Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat.

ABSTRACT:

Background

Multiple methods have been developed to infer behavioral states from animal movement data, but rarely has their accuracy been assessed from independent evidence, especially for location data sampled with high temporal resolution. Here we evaluate the performance of behavioral segmentation methods using acoustic recordings that monitor prey capture attempts.

Methods

We recorded GPS locations and ultrasonic audio during the foraging trips of 11 Mexican fish-eating bats, Myotis vivesi, using miniature bio-loggers. We then applied five different segmentation algorithms (k-means clustering, expectation-maximization and binary clustering, first-passage time, hidden Markov models, and correlated velocity change point analysis) to infer two behavioral states, forag

SUBMITTER: Hurme E 

PROVIDER: S-EPMC6567457 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature