Kim Dongmin, Thompson Peter R, Wolfson David W, Merkle Jerod A, Oliveira-Santos L G R, Forester James D, Avgar Tal, Lewis Mark A, Fieberg John
Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA.
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
Mov Ecol. 2024 Nov 18;12(1):72. doi: 10.1186/s40462-024-00510-9.
Incorporating memory (i.e., some notion of familiarity or experience with the landscape) into models of animal movement is a rising challenge in the field of movement ecology. The recent proliferation of new methods offers new opportunities to understand how memory influences movement. However, there are no clear guidelines for practitioners wishing to parameterize the effects of memory on moving animals. We review approaches for incorporating memory into step-selection analyses (SSAs), a frequently used movement modeling framework. Memory-informed SSAs can be constructed by including spatial-temporal covariates (or maps) that define some aspect of familiarity (e.g., whether, how often, or how long ago the animal visited different spatial locations) derived from long-term telemetry data. We demonstrate how various familiarity covariates can be included in SSAs using a series of coded examples in which we fit models to wildlife tracking data from a wide range of taxa. We discuss how these different approaches can be used to address questions related to whether and how animals use information from past experiences to inform their future movements. We also highlight challenges and decisions that the user must make when applying these methods to their tracking data. By reviewing different approaches and providing code templates for their implementation, we hope to inspire practitioners to investigate further the importance of memory in animal movements using wildlife tracking data.
将记忆（即对景观的某种熟悉感或体验）纳入动物运动模型是运动生态学领域日益严峻的挑战。最近新方法的大量涌现为理解记忆如何影响运动提供了新机遇。然而，对于希望对记忆对移动动物的影响进行参数化的从业者而言，尚无明确的指导方针。我们回顾了将记忆纳入步长选择分析（SSA）的方法，SSA是一种常用的运动建模框架。通过纳入时空协变量（或地图）来构建考虑记忆的SSA，这些协变量定义了从长期遥测数据得出的熟悉程度的某些方面（例如动物是否、多久或多久之前访问过不同的空间位置）。我们通过一系列编码示例展示了如何将各种熟悉程度协变量纳入SSA，在这些示例中，我们将模型拟合到来自广泛分类群的野生动物跟踪数据。我们讨论了如何使用这些不同方法来解决与动物是否以及如何利用过去经验的信息来指导其未来运动相关的问题。我们还强调了用户在将这些方法应用于其跟踪数据时必须做出的挑战和决策。通过回顾不同方法并提供其实现的代码模板，我们希望激励从业者利用野生动物跟踪数据进一步研究记忆在动物运动中的重要性。
