Empirical orthogonal function regression: Linking population biology to spatial varying environmental conditions using climate projections.

Ecologists and oceanographers inform population and ecosystem management by identifying the physical drivers of ecological dynamics. However, different research communities use different analytical tools where, for example, physical oceanographers often apply rank-reduction techniques (a.k.a. empirical orthogonal functions [EOF]) to identify indicators that represent dominant modes of physical variability, whereas population ecologists use dynamical models that incorporate physical indicators as covariates. Simultaneously modeling physical and biological processes would have several benefits, including improved communication across sub-fields; more efficient use of limited data; and the ability to compare importance of physical and biological drivers for population dynamics. Here, we develop a new statistical technique, EOF regression, which jointly models population-scale dynamics and spatially distributed physical dynamics. EOF regression is fitted using maximum-likelihood techniques and applies a generalized EOF analysis to environmental measurements, estimates one or more time series representing modes of environmental variability, and simultaneously estimates the association of this time series with biological measurements. By doing so, it identifies a spatial map of environmental conditions that are best correlated with annual variability in the biological process. We demonstrate this method using a linear (Ricker) model for early-life survival ("recruitment") of three groundfish species in the eastern Bering Sea from 1982 to 2016, combined with measurements and end-of-century projections for bottom and sea surface temperature. Results suggest that (a) we can forecast biological dynamics while applying delta-correction and statistical downscaling to calibrate measurements and projected physical variables, (b) physical drivers are statistically significant for Pacific cod and walleye pollock recruitment, (c) separately analyzing physical and biological variables fails to identify the significant association for walleye pollock, and (d) cod and pollock will likely have reduced recruitment given forecasted temperatures over future decades.

Trait-based climate vulnerability assessments in data-rich systems: An application to eastern Bering Sea fish and invertebrate stocks.

Trait-based climate vulnerability assessments based on expert evaluation have emerged as a rapid tool to assess biological vulnerability when detailed correlative or mechanistic studies are not feasible. Trait-based assessments typically view vulnerability as a combination of sensitivity and exposure to climate change. However, in some locations, a substantial amount of information may exist on system productivity and environmental conditions (both current and projected), with potential disparities in the information available for data-rich and data-poor stocks. Incorporating this level of detailed information poses challenges when conducting, and communicating uncertainty from, rapid vulnerability assessments. We applied a trait-based vulnerability assessment to 36 fish and invertebrate stocks in the eastern Bering Sea (EBS), a data-rich ecosystem. In recent years, the living marine resources of the EBS and Aleutian Islands have supported fisheries worth more than US $1 billion of annual ex-vessel value. Our vulnerability assessment uses projections (to 2039) from three downscaled climate models, and graphically characterizes the variation in climate projections between climate models and between seasons. Bootstrapping was used to characterize uncertainty in specific biological traits and environmental variables, and in the scores for sensitivity, exposure, and vulnerability. The sensitivity of EBS stocks to climate change ranged from "low" to "high," but vulnerability ranged between "low" and "moderate" due to limited exposure to climate change. Comparison with more detailed studies reveals that water temperature is an important variable for projecting climate impacts on stocks such as walleye pollock (Gadus chalcogrammus), and sensitivity analyses revealed that modifying the rule for determining vulnerability increased the vulnerability scores. This study demonstrates the importance of considering several uncertainties (e.g., climate projections, biological, and model structure) when conducting climate vulnerability assessments, and can be extended in future research to consider the vulnerability of user groups dependent on these stocks.

Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system.

Resource managers at the state, federal, and tribal levels make decisions on a weekly to quarterly basis, and fishers operate on a similar timeframe. To determine the potential of a support tool for these efforts, a seasonal forecast system is experimented with here. JISAO's Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE) features dynamical downscaling of regional ocean conditions in Washington and Oregon waters using a combination of a high-resolution regional model with biogeochemistry and forecasts from NOAA's Climate Forecast System (CFS). Model performance and predictability were examined for sea surface temperature (SST), bottom temperature, bottom oxygen, pH, and aragonite saturation state through model hindcasts, reforecast, and forecast comparisons with observations. Results indicate J-SCOPE forecasts have measurable skill on seasonal timescales. Experiments suggest that seasonal forecasting of ocean conditions important for fisheries is possible with the right combination of components. Those components include regional predictability on seasonal timescales of the physical environment from a large-scale model, a high-resolution regional model with biogeochemistry that simulates seasonal conditions in hindcasts, a relationship with local stakeholders, and a real-time observational network. Multiple efforts and approaches in different regions would advance knowledge to provide additional tools to fishers and other stakeholders.