Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests

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...

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Autores principales:	Faustinato Behivoke, Marie-Pierre Etienne, Jérôme Guitton, Roddy Michel Randriatsara, Eulalie Ranaivoson, Marc Léopold
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Boat movement Fishery map GPS track Madagascar Spatial data Speed threshold Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/0617482d707d4de8bbdaf3c7c39f7105
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id	oai:doaj.org-article:0617482d707d4de8bbdaf3c7c39f7105
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spelling	oai:doaj.org-article:0617482d707d4de8bbdaf3c7c39f71052021-12-01T04:42:34ZEstimating fishing effort in small-scale fisheries using GPS tracking data and random forests1470-160X10.1016/j.ecolind.2020.107321https://doaj.org/article/0617482d707d4de8bbdaf3c7c39f71052021-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20312632https://doaj.org/toc/1470-160XDuring 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.Faustinato BehivokeMarie-Pierre EtienneJérôme GuittonRoddy Michel RandriatsaraEulalie RanaivosonMarc LéopoldElsevierarticleBoat movementFishery mapGPS trackMadagascarSpatial dataSpeed thresholdEcologyQH540-549.5ENEcological Indicators, Vol 123, Iss , Pp 107321- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Boat movement Fishery map GPS track Madagascar Spatial data Speed threshold Ecology QH540-549.5
spellingShingle	Boat movement Fishery map GPS track Madagascar Spatial data Speed threshold Ecology QH540-549.5 Faustinato Behivoke Marie-Pierre Etienne Jérôme Guitton Roddy Michel Randriatsara Eulalie Ranaivoson Marc Léopold Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
description	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.
format	article
author	Faustinato Behivoke Marie-Pierre Etienne Jérôme Guitton Roddy Michel Randriatsara Eulalie Ranaivoson Marc Léopold
author_facet	Faustinato Behivoke Marie-Pierre Etienne Jérôme Guitton Roddy Michel Randriatsara Eulalie Ranaivoson Marc Léopold
author_sort	Faustinato Behivoke
title	Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
title_short	Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
title_full	Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
title_fullStr	Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
title_full_unstemmed	Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
title_sort	estimating fishing effort in small-scale fisheries using gps tracking data and random forests
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0617482d707d4de8bbdaf3c7c39f7105
work_keys_str_mv	AT faustinatobehivoke estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT mariepierreetienne estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT jeromeguitton estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT roddymichelrandriatsara estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT eulalieranaivoson estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT marcleopold estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests
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Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests

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