在细度息地选择中对行为进行核算:一项案例研究突出了方法学的复杂性
Larissa T Beumer1,2, Niels M Schmidt1,3, Jennifer Pohle4
1Department of Ecoscience, Aarhus University, Roskilde, Denmark.
The Journal of animal ecology
|July 16, 2023
概括
在息地选择模型中考虑动物行为会对结果产生重大影响. 将模型定制为特定的行为,如食或休息,可以改善预测,这对于有效的野生动物保护至关重要.
科学领域:
- 生态生态学 生态生态学
- 动物行为 动物行为
- 空间生态学空间生态学
背景情况:
- 息地选择是生态的基础,受食,捕食者回避和温度调节的影响.
- 步骤选择函数 (SSF) 分析基于移动和环境条件的细度息地选择.
- 使用SSF量化行为特异性息地选择需要仔细考虑可用性领域.
研究的目的:
- 评估行为特定可用性域和模型对SSF结构,系数和预测性能的影响.
- 用muskoxen作为案例研究来比较行为特定的SSF与行为非特定的方法.
- 确定在息地选择研究中可靠推断的最佳方法.
主要方法:
- 隐藏的马尔科夫模型 (HMMs) 用于从GPS数据中分类行为状态 (休息,食,迁移).
- 步骤选择函数 (SSF) 为每个行为单独安装,并与一般模型进行比较.
- 分析了与地形,植被和雪地条件有关的息地选择.
主要成果:
- 定义特定行为可用性域主要影响模型结构 (变量选择).
- 配合单独的特定行为模型主要影响了选择强度.
- 行为特异域改善了寻食物和迁移的预测性能,但减少了休息的预测性能.
结论:
- 在SSF中的行为会计影响模型结构,系数和预测性能.
- 应探索特定行为可用性领域和模型匹配,以进行强大的生态推理.
- 这种方法对于为野生动物管理和保护倡议提供信息至关重要.
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