Making sense of ultrahigh-resolution movement data: A new algorithm for inferring sites of interest
Making sense of ultrahigh-resolution movement data: A new algorithm for inferring sites of interest
复制标题
理解超高分辨率运动数据：一种推断感兴趣地点的新算法
DOI：
10.1002/ece3.4721
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发表时间：
2018
影响因子：
2.6
通讯作者：
Munden R
中科院分区：
文献类型：
--
作者：
Munden R
将动物的生命轨迹分解为行为片段是运动生态学的基本挑战。高分辨率数据的激增，通常每秒收集多次，为了解动物运动提供了很多机会。然而，现代数据集的庞大规模意味着越来越需要快速，新颖的计算技术来理解这些数据。大多数现有的方法都是根据较小的数据集设计的，因此速度非常慢。在这里，我们介绍一种方法，用于将高分辨率运动轨迹分割成感兴趣的部位和这些部位之间的过渡。这是基于Benhamou和Riotte‐Lambert（2012）的算法。使其适用于高分辨率数据。数据的分辨率消除了在连续位置之间进行插值的需要，使我们能够将算法的速度提高大约两个数量级，而基本上不会降低精度。此外，我们还采用了一种配色方案来测试算法推理的置信度（高=绿色，中=琥珀色，低=红色）。我们证明了我们的算法的速度和精度与应用程序的模拟和真实的数据（高山牛在1 Hz的分辨率）。在模拟数据上，我们的算法正确地识别了99%的“高置信度”路径的感兴趣的网站。对于牛的数据，该算法确定了两个已知的感兴趣的地点：一个水坑和一个挤奶站。它还确定了其他几个可能与假设的环境驱动因素有关的地点（例如，食物）。我们的算法提供了一种有效的方法，可以将长的高分辨率运动路径转化为大规模决策的示意图，从而可以直接链接到现有的点对点分析技术，如最优觅食理论。它被编码到一个名为SitesInterest的R包中，因此应该作为一个有价值的工具来理解这些越来越大的数据流。
Decomposing the life track of an animal into behavioral segments is a fundamental challenge for movement ecology. The proliferation of high‐resolution data, often collected many times per second, offers much opportunity for understanding animal movement. However, the sheer size of modern data sets means there is an increasing need for rapid, novel computational techniques to make sense of these data. Most existing methods were designed with smaller data sets in mind and can thus be prohibitively slow. Here, we introduce a method for segmenting high‐resolution movement trajectories into sites of interest and transitions between these sites. This builds on a previous algorithm of Benhamou and Riotte‐Lambert (2012). Adapting it for use with high‐resolution data. The data’s resolution removed the need to interpolate between successive locations, allowing us to increase the algorithm’s speed by approximately two orders of magnitude with essentially no drop in accuracy. Furthermore, we incorporate a color scheme for testing the level of confidence in the algorithm's inference (high = green, medium = amber, low = red). We demonstrate the speed and accuracy of our algorithm with application to both simulated and real data (Alpine cattle at 1 Hz resolution). On simulated data, our algorithm correctly identified the sites of interest for 99% of “high confidence” paths. For the cattle data, the algorithm identified the two known sites of interest: a watering hole and a milking station. It also identified several other sites which can be related to hypothesized environmental drivers (e.g., food). Our algorithm gives an efficient method for turning a long, high‐resolution movement path into a schematic representation of broadscale decisions, allowing a direct link to existing point‐to‐point analysis techniques such as optimal foraging theory. It is encoded into anRpackage calledSitesInterest, so should serve as a valuable tool for making sense of these increasingly large data streams.