Diel activity correlates with colour pattern morphology of heterobranch sea slugs

Visually hunting predators drive the evolution and maintenance of bold visual defences, including aposematic signalling. Such visual signals must be encountered by predators in lighting conditions where the perception of colour patterns is possible. Therefore, aposematic colouration is predicted to evolve in species encountered by visually hunting predators during daytime, with diurnal species most at risk of visual predation. However, colour patterns in diurnal and nocturnal species are highly diverse, and little is known about the relationships between prey colouration and daytime activity. To investigate correlations between daytime activity and colour pattern phenotypes across species, we quantified colour patterns in 45 species (n = 346 individuals) of eastern Australian sea slugs (Gastropoda: Heterobranchia). We used 158 descriptors of colour pattern morphology using the Quantitative Colour Pattern Analysis (QCPA) framework, which models pattern appearance using the visual system of a potential predator, a triggerfish (Rhinecanthus aculeatus). We then used phylogenetic factor analysis to identify a single factor that was strongly correlated with daytime activity, comprising 55 individual colour pattern descriptors. This dimension of pattern variation identified daytime activity in 87% of species in our dataset. We found that daytime activity in heterobranch sea slugs was associated with a distinct, highly descriptive set of spatiochromatic image statistics. Specifically, diurnal species were more boldly patterned than nocturnal species, exhibiting increased colour, luminance and pattern contrast, indicating the presence of visual signalling and, thus, visual predation as a key driver of heterobranch sea slug phenotype. Our study sheds light on the ecology and evolution of defensive colouration in sea slugs and provides a novel and robust workflow for comparative phylogenetic studies considering colour pattern spaces described by high-dimensional datasets, which can be applied to other species and ecosystems.