一个多种类的层次模型来整合计数和距离采样数据.
Neil A Gilbert1,2, Caroline M Blommel2,3, Matthew T Farr1,2,4
1Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA.
Ecology
|June 7, 2024
概括
一个新的综合社区模型结合了多种数据类型来估计野生动物的丰富性. 这种方法比传统方法提供了更准确的生态洞察力,揭示了各种物种对保护策略的反应.
科学领域:
- 生态生态学 生态生态学
- 野生动物管理 野生动物管理
- 统计建模 统计建模
背景情况:
- 传统的生态研究通常使用单个物种和单个数据源方法.
- 综合社区模型提供了一个框架,可以同时分析多个物种和数据源,以获得更广泛的生态推断.
研究的目的:
- 开发和验证一个新的综合社区模型,将距离采样和单次访问计数数据结合起来.
- 通过在数据源和物种之间共享信息来估计整个社区的丰度模式.
- 与传统方法相比,评估模型的性能和准确性.
主要方法:
- 开发了一个集成的社区模型,具有共同的概率和随机效应结构.
- 模拟数据测试模型提供无偏见的丰度和检测参数估计的能力.
- 将模型应用于肯尼亚马赛马拉国家保护区的11种食草动物物种社区.
主要成果:
- 模拟证实了模型的准确性和精度,优于单个物种模型.
- 该模型揭示了物种对被动与主动保护执法的反应的显著跨物种变化.
- 在被动执法下,五种物种更为丰富,在积极执法下有三种物种更为丰富,而三种物种没有表现出差异.
结论:
- 综合社区建模框架增强了跨空间和时间的生态推理.
- 该模型有效地揭示了不同物种对野生动物管理实践的不同反应.
- 实践者应该仔细考虑模型假设和特定应用的数据集成好处.
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