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Research article - Peer-reviewed, 2019

Using fine-scale movement patterns to infer ungulate parturition

Nicholson, Kerry L.; Warren, Matthew J.; Rostan, Camille; Månsson, Johan; Paragi, Thomas F.; Sand, Håkan


Quantifying a fundamental life history event like parturition for any species is important both for wildlife management and research purposes. Surveys to estimate reproductive success for large mammals are typically done by visual observations on the ground or from the air and are time consuming, expensive and labor intensive particularly when conducted in remote locations. An alternative to visual verification is remote monitoring of animal movement and locations which can identify and link movement behavior to different types of life history events, such as parturition. We used GPS locations of a large ungulate (moose) to identify a specific behavioral change in the movement pattern that would indicate a calving event. From these data we applied three methods, one of which is a novel technique, to search for localized movement patterns that characterize a calving event for female moose in Sweden (n = 60 moose-years, ground observations) and Alaska (n = 49 moose-years, aerial observations). The three methods include a computerized visual method using Tracking Analyst (R) (TA), a rolling window minimum convex polygon (rMCP), and behavioral change point analysis (BCPA), all of which provided nearly identical results. BCPA confirmed lack of a parturition date for 100% of the animals that were never observed with a calf, whereas the rMCP method yielded 1 false positive. For Sweden, parturition dates inferred using rMCP agreed exactly or +/- 1 day with the dates inferred using BCPA for 98% moose-years whereas TA vs BCPA and rMCP agreed 98% and 100% respectively; for Alaska parturition dates estimated from rMCP and BCPA agreed equally at 94%. In this study we showed that evaluation of wildlife movement patterns from remote monitoring can lead to increased precision and understanding of parturition with minimal bias from neonatal mortality, in addition to understanding spatiotemporal distribution, resource selection, and other behaviors.


Alces; Behavioral change point; Calving; Minimum convex polygon; Moose; Movement; Reproduction

Published in

Ecological Indicators
2019, Volume: 101, pages: 22-30

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