预测上海的鸟类道路杀伤风险:从小,机会性数据的集体建模的见解
Weiyu Yu1,2,3,4, Kun He5, Kun Zhao5
1Faculty of Urban Construction and Ecological Technology, Shanghai Institute of Technology, Shanghai, 201418, China. wyu@sit.edu.cn.
Environmental monitoring and assessment
|February 12, 2026
概括
在上海,鸟类道路杀伤热点被使用集体模型绘制,识别了道路密度和植被等关键驱动因素. 这种方法有助于在数据有限的情况下进行保护计划.
科学领域:
- 生态建模 生态建模
- 保护科学 保护科学
- 野生动物毒理学
背景情况:
- 在中国,公路上被杀害的鸟类是一个重要的生态问题,但数据的局限性阻碍了理解.
- 鸟与车辆碰撞的空间动力学和驱动因素仍未得到充分研究.
研究的目的:
- 开发一个集成建模框架,用于预测上海的鸟类道路杀伤风险.
- 为了确定关键的空间模式和驱动器的鸟类道路杀伤事件发生.
- 解决生态研究中的数据稀缺性和样本偏差问题.
主要方法:
- 通过使用46个鸟类道路杀伤事件和18个解释变量,开发了一个整体建模框架.
- 一个堆叠框架结合了来自最大值,增强回归树和随机森林算法的子模型.
- 弹性净回归被用作预测的元模型.
主要成果:
- 整体模型实现了高性能 (交叉验证AUC=0.964;独立测试AUC=0.772).
- 预测的风险热点集中在上海市中心城市区,沿海地区和崇明区.
- 确定的主要驱动因素包括距离工业场所的距离,水道密度,道路密度,植被指数和树木覆盖.
结论:
- 整体模型有效地预测了空间模式和鸟类道路杀伤的驱动因素,即使有有限的机会性数据.
- 这种方法为在数据有限的保护环境中进行风险映射和机械推理提供了有价值的工具.
- 调查结果可以为有针对性的缓解策略提供信息,以减少因道路碰撞造成的鸟类死亡率.
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