Model aversiveness and the evolution of imperfect Batesian mimics

Abstract

There are numerous examples of Batesian mimics that only imperfectly resemble their models. Given that inaccurate mimics are known to be predated more frequently than accurate ones, imperfect mimicry therefore poses something of a conundrum. One putative explanation, the relaxed selection hypothesis, predicts that when the cost of attacking a model is high relative to the benefit of consuming a mimic, selection against imperfect mimics will be relaxed, allowing mimics to be more imperfect for a given level of fitness. However, empirical support for this hypothesis is equivocal. Here, we report an experimental test of the relaxed selection hypothesis, in which human participants were tasked with discriminating between artificial stimuli representing models and mimics. In response to "attacking" a model (i.e., misclassifying it as palatable, or non-aversive) they received either a mild electric shock (high cost) or vibratory feedback (low cost). Consistent with the predictions of this hypothesis, we found that when the cost of attacking a model was high, mimetic phenotype could deviate more from the model (i.e., be more imperfect) for a given level of fitness than when the cost of attacking a model was low. Moreover, when the cost of attacking a model was high, participants showed an increased latency to attack. This finding shows that given sufficient costs, the relaxed selection hypothesis is a plausible explanation for the evolution of imperfect mimicry.

Keywords: Batesian mimicry; adaptation; color pattern; predation.

Figure 1

(a) Theoretical variation in fitness with respect to mimetic phenotype. When predators face a relatively high cost to attacking models (solid line), mimics can afford to be more imperfect for a given level of fitness (i.e., shifted right on the graph, away from the phenotype of their model) than when the cost of attacking models is low (dashed line). (b) The probability that participants chose to avoid a mimic when the cost of misclassification was comparatively high (i.e., they received an electric shock; solid line, circles) compared to when the cost of misclassification was low (i.e., they felt a vibration; dashed line, triangles). Data points show a participant’s decision (0, caught; 1, avoided) as a function of phenotype, where larger values indicate greater deviation from the phenotype of the model. Curves show fits from the generalized linear mixed-effects model.

Figure 2

Representative examples of artificial stimuli representing (a) models, and (b) mimics. Please see text for full details.

Figure 3

Latency to decide whether to (a) attack or (b) avoid a stimulus as a function of phenotype, where larger values indicate greater deviation from the phenotype of the model, when the cost of misclassification was either high (i.e., participants received an electric shock; triangles, solid lines) or low (i.e., they felt a vibration; circles, dashed lines).