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Using Delaunay triangulation to sample whole-specimen color from digital images

Cite this dataset

Valvo, Jennifer et al. (2022). Using Delaunay triangulation to sample whole-specimen color from digital images [Dataset]. Dryad.


Color variation is one of the most obvious examples of variation in nature, but biologically meaningful quantification and interpretation of variation in color and complex patterns is challenging. Many current methods for assessing variation in color patterns classify color patterns using categorical measures, provide aggregate measures that ignore spatial pattern, or both, losing potentially important aspects of color pattern.

Here, we present Colormesh, a novel method for analyzing complex color patterns that offers unique capabilities. Our approach is based on unsupervised color quantification combined with geometric morphometrics to identify regions of putative spatial homology across samples, from histology sections to whole organisms. Colormesh quantifies color at individual sampling points across the whole sample.

We demonstrate the utility of Colormesh using digital images of Trinidadian guppies (Poecilia reticulata), for which the evolution of color has been frequently studied. Guppies have repeatedly evolved in response to ecological differences between up- and downstream locations in Trinidadian rivers, resulting in extensive parallel evolution of many phenotypes. Previous studies have, for example, compared the area and quantity of discrete color (e.g., area of orange, number of black spots) between these up- and downstream locations neglecting spatial placement of these areas. Using the Colormesh pipeline, we show that patterns of whole-animal color variation do not match expectations suggested by previous work.

Colormesh can be deployed to address a much wider range of questions about color pattern variation than previous approaches. Colormesh is thus especially suited for analyses that seek to identify the biologically important aspects of color pattern when there are multiple competing hypotheses, or even no a priori hypotheses at all.


This is the dataset used for the guppy example described in the manuscript titled: "Using Delaunay triangulation to sample whole-specimen color from digital images." This dataset contains the RGB values measured from sampling locations determined in an unsupervised manner by the Colormesh pipeline. A total of 485 images were processed (landmarked and unwarped to a concensus shape) using the TPS Series software by James Rohlf. The Colormesh pipeline made 4 passes (i.e., performed 4 rounds of triangulation) and calculated 2,462 sampling locations within the consensus shape using Delaunay triangulation. The RGB values, on a scale of 0 to 1, were measured at each sampling point for each image.

For access to the images (*.TIF) that were sampled, please email Jennifer Valvo ( 

Usage notes

The RGB_colormesh_data.csv 

The color channels are identified by either r (red), g(green), or b(blue) in the header and the point sampled is given by the numerical value (e.g., the red color channel value for point #200 is under the column header r.200, the green and blue color channel values are under the headers g.200 and b.200, respectively). Information associated with the specimen identification is given in the first five columns. 


This file contains the x,y coordinates of the 2462 points sampled for color. The first 62 coordinates (point_ID 1-62) are the perimeter of the fish. These points are used in the first round of Delaunay triangulation. The centroid of the resulting trinagles serve as the vertices for the second round of triangulation. To generate the data set used in the manuscript, 4 passes of trinagulation were performed to produce the 2462 sampling locations. Only the pixel at this location was sampled for RGB values for this data set.


National Science Foundation, Award: DEB 1740466

Natural Sciences and Engineering Research Council

Rosemary Grant Award