Discover the latest in ARF Pacific Albacore Tuna research.

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

Dynamic human, oceanographic, and ecological factors mediate transboundary fishery overlap across the Pacific high seas
Timothy H. Frawley, Barbara Muhling, Stephanie Brodie, Hannah Blondin, Heather Welch, Martin C. Arostegui, Steven J. Bograd, Camrin D. Braun, Megan A. Cimino, Nima Farchadi, Elliott L. Hazen, Desiree Tommasi, Michael Jacox, Institute of Marine Sciences - University of California, Santa Cruz, NOAA Fisheries - Southwest Fisheries Science Center, Hopkins Marine Station - Stanford University, Woods Hole Oceanographic Institution, San Diego State University, University of California Davis, NOAA Earth System Research Laboratory Ericka Carlson Timothy H. Frawley, Barbara Muhling, Stephanie Brodie, Hannah Blondin, Heather Welch, Martin C. Arostegui, Steven J. Bograd, Camrin D. Braun, Megan A. Cimino, Nima Farchadi, Elliott L. Hazen, Desiree Tommasi, Michael Jacox, Institute of Marine Sciences - University of California, Santa Cruz, NOAA Fisheries - Southwest Fisheries Science Center, Hopkins Marine Station - Stanford University, Woods Hole Oceanographic Institution, San Diego State University, University of California Davis, NOAA Earth System Research Laboratory Ericka Carlson

Dynamic human, oceanographic, and ecological factors mediate transboundary fishery overlap across the Pacific high seas

The management and conservation of tuna and other transboundary marine species have to date been limited by an incomplete understanding of the oceanographic, ecological and socioeconomic factors mediating fishery overlap and interactions, and how these factors vary across expansive, open ocean habitats. Despite advances in fisheries monitoring and biologging technology, few attempts have been made to conduct integrated ecological analyses at basin scales relevant to pelagic fisheries and the highly migratory species they target. Here, we use vessel tracking data, archival tags, observer records, and machine learning to examine inter- and intra-annual variability in fisheries overlap (2013–2020) of five pelagic longline fishing fleets with North Pacific albacore tuna (Thunnus alalunga, Scombridae).

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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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