The role of signaling constraints in defining optimal marginal costs of reliable signals

Keith D Harris, Yair Daon, Vidyanand Nanjundiah and John QuinnEditor

Behavioral Ecology, 2020, vol. 31, issue 3, 784-791

Abstract: The handicap principle was originally proposed to resolve the question of why, in their competition for mates, certain species invest in exaggerated ornaments that are often detrimental to their survival. Zahavi suggested that the traits that are most suitable to serve as signals are precisely those that require the burden of extra investment to increase in magnitude: that burden enables the signal to be correlated with the signaler’s quality. According to his model, the additional investment in signaling results in a functional advantage. It does so by providing more accurate information regarding the signaler as it increases the distinction between males of similar quality. There are a number of formalizations of this model, and experimental studies of the handicap principle have focused on testing them. Nonetheless, there is little consensus whether 1) ensuring reliability requires an additional investment or 2) traits that require a relatively higher investment to increase (have higher marginal costs) are selected as signals over those with lower marginal costs. Here, we present an agent-based mate choice model that quantifies the relative stability of signals with different marginal costs. Our model demonstrates how quality-independent constraints (in signal production and perception) affect the range of marginal costs for which a signal is informative. In turn, receiver preference for informative signals drives the selection of signals according to marginal cost. The presence or absence of signaling constraints can determine the outcome of costly signaling models and, thus, explain the different conclusions of Zahavi’s verbal model and its subsequent formalizations.

Keywords: costly signaling; Fisherian runaway; handicap principle; mate choice (search for similar items in EconPapers)
Date: 2020
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