title: Co-mimics have a mutualistic relationship despite unequal defences.
abstract: In the first clear mathematical treatment of natural selection, Müller proposed that a shared warning signal (mimicry) would benefit defended prey species by sharing out the per capita mortality incurred during predator education. Although mimicry is a mainstay of adaptationist thinking, there has been repeated debate on whether there is a mutualistic or a parasitic relationship between unequally defended co-mimic species. Here we show that the relationship between unequally defended species is mutualistic. We examined this in a 'novel world' of artificial prey with wild predators (great tit, Parus major). We kept the abundance of a highly defended prey ('model') constant and increased the density of a moderately defended prey ('defended mimic') of either perfect or imperfect mimetic resemblance to the model. Both model and defended mimic showed a net benefit from a density-dependent decrease in their per capita mortality. Even when the effect of dilution through density was controlled for, defended mimics did not induce additional attacks on the model, but we found selection for accurate signal mimicry. In comparison, the addition of fully edible (batesian) mimics did increase additional attacks on the model, but as a result of dilution this resulted in no overall increase in per capita mortality. By ignoring the effects of density, current theories may have overestimated the parasitic costs imposed by less defended mimics on highly defended models.
authors.affiliation: School of Biological Sciences, University of Liverpool, Biosciences Building, Crown Street, Liverpool L69 7ZB, UK. h.m.rowland@liv.ac.uk
authors.forename: Hannah M
authors.initials: HM
authors.lastname: Rowland
authors.forename: Eira
authors.initials: E
authors.lastname: Ihalainen
authors.forename: Leena
authors.initials: L
authors.lastname: Lindström
authors.forename: Johanna
authors.initials: J
authors.lastname: Mappes
authors.forename: Michael P
authors.initials: MP
authors.lastname: Speed
mesh_terms: D000220:Adaptation, Biological; D000818:Animals; D001362:Avoidance Learning; D005075:Biological Evolution; D008954:Models, Biological; D046109:Passeriformes / Q000502:physiology*; D011235:Predatory Behavior