4.7 Article

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

Journal

ECOLOGICAL INDICATORS
Volume 123, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.ecolind.2020.107321

Keywords

Boat movement; Fishery map; GPS track; Madagascar; Spatial data; Speed threshold

Funding

  1. Laboratory of Excellence Corail (GEOFIA project), France [2AO2017]
  2. Critical Ecosystem Partnership Fund (CEPF-IH.SM) [66341]

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This study found that using a random forest algorithm is the most reliable and effective method for analyzing boat GPS tracks and detecting fishing events in small-scale fisheries. Both the random forest algorithm and speed threshold method showed high accuracy in predicting fishing effort across different gear types, with accuracies ranging from 89.4% to 97.0%.
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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