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colorist : An r package for colouring wildlife distributions in space–time
Author(s) -
Schuetz Justin G.,
StrimasMackey Matthew,
Auer Tom
Publication year - 2020
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13477
Subject(s) - computer science , raster graphics , visualization , set (abstract data type) , space (punctuation) , data mining , artificial intelligence , programming language , operating system
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. 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. 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. 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.