structuring at finer geographic scales among coastal shark species. Therefore, behavioural patterns (like philopatry) that inhibit reproductive mixing can also result in isolated adjacent populations in the absence of geophysical barriers (Chapman et al., 2015) in addition to environmental features restricting movement (Dudgeon et al., 2012).

Topology of haplotype networks Life history traits which influence the geographic structuring of evolutionary lineages could also affect the topology of a network. A star topology typically has a single widely-distributed haplotype that is positioned at the centre of the network (Jenkins, Castilho & Stevens, 2018). This central haplotype is thought to be the ancestral haplotype with the additional haplotypes linked to it differing by a single or few mutational steps (Jenkins, Castilho & Stevens, 2018). This was the predominant topology in which sharks occupy pelagic/semi pelagic habitat (eight) of which five were found in both coastal and oceanic waters. Seven reef-associated and/or demersal species also exhibited star topologies. However, four pelagic, one demersal and one reef-associated shark species with star topology did not exhibit genetic structuring. In rays, this topology was observed in three pelagic and one benthopelagic species found in both coastal and oceanic waters and all three pelagic species lacked genetic structuring. The benthopelagic species, A. narinari, exhibited weak but significant structuring possibly as a result of site affinity (Flowers et al., 2016). Star-like networks can indicate high connectivity, recent coalescence to a common ancestor, or population expansion. In the present study, star networks predominate among highly mobile species that lacked structuring. In strong contrast to sharks (predominantly star topology), the majority of rays showed complex mutational topology, where several mutations separate the central and peripheral haplotypes. Eight ray species exhibiting this topology were either demersal and/or were found around coral reefs in coastal waters, including four with genetic structure. This topology was also observed in one oceanic-pelagic and one benthopelagic species, both found in coastal and oceanic habitats and both lacked structuring. The benthopelagic species, A. ocellatus, possibly lacked structuring because studies so far have not reported site affinity in this species. In sharks, complex mutational topologies were found in four reef-associated, two semi pelagic, one pelagic and one benthopelagic species all of which had structured populations. Therefore, shark species with complex mutational topology were structured and all of them except Rhiz. acutus exhibited philopatry such as seasonal residency, site fidelity, or natal philopatry. Another difference between sharks and rays was the number of species that showed simple exclusive network topology. Three coastal-demersal rays (B. walga, H. leoparda, G. micrura) and one shark (Ca. dussumieri) showed this topology and all of them showed genetic structuring. Complex star topology was also observed in some pelagic sharks (four)

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and ray species (one) and one reef-associated shark. In this topology, there are multiple connections and high-frequency haplotypes (Jenkins, Castilho & Stevens, 2018). Networks with a single haplotype were observed in three sharks but not in rays. The tendency towards star mtDNA networks in sharks, and complex networks in demersal rays, may indicate a fundamental difference in phylogeographic patterns. Complex networks are common in terrestrial and freshwater organisms that inhabit highly structured habitats such as rivers and streams (e.g., Schönhuth et al., 2018). Complex networks are seldom observed in marine fishes but are a recurring pattern in marine invertebrates that lack a pelagic larval stage (Bowen et al., 2020).

Shallow coalescence Marine teleosts tend to show very shallow coalescence in haplotype networks, indicating a shared common ancestor on a timescale much shorter than the age of the species (Grant & Bowen, 1998). Furthermore, pelagic teleosts tend to have shallower coalescence than coastal fishes (Graves, 1998). The causes for this phenomenon have been debated in the literature for over 20 years (e.g., Copus et al., 2022). Here we extend these conclusions to sharks and rays, which have nearly uniformly shallow coalescence in haplotype networks (Figs. S1 & S2). What could cause shallow mtDNA coalescences in marine teleosts, sharks and rays, relative to freshwater and terrestrial organisms? Certainly, part of the answer for sharks and rays is the low mutation rate relative to other vertebrates, initially proposed by Martin, Naylor & Palumbi (1992) and confirmed with comparisons across the Isthmus of Panama (Duncan et al., 2006; Keeney & Heist, 2006; Schultz et al., 2008). A second explanation is the vast medium of the ocean with few barriers and high biological connectivity. This is a condition shared by marine teleosts and elasmobranchs and separates their environmental regime from those of freshwater and terrestrial biota. A third explanation for shallow coalescence, postulated for marine teleosts, is derived from r/K selection theory (MacArthur & Wilson, 1967). Marine teleost fishes are almost universally identified with an extreme version of the r-selected strategy, with high fecundity and no parental care. Thousands or millions of eggs are produced, but few survive to reproduce. This would result in a small effective population size (Ne ; Wright, 1931) relative to the census size of reproducing adults. Sweepstakes reproduction, wherein a small number of females produce most of the next generation by fortuitously placing progeny in optimal conditions for survival, would further reduce Ne (Hedgecock & Pudovkin, 2011). The r-selected strategy, combined with sweepstakes reproduction, could explain the shallow mtDNA coalescence in marine teleosts, but not in sharks and rays. Here, sharks and rays provide a unique insight into the genetic architecture of marine organisms. They are decidedly closer to the K-selected strategy, producing fewer progenies after a long gestation. Progeny are much further along in development, mostly arriving as miniature adults that can swim at birth. We conclude that since the r-selected teleosts and the K-selected sharks and rays both have shallow coalescence, the reproductive strategy may not drive this shared trait. The alternate explanation of high connectivity should be given greater weight and could be tested with genomic kinship analyses.

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Conservation implications A comparison of structuring at the family level shows that two of three species within Alopiidae exhibit genetic structuring within ocean basins, followed by Hemiscyllium (1 of 3) and Carcharhinidae (5 of 22). Overall 8ST was significant for all species belonging to 3 families–Odontaspididae, Stegostomatidae and Lamnidae. This was followed by Carcharhinidae (11 of 22), Alopiidae and Hemiscyllium (1 of 3) indicating structuring at some level. Structuring between ocean basins was observed in two of three species within Alopiidae, one species of Hemiscyllium and six species of Carcharhinidae. In rays, only Myliobatidae (1 of 2) and Dasyatidae (2 of 10) showed genetic structuring within ocean basins. However, on comparing overall 8ST , significant values were observed in species from all families–Dasyatidae (5 of 10), Myliobatidae (1 of 2), Mobulidae (1 of 5) and Gymnuridae (2 of 2). Two species belonging to Dasyatidae and one belonging to Myliobatidae showed structuring across ocean basins. Hence one conclusion from our review is that management units based on political boundaries may be too small to be effective, which points to the need for transboundary collaboration (see Shiffman & Hammerschlag, 2016). Resource managers need to understand the pattern and degree of population subdivision to prevent over-exploitation and loss of genetic diversity. The lesson from comparative phylogeography of sharks is that multiple population units with unique genetic signatures exist in most species, except in some of the large oceanic migrants. The corresponding lesson for pelagic rays is that whole ocean basins may be the scale of population units. Demersal rays may require management on a much smaller scale, based on the implications of complex haplotype networks. When population partitions exist, they are usually concordant with biogeographic boundaries such as those between ocean basins. Of course, while there will be exceptions to these trends, these can provide broad directions for management as well as point to species that urgently need genetic studies. In at least four cases, we detected genetic separations that approach or meet the criterion for evolutionary significant units (ESUs; Moritz, 1994). The smooth butterfly ray (Gymnura micrura) shared no haplotypes between North and South Atlantic samples (8ST = 0.989, p < 0.001). The reef-associated Indian-Ocean blue spotted maskray (Neotrygon indica, described in 2018 by Pavan-Kumar et al., 2018) shared no haplotypes between the Western and Central Indian Ocean (8ST = 0.357, p < 0.001). Likewise, the leopard whipray (Himantura leoparda) and longtailed butterfly ray (Gymnura poecilura) showed highly significant population structure (8ST = 0.918, p < 0.001 and 0.989, p = 0.001 respectively). It is not surprising that our survey revealed evidence for undescribed species; however, species misidentification cannot be ruled out, and where possible sequences were verified by carrying out a BLAST search on NCBI to compare and confirm the species identification. In a survey of 284 globally distributed fish species (both teleost and elasmobranch), at least 35 showed genetic evidence of cryptic evolutionary diversity (Gaither et al., 2016). In these cases, additional studies are strongly mandated to investigate the likelihood of cryptic evolutionary lineages at or below the species level. Taxonomic assignments would result in higher conservation priorities.

