Discover the latest in ARF Pacific Albacore Tuna research.

These studies are peer-reviewed and conducted by scientists at independently funded organizations.

Projecting species distributions using fishery-dependent data
Melissa A. Karp, Stephanie Brodie, James A. Smith, Kate Richerson, Rebecca L. Selden, Owen R. Liu, Barbara Muhling, Jameal Samhouri, Lewis A. K. Barnett, Elliott L. Hazen, Daniel Ovando, Jerome Fiechter, Michael Jacox, Mercedes Pozo Buil, ECS Tech, in support of, NOAA Fisheries Office of Science and Technology, NOAA Fisheries – Environmental Research Division, Southwest Fisheries Science Center, Institute of Marine Sciences - University of California, Santa Cruz, NOAA Fisheries - Fisheries Resources Division, Southwest Fisheries Science Center, NOAA Fisheries - Northwest Fisheries Science Center, Department of Biological Sciences - Wellesley College, NOAA Fisheries - Alaska Fisheries Science Center, School of Aquatic and Fishery Sciences - University of Washington, Ocean Sciences Department - University of California, Santa Cruz, NOAA Fisheries - Physical Sciences Laboratory, Oceanic and Atmospheric Research Ericka Carlson Melissa A. Karp, Stephanie Brodie, James A. Smith, Kate Richerson, Rebecca L. Selden, Owen R. Liu, Barbara Muhling, Jameal Samhouri, Lewis A. K. Barnett, Elliott L. Hazen, Daniel Ovando, Jerome Fiechter, Michael Jacox, Mercedes Pozo Buil, ECS Tech, in support of, NOAA Fisheries Office of Science and Technology, NOAA Fisheries – Environmental Research Division, Southwest Fisheries Science Center, Institute of Marine Sciences - University of California, Santa Cruz, NOAA Fisheries - Fisheries Resources Division, Southwest Fisheries Science Center, NOAA Fisheries - Northwest Fisheries Science Center, Department of Biological Sciences - Wellesley College, NOAA Fisheries - Alaska Fisheries Science Center, School of Aquatic and Fishery Sciences - University of Washington, Ocean Sciences Department - University of California, Santa Cruz, NOAA Fisheries - Physical Sciences Laboratory, Oceanic and Atmospheric Research Ericka Carlson

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.

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Recommendations for quantifying and reducing uncertainty in climate projections of species distributions
Stephanie Brodie, James A. Smith, Barbara Muhling, Lewis A. K. Barnett, Gemma Carroll, Paul Fiedler, Steven J. Bograd, Elliott L. Hazen, Michael Jacox, Kelly S. Andrews, Cheryl L. Barnes, Lisa G. Crozier, Jerome Fiechter, Alexa Fredston, Melissa A. Haltuch, Chris J. Harvey, Elizabeth Holmes, Melissa A. Karp, Owen R. Liu, Michael J. Malick, Mercedes Pozo Buil, Kate Richerson, Christopher N. Rooper, Jameal Samhouri, Rachel Seary, Rebecca L. Selden, Andrew R. Thompson, Desiree Tommasi, Eric J. Ward, Isaac C. Kaplan, Institute of Marine Sciences - University of California, Santa Cruz, NOAA Fisheries - Southwest Fisheries Science Center, NOAA Fisheries - Alaska Fisheries Science Center, Environmental Defense Fund, NOAA Earth System Research Laboratory, NOAA Fisheries - Northwest Fisheries Science Center, Cooperative Institute for Climate, Ocean, and Ecosystem Studies - University of Washington, Ocean Sciences Department - University of California, Santa Cruz, Department of Ecology, Evolution, and Natural Resources - Rutgers University, ECS Tech, in support of, NOAA Fisheries Office of Science and Technology, Pacific Biological Station - Fisheries and Oceans Canada, Department of Biological Sciences - Wellesley College Jade Gonzales Stephanie Brodie, James A. Smith, Barbara Muhling, Lewis A. K. Barnett, Gemma Carroll, Paul Fiedler, Steven J. Bograd, Elliott L. Hazen, Michael Jacox, Kelly S. Andrews, Cheryl L. Barnes, Lisa G. Crozier, Jerome Fiechter, Alexa Fredston, Melissa A. Haltuch, Chris J. Harvey, Elizabeth Holmes, Melissa A. Karp, Owen R. Liu, Michael J. Malick, Mercedes Pozo Buil, Kate Richerson, Christopher N. Rooper, Jameal Samhouri, Rachel Seary, Rebecca L. Selden, Andrew R. Thompson, Desiree Tommasi, Eric J. Ward, Isaac C. Kaplan, Institute of Marine Sciences - University of California, Santa Cruz, NOAA Fisheries - Southwest Fisheries Science Center, NOAA Fisheries - Alaska Fisheries Science Center, Environmental Defense Fund, NOAA Earth System Research Laboratory, NOAA Fisheries - Northwest Fisheries Science Center, Cooperative Institute for Climate, Ocean, and Ecosystem Studies - University of Washington, Ocean Sciences Department - University of California, Santa Cruz, Department of Ecology, Evolution, and Natural Resources - Rutgers University, ECS Tech, in support of, NOAA Fisheries Office of Science and Technology, Pacific Biological Station - Fisheries and Oceans Canada, Department of Biological Sciences - Wellesley College Jade Gonzales

Recommendations for quantifying and reducing uncertainty in climate projections of species distributions

Projecting the future distributions of commercially and ecologically important species has become a critical approach for ecosystem managers to strategically anticipate change, but large uncertainties in projections limit climate adaptation planning. Although distribution projections are primarily used to understand the scope of potential change—rather than accurately predict specific outcomes—it is nonetheless essential to understand where and why projections can give implausible results and to identify which processes contribute to uncertainty. Here, we use a series of simulated species distributions, an ensemble of 252 species distribution models, and an ensemble of three regional ocean climate projections, to isolate the influences of uncertainty from earth system model spread and from ecological modeling.

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