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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-04-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X20312632 |
| _version_ | 1818401957785632768 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T08:00:44Z |
| format | Article |
| id | doaj.art-0617482d707d4de8bbdaf3c7c39f7105 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-12-14T08:00:44Z |
| publishDate | 2021-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-0617482d707d4de8bbdaf3c7c39f71052022-12-21T23:10:25ZengElsevierEcological Indicators1470-160X2021-04-01123107321Estimating fishing effort in small-scale fisheries using GPS tracking data and random forestsFaustinato Behivoke0Marie-Pierre Etienne1Jérôme Guitton2Roddy Michel Randriatsara3Eulalie Ranaivoson4Marc Léopold5Institut Halieutique et des Sciences Marines (IH.SM), University of Toliara, BP 141, 601 Toliara, MadagascarUniversity of Rennes, Agrocampus Ouest, CNRS, UMR 6625 IRMAR, F-35000 Rennes, FranceESE, Agrocampus Ouest, INRAE, 35042 Rennes, FranceInstitut Halieutique et des Sciences Marines (IH.SM), University of Toliara, BP 141, 601 Toliara, MadagascarInstitut Halieutique et des Sciences Marines (IH.SM), University of Toliara, BP 141, 601 Toliara, MadagascarENTROPIE (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; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S1470160X20312632Boat movementFishery mapGPS trackMadagascarSpatial dataSpeed threshold |
| spellingShingle | 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 Ecological Indicators Boat movement Fishery map GPS track Madagascar Spatial data Speed threshold |
| title | 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_short | 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 |
| topic | Boat movement Fishery map GPS track Madagascar Spatial data Speed threshold |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X20312632 |
| work_keys_str_mv | AT faustinatobehivoke estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT mariepierreetienne estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT jeromeguitton estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT roddymichelrandriatsara estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT eulalieranaivoson estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests AT marcleopold estimatingfishingeffortinsmallscalefisheriesusinggpstrackingdataandrandomforests |