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The conservation outlook for elasmobranchs is dire. Over the last decade, fishing has moved into deeper regions of the world’s oceans (Morato et al., 2006) and several elasmobranchs found in deep waters have been exploited (Kyne & Simpfendorfer, 2010). Many elasmobranchs that are under immense pressure from fishing activities show low levels of genetic diversity while continued overfishing can result in changes in population subdivision and loss of genetic variation (Allendorf, Luikart & Aitken, 2013; Domingues, Hilsdorf & Gadig, 2018). Globally, 1,199 species of sharks and rays have been assessed for the IUCN Red List, including a minimum of 391 (32.6%) species assigned to three threatened categories–Critically Endangered, Endangered and Vulnerable (IUCN Red List Assessment, https://www.iucnredlist.org/, Dulvy et al., 2021). A total of 299 species (24.9%) are classified as Data Deficient and 44.1% are categorised as Least Concern indicating that a majority of them need proper assessment and conservation effort (IUCN SSC Shark Specialist Group, 2019). In addition, most of these species are classified based on abundance and geographic range size, which may not necessarily be important determinants of extinction risk (Payne et al., 2011; Harnik, Simpson & Payne, 2012). In the cases considered here, large range sizes and geographic scope of populations provide some buffer from depletion and extirpation. Abundance, on the other hand, is a more serious concern for sharks and rays, especially if demographic trends lead to the erosion of genetic diversity, the necessary building blocks to adapt to a changing world. Genetic diversity has largely been overlooked in conservation policy and fisheries management plans (Domingues, Hilsdorf & Gadig, 2018). Only about 10% of the 2014 IUCN listed shark and ray species have been studied for genetic diversity and structuring (Domingues, Hilsdorf & Gadig, 2018). Commonly caught by-catch species like pelagic sting ray (P. violacea) and the Critically Endangered daggernose shark (Isogomphodon oxyrhynchus) with narrow distribution have not yet been evaluated for discrete populations (Domingues, Hilsdorf & Gadig, 2018). It is therefore important to understand the nature of population subdivision and the type of structuring especially in those that are commercially exploited with narrow distributional ranges. This knowledge can aid in establishing policies and improving conservation plans that prevent overexploitation and aim to preserve natural genetic diversity.

CONCLUSIONS Our metadata analysis provides insights into how populations of sharks and rays are structured globally. It was evident that populations of sharks and rays primarily show genetic structuring across biogeographic provinces and ocean basins and, like marine teleosts, exhibit shallow coalescence in haplotype networks. No clear pattern of population subdivision could be observed for species occupying similar habitats because the reasons for structuring are complex and multifaceted. Apart from biogeographic barriers, philopatry also plays an important role in population connectivity and structure. This study was able to identify certain trends in structuring with populations of reef-associated shark species showing a higher proportion of genetic structuring across ocean basins when compared to demersal species. For rays, although non-significant, the results suggested that within basin

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genetic structuring could be higher for demersal species when compared to pelagic species. Network topologies of sharks were predominantly star-shaped while for rays (mostly demersal) they were complex mutational, indicating that the latter has more structured populations. Therefore, special recognition needs to be given to demersal rays which require management at a smaller scale. Since most of the shark and ray species in this study are migratory and showed genetic subdivisions among population samples, it is important that these ‘population units’ are assessed and managed individually. Conservation efforts need to move beyond political boundaries and require transboundary collaborations spanning neighbouring countries for the effective management of elasmobranchs.

LIMITATIONS OF THE STUDY The present study has used COI sequences given their availability for a large number of sharks and rays. Other mtDNA markers like the control region could reveal a different or more nuanced view of the observed population structure patterns. The present study also did not include skates (order Rajiformes) which are an important group of egg-laying elasmobranchs. Skates could potentially show tier 1 or 2 genetic structuring at regional and local levels (Misawa et al., 2019; however, further study is needed to compare sharks, rays and skates, the three most speciose groups of elasmobranchs.

ACKNOWLEDGEMENTS We thank Muralidharan Manoharakrishnan for comments and insights that improved the manuscript. SK conducted a part of this work during her Master’s dissertation at the TERI School of Advanced Studies. BWB thanks RJ Toonen and members of the ToBo Lab at Hawai’i Institute of Marine Biology for useful discussion and comment. We thank Fausto Tinti and two anonymous reviewers for insightful comments that improved the manuscript. We thank Shruthi Kottillil for proofreading the final manuscript and giving her inputs.

ADDITIONAL INFORMATION AND DECLARATIONS Funding The authors received no funding for this work. The open access funding for this paper has been provided by the Chan Zuckerberg Initiative. Brian W. Bowen is supported by the National Geographic Society Pristine Seas Initiative and the University of Hawai’i Sea Grant College Program. Kartik Shanker is supported by the DBT-IISc Partnership Programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Grant Disclosures The following grant information was disclosed by the authors: Chan Zuckerberg Initiative.

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National Geographic Society Pristine Seas Initiative and the University of Hawai’i Sea Grant College Program. DBT-IISc Partnership Programme.

Competing Interests Chetan Rao used to be an employee with Dakshin Foundation and this project was undertaken during his time with Dakshin Foundation. He is no longer an employee at Dakshin Foundation. Kartik Shanker is a founder trustee of Dakshin Foundation. The authors declare there are no competing interests.

Author Contributions • Sudha Kottillil conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. • Chetan Rao conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. • Brian W. Bowen analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft. • Kartik Shanker conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability The following information was supplied regarding data availability: The cytochrome c oxidase subunit I (COI) sequences of shark species are available at NCBI GenBank (Tables S1 and S2).

Supplemental Information Supplemental information for this article can be found online at http://dx.doi.org/10.7717/ peerj.15396#supplemental-information.

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Supplemental figures for: 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 Figures: Figure S1 Figure S2

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Figure S1: Median joining haplotype networks constructed using cytochrome c oxidase subunit I sequences of 40 shark species belonging to 17 genera.

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Figure S2: Median joining haplotype networks constructed using cytochrome c oxidase subunit I sequences of 19 ray species belonging to 11 genera.

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Table of Contents: Table S1 Table S2 Table S3 Table S4

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

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

Supplemental tables for:

Bineesh et al. (2016) Cardenosa et al. (2014) Chuang et al. (2016) Hacohen-Domene et al. (2018)* Jabado et al. (2015b)

Alopias pelagicus

Spaet and Berumen (2015) Velez-Zuazo et al. (2015) Wong et al. (2009)

Manjusha and Madhusoodanakurup (2010)* Marín et al. (2018) Sembiring et al. (2015)

Kumar et al. (2015) Liu et al. (2013)

Authors

Species name (sharks)

2

MH194433, MH194456 and MH194476 KF590224, KF590277, KF590278, KF590308–KF590313, KF590316, KF590328, KF590329, KF590332–KF590335, KF590351–KF590355, KF590439, KF590446, KF590503 and KC840948 KM396948 KJ146022–KJ146026 FJ518964–FJ518975, FJ519183–FJ519185

HQ589266 and HQ589267

KF899545–KF899548 and HM239672 KM218907–KM218923 KP719248–KP719286 MK188807 KP193429, KP193394, KP193357, KP193344, KP193340, KP193335, KP193328, KP193312, KP193299, KP193282, KP193273, KP193269, KP193264, KP193238.1, KP193237, KP193223 and KP193173 JX978330 KF606768, KF606777, KF606787, KF606798, KF606800, KF606824, KF606838, KF606840, KF606842, KF606848, KF606850, KF606852, KF606859 and KF606941– KF606943

Accession numbers of COI sequences in GenBank

Table S1: List of authors along with the corresponding GenBank accession numbers of cytochrome c oxidase subunit I (COI) sequences of shark species (n = 40) that have been used in the present study. Asterix (*) indicate sequences that have not been published but is available on GenBank.

