4.6 Article

Fine-scale roadkill risk models: understanding the intersection of wildlife and roads

期刊

BIODIVERSITY AND CONSERVATION
卷 30, 期 1, 页码 139-164

出版社

SPRINGER
DOI: 10.1007/s10531-020-02083-6

关键词

Roadkill hotspots; Species distribution model; MaxEnt; Road mortality; Wildlife-vehicle collision; Road ecology

资金

  1. National Science Foundation-East Asia and Pacific Summer Institute [1514955]
  2. U.S. Fulbright Student Program-Taiwan
  3. Texas A&M University-Ecology & Evolutionary Biology
  4. Friends of Sunset Zoo Conservation Grant
  5. International Herpetological Society research grant
  6. East Texas Herpetological Society-James R. Dixon Grant
  7. Taiwan Agricultural Council
  8. Environmental Protection Administration
  9. Australian Government Endeavour Research Fellowship
  10. Texas A&M University-Office of Graduate and Professional Studies Dissertation Fellowship
  11. Office Of The Director
  12. Office Of Internatl Science &Engineering [1514955] Funding Source: National Science Foundation

向作者/读者索取更多资源

Robust and spatially explicit approaches are necessary to identify environmental correlates of roadkill and set conservation priorities. By utilizing species distribution models and environmental covariates, this study predicted wildlife road mortality across a nationwide road network, highlighting the importance of systematic data collection and the production of individual models for different groups of interest. The research provides valuable insights into predicted high- and low-risk areas for various ecological guilds and endangered species, offering interactive roadkill risk maps as conservation tools for managers and practitioners.
Robust, spatially explicit approaches accounting for ecological drivers are needed to identify environmental correlates of roadkill and set conservation priorities. We predicted wildlife road mortality across a nationwide road network using species distribution models with environmental covariates. We applied MaxEnt to a citizen science database of > 60,000 roadkill records to predict roadkill probability. Twenty-eight environmental covariates at 50 m spatial resolution were included, such as road type and land cover composition. We focused on ecological guilds and endangered species: common venomous snakes (CVS), semiaquatic and aquatic snakes (SAS), turtles, and the Maki's keelback snake (Hebius miyajimae, HM). All predictive models performed well with AUCs > 0.7. Projected roadkill risks for CVS, SAS, turtles, and HM were highest in montane regions, coastal lowlands, the southwestern coast, and parts of central Taiwan, respectively. Roadkill projection models performed well across ecological levels and scales. Road-type strongly influenced roadkill risk. As predictions and variable importance differed across guild and species models, individual models need to be produced for each group of interest. Additionally, the project emphasizes the importance of systematic collection of roadkill data, which contributes to both informing conservation action and engaging the public in wildlife education. We discovered novel findings on predicted high- and low-risk areas for groups with conservation need and produced interactive roadkill risk maps as a conservation tool for managers and practitioners. Importantly, this methodology is not limited to Taiwan; it can be applied anywhere with sufficient roadkill and environmental data and is scalable to address the ecological question of interest.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据