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Climate change is likely to change the relationships between commonly used climate indices and underlying patterns of climate variability, but this complexity is rarely considered in studies using climate indices. Here, we show that the physical and ecological conditions mapping onto the Pacific Decadal Oscillation (PDO) index and North Pacific Gyre Oscillation (NPGO) index have changed over multidecadal timescales. These changes apparently began around a 1988/1989 North Pacific climate shift that was marked by abrupt northeast Pacific warming, declining temporal variance in the Aleutian Low (a leading atmospheric driver of the PDO), and increasing correlation between the PDO and NPGO patterns. Sea level pressure and surface temperature patterns associated with each climate index changed after 1988/1989, indicating that identical index values reflect different states of basinscale climate over time. The PDO and NPGO also show timedependent skill as indices of regional northeast Pacific ecosystem variability. Since the late 1980s, both indices have become less relevant to physical–ecological variability in regional ecosystems from the Bering Sea to the southern California Current. Users of these climate indices should be aware of nonstationary relationships with underlying climate variability within the historical record, and the potential for further nonstationarity with ongoing climate change.

Seasonal ice cover creates a pool of cold bottom water on the eastern Bering Sea continental shelf each winter. The southern edge of this cold pool, which defines the ecotone between arctic and subarctic communities, has retreated ~230 km northward since the early 1980s. Bottom trawl surveys of fish and invertebrates in the southeastern Bering Sea (1982-2006) show a coincident reorganization in community composition by latitude. Survey catches show community-wide northward distribution shifts, and the area formerly covered by the cold pool has seen increases in total biomass, species richness, and average trophic level as subarctic fauna have colonized newly favorable habitats. Warming climate has immediate management implications, as 57% of variability in commercial snow crab (Chionoecetes opilio) catch is explained by winter sea ice extent. Several measures of community distribution and structure show linear relationships with bottom temperature, suggesting warming climate as the primary cause of changing biogeography. However, residual variability in distribution not explained by climate shows a strong temporal trend, suggesting that internal community dynamics also contribute to changing biogeography. Variability among taxa in their response to temperature was not explained by commercial status or life history traits, suggesting that species-specific responses to future warming will be difficult to predict.

Common approaches for summarizing multivariate environmental or community data assume that relationships among variables are stationary over time, and this assumption is often not tested. Here we test the hypothesis that relationships among environmental and community time series are nonstationary in the Gulf of Alaska ecosystem (North Pacific Ocean) over multidecadal time scales. Dynamic factor analysis (DFA) is applied to environmental and community data from before and after 1988/1989, corresponding to the timing of an abrupt decline in temporal variance of the Aleutian Low atmospheric pattern, a leading driver of Gulf of Alaska climate. Results show that covariance among local atmosphere and ocean environmental variables weakened simultaneous to the decline in Aleutian Low variance. At the same time, community-wide responses of 14 fish and crustacean populations to physical forcing weakened, as indicated by nonstationary environment–biology regression coefficients. In line with theoretical predictions, this loss of a shared response to environmental variability was accompanied by weakening community covariance. Individual populations also showed nonstationary relationships with shared trends of community variability. We conclude that assumptions of fixed environmental and community relationships are likely to produce mistaken inference in this ecosystem. Similar concerns may apply in other ecosystems subject to changing climate patterns.

We apply climate attribution techniques to sea surface temperature time series from five regional North Pacific ecosystems to track the growth in human influence on ocean temperatures over the past seven decades (1950–2022). Using Bayesian estimates of the Fraction of Attributable Risk (FAR) and Risk Ratio (RR) derived from 23 global climate models, we show that human influence on regional ocean temperatures could first be detected in the 1970s and grew until 2014–2020 temperatures showed overwhelming evidence of human contribution. For the entire North Pacific, FAR and RR values show that temperatures have reached levels that were likely impossible in the preindustrial climate, indicating that the question of attribution is already obsolete at the basin scale. Regional results indicate the strongest evidence for human influence in the northernmost ecosystems (Eastern Bering Sea and Gulf of Alaska), though all regions showed FAR values > 0.98 for at least one year. Extreme regional SST values that were expected every 1000–10 000 years in the preindustrial climate are expected every 5–40 years in the current climate. We use the Gulf of Alaska sockeye salmon fishery to show how attribution time series may be used to contextualize the impacts of human-induced ocean warming on ecosystem services. We link negative warming effects on sockeye fishery catches to increasing human influence on regional temperatures (increasing FAR values), and we find that sockeye salmon migrating to sea in years with the strongest evidence for human effects on temperature (FAR ⩾ 0.98) produce catches 1.4 standard deviations below the long-term log mean. Attribution time series may be helpful indicators for better defining the human role in observed climate change impacts, and may thus help researchers, managers, and stakeholders to better understand and plan for the effects of climate change.

