"M2B" package in R: Deriving multiple variables from movement data to predict behavioural states with random forests
|Author(s)||Thiebault Andrea1, Dubroca Laurent2, Mullers Ralf H. E.3, Tremblay Yann4, Pistorius Pierre A.5|
|Affiliation(s)||1 : Nelson Mandela Univ, Dept Zool, Port Elizabeth, South Africa.
2 : IFREMER, Datacall Response Unit CREDO, Port En Bessin Huppain, France.
3 : Univ Limpopo, Dept Biodivers, Sovenga, South Africa.
4 : Inst Rech Dev, UMR MARBEC Marine Biodivers Exploitat & Conservat, Sete, France.
5 : Nelson Mandela Univ, Dept Zool, Percy FitzPatrick Inst, DST NRF Ctr Excellence, Port Elizabeth, South Africa.
|Source||Methods In Ecology And Evolution (2041-210X) (Wiley), 2018-06 , Vol. 9 , N. 6 , P. 1548-1555|
|WOS© Times Cited||1|
|Keyword(s)||Cape gannet, fisheries, GPS, local enhancement, machine learning, onboard observers, social interactions, video cameras|
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 https://CRAN.R-project.org/package=m2b. 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.