Improving salmon marine survival models with covariance map indices of sea surface temperature (CMISST) and sea surface height (CMISSH)

Pacific-basin scale atmospheric and oceanographic conditions cascade to regional environmental conditions. In turn, these regional conditions in the nearshore ocean have direct and indirect impacts on coastal ecology and influence everything from primary productivity to top predator abundance and distribution. Quantitatively representing these large-scale conditions with consolidated metrics allows the ocean environment to be represented in a small number of variables, or indices, and can aid in fisheries management and policy decisions. As an example, Pacific salmon face numerous challenges during their migration from freshwater rivers to the ocean and back, and managers must make accurate assessments of their marine survival to inform effective conservation and management strategies. From satellite data and earth systems models, we now have rich data sets with complete spatial and temporal coverage to use for these applications. Here, we create a spatial map of covariance values between salmon and sea surface temperature (or height) and describe a method to calculate a temporal index of similarity to that map. The resulting index, referred to as a Covariance Map Index (CMI), can be used in subsequent predictive models of salmon adult return abundance or survival. Importantly, and unlike standard indices such as PDO, this new metric is tailored for a specific stock of salmon, and can be easily applied to other marine species. By employing this innovative tool, researchers, policymakers, and conservationists can gain deeper insights into marine survival patterns, enabling more informed and effective strategies for the sustainable management of these iconic species. • Stock-specific Covariance Map Indices can offer practical benefits for fisheries management, enabling more targeted hatchery and harvest planning, improving conservation outcomes, and supporting adaptive strategies in the face of climate-driven changes and ecosystem non-stationarity. • Stock-specific indices significantly enhance predictive accuracy for salmon returns compared to traditional broad-scale indicators like the Pacific Decadal Oscillation and North Pacific Gyre Oscillation. • Sensitivity analyses revealed the importance of methodological decisions in shaping predictive performance, underscoring the need for flexible, tailored modeling approaches in fisheries forecasting. • Covariance maps provided valuable ecological insights, illustrating how differing migration routes and depth preferences among salmon stocks affect their exposure to, and relationship with, ocean conditions.