ArticleMovement ecology2021
Caution is warranted when using animal space-use and movement to infer behavioral states.
Article in Movement ecology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Statewide agent-based model for management of chronic wasting disease in white-tailed deer:MethodsX · 2026Article
- Intraspecific Contact Among White-Tailed Deer: A Literature Review and Chronic Wasting Disease Case Study.Ecology and evolution · 2026Review
- AccelerometerBehavior: R Package for Classifying Ungulate Behaviors Into Three States.Ecology and evolution · 2025Article
- An introduction to statistical models used to characterize species-habitat associations with animal movement data.Movement ecology · 2025Review
- Ecological drivers of movement for two sympatric marine predators in the California current large marine ecosystem.Movement ecology · 2025Article
- Combining video telemetry and wearable MEG for naturalistic imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- White-tailed deer detection rates increase when coyotes are present.Ecology and evolution · 2024Article
- Change-point models for identifying behavioral transitions in wild animals.Movement ecology · 2023Article
- Animal behaviour on the move: the use of auxiliary information and semi-supervision to improve behavioural inferences from Hidden Markov Models applied to GPS tracking datasets.Movement ecology · 2023Article
- Linking movement and dive data to prey distribution models: new insights in foraging behaviour and potential pitfalls of movement analyses.Movement ecology · 2023Article
- Joint use of location and acceleration data reveals influences on transitions among habitats in wintering birds.Scientific reports · 2023Article
- Behavioral "bycatch" from camera trap surveys yields insights on prey responses to human-mediated predation risk.Ecology and evolution · 2022Article
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9 authors.
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Abstract
backgroundIdentifying 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.
methodsUsing 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.
resultsFor 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.
conclusionsCaution 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.
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