Early warning signals ( EWS ) might dramatically improve our ability to manage nonlinear ecological change. However, the degree to which theoretical EWS predictions are supported in empirical systems remains unclear. The goal of this study is to make recommendations for identifying the types of ecological transitions that are expected to show EWS . We conducted a review and meta‐analysis of published studies and comparative analysis of eight northeast Pacific Ocean time series to illustrate the importance of testing for nonlinearity in empirical EWS studies. We found that published studies demonstrating nonlinearity in ecosystem dynamics are more likely to support EWS predictions than studies with linear or undetermined dynamics. The northeast Pacific time series in our analysis were often too short for formal tests of nonlinearity, a common problem in empirical studies. To assess the evidence for nonlinear dynamics in these data, we tested for state‐dependent driver–response relationships consistent with hysteresis, a central feature of nonlinear ecological models. This analysis supported the results of the literature meta‐analysis. Four time series with driver–response relationships consistent with hysteresis generally supported theoretical EWS predictions, while four without evidence of hysteresis failed to support EWS predictions. Theoretical support for EWS is largely generated from nonlinear models, and we conclude that tests for either nonlinear dynamics or hysteresis are needed before employing EWS .

Non‐stationarity (time‐varying mean or variance) in climate conditions can alter relationships between basin‐scale climate indices and the ecological conditions that map onto them. We consider evidence of time‐varying climate conditions in the California Current System (CCS) based on sea level pressure dynamics that characterize the North Pacific High (NPH), and evaluate the temporal stability of regional relationships between climate indices and physical and biological conditions across the CCS. We find relationships between climate indices and ecological conditions are relatively stable through time, but do not capture short‐term ecological trends. These results show that popular basin‐scale climate indices are insufficient in characterizing the North Pacific climate system, especially from ecosystem perspectives. Applications of associations between climate and ecological variables should consider proximate physical forcing mechanisms and the stability of relationships through time. Plain Language Summary The northeast Pacific has recently experienced abnormal climate conditions, characterized by frequent and severe marine heatwaves, making it difficult to know how species will respond to the environment. We examined how relationships between marine fauna and environmental conditions have changed over the past decade compared to earlier time periods over the past 50+ years. We found that an important atmospheric system, known as the North Pacific High, has been weaker and located further north during the spring upwelling season from 2013 to 2023, when ocean temperatures have been exceptionally warm. This shift impacts weather patterns and ocean currents, which in turn affect marine life and ecosystems. Our research shows that indicators of basin‐scale climate conditions do not track short‐term trends in upwelling and marine fauna. This means that the relationships between large‐scale climate and regional marine ecosystems are complex, making it difficult to predict how marine life will respond to current and future climate conditions without clearer mechanistic understanding. Key Points The springtime North Pacific High has been smaller, weaker, and less variable among years over the past decade compared to the long‐term average Physical and biological conditions have time‐varying relationships with basin‐scale climate indices Short‐term ecological trends do not track climate indices, contributing to time‐varying relationships

Sustainability—maintaining catches within the historical range of socially and ecologically acceptable values—is key to fisheries success. Climate change may rapidly threaten sustainability, and recognizing these instances is important for effective climate adaptation. Here, we present one approach for evaluating changing sustainability under a changing climate. We use Bayesian regression models to compare fish population processes under historical climate norms and emerging anthropogenic extremes. To define anthropogenic extremes we use the Fraction of Attributable Risk (FAR), which estimates the proportion of risk for extreme ocean temperatures that can be attributed to human influence. We illustrate our approach with estimates of recruitment (production of young fish, a key determinant of sustainability) for two exploited fishes (Pacific cod Gadus macrocephalus and walleye pollock G. chalcogrammus ) in a rapidly warming ecosystem, the Gulf of Alaska. We show that recruitment distributions for both species have shifted towards zero during anthropogenic climate extremes. Predictions based on the projected incidence of anthropogenic temperature extremes indicate that expected recruitment, and therefore fisheries sustainability, is markedly lower in the current climate than during recent decades. Using FAR to analyze changing population processes may help fisheries managers and stakeholders to recognize situations when historical sustainability expectations should be reevaluated.

