1. Step selection analysis (SSA) is a fundamental technique for uncovering the drivers of animal movement decisions. Its typical use has been to view an animal as ‘selecting’ each measured location, given its current (and possibly previous) locations. Although an animal is unlikely to make decisions precisely at the times its locations are measured, if data are gathered at a relatively low frequency (every few minutes or hours) this is often the best that can be done. Nowadays, though, tracking data are increasingly gathered at very high frequencies, often ≥1 Hz, so it may be possible to exploit these data to perform more behaviourally‐meaningful step selection analysis.
2. Here, we present a technique to do this. We first use an existing algorithm to determine the turning‐points in an animal's movement path. We define a ‘step’ to be a straight‐line movement between successive turning‐points. We then construct a generalised version of integrated SSA (iSSA), called time‐varying iSSA (tiSSA), which deals with the fact that turning‐points are usually irregularly spaced in time. We demonstrate the efficacy of tiSSA by application to data on both simulated animals and free‐ranging goats Capra aegagrus hircus, comparing our results to those of regular iSSA with locations that are separated by a constant time‐interval.
3. Using (regular) iSSA with constant time‐steps can give results that are misleading compared to using tiSSA with the actual turns made by the animals. Furthermore, tiSSA can be used to infer covariates that are dependent on the time between turns, which is not possible with regular iSSA. As an example, we show that our study animals tend to spend less time between successive turns when the ground is rockier and/or the temperature is hotter.
4. By constructing a step selection technique that works between observed turning‐points of animals, we enable step selection to be used on high‐frequency movement data, which are becoming increasingly prevalent in modern biologging studies. Furthermore, since turning‐points can be viewed as decisions, our method places step selection analysis on a more behaviourally‐meaningful footing compared to previous techniques.

中文翻译：

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动物为什么转身？时变步长选择分析，用于推断高频数据中观察到的转折点

1. 步骤选择分析（SSA）是发现动物运动决策动因的一项基本技术。它的典型用途是根据给定的当前（或先前）位置，将动物视为“选择”每个测量位置。尽管动物不太可能在测量其位置时准确地做出决定，但如果以相对较低的频率（每隔几分钟或几小时）收集一次数据，则通常是最好的选择。但是，如今，跟踪数据越来越多地以非常高的频率（通常≥1Hz）收集，因此有可能利用这些数据来执行更具行为意义的步骤选择分析。
2. 在这里，我们介绍一种执行此操作的技术。我们首先使用现有算法来确定动物运动路径的转折点。我们将“步骤”定义为连续转折点之间的直线运动。然后，我们构建集成的SSA（iSSA）的广义版本，称为时变iSSA（tiSSA），该版本处理转折点通常在时间上不规则间隔的事实。通过将我们的结果与常规iSSA的结果（以固定时间间隔分隔的位置）进行比较，我们通过将其应用于模拟动物和自由放养的山羊Capra aegagrus hircus的数据来证明tiSSA的功效。
3. 与对动物进行实际转弯的tiSSA相比，使用具有固定时间步长的（常规）iSSA可能会产生误导性的结果。此外，tiSSA可用于推断依赖于转弯之间时间的协变量，而常规iSSA则无法实现。例如，我们表明，当地面较硬和/或温度较高时，我们的研究动物倾向于在连续转弯之间花费更少的时间。
4. 通过构建在观察到的动物转折点之间起作用的步进选择技术，我们使步进选择可用于高频运动数据，而该数据在现代生物记录研究中正变得越来越普遍。此外，由于可以将转折点视为决策，因此与以前的技术相比，我们的方法将步选择分析置于更具行为意义的基础上。