Visintin Casey, van der Ree Rodney, McCarthy Michael A
Quantitative and Applied Ecology Group, School of BioSciences, University of Melbourne, Parkville, VIC, 3010, Australia.
School of BioSciences, University of Melbourne, Parkville, VIC, 3010, Australia; Ecology and Infrastructure International Pty Ltd, PO Box 6031, Wantirna, VIC, 3152, Australia.
J Environ Manage. 2017 Oct 1;201:397-406. doi: 10.1016/j.jenvman.2017.05.071. Epub 2017 Jul 10.
The occurrence and rate of wildlife-vehicle collisions are related to both anthropocentric and environmental variables, however, few studies compare collision risks for multiple species within a model framework that is adaptable and transferable. Our research compares collision risk for multiple species across a large geographic area using a conceptually simple risk framework. We used six species of native terrestrial mammal often involved with wildlife-vehicle collisions in south-east Australia. We related collisions reported to a wildlife organisation to the co-occurrence of each species and a threatening process (presence and movement of road vehicles). For each species, we constructed statistical models from wildlife atlas data to predict occurrence across geographic space. Traffic volume and speed on road segments (also modelled) characterised the magnitude of threatening processes. The species occurrence models made plausible spatial predictions. Each model reduced the unexplained variation in patterns and distributions of species between 29.5% (black wallaby) and 34.3% (koala). The collision models reduced the unexplained variation in collision event data between 7.4% (koala) and 19.4% (common ringtail possum) with predictor variables correlating similarly with collision risk across species. Road authorities and environmental managers need simple and flexible tools to inform projects. Our model framework is useful for directing mitigation efforts (e.g. on road effects or species presence), predicting risk across differing spatial and temporal scales and target species, inferring patterns of threat, and identifying areas warranting additional data collection, analysis, and study.
野生动物与车辆碰撞的发生情况和发生率与人为因素和环境变量都有关联，然而，很少有研究在一个具有适应性和可转移性的模型框架内比较多个物种的碰撞风险。我们的研究使用一个概念上简单的风险框架，比较了大地理区域内多个物种的碰撞风险。我们选取了澳大利亚东南部六种经常卷入野生动物与车辆碰撞事件的本土陆生哺乳动物。我们将向一个野生动物组织报告的碰撞事件与每个物种的共存情况以及一个威胁过程（道路车辆的存在和移动）联系起来。对于每个物种，我们根据野生动物地图集数据构建统计模型，以预测其在地理空间中的出现情况。路段的交通流量和速度（也进行了建模）表征了威胁过程的强度。物种出现模型做出了合理的空间预测。每个模型将物种分布模式和分布中无法解释的变异减少了29.5%（黑袋鼠）至34.3%（考拉）。碰撞模型将碰撞事件数据中无法解释的变异减少了7.4%（考拉）至19.4%（普通环尾袋貂），预测变量与各物种碰撞风险的相关性类似。道路管理部门和环境管理者需要简单灵活的工具来为项目提供信息。我们的模型框架有助于指导缓解措施（如针对道路影响或物种存在情况），预测不同空间和时间尺度以及目标物种的风险，推断威胁模式，并确定需要额外数据收集、分析和研究的区域。
