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<p>Linear infrastructure adversely affects wildlife by fragmenting habitats and increasing vehicle-animal collisions. This conflict between economic development and ecological processes can be reduced through appropriate mitigation measures. Planning such measures based on animal usage and crossing patterns can optimise investments and effectiveness, but this remains challenging in Open Natural Ecosystems (ONE) where animals do not follow fixed trails. Consequently, data-driven approaches for siting mitigation structures are scarce, especially in ONEs. Here, we describe a pipeline for collecting and analysing data to prioritise road segments for mitigation structures based on neighbouring habitat, connectivity, and vehicle-animal collision patterns. We demonstrate this approach on a 63&#xa0;km stretch of National Highway 11, crossing through Desert National Park, Rajasthan, India, as a case study, with Chinkara <i>Gazella bennettii</i> and Desert fox <i>Vulpes vulpes pusilla</i> as the focal species. We collected data on species occurrence and vehicle-animal collision through systematic field surveys. We identified core habitats of species through habitat use models, and modelled connectivity between them using graph theory within a 10&#xa0;km impact zone of the road. We calculated mechanistic and empirical collision probabilities using traversability and logistic regression models. Overlaying these indices of connectivity, collision and species’ abundances, we derived composite priority scores for road segments, thereafter selecting segments with top 10 percentile scores. Accounting for animal home ranges, one segment every 5&#xa0;km for chinkara and every 2&#xa0;km for desert fox were identified for wildlife crossing structures. Habitat use models based on 478 chinkara and 81 desert fox occurrences showed high classification accuracy. Collisions were influenced by vehicle frequency, roadside visibility obstruction, habitat connectivity, and animal encounter rates. Total 11 and 19 road segments were prioritised for chinkara and desert fox, respectively. Thus, we demonstrate a data-driven strategy for managing infrastructure-wildlife conflicts and addressing maximal covering location problem that can inform strategies to safeguard wildlife movements and landscape connectivity in threatened ONEs.</p>

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Optimal placement of wildlife crossing structures along roads in an open natural ecosystem

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Ashish Kumar Jangid,
  • Devendra Dutta Pandey,
  • Ashish Vyas,
  • Malvika Onial,
  • Sutirtha Dutta

摘要

Linear infrastructure adversely affects wildlife by fragmenting habitats and increasing vehicle-animal collisions. This conflict between economic development and ecological processes can be reduced through appropriate mitigation measures. Planning such measures based on animal usage and crossing patterns can optimise investments and effectiveness, but this remains challenging in Open Natural Ecosystems (ONE) where animals do not follow fixed trails. Consequently, data-driven approaches for siting mitigation structures are scarce, especially in ONEs. Here, we describe a pipeline for collecting and analysing data to prioritise road segments for mitigation structures based on neighbouring habitat, connectivity, and vehicle-animal collision patterns. We demonstrate this approach on a 63 km stretch of National Highway 11, crossing through Desert National Park, Rajasthan, India, as a case study, with Chinkara Gazella bennettii and Desert fox Vulpes vulpes pusilla as the focal species. We collected data on species occurrence and vehicle-animal collision through systematic field surveys. We identified core habitats of species through habitat use models, and modelled connectivity between them using graph theory within a 10 km impact zone of the road. We calculated mechanistic and empirical collision probabilities using traversability and logistic regression models. Overlaying these indices of connectivity, collision and species’ abundances, we derived composite priority scores for road segments, thereafter selecting segments with top 10 percentile scores. Accounting for animal home ranges, one segment every 5 km for chinkara and every 2 km for desert fox were identified for wildlife crossing structures. Habitat use models based on 478 chinkara and 81 desert fox occurrences showed high classification accuracy. Collisions were influenced by vehicle frequency, roadside visibility obstruction, habitat connectivity, and animal encounter rates. Total 11 and 19 road segments were prioritised for chinkara and desert fox, respectively. Thus, we demonstrate a data-driven strategy for managing infrastructure-wildlife conflicts and addressing maximal covering location problem that can inform strategies to safeguard wildlife movements and landscape connectivity in threatened ONEs.