Puget Sound Chum Salmon Growth Linked to Competitor Abundance, Climate Indices, and Copepod Species Richness

Abstract

An improved understanding of the mechanisms influencing productivity of fish populations is critical for accurately determining harvest rates and identifying years of conservation concern. Here we reconstruct yearly scale growth of three Puget Sound (PS) Chum Salmon Oncorhynchus keta stocks over 16 brood years (1997-2012) to better understand how variation in marine growth may be related to environmental factors, intra-and interspecific competition, and stock productivity. Generalized additive mixed models identified copepod species richness in the northern California Current and the abundance of PS Pink Salmon O. gorbuscha and Chum Salmon as strong predictors of first-year growth, the latter indicative of density-dependent effects as the abundance of local competitors (Pink and Chum salmon) increased. Second-year growth was negatively related to the Aleutian Low-Beaufort Sea Anticyclone (ALBSA), a recently defined metric of the Aleutian Low, and showed a nonlinear positive to negative relationship with sea surface temperature in the Gulf of Alaska. Puget Sound Chum and Pink Salmon abundances were also significantly related to second-year growth but did not suggest density-dependent effects like those observed in ocean year 1. Third-year growth was closely related to large-scale climate indicators, demonstrating a nonlinear negative to positive relationship with the North Pacific Gyre Oscillation, a negative relationship with ALBSA, and a negative relationship with North Pacific Pink Salmon abundance when catch statistics indicated that abundance was high. Models indicated that PS Chum Salmon stock productivity (recruits per spawner) was positively correlated with back-calculated firstyear growth and negatively correlated with second-and third-year growth, suggesting that for brood years whose surviving adults experienced rapid early marine growth, there were cohort survival benefits. As new relationships between large-scale indicators and Pacific salmon stocks are identified, incorporating these indicators into forecasting efforts is paramount for effective and sustainable management of fishery resources.

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“…3. Numerous studies document biennial fluctuations in various aspects of salmon productivity (Kaeriyama et al 2000;Ishida et al 2002;Ruggerone and Irvine 2018;Ruggerone and Nielsen 2004;Cline et al 2019), including studies that focused on chum (Azumaya and Ishida 2000;Kaga et al 2013;Litz et al 2021), sockeye (Bugaev et al 2001;Ruggerone et al 2003Ruggerone et al , 2005Ruggerone et al , 2007Ruggerone et al , 2016Ruggerone et al , 2019McKinnell & Reichardt 2012;Connors et al 2020), Chinook salmon (Ruggerone and Goetz 2004;Kendall et al 2020;Anderson et al 2021;Claiborne et al 2021;Buckner et al 2023) and steelhead (Myers 2018).…”