大多数基于摄像头的捕食者-猎物研究中的随机遇到模型密度估计是不可靠的
Sean M Murphy1, Benjamin S Nolan1, Felicia C Chen1
1U.S. Geological Survey, Western Ecological Research Center, Boulder City, NV 89005, USA.
Animals : an open access journal from MDPI
|December 17, 2024
概括
使用随机遭遇模型 (REM) 和摄像头陷来估计野生动物种群可能是不可靠的. 违反REM假设,特别是非随机的摄像机放置,显著膨胀掠食者和猎物的密度估计.
科学领域:
- 野生动物生态生态学
- 人口动态 人口动态
- 保护生物学 保护生物学
背景情况:
- 准确的野生动物种群估计对于理解捕食者-猎物动态至关重要.
- 摄像机陷是一种常见的方法,但估计未标记物种的种群存在挑战.
- 随机遭遇模型 (REM) 是未标记野生动物的关键分析方法,但依赖于严格的假设.
研究的目的:
- 评估违反随机遭遇模型 (REM) 假设对未标记的捕食者和猎物的密度估计的影响.
- 系统地审查公布的捕食者-猎物生态研究中的REM要求的应用和遵守.
主要方法:
- 从未标记的捕食者和猎物种群的多年相机陷数据集的实证分析.
- 在捕食者-猎物研究中使用随机遭遇模型 (REM) 发表的研究的系统文献综述.
- 评估由非随机的摄像机放置和借来的速度值产生的密度估计波动.
主要成果:
- 违反REM假设,特别是非随机的摄像机放置和使用借来的速度值,导致了挥发性密度估计.
- 战略性摄像机放置以最大限度地检测掠食者导致掠食者和猎物密度估计的大幅膨胀.
- 一项系统性审查发现,在掠食者和猎物的研究中,91%的REM密度估计使用了不符合REM要求的数据或速度值.
结论:
- 随机碰撞模型 (REM) 对其核心假设的违反非常敏感,特别是关于摄像头放置和移动数据.
- 在捕食者和猎物的研究中,REM的当前应用经常无法满足方法要求,从而损害了估计可靠性.
- 强烈建议在使用掠食者猎物研究中的随机遭遇模型 (REM) 密度估计用于保护和管理决策时谨慎使用.
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