Predicting bycatch hotspots in tropical tuna purse seine fisheries at the basin scale

Type Article
Date 2020-12
Language English
Author(s) Mannocci Laura1, Forget Fabien1, Travassos Tolotti Mariana1, Bach Pascal1, Bez Nicolas1, Demarcq Herve1, Kaplan David1, Sabarros Philippe1, Simier Monique1, Capello Manuela1, Dagorn Laurent1
Affiliation(s) 1 : MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, Sète, France
Source Global Ecology And Conservation (2351-9894) (Elsevier BV), 2020-12 , Vol. 24 , P. e01393 (11p.)
DOI 10.1016/j.gecco.2020.e01393
WOS© Times Cited 9
Keyword(s) Bycatch, Habitat modelling, Hotspots, Fisheries observer programs, Geographical extrapolation, Tropical oceans

Fisheries observer programs represent the most reliable way to collect data on fisheries bycatch. However, their limited coverage leads to important data gaps that preclude bycatch mitigation at the basin scale. Habitat models developed from available fisheries observer programs offer a potential solution to fill these data gaps. We focus on tropical tuna purse seine fisheries (TTPSF) that span across the tropics and extensively rely on floating objects (FOBs) for catching tuna schools, leading to the bycatch of other species associated with these objects. Bycatch under floating objects is dominated by five species, including the vulnerable silky shark Carcharhinus falciformis and four bony fishes (oceanic triggerfish Canthidermis maculata, rainbow runner Elagatis bipinnulata, wahoo Acanthocybium solandri, and dolphinfish Coryphaena hippurus). Our objective was to predict possible bycatch hotspots associated with FOBs for these five species across two tropical oceans. We used bycatch data collected from observer programs onboard purse seiners in the Atlantic and Indian oceans. We developed a generalized additive model per species and per ocean relating bycatch to a set of environmental covariates (depth, chlorophyll-a concentration, sea surface temperature, mixed layer depth, surface salinity, total kinetic energy and the density of floating objects) and temporal covariates (year and month). We extrapolated modeled relationships across each ocean within the range of environmental covariates associated with the bycatch data and derived quarterly predictions. We then detected bycatch hotspots as the 90th percentiles of predictions. In the Atlantic Ocean, bycatch hotspots were predicted throughout tropical and subtropical waters with little overlap between species. By contrast in the Indian Ocean, major overlapping hotspots were predicted in the Arabian Sea throughout most of the year for four species, including the silky shark. Our modeling approach provides a new analytical way to fill data gaps in fisheries bycatch. Even with the lack of evaluation inherent to extrapolations, our modeling effort represents the first step to assist bycatch mitigation in TTPSF and is applicable beyond these fisheries.

Full Text
File Pages Size Access
Publisher's official version 59 2 MB Open access
Supplementary data 653 KB Open access
Supplementary data 2 1 MB Open access
Supplementary data 3 183 KB Open access
Supplementary data 4 1 MB Open access
Supplementary data 5 1 MB Open access
Corrigendum 1 160 KB Open access
Top of the page

How to cite 

Mannocci Laura, Forget Fabien, Travassos Tolotti Mariana, Bach Pascal, Bez Nicolas, Demarcq Herve, Kaplan David, Sabarros Philippe, Simier Monique, Capello Manuela, Dagorn Laurent (2020). Predicting bycatch hotspots in tropical tuna purse seine fisheries at the basin scale. Global Ecology And Conservation, 24, e01393 (11p.). Publisher's official version : , Open Access version :