Where will it cross next? Optimal management of road collision risk for otters in Italy
Where will it cross next? Optimal management of road collision risk for otters in Italy
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DOI：
10.1016/j.jenvman.2019.109609
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发表时间：
2019-12-01
影响因子：
8.7
通讯作者：
Loy, Anna
中科院分区：
文献类型：
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作者：
Fabrizio, Mauro;Di Febbraro, Mirko;Loy, Anna
车辆碰撞是基础设施与野生动物之间的主要冲突，对人类和动物都造成了损害。至于后者，道路死亡是一个日益严重的现象，也是许多脊椎动物死亡的最大单一原因。在关注濒危物种时，欧亚水獭（Lutra Lutra）是最容易受到道路死亡的物种之一，这是欧洲记录的主要死亡原因。我们建议对意大利欧亚水獭的道路死亡风险进行大规模的空间明确评估，作为识别高碰撞风险路段的工具，从而优化缓解措施的位置。建模方法是为意大利中南部生产的，那里是意大利唯一幸存的水獭种群。我们使用了最大熵方法，包括通过公民科学计划在2004年至2016年期间记录的56起道路碰撞事件，以及在1公里网格单元上测量的7个环境预测因子。选择了四个预测因子来描述道路特征，即公路密度，以及州、区域和地方道路的密度。其余三个变量是指碰撞地点周围水獭栖息地的质量，即海拔、淡水水体密度和根据土地覆盖类别计算的景观异质性度量。该模型获得了较好的预测精度（AUC > 0.8； Boyce指数> 0.8）。碰撞概率主要受高程、国道密度和淡水水体密度的影响。具体而言，位于河流和湿地附近的低海拔、中等密度的国道碰撞风险较高。此外，模型预测表明，在研究区域10%的道路网络上实施缓解措施可能会减少研究期间记录的约50%的水獭伤亡。
Collisions with vehicles represent the main conflict between infrastructures and wildlife, causing damages to both humans and animals. As to the latter, road mortality is a growing phenomenon and the largest single cause of death for many vertebrates. When focusing on endangered species, the Eurasian otter (Lutra lutra) is among the most vulnerable to road-kills, which represent the predominant cause of deaths recorded in Europe. We propose a large scale spatially-explicit assessment of road-kill risk for the Eurasian otter in Italy as a tool to identify road stretches at high collision risk, thus optimizing the location of mitigation measures. The modelling approach was produced for South Central Italy, hosting the only remnant viable population of otters in Italy. We used a maximum entropy approach including 56 road collision events recorded between 2004 and 2016 through a citizen science initiative, along with seven environmental predictors measured on 1 km grid cells. Four predictors were selected to describe roads characteristics, i.e. density of highways, and of state, regional and local roads. The remaining three variables referred to the quality of otter habitat in the surrounding of the collision sites, i.e. elevation, density of freshwater bodies, and a measure of landscape heterogeneity calculated on land-cover categories. The model achieved a good predictive accuracy (AUC > 0.8; Boyce index > 0.8). The collision probability was mostly affected by elevation, density of state roads, and density of freshwater bodies. Specifically, collision risk was higher in areas at low elevation and medium density of state roads located near rivers and wetlands. In addition, model predictions evidenced that implementing mitigation measures along 10% of road network in the study area could have potentially hampered ca. 50% of otter casualties recorded during the study period.