一个集成的数据模型,以从时间依赖和不完善检测的计数中估计丰富度
Jay M Ver Hoef1, Brett T McClintock1, Peter L Boveng1
1Marine Mammal Laboratory, NOAA Fisheries, Alaska Fisheries Science Center, Seattle, Washington, USA.
Ecology
|May 20, 2025
概括
我们开发了一种贝叶斯模型,通过结合调查计数和检测数据来改进动物种群估计. 这种方法揭示了威廉王子海峡港口海的大量波动.
科学领域:
- 生态生态学 生态生态学
- 野生动物人口动力学
- 统计建模 统计建模
背景情况:
- 估计动物种群的丰富性对于保护至关重要.
- 传统的调查计数经常错过个人,导致低估.
- 可见性模型旨在纠正错过的个体,但可能是有限的.
研究的目的:
- 开发一个改进的贝叶斯层次模型来估计人口丰富度.
- 将动物调查计数与单独的检测数据整合起来,以计算错过的个体.
- 将模型应用于威廉王子海峡港湾海 (Phoca vitulina richardii) 种群动态.
主要方法:
- 开发了一个物流-二项式-波桑等级模型,将调查计数和检测数据结合起来.
- 使用逻辑回归与自身相关的随机效应建模的检测概率.
- 纳入了对真实丰度的临时自相关的Poisson模型.
- 利用AR1和随机步行模型的两阶段采样来提高计算效率.
主要成果:
- 确定一年中的时间和低潮后的时间作为检测概率的关键预测指标.
- 威廉王子海峡的港湾海数量出现了下降 (1996-2001),增加 (2001-2015),随后出现下降 (2015-2023).
- 该模型成功地解释了空中调查中错过的个体.
结论:
- 开发的贝叶斯模型提供了一个可靠的框架,用于使用联合计数和检测数据来估计人口丰度.
- 这种方法提高了长期人口监测计划的准确性.
- 该方法可适应各种物种和调查类型,包括传统可见度模型中使用的.
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