Gurarie Eliezer, Fleming Christen H, Fagan William F, Laidre Kristin L, Hernández-Pliego Jesús, Ovaskainen Otso
Department of Biology, University of Maryland, College Park, MD, 20742 USA.
Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA, USA.
Mov Ecol. 2017 May 10;5:13. doi: 10.1186/s40462-017-0103-3. eCollection 2017.
Continuous time movement models resolve many of the problems with scaling, sampling, and interpretation that affect discrete movement models. They can, however, be challenging to estimate, have been presented in inconsistent ways, and are not widely used.
We review the literature on integrated Ornstein-Uhlenbeck velocity models and propose four fundamental correlated velocity movement models (CVM's): random, advective, rotational, and rotational-advective. The models are defined in terms of biologically meaningful speeds and time scales of autocorrelation. We summarize several approaches to estimating the models, and apply these tools for the higher order task of behavioral partitioning via change point analysis.
An array of simulation illustrate the precision and accuracy of the estimation tools. An analysis of a swimming track of a bowhead whale () illustrates their robustness to irregular and sparse sampling and identifies switches between slower and faster, and directed vs. random movements. An analysis of a short flight of a lesser kestrel () identifies exact moments when switches occur between loopy, thermal soaring and directed flapping or gliding flights.
We provide tools to estimate parameters and perform change point analyses in continuous time movement models as an R package (smoove). These resources, together with the synthesis, should facilitate the wider application and development of correlated velocity models among movement ecologists.
连续时间运动模型解决了许多影响离散运动模型的缩放、采样和解释问题。然而，它们的估计可能具有挑战性，呈现方式不一致，且未得到广泛应用。
我们回顾了关于积分奥恩斯坦 - 乌伦贝克速度模型的文献，并提出了四种基本的相关速度运动模型（CVM）：随机、平流、旋转和旋转 - 平流。这些模型是根据生物学上有意义的速度和自相关时间尺度来定义的。我们总结了几种估计模型的方法，并将这些工具应用于通过变点分析进行行为划分的高阶任务。
一系列模拟说明了估计工具的精度和准确性。对一头弓头鲸游泳轨迹的分析表明了它们对不规则和稀疏采样的鲁棒性，并识别出较慢和较快运动之间以及定向与随机运动之间的转换。对一只红脚隼短距离飞行的分析确定了在循环、热气流翱翔与定向拍打或滑翔飞行之间转换发生的确切时刻。
我们提供了作为R包（smoove）在连续时间运动模型中估计参数和进行变点分析的工具。这些资源以及本综述应有助于相关速度模型在运动生态学家中得到更广泛的应用和发展。
