Scientific advances in ocean modeling and institutional investments such as NOAA’s Climate, Ecosystem, and Fisheries Initiative are leading to increased interest and feasibility in including environmental information in stock assessments. One of the most common linkages from environmental information to the stock assessment’s population model is via recruitment. However, research on when and how this additional source of data improves management advice from stock assessments is limited, providing little guidance on where to focus and how to implement efforts to include environmental data in stock assessments. In this study, we simulated age-structured populations using stock synthesis, one of the most widely-used tools for stock assessment today. We then sampled data from the simulated populations and fit stock assessment models to the simulated data. Estimation models included environmental recruitment indices of varying quality, included as a “survey” of recruitment deviations, as is common in stock synthesis (versus including the environmental data as part of the population’s recruitment model, more common in state-space approaches such as WHAM). Across multiple life histories, fishing histories, and data collection histories, we found that under this common implementation, recruitment indices improved estimates of spawning output during the forecast period due to improved estimates of recruitment during the final model years. However, models with high-quality indices also demonstrated increased bias in estimates of unfished recruitment and natural mortality. Work is ongoing to identify the source of this bias and strategies to minimize it.

ABSTRACT: Modelling has predicted that reductions in ocean pH and increases in temperature will reduce vital rates (survival and growth) of North Pacific crab stocks and hence the target levels of fishing mortality consistent with sustainable harvesting. However, these predictions have been based on best estimates of the effects of changes in ocean pH and temperature on vital rates. The effects of sources of uncertainty (in the relationship between ocean pH/temperature and vital rates, in economic parameters, whether prices and costs are non-linear functions of catches and effort, and that associated with the population dynamics model used to predict optimal fishing mortality rates) are quantified for red king crab and southern Tanner crab in Alaska. This shows that uncertainty related to the effects of ocean pH and temperature on vital rates and which Earth System Model / future emission scenario best reflects reality are the dominant sources of uncertainty. We then evaluate how additional experiments to explore the relationship between changes in ocean pH/temperature and vital rates (additional replicates and a wider range of levels of pH and temperature) could reduce the uncertainty in estimates of future time-trajectories of target fishing mortality rates.

ABSTRACT: There is growing interest from fisheries agencies and managers to incorporate climate indices into stock assessment. Environmental covariates are typically included as independent variable(s) and used to predict variation over time in some stock-assessment parameter as a dependent variable (a “regression paradigm”). However, this practice has several drawbacks: (1) it ignores collinearity among potential covariates resulting from mechanisms linking different ecological processes; (2) it forces analysts to select between long-time series for physical drivers, and intermittent time-series for biological drivers (e.g., forage indices); and (3) it does not easily incorporate stakeholder or scientific knowledge about relationships among covariates.
In the first hour of this Think Tank, we will introduce dynamic structural equation models (DSEM) as alternative to this “regression paradigm” for including environmental indices as covariates. DSEM generalizes a wide range of linear and causal models, including dynamic factor analysis (DFA), vector autoregressive, integrated, moving average (VARIMA) models, and causal models. In particular, we emphasize that DSEM (1) allows analysts to incorporate scientific and stakeholder knowledge to model collinearity among covariates, (2) jointly imputes missing values based on covariate relationships, (3) can incorporate Bayesian priors to generalized qualitative network models, while also (4) allowing analysts to specify how ocean physics can affect stock productivity via intermediate mechanisms such as forage time-series. We will provide a short summary of the statistical background, and a hands-on code tutorial for R-package dsem.
Next, we will show how DSEM can be incorporated directly into state-space, age-structured stock assessment models to account for climate or ecosystem-driven variation in population processes. We present preliminary results for a case study on Gulf of Alaska pollock, where we are using process research and expert knowledge to develop candidate causal maps to describe the physical environment, foraging conditions, and ecosystem drivers governing annual recruitment success for this commercially important stock. We discuss the lessons we’ve learned so far while developing and refining alternative causal maps, and our most recent results from performance tests when compared to the operational stock assessment model.

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