The evolution of signal form: effects of learned versus inherited recognition

Organisms can learn by individual experience to recognize relevant stimuli in the environment or they can genetically inherit this ability from their parents. Here, we ask how these two modes of acquisition affect signal evolution, focusing in particular on the exaggeration and cost of signals. We argue first, that faster learning by individual receivers cannot be a driving force for the evolution of exaggerated and costly signals unless signal senders are related or the same receiver and sender meet repeatedly. We argue instead that biases in receivers' recognition mechanisms can promote the evolution of costly exaggeration in signals. We provide support for this hypothesis by simulating coevolution between senders and receivers, using artificial neural networks as a model of receivers' recognition mechanisms. We analyse the joint effects of receiver biases, signal cost and mode of acquisition, investigating the circumstances under which learned recognition gives rise to more exaggerated signals than inherited recognition. We conclude the paper by discussing the relevance of our results to a number of biological scenarios.     

Frequency-dependent variation in mimetic fidelity in an intraspecific mimicry system

Contemporary theory predicts that the degree of mimetic similarity of mimics towards their model should increase as the mimic/model ratio increases. Thus, when the mimic/model ratio is high, then the mimic has to resemble the model very closely to still gain protection from the signal receiver. To date, empirical evidence of this effect is limited to a single example where mimicry occurs between species. Here, for the first time, we test whether mimetic fidelity varies with mimic/model ratios in an intraspecific mimicry system, in which signal receivers are the same species as the mimics and models. To this end, we studied a polymorphic damselfly with a single male phenotype and two female morphs, in which one morph resembles the male phenotype while the other does not. Phenotypic similarity of males to both female morphs was quantified using morphometric data for multiple populations with varying mimic/model ratios repeated over a 3 year period. Our results demonstrate that male-like females were overall closer in size to males than the other female morph. Furthermore, the extent of morphological similarity between male-like females and males, measured as Mahalanobis distances, was frequency-dependent in the direction predicted. Hence, this study provides direct quantitative support for the prediction that the mimetic similarity of mimics to their models increases as the mimic/model ratio increases. We suggest that the phenomenon may be widespread in a range of mimicry systems.     

Perspectives and Debates: Mimicry,Signalling and Co‐Evolution (Commentary on Wolfgang Wickler – Understanding Mimicry – With special reference to vocal mimicry)

In his stimulating discussion, Wolfgang Wickler criticizes fuzzy usage of term mimicry by drawing attention to its original definition by H. Bates. Mimicry refers to functional ‘model–mimic–selecting agent’ trinity (with varying number of species involved) when the selecting agent (i.e. signal receiver) responds similarly to mimic and model to the advantage of the mimic. Concurring with Wickler I argue that convergence is neither necessary nor sufficient to support similarity as evidence for mimicry and that it is artificial and unproductive to classify mimicry with respect to ontogeny (innate vs. learned similarity) or model species identity (learning from conspecifics vs. heterospecifics). Using butterfly ‘eye’‐spots, I argue that just identifying each of the supposed model, the mimic and the selective agent, and even demonstrating that mimic‐model similarity affects the agent's behaviour, provides no conclusive evidence for mimicry. Even a demonstration that the mimic benefits from receiver response may not provide conclusive evidence for mimicry. Using avian brood parasite–host egg and nestling mimicry, I emphasize that without experimental manipulation of the hypothesized mimetic traits, it is impossible to test the mimicry hypothesis robustly. Due to fundamental constraints on human perception, some cases of mimicry may in fact be just a by‐product of human inability to perceive relevant differences between animal phenotypes (what is similar for human eye, nose or ear may not be viewed, smelled or heard as similar for relevant animal observers), whereas many cases of real mimicry may escape our attention from the same reason (‘hidden’ mimicry). Surprisingly, the same mimetic phenotype may show completely different effects on selective agents under different ecological circumstances. Finally, relatively dissimilar species may be more mimetic than highly similar model–mimic pairs because mimicry may be more fruitfully understood as a co‐evolutionary process rather than a similarity.     

Aggressive mimics profit from a model-signal receiver mutualism

Mimetic species have evolved to resemble other species to avoid predation (protective mimicry) or gain access to food (aggressive mimicry). Mimicry systems are frequently tripartite interactions involving a mimic, model and 'signal receiver'. Changes in the strength of the relationship between model and signal receiver, owing to shifting environmental conditions, for example, can affect the success of mimics in protective mimicry systems. Here, we show that an experimentally induced shift in the strength of the relationship between a model (bluestreak cleaner fish, Labroides dimidiatus) and a signal receiver (staghorn damselfish, Amblyglyphidodon curacao) resulted in increased foraging success for an aggressive mimic (bluestriped fangblenny, Plagiotremus rhinorhynchos). When the parasite loads of staghorn damselfish clients were experimentally increased, the attack success of bluestriped fangblenny on damselfish also increased. Enhanced mimic success appeared to be due to relaxation of vigilance by parasitized clients, which sought cleaners more eagerly and had lower overall aggression levels. Signal receivers may therefore be more tolerant of and/or more vulnerable to attacks from aggressive mimics when the net benefit of interacting with their models is high. Changes in environmental conditions that cause shifts in the net benefits accrued by models and signal receivers may have important implications for the persistence of aggressive mimicry systems.     

