Signals are assumed to be honest in many cases. However sometimes cheats prosper. What factors determine the honesty of a signal and the likelihood of cheating?
Signalling is a ubiquitous form of communication that involves the sending of information to a ‘receiver’. Signals vary greatly and can take the form of acts or structures that alter the behaviour of the receiver. Examples of signals include the roar of a red deer stag (Cervus elaphus) to indicate size, or the presentation of a seagull’s (Larus argentatus) bill to its offspring. Even organisms as small as bacteria use forms of signalling (such as quorum sensing).
It is assumed that, on average, signals are beneficial and reliable to both sender and receiver (Stuart-Fox, 2005). It stands to reason that this is the case, due to the fact that signalling is so universal; if it was the case that signals only benefitted the sender, the perception of the signal would not have evolved. For example, a species of funnel web spider (Angelenopsis aperta) vibrate their web to indicate their weight to other individuals who are contesting for a specific web site. If it were the case that the receiver did not benefit from knowing the weight of their opponent, they would not have evolved to perceive web vibration as a signal.
So what comprises an honest signal? Honest signals convey correct or useful information to the receiver. On the outside, one might assume that signals are always meant to be honest and convey beneficial information. However, although signals must, on average, be reliable, much is to be gained through deception and cheating when there is conflict between sender and receiver. A dishonest or deceptive signal is one that manipulates the behaviour of the receiver to benefit the sender and detriment the receiver. Deceptive signals are successful because they exploit the honest signalling of others (Davies et al. 2012). Dawkins (1984) developed the idea that senders try to manipulate receivers and receivers attempt to mind-read senders. An example of signal deception is Batesian mimicry, in which a palatable species mimics an unpalatable ‘model’ to avoid being attacked by predators (the famous example being the Dismorphia butterflies mimicking the Ithomiini).
So why do all senders not give off dishonest signals? In many cases, the sender will have shared or common interest with the receiver. In this case, when there is no conflict, both the sender and receiver(s) will gain a fitness advantage if the sender is honest. For example, in some monogamous species, mating pairs that are matched in quality are able to raise more offspring (Davies et al. 2012). In this case, the likelihood of cheating would be extremely low.
Another factor affecting the honesty of a signal is Zahavi’s handicap principle. Zahavi’s principle indicates that reliable signals must be costly to the signaller, and therefore would not be possible to weak signallers (in turn making them honest signals) (Zahavi, 1975). This handicap is mostly implicated in mate choice. Using a common example, the peacock (Pavo cristatus) has a long and extravagant tail that hinders its flight speed and increases its vulnerability to predation. However, this shows to females that the individual is of good quality (seeing as it is still alive, regardless of its handicap). The superior mating success generated from the longer tail increases the peacock’s fitness enough to allow it to maintain the trait. If a trait is considered a handicap under this principle, it is seen as an honest signal.
Similarly, an alternative aspect that reduces the frequency of cheating signals are ‘index’ signals (Maynard Smith and Harper, 2003). These can be seen as truly honest signals; those that require the quality they indicate to express them. In other words, they cannot be faked. This can be exemplified using our example of the red deer. The indices here would be the roar of the stag; the size of the stag governs the quality of the sound produced; the physiological constraints generate honesty (Reby and McComb, 2003). This example also indicates a benefit of contest signals between opponents; signalling can minimise fighting costs by allowing each individual to size up the other. This will avoid heavily mismatched contests that would, almost certainly, lead to serious injury and gives one explanation as to how signalling has gained evolutionary stability.
These principles display that some communication mechanisms do not allow dishonest signalling. However, in most interactions there is a mixed equilibrium of cheats to honest signallers. What factors, then, will affect the honesty of a signal? It is important to consider that the effectiveness of dishonest signals must be frequency-dependant; a deceitful signal may only be evolutionarily stable when occurring at a low frequency. If all senders suddenly decided to send dishonest signals, receivers would adapt to stop responding to the signal through probing, or occasional assessment (Dawkins and Guilford, 1991). Therefore, dishonesty must exist as a mixed ESS (as proven by Számadó (2000) using game theory). Lindström et al. also demonstrated the mortality rates of butterfly mimics was strongly linked to their frequency in a population of models; the lower the frequency of mimic, the higher the survival rate. This backs up the theory that the frequency of a dishonest signal affects its effectiveness.
Although, as exemplified above, interactions between individuals are (on the whole) honest, this quality is exploited by occasional deceptive signals. Honest signals have evolved to be perceived without great investment to the receiver; it is this inherent quality of honest signalling that allows for deceptive signals to remain part of the equilibrium. There are, however, many principles that prohibit deceptive behaviour as well as those that maintain the evolutionary stability of honesty signalling.
References:
Davies, N.B., Krebs, J.R. & West, S.A. (2012). An introduction to behavioural ecology. John Wiley & Sons: Oxford.
Dawkins, M.S. & Guilford, T. (1991). The corruption of honest signalling. Animal Behaviour, 41, 865–873.
Krebs, J.R. & Dawkins, R. (1984). Animal signals: mind-reading and manipulation. Behavioural Ecology: an evolutionary approach, 2, 380-402.
Lindström, L., Alatalo, R. V. & Mappes, J. (1997). Imperfect Batesian mimicry—the effects of the frequency and the distastefulness of the model. Proceedings of the Royal Society B: Biological Sciences, 264, 149–153.
Reby, D. & McComb, K. (2003). Anatomical constraints generate honesty: acoustic cues to age and weight in the roars of red deer stags. Animal behaviour, 65, 519-530.
Smith, J.M. & Harper, D. (2003). Animal signals. Oxford University Press: Oxford.
Stuart-Fox, D. (2005). Deception and the origin of honest signals. Trends in Ecology and Evolution, 20, 521–523.
Számadó, S. (2000). Cheating as a mixed strategy in a simple model of aggressive communication. Animal behaviour, 59, 221–230.
Zahavi, A. (1975). Mate selection – A selection for a handicap. Journal of Theoretical Biology, 53, 205–214.