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      <source-app name="Horizon">Horizon</source-app>
      <rec-number>1</rec-number>
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      <ref-type name="Journal Article">17</ref-type>
      <work-type>ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES</work-type>
      <contributors>
        <authors>
          <author>
            <style face="normal" font="default" size="100%">Behivoke, F.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Etienne, M. P.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Guitton, J.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Randriatsara, R. M.</style>
          </author>
          <author>
            <style face="normal" font="default" size="100%">Ranaivoson, E.</style>
          </author>
          <author>
            <style face="bold" font="default" size="100%">Léopold, Marc</style>
          </author>
        </authors>
      </contributors>
      <titles>
        <title>Estimating fishing effort in small-scale fisheries using GPS tracking data and random forests</title>
        <secondary-title>Ecological Indicators</secondary-title>
      </titles>
      <pages>107321 [7 ]</pages>
      <keywords>
        <keyword>Boat movement</keyword>
        <keyword>Fishery map</keyword>
        <keyword>GPS track</keyword>
        <keyword>Madagascar</keyword>
        <keyword>Spatial data</keyword>
        <keyword>Speed threshold</keyword>
        <keyword>MADAGASCAR</keyword>
      </keywords>
      <dates>
        <year>2021</year>
      </dates>
      <call-num>fdi:010081012</call-num>
      <language>ENG</language>
      <periodical>
        <full-title>Ecological Indicators</full-title>
      </periodical>
      <isbn>1470-160X</isbn>
      <accession-num>ISI:000615921800004</accession-num>
      <electronic-resource-num>10.1016/j.ecolind.2020.107321</electronic-resource-num>
      <urls>
        <related-urls>
          <url>https://www.documentation.ird.fr/hor/fdi:010081012</url>
        </related-urls>
        <pdf-urls>
          <url>https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers21-03/010081012.pdf</url>
        </pdf-urls>
      </urls>
      <volume>123</volume>
      <remote-database-provider>Horizon (IRD)</remote-database-provider>
      <abstract>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.</abstract>
      <custom6>040 ; 020 ; 126</custom6>
      <custom1>UR250</custom1>
      <custom7>Madagascar</custom7>
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