Colour vision models: a practical guide, some simulations, and colourvision R package.

O_LIHuman colour vision differs from the vision of other animals. The most obvious differences are the number and type of photoreceptors in the retina. E.g., while humans are insensitive to ultraviolet (UV) light, most non-mammal vertebrates and insects have a colour vision that spans into the UV. The development of colour vision models allowed appraisals of colour vision independent of the human experience. These models are now widespread in ecology and evolution fields. Here I present a guide to colour vision modelling, run a series of simulations, and provide a R package - colourvision - to facilitate the use of colour vision models.\nC_LIO_LII present the mathematical steps for calculation of the most commonly used colour vision models: Chittka (1992) colour hexagon, Endler & Mielke (2005) model, and Vorobyev & Osorio (1998) linear and log-linear receptor noise limited models (RNL). These models are then tested using identical simulated and real data. These comprise of reflectance spectra generated by a logistic function against an achromatic background, achromatic reflectance against an achromatic background, achromatic reflectance against a chromatic background, and real flower reflectance data against a natural background reflectance.\nC_LIO_LIWhen the specific requirements of each model are met, between model results are, overall, qualitatively and quantitatively similar. However, under many common scenarios of colour measurements, models may generate spurious values and/or considerably different predictions. Models that log-transform data and use relative photoreceptor outputs are prone to generate unrealistic results when the stimulus photon catch is smaller than the background photon catch. Moreover, models may generate unrealistic results when the background is chromatic (e.g. leaf reflectance) and the stimulus is an achromatic low reflectance spectrum.\nC_LIO_LIColour vision models are a valuable tool in several ecology and evolution subfields. Nonetheless, knowledge of model assumptions, careful analysis of model outputs, and basic knowledge of calculation behind each model are crucial for appropriate model application, and generation of meaningful and reproducible results. Other aspects of vision not incorporated into these models should be considered when drawing conclusion from model results.\nC_LI