title: Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds.
abstract: Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.
authors.affiliation: Department of Biological Sciences, Boise State University, Boise, Idaho, United States of America.
authors.forename: Maitreyi
authors.identifier: http://orcid.org/0000-0002-7174-1813
authors.initials: M
authors.lastname: Sur
authors.affiliation: Sky Patrol Abatement, Simi Valley, California, United States of America.
authors.forename: Tony
authors.initials: T
authors.lastname: Suffredini
authors.affiliation: U.S. Geological Survey Henderson, Henderson, Nevada, United States of America.
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Wessells
authors.affiliation: Bloom Biological, Santa Ana, California, United States of America.
authors.forename: Peter H
authors.initials: PH
authors.lastname: Bloom
authors.affiliation: Cellular Tracking Technologies, Rio Grande, New Jersey, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Lanzone
authors.affiliation: Cellular Tracking Technologies, Rio Grande, New Jersey, United States of America.
authors.forename: Sheldon
authors.initials: S
authors.lastname: Blackshire
authors.affiliation: Department of Computer Science, Boise State University, Boise, Idaho, United States of America.
authors.forename: Srisarguru
authors.initials: S
authors.lastname: Sridhar
authors.affiliation: U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, Idaho, United States of America.
authors.forename: Todd
authors.initials: T
authors.lastname: Katzner
mesh_terms: D061725:Accelerometry; D000465:Algorithms; D000818:Animals; D001522:Behavior, Animal; D020307:Eagles; D005426:Flight, Animal; D000069550:Machine Learning; D008954:Models, Biological; D015233:Models, Statistical; D014741:Video Recording