Abstract

Wind power is a rapidly growing renewable energy sector that reduces greenhouse gas emissions and provides sustainable energy. However, environmental destruction in wind power plant areas, particularly in wind farms within forests, is an emerging issue. This study aimed to analyze the impact of wind-farm roads on terrestrial animals in forested areas. A camera trap survey was conducted to investigate the impact of road management on wildlife behavior. We installed 52 cameras along roads connecting wind turbines for three months (1st October to 30th December 2021) on the Yeongyang-gun wind farm in South Korea and evaluated animal occupancy and detection probabilities using an occupancy model. Factors related to terrain and vegetation were used to estimate the occupancy probability (station use). The detection analysis included the presence or absence of guardrails, wind turbines, shrublands, and retaining walls. Additional variables included camera type, number of camera-operating days, and survey time. During the survey period, seven terrestrial mammals (roe deer, wild boar, water deer, raccoon dogs, badgers, leopards, cats, and martens) were captured using cameras. Based on camera trap records, roe deer was the most dominant species, followed by wild boars, raccoon dogs, and water deer, with fewer badgers and martens. The presence of forests in the road area was a significant factor for most species in terms of use probability, and camera type was significant for detection probability. Detecting animals along roads shows that roads are passageways for wildlife, affecting animal behavior during vehicle movement and can cause habitat disconnection. Our results demonstrate that wind farms are indirectly linked to wildlife distribution and welfare. Effective management policies for mitigating wildlife disruption can support sustainable ecosystems and biodiversity. The results of this study can serve as a reference for supporting wildlife conservation, terrestrial ecosystems, and environmental impact assessments.

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