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Background <p>Effective conservation requires understanding the behavior of the targeted species. However, some species can be difficult to observe in the wild, which is why GPS collars and other telemetry devices can be used to “observe” these animals remotely. Combined with classification models, data collected by accelerometers on a collar can be used to determine an animal’s behaviors. Previous ungulate behavioral classification studies have mostly trained their models using data from captive animals, which may not be representative of the behaviors displayed by wild individuals. To fill this gap, we trained classification models, using a supervised learning approach with data collected from wild red deer (<i>Cervus elaphus</i>) in the Swiss National Park. While the accelerometer data collected on multiple axes served as input variables, the simultaneously observed behavior was used as the output variable. Further, we used a variety of machine learning algorithms, as well as combinations and transformations of the accelerometer data to identify those that generated the most accurate classification models. To determine which models performed most accurately, we derived a new metric which considered the imbalance between different behaviors.</p> Results <p>We found significant differences in the models’ performances depending on which algorithm, transformation method and combination of input variables was used. Discriminant analysis generated the most accurate classification models when trained with minmax-normalized acceleration data collected on multiple axes, as well as their ratio. This model was able to accurately differentiate between the behaviors lying, feeding, standing, walking, and running and can be used in future studies analyzing the behavior of wild red deer living in Alpine environments.</p> Conclusion <p>We demonstrate the possibility of using acceleration data collected from wild red deer to train behavioral classification models. At the same time, we propose a new type of metric to compare the accuracy of classification models trained with imbalanced datasets. We share our most accurate model in the hope that managers and researchers can use it to classify the behavior of wild red deer in Alpine environments.</p>

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Comparing the accuracy of machine learning methods for classifying wild red deer behavior based on accelerometer data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月8日

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  • Benjamin Bar-Gera,
  • Pia Anderwald,
  • Alina L. Evans,
  • Thomas Rempfler,
  • Claudio Signer

摘要

Background

Effective conservation requires understanding the behavior of the targeted species. However, some species can be difficult to observe in the wild, which is why GPS collars and other telemetry devices can be used to “observe” these animals remotely. Combined with classification models, data collected by accelerometers on a collar can be used to determine an animal’s behaviors. Previous ungulate behavioral classification studies have mostly trained their models using data from captive animals, which may not be representative of the behaviors displayed by wild individuals. To fill this gap, we trained classification models, using a supervised learning approach with data collected from wild red deer (Cervus elaphus) in the Swiss National Park. While the accelerometer data collected on multiple axes served as input variables, the simultaneously observed behavior was used as the output variable. Further, we used a variety of machine learning algorithms, as well as combinations and transformations of the accelerometer data to identify those that generated the most accurate classification models. To determine which models performed most accurately, we derived a new metric which considered the imbalance between different behaviors.

Results

We found significant differences in the models’ performances depending on which algorithm, transformation method and combination of input variables was used. Discriminant analysis generated the most accurate classification models when trained with minmax-normalized acceleration data collected on multiple axes, as well as their ratio. This model was able to accurately differentiate between the behaviors lying, feeding, standing, walking, and running and can be used in future studies analyzing the behavior of wild red deer living in Alpine environments.

Conclusion

We demonstrate the possibility of using acceleration data collected from wild red deer to train behavioral classification models. At the same time, we propose a new type of metric to compare the accuracy of classification models trained with imbalanced datasets. We share our most accurate model in the hope that managers and researchers can use it to classify the behavior of wild red deer in Alpine environments.