Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

Effects of fishery bycatch-mitigation measures on vulnerable marine fauna and target catch

Nature Sustainability (2024)Cite this article

Subjects

Abstract

Reducing fisheries bycatches of vulnerable species is critical to marine biodiversity conservation and sustainable fisheries development. Although various preventive technical measures have been implemented, their overall effects are poorly understood. Here, we used a meta-analysis approach to quantify the effects of 42 technical measures on the target catch and the bycatch of seabirds, elasmobranchs, marine mammals and sea turtles. We showed that these measures generally reduced the bycatch while having no statistically significant effect on the target catch. Sensory-based measures generally outperformed physical-based ones in reducing the bycatch. Mitigation measures that worked well for several fishing gears or taxa, although useful, were very rare. Most of the adoptions by regional fisheries management organizations (59%) were supported by our findings, although many others are yet to be robustly evaluated. Our study encourages the innovation and adoption of technical measures and provides crucial insights for policy-making and further research in sustainable bycatch management.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Technical mitigation measures and their overall effects on the bycatch of seabirds, elasmobranchs, marine mammals (cetaceans and pinnipeds) and sea turtles and the target catch after accounting for publication bias.
Fig. 2: Effects of measures on subgroups after accounting for publication bias for subgroups with at least three experiments (or three data points) from the included case studies.
Fig. 3: Effects of a measure for a specific fishing gear and SEMS group after accounting for publication bias for combinations with at least three experiments (or three data points).
Fig. 4: Number of included experimental studies interventions by EEZs of a specific country (in red bubble) and the convention areas of RFMOs (in blue bubble) defined by the FAO.
Fig. 5: Topography and distribution of technical measures adopted by RFMOs.

Similar content being viewed by others

Data availability

The data supporting the findings of this study are provided in Supplementary Data 1 and 2.

Code availability

The analysis processes and codes were recorded in Supplementary Code.

References

  1. Queiroz, N. et al. Global spatial risk assessment of sharks under the footprint of fisheries. Nature 572, 461–466 (2019).

    Article  CAS  Google Scholar 

  2. Lewison, R. L. et al. Global patterns of marine mammal, seabird, and sea turtle bycatch reveal taxa-specific and cumulative megafauna hotspots. Proc. Natl Acad. Sci. USA 111, 5271–5276 (2014).

    Article  CAS  Google Scholar 

  3. The State of World Fisheries and Aquaculture 2022: Towards Blue Transformation (FAO, 2022).

  4. Kroodsma, D. A. et al. Tracking the global footprint of fisheries. Science 359, 904–908 (2018).

    Article  CAS  Google Scholar 

  5. Costello, C. et al. Global fishery prospects under contrasting management regimes. Proc. Natl Acad. Sci. USA 113, 5125–5129 (2016).

    Article  CAS  Google Scholar 

  6. Dulvy, N. K. et al. Challenges and priorities in shark and ray conservation. Curr. Biol. 27, R565–R572 (2017).

    Article  CAS  Google Scholar 

  7. Tixier, P. et al. When large marine predators feed on fisheries catches: global patterns of the depredation conflict and directions for coexistence. Fish Fish. 22, 31–53 (2020).

    Article  Google Scholar 

  8. Wilson, S. M., Raby, G. D., Burnett, N. J., Hinch, S. G. & Cooke, S. J. Looking beyond the mortality of bycatch: sublethal effects of incidental capture on marine animals. Biol. Conserv. 171, 61–72 (2014).

    Article  Google Scholar 

  9. Gilman, E. et al. Bycatch-neutral fisheries through a sequential mitigation hierarchy. Mar. Policy https://doi.org/10.1016/j.marpol.2023.105522 (2023).

  10. Žydelis, R., Small, C. & French, G. The incidental catch of seabirds in gillnet fisheries: a global review. Biol. Conserv. 162, 76–88 (2013).

    Article  Google Scholar 

  11. Read, A. J., Drinker, P. & Northridge, S. Bycatch of marine mammals in U.S. and global fisheries. Conserv. Biol. 20, 163–169 (2006).

    Article  Google Scholar 

  12. Hammerschlag, N. et al. Ecosystem function and services of aquatic predators in the Anthropocene. Trends Ecol. Evol. 34, 369–383 (2019).

