The Royal Society
Browse
rspb20222570_si_001.docx (1.32 MB)

Supplementary material: Knowing the fishery to know the bycatch from Knowing the fishery to know the bycatch: bias-corrected estimates of harbour porpoise bycatch in gillnet fisheries

Download (1.32 MB)
journal contribution
posted on 2023-06-22, 02:45 authored by Lotte Kindt-Larsen, Gildas Glemarec, Casper W. Berg, Sara Königson, Anne-Mette Kroner, Mathias Søgaard, David Lusseau
Incidental captures (bycatch) remain a key global conservation threat for cetaceans. Bycatch of harbour porpoise Phocoena phocoena in set gillnets is routinely monitored in European Union fisheries, but generally relies on data collected at low spatio-temporal resolution or over short periods. In Denmark, a long-term monitoring programme started in 2010 using electronic monitoring to collect data on porpoise bycatch and gillnet fishing effort at a fine spatial and temporal scale, including time and position of each fishing operation, together with every associated bycatch event. We used these observations to model bycatch rates, given the operational and ecological characteristics of each haul observed in Danish waters. Fishing effort from the Danish and Swedish gillnet fleets was collected to predict fleet-wide porpoise bycatch in gillnets at regional level. Between 2010 and 2020, yearly total bycatch averaged 2088 animals (95% Cl: 667–6798). For the Western Baltic assessment unit, bycatch levels were above sustainability thresholds. These results demonstrate that fishing characteristics are key determinants of porpoise bycatch and that classical approaches ignoring these features would produce biased estimates. It emphasizes the need for efficient and informative monitoring methods to understand possible conservation impacts of marine mammal bycatch and implement tailored mitigation techniques.

History

Usage metrics

    Proceedings of the Royal Society B: Biological Sciences

    Categories

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC