ABSTRACT
Modern fisheries management strives to balance opposing goals of protection for weak stocks and opportunity for harvesting healthy stocks. Test fisheries can aid management of anadromous fishes if they can forecast the strength and timing of an annual run with adequate time to allow fisheries planning. Integration of genetic stock identification (GSI) can further maximize utility of test fisheries by resolving run forecasts into weak- and healthy-stock subcomponents. Using 5 years (2017-2022) of test fishery data, our study evaluated accuracy, resolution, and lead time of predictions for stock-specific run timing and abundance of Columbia River spring Chinook salmon (Oncorhynchus tshawytscha). We determined if this test fishery (1) could use visual stock identification (VSI) to forecast at the coarse stock resolution (i.e., classification of "lower" vs. "upriver" stocks) upon which current management is based and (2) could be enhanced with GSI to forecast at higher stock resolution. VSI accurately identified coarse stocks (83.3% GSI concordance), and estimated a proxy for abundance (catch per unit effort, CPUE) of the upriver stock in the test fishery that was correlated (R 2 = 0.90) with spring Chinook salmon abundance at Bonneville dam (Rkm 235). Salmon travel rates (~8.6 Rkm/day) provided predictions with 2-week lead time prior to dam passage. Importantly, GSI resolved this predictive ability as finely as the hatchery broodstock level. Lower river stock CPUE in the test fishery was correlated with abundance at Willamette Falls (Rkm 196, R 2 = 0.62), but could not be as finely resolved as achieved for upriver stocks. We described steps to combine VSI and GSI to provide timely in-season information and with prediction accuracy of ~12.4 mean absolute percentage error and high stock resolution to help plan Columbia River mainstem fisheries.
ABSTRACT
The stock-specific distribution of maturing salmon in the North Pacific has been a persistent information gap that has prevented us from determining the ocean conditions experienced by individual stocks. This continues to impede understanding of the role of ocean conditions in stock-specific population dynamics. We assessed scale archives for 17 sockeye salmon (Oncorhynchus nerka) stocks covering the entire North Pacific, from the Columbia River (Washington State and British Columbia) to Kamchatka Peninsula (Russia), to infer salmon locations during their last growing season before returning to their spawning grounds. The approach used, first pioneered in salmon stocks in the Atlantic, relies on the relationship between temporal changes in δ13C in salmon scales and sea surface temperature to estimate salmon distribution based on correlation strength. An advantage of this approach is that it does not require fish sampling at sea, but relies on existing fishery agency collections of salmon scales. Significant correlations were found for 7 of the stocks allowing us to propose plausible feeding grounds. Complementary information from δ15N, historical tagging studies, and connectivity analysis were used to further refine distribution estimates. This study is a first step toward estimating stock-specific distributions of salmon in the North Pacific and provides a basis for the application of the approach to other salmon scale archives. This information has the potential to improve our ability to relate stock dynamics to ocean conditions, ultimately enabling improved stock management. For example, our estimated distributions of Bristol Bay and NE Pacific stocks demonstrated that they occupy different areas with a number of the former being distributed in the high productivity shelf waters of the Aleutian Islands and Bering Sea. This may explain why these stocks seem to have responded differently to changes in ocean conditions, and the long-term trend of increased productivity of Bristol Bay sockeye.