Alopias superciliosus

da Silva Ferrette et al. (2019) Vella et al. (2017) Ward et al. (2005) Ward et al. (2008) Wong et al. (2009) Zacharia et al. (2016)*

Kumar et al. (2015) Liu et al. (2013) Manjusha and Madhusoodanakurup (2010)* Sembiring et al. (2015)

Deepak et al. (2013)* Hastings and Burton (2008)* Hastings and Burton (2010)* Jabado et al. (2015b)

Bineesh et al. (2016) Chuang et al. (2016)

3

KF590221, KF590226, KF590228, KF590248, KF590331, KF590337, KF590404, KF590406–KF590409, KF590417–KF590419, KF590483, KF590487–KF590490, KF590499, KF793744, KF793770 and KC840955 MH911331–MH911335 MF405097 DQ108329, DQ108330 EU398519–EU398521 FJ518976–FJ518986, FJ519032–FJ519034, FJ519601 and FJ519603 KX063632

HM990646–HM990649 and HM990651

HM239673 and KF899549–KF899556 KP719264–KP719280, KP719469, KP719470 and KP719487– KP719490 KF700944 and KF700945 EU400162 GU440213 and GU440214 KP193168, KP193210, KP193226, KP193272, KP193297, KP193301, KP193322, KP193342, KP193381, KP193400, KP193414, KP193419, KP193425, KP193436 and KP193454 JX978336 KF606813

Carcharhinus amblyrhynchoides

Appleyard et al. (2018) Berumen et al. (2019) Bineesh et al. (2016) International Barcode of Life (2011)* Jabado et al. (2015b)

Carcharhinus altimus

Appleyard et al. (2018) Haque et al. (2019) Jabado et al. (2015a) Moore et al. (2011) Ovenden et al. (2010)

Moftah et al. (2011) Spaet and Berumen (2015) Ward et al. (2008) Wong et al. (2009) Yokes (2016)

Aguilar et al. (2020)* Bineesh et al. (2016) Cariani et al. (2017) Keskin, (2010) Lakra et al. (2011) Steinke et al. (2015) Vella et al. (2017) Ward et al. (2008) Wong et al. (2009)

Alopias vulpinus

4

MF508663 and MF508664 MH841990 KM973137–KM973141 JN034896–JN034898 and JN082181–JN082185 GQ227287

KP193252, KP193277, KP193296, KP193376, KP193408, KP193441 and KP193445 JN641206 and JN641207 KM396952 EU398587–EU398589 FJ519047 and FJ519049–FJ519052 KY176421

MF508661 and MF508662 MH331689 KF899783–KF899788 JN313266

MT456029 and MT456102 KF899557 and KF899558 KT307158–KT307160 HQ167643 FJ347901–FJ347904 JF492808 and JF492809 KY909334–KY909344 EU398522 FJ518987–FJ518998, FJ519186–FJ519190 and FJ519605

Carcharhinus amboinensis

Khalil et al. (2015)* Noorul-Azliana and Wahidah, (2017)* Sembiring et al. (2015) Spaet and Berumen (2015) Steinke et al. (2015) Wainwright et al. (2018) Ward et al. (2008) Wong et al. (2009) Zacharia et al. (2016)*

Jabado et al. (2015a) Jabado et al. (2015b)

Ahmed et al. (2021) Appleyard et al. (2018) Bineesh et al. (2016) Chuang et al. (2016) Doukakis et al. (2011) Haque et al. (2019)

Ward and Holmes, (2007) Ward et al. (2008)

Wainwright et al. (2018)

5

KF590340 KM396937 JF493045–JF493048 MH243193 EU398599, EU398600 FJ519053–FJ519056 KX063634

MH230957 MF508671 and MF508672 KF899796 KP719472 HQ171612 and HQ171613 MH841980, MH841984, MH841992, MH841997, MH842005, MH842006 and MH842008 KM973111–KM973115 KP193152, KP193155, KP193158, KP193162, KP193166, KP193169, KP193172, KP193177, KP193179, KP193192, KP193194, KP193218, KP193222, KP193224, KP193230, KP193244, KP193246, KP193247, KP193249, KP193250, KP193253, KP193321, KP193348, KP193380, KP193383, KP193392, KP193407, KP193422, KP193422 KP193431, KP193432, KP193434 and KP193442 KU366634 MG644325

MH243097–MH243100, MH243152, MH243153, MH243161, MH243164, MH243183 and MH243233 EF609307 EU398590–EU398593

Carcharhinus brevipinna

Spaet and Berumen (2015) Steinke et al. (2015) Wainwright et al. (2018)

6

KF793760, KF793745, KF590405, KF590390, KF590386, KF590342, KF590293, KF590282 and KC840954 KM396945 JF493053–JF493059 and GU804990 MH243095, MH243105, MH243150, MH243168, MH243174, MH243176, and MH243178

KC175450 KF606833 JN989309 MG644347, MG644275–MG644277

MF508673 KF899797–KF899801 KP719473–KP719475 KF461149 HQ171614–HQ171641 MH841978 KP193451, KP193433, KP193424, KP193378, KP193374, KP193368, KP193359, KP193354, KP193346, KP193338, KP193336, KP193332, KP193327, KP193324, KP193313, KP193311, KP193307, KP193294, KP193283, KP193270, KP193267, KP193241, KP193235, KP193216, KP193198, KP193196, KP193174, KP193171, KP193144 and KM973097–KM973101 MG594062, MG594055 and MG594049

Appleyard et al. (2018) Bineesh et al. (2016) Chuang et al. (2016) Deeds et al. (2014) Doukakis et al. (2011) Haque et al. (2019) Jabado et al. (2015b)

Jamaludin and Mohd-Arshaad (2017)* Kumar et al. (2015) Liu et al. (2013) Moore et al. (2012) Noorul-Azliana and Wahidah (2017)* Semberring et al. (2017)

MT456076, MT455809, MT455794, MT455455, MT455450, MT455293, MT455246, MT455095 and MT455080

DQ885075, DQ885076 and DQ884978

Aguilar et al. (2020)*

Zemlak et al. (2009)

International Barcode of Life (2010) * Jabado et al. (2015a) Ward et al. (2005) Ward et al. (2008) Wong et al. (2009)

Wainwright et al. (2018)

da Silva Ferrette et al. (2019)

Johri et al. (2019) Sembiring et al. (2015)

Appleyard et al. (2018) Bineesh et al. (2016) Camacho and Moreno (2018) Jabado et al. (2015a) Jabado et al. (2015b)

Carcharhinus falciformis Almanza et al. (2014)

Carcharhinus dussumieri

Ward et al. (2008) Wong et al. (2009) Zacharia et al. (2016)*

7

KM987408–KM987411 MF508674–MF508677 KF899803–KF899807 MG837900–MG837909 KM973147–KM973148 KP193153, KP193184, KP193208, KP193233, KP193304, KP193314, KP193326, KP193375, KP193386, KP193449 and KP193452 MK092088 KF590281, KF590283, KF590292, KF590294-KF590301, KF590303, KF590304, KF590324–KF590326, KF590344, KF590345, KF590362– KF590373, KF590392, KF590411, KF590423–KF590426, KF590428, KF590429, KF590456, KF590484–KF590486, KF590491–KF590494, KF590497, KF590498, KF590505, KF590511, KF590515, KF793743, KF793756, KF793759, KC840952 and KC840953 MH719952, MH719954–MH719956, MH719958, MH719960, MH719965, MH719977 and MH719978 MH243106, MH243156-MH243159, MH243170, MH243172, MH243177, MH243180–MH243182 and MH243225

KM973102–KM973106 DQ108301 EU398608–EU398610 FJ519072–FJ519078

GU673375, GU673386, GU673579 and GU673585

EU398603–EU398601 FJ519606 and FJ519062-FJ519070 KX063635

Carcharhinus limbatus

Carcharhinus leucas

Aguilar et al. (2020)* Appleyard et al. (2018) Bineesh et al. (2016) Doukakis et al. (2011) Feitosa et al. (2018)

Wynen et al. (2009)

