Reliably discriminating stock structure with genetic markers: Mixture models with robust and fast computation

Implications of past and present genetic connectivity for management of the saltwater crocodile (Crocodylus porosus)

Effective management of protected species requires information on appropriate evolutionary and geographic population boundaries and knowledge of how the physical environment and life-history traits combine to shape the population structure and connectivity. Saltwater crocodiles (Crocodylus porosus) are the largest and most widely distributed of living crocodilians, extending from Sri Lanka to Southeast Asia and down to northern Australia. Given the long-distance movement capabilities reported for C. porosus, management units are hypothesised to be highly connected by migration. However, the magnitude, scale, and consistency of connection across managed populations are not fully understood. Here we used an efficient genotyping method that combines DArTseq and sequence capture to survey≈ 3000 high-quality genome-wide single nucleotide polymorphisms from 1176 C. porosus sampled across nearly the entire range of the species in Queensland, Australia. We investigated historical and present-day connectivity patterns using fixation and diversity indices coupled with clustering methods and the spatial distribution of kin pairs. We inferred kinship using forward simulation coupled with a kinship estimation method that is robust to unspecified population structure. The results demonstrated that the C. porosus population has substantial genetic structure with six broad populations correlated with geographical location. The rate of gene flow was highly correlated with spatial distance, with greater differentiation along the east coast compared to the west. Kinship analyses revealed evidence of reproductive philopatry and limited dispersal, with approximately 90% of reported first and second-degree relatives showing a pairwise distance of <50 km between sampling locations. Given the limited dispersal, lack of suitable habitat, low densities of crocodiles and the high proportion of immature animals in the population, future management and conservation interventions should be considered at regional and state-wide scales.

Stepping up to genome scan allows stock differentiation in the worldwide distributed blue shark Prionace glauca

The blue shark Prionace glauca is a top predator with one of the widest geographical distributions of any shark species. It is classified as Critically Endangered in the Mediterranean Sea, and Near Threatened globally. Previous genetic studies did not reject the null hypothesis of a single global population. The blue shark was proposed as a possible archetype of the "grey zone of population differentiation," coined to designate cases where population structure may be too recent or too faint to be detected using a limited set of markers. Here, blue shark samples collected throughout its global range were sequenced using a specific RAD method (DArTseq), which recovered 37,655 genome-wide single nucleotide polymorphisms (SNPs). Two main groups emerged, with Mediterranean Sea and northern Atlantic samples (Northern population) differentiated significantly from the Indo-west Pacific samples (Southern population). Significant pairwise F_ST values indicated further genetic differentiation within the Atlantic Ocean, and between the Atlantic Ocean and the Mediterranean Sea. Reconstruction of recent demographic history suggested divergence between Northern and Southern populations occurred about 500 generations ago and revealed a drastic reduction in effective population size from a large ancestral population. Our results illustrate the power of genome scans to detect population structure and reconstruct demographic history in highly migratory marine species. Given that the management plans of the blue shark (targeted or bycatch) fisheries currently assume panmictic regional stocks, we strongly recommend that the results presented here be considered in future stock assessments and conservation strategies.

Sample size requirements for genetic studies on yellowfin tuna

In population genetics, the amount of information for an analytical task is governed by the number of individuals sampled and the amount of genetic information measured on each of those individuals. In this work, we assessed the numbers of individual yellowfin tuna (Thunnus albacares) and genetic markers required for ocean-basin scale inferences. We assessed this for three distinct data analysis tasks that are often employed: testing for differences between genetic profiles; stock delineation, and; assignment of individuals to stocks. For all analytical tasks, we used real (not simulated) data from four sampling locations that span the tropical Pacific Ocean. Whilst spatially separated, the genetic differences between the sampling sites were not substantial, a maximum of approximately F_st = 0.02, which is quite typical of large pelagic fish. We repeatedly sub-sampled the data, mimicking a new survey, and performed the analyses. False positive rates were also assessed by re-sampling and randomly assigning fish to groups. Varying the sample sizes indicated that some analytical tasks, namely profile testing, required relatively few individuals per sampling location (n ≳ 10) and single nucleotide polymorphisms (SNPs, m ≳ 256). Stock delineation required more individuals per sampling location (n ≳ 25). Assignment of fish to sampling locations required substantially more individuals, more in fact than we had available (n > 50), although this sample size could be reduced to n ≳ 30 when individual fish were assumed to belong to one of the groups sampled. With these results, designers of molecular ecological surveys for yellowfin tuna, and users of information from them, can assess whether the information content is adequate for the required inferential task.

