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

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datacite.RelatedIdentifierhttps://doi.org/10.1186/s40462-019-0163-7
datacite.RelatedIdentifier.relatedIdentifierTypeDOI
datacite.RelatedIdentifier.relationTypeIsSupplementTo
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.date.accessioned2019-06-26T16:54:25Z
dc.date.available2019-06-26T16:54:25Z
dc.date.issued2019-06-26
dc.date.submitted2019
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.identifier.doidoi:10.5441/001/1.kk3bg2f4
dc.identifier.urihttps://datarepository.movebank.org/handle/10255/move.891
dc.language.isoeng
dc.relationMovement Ecology
dc.relation.haspartdoi:10.5441/001/1.kk3bg2f4/1
dc.relation.haspartdoi:10.5441/001/1.kk3bg2f4/2
dc.relation.isreferencedbydoi:10.1186/s40462-019-0163-7
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectMyotis vivesi
dc.subjectanimal foraging
dc.subjectanimal movement
dc.subjectanimal tracking
dc.subjectbio-logging
dc.subjectGPS logger
dc.subjectMexican fish-eating bat
dc.subjectMyotis vivesi
dc.subjectpath segmentation
dc.subjectultrasonic audio
dc.titleData from: Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat
dc.typeArticle
dspace.entity.typeData package
dwc.ScientificNameMyotis vivesi
mdr.animal.count11
mdr.citation.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}
}
mdr.citation.CSE
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
mdr.citation.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  -
mdr.journal.titleMovement Ecology
mdr.location.count14328
mdr.study.id797650867
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sensor.nameGPS
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