8

MT455925 and MT456066 MF508680 KF899813–KF899816 HQ171642 MF686570

EU818710

FJ518999, FJ519000–FJ519009, FJ519079-FJ519086, FJ519088 and FJ519106– FJ519109

EU398616, EU398617, EU398619, EU398618 and EU398611–EU398614

Ward et al. (2008) Wong et al. (2009)

Wainwright et al. (2018)

da Silva Ferrette et al. (2019)

Johri et al. (2019) Sembiring et al. (2015)

KF899803–KF899807 MG837900–MG837909 KM973147–KM973148 KP193153, KP193184, KP193208, KP193233, KP193304, KP193314, KP193326, KP193375, KP193386, KP193449 and KP193452 MK092088 KF590281, KF590283, KF590292, KF590294-KF590301, KF590303, KF590304, KF590324–KF590326, KF590344, KF590345, KF590362KF590373, KF590392, KF590411, KF590423–KF590426, KF590428, KF590429, KF590456, KF590484–KF590486, KF590491–KF590494, KF590497, KF590498, KF590505, KF590511, KF590515, KF793743, KF793756, KF793759, KC840952, KC840953 and JF493060–JF493063 MH719952, MH719954–MH719956, MH719958, MH719960, MH719965, MH719977 and MH719978 MH243106, MH243156–MH243159, MH243170, MH243172, MH243177, MH243180–MH243182, MH243184, MH243185, MH243187 and MH243225

EU398611–EU398614 FJ519079–FJ519086, FJ519088

Bineesh et al. (2016) Camacho and Moreno (2018) Jabado et al. (2015a) Jabado et al. (2015b)

Ward et al. (2008) Wong et al. (2009)

Carcharhinus longimanus

Ward et al. (2007)

da Silva Ferrette et al. (2019) Spaet and Berumen (2015) Steinke et al. (2015) Ward et al. (2008) Wong et al. (2009) Zemlak et al. (2009) Bineesh et al. (2016) Hastings and Burton (2010)* Jabado et al. (2015a) Jabado et al. (2015b) Moore et al. (2012) Sembiring et al. (2015)

Handy et al. (2013) International Barcode of Life (2011) * Jabado et al. (2015a) Jabado et al. (2015b) Lakra et al. (2011) Moore et al. (2011) Moore et al. (2012) Noorul-Azliana and Wahidah (2017)* Ovenden et al. (2010) Persis et al. (2009) Ribeiro et al. (2012) Sembiring et al. (2015)

9

GQ227280–GQ227282 EU541307 JQ365259–JQ365263 KF590251, KF590257, KF590259–KF590263, KF590291, KF590382, KF590500, KF590506, KF793730–KF793732, KF793747 and KF793748 MH911050 and MH911051 KM396943 JF493064 and JF493065 EU398620–EU398625 FJ519110–FJ519116 and FJ519613–FJ519616 DQ884979–DQ884981 KF913239 and KF913240–KF913242 GU440259 KM973123–KM973126 KP193281 JN989311 KF590242, KF590246, KF590412, KF590413, KF590427, KF590501, KF590504 and KF793726 EF609312

KM973118–KM973122 KP193156, KP193190, KP193193, KP193195, KP193274 and KP193401 FJ237542 and FJ237543 JN082186–JN082188 JN989310 MG644311 and MG644365

KF461152 JN313301

Carcharhinus melanopterus

Carcharhinus macloti

EU398630–EU398633 FJ519120–FJ519127

Ward et al. (2008) Wong et al. (2009)

10

KF590379, KF590380, KF590383, KF590389, KF793769 and KF793772 KM396936 MH243165 and MH243191 EF609313

JN082189 and JN082190 MH235615

KM973092–KM973096 KP193207, KP193215, KP193231, KP193236, KP193260, KP193341, KP193362, KP193371, KP193410, KP193413, KP193418, KP193421, KP193443, KP193453 and KP193157

JN313260 and JN313261

EU398627–EU398629 FJ518919–FJ518928, FJ519117–FJ519119, FJ519368 and FJ519617– FJ519620 KF913239–KF913242 KM973123–KM973126 JN989311 EF609312 EU398628 and EU398629 MF508681 KF899824, KF899823 and MK422108 JQ431553 HM387141

Moore et al. (2011) Segura-Garcia and Yain Tun (2018)* Sembiring et al. (2015) Spaet and Berumen (2015) Wainwright et al. (2018) Ward et al. (2007)

Bineesh et al. (2016) Jabado et al. (2015) Moore et al. (2012) Ward et al. (2007) Ward et al. (2008) Appleyard et al. (2018) Bineesh et al. (2016) Hubert et al. (2012) International Barcode of Life (2010)* International Barcode of Life (2011)* Jabado et al. (2015a) Jabado et al. (2015b)

Ward et al. (2008) Wong et al. (2009)

Carcharhinus sorrah

Carcharhinus sealei

Carcharhinus plumbeus

11

KM973132–KM973136

MG644363, MK422103 and MK422104–MK422106 KF590375–KF590378, KF590393. KF590395 and KF793767 MH243171 EU398640–EU398644 MF508683 MH429287 and MH429296 KF899817–KF899822 KP719305, KP719603KP719611 and KP719830 HQ171653–HQ171667 MH841979, MH841999 and MH842000 JN313264

KF590222, KF590225 and KF590227 KM396951 JF493067–JF493070 EU398638 and EU398639 FJ519152–FJ519156 and FJ519623

Sembiring et al. (2015) Spaet and Berumen (2015) Steinke et al. (2015) Ward et al. (2008) Wong et al. (2009) Bineesh et al. (2016) Sembiring et al. (2015) Wainwright et al. (2018) Ward et al. (2008) Appleyard et al. (2018) Ahmed et al. (2021) Bineesh et al. (2016) Chuang et al. (2016) Doukakis et al. (2011) Haque et al. (2019) International Barcode of Life (2010)* Jabado et al. (2015a)

MT455294, MT455848, MT455995, MT456051 and MT456060 MF508682 KP719303 sand KP719304 HQ171649–HQ171652 KM973127–KM973131 KP193149, KP193151, KP193178, KP193189, KP193191, KP193206, KP193212, KP193229, KP193279, KP193286, KP193317, KP193365, KP193369, KP193403 and KP193409

Aguilar et al. (2020)* Appleyard et al. (2018) Chuang et al. (2016) Doukakis et al. (2011) Jabado et al. (2015a) Jabado et al. (2015b)

Carcharodon carcharias

Ward et al. (2008) Wong et al. (2009) Zacharia et al. (2016)* Hastings and Burton (2010)* Iglesia et al. (2014) International Barcode of Life (2011)* Keskin (2010)* Steinke et al. (2015) Vella et al. (2017) Ward et al. (2005) Ward et al. (2008) Yokes (2016)*

Spaet and Berumen (2015) Wainwright et al. (2018)

Khalil et al. (2015)* Kumar et al. (2013) Moore et al. (2011) Sembiring et al. (2015)

Jabado et al. (2015b)

12

HQ167639 JF493076 KY909355 DQ108328 FJ518939–FJ518944 and EU398646 KY176701

EU398645 FJ519167–FJ519172 KX063636 GU440260 KM212005 JN312956

KP193145, KP193147, KP193161, KP193163, KP193164, KP19317, KP193180–KP193182, KP193221, KP193225, KP193232, KP193234, KP193239, KP193245, KP193248, KP193254, KP193259, KP193265, KP193268, KP193271, KP193275, KP193284, KP193288, KP193290, KP193295, KP193308, KP193315, KP193325, KP193351, KP193364, KP193366, KP193372, KP193388, KP193391, KP193398, KP193412, KP193423, KP193435 and KP193448 KU366614, KU366624, KU366628 and KU366630 JX978335 JN082191 and JN034904 KF590258, KF590268, KF590269, KF590343, KF590356, KF590357, KF590384, KF590440, KF590442–KF590444, KF590458, KF793728, KF793746, KF793761, KF793763, KF793764 and KC840949–KC840951 KM396941 MH243104, MH243173, MH243129, MH243198, MH243206, MH243208, MH243212 and MH243221