Investigating the morphological and genetic divergence of arctic char (Salvelinus alpinus) populations in lakes of arctic Alaska

Polymorphism facilitates coexistence of divergent morphs (e.g., phenotypes) of the same species by minimizing intraspecific competition, especially when resources are limiting. Arctic char (Salvelinus sp.) are a Holarctic fish often forming morphologically, and sometimes genetically, divergent morphs. In this study, we assessed the morphological and genetic diversity and divergence of 263 individuals from seven populations of arctic char with varying length-frequency distributions across two distinct groups of lakes in northern Alaska. Despite close geographic proximity, each lake group occurs on landscapes with different glacial ages and surface water connectivity, and thus was likely colonized by fishes at different times. Across lakes, a continuum of physical (e.g., lake area, maximum depth) and biological characteristics (e.g., primary productivity, fish density) exists, likely contributing to characteristics of present-day char populations. Although some lakes exhibit bimodal size distributions, using model-based clustering of morphometric traits corrected for allometry, we did not detect morphological differences within and across char populations. Genomic analyses using 15,934 SNPs obtained from genotyping by sequencing demonstrated differences among lake groups related to historical biogeography, but within lake groups and within individual lakes, genetic differentiation was not related to total body length. We used PERMANOVA to identify environmental and biological factors related to observed char size structure. Significant predictors included water transparency (i.e., a primary productivity proxy), char density (fish·ha-1), and lake group. Larger char occurred in lakes with greater primary production and lower char densities, suggesting less intraspecific competition and resource limitation. Thus, char populations in more productive and connected lakes may prove more stable to environmental changes, relative to food-limited and closed lakes, if lake productivity increases concomitantly. Our findings provide some of the first descriptions of genomic characteristics of char populations in arctic Alaska, and offer important consideration for the persistence of these populations for subsistence and conservation.

One panel to rule them all: DArTcap genotyping for population structure, historical demography, and kinship analyses, and its application to a threatened shark

With recent advances in sequencing technology, genomic data are changing how important conservation management decisions are made. Applications such as Close-Kin Mark-Recapture demand large amounts of data to estimate population size and structure, and their full potential can only be realised through ongoing improvements in genotyping strategies. Here we introduce DArTcap, a cost-efficient method that combines DArTseq and sequence capture, and illustrate its use in a high resolution population analysis of Glyphis garricki, a rare, poorly known and threatened euryhaline shark. Clustering analyses and spatial distribution of kin pairs from four different regions across northern Australia and one in Papua New Guinea, representing its entire known range, revealed that each region hosts at least one distinct population. Further structuring is likely within Van Diemen Gulf, the region that included the most rivers sampled, suggesting additional population structuring would be found if other rivers were sampled. Coalescent analyses and spatially explicit modelling suggest that G. garricki experienced a recent range expansion during the opening of the Gulf of Carpentaria following the conclusion of the Last Glacial Maximum. The low migration rates between neighbouring populations of a species that is found only in restricted coastal and riverine habitats show the importance of managing each population separately, including careful monitoring of local and remote anthropogenic activities that may affect their environments. Overall we demonstrated how a carefully chosen SNP panel combined with DArTcap can provide highly accurate kinship inference and also support population structure and historical demography analyses, therefore maximising cost-effectiveness.