Department of Fisheries, Wildlife, and Conservation Biology, University of Minnesota, St. Paul, MN, USA.
Wildlife Science, Faculty of Forestry and Forest Ecology, University of Goettingen, Göttingen, Germany.
J Anim Ecol. 2021 May;90(5):1027-1043. doi: 10.1111/1365-2656.13441. Epub 2021 Mar 12.
Habitat-selection analyses allow researchers to link animals to their environment via habitat-selection or step-selection functions, and are commonly used to address questions related to wildlife management and conservation efforts. Habitat-selection analyses that incorporate movement characteristics, referred to as integrated step-selection analyses, are particularly appealing because they allow modelling of both movement and habitat-selection processes. Despite their popularity, many users struggle with interpreting parameters in habitat-selection and step-selection functions. Integrated step-selection analyses also require several additional steps to translate model parameters into a full-fledged movement model, and the mathematics supporting this approach can be challenging for many to understand. Using simple examples, we demonstrate how weighted distribution theory and the inhomogeneous Poisson point process can facilitate parameter interpretation in habitat-selection analyses. Furthermore, we provide a 'how to' guide illustrating the steps required to implement integrated step-selection analyses using the amt package By providing clear examples with open-source code, we hope to make habitat-selection analyses more understandable and accessible to end users.
栖息地选择分析允许研究人员通过栖息地选择或步长选择函数将动物与其环境联系起来，通常用于解决与野生动物管理和保护工作相关的问题。整合了运动特征的栖息地选择分析，即综合步长选择分析，特别吸引人，因为它们允许同时对运动和栖息地选择过程进行建模。尽管它们很受欢迎，但许多用户在解释栖息地选择和步长选择函数中的参数时遇到困难。综合步长选择分析还需要几个额外的步骤将模型参数转化为成熟的运动模型，并且支持这种方法的数学对于许多人来说可能难以理解。使用简单的示例，我们演示了加权分布理论和非齐次泊松点过程如何促进栖息地选择分析中的参数解释。此外，我们提供了一个“如何做”指南，说明了使用 amt 包实施综合步长选择分析所需的步骤。通过提供带有开源代码的清晰示例，我们希望使栖息地选择分析更容易被最终用户理解和使用。
