IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0084499.html
   My bibliography  Save this article

Mitigating Seabird Bycatch during Hauling by Pelagic Longline Vessels

Author

Listed:
  • Eric Gilman
  • Milani Chaloupka
  • Brett Wiedoff
  • Jeremy Willson

Abstract

Bycatch in longline fisheries threatens the viability of some seabird populations. The Hawaii longline swordfish fishery reduced seabird captures by an order of magnitude primarily through mitigating bycatch during setting. Now, 75% of captures occur during hauling. We fit observer data to a generalized additive regression model with mixed effects to determine the significance of the effect of various factors on the standardized seabird haul catch rate. Density of albatrosses attending vessels during hauling, leader length and year had largest model effects. The standardized haul catch rate significantly increased with increased albatross density during hauling. The standardized catch rate was significantly higher the longer the leader: shorter leaders place weighted swivels closer to hooks, reducing the likelihood of baited hooks becoming available to surface-scavenging albatrosses. There was a significant linear increasing temporal trend in the standardized catch rate, possibly partly due to an observed increasing temporal trend in the local abundance of albatrosses attending vessels during hauling. Swivel weight, Beaufort scale and season were also significant but smaller model effects. Most (81%) haul captures were on branchlines actively being retrieved. Future haul mitigation research should therefore focus on reducing bird access to hooks as crew coil branchlines, including methods identified here of shorter leaders and heavier swivels, and other potentially effective methods, including faster branchline coiling and shielding the area where hooks becomes accessible. The proportion of Laysan albatross (Phoebastria immutabilis) captures that occurred during hauling was significantly, 1.6 times, higher than for black-footed albatrosses (P. nigripes), perhaps due to differences in the time of day of foraging and in daytime scavenging competitiveness; mitigating haul bycatch would therefore be a larger benefit to Laysans. Locally, findings identify opportunities to nearly eliminate seabird bycatch. Globally, findings fill a gap in knowledge of methods to mitigate seabird bycatch during pelagic longline hauling.

Suggested Citation

  • Eric Gilman & Milani Chaloupka & Brett Wiedoff & Jeremy Willson, 2014. "Mitigating Seabird Bycatch during Hauling by Pelagic Longline Vessels," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
  • Handle: RePEc:plo:pone00:0084499
    DOI: 10.1371/journal.pone.0084499
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0084499
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0084499&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0084499?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gilman, Eric L., 2011. "Bycatch governance and best practice mitigation technology in global tuna fisheries," Marine Policy, Elsevier, vol. 35(5), pages 590-609, September.
    2. Gilman, Eric & Clarke, Shelley & Brothers, Nigel & Alfaro-Shigueto, Joanna & Mandelman, John & Mangel, Jeff & Petersen, Samantha & Piovano, Susanna & Thomson, Nicola & Dalzell, Paul & Donoso, Miguel &, 2008. "Shark interactions in pelagic longline fisheries," Marine Policy, Elsevier, vol. 32(1), pages 1-18, January.
    3. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gilman, Eric & Owens, Matthew & Kraft, Thomas, 2014. "Ecological risk assessment of the Marshall Islands longline tuna fishery," Marine Policy, Elsevier, vol. 44(C), pages 239-255.
    2. Chan, Valerie & Clarke, Raymond & Squires, Dale, 2014. "Full retention in tuna fisheries: Benefits, costs and unintended consequences," Marine Policy, Elsevier, vol. 45(C), pages 213-221.
    3. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    4. Thomas A. Murray & Brian P. Hobbs & Theodore C. Lystig & Bradley P. Carlin, 2014. "Semiparametric Bayesian commensurate survival model for post-market medical device surveillance with non-exchangeable historical data," Biometrics, The International Biometric Society, vol. 70(1), pages 185-191, March.
    5. Lawrence N Kazembe, 2013. "A Bayesian Two Part Model Applied to Analyze Risk Factors of Adult Mortality with Application to Data from Namibia," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-10, September.
    6. Rouven E. Haschka & Helmut Herwartz, 2022. "Endogeneity in pharmaceutical knowledge generation: An instrument‐free copula approach for Poisson frontier models," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(4), pages 942-960, November.
    7. Shuxi Zeng & Elizabeth C. Lange & Elizabeth A. Archie & Fernando A. Campos & Susan C. Alberts & Fan Li, 2023. "A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 197-218, June.
    8. Xin Fang & Bo Fang & Chunfang Wang & Tian Xia & Matteo Bottai & Fang Fang & Yang Cao, 2019. "Comparison of Frequentist and Bayesian Generalized Additive Models for Assessing the Association between Daily Exposure to Fine Particles and Respiratory Mortality: A Simulation Study," IJERPH, MDPI, vol. 16(5), pages 1-20, March.
    9. Serinaldi, Francesco, 2011. "Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape," Energy Economics, Elsevier, vol. 33(6), pages 1216-1226.
    10. Belitz, Christiane & Lang, Stefan, 2008. "Simultaneous selection of variables and smoothing parameters in structured additive regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 61-81, September.
    11. Volker Schmid & Leonhard Held, 2004. "Bayesian Extrapolation of Space–Time Trends in Cancer Registry Data," Biometrics, The International Biometric Society, vol. 60(4), pages 1034-1042, December.
    12. Birgit Schrödle & Leonhard Held, 2011. "A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$," Computational Statistics, Springer, vol. 26(2), pages 241-258, June.
    13. Aliaksandr Hubin & Geir Storvik, 2024. "Sparse Bayesian Neural Networks: Bridging Model and Parameter Uncertainty through Scalable Variational Inference," Mathematics, MDPI, vol. 12(6), pages 1-28, March.
    14. Klein, Nadja & Herwartz, Helmut & Kneib, Thomas, 2020. "Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales," Journal of Econometrics, Elsevier, vol. 214(2), pages 513-539.
    15. Rasheed A. Adeyemi & Temesgen Zewotir & Shaun Ramroop, 2016. "Semiparametric Multinomial Ordinal Model to Analyze Spatial Patterns of Child Birth Weight in Nigeria," IJERPH, MDPI, vol. 13(11), pages 1-22, November.
    16. Evans, K. & Young, J.W. & Nicol, S. & Kolody, D. & Allain, V. & Bell, J. & Brown, J.N. & Ganachaud, A. & Hobday, A.J. & Hunt, B. & Innes, J. & Gupta, A. Sen & van Sebille, E. & Kloser, R. & Patterson,, 2015. "Optimising fisheries management in relation to tuna catches in the western central Pacific Ocean: A review of research priorities and opportunities," Marine Policy, Elsevier, vol. 59(C), pages 94-104.
    17. David O'Donnell & Alastair Rushworth & Adrian W. Bowman & E. Marian Scott & Mark Hallard, 2014. "Flexible regression models over river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 47-63, January.
    18. Khaled Khatab & Maruf A Raheem & Benn Sartorius & Mubarak Ismail, 2019. "Prevalence and risk factors for child labour and violence against children in Egypt using Bayesian geospatial modelling with multiple imputation," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-20, May.
    19. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    20. Riccardo Borgoni & Francesco Billari, 2003. "Bayesian spatial analysis of demographic survey data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 8(3), pages 61-92.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0084499. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.