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