Abstract
Preseason abundance forecasts drive management of US West Coast salmon fisheries, yet little is known about how environmental variability influences forecast performance. We compared forecasts of Chinook salmon (Oncorhynchus tshawytscha) against returns for (i) key California-Oregon ocean fishery stocks and (ii) high priority prey stocks for endangered Southern Resident Killer Whales (Orcinus orca) in Puget Sound, Washington. We explored how well environmental indices (at multiple locations and time lags) explained performance of forecasts based on different methods (i.e. sibling-based, production-based, environment-based, or recent averages), testing for nonlinear threshold dynamics. For the California stocks, no index tested explained >50% of the variation in forecast performance, but spring Pacific Decadal Oscillation and winter North Pacific Index during the year of return explained >40% of the variation for the sibling-based Sacramento Fall Chinook forecast, with nonlinearity and apparent thresholds. This suggests that oceanic conditions experienced by adults (after younger siblings returned) have the most impact on sibling-based forecasts. For Puget Sound stocks, we detected nonlinear/threshold relationships explaining >50% of the variation with multiple indices and lags. Environmental influences on preseason forecasts may create biases that render salmon fisheries management more or less conservative, and therefore could motivate the development of ecosystem-based risk assessments.
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Preseason abundance forecasts drive management of US West Coast salmon fisheries, yet little is known about how environmental variability influences forecast performance. We compared forecasts of Chinook salmon (Oncorhynchus tshawytscha) against returns for (i) key California-Oregon ocean fishery stocks and (ii) high priority prey stocks for endangered Southern Resident Killer Whales (Orcinus orca) in Puget Sound, Washington. We explored how well environmental indices (at multiple locations and time lags) explained performance of forecasts based on different methods (i.e. sibling-based, production-based, environment-based, or recent averages), testing for nonlinear threshold dynamics. For the California stocks, no index tested explained >50% of the variation in forecast performance, but spring Pacific Decadal Oscillation and winter North Pacific Index during the year of return explained >40% of the variation for the sibling-based Sacramento Fall Chinook forecast, with nonlinearity and apparent thresholds. This suggests that oceanic conditions experienced by adults (after younger siblings returned) have the most impact on sibling-based forecasts. For Puget Sound stocks, we detected nonlinear/threshold relationships explaining >50% of the variation with multiple indices and lags. Environmental influences on preseason forecasts may create biases that render salmon fisheries management more or less conservative, and therefore could motivate the development of ecosystem-based risk assessments.
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