1. Maps are essential tools for communicating information about wildlife distributions in space and time. As observational datasets grow and enable description of distributions at broader spatiotemporal extents and finer spatiotemporal resolutions, new opportunities arise for visualizing when and where animals occur.
2. Here we present a package for the r statistical computing environment, colorist, that facilitates visualization of animal distributions in space and time using raster inputs. In addition to enabling display of sequential change in distributions through the use of small multiples, colorist provides functions for extracting several features of interest from a sequence of distributions and for visualizing those features within an HCL (hue–chroma–luminance) colour space.
3. Resulting maps allow for ‘fair’ visual comparison of occurrence, abundance or probability density values across space and time and can be used to address questions about where, when and how consistently a species or individual is likely to be found. Functions can also be harnessed to visualize distributions of multiple species or individuals within a single time period when research questions are focused on understanding the degree to which species or individuals partition space.
4. By colouring temporal features of distributions, while simultaneously controlling their perceptual weight, we expand the set of tools available for exploring wildlife movements through space–time and for communicating research results. We expect colorist functions will prove useful for visualizing other types of multivariate data that we have yet to consider.

中文翻译：

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colorist：用于在时空上为野生生物分布着色的r包

1. 地图是传达有关时空野生动植物分布信息的重要工具。随着观测数据集的增长，并能够在更广泛的时空范围和更好的时空分辨率下描述分布，出现可视化动物何时何地的新机会。
2. 这里，我们提出一个包为[R统计计算环境中，调色，有利于动物的分布可视化在空间和时间使用光栅输入。除了可以通过使用较小的倍数来显示分布的顺序变化之外，colorist还提供了从一系列分布中提取感兴趣的多个特征并在HCL（色相-色度-亮度）色彩空间中可视化这些特征的功能。
3. 生成的地图可以对空间和时间上的发生，丰度或概率密度值进行“公平”的视觉比较，并可用于解决有关在何处，何时以及如何持续找到物种或个体的问题。当研究问题集中在理解物种或个体划分空间的程度时，也可以利用功能来可视化单个时间段内多个物种或个体的分布。
4. 通过对分布的时间特征进行着色，同时控制其感知权重，我们扩展了可用于通过时空探索野生动植物运动以及传达研究结果的工具集。我们期望色彩功能对于证明我们尚未考虑的其他类型的多元数据非常有用。