Ferretti, F., Worm, B., Britten, G., Heithaus, M. & Lotze, H. Patterns and ecosystem consequences of shark declines in the ocean. Ecol. Lett. 13, 1055–1071 (2010).
Google Scholar
Heithaus, M. et al. Seagrasses in the age of sea turtle conservation and shark overfishing. Front. Mar. Sci. 1, 1–6 (2014).
Google Scholar
Estes, J. et al. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Env. Resour. 41, 83–116 (2016).
Google Scholar
Anderson, O. et al. Global seabird bycatch in longline fisheries. Endanger. Species Res. 14, 91–106 (2011).
Google Scholar
Dias, M. et al. Threats to seabirds: A global assessment. Biol. Conserv. 237, 525–537 (2019).
Google Scholar
Phillips, R. et al. The conservation status and priorities for albatrosses and large petrels. Biol. Conserv. 201, 169–183 (2016).
Google Scholar
Werner, T., Kraus, S., Read, A. & Zollett, E. Fishing techniques to reduce the bycatch of threatened marine animals. Mar. Technol. Soc. J. 40, 50–68 (2006).
Google Scholar
Hall, M., Gilman, E., Minami, H., Mituhasi, T. & Carruthers, E. Mitigating bycatch in tuna fisheries. Rev. Fish Biol. Fish. 27, 881–908 (2017).
Google Scholar
Gilman, E., Brothers, N. & Kobayashi, D. Principles and approaches to abate seabird bycatch in longline fisheries. Fish Fish. 6, 35–49 (2005).
Google Scholar
Gilman, E., Chaloupka, M., Wiedoff, B. & Willson, J. Mitigating seabird bycatch during hauling by pelagic longline vessels. PLoS ONE 9, e84499 (2014).
Google Scholar
Juan-Jorda, M., Murua, H., Arrizabalaga, H., Dulvy, N. & Restrepo, V. Report card on ecosystem-based fisheries management in tuna regional fisheries management organizations. Fish Fish. 19, 321–339 (2018).
Google Scholar
ACAP. Review and best practice advice for reducing the impact of pelagic longline fisheries on seabirds. Agreement on the Conservation of Albatrosses and Petrels, Hobart, Australia (2019).
Crespo, P. & Crawford, R. Bycatch and the Marine Stewardship Council (MSC): A Review of the Efficacy of the MSC Certification Scheme in Tackling the Bycatch of Non-target Species (Birdlife International, 2019).
Nakano, H., Okazaki, M. & Okamoto, H. Analysis of catch depth by species for tuna longline fishery based on catch by branch lines. Bull. Nat. Res. Inst. Far Seas Fish. 34, 43–62 (1997).
Musyl, M. et al. Postrelease survival, vertical and horizontal movements, and thermal habitats of five species of pelagic sharks in the central Pacific Ocean. Fish. Bull. 109, 341–368 (2011).
Gabr, M. & El-Haweet, A. Pelagic longline fishery for albacore in the Mediterranean Sea off Egypt. Turk. J. Fish. Aquat. Sci. 12, 735–741 (2012).
Google Scholar
MEC. Marine Stewardship Council Public Certification Report. French Polynesia Albacore and Yellowfin Longline Fishery. ME Certification Ltd., Lymington, UK (2018).
Gilman, E. et al. Robbing Peter to pay Paul: Replacing unintended cross-taxa conflicts with intentional tradeoffs by moving from piecemeal to integrated fisheries bycatch management. Rev. Fish Biol. Fish. 29, 93–123 (2019).
Google Scholar
SCS. Tri Marine Atlantic albacore (Thunnus alalunga) Longline Fishery. MSC Fishery Assessment Report. SCS Global Services, Emeryville, USA (2022).
Gilman, E. et al. Phylogeny explains capture mortality of sharks and rays in pelagic longline fisheries: A global meta-analytic synthesis. Sci. Rep. https://doi.org/10.1038/s41598-022-21976-w (2022).
Google Scholar
WCPFC. Conservation and Management Measure to Mitigate the Impact of Fishing for Highly Migratory Fish Stocks on Seabirds. CMM 2018-03. Western and Central Pacific Fisheries Commission, Kolonia, Federated States of Micronesia (2018).
IATTC. Resolution to Mitigate the Impact on Seabirds of Fishing for Species Covered by the IATTC. Resolution C-11-02. Inter-American Tropical Tuna Commission, La Jolla, USA (2011).
Melvin, E., Guy, T. & Read, L. Best practice seabird bycatch mitigation for pelagic longline fisheries targeting tuna and related species. Fish. Res. 149, 5–18 (2014).
Google Scholar
Huang, H. Incidental catch of seabirds and sea turtles by Taiwanese longline fleets in the Pacific Ocean. Fish. Res. 170, 179–189 (2015).
Google Scholar
Jimenez, S. et al. Towards mitigation of seabird bycatch: Large-scale effectiveness of night setting and tori lines across multiple pelagic longline fleets. Bio. Cons. 247, 108642 (2020).
