Sexual selection based on egg colour: physiological models and egg discrimination experiments in a cavity-nesting bird

Abstract

It has been proposed that the blue-green bird egg colourations of many avian species may constitute a sexually selected female signal that males can use to modulate their parental investment. A fundamental untested assumption for the validation of this hypothesis is that males can accurately assess differences in the colour of eggs. A recent review suggests that this could be particularly problematic when egg clutches were located within a dimly lit nest cavity, due to limitations of the visual system in low light conditions. Here, we first used a photoreceptor noise-limited model of colour discrimination ability that accounts for visual performance under low light conditions to study whether a typical cavity-nesting passerine, the spotless starling Sturnus unicolor, can discriminate their eggs under the ambient illumination in their nest-holes. Secondly, we tested the validity of model predictions with behavioural data collected in two egg discrimination experiments performed in this species. Estimated egg detectability depended entirely on model assumptions about visual limitations linked to light intensity. Starlings would not be able to discriminate egg differences in their nests if the model was based on the assumption that light intensity limited detectability, whereas they could potentially perceive as different many possible pairwise clutch comparisons if the model assumption was that light intensity did not limit detectability. Results of behavioural experiments fitted the prediction of the visual model where light intensity did not limit detectability. Our results suggest that photoreceptor noise-limited colour models based on stimulation of single photoreceptors cannot, at present, be used to predict egg discrimination ability in spotless starlings under low light conditions. Future studies aiming to test egg discrimination constraints in the frame of the sexual selection hypothesis should therefore combine both modelling and behavioural experiments to determine which are the components of the models that produce the mismatch with the behavioural conditions.