Incorporating spatial and temporal dynamics into evaluations of fish populations and habitat

Abstract

The ocean is rapidly changing, with impacts on both the physical environment and ecological systems. In my dissertation, I seek to understand how variations in fish populations over space and time are driven by environmental conditions in the ocean, and to improve statistical methods to accomodate this variation and thereby contribute to sustainable fisheries. In the following four chapters of my dissertation, I develop, test, and apply improved methodologies that link fish demographics to environmental conditions and address pressing management concerns. A spatio-temporal model of weight-at-age of walleye pollock improved our understanding of the dynamics of local and population-level demographic processes and can be used in future stock assessment models. I developed a statistical model that incorporated a physiological response to temperature and oxygen into distribution modeling to better capture this joint effect, in the context of predicting impacts of climate change on local fish densities. Because spatial statistical models rely on environmental data, I used statistical approaches to expand oxygen data available and test the sensitivity of ecological models to environmental data. Lastly, I applied these improved techniques in retrospective statistical models to evaluate evidence for oxygen limitation on the distribution of 32 groundfish species in the northeastern Pacific Ocean. Overall this dissertation advances statistical solutions for accomodating spatio-temporal data in estimates and predictions of fish ecological responses to environmental change.