使用密度表面量化动物群的空间尺度
Max van Mulken1, Jasper Eikelboom2,3, Kevin Verbeek1
1Algorithms, Eindhoven University of Technology, Eindhoven, The Netherlands.
Journal of the Royal Society, Interface
|September 23, 2025
概括
这项研究引入了一种新的方法来量化动物在各种空间尺度上的聚类. 该技术准确地识别了集群大小,即使在复杂的异构环境中也是如此.
科学领域:
- 生态生态学 生态生态学
- 空间统计的空间统计.
- 动物行为 动物行为
背景情况:
- 目前用于量化动物聚类的现有方法通常是规模独立的,或者无法代表动物的感知.
- 目前的近距离模型并没有很好地捕捉到异型和层次的集群模式.
研究的目的:
- 开发一种新的,无参数的方法来量化动物在多个空间尺度上的聚类.
- 为了自动检测反映动物感知社区的集群直径.
- 将该方法应用于人工和现实世界的动物数据集.
主要方法:
- 利用内核密度估计,从点位置数据创建密度函数.
- 采用光滑芯来自动检测集群直径,模仿动物的感知.
- 在各种人工数据集和非洲林大象数据集上测试了该方法.
主要成果:
- 该方法准确地将更高的集群值分配给更大的集群规模.
- 成功识别了与实际集群直径相对应的空间尺度.
- 经过证明的稳定性,精度对内核功能的选择无关.
结论:
- 开发的方法提供了一个强大的,无参数的方法来分析动物聚类.
- 它对于异型和等级聚类系统特别有效.
- 方便在动物群体动态中检测相关的空间尺度.
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