Gawryszewski, F. M. | bioRxiv Science

bioRxiv2017

Orb-web spider color polymorphism through the eyes of multiple prey and predators

The sensory drive theory predicts that signals, sensory systems, and signaling behavior should coevolve. Variation in the sensory systems of prey and predators may explain the diversity of color signals, such as color polymorphism. The spider Gasteracantha cancriformis (Araneidae) possesses several conspicuous color morphs. The aim of the present study was to assess whether the color polymorphism of G. cancriformis may be maintained by pressure from multiple signal receivers, such as prey and predators with distinct color vision systems. Although, the multiple receivers world is a more realistic scenario, it has received little attention. In orb-web spiders, the prey attraction hypothesis states that conspicuous colors are prey lures that increase spider foraging success via flower mimicry. However, in highly defended species, conspicuous colors could also be a warning signal to predators. We used color vision modelling to estimate chromatic and achromatic contrast of G. cancriformis morphs as perceived by potential prey and predator taxa. Our results revealed that individual prey and predator taxa perceive the conspicuousness of morphs differently. For instance, the red morph is perceived as quite conspicuous to lepidopteran prey and avian predators, but not by other insects. Therefore, the multiple prey and predator hypotheses may explain the evolution of color polymorphism in G. cancriformis. However, flower mimicry hypothesis was weakly corroborated. Other parameters that are not evaluated by color vision models, such as distance, shape, angle, and pattern geometry could also affect the perception of color morphs by both prey and predators and thereby influence morph survival.

animal behavior and cognition↗

bioRxiv2017

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

animal behavior and cognition↗