Dean C. Blower

Aim: The white shark (Carcharodon carcharias) is one of the world's largest and most recognisable marine predators but has suffered significant declines since the mid‐twentieth century. Conservation efforts remain complicated by persistent knowledge gaps associated with white shark biology and ecology, including the biological connectedness of white shark populations. We re‐assess patterns of population genetic structure in Australian white sharks, where two subpopulations—eastern and southern‐western—are currently recognised based on previous animal tracking and genetic assessments.Methods: Population genomic analyses are performed using tissues from ~650 individual white sharks and ~7000 single nucleotide polymorphism (SNP) loci generated through reduced genome representation sequencing. We test for evidence of genetic structure and relatedness among sharks from eastern and southern Australia and use population genetic simulations to assess the likely strength of inter‐generational migration between regions.Results: This study challenges the current paradigm of population structure in Australian white sharks, showing a lack of genetic structure between white sharks from eastern and southern Australia. These findings are further supported by population genetic simulations and kinship analyses indicating high levels of intergenerational migration and relatedness between regions. Consistent with recent reports from eastern Australia, we also detected high levels of relatedness among juvenile and subadult white sharks and estimated the overall effective population size (Ne) of Australian white sharks to be less than 500 individuals. Furthermore, we provide evidence of a potential reduction in Ne over the last two generations.Main Conclusions: Overall, these findings highlight the need to consider this revised estimate of genetic structure when discussing the management and conservation of the species. Our results also raise concerns for the conservation of Australian white sharks highlighting risks of potential inbreeding, and reductions in population fitness and resilience. We discuss the need for further research and the importance of ongoing population monitoring.

A landmark study published in 2002 estimated a very small N_e/N ratio (around 10^‐5) in a population of pink snapper (Chrysophrys auratus, Forster, 1801) in the Hauraki Gulf in New Zealand. It epitomized the tiny N_e/N ratios (‐3) reported in marine species due to the hypothesized operation of sweepstakes reproductive success (SRS). Here we re‐evaluate the occurrence of SRS in marine species and the potential effect of fishing on the N_e/N ratio by studying the same species in the same region, but in a population that has been protected from fishing since 1975. We combine empirical, simulation and model‐based approaches to estimate N_e (and N_b) from genotypes of 1044 adult fish and estimate N using recapture‐probabilities. The estimated N_e/N ratio was much larger (0.33, SE: 0.14) than expected. The magnitude of estimates of population‐wide variance in individual lifetime reproductive success (10 ‐ 18) suggested that the sweepstakes effect was negligible in the study population. After evaluating factors that could explain the contrast between studies – experimental design, life‐history differences, environmental effects and the influence of exploitation on the N_e/N ratio – we conclude that the low N_e of the Hauraki Gulf population is associated with demographic instability in the harvested compared to the protected population despite circumstantial evidence that the 2002 study may have underestimated N_e. This study has broad implications for the prevailing view that reproductive success in the sea is largely driven by chance, and for genetic monitoring of populations using the N_e/N ratio and N_b.

Molecular genetic estimates of population effective size (N_e) lose accuracy and precision when insufficient numbers of samples or loci are used. Ideally, researchers would like to forecast the necessary power when planning their project. neogen (genetic N_e for Overlapping Generations) enables estimates of precision and accuracy in advance of empirical investigation and allows exploration of the power available in different user‐specified age‐structured sampling schemes. neogen provides a population simulation and genetic power analysis framework that simulates the demographics, genetic composition, and N_e, from species‐specific life history, mortality, population size, and genetic priors. neogen guides the user to establish a tractable sampling regime and to determine the numbers of samples and microsatellite or SNP loci required for accurate and precise genetic N_e estimates when sampling a natural population. neogen is useful at multiple stages of a study's life cycle: when budgeting, as sampling and locus development progresses, and for corroboration when empirical N_e estimates are available. The underlying model is applicable to a wide variety of iteroparous species with overlapping generations (e.g., mammals, birds, reptiles, long‐lived fishes). In this paper, we describe the neogen model, detail the workflow for the point‐and‐click software, and explain the graphical results. We demonstrate the use of neogen with empirical Australian east coast zebra shark (Stegostoma fasciatum) data. For researchers wishing to make accurate and precise genetic N_e estimates for overlapping generations species, neogen facilitates planning for sample and locus acquisition, and with existing empirical genetic N_e estimates neogen can corroborate population demographic and life history properties.

Sustainable exploitation of fisheries populations is challenging to achieve when the size of the population prior to exploitation and the actual numbers removed over time and across fishing zones are not clearly known. Quantitative fisheries' modeling is able to address this problem, but accurate and reliable model outcomes depend on high quality input data. Much of this information is obtained through the operation of the fishery under consideration, but while this seems appropriate, biases may occur. For example, poorly quantified changes in fishing methods that increase catch rates can erroneously suggest that the overall population size is increasing. Hence, the incorporation of estimates of abundance derived from independent data sources is preferable. We review and evaluate a fisheries‐independent method of indexing population size; inferring adult abundance from estimates of the genetic effective size of a population (N_e). Recent studies of elasmobranch species have shown correspondence between N_e and ecologically determined estimates of the population size (N). Simulation studies have flagged the possibility that the range of N_e/N ratios across species may be more restricted than previously thought, and also show that declines in N_e track declines in the abundance of model fisheries species. These key developments bring this new technology closer to implementation in fisheries science, particularly for data‐poor fisheries or species of conservation interest.

The sandbar shark, Carcharhinus plumbeus, a major representative species in shark fisheries worldwide is now considered vulnerable to overfishing. A pool of 774,234 Roche 454 shotgun sequences from one individual were assembled into a 16,706 bp mitogenome with 33× average coverage depth. It comprised 13 protein coding genes, 22 transfer RNA's, 2 ribosomal genes and 2 non-coding regions, typical of a vertebrate mitogenome. As expected for sharks, an A-T nucleotide bias was evident. This adds to rapidly growing number of mitogenome assemblies for the economically important Carcharhinidae family. The C. plumbeus mitogenome will assist researchers, fisheries and conservation managers interested in shark molecular systematics, phylogeography, conservation genetics, population and stock structure.