Galeocerdo cuvier

Chiloscyllium indicum Chiloscyllium punctatum

Chiloscyllium griseum

Cetorhinus maximus

Carcharias taurus

Zemlak et al. (2009) Aguilar et al. (2020)* Jabado et al. (2015a) Keskin (2010)* Momigliano et al. (2015) Steinke et al. (2015) Wong et al. (2009) Cariani et al. (2017) Keskin (2010)* Wong et al. (2009) Yokes (2016)* Bamaniya et al. (2016)* Bineesh et al. (2016) Kumar et al. (2015) Steinke et al. (2009) Ward et al. (2008) International Barcode of Life (2011)* Segura-Garcia and Yain Tun (2018)* Steinke et al. (2009) Ward et al. (2007) Ward et al. (2008) Aguilar et al. (2020)* Ahmed et al. (2021) Appleyard et al. (2018) Bineesh et al. (2016) Chuang et al. (2016) 13

FJ583142–FJ583144 EF609326 EU398697–EU398707 MT455235, MT455562 and MT455748 MN013428 and MH429290 MF508685 KF899429–KF899436, HM239674 KP719330, KP719613 and KP719614

MH235621

DQ884985–DQ884987 MT455256 and MT456108 KM973199 HQ167637 KR003980–KR003983 JF493071–JF493075 FJ519624 and FJ519628–FJ519699 KT307166–KT307170 HQ167642 FJ519292–FJ519322, FJ519325–FJ519328 and FJ519335–FJ519339 KY176425 and KY176702 KJ093271–KJ093276 KF899625–KF899628 KC175451 FJ583140 and FJ583141 EF609325 and EU398680–EU398692 HQ955999, HQ956000 and JN313263

Isurus oxyrinchus

KP719234–KP719246

Chuang et al. (2016)

14

KF899536–KF899541

Bineesh et al., (2016)

Bengil et al. (2019)

FM164462, FM164463, FM164465, FM164467, FM164468, FM164470, FM164473, FM164474 and FM164475 KY290584 and MG214784

MH911009–MH911011 JN657190 HQ171668–HQ171676 MF686571 and MF686584 MH841986 KM973198 KP193148, KP193160, KP193187, KP193200, KP193205, KP193214, KP193261, KP193262, KP193289, KP193309, KP193329, KP193349, KP193367, KP193370 and KP193385 KF606776 and KF606778 MK359168–MK359171 MG837930–MG837933 KF590290 and KF590299 KM396946 MN017113 MH243175, MH243201, MH243219 and MH243228 EU398785–EU398788 FJ519801–FJ519959 KJ475202 DQ885011–DQ885013, DQ885091 and DQ885091 MT456007

Barbuto et al. (2010)

Liu et al. (2013) Pirog (2019) Sarmiento-Camacho et al. (2018) Sembiring et al. (2015) Spaet and Berumen (2015) Vysakh (2019)* Wainwright et al. (2018) Ward et al. (2008) Wong et al. (2009) Zacharia et al. (2014)* Zemlak et al. (2009) Aguilar et al. (2020)*

da Silva Ferrette et al. (2019) Deepak et al. (2011)* Doukakis et al. (2011) Feitosa et al. (2018) Haque et al. (2019) Jabado et al. (2015a) Jabado et al. (2015b)

Isurus paucus

EU398889–EU398898 and EU869822 FJ518945–FJ518952 and FJ519206–FJ519210 KX063638

Ward et al. (2008) Wong et al. (2009) Zacharia et al. (2016)* Bineesh et al., (2016) Chuang et al. (2016) da Silva Ferrette et al. (2019)

15

KF899542–KF899544 KP719247 MH911336–MH911341, MH719923–MH719928, MH719928, MH719931, MH719932, MH719939, MH719940, MH719964 and MH719976

JF493694–JF493697 KT444008 KJ146030–KJ146038 KY909421–KY909429

Steinke et al. (2015) Townsend and McCracken (2015) Velez-Zuazo et al. (2015) Vella et al. (2017)

Keskin (2010)* Kumar et al. (2015) McCusker et al. (2013) Ribeiro et al. (2012) Sembiring et al. (2015)

de Carvalho (2014) Jabado et al. (2015b)

MH911342–MH911346, MH194507, MH719959, MH719963, MH719966 and MH719979 KF771232 KP193143, KP193150, KP193176, KP193183, KP193197, KP193203, KP193204, KP193209, KP193213, KP193240, KP193251. KP193255, KP193258. KP193266, KP193291, KP193303, KP193330, KP193331, KP193347, KP193352, KP193353, KP193373, KP193377, KP193379, KP193384, KP193387, KP193389, KP193390, KP193395, KP193396, KP193402, KP193427, KP193430, KP193439 and KP193440 HQ167640 JX978337 KC015501 and KC015502 JX124792 and JX034003–JX034006 KF590247, KF590279, KF590306, KF590307, KF590330, KF590336, KF590338, KF590339, KF590381, KF590420, KF590421, KF590422, KF590437, KF590438. KF793721 and KF793722

da Silva Ferrette et al. (2019)

FM164482, FM164428, FM164458 and FM164459 KP975821–KP975822 KF899650–KF899653

Barbuto et al. (2010) Bénard-Capelle et al. (2015) Bineesh et al. (2016)

16

MG703523–MG703531, MG703533, MG703534 and MG703536

Almerón et al. (2018)

Prionace glauca

KM973164 and KM973165 KP193167, KP193186, KP193199, KP193211, KP193243, KP193280, KP193293, KP193300, KP193318, KP193320, KP193323, KP193406, KP193428, KP193437, KP193428, KP193437 and KP193438 KM396935 DQ108284 EU398935–EU398940 FJ519224–FJ519225 FJ519226–FJ519235 and FJ519631

HQ167641 KJ146039–KJ146041 FJ519648–FJ519724 MF508686 and MF508686 GU674232

Negaprion brevirostris

Keskin (2010)* Velez-Zuazo et al. (2015) Wong et al. (2009) Appleyard et al. (2018) International Barcode of Life (2010)* Jabado et al. (2015a) Jabado et al. (2015b)

EU398899 and EU398900 FJ519010, FJ519012–FJ519019 and FJ519627–FJ519629 KF918878 HQ712511 FJ519020–FJ519031 JF952773

Ward et al. (2008) Wong et al. (2009) Elz et al. (2013) Mecklenburg et al. (2011) Wong et al. (2009) Zhang and Hanner (2011)

Spaet and Berumen (2015) Ward et al. (2005) Ward et al. (2008) Wong et al. (2009) Wong et al. (2009)

Negaprion acutidens

Lamna nasus

Lamna ditropis

KF590434–KF590436, KF590460, KF590464, KF590468, KF590472, KF590474, KF590478, KF590495 and KF590496

Sembiring et al. (2015)

MH911185–MH911253 and MH719774–MH719984 MN447694–MN447697 MN641758, MN641761, MN641763, MN641766, MN641769, MN641770, MN641777, MN641781, MN641784, MN641785, MN641788, MN641789, MN641798, MN641799, MN641800 and MN641801 KP192409 EU400175 JN312503–JN312505

da Silva Ferrette et al. (2019) Ferrito et al. (2019) Giovos et al. (2020)

Wainwright et al. (2018)

17

MH243131–MH243139, MH243143, MH243144, MH243160, MH243163 and MH243166

KC015829–KC015834 GU324182 KX586223–KX586225 KF590231–KF590241, KF590244, KF590245, KF590280, KF590302, KF590459, KF590461 –KF590463, KF590465 - KF590467, KF590470, KF590471, KF590473, KF590475–KF590477, KF590479–KF590482, KF793717–KF793720, KF793723–KF793725, KF793727, KF793750, KF793751, KF793758 and KF793771 KJ146042–KJ146044 KY909463–KY909469

McCuster et al. (2013) Nicolé et al. (2011) Oliveira et al. (2016) Sembiring et al. (2015)

Velez-Zuazo et al. (2013) Vella et al. (2017)

MH194440, MH194441, MH194480, MH194481, and MH194484

Marín et al. (2018)

Liu et al. (2013)

