Abstract
Introduction Species distribution models (SDMs) are increasingly used in fisheries science to understand species’ spatial patterns and improve stock assessments. Method This study developed SDMs for eulachon smelt ( Thaleichthys pacificus ), a threatened, demersal forage fish often caught as bycatch in the United States West Coast ocean shrimp trawl fishery. Using ten years of observer data (2012–2021, n=19,749 with 25.4% being zeros), the study assessed the influence of static (e.g., substrate) and dynamic (e.g., ocean temperature, currents) environmental variables on eulachon abundance. Results The best-performing SDM included near-bottom temperature and current data, outperforming SDMs using only surface variables. Eulachon abundance peaked at ~150 m depth, especially over gravel substrates, and during nighttime. Although none of the SDM-based abundance indices significantly correlated with the Columbia River stock assessment index, bottom-based SDMs showed stronger alignment with the stock assessment (R=0.61, p=0.08 & R=0.62, p=0.07). Discussion Management implications are significant. Rock habitats were associated with higher eulachon bycatch, and vessels can use bottom-typing tools to avoid them. Also, delaying the season opener could reduce bycatch, as eulachon catch was reduced by 0.0109 mt per trawl over the interquartile range of day of the year. These findings can inform Best Management Practices (BMPs), which have historically led to regulatory changes such as the adoption of excluder grates and LED lights. Overall, incorporating near-bottom oceanographic data greatly enhanced predictive performance, especially for demersal species like eulachon. These mechanistic SDMs can be projected forward, aiding future management amid changing ocean conditions. While some discrepancies remain, this approach offers promising insights for adaptive fishery management and conservation of imperiled species like eulachon.