Evolutionary Ecology

, Volume 23, Issue 4, pp 491–501

Identifying the ecological conditions that select for intermediate levels of aposematic signalling

Original Paper

DOI: 10.1007/s10682-008-9247-3

Cite this article as:
Ruxton, G.D., Speed, M.P. & Broom, M. Evol Ecol (2009) 23: 491. doi:10.1007/s10682-008-9247-3


Chemically defended species often have conspicuous signals that warn potential predators of these defences. Recent evidence suggests that some such aposematic prey are not as conspicuous as possible, even though increased conspicuousness would bring additional anti-predator benefits. Here we present a simple model to explore the generality of these observations. Our model predicts that optimal fitness will often be achieved at an intermediate level of conspicuousness and not simply by maximising conspicuousness. This comes about because of the ubiquitous trade-off that increased conspicuousness has an ecological cost in increasing the encounter rate with predators, as well as a benefit in terms of enhancing learned aversion by predators of defended prey. However, importantly, we also predict that a small deviation away from maximal crypsis generally causes a decrease in fitness, even if a larger deviation would lead to an intermediate level of conspicuousness that maximises fitness. Hence, further consideration of whether intermediate levels of aposematism are as common in nature as predicted in this model will require consideration of the underlying evolution of appearance, and the plausibility of evolution across the fitness trough, from maximal crypsis to an intermediate level of aposematism.


AposematismConspicuousnessOptimisationPredationSignalling and communication

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.Division of Environmental & Evolutionary Biology, Institute of Biomedical and Life Sciences, Graham Kerr BuildingUniversity of GlasgowGlasgowUK
  2. 2.School of Biological SciencesUniversity of LiverpoolLiverpoolUK
  3. 3.Centre for Statistics and Stochastic Modelling, Department of MathematicsUniversity of SussexBrightonUK