Deciphering behavioral changes in animal movement with a “multiple change point algorithm- classification tree” framework
Deciphering behavioral changes in animal movement with a “multiple change point algorithm- classification tree” framework
复制标题
用“多变化点算法-分类树”框架解读动物运动中的行为变化
DOI：
--
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发表时间：
2014
影响因子：
3
通讯作者：
Y. Hingrat
中科院分区：
文献类型：
--
作者：
B. Madon;Y. Hingrat
跟踪工具的最新发展提高了我们对动物运动的新知识。由于模型的复杂性、不切实际的先验假设和繁重的计算资源，沿着动物路径的行为变化仍然经常被视觉评估。最近用变点算法开辟了一条新的途径，因为跟踪数据可以被组织成时间序列，其中潜在的周期性变化点将运动分隔在不同统计特性的分段中。到目前为止，这种方法仅限于单变点检测，我们基于最近的多变点算法提出了一个简单的分析框架：PELT算法，这是一种动态规划剪枝搜索方法，用于在时间序列中寻找变点的数量和位置的最佳组合。然后，使用有监督的分类树过程对由该算法找到的数据段进行分类，以按照运动类别来组织段。我们应用这一框架研究了一种候鸟--马奎氏扇贝的日距离变化，并将其运动分为三类：阶段性运动、非迁移性运动和迁移性运动。通过仿真实验，我们证明了该算法对于识别准确的行为变化(平均超过80%的时间)是稳健的，但当存在正自相关时，可能导致错误的变化点的检测(在36%的迭代中，平均1.97(se=0.06)个额外的变化点)。提供了一个案例研究，以说明与我们的分析框架的可靠性相比，与运动模式的视觉分析相关联的偏差。技术进步将为动物行为研究提供新的机会，带来巨大而多样的数据集，这对生物学家来说是一个越来越大的挑战，这个简单而标准化的框架可能是试图破译动物行为的一笔财富。
The recent development of tracking tools has improved our nascent knowledge on animal movement. Because of model complexity, unrealistic a priori hypotheses and heavy computational resources, behavioral changes along an animal path are still often assessed visually. A new avenue has recently been opened with change point algorithms because tracking data can be organized as time series with potential periodic change points segregating the movement in segments of different statistical properties. So far this approach was restricted to single change point detection and we propose a straightforward analytical framework based on a recent multiple change point algorithm: the PELT algorithm, a dynamic programming pruning search method to find, within time series, the optimal combination of number and locations of change points. Data segments found by the algorithm are then sorted out with a supervised classification tree procedure to organize segments by movement classes. We apply this framework to investigate changes in variance in daily distances of a migratory bird, the Macqueen’s Bustard, Chlamydotis macqueenii, and describe its movements in three classes: staging, non-migratory and migratory movements. Using simulation experiments, we show that the algorithm is robust to identify exact behavioral shift (on average more than 80% of the time) but that positive autocorrelation when present is likely to lead to the detection of false change points (in 36% of the iterations with an average of 1.97 (se = 0.06) additional change points). A case study is provided to illustrate the biases associated with visual analysis of movement patterns compared to the reliability of our analytical framework. Technological improvement will provide new opportunities for the study of animal behavior, bringing along huge and various data sets, a growing challenge for biologists, and this straightforward and standardized framework could be an asset in the attempt to decipher animal behavior.
影响因子：
2.1
作者：
Olshen, AB;Venkatraman, ES;Wigler, M
通讯作者：
Wigler, M
影响因子：
3.5
作者：
AUGER, IE;LAWRENCE, CE
通讯作者：
LAWRENCE, CE