Predicting moose behaviors from tri-axial accelerometer data using a supervised classification algorithm ...

• Main Authors: Kirchner, Theresa M., Devineau, Olivier, Chimienti, Marianna, Thompson, Daniel P., Crouse, John, Evans, Alina L., Zimmermann, Barbara, Eriksen, Ane
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: figshare 2024
• Subjects: Ecology; FOS: Biological sciences; Alces alces
• Online Access: https://dx.doi.org/10.6084/m9.figshare.c.6815591; https://springernature.figshare.com/collections/Predicting_moose_behaviors_from_tri-axial_accelerometer_data_using_a_supervised_classification_algorithm/6815591

Abstract Background Monitoring the behavior of wild animals in situ can improve our understanding of how their behavior is related to their habitat and affected by disturbances and changes in their environment. Moose (Alces alces) are keystone species in their boreal habitats, where they are facing environmental changes and disturbances from human activities. How these potential stressors can impact individuals and populations is unclear, in part due to our limited knowledge of the physiology and behavior of moose and how individuals can compensate for stress and disturbances they experience. We collected data from collar-mounted fine-scale tri-axial accelerometers deployed on captive moose in combination with detailed behavioral observations to train a random forest supervised classification algorithm to classify moose accelerometer data into discrete behaviors. To investigate the generalizability of our model to collared new individuals, we quantified the variation in classification performance among ...