FN Archimer Export Format
PT J
TI Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
BT 
AF Behivoke, Faustinato
   Etienne, Marie-Pierre
   Guitton, Jérôme
   Randriatsara, Roddy Michel
   Ranaivoson, Eulalie
   Léopold, Marc
AS 1:1;2:2;3:3;4:1;5:1;6:4;
FF 1:;2:;3:;4:;5:;6:;
C1 Institut Halieutique et des Sciences Marines (IH.SM), University of Toliara, BP 141, 601 Toliara, Madagascar
   University of Rennes, Agrocampus Ouest, CNRS, UMR 6625 IRMAR, F-35000 Rennes, France
   ESE, Agrocampus Ouest, INRAE, 35042 Rennes, France
   ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), 97400 Saint-Denis, La Reunion c/o IH.SM, University of Toliara, BP 141, 601 Toliara, Madagascar
C2 UNIV TOLIARA, MADAGASCAR
   UNIV RENNES, FRANCE
   AGROCAMPUS OUEST, FRANCE
   IRD, FRANCE
UM ENTROPIE
IN WOS Cotutelle UMR
   DOAJ
   copubli-france
   copubli-univ-france
   copubli-int-hors-europe
   copubli-sud
IF 6.263
TC 36
UR https://archimer.ifremer.fr/doc/00669/78059/80289.pdf
LA English
DT Article
DE ;Boat movement;Fishery map;GPS track;Madagascar;Spatial data;Speed threshold
AB During the last decade spatial patterns of industrial fisheries have been increasingly characterized using tracking technologies and machine learning analytical algorithms. In contrast, for small-scale fisheries, fishers’ behaviour for estimating and mapping fishing effort has only been anecdotally explored. Following a comparative approach, we conducted a boat tracking survey in a small-scale reef fishery in Madagascar and investigated the performance of a learning random forest algorithm and a speed threshold for estimating and mapping fishing effort. We 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). To ground-truth GPS location data, fishers’ behaviour was simultaneously recorded by a single on-board observer for 49 tracks. Typical, gear-specific track patterns were observed. Overall, 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. Predictions of mean fishing effort per trip showed that both methods reached from 89.4% to 97.0% accuracy across gear types. Our 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. Selection of the most appropriate GPS data processing method is dependent on local gear use, fishing effort indicators, and available analytical expertise. 
PY 2021
PD APR
SO Ecological Indicators
SN 1470-160X
PU Elsevier BV
VL 123
UT 000615921800004
DI 10.1016/j.ecolind.2020.107321
ID 78059
ER

EF