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Description

Abstract

Abundant deer populations often cause conflicts in suburban communities, but traditional population reduction methods, such as controlled hunting, can be challenging to implement in these contexts. Fertility control, specifically through ovariectomy, can limit reproduction and reduce populations in certain settings, yet its effects on movement behavior remain poorly understood. Concerns persist that hormonal changes affecting movement could lead to increased physiological demands and influence deer-vehicle collision (DVC) risks. We evaluated the effects of ovariectomy-induced anestrus on the 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 untreated (control) female deer in South Euclid, Ohio, USA, and quantified seasonal movement patterns using 7-day home-range size, daily diffusion, and daily excursivity. Both groups had overall similar home-range size, diffusion, and excursivity. Untreated females exhibited seasonal increases in home-range size, diffusion, and excursivity leading up to and during the breeding season (late July–November). During and after parturition (late May), untreated females reduced home-range size and overall mobility. Treated females did not exhibit strong seasonal behaviors; however, diffusion differed significantly between groups. These findings suggest that ovariectomy removes hormonal triggers that typically drive seasonal movement changes. We found no clear evidence that ovariectomy increased excursive behavior, nor that it led to higher DVC risk, although our sample size was limited.  LPWAN-enabled IoT tracking proved to be a low-cost, low-power, lightweight method for wildlife monitoring, compared to Global Positioning System biologgers, but data frequency was irregular at times. Our findings suggest ovariectomy is a safe management tool for abundant deer populations. However, we recommend further research to build on our findings and highlight both the potential and limitations of IoT-based biologging for wildlife research.