Data from: Predicting local and non-local effects of resources on animal space use using a mechanistic step-selection model

1. Predicting space use patterns of animals from their interactions with the environment is fundamental for understanding the effect of habitat changes on ecosystem functioning. Recent attempts to address this problem have sought to unify resource selection analysis, where animal space use is derived from available habitat quality, and mechanistic movement models, where detailed movement processes of an animal are used to predict its emergent utilisation distribution. Such models bias the animal's movement towards patches that are easily available and resource-rich, and the result is a predicted probability density at a given position being a function of the habitat quality at that position. However, in reality, the probability that an animal will use a patch of the terrain tends to be a function of the resource quality in both that patch and the surrounding habitat. 2. We propose a mechanistic model where this non-local effect of resources naturally emerges from the local movement processes, by taking into account the relative utility of both the habitat where the animal currently resides and that of where it is moving. We give statistical techniques to parametrize the model from location data, and demonstrate application of these techniques to GPS location data of caribou (Rangifer tarandus) in Newfoundland. 3. Steady-state animal probability distributions arising from the model have complex patterns that cannot be expressed simply as a function of the local quality of the habitat. In particular, large areas of good habitat are used more intensively than smaller patches of equal quality habitat, whereas isolated patches are used less frequently. Both of these are real aspects of animal space use missing from previous mechanistic resource-selection models. 4. Whilst we focus on habitats in this paper, our modelling framework can be readily used with any environmental covariates, and therefore represents a unification of mechanistic modelling and step-selection approaches to understanding animal space use.

1. 基于动物与环境的交互行为预测其空间利用模式，是解析生境变化对生态系统功能影响的核心基础。此前针对该问题的研究尝试整合两类方法：一类是资源选择分析（Resource Selection Analysis），即通过可获取的生境质量推导动物空间利用模式；另一类是机制性运动模型（Mechanistic Movement Models），即借助动物的详细运动过程来预测其涌现利用分布（Emergent Utilisation Distribution）。此类模型会将动物的运动偏向于易抵达且资源丰富的生境斑块，最终得到的给定位置预测概率密度仅为该位置生境质量的函数。但实际情况中，动物利用某一地形斑块的概率，往往同时取决于该斑块及其周边生境的资源质量。
2. 本研究提出一种机制性模型，通过同时考量动物当前所处生境与运动目标生境的相对效用（Relative Utility），使资源的非局部效应（Non-local Effect）能够从局部运动过程中自然涌现。我们提出了基于位置数据对该模型进行参数化的统计方法，并将这些方法应用于纽芬兰地区驯鹿（Rangifer tarandus）的GPS位置数据。
3. 由该模型得到的动物稳态概率分布（Steady-state Probability Distribution）具有复杂的模式，无法仅通过生境局部质量进行简单表征。具体而言，连片优质生境的利用强度会高于同等质量的小型生境斑块，而孤立斑块的利用频率则相对更低。上述两类特征均为真实存在的动物空间利用现象，但此前的机制性资源选择模型并未涵盖。
4. 尽管本文聚焦于生境场景，但本建模框架可直接适配任意环境协变量（Environmental Covariates），因此实现了机制性建模与步长选择方法（Step-selection Approaches）在动物空间利用研究中的统一。