Estes, J. et al. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116 (2016).
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. R. et al. Seagrasses in the age of sea turtle conservation and shark overfishing. Front. Mar. Sci. 1, 1–6 (2014).
McCauley, D. et al. Marine defaunation: Animal loss in the global ocean. Science 347, 1255641 (2015).
Google Scholar
Pereira, H. et al. Scenarios for global biodiversity in the 21st century. Science 330, 1496–1501 (2010).
Google Scholar
Oliver, S., Braccini, M., Newman, S. & Harvey, E. S. Global patterns in the bycatch of sharks and rays. Mar. Policy 54, 86–97 (2015).
Pacoureau, N. et al. Half a century of global decline in oceanic sharks and rays. Nature 589, 567–574 (2021).
Google Scholar
Gilman, E. et al. Shark interactions in pelagic longline fisheries. Mar. Policy 32, 1–18 (2008).
Worm, B. et al. Global catches, exploitation rates, and rebuilding options for sharks. Mar. Policy 40, 194–204 (2013).
Bowlby, H. & Gibson, A. Implications of life history uncertainty when evaluating status in the Northwest Atlantic population of white shark (Carcharodon carcharias). Ecol. Evol. 10, 4990–5000 (2020).
Google Scholar
Dulvy, N. et al. Overfishing drives over one third of all sharks and rays toward a global extinction crisis. Curr. Biol. 31, 4773-4787.e8 (2021).
Google Scholar
Heino, M., Pauli, B. & Dieckmann, U. Fisheries-induced evolution. Annu. Rev. Ecol. Evol. Syst. 46, 461–480 (2015).
Mitchell, J., McLean, D., Collins, S. & Langlois, T. Shark depredation in commercial and recreational fisheries. Rev. Fish Biol. Fish 28, 715–748 (2018).
Jaiteh, V. F., Loneragan, N. & Warren, C. The end of shark finning? Impacts of declining catches and fin demand on coastal community livelihoods. Mar. Policy 82, 224–233 (2017).
Seidu, I. et al. Fishing for survival: Importance of shark fisheries for the livelihoods of coastal communities in Western Ghana. Fish. Res. 246, 106157 (2022).
Gilman, E., Weijerman, M. & Suuronen, P. Ecological data from observer programs underpin ecosystem-based fisheries management. ICES J. Mar. Sci. 74, 1481–1495 (2017).
Melnychuk, M. et al. Identifying management actions that promote sustainable fisheries. Nat. Sustain. https://doi.org/10.1038/s41893-020-00668-1 (2021).
Google Scholar
Musyl, M. & Gilman, E. Meta-analysis of post-release fishing mortality in apex predatory pelagic sharks and white marlin. Fish Fish. 20, 466–500 (2019).
Clarke, S. A status snapshot of key shark species in the western and central pacific and potential management options. in WCPFC-SC7-2011/EB-WP-04. Western and Central Pacific Fisheries Commission, Kolonia, Federated States of Micronesia (2011).
Dapp, D., Walker, T., Huveneers, C. & Reina, R. Respiratory mode and gear type are important determinants of elasmobranch immediate and post-release mortality. Fish Fish. 17, 507–524 (2016).
ICES. Report of the working group on elasmobranch fishes. in ICES CM 2018/ACOM:16. International Council for the Exploration of the Sea, Copenhagen (2018).
Dicks, L. et al. A transparent process for “evidence-informed” policy making. Conserv. Lett. 7, 119–125 (2014).
Nichols, J., Kendall, W. & Boomer, G. Accumulating evidence in ecology: Once is not enough. Ecol. Evol. 9, 13991–14004 (2019).
Google Scholar
Nakagawa, S. et al. Meta-analysis of variation: Ecological and evolutionary applications and beyond. Methods Ecol. Evol. 6, 143–152 (2015).
Pfaller, J., Chaloupka, M., Bolten, A. & Bjorndal, K. Phylogeny, biogeography and methodology: A meta-analytic perspective on heterogeneity in adult marine turtle survival rates. Sci. Rep. 8, 5852. https://doi.org/10.1038/s41598-018-24262-w (2018).
Google Scholar
Godin, A., Carlson, J. & Burgener, V. The effect of circle hooks on shark catchability and at-vessel mortality rates in longlines fisheries. Bull. Mar. Sci. 88, 469–483 (2012).
