综合距离采样模型用于简单的点数计数
Marc Kéry1, J Andrew Royle2, Tyler Hallman1,3,4
1Swiss Ornithological Institute, Sempach, Switzerland.
Ecology
|March 27, 2024
概括
综合远距离采样 (IDS) 模型将远距离采样与点数或检测/不检测数据相结合,以准确估计野生动物密度,并考虑到生物多样性调查中的可检测性偏差.
科学领域:
- 生态生态学 生态生态学
- 野生动物生物学 野生动物生物学
- 统计建模 统计建模
背景情况:
- 点数计数 (PC) 是常见的生物多样性调查方法,但受到未知的检测能力的影响,导致偏见的丰度估计.
- 可检测度的时空变化和未知的调查区域阻碍了准确的密度估计和景观层面的缩放.
- 现有的公民科学数据往往缺乏信息来纠正检测偏差,从而限制了它们的生态效用.
研究的目的:
- 引入综合距离采样 (IDS) 模型,以解决传统点数和检测/不检测数据的局限性.
- 通过结合多种数据类型,实现精确的密度估计和景观层次推断.
- 通过纠正检测偏差来提高公民科学数据的实用性.
主要方法:
- IDS模型将距离采样 (DS) 与点计数 (PC) 或检测/不检测 (DND) 数据相结合.
- 将PC和DND数据视为潜伏的DS调查的汇总,以估计单独的检测功能和共变效应.
- 使用重复或时间移除调查来估计可用性和可感知性的可检测性组件.
主要成果:
- IDS模型调和不同数据集之间的空间和时间不匹配.
- 成功地解决了简单PC和DND数据中固有的可检测性和调查区域问题.
- 提供 JAGS 代码和一个 R 包函数 ("IDS") 来安装这些模型.
结论:
- IDS模型提供了一个强大的解决方案,用于在生态调查中准确估计密度.
- 通过纠正检测偏差,显著扩大公民科学数据的实用性和覆盖范围.
- 适用于混合调查设计,将DS与无距离方法相结合,对生态和保护管理具有广泛的影响.
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