Abstract
Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86–97% sensitivity, 89–98% precision) and a reasonable recognition of flushing (79–86%, 66–80%) and landing behaviour(73–91%, 79–92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system.
Highlights
In many parts of the world, damage caused by wildlife creates significant economic challenges to human communities
The principal components are derived via principal component analysis (PCA) [47], and are the linear combination of the selected features which preserves the most variance in a smaller dimensional space
It is possible to distinguish between landing, foraging and flushing behaviour based on acoustic information
Summary
In many parts of the world, damage caused by wildlife creates significant economic challenges to human communities. A wide range of devices to detect and deter animals causing conflict are used in wildlife damage management, their effectiveness is often highly variable [2]. In most cases scaring devices are non-specific, so they can be activated by any animal, when individuals of the target species enters the area. This increases the risk of habituation, which is often the major limitation on the use of scaring devices [3]. Random or animal-activated scaring devices may reduce habituation and prolong the protection period over non-random devices [3], to our knowledge no cost-effective concept circumventing the problems of habituation has yet been developed
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