John Wenburg | Geology Reports

USGS2021

Genetic variation in sea otters (Enhydra lutris) from the North Pacific with relevance to the threatened Southwest Alaska Distinct Population Segment

For the sea otter ( Enhydra lutris ), genetic population structure is an area of research that has not received significant attention, especially in Southwest Alaska where that distinct population segment has been listed as threatened since 2005 pursuant to the U.S. Endangered Species Act. In this study, 501 samples from 14 locations from Prince William Sound, Alaska to the Commander Islands in Russia were analyzed for variation at 13 microsatellite loci. Our results indicate a high degree of genetic divergence among the 14 locations ( F ST = 0.120) with gene flow conforming to the isolation by distance (IBD) model ( r 2 = 0.491, p < .05). The 14 sampling locations formed six geographic associations in clustering and ordination analyses that likely correspond to remnant population lineages: (1) Southcentral Alaska, (2) Kodiak and North Alaska Peninsula, (3) South Alaska Peninsula and Bristol Bay, (4) Eastern Aleutian, (5) Western Aleutian, and (6) the Commander Islands. Except for South Alaska Peninsula and Bristol Bay, these clusters closely agree with previously defined stock and management unit boundaries. Our results reveal significant genetic population structure and are generally congruent with current management strategies for the threatened Southwest Alaska distinct population segment.

Alaska↗

USGS2016

Accurate recapture identification for genetic mark–recapture studies with error-tolerant likelihood-based match calling and sample clustering

Error-tolerant likelihood-based match calling presents a promising technique to accurately identify recapture events in genetic mark–recapture studies by combining probabilities of latent genotypes and probabilities of observed genotypes, which may contain genotyping errors. Combined with clustering algorithms to group samples into sets of recaptures based upon pairwise match calls, these tools can be used to reconstruct accurate capture histories for mark–recapture modelling. Here, we assess the performance of a recently introduced error-tolerant likelihood-based match-calling model and sample clustering algorithm for genetic mark–recapture studies. We assessed both biallelic (i.e. single nucleotide polymorphisms; SNP) and multiallelic (i.e. microsatellite; MSAT) markers using a combination of simulation analyses and case study data on Pacific walrus ( Odobenus rosmarus divergens ) and fishers ( Pekania pennanti ). A novel two-stage clustering approach is demonstrated for genetic mark–recapture applications. First, repeat captures within a sampling occasion are identified. Subsequently, recaptures across sampling occasions are identified. The likelihood-based matching protocol performed well in simulation trials, demonstrating utility for use in a wide range of genetic mark–recapture studies. Moderately sized SNP (64+) and MSAT (10–15) panels produced accurate match calls for recaptures and accurate non-match calls for samples from closely related individuals in the face of low to moderate genotyping error. Furthermore, matching performance remained stable or increased as the number of genetic markers increased, genotyping error notwithstanding.

Royal Society Open Science↗