Correcting a bias in the computation of behavioural time budgets that are based on supervised learning
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Peer-reviewed
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Authors
Resheff, Yehezkel S
Bensch, Hanna M
Zöttl, Markus
Rotics, Shay
Abstract
- Supervised learning of behavioral modes from body-acceleration data has become a widely used research tool in Behavioral Ecology over the past decade. One of the primary usages of this tool is to estimate behavioral time budgets from the distribution of behaviors as predicted by the model. These serve as the key parameters to test predictions about the variation in animal behavior. In this paper we show that the widespread computation of behavioral time budgets is biased, due to ignoring the classification model confusion probabilities.
- Next, we introduce the confusion matrix correction for time budgets - a simple correction method for adjusting the computed time budgets based on the model's confusion matrix.
- Finally, we show that the proposed correction is able to eliminate the bias, both theoretically and empirically in a series of data simulations on body acceleration data of a fossorial rodent species (Damaraland mole-rat, Fukomys damarensis).
- Our paper provides a simple implementation of the confusion matrix correction for time budgets, and we encourage researchers to use it to improve accuracy of behavioral time budget calculations.
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Keywords
3109 Zoology, 31 Biological Sciences
Journal Title
Methods in Ecology and Evolution
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Journal ISSN
2041-210X
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Publisher
Wiley
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All rights reserved
Sponsorship
We thank the Wenner-Gren and the Blavatnik foundations for (non-parallel) stipends granted to S.R. The data collection was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme
(Grant agreement No. 742808) and by the Crafoordska Stiftelsen (2018-2259 & 2020-0976). We are grateful to the Kalahari Research Trust and the Kalahari Meerkat Project for access to facilities in the Kuruman River Reserve, and to Prof Marta Manser
for her contribution to the management of the reserve. We would like to thank the Northern Cape Department of Environment and Nature Conservation for permission to conduct the data collection.