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,W David Walter

doi:10.1371/journal.pone.0242328

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.

Highlights

A thorough understanding of animal space use across a landscape is necessary to effectively implement fundamental principles of conservation biology and wildlife management
Extra-home range movements that were not available for detection by both methods were excluded from further analysis (n = extra-home range movements (EHRMs) detected by the Fixed-Period method that occurred during the establishment of the first 60-day pre-EHRM home range (PreHR) of the Moving-Window method and were not available for detection by the latter, and n = EHRMs detected by the Moving-Window method that occurred when less than 60 days of data were available in a given season, and were not available for detection by the Fixed-Period method)
The Fixed-Period approach for identifying EHRMs in animal movement studies typically involves constructing a temporally-fixed, seasonal or annual home range for an individual and examining global positioning system (GPS) locations recorded during that fixed period for any long-distance movements extending outside of that home range (e.g., [61, 68, 77,78,79])

Summary

Introduction

A thorough understanding of animal space use across a landscape is necessary to effectively implement fundamental principles of conservation biology and wildlife management Space use metrics such as home range size, home range fidelity, activity rate, dispersal distance, and path tortuosity can be used to draw inferences concerning resource selection and habitat use [1], predator-prey interactions [2, 3], disease epidemiology [4], bio-climatic relationships [5], and the effects of anthropogenic factors on wildlife populations [6, 7]. Advancements in methodology have occurred through the emergence of home range estimators that incorporate time, variance in Brownian motion, or other animal movement parameters into their models and result in more representative utilization distributions that reflect associated movements across a landscape, rather than independent relocation points (e.g., Brownian and dynamic Brownian bridge movement models, movement kernel density estimators [22,23,24,25])

Methods

Results

Conclusion