knitr::opts_chunk$set(dev='png')
Animal behaviours affect animal distributions and trophic relationships and are therefore fundamental in determining ecological patterns. 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 behavioural states, using the random forest algorithm. The behavioural state of an animal can refer here to any categorization of the observed behaviour. 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.
This tutorial presents a new class named xytb
, the functions that were
implemented for the use of this package, and an example of application.
The package can be installed using the CRAN system (install.package("m2b")
),
but the development version can be found on github
(https://github.com/ldbk/m2b).
xytb
is a S4 class built to provide in a single object all the information
associated to a track. This includes the tracking data (two dimension space
coordinates, time and behavioural states), the predictor variables derived from
the movement data, the resulting model (to be used on other datasets) and the
prediction (on the given dataset).
xyt
relates to information about the movement data, and b
to the behavioural
state.
These information are containted into 8 slots, each of them deriving from
different methods and functions (see Figure 1 for details). This object was
created for the user to keep everything (data, model and prediction) in a single
container, but also, by
extension, for the user to (1) keep track of the precision of the model
predictions and (2) exchange the analyses and results with different users
easily.
DiagrammeR::grViz(width=800,height=800,diagram=" digraph rmarkdown { graph[center=true,ratio=auto,rankdir=TD,compound=true]; fieldwork[label='Fieldwork',color=white]; fieldwork->behaviour[lhead=cluster0]; fieldwork->descdata[lhead=cluster0]; fieldwork->track[lhead=cluster0]; subgraph cluster0{ track[label='track data']; behaviour[label='behavioural data']; descdata[label='meta data']; label = 'data'; } descdata->desc[fontcolor=blue,color=blue]; behaviour->b[fontcolor=blue,color=blue]; track->xyt[fontcolor=blue,color=blue]; subgraph cluster1 { xytb[label='class xytb',shape=diamond]; xytb-> desc[type=tee,style=dotted,dir=none]; xytb-> xyt[type=tee,style=dotted,dir=none]; xytb-> b[type=tee,style=dotted,dir=none]; xytb-> dxyt[type=tee,style=dotted,dir=none]; xytb-> befdxyt[type=tee,style=dotted,dir=none]; xytb-> model[type=tee,style=dotted,dir=none]; xytb-> rfcv[type=tee,style=dotted,dir=none]; xytb-> predb[type=tee,style=dotted,dir=none]; xyt->dxyt->befdxyt[color=blue,style=dashed]; b->model[color=red,style=dashed]; dxyt->model[color=red,style=dashed]; befdxyt->model[color=red,style=dashed]; model->predb[color=red,style=dashed]; rfcv->model[dir=both,color=red]; desc[shape=diamond,label='@desc:\nshort\ndescription'] xyt[shape=diamond,label='@xyt:\ntrack'] b[shape=diamond,label='@b:\nbehaviour'] dxyt[shape=diamond,label='@dxyt:\ntrack\nderivative'] befdxyt[shape=diamond,label='@befdxyt:\n@dxyt\nshifted'] model[shape=diamond,label='@model:\nrandom forest\nmodel'] rfcv[shape=diamond,label='@rfcv:\ncross validation\nof @model'] predb[shape=diamond,label='@predb:\nprediction of \n@b using @model'] label = 'xytb object'; } subgraph cluster3 { label='Results'; Plots[shape=box]; Tables[shape=box]; } xytb->Plots[color=green]; xytb->Tables[color=green]; ltraj[label='ltraj object',shape=diamond]; xytb->ltraj[color=pink,dir=both]; hmm[label='moveHMM object',shape=diamond]; xytb->hmm[color=pink,dir=both]; subgraph cluster100 { label='Legend' out[label='Output',shape=box]; R[label='R object',shape=diamond]; slot[label='slot',shape=diamond]; fun[label='Functions\n& Methods',color=white]; leg1[label='xytb()',color=blue,fontcolor=blue]; leg2[label='modelRF()',color=red,fontcolor=red]; leg3[label='resRF()\n resB()',color=green,fontcolor=green]; leg4[label='xytb2ltraj()\n ltraj2xytb()\n xytb2hmm()',color=pink,fontcolor=pink]; fun->leg1[color=blue]; fun->leg2[color=red]; fun->leg3[color=green]; fun->leg4[color=pink]; R->slot[style=dotted,dir=none]; file[label='Data']; } }")
The analytical procedure is summarized in 4 main functions and methods. For a full description of the use of the functions, please refer to the help provided for each function.
xytb
xytb
is a class, but also a method.
Used as a function xytb
will calculte the predictor variables and store all
the data in a newly created xytb
object.
4 distinct signatures help the user to load
trackings and behavioural data in the object.
