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一个灵活的框架用于空间捕获-重新捕获与未知的身份
Paul van Dam-Bates1, Michail Papathomas1, Ben C Stevenson2
1School of Mathematics and Statistics, University of St Andrews, St Andrews, Fife, KY16 9LZ, United Kingdom.
Biometrics
|February 19, 2024
概括
本研究引入了一种新的空间捕获-重新捕获 (SCR) 方法来估计野生动物种群密度,适用于相机陷和声学数据. 该方法有效地使用部分动物识别,提高了人口评估的准确性.
科学领域:
- 生态生态学 生态生态学
- 野生动物人口动力学
- 生物声学是一种生物声学.
背景情况:
- 摄像机陷和声学记录器是野生动物群体采样的标准工具.
- 空间捕获-重新捕获 (SCR) 方法需要个别动物的识别,这往往是劳动密集型,并不总是可行的.
- 现有的方法与个人识别不完整的数据集扎.
研究的目的:
- 开发一个通用的空间捕获-重新捕获 (SCR) 框架,以适应不完美的个人识别.
- 将声学数据集成到SCR模型中,使得从发音中进行密度估计.
- 提供一种统一的方法来分析来自摄像头陷和声学记录器的数据.
主要方法:
- 制定SCR作为一个标记的Poisson过程,所有检测都采用单一的计数过程.
- 定义了一个灵活的标记分布,以结合各种观察到的特征 (例如,身份,位置,性别,时间).
- 将一般化的SCR模型应用于摄像头陷数据 (渔民) 和声学数据 (开普半岛青),通过模拟验证.
主要成果:
- 拟议的方法成功地从摄像头陷和声学数据中估计了动物密度.
- 包含额外标记 (性别,时间) 的隐藏身份SCR模型被证明可靠用于密度估计.
- 该框架整合了声学SCR,并将现有的潜在身份SCR模型推广为一般化.
结论:
- 一般化的SCR框架为未完善识别的野生动物密度估计提供了强大的解决方案.
- 这种综合方法提高了摄像头陷和声学监控数据的实用性.
- 该方法为生态研究和野生动物管理提供了可靠的工具.
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