    Article  Google Scholar 

  13. Subroy, V., Gunawardena, A., Polyakov, M., Pandit, R. & Pannell, D. J. The worth of wildlife: a meta-analysis of global non-market values of threatened species. Ecol. Econ. https://doi.org/10.1016/j.ecolecon.2019.106374 (2019).

  14. Moore, J. E. et al. Evaluating sustainability of fisheries bycatch mortality for marine megafauna: a review of conservation reference points for data-limited populations. Environ. Conserv. 40, 329–344 (2013).

    Article  Google Scholar 

  15. Gilman, E. L. Bycatch governance and best practice mitigation technology in global tuna fisheries. Mar. Policy 35, 590–609 (2011).

    Article  Google Scholar 

  16. Werner, T. B., Northridge, S., Press, K. M. & Young, N. Mitigating bycatch and depredation of marine mammals in longline fisheries. ICES J. Mar. Sci. 72, 1576–1586 (2015).

    Article  Google Scholar 

  17. Lucas, S. & Berggren, P. A systematic review of sensory deterrents for bycatch mitigation of marine megafauna. Rev. Fish Biol. Fish. https://doi.org/10.1007/s11160-022-09736-5 (2023).

    Article  Google Scholar 

  18. Favaro, B. & Côté, I. M. Do by-catch reduction devices in longline fisheries reduce capture of sharks and rays? A global meta-analysis. Fish Fish. 16, 300–309 (2015).

    Article  Google Scholar 

  19. Reinhardt, J. F. 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).

    Article  Google Scholar 

  20. Avery, J. D., Aagaard, K., Burkhalter, J. C. & Robinson, O. J. Seabird longline bycatch reduction devices increase target catch while reducing bycatch: a meta-analysis. J. Nat. Conserv. 38, 37–45 (2017).

    Article  Google Scholar 

  21. Garvey, P. M. et al. Leveraging motivations, personality, and densory cues for vertebrate pest management. Trends Ecol. Evol. 35, 990–1000 (2020).

    Article  Google Scholar 

  22. Januma, S., Miyajima, K. & Abe, T. Development and comparative test of squid liver artificial bait for tuna longline. Fish. Sci. 69, 288–292 (2003).

    Article  CAS  Google Scholar 

  23. Gilman, E. & Chaloupka, M. Applying a sequential evidence hierarchy, with caveats, to support prudent fisheries bycatch policy. Rev. Fish Biol. Fish. https://doi.org/10.1007/s11160-022-09745-4 (2023).

    Article  Google Scholar 

  24. Gilman, E., Passfield, K. & Nakamura, K. Performance of regional fisheries management organizations: ecosystem-based governance of bycatch and discards. Fish Fish. 15, 327–351 (2014).

    Article  Google Scholar 

  25. Elliott, B., Tarzia, M. & Read, A. J. Cetacean bycatch management in regional fisheries management organizations: current progress, gaps, and looking ahead. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.1006894 (2023).

  26. Løbach, T., Petersson, M., Haberkon, E. & Mannini, P. Regional Fisheries Management Organizations and Advisory Bodies: Activities and Developments, 2000–2017 (FAO, 2020).

  27. Gilman, E., Musyl, M., Wild, M., Rong, H. & Chaloupka, M. Investigating weighted fishing hooks for seabird bycatch mitigation. Sci. Rep. 12, 2833 (2022).

    Article  CAS  Google Scholar 

  28. Hamilton, S. & Baker, G. B. Technical mitigation to reduce marine mammal bycatch and entanglement in commercial fishing gear: lessons learnt and future directions. Rev. Fish Biol. Fish. 29, 223–247 (2019).

    Article  Google Scholar 

  29. Kennelly, S. J. & Broadhurst, M. K. A review of bycatch reduction in demersal fish trawls. Rev. Fish Biol. Fish. 31, 289–318 (2021).

    Article  Google Scholar 

  30. Rousseau, Y., Watson, R. A., Blanchard, J. L. & Fulton, E. A. Defining global artisanal fisheries. Mar. Policy 108, 103634 (2019).

    Article  Google Scholar 

  31. He, P., Chopin, F., Suuronen, P., Ferro, R. S. & Lansley, J. Classification and Illustrated Definition of Fishing Gears (FAO, 2021).