Reinhardt, J. et al. Catch rate and at-vessel mortality of circle hooks versus J-hooks in pelagic longline fisheries: A global meta-analysis. Fish Fish. 19, 413–430 (2018).
Rosa, D., Santos, C. & Coelho, R. Assessing the effects of hook, bait and leader type as potential mitigation measures to reduce bycatch and mortality rates of shortfin mako: A meta-analysis with comparisons for target, bycatch and vulnerable fauna interactions. in ICCAT Collective Volume of Scientifics Papers 76, 247–278 (2020).
Santos, C., Rosa, D. & Coelho, R. Hook, bait and leader type effects on surface pelagic longline retention and mortality rates: A meta-analysis with comparisons for target, bycatch and vulnerable fauna interactions. in IOTC-2019-WPEB15-39. Indian Ocean Tuna Commission, Mahe, Seychelles (2019).
Santos, C., Rosa, D. & Coelho, R. Progress on a meta-analysis for comparing hook, bait and leader effects on target, bycatch and vulnerable fauna interactions. in Collective Volume of Scientifics Papers ICCAT 77, 182–217 (2020).
Condamine, F., Romieu, J. & Guinot, G. Climate cooling and clade competition likely drove the decline of lamniform sharks. PNAS 116, 20584–20590 (2019).
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
Cinar, O., Nakagawa, S. & Viechtbauer, W. Phylogenetic multilevel meta-analysis: A simulation study on the importance of modelling the phylogeny. Methods Ecol. Evol. 13, 383–395 (2022).
Lajeunesse, M. Meta-analysis and the comparative phylogenetic method. Am. Nat. 174, 369–381 (2009).
Google Scholar
Chamberlain, S. et al. Does phylogeny matter? Assessing the impact of phylogenetic information in ecological meta-analysis. Ecol. Lett. 15, 627–636 (2012).
Google Scholar
Burns, J. & Strauss, S. More closely related species are more ecologically similar in an experimental test. Proc. Natl. Acad. Sci. USA 108, 5302–5307 (2011).
Google Scholar
Cachera, M. & Le Loc’h, F. Assessing the relationships between phylogenetic and functional singularities in sharks (Chondrichthyes). Ecol. Evol. 7, 6292–6303 (2017).
Google Scholar
Münkemüller, T., Boucher, F. C., Thuiller, W. & Lavergne, S. Phylogenetic niche conservatism—Common pitfalls and ways forward. Funct. Ecol. 29, 627–639 (2015).
Google Scholar
Bazzi, M., Campione, N., Kear, B., Pimiento, C. & Ahlberg, P. Feeding ecology has shaped the evolution of modern sharks. Curr. Biol. 31, 5138–5148 (2021).
Google Scholar
Sepulveda, C., Wegner, N., Bernal, D. & Graham, J. The red muscle morphology of the thresher sharks (family Alopiidae). J. Exp. Biol. 208, 4255–4261 (2005).
Google Scholar
Wosnick, N. et al. Multispecies thermal dynamics of air-exposed ectothermic sharks and its implications for fisheries conservation. J. Exp. Mar. Biol. Ecol. 513, 1–9 (2019).
French, R. et al. High survivorship after catch-and-release fishing suggests physiological resilience in the endothermic shortfin mako shark (Isurus oxyrinchus). Conserv. Physiol. https://doi.org/10.1093/conphys/cov044 (2015).
Google Scholar
Davis, M. Key principles for understanding fish bycatch discard mortality. Can. J. Fish. Aquat. Sci. 59, 1834–1843 (2002).
Massey, Y., Sabarros, P., Rabearisoa, N. & Bach, P. Drivers of at-haulback mortality of sharks caught during pelagic longline fishing experiments. in IOTC-2019-WPEB15-14_Rev1. Indian Ocean Tuna Commission, Mahe, Seychelles (2019).
Musyl, M., Moyes, C., Brill, R. & Fragoso, N. Factors influencing mortality estimates in post-release survival studies: Comment on Campana et al. (2009). Mar. Ecol. Prog. Ser. 396, 157–159 (2009).
Pimiento, C., Cantalapiedra, J., Shimada, K., Field, D. & Smaers, J. Evolutionary pathways towards gigantism in sharks and rays. Evolution 73, 588–599 (2019).
