Bayesian Spatiotemporal Modeling Approaches to Assess Green Sturgeon Bycatch Patterns in U.S. West Coast Groundfish Fisheries

Fish species of conservation concern, such as West Coast green sturgeon, are thought to be low in abundance, and only rarely encountered in fisheries, making their population trends difficult to assess. In particular, when green sturgeon co-occur with commercially targeted species, understanding the spatial and temporal characteristics of bycatch patterns is critical for conservation planning and management. We used Bayesian spatiotemporal models to analyze patterns of green sturgeon bycatch data collected by the West Coast Groundfish Observer Program in groundfish fisheries (2002-13). We model the spatiotemporal patterns of bycatch based on a novel Bayesian modeling approach with time varying random effects. Preliminary results suggest that green sturgeon is encountered in the fisheries consistently in a few confined nearshore coastal habitats. In addition, we illustrate how different metrics of effort influence bycatch estimates and interpretation. The models indicate that the patterns of encounters within these areas vary spatially and temporally, but there are consistent hotspots of encounters. The study illustrates how new methods can be applied to observer data to assess a species of high conservation concern, and have significant implications for conservation planning and management.