Abstract:
Accurate information provided by reliable models is essential for identifying hotspots and mitigating roadkill. However, existing methods, such as kernel density estimation (KDE) and maximum entropy modeling (ME) may individually identify only a subset of the suitable locations for mitigation, because KDE cannot detect hotspots once local abundances are depressed, and ME may only partially identify current hotspots due to imperfect discrimination skill. Here, we propose a hybrid consensus modeling (HCM) approach that leverages the strengths of both KDE and ME by using their consensus to identify the core subset of hotspots. We collected herpetofauna (amphibians and reptiles) roadkill data (N = 839) along four roads in Taiwan (R.O.C.) to evaluate the statistical performance and theoretical mitigation efficiency of HCM, KDE and ME, and to compare the allocation among roads, spatial clustering, and environmental conditions in the identified hotspots. HCM was applied on the herpetofauna dataset as well as separately on amphibians and reptiles. Although the discrimination skill of KDE and ME models for both target clades together was good to excellent (AUCKDE = 0.944, AUCME = 0.822), the highest theoretical mitigation efficiency, was displayed by HCM Consensus (2.89), followed by KDE (2.58), and ME (1.91). Furthermore, we show that theoretical mitigation efficiency increases with decreasing spatial clustering (Moran\'s I). Given pervasive budget constraints, we recommend to limit permanent mitigation measures such as fenced culverts to HCM Consensus hotspots, temporary measures to KDE hotspots, and to target additional monitoring at ME hotspots.
摘要:
可靠模型提供的准确信息对于识别热点和减轻道路伤害至关重要。然而,现有方法,例如核密度估计(KDE)和最大熵建模(ME)可以单独识别用于缓解的合适位置的子集,因为一旦局部丰度下降,KDE就无法检测到热点,由于辨别技能不完善,ME可能只能部分识别当前的热点。这里,我们提出了一种混合共识建模(HCM)方法,该方法利用KDE和ME的优势,通过使用它们的共识来识别热点的核心子集。我们收集了台湾(R.O.C.)四条道路上的爬行动物(两栖动物和爬行动物)道路杀伤数据(N=839),以评估HCM的统计性能和理论缓解效率,KDE和我,并比较道路之间的分配,空间聚类,和确定的热点地区的环境条件。HCM已应用于Herpetofauna数据集,并分别应用于两栖动物和爬行动物。尽管KDE和ME模型对两个目标进化枝的辨别技巧都很好(AUCKDE=0.944,AUCME=0.822),最高的理论缓解效率,由HCM共识(2.89)显示,其次是KDE(2.58),和我(1.91)。此外,我们表明,理论缓解效率随着空间聚类的减少而增加(Moran’sI)。鉴于普遍的预算限制,我们建议将永久性缓解措施限制为HCM共识热点,例如围栏涵洞,对KDE热点的临时措施,并针对ME热点进行额外监测。