Abstract
BackgroundPopulation 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.ResultsWe 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.ConclusionsWhile 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.
Highlights
Population parameters such as reproductive success are critical for sustainably managing ungulate populations, obtaining these data is often difficult, expensive, and invasive
Our goal was to assess the feasibility of using low-fix rate Global Positioning System (GPS) telemetry to identify parturition, providing the opportunity to obtain an estimate of initial reproductive success without using Vaginal implant transmitter (VIT) or visual observation of a female with a calf
Two cows died before giving birth, three expelled their VITs early and no fetus was recovered, two had VITs malfunction, two were determined to be not pregnant via pregnancy-specific protein B (PSPB) assay, one had its collar fail before giving birth, and two cows gave birth outside of the study period (15 May–15 Jul) in 2021
Summary
Population parameters such as reproductive success are critical for sustainably managing ungulate populations, obtaining these data is often difficult, expensive, and invasive. Ungulate population management heavily relies on obtaining reproductive parameters and neonatal survival estimates [13,14,15,16] which may be difficult. Established field methods to obtain these data included opportunistic sampling of reproductive organs from harvested females [17], pregnancy determination via palpation [18, 19] or serological analysis [20,21,22], characterization of juvenile/adult ratios from visual observation [21, 23,24,25], and radio-marking and monitoring of juveniles [26,27,28]. Failure to incorporate an estimate of fetal survival into population models may lead to overestimation of the proportion of sexually mature females that produce and recruit offspring in a given biological year
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