KP193159, KP193306, KP193339, KP193350, KP193446 and KP193455 KF606771, KF606774, KF606779, KF606788, KF606807, KF606817, KF606823, KF606830, KF606846 and KF606857

KP719342, KP719343 and KP719345–KP719450

Chuang et al. (2016)

Gkafas et al. (2015) Hastings and Burton (2008)* International Barcode of Life (2011)* Jabado et al. (2015b)

KT307365 and KT307366

Cariani et al. (2017)

Rhizoprinodon oligolinx

Rhizoprionodon acutus

Rhincodon typus

KM973176, KM973178 and KM973180 JX978338 KF590219, KF590220, KF590223, KF590229, KF590256, KF590264– KF590267, KF793733–KF793737 and KF793749 KM396933 MH243103 and MH243140 DQ108275–DQ108278 and DQ108290 FJ519253

Jabado et al. (2015a) Kumar et al. (2015) Sembiring et al. (2015)

Andriyono et al. (2020) Jabado et al. (2015a)

18

MH085756 KM973185–KM973188

HQ171695–HQ171734

Doukakis et al. (2011)

Spaet and Berumen (2015) Wainwright et al. (2018) Ward et al. (2005) Wong et al. (2009)

KF899632–KF899634 MH842010 GU440502 KM973184 MH194467 HQ945887–HQ945889 MN759737–MN759764 EU398993 FJ519244–FJ519252 KF899684–KF899688 and MK422110

DQ108286, DQ108288 and DQ108289 EU869837 FJ518955–FJ518963, FJ519237–FJ519243, FJ519632 and FJ519633 KY176584

Bineesh et al. (2016) Haque et al. (2019) Hastings and Burton (2010)* Jabado et al. (2015a) Marín et al. (2018) Steinke et al. (2015) Toha et al. (2020) Ward et al. (2008) Wong et al. (2009) Bineesh et al. (2016)

Ward et al. (2005) Ward et al.,(2008) Wong et al. (2009) Yokes (2016)*

Sphyrna lewini

Scoliodon laticaudus

MH230949, MH429288 and MH429289 LC422406–LC422410 MF508689–MF508691 KF899746–KF899751, MK422114 and HM239675 JQ365581–JQ365585 HQ171735–HQ171776 GU440527 KM973194–KM973197 KP177233–KP177307 MG837998–MG838000

Ahmed et al. (2021) Alghozali et al. (2019) Appleyard et al. (2018) Bineesh et al. (2016) de Oliveira Ribeiro et al. (2012) Doukakis et al. (2011) Hastings and Burton (2010)* Jabado et al. (2015a) Jabado et al. (2015b) Sarmiento-Camacho et al. (2018) Segura-Garcia and Yain Tun (2018)* Sembiring et al. (2015)

19

KF590254, KF590255, KF590271 - KF590276, KF590305, KF590315, KF590317–KF590323, KF590327, KF590347, KF590348, KF590358, KF590359, KF590394, KF590431, KF590449–KF590455, KF793729, KF793738 - KF793742, KF793753 and KF793757

MH235722 and MH235723

JX978326–JX978328 and KC175448 MH243114–MH243122 MT455337, MT455567 and MT456041

MH429294, MH429295, MH311279, MH311281, MH311285, MH243096, MH243142, MH243149, MH243151 and MH243154 MH230956, MH429292 MH087056 and MH087057 KF899694–KF899700 MN458374 MH841994 HQ956149

Ahmed et al. (2021) Bineesh et al. (2016) Habib et al. (2019)* Haque et al. (2019) International Barcode of Life (2011) * Kumar et al. (2015) Wainwright et al. (2018) Aguilar et al. (2020)*

Wainwright et al. (2018)

Sphyrna zygaena

Sphyrna mokarran

MH243101, MH243108, MH243124, MH243126, MH243128, MH243141, MH243147 and MH243148 EU399011–EU399014 FJ519373–FJ519458 and FJ519635–FJ519637 DQ885056, DQ885057 and DQ885126–DQ885127 FJ237951–FJ237956

Wainwright et al. (2018)

Wong et al. (2009) Zemlak et al. (2012) Zhang and Hanner (2012)

MK188824 KM973189–KM973193 KP193257 and KP177308–KP177317 JN989316 KM396934 EU399015–EU399017 FJ519459–FJ519488, FJ519639 and FJ519641 MT455636 and MT455637 MG703560 MF508694 KF899752–KF899757 KP719454–KP719458 HM909793

Hacohen-Domene et al. (2018)* Jabado et al. (2015a) Jabado et al. (2015b) Moore et al. (2012) Spaet and Berumen (2015) Ward et al. (2008) Wong et al. (2009) Aguilar et al. (2020)* Almerón-Souza et al. (2018) Appleyard et al. (2018) Bineesh et al. (2016) Chuang et al. (2016) International Barcode of Life (2011)*

20

MF508693 MH911047, MH911048 and MK188802 MF686574

Appleyard et al. (2018) da Silva Ferrette et al. (2019) Feitosa et al. (2018)

Ward et al. (2008)

JF494559–JF494561 MH593266–MH593272 and MH593283–MH593289

Steinke et al. (2015) Sukumar et al. (2020)

Triaenodon obesus

Stegostoma fasciatum

KF606765, KF606766, KF606815 and KF606858 MH194422, MH194471 - MH194475, MH194487, MH194504, MH194431 and MK070513 MK545100 and MK545101 KF590243, KF590430, KF590432 and KF590433 JF494562–JF494564 KJ146045 EU399018 FJ519525–FJ519544 HM239676 HQ171777 FJ178398–FJ178402 KM973166–KM973170 KP193228 and KP193405 KF590349 EU399050–EU399053 KX063639 KF899764–KF899767 KT275201–KT275239 KF590341, KF590361 and KF590388 MK492925 KM396947 FJ519288 and FJ519289

Liu et al. (2013) Marín et al. (2018) Mwangi et al. (2019)* Sembiring et al. (2015) Steinke et al. (2015) Velez-Zuazo et al. (2015) Ward et al. (2008) Wong et al. (2009) Bineesh et al. (2016) Doukakis et al. (2011) Dudgeon et al. (2009) Jabado et al. (2015a) Jabado et al. (2015b) Sembiring et al. (2015) Ward et al. (2008) Zacharia et al. (2016)* Bineesh et al. (2016) Diaz-Ferguson et al. (2016) Sembiring et al. (2015) Sijo et al. (2019)* Spaet and Berumen (2015) Wong et al. (2009) 21

KP193202, KP193220, KP193242, KP193256, KP193263, KP193278, KP193292, KP193302, KP193305, KP193316, KP193333, KP193343, KP193355, KP193356, KP193360, KP193361, KP193397, KP193404, KP193417, KP193426, KP193444 and KP177224–KP177232

Jabado et al. (2015b)

Yambot et al. (2012)*

22

KC970512

Brevitrygon walga

Brevitrygon imbricata

Aetobatus ocellatus

KF604909 and KF604917 FJ384709 KU317892 and KU317893

Last et al. (2013) Prasannakumar et al. (2008)* Rabaoui et al. (2019)

Basumatary et al. (2017)

23

MH429304, MH429305, MH429306, MH429310, MN013425, MH230948 and MN083136 MF495712 and MF495713

GU673374

International Barcode of Life (2010)*

Ahmed et al. (2021)

MF611582

Habib et al. (2017a)

EU398508

Ward et al. (2008)

KF899354–KF899356 and KF899512–KF899521

MK422135–MK422137

Ravi et al. (2019)*

Bineesh et al. (2016)

KX219586

Mohd-Arshaad et al. (2016)*

MK331965 and MK331966

KM073028 and KM073029

Lim et al. (2015)

Bhaskar and Das (2018)*

MG774904

John et al. (2020)

MT776900

LC505460

Hartoko et al. (2020)

Bhagyalekshmi and Kumar (2021)

KT208286–KT208291

MK340509–MK340527

Berthe et al. (2016)

Sales et al. (2019)

