Mapping the adaptive landscape of Batesian mimicry using 3D-printed stimuli
Christopher H. Taylor, David Watson, John Skelhorn, Danny Bell, Simon Burdett, Aoife Codyre, Kathryn Cooley, James R. Davies, Joshua Joseph Dawson, Tahiré D'Cruz, Samir Raj Gandhi, Hannah Jackson, Ros Lowe, Elizabeth Ogilvie, Alexandra Lei Pond, H.D. Rees, Joseph Richardson, Joshua Sains, Francis Short, Christopher Brignell, Gabrielle L. Davidson, Hannah M. Rowland, Mark East, Ruth Goodridge, Francis Gilbert, Tom Reader
Abstract
Abstract In a classic example of adaptation, harmless Batesian mimics gain protection from predators through resemblance to one or more unpalatable models 1,2 . Mimics vary greatly in accuracy, and explaining the persistence of inaccurate mimics is an ongoing challenge for evolutionary biologists 3,4 . Empirical testing of existing hypotheses is constrained by the difficulty of assessing the fitness of phenotypes absent among extant species, leaving large parts of the adaptive landscape unexplored 5 —a problem affecting the study of the evolution of most complex traits. Here, to address this, we created mimetic phenotypes that occupy hypothetical areas of trait space by morphing between 3D images of real insects (flies and wasps), and tested the responses of real predators to high-resolution, full-colour 3D-printed reproductions of these phenotypes. We found that birds have an excellent ability to learn to discriminate among insects on the basis of subtle differences in appearance, but this ability is weaker for pattern and shape than for colour and size traits. We found that mimics gained no special protection from intermediate resemblance to multiple model phenotypes. However, discrimination ability was lower in some invertebrate predators (especially crab spiders and mantises), highlighting that the predator community is key to explaining the apparent inaccuracy of many mimics.