The two-dimensional track of an animal on a landscape has progressed over the past three decades from hourly to second-by-second recordings of locations. Track segmentation methods for analyzing the behavioral information in such relocation data has lagged somewhat behind, with scales of analysis currently at the sub-hourly to minute level. A new approach is needed to bring segmentation analysis down to a second-by-second level. Here, such an approach is presented that rests heavily on concepts from Shannon's Information Theory. In this paper, we first briefly review and update concepts relating to movement path segmentation. We then discuss how cluster analysis can be used to organize the smallest viable statistical movement elements (StaMEs), which are $\mu$ steps long, and to code the next level of movement elements called ``words'' that are $m \mu$ steps long. Centroids of these word clusters are identified as canonical activity modes (CAMs). Unlike current segmentation schemes, the approach presented here allows us to provide entropy measures for movement paths, compute the coding efficiencies of derived StaMEs and CAMs, and assess error rates in the allocation of strings of $m$ StaMEs to CAM types. In addition our approach allows us to employ the Jensen-Shannon divergence measure to assess and compare the best choices for the various parameters (number of steps in a StaME, number of StaME types, number of StaMEs in a word, number of CAM types), as well as the best clustering methods for generating segments that can then be used to interpret and predict sequences of higher order segments. The theory presented here provides another tool in our toolbox for dealing with the effects of global change on the movement and redistribution of animals across altered landscapes

翻译：在过去三十年中，动物在景观上的二维轨迹记录已从每小时观测发展到每秒定位。用于分析此类重定位数据中行为信息的轨迹分割方法在规模上略有滞后，当前分析尺度仍停留在亚小时至分钟级别。亟需一种新方法将分割分析推进至秒级。本文提出了一种基于香农信息论概念的方法。我们首先简要回顾并更新了运动路径分割的相关概念，随后探讨如何利用聚类分析组织最小可行统计运动单元（StaMEs），其长度为μ步，并编码下一级运动单元（称为“词”），长度为mμ步。这些词簇的质心被识别为典型活动模式（CAMs）。与现有分割方案不同，本方法能够为运动路径提供熵度量、计算导出StaMEs和CAMs的编码效率，并评估将m个StaMEs字符串分配给CAM类型时的错误率。此外，本方法还允许使用Jensen-Shannon散度来评估和比较各参数（StaME中的步数、StaME类型数量、词中的StaME数量、CAM类型数量）的最佳选择，以及生成可用于解释和预测高阶片段序列的最佳聚类方法。本文提出的理论为我们应对全球变化对动物在景观变迁中的移动与再分布影响提供了另一种工具。