Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
Type | Article | ||||||||
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Date | 2021-04 | ||||||||
Language | English | ||||||||
Author(s) | Behivoke Faustinato1, Etienne Marie-Pierre2, Guitton Jérôme3, Randriatsara Roddy Michel1, Ranaivoson Eulalie1, Léopold Marc4 | ||||||||
Affiliation(s) | 1 : Institut Halieutique et des Sciences Marines (IH.SM), University of Toliara, BP 141, 601 Toliara, Madagascar 2 : University of Rennes, Agrocampus Ouest, CNRS, UMR 6625 IRMAR, F-35000 Rennes, France 3 : ESE, Agrocampus Ouest, INRAE, 35042 Rennes, France 4 : 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 |
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Source | Ecological Indicators (1470-160X) (Elsevier BV), 2021-04 , Vol. 123 , P. 107321 (7p.) | ||||||||
DOI | 10.1016/j.ecolind.2020.107321 | ||||||||
WOS© Times Cited | 36 | ||||||||
Keyword(s) | Boat movement, Fishery map, GPS track, Madagascar, Spatial data, Speed threshold | ||||||||
Abstract | 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. |
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