The Use of Acceleration to Code for Animal Behaviours; A Case Study in Free-Ranging Eurasian Beavers <i>Castor fiber</i>

<div><p>Recent technological innovations have led to the development of miniature, accelerometer-containing electronic loggers which can be attached to free-living animals. Accelerometers provide information on both body posture and dynamism which can be used as descriptors to define behaviour. We deployed tri-axial accelerometer loggers on 12 free-ranging Eurasian beavers <i>Castor fiber</i> in the county of Telemark, Norway, and on four captive beavers (two Eurasian beavers and two North American beavers <i>C</i>. <i>canadensis</i>) to corroborate acceleration signals with observed behaviours. By using random forests for classifying behavioural patterns of beavers from accelerometry data, we were able to distinguish seven behaviours; standing, walking, swimming, feeding, grooming, diving and sleeping. We show how to apply the use of acceleration to determine behaviour, and emphasise the ease with which this non-invasive method can be implemented. Furthermore, we discuss the strengths and weaknesses of this, and the implementation of accelerometry on animals, illustrating limitations, suggestions and solutions. Ultimately, this approach may also serve as a template facilitating studies on other animals with similar locomotor modes and deliver new insights into hitherto unknown aspects of behavioural ecology.</p></div

Statistics of the static surge, sway and heave acceleration signal and overall dynamic body acceleration for the seven identified Eurasian beaver behaviours.

<p>^aSimilar-shaped, comparable dive types were only found in 8 individuals.</p><p>^bSleeping consists of a series of different postures (e.g. lying on the belly, on the sides, or on the back), thereby impeding the specification of mean values.</p><p>Statistics of the static surge, sway and heave acceleration signal and overall dynamic body acceleration for the seven identified Eurasian beaver behaviours.</p

Partial dependence plot for the most important predictor variable, the mean heave acceleration.

<p>The partial dependence function plots a grid of values over the range of the mean heave acceleration on the x-axis, with decile rugs at the bottom of the plot representing the distribution of the total mean heave acceleration. The y-axis is on the logit scale and is centred to have zero mean over the data distribution.</p

Changes in the acceleration signal of Eurasian beavers during a whole sleeping session.

<p>Example of the static surge, sway and heave acceleration of Eurasian beavers during a whole sleeping session. (a) Static heave acceleration values towards -1 <i>g</i> indicate lying on the back. (b) Static surge acceleration values towards +1 <i>g</i> and static sway acceleration values towards 0 <i>g</i> imply that the animal is sleeping in a sitting posture on its belly. (c) Static sway acceleration values towards -1 <i>g</i> indicate that the animal is lying on the left side, while (d) static sway acceleration values towards +1 <i>g</i> indicate lying on the right side.</p

Class center plot for Eurasian beaver behaviours.

<p>The plot contains prototypes (big dots) for the six behavioural classes modelled by random forests. Prototypes are median values of samples with the largest number of k-nearest neighbours of the same class. In this way, they mark the center of each class and indicate how the variables relate to the classification.</p

Variable importance plot for Eurasian beaver behaviours.

<p>A relative ranking of significant predictors, the permutation-based variable importance measure of the random forest model, is shown as the mean decrease in accuracy in percent. Higher values of mean decrease in accuracy indicate variables that contribute more to the accuracy of the classification.</p

An example plot of uptake against <i>ODBA</i> (black circles) and <i>VeDBA</i> (grey triangles) over the duration of the trial following removal of the points above the participant's anaerobic threshold.

<p>An example plot of uptake against <i>ODBA</i> (black circles) and <i>VeDBA</i> (grey triangles) over the duration of the trial following removal of the points above the participant's anaerobic threshold.</p

Dynamic body accelerations (<i>ODBA</i> – circles, and <i>VeDBA</i> –crosses) from straight- versus skew-mounted accelerometers (for details see text).

<p>Each point denotes a mean value derived from a three-minute trial of a participant moving at one particular speed below the lactate threshold. Data from all participants are included.</p

Heave (continuous line), sway (dotted line) and surge (dashed line) acceleration axes displayed graphically over one stride (from each leg) during walking (i) and running (ii).

<p>Heave (continuous line), sway (dotted line) and surge (dashed line) acceleration axes displayed graphically over one stride (from each leg) during walking (i) and running (ii).</p

Relationship between mean <i>ODBA</i> and mean <i>VeDBA</i> (means taken for each running speed) for a test participant during a max test.

<p>Only data during the period when the participant did not exceed the ventilatory threshold (for definition see text) are included. as with all other participants, the relationship between <i>ODBA</i> and <i>VeDBA</i> was highly significant (<i>VeDBA</i> = 0.014+0.6418 <i>ODBA</i>, r² = 0.987, P<0.001).</p