Abstract Animal movement study often relies on individual tracking. The data scale (in time and space) varies according to the species, the environment where individuals live, or the exogenous processes that drive movement. To explore freshwater fish movement in rivers, fine-scale data are needed. Also, in rivers, recorded telemetry frequently shows missing data and location errors. The irregular time-steps, huge amount of data, environmental complexity (river section) and how fish move in such anisotropic environments undermine the use of statistical frameworks such as state-space models. To deal with these specificities, data pre-treatment can be required. We propose a generic method of telemetry data pre-processing, which can be transposed to other datasets. This framework includes interpolation to handle trajectories at fine time scales and performs data analysis within a state-space model. We combined analyses on observed and simulated data at various interpolation time-steps to choose the one that best preserves the general movement while reducing the total amount of data required. First, we directly compared raw and interpolated data, and the results of parameter inference of a simple state-space model using the interpolated data. The state-space model infers behavioural state based on speed and turning angle between successive locations in animal trajectories. We also included two additional variables computed from raw data: a quantitative indicator of the correspondence between the interpolated trajectory and the raw data, and the variance of turning angles of raw data within the interpolation time-step. We were finally able to determine the most appropriate time-step to obtain locations that were regularly spaced in time and to reduce the amount of data while maintaining the precision of the raw data. Computational time was reduced 12-fold by using a 30-second time-step to interpolate data simulated at 3-second intervals. The inclusion of the two variables derived from raw data compensated for the loss of information in interpolated trajectories and allowed more efficient discrimination between behaviours.

中文翻译：

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河段淡水鱼个体位置遥测数据预处理框架

摘要 动物运动研究往往依赖于个体追踪。数据规模（在时间和空间上）因物种、个体居住的环境或驱动运动的外生过程而异。为了探索河流中淡水鱼的运动，需要精细的数据。此外，在河流中，记录的遥测数据经常显示缺失数据和位置错误。不规则的时间步长、大量数据、环境复杂性（河段）以及鱼类在这种各向异性环境中的移动方式破坏了状态空间模型等统计框架的使用。为了处理这些特殊性，可能需要进行数据预处理。我们提出了一种通用的遥测数据预处理方法，可以将其转换为其他数据集。该框架包括插值以处理精细时间尺度的轨迹并在状态空间模型内执行数据分析。我们结合了对不同插值时间步长的观测数据和模拟数据的分析，以选择最能保留一般运动同时减少所需数据总量的数据。首先，我们直接比较了原始数据和插值数据，以及使用插值数据的简单状态空间模型的参数推断结果。状态空间模型根据动物轨迹中连续位置之间的速度和转向角来推断行为状态。我们还包括从原始数据计算的两个额外变量：内插轨迹与原始数据之间对应关系的定量指标，以及插值时间步长内原始数据的转角方差。我们最终能够确定最合适的时间步长，以获取时间间隔有规律的位置，并在保持原始数据精度的同时减少数据量。通过使用 30 秒的时间步长对以 3 秒间隔模拟的数据进行插值，计算时间减少了 12 倍。包含源自原始数据的两个变量补偿了内插轨迹中的信息丢失，并允许更有效地区分行为。通过使用 30 秒的时间步长对以 3 秒间隔模拟的数据进行插值，计算时间减少了 12 倍。包含源自原始数据的两个变量补偿了内插轨迹中的信息丢失，并允许更有效地区分行为。通过使用 30 秒的时间步长对以 3 秒间隔模拟的数据进行插值，计算时间减少了 12 倍。包含源自原始数据的两个变量补偿了内插轨迹中的信息丢失，并允许更有效地区分行为。