4.7 Article

Incorporating habitat use in models of fauna fatalities on roads

期刊

DIVERSITY AND DISTRIBUTIONS
卷 15, 期 2, 页码 222-231

出版社

WILEY
DOI: 10.1111/j.1472-4642.2008.00523.x

关键词

Common wombats; Getis-Ord clustering; habitat use; predictive modelling; road-kill; spatial analysis; Vombatus ursinus

资金

  1. ARC Linkage Project [LP0346925]
  2. International Fund for Animal Welfare
  3. New South Wales Department of Environment and Conservation
  4. New South Wales Wildlife Information and Rescue Service and Roe Koh and Associates
  5. MA Ingram Foundation
  6. Foundation for National Parks and Wildlife
  7. Sherman Foundation
  8. Australian Research Council [LP0346925] Funding Source: Australian Research Council

向作者/读者索取更多资源

To highlight the benefit of using habitat use to improve the accuracy of predictive road fatality models. The Snowy Mountains Highway in southern New South Wales, Australia. A binary logistic regression model was constructed using wombat fatality presences and randomly generated absences. Species-specific habitat variables were included as predictors in the model selection process as well as two spatially explicit measures of wombat habitat use. Generalized additive models (GAMs) were constructed for each possible combination of predictors in R. The final model was selected by comparing all models subsets for the eight predictors and employing the one standard error rule to select the best model set. The final predictive model had high discriminatory power and incorporated both measures of species habitat use, greatly exceeding the variation explained by a previously published model for the same species and road. Our findings highlight the importance of incorporating variables that describe habitat use by fauna for predictive modelling of animal-vehicle crashes. Reliance upon models that ignore landscape patterns are limited in their capacity to identify hotspots and inform managers of locations to engage in mitigation.

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

4.7
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