Evidence map›Paper›PMID 34116712›Full record

ArticleMovement ecology2021

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

Frances E Buderman, Tess M Gingery, Duane R Diefenbach, Laura C Gigliotti, Danielle Begley-Miller, Marc M McDill, Bret D Wallingford, Christopher S Rosenberry, Patrick J Drohan

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Review
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  5. Article
  6. Combining video telemetry and wearable MEG for naturalistic imaging.Imaging neuroscience (Cambridge, Mass.) · 2025
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Frances E BudermanDepartment of Ecosystem Science and Management, Pennsylvania State University, University Park, PA, 16802, USA. fbuderman@psu.edu.ORCID http://orcid.org/0000-0001-9778-9906
Tess M GingeryPennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, University Park, PA, 16802, USA.
Duane R DiefenbachU. S. Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, University Park, PA, 16802, USA.
Laura C GigliottiDepartment of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley, CA, 94720, USA.
Danielle Begley-MillerTeatown Lake Reservation, Ossining, NY, 10562, USA.
Marc M McDillDepartment of Ecosystem Science and Management, Pennsylvania State University, University Park, PA, 16802, USA.
Bret D WallingfordPennsylvania Game Commission, Harrisburg, PA, 17110, USA.
Christopher S RosenberryPennsylvania Game Commission, Harrisburg, PA, 17110, USA.
Patrick J DrohanDepartment of Ecosystem Science and Management, Pennsylvania State University, University Park, PA, 16802, USA.

Funding

National Institute of Food and Agriculture Hatch 1024904Pennsylvania Department of Conservation and Natural Resources 4400015622Pennsylvania Game Commission Cooperative Agreement No. 1434-03HQRU1548, Research Project No. 30
6 · The paper itself

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.

Indexed as

Behavioral stateBreedingBrownian bridgeHidden Markov modelsHome-rangeMate search strategyOdocoileus virginianusState identificationUtilization distributionWhite-tailed deer

Identifiers

PMID34116712
PMCPMC8196457

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.