Genetic population structure and demography of an apex predator, the tiger shark Galeocerdo cuvier

Abstract

Population genetics has been increasingly applied to study large sharks over the last decade. Whilst large shark species are often difficult to study with direct methods, improved knowledge is needed for both population management and conservation, especially for species vulnerable to anthropogenic and climatic impacts. The tiger shark, Galeocerdo cuvier, is an apex predator known to play important direct and indirect roles in tropical and subtropical marine ecosystems. While the global and Indo-West Pacific population genetic structure of this species has recently been investigated, questions remain over population structure and demographic history within the western Indian (WIO) and within the western Pacific Oceans (WPO). To address the knowledge gap in tiger shark regional population structures, the genetic diversity of 286 individuals sampled in seven localities was investigated using 27 microsatellite loci and three mitochondrial genes (CR,COI, and cytb). A weak genetic differentiation was observed between the WIO and the WPO, suggesting high genetic connectivity. This result agrees with previous studies and highlights the importance of the pelagic behavior of this species to ensure gene flow. Using approximate Bayesian computation to couple information from both nuclear and mitochondrial markers, evidence of a recent bottleneck in the Holocene (2,000-3,000 years ago) was found, which is the most probable cause for the low genetic diversity observed. A contemporary effective population size as low as 111 [43,369] was estimated during the bottleneck. Together, these results indicate low genetic diversity that may reflect a vulnerable population sensitive to regional pressures. Conservation measures are thus needed to protect a species that is classified as Near Threatened.

Keywords: approximate Bayesian computation; bottleneck; effective population size; microsatellite DNA; mitochondrial DNA; tiger shark.

Figure 1

Map of tiger shark (Galeocerdo cuvier) sampling locations (AS: Andaman Sea; AUS1: Western Australian coast; AUS2: Queensland, Australia; AUS3: New South Wales, Australia; AUS4: Northern Territories, Australia; BAH: Bahamas; COR; BRA: Brazil; Coral Sea; FLE: Florida East Coast; HAW: Hawaii; GOM: Gulf of Mexico; NCA: New Caledonia; RUN: Reunion Island; SEY: Seychelles; SAF: South Africa;USVI:US Virgin Islands; ZAN: Zanzibar). In brackets are sample sizes. In green are indicated samples collected for this study and genotyped with 27 microsatellite loci and three mitochondrial genes. Red indicates samples genotyped by Holmes et al. (2017) with the nine microsatellite loci developed by Bernard et al. (2015). Blue indicates samples sequenced at the control region by Bernard et al. (2016)

Figure 2

Graphical representations of the seven scenarios depicting possible variations in effective population size of Galeocerdo cuvier population, using individuals from Reunion Island (RUN). The time was measured backward in generations before present. N ₀, the actual effective population size; N ₁, the ancestral effective population size; N _b, the effective population size during a bottleneck; N _e, the effective population size during an expansion; t ₁, beginning of decrease or expansion for Scenarios 1 and 3; t ₂, beginning of decrease or expansion for Scenarios 2 and 4; t, beginning of the expansion or bottleneck period for Scenarios 5 and 6

Figure 3

TCS statistical parsimony networks for the tiger shark Galeocerdo cuvier constructed with (a) the CR‐COI‐cytb dataset (22 haplotypes) and (b) the CR dataset (25 haplotypes). Each circle represents a haplotype and each trait, a mutation. Circle size is proportional to the number of individuals harboring each haplotype and colors correspond to sampling locations (AS: Andaman Sea; AUS1: Western Australian coast; AUS2: Queensland, Australia; AUS3: New South Wales, Australia; AUS4: Northern Territories, Australia; BAH: Bahamas; COR; BRA: Brazil; Coral Sea; FLE: Florida East Coast; HAW: Hawaii; GOM: Gulf of Mexico; NCA: New Caledonia; RUN: Reunion Island; SEY: Seychelles; SAF: South Africa;USVI:US Virgin Islands; ZAN: Zanzibar)

Figure 4

Galeocerdo cuvier assignment probabilities of individuals to putative clusters assuming correlated allele frequencies and admixture as performed by STRUCTURE. (a) Average probability of membership (y axis) of individuals (N = 275, x axis) for major modes of K varying from 2 to 3, with the 27‐msat dataset and no a priori sampling location information. (b) Average probability of membership (y axis) of individuals (N = 275, x axis) for major modes of K varying from 2 to 3, with the 27‐msat dataset and the LOCPRIOR model. (c) Average probability of membership (y axis) of individuals (N = 606, x axis) for major modes of K varying from 2 to 3, with the 8‐msat dataset and no a priori sampling location information. (d) Average probability of membership (y axis) of individuals (N = 606, x axis) for major modes of K varying from 2 to 4, with the 8‐msat dataset and the LOCPRIOR model. AUS1, Western Australian coast; AUS2, Queensland, Australia; AUS3, New South Wales, Australia; AUS4, Northern Territories, Australia; BRA, Brazil; COR, Coral Sea; HAW, Hawaii; NCA, New Caledonia; RUN, Reunion Island; SEY, Seychelles; SAF, South Africa; ZAN, Zanzibar

Figure 5

Galeocerdo cuvier scatterplot output from DAPC analyses performed using the four microsatellite datasets, and using the first and second components (a) 27‐msat dataset, (b) 8‐msat dataset, (c) Holmes 8‐msat dataset, (d) Bernard 8‐msat dataset. Dots represent individuals colored by their sampling location (AS: Andaman Sea; AUS1: Western Australian coast; AUS2: Queensland, Australia; AUS3: New South Wales, Australia; AUS4: Northern Territories, Australia; BAH: Bahamas; BRA: Brazil; COR: Coral Sea; HAW: Hawaii; GOM: Gulf of Mexico; FLE: Florida East Coast; NCA: New Caledonia; RUN: Reunion Island; SEY: Seychelles; SAF: South Africa; USVI:US Virgin Islands; ZAN: Zanzibar)