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The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths

机译：贝叶斯状态空间模型从运动路径估计行为状态的有效性

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1. Bayesian state-space movement models have been proposed as a method of inferring behavioural states from movement paths (Morales et al. 2004), thereby providing insight into the behavioural processes from which patterns of animal space use arise inheterogeneous environments. It is not clear, however, how effective state-space models are at estimating behavioural states. 2. We use stochastic simulations of two movement models to quantify how behavioural state movement characteristics affect classification error. State-space movement models can be a highly effective approach to estimating behavioural states from movement paths. 3. Classification accuracy was contingent upon the degree of separation between the distributions that characterize the states (e.g. step length and turn angle distributions) and the relative frequency of the behavioural states. In the best case scenarios classification accuracy approached 100%, but was close to 0% when step length and turn angle distributions of each state were similar, or when one state was rare. Mean classification accuracy was un-correlated with path length, but the variance in classification accuracy was inversely related to path length. 4. Importantly, we find that classification accuracy can be predicted based on the separation between distributions that characterize the movement paths, thereby providing a method of estimating classification accuracy for real movement paths. We demonstrate this approach using radiotelemetry relocation data of 34 moose (Atces alces). 5. We conclude that Bayesian state-space models offer powerful new opportunities for inferring behavioural states from relocation data.
机译：1.已经提出了贝叶斯状态空间运动模型作为从运动路径推断行为状态的方法（Morales等人2004），从而提供了对在异质环境中动物空间利用方式出现的行为过程的深入了解。但是，尚不清楚状态空间模型在估计行为状态方面如何有效。 2.我们使用两个运动模型的随机模拟来量化行为状态运动特征如何影响分类误差。状态空间运动模型可以是一种从运动路径估计行为状态的高效方法。 3.分类的准确性取决于表征状态的分布（例如步长和转弯角度分布）与行为状态的相对频率之间的分离程度。在最佳情况下，分类精度接近100％，但是当每种状态的步长和转角分布相似时，或者在一种状态很少时，分类精度接近0％。平均分类精度与路径长度不相关，但分类精度的方差与路径长度成反比。 4.重要的是，我们发现可以基于表征运动路径的分布之间的分离来预测分类精度，从而提供一种估算实际运动路径的分类精度的方法。我们证明了这种方法使用34麋（Atces alces）的无线电遥测重定位数据。 5.我们得出结论，贝叶斯状态空间模型为从重定位数据推断行为状态提供了强大的新机会。

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