Fitting probability distributions to animal movement trajectories: Using artificial neural networks to link distance, resources, and memory
Fitting probability distributions to animal movement trajectories: Using artificial neural networks to link distance, resources, and memory
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DOI：
10.1086/589448
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发表时间：
2008-08-01
影响因子：
2.9
通讯作者：
Fryxell, John M.
中科院分区：
文献类型：
--
作者：
Dalziel, Benjamin D.;Morales, Juan M.;Fryxell, John M.
动物的运动路径通常被认为是行为过程和景观模式的汇合。然而，事实证明，很难开发出分析动物运动的框架来测试这些相互作用。在这里，我们描述了一种新的方法，用于将运动模型与数据进行拟合，这些数据可以包含景观和行为的不同方面。利用安大略省中部重新引入的五只加拿大梅花鹿的数据，我们使用人工神经网络来估计作为三个景观行为过程的函数的运动概率核。这些测量包括动物对景观的物理空间结构的反应，资源的空间变异性，以及对以前访问过的地点的记忆。研究结果支持这样的观点，即动物移动是景观结构和行为要素之间相互作用的结果，激发了与背景相关的移动概率，而不是像一些传统的移动和资源选择模型所假设的那样，是静态分布的连续实现。因此，灵活的、非线性的模型可能有助于理解控制动物运动模式的机制。
Animal movement paths are often thought of as a confluence of behavioral processes and landscape patterns. Yet it has proven difficult to develop frameworks for analyzing animal movement that can test these interactions. Here we describe a novel method for fitting movement models to data that can incorporate diverse aspects of landscapes and behavior. Using data from five elk (Cervus canadensis) reintroduced to central Ontario, we employed artificial neural networks to estimate movement probability kernels as functions of three landscape-behavioral processes. These consisted of measures of the animals' response to the physical spatial structure of the landscape, the spatial variability in resources, and memory of previously visited locations. The results support the view that animal movement results from interactions among elements of landscape structure and behavior, motivating context-dependent movement probabilities, rather than from successive realizations of static distributions, as some traditional models of movement and resource selection assume. Flexible, nonlinear models may thus prove useful in understanding the mechanisms controlling animal movement patterns.