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Auteur (up) Thiebault, A.; Dubroca, L.; Mullers, R.H.E.; Tremblay, Y.; Pistorius, P.A. doi  openurl
  Titre “M2B” package in R: Deriving multiple variables from movement data to predict behavioural states with random forests Type Article scientifique
  Année 2018 Publication Revue Abrégée Methods Ecol. Evol.  
  Volume 9 Numéro 6 Pages 1548-1555  
  Mots-Clés Cape gannet; classification; ecology; fisheries; gps; local enhancement; machine learning; onboard observers; social interactions; video cameras  
  Résumé 1. The behaviour of individuals affect their distributions and is therefore fundamental in determining ecological patterns. While, the direct observation of behaviour is often limited due to logistical constraints, collection of movement data has been greatly facilitated through the development of bio-logging. Movement data obtained through tracking instrumentation may potentially constitute a relevant proxy to infer behaviour. 2. To infer behaviour from movement data is a key focus within the “movement ecology” discipline. Statistical learning constitutes a number of methods that can be used to assess the link between given variables from a fully informed training dataset and then predict the values on a non-informed variable. We chose the random forest algorithm for its high prediction accuracy and its ease of implementation. The strength of random forest partly lies in its ability to handle a very large number of variables. Our methodology is accordingly based on the derivation of multiple predictor variables from movement data over various temporal scales, to capture as much information as possible from changes and variations in movement. 3. The methodology is described in four steps, using examples on foraging seabirds and fishing vessels for illustration. The models showed very high prediction accuracy (92%-97%), thereby confirming the influence of behaviour on movement decisions and demonstrating the ability to derive multiple variables from movement data to predict behaviour with random forests. 4. The codes developed for this methodology are published in the “M2B” (Movement to Behaviour) R package, available at They can be used and adapted to datasets where movement was sampled from a wide range of taxa, sampling schemes or tracking devices. Observations are needed for a subset of the data, but once the model is trained, it can be used on any dataset with similar movement data.  
  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 2041-210x ISBN Médium  
  Région Expédition Conférence  
  Notes Approuvé pas de  
  Numéro d'Appel MARBEC @ alain.herve @ collection 2382  
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