| Summary: | Thesis (Master's)--University of Washington, 2013 I investigated common patterns in North Pacific salmon abundance and marine fish recruitment and environmental drivers of these patterns. In Chapter 1 I identified patterns of covariation in the abundance of regional groups of wild pink salmon ( Oncorhynchus gorbuscha ), chum salmon ( O. keta ), and sockeye salmon ( O. nerka ) from 34 population groups from Asia and western North America using multivariate analysis. The most prominent pattern of abundance variation was dominated by the Alaskan population groups and greater abundance was related to warm sea surface temperatures in the eastern Bering Sea and Gulf of Alaska. The second dominant pattern captured a dipole between North American and Asian salmon population groups that was associated with an intense, large-scale Aleutian Low. This highlights the existence of basin-wide covariations in wild salmon abundance that are associated with spatially coherent and regionally distinct patterns in North Pacific climate. In Chapter 2 I tested the hypothesis that synchronous Northeast Pacific marine fish recruitment is due to a shared susceptibility to environmental variability. For 52 marine fish stocks in the Eastern Bering Sea and Aleutian Islands, Gulf of Alaska (GOA), and California Current (CC) ecosystems, I tested for synchrony in stock-recruitment residuals within each ecosystem. Based on evidence of synchrony, I used Bayesian hierarchical models to model stock-recruitment residuals for groups of stocks with similar life histories as a linear function of environmental covariates. Within the GOA several stocks had a similar strong relationship with Northeast Pacific sea surface height. In the CC many stocks had a strong relationship with San Francisco sea level, experiencing higher recruitment during periods of high sea level and low upwelling the year before spawning and low sea level and high upwelling the year of spawning. Although for most stocks these models explained only a small portion of the variance ...
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