Camouflage research has long shaped our understanding of evolution by natural selection, and elucidating the mechanisms by which camouflage operates remains a key question in visual ecology. However, the vast diversity of colour patterns found in animals and their backgrounds, combined with the scope for complex interactions with receiver vision presents a fundamental challenge for investigating optimal camouflage strategies. Genetic algorithms have provided a potential method for accounting for these interactions, but with limited accessibility. Here, we present CamoEvo, an open-access toolbox for investigating camouflage pattern optimisation by using tailored genetic algorithms, animal and egg maculation theory and artificial predation experiments. This system allows for camouflage evolution within the span of just 10-30 generations (~1-2 min per generation), producing patterns that are both significantly harder to detect and that are optimised to their background. CamoEvo was built in ImageJ to allow for integration with an array of existing open access camouflage analysis tools. We provide guides for editing and adjusting the predation experiment and genetic algorithm as well as an example experiment. The speed and flexibility of this toolbox makes it adaptable for a wide range of computer based phenotype optimisation experiments.

Background Treatment Photos.

Background photos were photographed using an ASUS A002 smartphone calibrated with imageJ using a 5% reflectance standard. The scrubland backgrounds were photographed in Fordingbridge, Hampshire, England [50.93200,-1.78300], while the leaf litter and spring vegetation photos were collected from Headley, Surrey, England [51.26549,-0.26992]. Uncalibrated copies of the photos are available on request (ghancockzoology@gmail.com).

Prey Phenotypes and Capture Data.

All prey patterns were generated by the CamoEvo toolbox under selection from 9 volunteers (age 20-30) recruited at the University of Exeter. Each volunteer was randomly assigned treatment and given an hour to complete the experiment on their own computer. The capture time and image measurements were then submitted back to the experimenters. The images were then remeasured to provide measures for GabRat A and GabRat B, in addition to measures for the whole background. Survival status and presentation order was also added manually using Excel as all the data rows are in order.

Running the statistical analyses simply requires the R code and the Population Data. If the user wishes to regenerate the stimuli used, they will need a copy of CamoEvo available here.