Award: OCE-1558648

For much of the 20th century, productivity and abundance for many fish and crustacean populations in the Gulf of Alaska was tightly coupled with climate. Swings in temperature variability that were part of the Pacific Decadal Oscillation (PDO) climate pattern drove dramatic changes in salmon, groundfish, small pelagic fish, shrimp, and crab populations that created a series of booms and busts in Alaskan fisheries. However, beginning in the late 1980s, these long-standing population responses to climate largely disappeared. This project tested a novel explanation for these climate-biology relationships that change over time. We hypothesized that increasing importance in another large-scale climate pattern, the North Pacific Gyre Oscillation (NPGO) changed relationships among different Gulf of Alaska climate variables in a way that invalidated previously strong links between temperature and population processes. We found that when the NPGO climate pattern became more important, the Aleutian Low atmospheric pattern, which is a leading driver of the PDO, showed decreasing year to year variability. The Aleutian Low is also a dominant feature of Gulf of Alaska oceanography, as it drives the wind patterns that are leading drivers of a range of important ocean climate variables, including coastal freshwater discharge and salinity, coastal downwelling, wind mixing, large-scale current patterns, heat fluxes, and temperature. Our research showed that prior to the late 1980s, when the Aleutian Low was more variable, all of these climate variables tended to be strongly correlated. As a result, conditions for fish and crustacean populations across a range of climate variables tended to be either good or bad in concert. And temperature, which was correlated with the full suite of climate variables, acted as a good statistical predictor of population variability. But when Aleutian Low variability declined, individual climate variables were no longer strongly correlated, temperature was no longer a good measure of overall climate conditions, and statistical temperature-biology relationships decayed towards zero. Our finding that relationships among climate variables changed in ecologically relevant ways over the relatively short time scale of the observed record has important implications for the statistical tools that are commonly used for understanding climate effects on marine ecosystems. Statistical approaches that are used to link climatic and biological variability, or to simplify patterns of variability across many climate or population time series, commonly assume that relationships among climate and population variables are fixed in time, so that the patterns observed in one time period are valid for explaining ecological variability in another period, even decades later. We showed that in the Gulf of Alaska these assumptions of fixed relationships are likely to produce mistaken conclusions about climate-biology links and community variability. Relaxing this assumption and allowing ecological relationships to change over time greatly increased our ability to construct statistical models of ecological variability over multidecadal time scales. This project was a limited-scope, two-year study aimed at investigating a potentially transformative but untested idea. The intellectual contributions of this project have resulted in one published peer-reviewed paper, a second paper submitted for publication and a third nearing submission, and eight presentations at national and international scientific conferences. In addition, this project has had a number of broader impacts. Our results are currently being applied to improve Federal fisheries management in California, Oregon, Washington, and Alaska through a spin-off study funded by the National Oceanic and Atmospheric Administration. This study has also had an extensive early-career component, with a post-doctoral researcher and undergraduate researcher supported at Oregon State University, and a graduate student supported at the University of South Carolina. Last Modified: 11/29/2018 Submitted by: Lorenzo Ciannelli