Landscape and Ecological Engineering最新文献

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Correction: Nine-year bird community development on Radovesická spoil heap: impacts of restoration approach and vegetation characteristics 更正：拉多维西卡垃圾堆上九年的鸟类群落发展：恢复方法和植被特征的影响

IF 2 4区环境科学与生态学 Q2 BIODIVERSITY CONSERVATION

Landscape and Ecological Engineering

Pub Date : 2023-12-16 DOI: 10.1007/s11355-023-00590-6

Kryštof Korejs, Miroslav Šálek, V. Bejček, Petr Musil, Karel Šťastný, Ondřej Volf, Jan Riegert

引用次数: 0

Prediction of areas with high risk of roadkill wildlife applying maximum entropy approach and environmental features: East Antioquia, Colombia 应用最大熵法和环境特征预测道路野生动物死亡高风险地区:哥伦比亚东安蒂奥基亚

IF 2 4区环境科学与生态学 Q2 BIODIVERSITY CONSERVATION

Landscape and Ecological Engineering

Pub Date : 2023-11-24 DOI: 10.1007/s11355-023-00581-7

J. Perez-Guerra, J. Gonzalez-Velez, J. Murillo-Escobar, J. C. Jaramillo-Fayad

Linear infrastructures such as roads are known to cause adverse effects on the surrounding ecosystems. Wildlife–vehicle collisions (WVC) are considered to be one of the main causes of biodiversity loss. Several studies have demonstrated that WVC occurs on Colombian roads. However, studies have focused on a body count, the most affected species, and places with high mortality rates. We aim to propose a methodology for predicting WVC risk in the east of Antioquia, Colombia employing a machine learning approach to identify road segments with a high risk of WVC. Additionally, we present a novel validation technique for the "MaxEnt" approach. During this investigation, 499 reports were collected through road surveys between 2015 and 2016. We identified 160 road segments with high mortality rates with a 2D Hotspots analysis. 15 environmental descriptors were collected for each road segment. Validation of the predictive capabilities of the algorithm was performed using the area under the Receiver Operating Characteristic curve (AUC-ROC). The model achieved a good predictive ability (AUC>0.77). The response curves evidenced that features like distance to forest, land cover, resistance, and land use increase the probability of WVC, specifically, collision risk was higher in zones with high resistance values, crops, and pastures. This methodology has the potential to become an important tool for the prioritization of resources to mitigate WVC.

众所周知，道路等线性基础设施会对周围的生态系统造成不利影响。野生动物车辆碰撞(WVC)被认为是生物多样性丧失的主要原因之一。几项研究表明，在哥伦比亚的道路上发生了WVC。然而，研究主要集中在死亡人数、受影响最严重的物种和死亡率高的地方。我们的目标是提出一种预测哥伦比亚安蒂奥基亚东部WVC风险的方法，采用机器学习方法来识别WVC高风险的路段。此外，我们提出了一种新的“MaxEnt”方法验证技术。在本次调查中，2015年至2016年期间通过道路调查收集了499份报告。我们通过二维热点分析确定了160个高死亡率路段。每个路段收集了15个环境描述符。采用受试者工作特征曲线下面积(AUC-ROC)验证算法的预测能力。该模型具有较好的预测能力(AUC>0.77)。响应曲线表明，与森林的距离、土地覆盖、阻力和土地利用等特征增加了WVC发生的概率，其中阻力值高的区域、农作物和牧场的碰撞风险更高。这种方法有可能成为资源优先排序以减轻WVC的重要工具。

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引用次数: 0

Nine-year bird community development on Radovesická spoil heap: impacts of restoration approach and vegetation characteristics radovesicks <e:1>废弃地9年鸟类群落发展:恢复方式和植被特征的影响