CamoEvo: an open access toolbox for artificial camouflage evolution experiments

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.