  32. Eriksen, M. B. & Frandsen, T. F. The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review. J. Med. Libr. Assoc. 106, 420 (2018).

    Article  Google Scholar 

  33. Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. Introduction to Meta-analysis (John Wiley & Sons, 2011).

  34. Shi, L. & Lin, L. The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses. Medicine 98, e15987 (2019).

    Article  Google Scholar 

  35. 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).

    Article  Google Scholar 

  36. Hutchinson, M. et al. The effects of a lanthanide metal alloy on shark catch rates. Fish. Res. 131, 45–51 (2012).

    Article  Google Scholar 

  37. Mangel, J. C. et al. Illuminating gillnets to save seabirds and the potential for multi-taxa bycatch mitigation. R. Soc. Open Sci. 5, 180254 (2018).

    Article  Google Scholar 

  38. Restrepo, V. et al. Compendium of ISSF At-Sea Bycatch Mitigation Research Activities as of July, 2016 (ISSF, 2017).

  39. Ortiz, N. et al. Reducing green turtle bycatch in small-scale fisheries using illuminated gillnets: the cost of saving a sea turtle. Mar. Ecol. Prog. Ser. 545, 251–259 (2016).

    Article  Google Scholar 

  40. Senko, J. F., Peckham, S. H., Aguilar-Ramirez, D. & Wang, J. H. Net illumination reduces fisheries bycatch, maintains catch value, and increases operational efficiency. Curr. Biol. 32, 911–918 (2022).

    Article  CAS  Google Scholar 

  41. Dickman, A. J. Complexities of conflict: the importance of considering social factors for effectively resolving human–wildlife conflict. Anim. Conserv. 13, 458–466 (2010).

    Article  Google Scholar 

  42. Zollett, E. A. & Swimmer, Y. Safe handling practices to increase post-capture survival of cetaceans, sea turtles, seabirds, sharks, and billfish in tuna fisheries. Endanger. Species Res. 38, 115–125 (2019).

    Article  Google Scholar 

  43. Haas, B., McGee, J., Fleming, A. & Haward, M. Factors influencing the performance of regional fisheries management organizations. Mar. Policy https://doi.org/10.1016/j.marpol.2019.103787 (2020).

  44. Fan, H. et al. Conservation priorities for global marine biodiversity across multiple dimensions. Natl Sci. Rev. 10, nwac241 (2023).

    Article  Google Scholar 

  45. Fernando, D. & Stewart, J. D. High bycatch rates of manta and devil rays in the “small-scale” artisanal fisheries of Sri Lanka. PeerJ 9, e11994 (2021).

    Article  Google Scholar 

  46. Swimmer, Y., Zollett, E. A. & Gutierrez, A. Bycatch mitigation of protected and threatened species in tuna purse seine and longline fisheries. Endanger. Species Res. 43, 517–542 (2020).

    Article  Google Scholar 

  47. Christie, A. P. et al. Simple study designs in ecology produce inaccurate estimates of biodiversity responses. J. Appl. Ecol. 56, 2742–2754 (2019).

    Article  Google Scholar 

  48. Lüdecke, D. Esc: effect size computation for meta-analysis. R package version 0.4.1 (2018).

  49. Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).

    Article  Google Scholar 

  50. Chaplin-Kramer, R., O’Rourke, M. E., Blitzer, E. J. & Kremen, C. A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).

    Article  Google Scholar 

  51. Begg, C. B. & Mazumdar, M. Operating characteristics of a rank correlation test for publication bias. Biometrics 50, 1088–1101 (1994).

    Article  CAS  Google Scholar 

  52. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by the National Natural Science Foundation of China no. 31572291/32370545 to Y.L., the Guangdong Basic and Applied Basic Research Foundation (2022A1515010640) to X.Z.; the Science and Technology Planning Projects of Guangdong Province (2021B1212110002) to Y.L. and X.Z.; and the Overseas High-level Talent Research Program in China (41180953) and the Guangdong Provincial Research Fund (42150016) to T.M.L. J.R. became Chief Scientist – Emeritus in 2016, and retired from that position in 2023. We thank L. Nelson of IOTC, V. Radchenko of NPAFC, L. Voges of SEAFO and M. Hutchinson and J. Lopez of IATTC for promptly providing information about the conservation management and measures of the respective RFMOs. We thank Y. Hong for the assistance in data collection. We thank Z. Liang (Toto) for the English edit.

