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Linking Northeast Pacific recruitment synchrony to environmental variability
Authors:Megan M Stachura  Timothy E Essington  Nathan J Mantua  Anne B Hollowed  Melissa A Haltuch  Paul D Spencer  Trevor A Branch  Miriam J Doyle
Institution:1. School of Aquatic and Fishery Sciences, University of Washington, , Seattle, WA, 98195 U.S.A;2. Southwest Fisheries Science Center, National Marine Fisheries Service, , Santa Cruz, CA, 95060 U.S.A;3. Alaska Fisheries Science Center, National Marine Fisheries Service, , Seattle, WA, 98115 U.S.A;4. Fisheries Resource Analysis and Monitoring, Northwest Fisheries Science Center, National Marine Fisheries Service, , Seattle, WA, 98112 U.S.A;5. Joint Institute for the Study of the Atmosphere and Oceans, University of Washington, Alaska Fisheries Science Center, National Marine Fisheries Service, , Seattle, WA, 98115 U.S.A
Abstract:We investigated the hypothesis that synchronous recruitment is due to a shared susceptibility to environmental processes using stock–recruitment residuals for 52 marine fish stocks within three Northeast Pacific large marine ecosystems: the Eastern Bering Sea and Aleutian Islands, Gulf of Alaska, and California Current. There was moderate coherence in exceptionally strong and weak year‐classes and correlations across stocks. Based on evidence of synchrony from these analyses, we used Bayesian hierarchical models to relate recruitment to environmental covariates for groups of stocks that may be similarly influenced by environmental processes based on their life histories. There were consistent relationships among stocks to the covariates, especially within the Gulf of Alaska and California Current. The best Gulf of Alaska model included Northeast Pacific sea surface height as a predictor of recruitment, and was particularly strong for stocks dependent on cross‐shelf transport during the larval phase for recruitment. In the California Current the best‐fit model included San Francisco coastal sea level height as a predictor, with higher recruitment for many stocks corresponding to anomalously high sea level the year before spawning and low sea level the year of spawning. The best Eastern Bering Sea and Aleutian Islands model included several environmental variables as covariates and there was some consistent response across stocks to these variables. Future research may be able to utilize these across‐stock environmental influences, in conjunction with an understanding of ecological processes important across early life history stages, to improve identification of environmental drivers of recruitment.
Keywords:Bayesian hierarchical models  environment  fish recruitment  Northeast Pacific Ocean  synchrony
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