| Registro completo |
| Provedor de dados: |
ArchiMer
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| País: |
France
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| Título: |
Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests
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| Autores: |
Behivoke, Faustinato
Etienne, Marie-pierre
Guitton, Jérôme
Randriatsara, Roddy Michel
Ranaivoson, Eulalie
Léopold, Marc
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| Data: |
2021-04
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| Ano: |
2021
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| Palavras-chave: |
Boat movement
Fishery map
GPS track
Madagascar
Spatial data
Speed threshold
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| Resumo: |
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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| Tipo: |
Text
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| Idioma: |
Inglês
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| Identificador: |
https://archimer.ifremer.fr/doc/00669/78059/80289.pdf
DOI:10.1016/j.ecolind.2020.107321
https://archimer.ifremer.fr/doc/00669/78059/
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| Editor: |
Elsevier BV
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| Formato: |
application/pdf
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| Fonte: |
Ecological Indicators (1470-160X) (Elsevier BV), 2021-04 , Vol. 123 , P. 107321 (7p.)
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| Direitos: |
info:eu-repo/semantics/openAccess
restricted use
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