Monitoring the effects of ovariectomy on seasonal movement behavior in suburban female white‐tailed deer using internet of things‐enabled devices
Bibliographic record
Abstract
Abundant deer populations often cause conflicts in suburban communities, yet traditional population reduction methods, such as controlled hunting, can be challenging to implement. Fertility control, specifically through ovariectomy, can limit reproduction and reduce populations in certain settings, but its effect on movement behavior remains poorly understood. Concerns persist that hormonal changes may affect movement behavior, potentially leading to increased physiological demands and influencing deer–vehicle collision (DVC) risks. We evaluated the effects of ovariectomy‐induced anestrus on seasonal movement behavior in female white‐tailed deer Odocoileus virginianus using internet of things (IoT) biologging devices connected via a low‐power wide‐area network (LPWAN). From 17 January 2023 to 1 June 2024, we collected telemetry data from ovariectomized (treated) and control (untreated) female deer in South Euclid, Ohio, USA, and quantified seasonal movement patterns using 7‐day home‐range size, daily diffusion, and daily excursivity estimated through continuous‐time movement models. Of the three metrics, only diffusion differed significantly between groups. Untreated females exhibited seasonal increases in all three metrics leading up to and during the breeding season (late July to November) and reduced movement around parturition (late May). Treated females did not exhibit strong seasonal behaviors. Although our sample size was small, our findings suggest that ovariectomy removes hormonal triggers without leading to otherwise unusual movement behavior or increasing excursive behavior. Biologging devices using LPWAN‐enabled IoT for data transmission proved to be a low‐cost, low‐power, lightweight alternative to traditional devices that transmit data using satellite or cellular technology, though data transmission was irregular. We conclude that ovariectomy is a safe management tool for controlling abundant deer populations; however, further research is needed to more precisely quantify the effects of ovariectomy and explore the potential and limitations of IoT‐based biologging for wildlife research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".