Projecting species distributions using fishery‐dependent data

Many marine species are shifting their distributions in response to changing ocean conditions, posing significant challenges and risks for fisheries management. Species distribution models (SDMs) used project future the face of a climate. Information fit SDMs generally comes from two main sources: fishery-independent (scientific surveys) fishery-dependent (commercial catch) data. A concern with data is that fishing locations not independent underlying abundance, potentially biasing predictions distributions. However, resources surveys increasingly limited; therefore, it critical we understand strengths limitations developed We simulation approach evaluate potential inform abundance estimates quantify bias resulting different sampling scenarios California Current System (CCS). then evaluated ability changes spatial over time compare scale which model performance degrades between as function climate novelty. Our results show generated can still result high predictive skill several decades into future, given specific forms preferential low Therefore, may be able supplement information reduced or eliminated budgetary reasons future.