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Auteur (up) Baidai, Y.; Dagorn, L.; Amande, M.J.; Gaertner, D.; Capello, M. doi  openurl
  Titre Machine learning for characterizing tropical tuna aggregations under Drifting Fish Aggregating Devices (DFADs) from commercial echosounder buoys data Type Article scientifique
  Année 2020 Publication Revue Abrégée Fish Res.  
  Volume 229 Numéro Pages 105613  
  Mots-Clés behavior; classification; communities; Direct abundance indicator; echo-sounder buoys; Echosounder buoys; estimate biomass; fads; Fish aggregating devices; Purse seiner; purse seiners; target strength; temperature; Tropical tunas; yellowfin thunnus-albacares  
  Résumé The use of echosounder buoys deployed in conjunction with Drifting Fish Aggregating Devices (DFADs) has progressively increased in the tropical tuna purse seine fishery since 2010 as a means of improving fishing efficiency. Given the broad distribution of DFADs, the acoustic data provided by echosounder buoys can provide an alternative to the conventional CPUE index for deriving trends on tropical tuna stocks. This study aims to derive reliable indices of presence of tunas (and abundance) using echosounder buoy data. A novel methodology is presented which utilizes random forest classification to translate the acoustic backscatter from the buoys into metrics of tuna presence and abundance. Training datasets were constructed by cross-referencing acoustic data with logbook and observer data which reported activities on DFADs (tuna catches, new deployments and visits of DFADs) in the Atlantic and Indian Oceans from 2013 to 2018. The analysis showed accuracies of 75 and 85 % for the recognition of the presence/absence of tuna aggregations under DFADs in the Atlantic and Indian Oceans, respectively. The acoustic data recorded at ocean-specific depths (6-45m in the Atlantic and 30-150m in the Indian Ocean) and periods (4 a.m.-4 p.m.) were identified by the algorithm as the most important explanatory variables for detecting the presence of tuna. The classification of size categories of tuna aggregations showed a global accuracy of nearly 50 % for both oceans. This study constitutes a milestone towards the use of echosounder buoys data for scientific purposes, including the development of promising fisheries-independent indices of abundance for tropical tunas.  
  Auteur institutionnel Thèse  
  Editeur Lieu de Publication Éditeur  
  Langue English Langue du Résumé Titre Original  
  Éditeur de collection Titre de collection Titre de collection Abrégé  
  Volume de collection Numéro de collection Edition  
  ISSN 0165-7836 ISBN Médium  
  Région Expédition Conférence  
  Notes WOS:000539099200018 Approuvé pas de  
  Numéro d'Appel MARBEC @ isabelle.vidal-ayouba @ collection 2816  
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