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Identification methods and deterministic factors of owl roadkill hotspot locations in Mediterranean landscapes

机译：地中海景观中猫头鹰道路杀伤热点地区的识别方法和确定因素

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摘要

Road fatalities are among the major causes of mortality for Strigiformes species and may affect the population's survival. The use of mitigation strategies must be considered to overcome this problem. However, because mitigation along the total length of all roads is not financially feasible, the locations where Strigiformes roadkills are more frequent (i.e., road fatality hotspots) must be identified. In addition to hotspot identification, factors that influence the occurrence of such fatalities should be recognized to allow mitigation measures to be delineated. We used road fatality data collected from 311 km of southern Portugal roads over a 2-year period to compare the performance of five hotspot identification methods: binary logistic regression (BLR), ecological niche factor analysis (ENFA), Kernel density estimation, nearest neighbor hierarchical clustering (NNHC), and Malo's method. BLR and ENFA modelling were also used for recognizing roadkill deterministic factors. Our results suggest that Malo's method should be preferred for hotspot identification. The main factors driving owl roadkillings are those associated with good habitat conditions for species occurrence and specific conditions that promote hunting behavior near roads. Based on these factors, several mitigation measures are recommended.
机译：道路交通事故是造成剑形目物种死亡的主要原因之一，可能会影响人口的生存。必须考虑使用缓解策略来克服此问题。但是，由于在所有道路的全长上进行缓解措施在经济上都不可行，因此必须确定剑形目击杀行为更为频繁的位置（即道路致死性热点）。除了识别热点之外，还应认识到影响此类死亡事件发生的因素，以便确定缓解措施。我们使用了在两年时间内从葡萄牙南部道路311公里处收集的道路死亡数据来比较五种热点识别方法的性能：二进制逻辑回归（BLR），生态位因子分析（ENFA），内核密度估计，最近邻层次聚类（NNHC）和Malo方法。 BLR和ENFA建模也用于识别道路杀伤性确定性因素。我们的结果表明，Malo方法应首选用于热点识别。导致猫头鹰道路杀害的主要因素是与物种发生的良好栖息地条件以及促进道路附近狩猎行为的特定条件有关的因素。基于这些因素，建议采取几种缓解措施。

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