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Caution is warranted when using animal space-use and movement to infer behavioral states

BACKGROUND: Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, be...

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Autores principales: Buderman, Frances E., Gingery, Tess M., Diefenbach, Duane R., Gigliotti, Laura C., Begley-Miller, Danielle, McDill, Marc M., Wallingford, Bret D., Rosenberry, Christopher S., Drohan, Patrick J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196457/
https://www.ncbi.nlm.nih.gov/pubmed/34116712
http://dx.doi.org/10.1186/s40462-021-00264-8
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author Buderman, Frances E.
Gingery, Tess M.
Diefenbach, Duane R.
Gigliotti, Laura C.
Begley-Miller, Danielle
McDill, Marc M.
Wallingford, Bret D.
Rosenberry, Christopher S.
Drohan, Patrick J.
author_facet Buderman, Frances E.
Gingery, Tess M.
Diefenbach, Duane R.
Gigliotti, Laura C.
Begley-Miller, Danielle
McDill, Marc M.
Wallingford, Bret D.
Rosenberry, Christopher S.
Drohan, Patrick J.
author_sort Buderman, Frances E.
collection PubMed
description BACKGROUND: Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. METHODS: Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. RESULTS: For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. CONCLUSIONS: Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-021-00264-8.
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spelling pubmed-81964572021-06-15 Caution is warranted when using animal space-use and movement to infer behavioral states Buderman, Frances E. Gingery, Tess M. Diefenbach, Duane R. Gigliotti, Laura C. Begley-Miller, Danielle McDill, Marc M. Wallingford, Bret D. Rosenberry, Christopher S. Drohan, Patrick J. Mov Ecol Research BACKGROUND: Identifying the behavioral state for wild animals that can’t be directly observed is of growing interest to the ecological community. Advances in telemetry technology and statistical methodologies allow researchers to use space-use and movement metrics to infer the underlying, latent, behavioral state of an animal without direct observations. For example, researchers studying ungulate ecology have started using these methods to quantify behaviors related to mating strategies. However, little work has been done to determine if assumed behaviors inferred from movement and space-use patterns correspond to actual behaviors of individuals. METHODS: Using a dataset with male and female white-tailed deer location data, we evaluated the ability of these two methods to correctly identify male-female interaction events (MFIEs). We identified MFIEs using the proximity of their locations in space as indicators of when mating could have occurred. We then tested the ability of utilization distributions (UDs) and hidden Markov models (HMMs) rendered with single sex location data to identify these events. RESULTS: For white-tailed deer, male and female space-use and movement behavior did not vary consistently when with a potential mate. There was no evidence that a probability contour threshold based on UD volume applied to an individual’s UD could be used to identify MFIEs. Additionally, HMMs were unable to identify MFIEs, as single MFIEs were often split across multiple states and the primary state of each MFIE was not consistent across events. CONCLUSIONS: Caution is warranted when interpreting behavioral insights rendered from statistical models applied to location data, particularly when there is no form of validation data. For these models to detect latent behaviors, the individual needs to exhibit a consistently different type of space-use and movement when engaged in the behavior. Unvalidated assumptions about that relationship may lead to incorrect inference about mating strategies or other behaviors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40462-021-00264-8. BioMed Central 2021-06-11 /pmc/articles/PMC8196457/ /pubmed/34116712 http://dx.doi.org/10.1186/s40462-021-00264-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Buderman, Frances E.
Gingery, Tess M.
Diefenbach, Duane R.
Gigliotti, Laura C.
Begley-Miller, Danielle
McDill, Marc M.
Wallingford, Bret D.
Rosenberry, Christopher S.
Drohan, Patrick J.
Caution is warranted when using animal space-use and movement to infer behavioral states
title Caution is warranted when using animal space-use and movement to infer behavioral states
title_full Caution is warranted when using animal space-use and movement to infer behavioral states
title_fullStr Caution is warranted when using animal space-use and movement to infer behavioral states
title_full_unstemmed Caution is warranted when using animal space-use and movement to infer behavioral states
title_short Caution is warranted when using animal space-use and movement to infer behavioral states
title_sort caution is warranted when using animal space-use and movement to infer behavioral states
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8196457/
https://www.ncbi.nlm.nih.gov/pubmed/34116712
http://dx.doi.org/10.1186/s40462-021-00264-8
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