Mexican fish-eating bat in Isla Partida Norte, Mexico

dc.contributor.authorHurme, Edward
dc.contributor.authorGurarie, Eliezer
dc.contributor.authorGreif, Stefan
dc.contributor.authorHerrera M., L. Gerardo
dc.contributor.authorFlores-Martínez, José Juan
dc.contributor.authorWilkinson, Gerald S.
dc.contributor.authorYovel, Yossi
dc.description.abstractBackground: 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.
dc.subjectanimal behavior
dc.subjectanimal foraging
dc.subjectanimal movement
dc.subjectanimal tracking
dc.subjectGPS logger
dc.subjectMexican fish-eating bat
dc.subjectMyotis vivesi
dc.subjectpath segmentation
dc.subjectultrasonic audio
dc.titleMexican fish-eating bat in Isla Partida Norte, Mexico
dspace.entity.typeData package
dwc.ScientificNameMyotis vivesi
  title = {Mexican fish-eating bat in Isla Partida Norte, Mexico},
  author = {Hurme, E and Gurarie, E and Greif, S and Herrera, M., LG and Flores-Martínez, JJ and Wilkinson, GS and Yovel, Y},
  URL = {},
  doi = {doi:10.5441/001/1.kk3bg2f4/1},
  publisher = {Movebank data repository}
Hurme E, Gurarie E, Greif S, Herrera M. LG, Flores-Martínez JJ, Wilkinson GS, Yovel Y. Mexican fish-eating bat in Isla Partida Norte, Mexico. Movebank Data Repository.
ID  - doi:10.5441/001/1.kk3bg2f4/1
T1  - Mexican fish-eating bat in Isla Partida Norte, Mexico
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
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  -
DO  - doi:10.5441/001/1.kk3bg2f4/1
ER  -