Table S2. List of authors along with the corresponding GenBank accession numbers of cytochrome c oxidase subunit I (COI) sequences of ray species ( n = 19) that have been used in the present study. Asterix (*) indicate sequences that have not been published but is available on GenBank. Species name (rays) Authors Accession numbers of COI sequences in GenBank Aetobatus narinari Richards et al. (2009) FJ812200–FJ812203

Himantura leoparda

Gymnura poecilura

Gymnura micrura

KU499632 and KU499714 EU398804

John et al. (2020)

Kumar et al. (2015) Rabaoui et al. (2019) Ward et al. (2008)

Lim et al. (2015)

24

MG792078, MG792101, MG792112, MG792113, MG792124, MG792125, MG774902, MG774903, MG774913.1, MG774915 and MG774922 KM072996–KM072998

MG774900, MG774901, MG774908, MG774909, MG774911, MG774912, MG774920, MG774921, MG792071–MG792073.1, MG792081, MG792085–MG792087, MG792089 and MG792090 JX978320–JX978324

Haque et al. (2019)

John et al. (2020)

MH842007

Habib et al. (2019)*

KF899353 and KF899500–KF899502

MN458407

Bineesh et al. (2016)

Bineesh et al. (2016)

KF899442, KF899443, KF899444, KF899445 and KF89944

Basumatary et al. (2017)

JX263361–JX263417

MF495714 and MF495715

Ahmed et al. (2021)

Arlyza et al. (2013a)

MN105752, MN105825–MN105830, MN105832, MN105833 and MN105836–MN105838 MH230947

EU398872–EU398876

Ward et al. (2008) Sales et al. (2019)

KF604912 KM072994 and KM072995

Last et al. (2013) Lim et al. (2015) HQ575754 and HQ575767

HQ955940 and HQ955948

International Barcode of Life (2011)*

International Barcode of Life (2010)*

MF614769

Habib et al. (2017b)*

Mobula birostris

Maculabatis gerrardi

Himantura uarnak

MH243129 and MH243130 DQ108164 and DQ108177 EU398840–EU398840 KJ475200

Wainwright et al. (2018) Ward et al. (2005) Ward et al. (2008) Zacharia et al. (2014)*

25

JF493862–JF493866

JF493648–JF493650

Steinke et al. (2015)

Steinke et al. (2011)

MH235645

Segura-Garcia and Yain Tun (2018)*

KM364883 and KM364884

MK422126–MK422129

Ravi et al. (2019)*

Poortvliet et al. (2015)

KM073002 and KM073003

Lim et al. (2015)

KF899564–KF899569

KC175446 and JX978325

Kumar et al. (2015)

Bineesh et al. (2016)

KF899364, KF899476–KF899487

KY176708

Yokes (2016)*

Bineesh et al. (2016)

KM072999 and KM073000

Lim et al. (2015)

JX263423 and JX263424

MG792110 and MG792123

John et al. (2020)

Arlyza et al. (2013a)

JQ765509, JQ765519–JQ765530, JQ765594 and JQ765595

Cerutti-Pereyra et al. (2012)

MH230945

KF899507–KF899511

Bineesh et al. (2016)

Ahmed et al. (2021)

JX263337–JX263360

JF493651 and JF493652

Steinke et al. (2015) Arlyza et al. (2013a)

MK422130

Ravi et al. (2019)*

Mobula mobular1

Mobula kuhlii

MH842002 GU674092, GU674234–GU674236, GU674396, GU674397, GU674399 and GU674401 HQ956137 MG774910 KC175452 HQ589285 and HQ589286

Haque et al. (2019) International Barcode of Life (2010)*

John et al. (2020) Kumar et al. (2015) Manjusha and Madhusoodanakurup (2010)* Marín et al. (2018) Poortvliet et al. (2015)

26

KM364889–KM364892, KM364897 and KP175584–KP175670

MH194451–MH194453, MH194455, MH194457, MH194461, MH194464, MH194469 and MK070516

KP719334–KP719338

Chuang et al. (2016)

International Barcode of Life (2011)*

KT307247

EU398907

Ward et al. (2008)

Cariani et al. (2017)

JF493895–JF493899

Steinke et al. (2011)

KF899570

MH235671

Segura-Garcia and Yain Tun (2018)*

Bineesh et al. (2016)

KM364893–KM364896

Poortvliet et al. (2015)

KJ093277 and KJ093278

KM073011

Lim et al. (2015)

Bamaniya et al. (2016)*

GU673390

International Barcode of Life (2010)*

MH230952

MH841993

Haque et al. (2019)

Ahmed et al. (2021)

KF899581–KF899583

EU398902–EU398904

Bineesh et al. (2016)

Ward et al. (2008)

MH243244, MH243247 and MH243249 EU398908 and EU398909

Wainwright et al. (2018) Ward et al. (2008)

Mobula thurstoni

KX060796 KM073012 KM364904–KM364906 MH235673 and MH235674 MH243242, MH243243, MH243245, MH243246, MH243250, MH243251, MH243255, MH243258 and MH243259 EU398914–EU398918

Li et al. (2016)* Lim et al. (2015) Poortvliet et al. (2015) Segura-Garcia and Yain Tun (2018)* Wainwright et al. (2018) Ward et al. (2008)

27

MG792109 and MG792121

John et al. (2020)

EU398910–EU398913

Ward et al. (2008)

GU673712, GU673713, GU674394 and GU674398

MH243238 and MH243252

Wainwright et al. (2018)

International Barcode of Life (2010)*

KM364901–KM364903

Poortvliet et al. (2015)

MK085566, MK085584 and MK085596

KX060791–KX060795

Li et al. (2016)*

da Silva Ferrette et al. (2019)

GU673481

International Barcode of Life (2010)*

KP719340

KY454873

Gargan et al. (2017)

Chuang et al. (2016)

KP719339

Chuang et al. (20016)

Sequences of Mobular japanica have been included under Mobula mobular as the former is a junior synonym of M. mobular and both species are conspecifics (Last et al., 2016; White et al., 2017) Mobula tarapacana Bineesh et al. (2016) KF899576–KF899580

1

MH235670

Segura-Garcia and Yain Tun (2018)*

Pteroplatytrygon violacea

Pateobatis jenkinsii

Neotrygon kuhlii

Neotrygon indica

JX978329 KC249906 KT794001

Kumar et al. (2015) Puckridge et al. (2013) Santhanakrishnan et al. (2015)*

MK422144 MH235683 DQ108168 and DQ108169 EU398850 and EU398851

Ravi et al. (2019)* Segura-Garcia and Yain Tun (2018)* Ward et al. (2005) Ward et al. (2008)

28

KT307370–KT307373

KM072991– KM072993

Lim et al. (2015)

Cariani et al. (2017)

MG792059, MG792066, MG792068, MG792094–MG792096

John et al. (2020)

KF899654–KF899658 and HM239671

GU673708

International Barcode of Life (2010)*

Bineesh et al. (2016)

JQ765516–JQ765518

Cerutti-Pereyra et al. (2012)

KF930342

KF913237 and KF913238

Bineesh et al. (2016)

Bentley and Wiley (2013)*

MH230946

Ahmed et al. (2021)

KU497940–KU497945 and KU497952–KU497960

KU498035–KU498038

Borsa et al. (2016)

Borsa et al. (2016)

KC295416

Borsa et al. (2013)

JX304892–JX304915

HM467799 and KF899609–KF899613

Bineesh e al. (2016)

Arlyza et al. (2013b)

JX263421

KX063641

Arlyza et al. (2013a)

Zacharia et al. (2016)*

Taeniura lymma

KM073026 and KM073027 KC250631 and KC250633 FJ584168–FJ584170

Lim et al. (2015) Puckridge et al. (2013) Steinke et al. (2009)

29

MG792060 and MG774926

Song and Kim (2017)*

John et al. (2020)

MG573151

Ramírez‐Amaro et al. (2018)

JQ765547–JQ765553, JQ815396 and JQ929048

KY949117–KY949119

Marín et al. (2018)

Cerutti-Pereyra et al. (2012)

MH194458

International Barcode of Life (2010)*

KF930495

GU674230

Iglesias et al. (2013)*

Bentley and Wiley (2013)*

KF808209

Hastings and Burton (2010)*

KY849556

GU440486

da Silva Rodrigues Filho et al. (2020)

