Whetten Andrew B, Hefley Trevor J, Haukos David A, Brewer Dustin E
Department of Statistics Kansas State University Manhattan Kansas USA.
Division of Biology Kansas State University Manhattan Kansas USA.
Ecol Evol. 2025 Sep 7;15(9):e72060. doi: 10.1002/ece3.72060. eCollection 2025 Sep.
Determining which statistical methods are appropriate for data is both user and data dependent and prone to change as new methodology becomes available. This process encompasses model ideation, model selection, and determining appropriate use of statistical methods. Literature on models for animal movement emerging in the past two decades has yielded a rich collection of statistical methods garnering much deserved positive attention. Among such efforts, there is limited investigation of the broader place for simple machine learning methodology in animal movement modeling. We propose a bagged (i.e., bootstrap aggregated) animal movement model using simple, off-the-shelf machine learning algorithms. The model is intuitive, retains statistical inference about characteristics of animal movement (i.e., estimated from model-based summary statistics), and only requires knowledge of elementary statistical and machine learning analysis to understand. We show by simulation that our model can provide unbiased estimates of pertinent characteristics of animal movement (e.g., daily displacement) in the presence of large and realistic location error. We believe that increasing accessible literature on simple machine learning animal movement models provides valuable pedagogical and practical support for researchers using statistical models to study animal movement.
确定哪些统计方法适用于数据既取决于用户，也取决于数据，并且随着新方法的出现容易发生变化。这个过程包括模型构思、模型选择以及确定统计方法的适当使用。过去二十年来出现的关于动物运动模型的文献产生了丰富的统计方法集合，这些方法受到了应有的积极关注。在这些努力中，对于简单机器学习方法在动物运动建模中的更广泛作用的研究有限。我们提出了一种使用简单的现成机器学习算法的袋装（即自助聚合）动物运动模型。该模型直观，保留了关于动物运动特征的统计推断（即从基于模型的汇总统计中估计），并且只需要基本的统计和机器学习分析知识就能理解。我们通过模拟表明，在存在大的现实位置误差的情况下，我们的模型可以对动物运动的相关特征（例如每日位移）提供无偏估计。我们相信，增加关于简单机器学习动物运动模型的可获取文献，为使用统计模型研究动物运动的研究人员提供了有价值的教学和实践支持。
