Abstract
We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be , but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.
Highlights
Advances in technology are allowing biologists to collect large amounts of high resolution data without the need to be physically present
We developed a Support Vector Machine (SVM)-based classifier that allows us to classify behaviour of animals over a long period of time based on activity measurements and a small set of labeled data
Evaluation We evaluated our method in two different ways: 1) we estimated the classification performance with a leave-one-out cross validation; 2) we tested the dependency of the Hidden Markov Model (HMM) performance on a critical parameter
Summary
Advances in technology are allowing biologists to collect large amounts of high resolution data without the need to be physically present. To date, most studies have used simple data-oriented methods of analysis: for example, threshold-based detection of active/inactive states [1] or classification into slightly richer behavioural states [2,3,4]. These techniques have often been used in marine systems where the animals in question are near to impossible to follow and observe [4,5,6]. Despite the fact that these techniques have given a valuable insight into behaviours such as underwater foraging in free-living penguins [7] and swimming and diving in marine mammals and birds [4,5], it is hard to draw robust conclusions about actual/true behaviour from such analysis
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