<?xml version="1.0"?>
<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests</dc:title>
  <dc:creator>Behivoke, F.</dc:creator>
  <dc:creator>Etienne, M. P.</dc:creator>
  <dc:creator>Guitton, J.</dc:creator>
  <dc:creator>Randriatsara, R. M.</dc:creator>
  <dc:creator>Ranaivoson, E.</dc:creator>
  <dc:creator>/L&#xE9;opold, Marc</dc:creator>
  <dc:subject>Boat movement</dc:subject>
  <dc:subject>Fishery map</dc:subject>
  <dc:subject>GPS track</dc:subject>
  <dc:subject>Madagascar</dc:subject>
  <dc:subject>Spatial data</dc:subject>
  <dc:subject>Speed threshold</dc:subject>
  <dc: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.</dc:description>
  <dc:date>2021</dc:date>
  <dc:type>text</dc:type>
  <dc:identifier>https://www.documentation.ird.fr/hor/fdi:010081012</dc:identifier>
  <dc:identifier>fdi:010081012</dc:identifier>
  <dc:identifier>Behivoke F., Etienne M. P., Guitton J., Randriatsara R. M., Ranaivoson E., L&#xE9;opold Marc. Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests. 2021, 123, 107321 [7 ]</dc:identifier>
  <dc:language>EN</dc:language>
  <dc:coverage>MADAGASCAR</dc:coverage>
</oai_dc:dc>
