Projecting species distributions using fishery-dependent data

Contributors: Melissa A. Karp, Stephanie Brodie, James A. Smith, Kate Richerson, Rebecca L. Selden, Owen R. Liu, Barbara A. Muhling, Jameal F. Samhouri, Lewis A. K. Barnett, Elliott L. Hazen, Daniel Ovando, Jerome Fiechter, Michael G. Jacox, Mercedes Pozo Buil

Organizations: ECS Tech, in support of, Office of Science & Technology, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Silver Spring, MD • Environmental Research Division, NOAA Southwest Fisheries Science Center, Monterey, CA • Institute of Marine Sciences, University of California Santa Cruz, Monterey, CA • Fisheries Resources Division, NOAA Southwest Fisheries Science Center, La Jolla, CA • NOAA Fisheries, Northwest Fisheries Science Center, Seattle, WA • Department of Biological Sciences, Wellesley College, Wellesley, MA • NOAA Fisheries, Alaska Fisheries Science Center, Seattle, WA • School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA • Ocean Sciences Department, University of California at Santa Cruz, Santa Cruz, CA • Physical Sciences Laboratory, NOAA Oceanic and Atmospheric Research, Boulder, CO

Summary: 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.