The data must be presented in a dataframe with locations in lines, and 5
variables in column: x
the longitude, y
the latitude, t
the time in
POSIXct format, b
the behavioural state and id
the individual
identification for the track.
The function xytb
can then be used with this dataframe as an input, so the
predictor variables are calculated and stored in the slots dxyt
and befdxyt
of the xytb
created object.
For the calculation of predictor
variables, three parameters can be set:
winsize
specifies the sizes of sliding windows on which to compute the statistical operators,idquant
specifies the quantiles to be computed, move
(optional) specifies the number of points for the calculated variables to be shifted backward (
variables to be added to the ones calculated at the time).If included, the latter parameter will account for a delay between the reaction of the animal captured in the movement data, and its behaviour as recorded by the observer. In all cases, the original data are not modified, but derivated data are saved in the corresponding slots.
modelRF
This function computes a random forest model to predict the behavioural
state (response) from the movement data (predictors). This is a simple wrapper calling the
randomForest function from the randomForest package
(https://CRAN.R-project.org/package=randomForest).
This function will update the xytb
object and store the outputs in the slots
rfcv
(cross-validation for the choice of the parameter mtry
), model
(model itself) and predb
(predictions). Cross-validation has to be done
independently to set up the mtry
parameters for the model.
resRF
and resB
These two functions compute and plot the diagnostics and results of the model.
The function resRF
provides the error rate, the convergence of the model, a
confusion matrix and the importance of variables. The function resB
plots the
predictions vs observations, over time or space.
xytb2ltraj
and ltraj2xytb
These functions import or export a xytb
object to an object of class ltraj
. The
latter is used in the adehabitatLT
package where numerous function
are dedicated to the analysis of trajectories (see @calenge2008).
xytb2hmm
This function import a xytb
object to an object of class moveHMM
.
The latter is used in the moveHMM
package which provides functions
dedicated to the analysis of trajectories using hidden Markov models (see @michelot2016).
The data frame track_CAGA_005
contains the tracking and behavioural data
collected from a Cape gannet (\emph{Morus capensis}, Lichtenstein 1823). Tracking data
include latitude, longitude and time (class POSIXct). Behavioural data include
three states coded as '1' (bird diving), '2' (bird sitting on the water), '3'
(bird flying), and a state -1
for data points where the behaviour could not
be observed.
A state with no observation can be declared in some functions (resB
for
example) using the parameter nob
, equal to '-1' in our case (see functions'
help).
library(m2b) str(track_CAGA_005)
Different methods are available to build a xytb
objec. Here, the tracking and
behavioural data are directly taken from the dataframe, and the predictor
variables deriving from the tracking data are computed at the same time using
the function xytb
. The variables are
computed over sliding windows of sizes 3, 5, 7, 9, 11, 13 and 15 locations (the
winsize
parameter). In addition to the standard statistical operators (mean,
standard deviation
and median absolute deviation), the quantiles at 0, 25,
50, 75 and 100\% are computed (the idquant
parameter). All those values
calculated can be
then shifted in time to 5, 10 and 15 points backwards (the move
parameter), if
the user is interested to investigate the effect of the delay
between the reaction
of the animal captured in the movement data, and its behaviour as recorded by
the observer.
The rationale behind this operation is based on the fact that
some changes in movement can be related to a change in behavioural state
observed only later by the scientist. For example, an animal may react to
something detected from a distance (like a possible feeding area), a change
immediately captured in the movement data, but the reason for its movement
(starting to feed) will only appear later in the observation data.
library(m2b) #convert to xybt object with computation of windows operators and some quantiles xytb<-xytb(track_CAGA_005,desc="example track", winsize=seq(3,15,2),idquant=seq(0,1,.25),move=c(5,10,15)) #a simple plot method plot(xytb)
To build a random forest predicting the behavioural states based on the movement
information, the function modelRF
is used. It's a simple wrapper calling the
randomForest
function of the randomForest
package, using the behavioural
observation as response, and movement information as predictors.
#a model (the function modelRF updates the model inside the xytb object) xytb<-modelRF(xytb,type="actual",ntree=501,mtry=15)
Some diagnostic plots are available using the resRF
function to check the fit
of the model.
In addition, the function extractRF
can be used to export the resulting model to the randomForest
format, so that other function from the randomForest
package can be used to
perform a deep analysis of the model.
resRF(xytb) resRF(xytb,"importance") resRF(xytb,"confusion")
The results regarding the behavioural states predicted vs the one observed are
illustrated thanks to the resB
functions.
resB(xytb,"time",nob="-1") resB(xytb,"space",nob="-1") resB(xytb,"density",nob="-1")
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