@article{fdi:010081012,
  title = {{E}stimating fishing effort in small-scale fisheries using {GPS} tracking data and random forests},
  author = {{B}ehivoke, {F}. and {E}tienne, {M}. {P}. and {G}uitton, {J}. and {R}andriatsara, {R}. {M}. and {R}anaivoson, {E}. and {L}{\'e}opold, {M}arc},
  editor = {},
  language = {{ENG}},
  abstract = {{D}uring the last decade spatial patterns of industrial fisheries have been increasingly characterized using tracking technologies and machine learning analytical algorithms. {I}n contrast, for small-scale fisheries, fishers' behaviour for estimating and mapping fishing effort has only been anecdotally explored. {F}ollowing a comparative approach, we conducted a boat tracking survey in a small-scale reef fishery in {M}adagascar and investigated the performance of a learning random forest algorithm and a speed threshold for estimating and mapping fishing effort. {W}e monitored the movements of a sample of 31 traditional sailing fishing boats at around 45 s time interval using small {GPS} trackers. {A} total of 306 daily tracks were recorded among five gear types (beach seine, mosquito trawl net, gillnet, handline, and speargun). {T}o ground-truth {GPS} location data, fishers' behaviour was simultaneously recorded by a single on-board observer for 49 tracks. {T}ypical, gear-specific track patterns were observed. {O}verall, the random forest model was found to be the most reliable, generic, and complex method for processing boat {GPS} tracks and detecting spatially-explicit fishing events regardless gear type. {P}redictions of mean fishing effort per trip showed that both methods reached from 89.4% to 97.0% accuracy across gear types. {O}ur findings showed that boat tracking combined with on-board observation would improve the reliability of spatial fishing effort indicators in small-scale fisheries and contribute to more efficient management. {S}election of the most appropriate {GPS} data processing method is dependent on local gear use, fishing effort indicators, and available analytical expertise.},
  keywords = {{B}oat movement ; {F}ishery map ; {GPS} track ; {M}adagascar ; {S}patial data ; {S}peed threshold ; {MADAGASCAR}},
  booktitle = {},
  journal = {{E}cological {I}ndicators},
  volume = {123},
  numero = {},
  pages = {107321 [7 ]},
  ISSN = {1470-160{X}},
  year = {2021},
  DOI = {10.1016/j.ecolind.2020.107321},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081012},
}