Mixed-stock analysis using Rapture genotyping to evaluate stock-specific exploitation of a walleye population despite weak genetic structure

Mixed-stock analyses using genetic markers have informed fisheries
management in cases where strong genetic differentiation occurs among
local spawning populations, yet many fisheries are supported by multiple
spawning stocks that are weakly differentiated. Freshwater fisheries
exemplify this problem, with many harvested populations supported by
multiple stocks of young evolutionary age and that are isolated across
small spatial scales. As a result, attempts to conduct genetic mixed-stock
analyses of inland fisheries have often been unsuccessful. Advances in
genomic sequencing now offer the ability to discriminate among populations
with weak population structure, by providing the necessary resolution to
conduct mixed-stock assignment among previously indistinguishable stocks.
We demonstrate the use of genomic data to conduct a mixed-stock analysis
of Lake Erie's commercial and recreational walleye (Sander vitreus)
fisheries and estimate the relative harvest of weakly differentiated
stocks. We used RAD-capture (Rapture) to sequence and genotype individuals
at 12,081  loci that had been previously determined to be capable
of discriminating between western and eastern basin stocks (mean pairwise
FST = 0.001) with 95% reassignment accuracy. An outcome not possible in
the past with microsatellite markers. Genetic assignment of 1,075 fish
harvested from recreational and commercial fisheries in the eastern basin
indicated that western basin stocks supported the majority of the harvest
during peak harvest (July – September). Composition of harvest changed
seasonally, with eastern basin fish comprising much of the early season
harvest (May – June). Clear spatial structure in stock-specific harvest
existed; more easterly sites contained more individuals of east basin
origin than did westerly sites. Our study provides important stock
contribution estimates for Lake Erie fishery management and demonstrates
the power of genomic data to facilitate mixed-stock analysis in exploited
fish populations with weak population structure or limited existing
genetic resources.