Background: 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. Results: 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. Conclusions: 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.
Bibliographical noteFunding 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.
We would like to thank our reviewers for providing feedback that improved this manuscript.?We also thank the Department of Forestry and Natural Resources at the University of Kentucky (UK) for logistical support and UK?s Robinson Center for Appalachian Resource Sustainability for providing field housing. We also want to acknowledge the staff and volunteers who assisted in the field with elk capture and data collection, especially: C. Casey, J. Fusaro, K. Sams, T. Curry, D. Brewster, Z. Hahn, J. Lane, E. Evers, K. Davis, S. Maywald, A. Riggs, J. Wissmann, P. Clements, K. Bosch, D. Yancy, and M. Peterson. Finally, we thank the landowners and managers for allowing us to capture elk on their properties across the KERZ.
© 2022, The Author(s).
- GPS telemetry
- Vital rates
ASJC Scopus subject areas
- Signal Processing
- Animal Science and Zoology
- Computer Networks and Communications