Google Scholar
Musyl, M. & Gilman, E. Post-release fishing mortality of blue (Prionace glauca) and silky shark (Carcharhinus falciformes) from a Palauan-based commercial longline fishery. Rev. Fish Biol. Fish. 28, 567–658 (2018).
Childs, D., Sheldon, B. & Rees, M. The evolution of labile traits in sex- and age-structured populations. J. Anim. Ecol. 85, 329–342 (2016).
Google Scholar
Comte, L., Murienne, J. & Grenouillet, G. Species traits and phylogenetic conservatism of climate-induced range shifts in stream fishes. Nat. Commun. 5, 5053 (2014).
Google Scholar
IUCN. The IUCN Red List of Threatened Species. Version 2021-3. www.iucnredlist.org. ISSN 2307-8235 (International Union for the Conservation of Nature, Gland, Switzerland, 2022).
García, V., Lucifora, L. & Ransom, M. The importance of habitat and life history to extinction risk in sharks, skates, rays and chimaeras. Proc. R. Soc. B 275, 83–89 (2008).
Google Scholar
Cortes, E. Perspectives on the intrinsic rate of population growth. Methods Ecol. Evol. 7, 1136–1145 (2016).
Ellis, J. et al. A review of capture and post-release mortality of elasmobranchs. J. Fish Biol. 90, 653–722 (2017).
Google Scholar
Gallagher, A., Orbesen, E., Hammerschlag, N. & Serafy, J. Vulnerability of oceanic sharks as pelagic longline bycatch. Glob. Ecol. Conserv. 1, 50–59 (2014).
Afonso, A., Santiago, R., Hazin, H. & Hazin, F. Shark bycatch and mortality and hook bite-offs in pelagic longlines: Interactions between hook types and leader materials. Fish. Res. 131–133, 9–14 (2012).
Gilman, E., Chaloupka, M. & Musyl, M. Effects of pelagic longline hook size on species- and size-selectivity and survival. Rev. Fish Biol. Fish. 28, 417–433 (2018).
Epperly, S., Watson, J., Foster, D. & Shah, A. Anatomical hooking location and condition of animals captured with pelagic longlines: The grand banks experiments 2002–2003. Bull. Mar. Sci. 88, 513–527 (2012).
Amorim, S., Santos, M., Coelho, R. & Fernandez-Carvalho, J. Effects of 17/0 circle hooks and bait on fish catches in a southern Atlantic swordfish longline fishery. Aquat. Conserv. 25, 518–533 (2014).
Coelho, R., Fernandez-Carvalho, J., Lino, P. & Santos, M. An overview of the hooking mortality of elasmobranchs caught in a swordfish pelagic longline fishery in the Atlantic Ocean. Aquat. Living Resour. 25, 311–319 (2012).
Gilman, E. et al. A decision support tool for integrated fisheries bycatch management. Rev. Fish Biol. Fish. https://doi.org/10.1007/s11160-021-09693-5 (2022).
Google Scholar
Pascoe, S. et al. Use of incentive-based management systems to limit bycatch and discarding. Int. Rev. Environ. Resour. Econ. 4, 123–161 (2010).
Somers, K., Pfeiffer, L., Miller, S. & Morrison, W. Using incentives to reduce bycatch and discarding: Results under the west coast catch share program. Coast. Manag. 46, 1–17 (2019).
Abbott, J. & Wilen, J. Regulation of fisheries bycatch with common-pool output quotas. J. Environ. Econ. Manag. 57, 195–204 (2009).
Google Scholar
Gilman, E. et al. Increasing the functionalities and accuracy of fisheries electronic monitoring systems. Aquat. Conserv. 29, 901–926 (2019).
Watling, J. Fishing observers ‘intimidated and bribed by EU crews’. Quota checks allegedly being compromised aboard Northwest Atlantic fishery boats, as observers report surveillance and theft. The Guardian (2012, accessed 21 July 2022). https://www.theguardian.com/environment/2012/may/18/fishing-inspectors-intimidated-bribed-crews.
Clarke, S., Harley, S., Hoyle, S. & Rice, J. Population trends in Pacific oceanic sharks and the utility of regulations on shark finning. Conserv. Biol. 27, 197–209 (2013).
Google Scholar
Tolotti, M. T. et al. Banning is not enough: The complexities of oceanic shark management by tuna regional fisheries management organizations. Glob. Ecol. Conserv. 4, 1–7 (2015).