Author information

Authors and Affiliations

  1. School of Ecology and State Key Lab of Biocontrol, Sun Yat-sen University, Shenzhen, China

    Cheng Huang

  2. China Center for Special Economic Zone Research, Shenzhen University, Shenzhen, China

    Cheng Huang, Xiong Zhang, Tien Ming Lee & Yang Liu

  3. Fisheries and Oceans Canada, Ottawa, Ontario, Canada

    Jake Rice

  4. Environmental Economics and Natural Resources Group, Wageningen University, Wageningen, the Netherlands

    Andries Richter

  5. School of Life Sciences, Sun Yat-sen University, Guangzhou, China

    Kaiwen Zhou, Yi Wang, Chentao Wei, Tien Ming Lee & Yang Liu

  6. State Environmental Protection Key Laboratory of Urban Ecological Environment Simulation and Protection, South China Institute of Environmental Sciences, MEE, Guangzhou, China

    Chentao Wei

  7. Department of Health and Environmental Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Emilio Pagani-Núñez

  8. Centre for Conservation and Restoration Science, School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK

    Emilio Pagani-Núñez

  9. School of Applied Sciences, Edinburgh Napier University, Edinburgh, UK

    Emilio Pagani-Núñez

  10. Wildlife Conservation Society, Arctic Beringia Regional Program, Fairbanks, AK, USA

    Philipp N. Maleko

Authors
  1. Cheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jake Rice
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andries Richter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaiwen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chentao Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Emilio Pagani-Núñez
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Philipp N. Maleko
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xiong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tien Ming Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.H., T.M.L. and Y.L. conceived the idea. C.H., K.Z., C.W., E.P.-N., P.N.M., X.Z. and Y.L. collected data. C.H., Y.W. and T.M.L. analysed the data. C.H., K.Z., X.Z., T.M.L. and Y.L. drafted the manuscript with the inputs from J.R. and A.R. All authors revised and approved the final draft of the manuscript.

Corresponding authors

Correspondence to Cheng Huang, Xiong Zhang, Tien Ming Lee or Yang Liu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Sustainability thanks Divya Karnad, Valentina Melli, Ming Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 PRISMA workflow of this study.

The value ‘n’ represents is the number of literature available.

Extended Data Fig. 2 Effect of mitigation mechanism for a specific fishing gear and SEMS group after accounting for publication bias for combinations with at least three experiments (or three effect sizes).

The plotted data points are the estimated overall g metric of the SEMS bycatch (green), the change ratio of SEMS bycatch (grey), and the g metric of target catch (blue points) based on the individual effect sizes across subgroup studies. The error bars represent the 95% confidence interval. The value in the parenthesis indicates the number of the SEMS bycatch g, the SEMS bycatch change ratio, and the target catch g, respectively, for estimating an overall effect size. The bigger point indicates that the sample size is at least three and the smaller point indicates that the sample size is less than three. If a significant overall effect size is affected by publication bias, the result of the trim-fill recalculations was plotted instead. A positive value of effect size (g metric) means a mitigation mechanism increases the SEMS bycatch or target catch and vice versa. If an effect size of a mitigation mechanism is not significantly different from zero, we treat the effect as “no statistically significant effect”.

Extended Data Fig. 3 Number of included experimental studies by the Exclusive Economic Zones of a specific country (in red bubble) and the major marine areas (in blue bubble) as defined by the Food and Agriculture Organization.

The size of each bubble is proportional to the number of included studies.

Supplementary information

Supplementary Information

Systematic searches of case studies and the list of included studies.

Reporting Summary

Supplementary Data 1

Experimental data from the included case studies.

Supplementary Data 2

Adopted measures by RFMOs.

Supplementary Tables

Supplementary Tables 1–8.

Supplementary Code

R code for data analysis.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, C., Rice, J., Richter, A. et al. Effects of fishery bycatch-mitigation measures on vulnerable marine fauna and target catch. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01422-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41893-024-01422-7

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing