Population parameters such as reproductive success are critical for sustainably managing ungulate populations, however obtaining these data is often difficult, expensive, and invasive. Movement-based methods that leverage Global Positioning System (GPS) relocation data to identify parturition offer an alternative to more invasive techniques such as vaginal implant transmitters, but thus far have only been applied to relocation data with a relatively fine (one fix every  < 8 h) temporal resolution. We employed a machine learning method to classify parturition/calf survival in cow elk in southeastern Kentucky, USA, using 13-h GPS relocation data and three simple movement metrics, training a random forest on cows that successfully reared their calf to a week old.


We developed a decision rule based upon a predicted probability threshold across individual cow time series, accurately classifying 89.5% (51/57) of cows with a known reproductive status. When used to infer status of cows whose reproductive outcome was unknown, we classified 48.6% (21/38) as successful, compared to 85.1% (40/47) of known-status cows.


While our approach was limited primarily by fix acquisition success, we demonstrated that coarse collar fix rates did not limit inference if appropriate movement metrics are chosen. Movement-based methods for determining parturition in ungulates may allow wildlife managers to extract more vital rate information from GPS collars even if technology and related data quality are constrained by cost.

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Notes/Citation Information

Published in Animal Biotelemetry, v. 10, article no. 5.

© The Author(s) 2022

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (https://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Funding Information

Funding for elk capture and telemetry was primarily funded by Pittman-Robertson federal aid administered by KDFWR and supplemented by the U.S. Department of Agriculture McIntire-Stennis program (Project #1021936). NDH was supported by a teaching assistantship through the Department of Forestry and Natural Resources at the University of Kentucky during part of this study.

Related Content

The GPS collar-generated dataset analyzed in this study is not publicly available due to potential ethical implications of access to raw relocation data, but are available from the corresponding author on reasonable request. Other datasets used in this study are available in the Zenodo repository, http://doi.org/10.5281/zenodo.5608744, and all R scripts are archived on GitHub, https://github.com/nhooven/elk-repro-success.

40317_2022_276_MOESM1_ESM.docx (748 kB)
Additional file 1: Tables and figures

40317_2022_276_MOESM2_ESM.pdf (290 kB)
Additional file 2: User-friendly R workflow for using low-fix rate GPS telemetry to determine ungulate reproductive success. RMarkdown document detailing the basic workflow preparing data, generating movement metrics, and producing RF predictions.