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JOINT ANALYSIS OF GENETIC AND AGE DATA TO ESTIMATE TRENDS IN STRAIN-SPECIFIC RECRUITMENT OF EMERGING WILD LAKE TROUT POPULATIONS IN LAKE HURON
Kim Scribner1,2, IyobTsehaye1,3*, Travis Brenden1,3, Wendylee Stott1,4, Jeannette Kanefsky1, James Bence1,3
1Dept. Fisheries & Wildlife, Michigan State Univ., 480 Wilson Rd., 13 Natural Resources Bldg, East Lansing, MI 48824
2Dept. Integrative Biology, Michigan State Univ., 201 Natural Sciences Bldg, East Lansing, MI 48824 3Quantitative Fisheries Center, 293 Farm Ln., 153 Giltner Hal, Michigan State Univ., East Lansing, MI 48824
4Great Lakes Science Center, 1451 Green Rd., Ann Arbor, MI 48105-2807 (email@example.com)
*present address – Wisconsin Dept. Natural Resources, Madison, WI
During the past decade, assessments have indicated the emergence of wild lake trout (Salvelinus namaycush) recruitment in many areas of Lake Huron based on collections of young-of–the-year (YOY) and unmarked adult and sub-adult fish. Because wild fish derived from different strains are not phenotypically distinct and are unmarked, managers lack the ability to distinguish what lake trout hatchery strains are contributing to this wild recruitment or whether there are any temporal and/or spatial differences in hatchery strain contributions. We used 15 microsatellite loci to characterize the originating strains of wild lake trout (N=1567) collected in assessment fisheries conducted by agency cooperators during an early (2002-2004) and late (2009-2012) sampling period. Individuals from 13 US and Canadian hatchery strains stocked into Lake Huron (N=1143) were genotyped to develop standardized baseline information on the potential source strains. The basis for our analysis was a model originally proposed to quantify the contributions of different source populations to newly founded colonies. Models were estimated within a Bayesian context and deviance information criteria (DIC) was used to compare competing models evaluating strain contributions at different spatial and temporal scales. Known annual stocking numbers of hatchery strains and stocking locations combined with survival estimates based on statistical catch-at-age (SCAA) assessment models and movement patterns of fish among different regions of the lake based on analyses of coded-wire tag returns were used to derive expectations of strain contributions for the samples. The best performing models based on DIC were the most complex models, suggesting that hatchery strain contributions to wild lake trout differed both spatially and temporally. Contributions of Seneca strain lake trout were consistently high across Lake Huron statistical districts, with contributions increasing from early to late time periods. Strain contributions deviated from expectations based on historical stocking levels, suggesting that hatchery strains differed with respect to survival, reproductive success, and/or dispersal. Strain type was extremely important to emergence of wild stocks, which underlies the importance of genetic bases of adaptation in the development of more efficient and effective rehabilitation strategies across the Great Lakes. Knowledge of recruitment levels of hatchery strains stocked in different management units and how strain-specific recruitment varies across years, locations, and as a function of local or regional stocking efforts is important to target strains to prioritize future stocking and management of the transition process from primarily hatchery stocks to wild stocks in Lake Huron and potentially other Great Lakes as well.