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如何在步骤选择分析中解释行为状态:模型比较模型

Jennifer Pohle¹, Johannes Signer², Jana A Eccard³

¹Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany.

|March 1, 2024

概括

将隐藏的马尔科夫模型 (HMM) 与集成步骤选择分析 (iSSA) 集成,可以提高对动物行为的理解. 这种方法共同估计行为状态和息地选择,优于标准方法.

科学领域:

生态生态学生态生态学
运动生态学运动生态学
野生动物生物学野生动物生物学

背景情况:

步骤选择模型分析动物移动数据以选择息地.
没有观察到的行为状态 (例如,食,休息) 影响息地偏好和移动.
标准的步骤选择分析经常忽略这些行为状态,可能会导致结果偏差.

研究的目的:

评估结合集成阶段选择分析 (iSSA) 与隐藏的马尔科夫模型 (HMM) 的性能.
将HMM-iSSA方法与标准iSSA和基于分类的iSSA进行比较.
在步骤选择分析中,量化忽视行为状态的后果.

主要方法:

开发并应用了一种隐藏马尔科夫模型集成步骤选择分析 (HMM-iSSA).
进行了广泛的模拟研究,以测试模型性能.
为了经验性比较,利用了涉及同时跟踪的银行 (Myodes glareolus) 的案例研究.

主要成果:

HMM-iSSA方法允许对行为状态和状态依赖性息地选择进行联合估计.
经验性比较显示HMM-iSSA的表现优于标准和基于分类的iSSA.
该研究量化了未观察到状态对息地选择推断的影响.

结论:

将HMM与iSSA相结合,为分析国家依赖的息地选择提供了一个强大的框架.
在步骤选择模型中忽略行为状态可能导致不准确的结论.
在R包HMMiSSA中实现了HMM-iSSA方法,以实现更广泛的可访问性.

关键词:

动物的运动动物的运动.微量级的相互作用.息地选择息地选择隐藏的马尔科夫模型综合步骤选择分析.马尔科夫切换回归的回归方法运动行为运动行为国家切换转换.

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