@article{fdi:010080659,
  title = {{P}redicting bycatch hotspots in tropical tuna purse seine fisheries at the basin scale [+ {C}orrigendum, 2021, vol. 25, art. e01427]},
  author = {{M}annocci, {L}. and {F}orget, {F}abien and {T}olotti, {M}. {T}. and {B}ach, {P}ascal and {B}ez, {N}icolas and {D}emarcq, {H}erv{\'e} and {K}aplan, {D}avid and {S}abarros, {P}hilippe and {S}imier, {M}onique and {C}apello, {M}anuela and {D}agorn, {L}aurent},
  editor = {},
  language = {{ENG}},
  abstract = {{F}isheries observer programs represent the most reliable way to collect data on fisheries bycatch. {H}owever, their limited coverage leads to important data gaps that preclude bycatch mitigation at the basin scale. {H}abitat models developed from available fisheries observer programs offer a potential solution to fill these gaps. {W}e focus on tropical tuna purse seine fisheries ({TTPSF}) that span across the tropics and extensively rely on floating objects ({FOB}s) for catching tuna schools, leading to the bycatch of other species associated with these objects. {B}ycatch under floating objects is dominated by five species, including the vulnerable silky shark {C}archarhinus falciformis and four bony fishes (oceanic triggerfish {C}anthidermis maculata, rainbow runner {E}lagatis bipinnulata, wahoo {A}canthocybium solandri, and dolphinfish {C}oryphaena hippurus). {O}ur objective was to predict possible bycatch hotspots associated with {FOB}s for these five species across two tropical oceans. {W}e used bycatch data collected from observer programs onboard purse seiners in the {A}tlantic and {I}ndian oceans. {W}e 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). {W}e extrapolated modeled relationships across each ocean within the range of environmental covariates associated with the bycatch data and derived quarterly predictions. {W}e then detected bycatch hotspots as the 90th percentiles of predictions. {I}n the {A}tlantic {O}cean, bycatch hotspots were predicted throughout tropical and subtropical waters with little overlap between species. {B}y contrast in the {I}ndian {O}cean, major overlapping hotspots were predicted in the {A}rabian {S}ea throughout most of the year for four species, including the silky shark. {O}ur modeling approach provides a new analytical way to fill data gaps in fisheries bycatch. {E}ven 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.},
  keywords = {{B}ycatch ; {H}abitat modelling ; {H}otspots ; {F}isheries observer programs ; {G}eographical extrapolation ; {T}ropical oceans ; {ATLANTIQUE} ; {OCEAN} {INDIEN} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{G}lobal {E}cology and {C}onservation},
  volume = {24},
  numero = {},
  pages = {e01393 [11  + {C}orrigendum, 2021, vol. 25, art. e01427]},
  ISSN = {2351-9894},
  year = {2020},
  DOI = {10.1016/j.gecco.2020.e01393},
  URL = {https://www.documentation.ird.fr/hor/fdi:010080659},
}