Life Sciences Commons^™

A Probabilistic Framework For Behavioral Identification From Animal-Borne Accelerometers, Jane Dentinger, Luca Börger, Mark D. Holton, Ruholla Jafari-Marandi, Durham A. Norman, Brian K. Smith, Seth F. Oppenheimer, Bronson K. Strickland, Rory P. Wilson, Garrett M. Street

College of Forest Resources Publications and Scholarship

Many studies of animal distributions use habitat and climactic variables to explain patterns of observed space use. However, without behavioral information, we can only speculate as to why and how these characteristics are important to species persistence.

Animal-borne accelerometer and magnetometer data loggers can be used to detect behaviors and when coupled with telemetry improve our understanding of animal space use and habitat requirements. However, these loggers collect tremendous quantities of data requiring automated machine learning techniques to identify patterns in the data. Supervised machine learning requires a set of training signals with known behaviors to train the model to …