Learning and memory in mimicry: II. Do we understand the mimicry spectrum?

Abstract

The evolution of mimicry is driven by the behaviour of predators. However, there has been little systematic testing of the sensitivity of evolutionary predictions to variations in assumptions about predator learning and forgetting. T o test how robust mimicry theory is to such behavioural modifications we combined sets of rules describing ways in which learning and forgetting might operate in vertebrate predators into 29 computer predator behaviour systems. These systems were applied in simulations of simplified natural mimicry situations, particularly investigating the nature of density-dependence and the benefits and losses conferred by mimicry across a spectrum of palatabilities. The classical Batesian-Muellerian spectrum was generated only by two of our 29 predator behaviour systems. Both of these 'classical predators' had extreme asymptotes of learning and fixed rate, time dependent forgetting. All edible mimics were treated by them as Batesian in that they parasitized their model's protection and had positive monotonic effects of density on model-mimic attack rates. All defended mimics were treated as Muellerian (Mullerian) in that their presence benefited their Model's protection, and showed negative monotonic density effects on attack rates. With the remaining 27 systems Batesian or Muellerian relationships extended beyond their conventional edibility boundaries. In some cases, Muellerian mimicry extended into the edible region of the 'palatability spectrum' (we term this quasi-Muellerian mimicry), and in others Batesian mimicry extended into the 'unpalatable', defended half of the spectrum (quasi-Batesian mimicry). Although most of the 29 behaviour systems included at least some regions of true Batesian and Muellerian mimicries, if forgetting was triggered by avoidance events (as suggested by J.E. Huheey) rather than by the passage of time then the mimicry spectrum excluded Mullerian mimicry altogether, and was composed of Batesian and quasiBatesian mimicries. In addition the classical prediction of monotonic density-dependent predation was shown not to be robust against variations in the forgetting algorithm. Time based forgetting which is retarded by observations of prey, or which varies its rate according to the degree of pleasantness or unpleasantness of a prey generates non-monotonic results. At low mimic densities there is a positive effect on attack rates and at higher densities a negative effect. Overall, the mode of forgetting has a more significant effect on mimetic relationships than the rate of learning. It seems to matter little whether learning and forgetting are switched or gradual functions. Predictions about mimetic evolution are therefore sensitive to assumptions about predator behaviour, though more so to variations in forgetting than learning rate. Based on findings from animal psychology and mimetic populations, we are able to rule out a number of predator behaviour systems. We suggest that the most credible of our 29 predators are those which generate results which incorporate Bates...

The Effect of Alternative Prey on the Dynamics of Imperfect Batesian and Müllerian Mimicries

“…We did not directly test quasi-Batesian mimicry theory, as it is based on an accurate resemblance between models and mimics. Interestingly, however, our data shows that the signal seemed to be more important to the avoidance learning of the predators than the relative unpalatability difference of the prey, suggesting that the variability in taste might not be as significant for the Mü llerian mimicry systems as suggested by unconventional theory (Speed 1993;Speed and Turner 1999;Turner and Speed 1996). Although our results illuminate the effects alternative prey can have in both Batesian and Mü llerian mimicries, our focus was on testing imperfect mimicry.…”

“…Experimental evidence for Mü llerian mimicry is based largely on field studies (Benson 1972;Mallet and Barton 1989;Kapan 2001), in which predator behavior is not directly observed. Despite the lack of understanding of exactly how predators promote similarity in Mü llerian co-mimics, predator behavior forms a basis of the mathematical models (e.g., Speed 1993;Turner and Speed 1996;Speed and Turner 1999;Mallet and Joron 2000). There is a clear need for more detailed studies of predator behavior (e.g., Alatalo and Mappes 1996;Speed et al 2000;Rowe et al 2004).…”

Molecular phylogenetic evidence for a mimetic radiation in Peruvian poison frogs supports a Müllerian mimicry hypothesis

“…These factors could potentially in£uence divergence between poison-frog populations as well. Coevolutionary chase of models by`pseudoBatesian' mimics has also been proposed as an explanation for polymorphism and diversi¢cation in toxic butter£ies (Speed & Turner 1999). This explanation is unlikely to apply to the species described here because D. imitator is probably more toxic than its models (Schulte 2001).…”

“…MÏller's (1879) hypothesis that toxic species would obtain a selective advantage by sharing similar colours and patterns has generated great interest and considerable controversy among scientists (Mallet & Joron 1999;Speed & Turner 1999). Most of the evidence for MÏllerian mimicry comes from studies of insects (e.g.…”

What kind of signals do mimetic tiger moths send? A phylogenetic test of wasp mimicry systems (Lepidoptera: Arctiidae: Euchromiini)

“…Conversely, in faunas with fewer obligate insectivores, the selection pressure should be less and imperfect mimics should be more common. In addition, these conditions will shift depending on the composition, palatability and densities of prey species (Speed & Turner 1999). Thus, mimicry dynamics function across historical space (phylogeny), biogeographic space and predatorprey community assemblages.…”