Azmir et al. (2017)

MK085554, MK085585, MK085660, MK085682, MK085718 and MK085720 MN105751

da Silva Ferrette et al. (2019)

0.004 ± 0.066 0.449 ± 0.081 0.15 ± 0.126 0.527 ± 0.064

Simple Star Single Simple Star Star Complex star

Ch. punctatum (n=20)

G. cuvier (n=228) I. oxyrinchus (n=140)

30

0.44 ± 0.024 0.785 ± 0.024

0.826 ± 0.061

0.577 ± 0.043 0.164 ± 0.049 0.40 ± 0.093 0.64 ± 0.092 0.659 ± 0.083 0.108 ± 0.049 0.34 ± 0.054 0.594 ± 0.083 0.386 ± 0.055 0.30 ± 0.07 0.535 ± 0.063 0.179 ± 0.088 0.561 ± 0.015 0.382 ± 0.078 0.671 ± 0.033 0.529 ± 0.001 0.181 ± 0.046 0.535 ± 0.023

Complex star Star Star Simple linear Complex star Star Star Simple exclusive Complex mutational Star Complex mutational Star Star Complex mutational Simple linear Star Complex mutational Complex mutational

A. pelagicus (n=146) A. superciliosus (n=104) A. vulpinus (n=44) Ca. altimus (n=29) Ca. amblyrhynchoides (n=32) Ca. amboinensis (n=72) Ca. brevipinna (n=132) Ca. dussumieri (n=24) Ca. falciformis (n=116) Ca. leucas (n=66) Ca. limbatus (n=78) Ca. longimanus (n=30) Ca. macloti (n=12) Ca. melanopterus (n=54) Ca. plumbeus (n=48) Ca. sealei (n=18) Ca. sorrah (n=124) Carcharias taurus Carcharodon carcharias (n=18) Ce. maximus (n=56) Ch. griseum (n=12) Ch. indicum (n=14)

h

Topology

Species name

0.002 ± 0.0002 0.009 ± 0.0005

0.005 ± 0.0001

0.001 ± 0.00035 0.003 ± 0.0059 0.0008 ± 0.0001

0.003 ± 0.0003

0.005 ± 0.0005 0.0005 ± 0.0001 0.015 ± 0.006 0.002 ± 0.0004 0.004 ± 0.001 0.002 ± 0.0011 0.005 ± 0.0013 0.027 ± 0.0039 0.003 ± 0.001 0.003 ± 0.0007 0.018 ± 0.0084 0.0003 ± 0.0001 0.002 ± 0.00053 0.003 ± 0.0012 0.002 ± 0.0002 0.003 ± 0.00094 0.002 ± 0.001 0.015± 0.0005

π

3 17

8

5 1 2

5

14 6 5 7 8 3 14 5 9 4 6 3 4 4 4 4 5 5

HAP

1 10

7

2 1 2

2

9 5 4 5 5 2 11 4 7 3 5 2 3 1 1 3 3 3

UHAP

1 (Hap 4) 1 (Hap2) -

-

1 (Hap1) 1 (Hap1) 1 (Hap1) 1 (Hap3) 1 (Hap1) 1 (Hap1) 1 (Hap5) 1 (Hap1) 1 (Hap1) 1 (Hap1) 1 (Hap1) 1 (Hap1) 1 (Hap2)

DHAP

1 7

-

2 -

3

4 1 3 2 1 1 1 2 3 4 1 1

SHAP

Table S3. Diversity indices for shark species, using the mitochondrial cytochrome C oxidase subunit I: n, sample size; h, haplotype diversity; π, nucleotide diversity; HAP, number of haplotypes observed; UHAP, number of unique haplotypes; DHAP, number of dominant haplotypes observed; SHAP, number of shared haplotypes other than the dominant haplotype.

Star Star Complex star Simple linear Single Single Simple Complex mutational Complex star Simple Complex mutational Complex mutational Single Star Star

I. paucus (n=46) L. ditropis (n=15) L. nasus (n=81) N. acutidens (n=31) N. brevirostris (n=11) P. glauca (n=534) Rhin. typus (n=48) Rhiz. acutus (n=78) Rhiz. oligolinx (n=15) S. laticaudus (n=27)

Sp. lewini (n=323)

Sp. mokarran (n=59) Sp. zygaena (n=91) St. fasciatum (n=26) T. obesus (n=53)

31

0.1 ± 0.053 0 0.46 ± 0.116 0.428 ± 0.075

0.53 ± 0.026

0.63 ± 0.039 0.91 ± 0.047 0.844 ± 0.034 0.701 ± 0.034 0 0 0.12 ± 0.061 0.461 ± 0.049 0.02 ± 0.142 0.74 ± 0.062

0.0007 ± 0.0004 0 0.001 ± 0.0004 0.0012 ± 0.0003

0.023 ± 0.001

0.002 ± 0.0002 0.003 ± 0.0089 0.007 ± 0.0089 0.003 ± 0.0019 0 0 0.0002 ± 0.0001 0.0004 ± 0.0005 0.119 ± 0.0079 0.047 ± 0.024

4 1 5 5

12

4 9 23 4 1 1 2 5 3 6

3 4 3

9

1 9 19 3 3 2 4

1 (Hap1) 1 (Hap1) 1 (Hap1) 1 (Hap2) 1 (Hap1) 1 (Hap 3) 1 (Hap1) 1 (Hap1) 1 (Hap1) 1 (Hap2) 1

2

3 4 1 1 1 2

A. narinari (n=29) A. ocellatus (n=18) B. imbricata (n=23) B. walga (n = 20) G. micrura (n=15) G. poecilura (n=39) H. leoparda (n=78) H. uarnak (n=49) Mac. gerrardi Mob. birostris (n=16) Mob. kuhlii (n=18) Mob. mobular (n=166) Mob. tarapacana (n=23) Mob. thurstoni (n=32) N. indica (n=15) N. kuhlii (n=59) P. jenkinsii P. violacea T. lymma (n=20)

Species name Star Complex mutational Complex mutational Simple exclusive Simple exclusive Complex mutational Simple exclusive Complex mutational Complex mutational Star Simple Complex star Star Star Complex mutational Complex mutational Complex mutational Complex mutational Complex mutational

Topology

32

0.25 ± 0.12 0.76 ± 0.066 0.838 ± 0.053 0.963 ± 0.028 0.848 ± 0.088 0.84 ± 0.047 0.9 ± 0.019 0.91 ± 0.0003 0.875 ± 0.029 0.61 ± 0.130 0.853 ± 0.053 0.51 ± 0.034 0.17 ± 0.102 0.712 ± 0.037 0.905 ± 0.084 0.8 ± 0.045 0.823 ± 0.06 0.671 ± 0.082 0.82 ± 0.058

h 0.001 ± 0.0008 0.026 ± 0.059 0.036 ± 0.0096 0.023 ± 0.0014 0.028 ± 0.0127 0.042 ± 0.0087 0.030 ± 0.019 0.036 ± 0.0074 0.023 ± 0.003 0.002± 0.0005 0.005 ± 0.0014 0.003 ± 0.0007 0.002 ± 0.0051 0.003 ± 0.0003 0.011 ± 0.0012 0.015 ± 0.00103 0.032 ± 0.0661 0.007 ± 0.004 0.024 ± 0.014

π 8 5 5 14 9 14 20 12 11 5 7 13 3 4 9 12 6 5 5

HAP 7 4 5 13 9 13 20 12 9 4 5 11 2 2 9 12 5 2 4

UHAP

1 (Hap10) 1(Hap 2) 1 (Hap1) 1 1 1 (Hap 4) 1 (Hap 3)

DHAP

1 1 1 2 2 1 2 2 -

SHAP

Table S4. Diversity indices for ray species, based on the mitochondrial cytochrome C oxidase subunit I: n, sample size; h, haplotype diversity; π, nucleotide diversity; HAP, number of haplotypes observed; UHAP, number of unique haplotypes; DHAP, number of dominant haplotypes observed; OHAP, number of shared haplotypes other than the dominant haplotype.

33

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