Data from: aniMotum, an R package for animal movement data: rapid quality control, behavioural estimation and simulation

1.  Animal tracking data are indispensable for understanding the
ecology, behaviour and physiology of mobile or cryptic species. Meaningful
signals in these data can be obscured by noise due to imperfect
measurement technologies, requiring rigorous quality control as part of
any comprehensive analysis.   2.  State-space models are
powerful tools that separate signal from noise. These tools are ideal for
quality control of error-prone location data and for inferring where
animals are and what they are doing when they record or transmit other
information. However, these statistical models can be challenging and
time-consuming to fit to diverse animal tracking data sets.   3.
 The R package aniMotum eases the tasks of conducting quality
control on and inference of changes in movement from animal tracking data.
This is achieved via: 1) a simple but extensible workflow that
accommodates both novice and experienced users; 2) automated processes
that alleviate complexity from data processing and model
specification/fitting steps; 3) simple movement models coupled with a
powerful numerical optimization approach for rapid and reliable model
fitting.   4.  We highlight
aniMotum's capabilities through three applications to real
animal tracking data. Full R code for these and additional applications
are included as Supporting Information so users can gain a deeper
understanding of how to use aniMotum for their own analyses.

1. 动物追踪数据对于解析移动性或隐匿性物种的生态学、行为学与生理学特征不可或缺。但由于测量技术存在局限性，数据中的有效信号常被噪声掩盖，因此在开展任何全面分析时，严格的质量控制环节必不可少。
2. 状态空间模型（State-space models）是分离信号与噪声的有力工具，非常适用于对易出错的定位数据开展质量控制，同时可推断动物在记录或传输其他信息时的所处位置与行为状态。不过，将这类统计模型适配至多样化的动物追踪数据集，往往颇具挑战性且耗时较长。
3. R语言包aniMotum可简化动物追踪数据的质量控制以及运动变化推断流程，具体实现途径包括：1） 简洁且可扩展的工作流，可兼顾新手与资深用户的使用需求；2） 自动化处理流程，可降低数据处理与模型设定/拟合环节的复杂度；3） 轻量化运动模型结合高性能数值优化方法，实现快速且可靠的模型拟合。
4. 我们通过三个基于真实动物追踪数据的应用案例，展示了aniMotum的功能特性。本研究附带的补充材料包含了上述案例及其他应用场景的完整R代码，以便用户深入学习如何将aniMotum应用于自身的分析工作中。