Ecosystem-based fisheries management requires the successful integration of ecosystem information into the fisheries management process. In the Northeast Pacific Ocean, ecosystem data collection and accessibility have achieved successful milestones, yet application to the harvest specification process remains challenging. The synthesis, interpretation, and application of ecosystem information to groundfish fisheries management in the Gulf of Alaska (GOA) can be supported by the identification of common ecosystem trends and ecosystem states across a diverse set of indicators. In this study, we used Dynamic Factor Analysis (DFA) and hidden Markov models (HMM) to analyze 92 indicators in climate, lower-trophic, mid-trophic, and seabird models for the western and eastern GOA marine ecosystems. Time series ranged from 25 to 52 years in length, analyzed through 2022. The DFA identified common trends across indicators and groups of covarying indicators (e.g., biomass of zooplankton species), highlighting opportunities to streamline communication of these data to management. Non-stationarity analyses revealed past changes in relationships, and can provide early warnings in future annual updates if previously identified correlations change. The HMM identified two to three ecosystem states in each sub-model that largely aligned with previously observed long- and short-term shifts in ecosystem dynamics in the region (i.e., shifts starting in 1975, 1988, and 2014). Annually updating these analyses, within an existing framework of reporting ecosystem information to management bodies, can streamline communication and improve early warning of changes in ecosystem dynamics. These tools can provide ecosystem support to management decisions relative to groundfish productivity and resulting harvest specifications.

The Exxon Valdez oil spill occurred in March 1989 in Prince William Sound, Alaska, and was one of the worst environmental disasters on record in the United States. Despite long-term data collection over the nearly three decades since the spill, tremendous uncertainty remains as to how significantly the spill affected fishery resources. Pacific herring (Clupea pallasii) and some wild Pacific salmon populations (Oncorhynchus spp.) in Prince William Sound declined in the early 1990s, and have not returned to the population sizes observed in the 1980s. Discerning if, or how much of, this decline resulted from the oil spill has been difficult because a number of other physical and ecological drivers are confounded temporally with the spill; some of these drivers include environmental variability or changing climate regimes, increased production of hatchery salmon in the region, and increases in populations of potential predators. Using data pre- and post-spill, we applied time-series methods to evaluate support for whether and how herring and salmon productivity has been affected by each of five drivers: (1) density dependence, (2) the EVOS event, (3) changing environmental conditions, (4) interspecific competition on juvenile fish, and (5) predation and competition from adult fish or, in the case of herring, humpback whales. Our results showed support for intraspecific density-dependent effects in herring, sockeye, and Chinook salmon, with little overall support for an oil spill effect. Of the salmon species, the largest driver was the negative impact of adult pink salmon returns on sockeye salmon productivity. Herring productivity was most strongly affected by changing environmental conditions; specifically, freshwater discharge into the Gulf of Alaska was linked to a series of recruitment failures-before, during, and after EVOS. These results highlight the need to better understand long terms impacts of pink salmon on food webs, as well as the interactions between nearshore species and freshwater inputs, particularly as they relate to climate change and increasing water temperatures.

Fisheries-independent surveys provide critical data products used to estimate stock status and inform management decisions. While it can be possible to redistribute sampling effort to improve survey efficiency and address changing monitoring needs in the face of unforeseen challenges, it is important to assess the consequences of such changes. Here, we present an approach that relies on existing survey data and simulations to evaluate the impacts of strategic reductions in survey sampling effort. We apply this approach to assess the potential effects of reducing high density sampling near St. Matthew Island and the Pribilof Islands in the NOAA eastern Bering Sea (EBS) bottom trawl survey. These areas contain high density “corner stations” that were implemented for finer-scale monitoring of associated blue king crab stocks ( Paralithodes platypus ) which historically supported commercial fisheries but have since declined and are seldom eligible for harvest. We investigate the effects of removing these corner stations on survey data quality for focal P. platypus stocks and other crab and groundfish species monitored by the EBS survey. We find that removing the St. Matthew and Pribilof Islands corner stations has negligible effects on data quality for most stocks, except for those whose distributions are concentrated in these areas. However, the data quality for such stocks was relatively low even with higher density sampling, and corner station removal had only minor effects on stock assessment outcomes. The analysis we present here provides a generic approach for evaluating strategic reductions in sampling effort for systematic survey designs and can be applied by scientists and managers facing similar decisions elsewhere.