利用方向持久性和吸引点建模动物运动
Modeling animal movement with directional persistence and attractive points
摘要：
目前，研究人员很容易获得GPS技术，并且可以获得许多动物运动数据集。描述动物路径的模型可以具有的两个主要特征是方向持久性和对空间中某一点的吸引力。在这项工作中，我们提出了一种新的方法，可以有这两个特点。我们的建议是一个隐马尔可夫模型与一个新的排放分布。排放分布模拟了上述两个特征，而隐马尔可夫模型的潜在状态需要考虑行为模式。我们表明，该模型是很容易实现的贝叶斯框架。我们估计我们的建议的激励数据，代表GPS位置的Maremma牧羊犬记录在澳大利亚。所得到的结果是很容易解释的，我们表明，我们的建议优于主要的竞争模型。
英文摘要：
GPS technology is currently easily accessible to researchers, and many animal movement datasets are available. Two of the main features that a model which describes an animal's path can possess are directional persistence and attraction to a point in space. In this work, we propose a new approach that can have both characteristics. Our proposal is a hidden Markov model with a new emission distribution. The emission distribution models the two aforementioned characteristics, while the latent state of the hidden Markov model is needed to account for the behavioral modes. We show that the model is easy to implement in a Bayesian framework. We estimate our proposal on the motivating data that represent GPS locations of a Maremma Sheepdog recorded in Australia. The obtained results are easily interpretable and we show that our proposal outperforms the main competitive model.
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