@article{fdi:010090509,
  title = {{M}odeling bycatch abundance in tropical tuna purse seine fisheries on floating objects using the Δ method},
  author = {{D}umont, {A}. and {D}uparc, {A}ntoine and {S}abarros, {P}hilippe and {K}aplan, {D}avid},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ycatch rates are essential to estimating fishery impacts and making management decisions, but data on bycatch are often limited. {T}ropical tuna purse seine ({PS}) fisheries catch numerous bycatch species, including vulnerable silky sharks. {E}ven if bycatch proportion is relatively low, impacts on pelagic ecosystems may be important due to the large size of these fisheries. {P}artial observer coverage of bycatch is a major impediment to assessing impacts. {H}ere we develop a generic {D}elta modeling approach for predicting catch of four major bycatch species, including silky sharks, in floating object-associated fishing sets of the {F}rench {I}ndian {O}cean {PS} fleet from 2011 to 2018 based on logbook and observer data. {C}ross-validation and variable selection are used to identify optimal models consisting of a random forest model for presence-absence and a negative binomial general-additive model for abundance when present. {T}hough models explain small to moderate amounts of variance (5-15%), they outperform a simpler approach commonly used for reporting, and they allow us to estimate total annual bycatch for the four species with robust estimates of uncertainty. {I}nterestingly, uncertainty relative to mean catch is lower for top predators than forage species, consistent with these species having similar behavior and ecological niches to tunas.},
  keywords = {prediction intervals ; random forest ; general additive model ({GAM}) ; silky ; shark ({C}archarhinus falciformis) ; dolphinfish ({C}oryphaena hippurus) ; rainbow runner ({E}lagatis bipinnulata) ; rough triggerfish ({C}anthidermis ; maculata)},
  booktitle = {},
  journal = {{ICES} {J}ournal of {M}arine {S}cience},
  volume = {[{E}arly access]},
  numero = {},
  pages = {[22 p.]},
  ISSN = {1054-3139},
  year = {2024},
  DOI = {10.1093/icesjms/fsae043},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090509},
}