Quantifying spatiotemporal change and variability in natural mortality and other population parameters (growth, productivity, etc.) is essential for developing reliable forecasting and assessment models used to manage salmon stocks. Productivity of numerous coho salmon populations in the eastern Pacific has decreased since the 1970s (Zimmerman et al. 2015), and recent research has identified several factors that may be causing increased mortality in both the marine (Thomas et al. 2017) and freshwater environments (Feist et al. 2017; Ohlberger et al. 2018a). However, there has been minimal effort to incorporate these findings into a rigorous modeling approach that can be used for multiple coho stocks to address fishery, conservation, and ecosystem concerns. We propose to develop spatially explicit hierarchical models for coho salmon stocks, ranging from Oregon to British Columbia. These models will be complete life cycle models, allowing us to evaluate the relative contributions of different factors in space and time.
A better understanding of how drivers such as environmental variation, ecosystem interactions, and fishing pressure have changed over time will also allow us to forecast likely scenarios of future change. By modeling the stocks jointly, and including information about both freshwater and marine drivers of productivity, we aim to address Southern Panel Priority # 3, improving “abundance forecasting and escapement estimation for Coho Management Units (MUs), including better understanding of the impacts of environmental variability and uncertainty.”