Movement to Behaviour Inference using Random Forest

Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.


A package to build behavioural model on track data, using randomForest.

Installation

In a R console:

install.packages("devtools")

library(devtools)

install_github("ldbk/m2b")

Description

Animal behaviour, including social interactions, are fundamental to the field of ecology. Whereas the direct observation of animal behaviour is often limited due to logistical constraints, collection of movement data have been greatly facilitated through the development of bio-logging. Animal movement data obtained through tracking instrumentation may potentially constitute a relevant proxy to infer animal behaviour. This is, however, based on the premise that a range of movement patterns can be linked to specific behaviours.

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 capacity to deal with imbalanced data (particularly relevant for behavioural data), its high prediction accuracy and its ease of implementation (@breiman2001b, @chen2004). The strength of random forest partly relies in its ability to handle a very large number of variables. Hence, our methodology is based on the derivation of multiple predictor variables from the movement data over various temporal scales, in order to capture as much information as possible on the changes and variations of movement.

In this package we developed a method to link the movement patterns of animals with their behaviour, using the random forest algorithm. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any dataset providing movement data together with observation of behaviour.

News

Reference manual

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install.packages("m2b")

1.0 by Laurent Dubroca, a year ago


https://github.com/ldbk/m2b


Browse source code at https://github.com/cran/m2b


Authors: Laurent Dubroca [aut, cre], Andréa Thiebault [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports geosphere, caTools, ggplot2, randomForest, e1071, caret, methods, graphics, stats, utils

Suggests adehabitatLT, moveHMM, knitr, DiagrammeR, rmarkdown


See at CRAN