具有不完美的检测的联合物种分布模型,用于高维空间数据
Jeffrey W Doser1,2, Andrew O Finley2,3, Sudipto Banerjee4
1Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA.
Ecology
|July 10, 2023
概括
一个新的空间因素多种占用模型通过考虑物种相关性,不完美的检测和空间自相关性来有效估计物种分布. 与忽视这些因素的模型相比,这种方法改善了生态预测.
科学领域:
- 生态生态学 生态生态学
- 保护生物学 保护生物学
- 计算生物学 计算生物学
背景情况:
- 估计物种分布和生物多样性对于生态和保护至关重要.
- 联合物种分布模型 (JSDMs) 分析了多个物种的检测-不检测数据,但面临着诸如物种相关性,不完美的检测和空间自相关性等挑战.
- 很少有方法可以同时解决JSDM中的所有三种复杂性.
研究的目的:
- 开发一个空间因素多物种占用模型,同时考虑物种相关性,不完美的检测和空间自相关性.
- 为了确保大量物种和空间位置的大型数据集的计算效率.
- 为分析复杂的生态数据提供一个用户友好的工具.
主要方法:
- 开发了一个空间因子多种占用模型,使用空间因子维度减小和最近邻居高斯过程.
- 将拟议模型的性能与解决复杂性子集的五种替代模型进行了比较.
- 实现了开源R包spOccupancy中的模型.
主要成果:
- 模拟表明,忽视物种相关性,不完美的检测或空间自相关性导致预测性能较差.
- 拟议的空间因素多种占用模型在对美国大陆98种鸟类的案例研究中表现出卓越的预测性能.
- 忽视复杂性的影响因研究目标而异.
结论:
- 开发的空间因子多种占用模型为了解物种分布和生物多样性的空间变化提供了强大的框架.
- spOccupancy R包为生态学家提供了一个可访问的工具,可以应用先进的占用模型技术.
- 同时处理多个数据复杂性对于准确的生态推断和保护规划至关重要.
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