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

Hurme E, Gurarie E, Greif S, Herrera M. LG, Flores-Martínez JJ, Wilkinson GS, Yovel Y. 2019. Data from: Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat. Movebank Data Repository. https://doi.org/10.5441/001/1.kk3bg2f4

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, foraging and commuting, from the GPS data. To evaluate the inference, we independently identified characteristic patterns of biosonar calls (“feeding buzzes”) that occur during foraging in the audio recordings. We then compared segmentation methods on how well they correctly identified the two behaviors and if their estimates of foraging movement parameters matched those for locations with buzzes. Results: While the five methods differed in the median percentage of buzzes occurring during predicted foraging events, or true positive rate (44–75%), a two-state hidden Markov model had the highest median balanced accuracy (67%). Hidden Markov models and first-passage time predicted foraging flight speeds and turn angles similar to those measured at locations with feeding buzzes and did not differ in the number or duration of predicted foraging events. Conclusion: The hidden Markov model method performed best at identifying fish-eating bat foraging segments; however, first-passage time was not significantly different and gave similar parameter estimates. This is the first attempt to evaluate segmentation methodologies in echolocating bats and provides an evaluation framework that can be used on other species.

Keywords

Myotis vivesi,animal foraging,animal movement,animal tracking,bio-logging,GPS logger,Mexican fish-eating bat,Myotis vivesi,path segmentation,ultrasonic audio

Taxa

Taxon

Myotis vivesi

Fish-eating Bat, Fish-eating Myotis

Sensors

Sensor

GPS

DOIs of related Publications

https://doi.org/10.1186/s40462-019-0163-7

URI

https://datarepository.movebank.org/handle/10255/move.891

BibTex

@misc{001/1_kk3bg2f4,
  title = {Data from: Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat},
  author = {Hurme, E and Gurarie, E and Greif, S and Herrera, M., LG and Flores-Martínez, JJ and Wilkinson, GS and Yovel, Y},
  year = {2019},
  URL = {http://dx.doi.org/10.5441/001/1.kk3bg2f4},
  doi = {doi:10.5441/001/1.kk3bg2f4},
  publisher = {Movebank data repository}
}

RIS

TY  - DATA
ID  - doi:10.5441/001/1.kk3bg2f4
T1  - Data from: Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat
AU  - Hurme, Edward
AU  - Gurarie, Eliezer
AU  - Greif, Stefan
AU  - Herrera M., L. Gerardo
AU  - Flores-Martínez, José Juan
AU  - Wilkinson, Gerald S.
AU  - Yovel, Yossi
Y1  - 2019/06/26
KW  - Myotis vivesi
KW  - animal behavior
KW  - animal foraging
KW  - animal movement
KW  - animal tracking
KW  - bio-logging
KW  - GPS logger
KW  - Mexican fish-eating bat
KW  - Myotis vivesi
KW  - path segmentation
KW  - ultrasonic audio
KW  - Myotis vivesi
PB  - Movebank data repository
UR  - http://dx.doi.org/10.5441/001/1.kk3bg2f4
DO  - doi:10.5441/001/1.kk3bg2f4
ER  -

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