Gilman, E., Chaloupka, M., Merrifield, M., Malsol, N. & Cook, C. Standardized catch and survival rates, and effect of a ban on shark retention, Palau pelagic longline fishery. Aquat. Conserv. 26, 1031–1062 (2016).
CITES. Appendices I, II and III. Valid from 22 June 2021. Convention on International Trade in Endangered Species of Wild Fauna and Flora, United Nations Environment Program, Geneva (2021).
Ward-Paige, C. A global overview of shark sanctuary regulations and their impact on shark fisheries. Mar. Policy 82, 87–97 (2017).
E.U. Regulation (E.U.) No 1380/2013 of the European Parliament and of the Council of 11 December 2013 on the Common Fisheries Policy, amending Council Regulations (EC) No 1954/2003 and (EC) No 1224/2009 and repealing Council Regulations (EC) No 2371/2002 and (EC) No 639/2004 and Council Decision 2004/585/EC. Official Journal of the European Union L354, 22–61 (2013).
FAO. International Guidelines on Bycatch Management and Reduction of Discards (Food and Agriculture Organization of the United Nations, Rome, 2011).
CCSBT. Resolution to Align CCSBT’s Ecologically Related Species Measures with those of other Tuna RFMOs (Commission for the Conservation of Southern Bluefin Tuna, Deakin West, Australia, 2021).
IATTC. Active Resolutions and Recommendations (Inter-American Tropical Tuna Commission, La Jolla, 2022).
ICCAT. Compendium. Management Recommendations and Resolutions Adopted by ICCAT for the Conservation of Atlantic Tunas and Tuna-like Species (International Commission for the Conservation of Atlantic Tunas, Madrid, 2021).
IOTC. Compendium of Active Conservation and Management Measures for the Indian Ocean Tuna Commission (Indian Ocean Tuna Commission, Mahe, 2021).
WCPFC. Conservation and Management Measures and Resolutions of the Western and Central Pacific Fisheries Commission. Compiled 31 August 2021 (Western and Central Pacific Fisheries Commission, Kolonia, Federated States of Micronesia, 2021).
Faith, D. Threatened species and the potential loss of phylogenetic diversity: Conservation scenarios based on estimated extinction probabilities and phylogenetic risk analysis. Conserv. Biol. 22, 1461–1470 (2008).
Google Scholar
Dolce, J. & Wilga, C. Evolutionary and ecological relationships of gill slit morphology in extant sharks. Bull. Mus. Comp. 161, 79–109 (2013).
MacLeod, N. & Forey, P. Morphology, Shape and Phylogeny (CRC Press, 2002).
Haddaway, N., Macura, B., Whaley, P. & Pullin, A. ROSES RepOrting standards for Systematic Evidence Syntheses: pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environ. Evid. https://doi.org/10.1186/s13750-018-0121-7 (2018).
Google Scholar
Pullin, A., Frampton, G., Livoreil, B. & Petrokofsky, G., Eds. Section 5. Conducting a Search. Key CEE Standards for Conduct and Reporting. In Pullin, A., Frampton, G., Livoreil, B., Petrokofsky, G., Eds. Guidelines and Standards for Evidence Synthesis in Environmental Management. Version 5.0. Collaboration for Environmental Evidence (2020).
Pullin, A., Frampton, G., Livoreil, B. & Petrokofsky, G. (eds) Section 3. Planning a CEE Evidence Synthesis. In Pullin, A., Frampton, G., Livoreil, B., Petrokofsky, G. (eds) Guidelines and Standards for Evidence Synthesis in Environmental Management. Version 5.0. Collaboration for Environmental Evidence (2021).
Page, M. et al. The PRISMA statement: An updated guideline for reporting systematic reviews. BMJ https://doi.org/10.1136/bmj.n.71 (2020).
Google Scholar
Tuyl, F., Gerlach, R. & Mengersen, K. Comparison of Bayes-Laplace, Jeffreys, and other priors: The case of zero events. Am. Stat. 62, 40–44 (2008).
Google Scholar
Dorai-Raj, S. binom: Binomial confidence intervals for several parameterizations. R package version 1.1-1. https://CRAN.R-project.org/package=binom (2014).
van Lissa, C. Small sample meta-analyses: Exploring heterogeneity using MetaForest. Chapter 13. In Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners (eds Van De Schoot, R. & Miočević, M.) 186–202 (Routledge, Oxford, 2020).
Wright, M. & Ziegler, A. ranger: A fast implementation of random forests for high dimensional data in C++ and R. J. Stat. Softw. 77, 1–17 (2017).
Janitza, S., Celik, E. & Boulesteix, A. A computationally fast variable importance test for random forests for high-dimensional data. Adv. Data Anal. Classif. 12, 885–915 (2018).
Google Scholar
Mayer, M. missRanger: Fast imputation of missing values. R package version 2.1.3. https://CRAN.R-project.org/package=missRanger (2021).
Konstantopoulos, S. Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis. Methods 2, 61–76 (2011).
Amaral, C., Pereira, F., Silva, D., Amorim, A. & de Carvalho, E. The mitogenomic phylogeny of the Elasmobranchii (Chondrichthyes). Mitochondrial DNA A 29, 867–878 (2017).
Hara, Y. et al. Shark genomes provide insights into elasmobranch evolution and the origin of vertebrates. Nat. Ecol. Evol. 2, 1761–1771 (2018).
Google Scholar
Naylor, G. et al. A DNA sequence-based approach to the identification of shark and ray species and its implications for global elasmobranch diversity and parasitology. Bull. Am. Mus. Nat. 367, 1–262 (2012).
Stein, R. et al. Global priorities for conserving the evolutionary history of sharks, rays and chimaeras. Nat. Ecol. Evol. 2, 288–298 (2018).
Google Scholar
Maddison, D., Swofford, D., Maddison, W. & Cannatella, D. Nexus: An extensible file format for systematic information. Syst. Biol. 46, 590–621 (1997).
Google Scholar
Upham, N., Esselstyn, J. & Jetz, W. Inferring the mammal tree: Species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).
Google Scholar
Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).
Google Scholar
Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinform. 69, e96. https://doi.org/10.1002/cpbi.96 (2020).
Google Scholar
Hadfield, J. & Nakagawa, S. General quantitative genetic methods for comparative biology: Phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol 23, 494–508 (2010).
Google Scholar
Lin, L. & Chu, H. Meta-analysis of proportions using generalized linear mixed models. Epidemiology 31, 713–717 (2020).
Google Scholar
Carpenter, B. et al. Stan: A probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).
Bürkner, P. brms: An R Package for Bayesian multilevel models using Stan. J. Stat. Softw. 81, 1–28 (2017).
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
Pappalardo, P. et al. Comparing traditional and Bayesian approaches to ecological meta-analysis. Methods Ecol. Evol. 11, 1286–1295 (2020).
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
Wood, S. Generalized Additive Models: An Introduction with R 2nd edn. (Chapman and Hall, 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
Kay, M. tidybayes: Tidy data and geoms for Bayesian models. R package version 2.1.1. https://doi.org/10.5281/zenodo.1308151 (2020).
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
Searle, S., Speed, F. & Milliken, G. Population marginal means in the linear model: An alternative to least squares means. Am. Stat. 34, 216–221 (1980).
Google Scholar
Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.5.2-1. https://CRAN.R-project.org/package=emmeans (2020).
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).
Google Scholar
Münkemüller, T. et al. How to measure and test phylogenetic signal. Methods Ecol. Evol. 3, 743–756 (2012).
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
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
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
Lazic, S., Mellor, J., Ashby, M. & Munafo, M. A Bayesian predictive approach for dealing with pseudoreplication. Sci. Rep. 10, 2020. https://doi.org/10.1038/s41598-020-59384-7 (2020).
Google Scholar
Page, M., Sterne, J., Higgins, J. & Egger, M. Investigating and dealing with publication bias and other reporting biases in meta-analyses of health research: A review. Res. Synth. Methods 12, 248–259 (2021).
Google Scholar
Peters, J., Sutton, A., Jones, D., Abrams, K. & Rushton, L. Contour-enhanced meta-analysis funnel plots help distinguish publication bias from other causes of asymmetry. J. Clin. Epidemiol. 61, 991–996 (2008).
Google Scholar
Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).
Gasparrini, A., Armstrong, B. & Kenward, M. Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat. Med. 31, 3821–3839 (2012).
Google Scholar
Source: Ecology - nature.com