Google Scholar
IUCN. The IUCN Red List of Threatened Species. Version 2022–1. Online resource www.iucnredlist.org. ISSN 2307–8235. International Union for the Conservation of Nature, Gland, Switzerland (2022).
Gilman, E., Castejon, V., Loganimoce, E. & Chaloupka, M. Capability of a pilot fisheries electronic monitoring system to meet scientific and compliance monitoring objectives. Mar. Policy 113, 103792 (2020).
Google Scholar
Gilman, E., Chaloupka, M. & Sieben, C. Ecological risk assessment of a data-limited fishery using an ensemble of approaches. Mar. Policy 133, 104752 (2021).
Google Scholar
WPRFMC. Appendix 5. Fact Sheets on Seabird Bycatch Mitigation Methods for Pelagic Longline Fisheries. Report of the Workshop to Review Seabird Bycatch Mitigation Measures for Hawaii’s Pelagic Longline Fisheries. ISBN: 978–1–944827–37–3. Western Pacific Regional Fishery Management Council, Honolulu (2019).
Melvin, E., Dietrich, K., Suryan, R. & Fitzgerald, S. Lessons from seabird conservation in Alaskan longline fisheries. Cons. Biol. 33, 842–852 (2019).
Google Scholar
Ward, P. & Myers, R. Inferring the depth distribution of catchability for pelagic fishes and correcting for variations in the depth of longline fishing gear. Can. J. Fish. Aquat. Sci. 62, 1130–1142 (2005).
Google Scholar
Rice, P., Goodyear, C., Prince, E., Snodgrass, D. & Serafy, J. Use of catenary geometry to estimate hook depth during near-surface pelagic longline fishing: Theory versus practice. N. Am. J. Fish. Manag. 27, 1148–1161 (2007).
Google Scholar
Zhou, C. & Brothers, N. Interaction frequency of seabirds with longline fisheries: Risk factors and implications for management. ICES J. Mar. Sci. 78, 1278–1287 (2021).
Google Scholar
Childers, J., Snyder, S. & Kohin, S. Migration and behavior of juvenile North Pacific albacore (Thunnus alalunga). Fish. Oceanogr. 20, 157–173 (2011).
Google Scholar
Cosgrove, R., Arregui, I., Arrizabalaga, H., Goni, N. & Sheridan, M. New insights to behavior of North Atlantic albacore tuna (Thunnus alalunga) observed with pop-up satellite archival tags. Fish. Res. 150, 89–99 (2014).
Google Scholar
Williams, et al. Vertical behavior and diet of albacore tuna (Thunnus alalunga) vary with latitude in the South Pacific Ocean. Deep -Sea Res. II 113, 154–169 (2015).
Punt, A., Butterworth, D., de Moor, C., De Oliveira, J. & Haddon, M. Management strategy evaluation: Best practices. Fish Fish. 17, 303–334 (2016).
Google Scholar
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Soc. Ser. A 182, 1–14 (2019).
Google Scholar
Gelman, A., et al. Bayesian Workflow. arXiv:2011.01808v1 (2020).
Fahrmeir, L. & Lang, S. Bayesian inference for generalised additive mixed models based on Markov random field priors. Appl. Stat. 50, 201–220 (2001).
Yao, Y., Vehtari, A., Simpson, D. & Gelman, A. Using stacking to average Bayesian predictive distributions (with Discussion). Bayesian Anal. 13, 917–1003 (2018).
Google Scholar
Fávero, L., Hair, J., Souza, R., Albergaria, M. & Brugni, T. Zero-inflated generalized linear mixed models: a better way to understand data relationships. Mathematics 9, 1100 (2021).
Google Scholar
Gilman, E. et al. Tori lines mitigate seabird bycatch in a pelagic longline fishery. Rev. Fish Biol. Fish. 31, 653–666 (2021).
Google Scholar
Makowski, D., Ben-Shachar, M. & Lüdecke, D. bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).
Google Scholar
Yau, K., Wang, K. & Lee, A. Zero-Inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros. Biom. J. 45, 437–452 (2003).
Google Scholar
Congdon, P. Applied Bayesian Modelling. Wiley and Sons Ltd, UK. (2003).
Günhan, B., Röver, C. & Friede, T. Random-effects meta-analysis of few studies involving rare events. Res. Synth. Methods 11, 74–90 (2020).
Google Scholar
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).
Google Scholar
Bürkner, P. brms: An R Package for Bayesian multilevel models using Stan. J. Stat. Softw. 81, 1–28 (2017).
Ott, M., Plummer, M. & Roos, M. How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis. Stat. Med. 40, 4505–4521 (2021).
Google Scholar
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B. & Bürkner, P. Rank-normalization, folding, and localization: an improved Rhat for assessing convergence of MCMC (with Discussion). Bayesian Anal. 16, 667–718 (2021).
Google Scholar
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).
Google Scholar
Kruschke, J. & Liddell, T. The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychon. Bull. Rev. 25, 178–206 (2018).
Google Scholar
Lenth, R. Least-squares means: the R package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Google Scholar
Lenth R (2020) emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.2-1. https://CRAN.R-project.org/package=emmeans
Source: Ecology - nature.com
