Evidence map›Paper›PMID 33253220›Full record

ArticlePloS one2020

A novel method for detecting extra-home range movements (EHRMs) by animals and recommendations for future EHRM studies.

Todd C Jacobsen, Kevyn H Wiskirchen, Stephen S Ditchkoff

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Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
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4citing papers in PubMed
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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Todd C JacobsenSchool of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama, United States of America.ORCID 0000-0001-5019-5836
Kevyn H WiskirchenSchool of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama, United States of America.
Stephen S DitchkoffSchool of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infrequent, long-distance animal movements outside of typical home range areas provide useful insights into resource acquisition, gene flow, and disease transmission within the fields of conservation and wildlife management, yet understanding of these movements is still limited across taxa. To detect these extra-home range movements (EHRMs) in spatial relocation datasets, most previous studies compare relocation points against fixed spatial and temporal bounds, typified by seasonal home ranges (referred to here as the "Fixed-Period" method). However, utilizing home ranges modelled over fixed time periods to detect EHRMs within those periods likely results in many EHRMs going undocumented, particularly when an animal's space use changes within that period of time. To address this, we propose a novel, "Moving-Window" method of detecting EHRMs through an iterative process, comparing each day's relocation data to the preceding period of space use only. We compared the number and characteristics of EHRM detections by both the Moving-Window and Fixed-Period methods using GPS relocations from 33 white-tailed deer (Odocoileus virginianus) in Alabama, USA. The Moving-Window method detected 1.5 times as many EHRMs as the Fixed-Period method and identified 120 unique movements that were undetected by the Fixed-Period method, including some movements that extended nearly 5 km outside of home range boundaries. Additionally, we utilized our EHRM dataset to highlight and evaluate potential sources of variation in EHRM summary statistics stemming from differences in definition criteria among previous EHRM literature. We found that this spectrum of criteria identified between 15.6% and 100.0% of the EHRMs within our dataset. We conclude that variability in terminology and definition criteria previously used for EHRM detection hinders useful comparisons between studies. The Moving-Window approach to EHRM detection introduced here, along with proposed methodology guidelines for future EHRM studies, should allow researchers to better investigate and understand these behaviors across a variety of taxa.

Indexed as

AnimalsAnimals, WildDeerGeographic Information SystemsHoming Behavior

Identifiers

PMID33253220
PMCPMC7703910

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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.