Animal-borne acoustic data alone can provide high accuracy classification of activity budgets

This telemetry case report explores the use of animal-borne acoustic recorders to automatically infer activities in seabirds.

Date published: 2021/09/17

Title: Animal-borne acoustic data alone can provide high accuracy classification of activity budgets

Authors: Andréa Thiebault, Chloé Huetz, Pierre Pistorius, Thierry Aubin & Isabelle Charrier 

Journal: Animal Biotelemetry

Citation: Thiebault, A., Huetz, C., Pistorius, P. et al. Animal-borne acoustic data alone can provide high accuracy classification of activity budgets. Anim Biotelemetry 9, 28 (2021). https://doi.org/10.1186/s40317-021-00251-1

Open Access: Yes

Abstract

Background

Studies on animal behaviour often involve the quantification of the occurrence and duration of various activities. When direct observations are challenging (e.g., at night, in a burrow, at sea), animal-borne devices can be used to remotely record the movement and behaviour of an animal (e.g., changing body posture and movement, geographical position) and/or its immediate surrounding environment (e.g., wet or dry, pressure, temperature, light). Changes in these recorded variables are related to different activities undertaken by the animal. Here we explored the use of animal-borne acoustic recorders to automatically infer activities in seabirds.

Results

We deployed acoustic recorders on Cape gannets and analysed sound data from 10 foraging trips. The different activities (flying, floating on water and diving) were associated with clearly distinguishable acoustic features. We developed a method to automatically identify the activities of equipped individuals, exclusively from animal-borne acoustic data. A random subset of four foraging trips was manually labelled and used to train a classification algorithm (k-nearest neighbour model). The algorithm correctly classified activities with a global accuracy of 98.46%. The model was then used to automatically assess the activity budgets on the remaining non-labelled data, as an illustrative example. In addition, we conducted a systematic review of studies that have previously used data from animal-borne devices to automatically classify animal behaviour (n = 61 classifications from 54 articles). The majority of studies (82%) used accelerometers (alone or in combination with other sensors, such as gyroscopes or magnetometers) for classifying activities, and to a lesser extent GPS, acoustic recorders or pressure sensors, all potentially providing a good accuracy of classification (> 90%).

Conclusion

This article demonstrates that acoustic data alone can be used to reconstruct activity budgets with very good accuracy. In addition to the animal’s activity, acoustic devices record the environment of equipped animals (biophony, geophony, anthropophony) that can be essential to contextualise the behaviour of animals. They hence provide a valuable alternative to the set of tools available to assess animals’ behaviours and activities in the wild.

Keywords: Bioacoustics, Biologging Behaviour, Machine learning, Seabird, Supervised learning, Systematic review

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