Information,influence, and the causal-explanatory role of content in understanding receiver responses

Sceptics of informational terminology argue that by attributing content to signals, we fail to address nonhuman animal communication on its own terms. Primarily, we ignore that communication is sender driven: i.e. driven by the intrinsic physical properties of signals, themselves the result of selection pressures acting on signals to influence receivers in ways beneficial for senders. In contrast, information proponents argue that this ignores the degree to which communication is, in fact, receiver driven. The latter argue that an exclusive focus on the intrinsic mechanical properties of signals cannot explain why receivers respond as they do. This is because receivers are not prisoners of sender influence. They possess response flexibility, and so we can only explain why receivers respond to signals as they do by positing that receivers ‘derive information’ from signals. I argue that, while basically true, this response flexibility can take one of two forms depending on the causal-explanatory role of information in understanding the response of the receiver: diachronic, on the one hand; and synchronic, on the other. In species with diachronic response flexibility only, information is derived by receivers from signals in a minimal sense. In such cases, information is an ultimate explanatory construct: one underpinned by historical facts at the population level. Alternatively, in species with synchronic response flexibility, information is derived by receivers from signals in a richer sense. Here, information is a proximate explanatory construct: one underpinned by cognitive-mechanistic facts at the level of the individual organism. Without recognising the different ways information can be derived from signals, and the different causal-explanatory roles (ultimate vs proximate) information can play in understanding alternate kinds of receiver flexibility (diachronic vs synchronic), proponents of information leave themselves open to the charge of anthropomorphising some signalling systems.     

On the definition of mimicry

An operational distinction between crypsis and mimicry is made in terms of the cognitive and perceptual systems of signal-receivers. Cryptic organisms specialize in generating information of the type not attended to or filtered out (reference frame) by the receivers, whereas mimetic organisms specialize in producing information (signals) of the type sought out by and of interest to a receiver. Mimicry is defined in terms of a system of three living organisms, model, mimic and operator (signal-receiver), in which the mimic gains in fitness by the operator identifying it with the model. Some advantages and applications of the definition are briefly discussed.     

Mimicry for all modalities

Mimicry is a canonical example of adaptive signal design. In principle, what constitutes mimicry is independent of the taxonomic identity of the mimic, the ecological context in which it operates, and the sensory modality through which it is expressed. However, in practice the study of mimicry is inconsistent across research fields, with theoretical and empirical advances often failing to cross taxonomic and sensory divides. We propose a novel conceptual framework whereby mimicry evolves if a receiver perceives the similarity between a mimic and a model and as a result confers a selective benefit onto the mimic. Here, misidentification and/or deception are no longer formal requirements, and mimicry can evolve irrespective of the underlying proximate mechanisms. The centrality of receiver perception in this framework enables us to formally distinguish mimicry from perceptual exploitation and integrate mimicry and multicomponent signalling theory for the first time. In addition, it resolves inconsistencies in our understanding of the role of learning in mimicry evolution, and shows that imperfect mimicry is expected to be the norm. Mimicry remains a key model for understanding signal evolution and cognition, and we recommend the adoption of a unified approach to stimulate future interdisciplinary developments in this fascinating area of research.     

THE EVOLUTION OF INDIVIDUAL VARIATION IN COMMUNICATION STRATEGIES

Communication is a process in which senders provide information via signals and receivers respond accordingly. This process relies on two coevolving conventions: a “sender code” that determines what kind of signal is to be sent given the sender's state; and a “receiver code” that determines the appropriate responses to different signal types. By means of a simple but generic model, we show that polymorphic sender and receiver strategies emerge naturally during the evolution of communication, and that the number of alternative strategies observed at equilibrium depends on the potential for error in signal production. Our model suggests that alternative communication strategies will evolve whenever senders possess imperfect information about their own quality or state, signals are costly, and genetic mechanisms allow for a correlation between sender and receiver behavior. These findings provide an explanation for recent reports of individual differences in communication strategies, and suggest that the amount of individual variation that can be expected in communication systems depends on the type of information being conveyed. Our model also suggests a link between communication and the evolution of animal personalities, which is that individual differences in the production and interpretation of signals can result in consistent differences in behavior.     

Natural selection in mimicry

Biological mimicry has served as a salient example of natural selection for over a century, providing us with a dazzling array of very different examples across many unrelated taxa. We provide a conceptual framework that brings together apparently disparate examples of mimicry in a single model for the purpose of comparing how natural selection affects models, mimics and signal receivers across different interactions. We first analyse how model–mimic resemblance likely affects the fitness of models, mimics and receivers across diverse examples. These include classic Batesian and Müllerian butterfly systems, nectarless orchids that mimic Hymenoptera or nectar‐producing plants, caterpillars that mimic inert objects unlikely to be perceived as food, plants that mimic abiotic objects like carrion or dung and aggressive mimicry where predators mimic food items of their own prey. From this, we construct a conceptual framework of the selective forces that form the basis of all mimetic interactions. These interactions between models, mimics and receivers may follow four possible evolutionary pathways in terms of the direction of selection resulting from model–mimic resemblance. Two of these pathways correspond to the selective pressures associated with what is widely regarded as Batesian and Müllerian mimicry. The other two pathways suggest mimetic interactions underpinned by distinct selective pressures that have largely remained unrecognized. Each pathway is characterized by theoretical differences in how model–mimic resemblance influences the direction of selection acting on mimics, models and signal receivers, and the potential for consequent (co)evolutionary relationships between these three protagonists. The final part of this review describes how selective forces generated through model–mimic resemblance can be opposed by the basic ecology of interacting organisms and how those forces may affect the symmetry, strength and likelihood of (co)evolution between the three protagonists within the confines of the four broad evolutionary possibilities. We provide a clear and pragmatic visualization of selection pressures that portrays how different mimicry types may evolve. This conceptual framework provides clarity on how different selective forces acting on mimics, models and receivers are likely to interact and ultimately shape the evolutionary pathways taken by mimetic interactions, as well as the constraints inherent within these interactions.     

Understanding Mimicry – with Special Reference to Vocal Mimicry

The term mimicry was introduced to biology in 1862 by Henry Walter Bates in his evolutionary explanation of deceptive communication in nature, based on a three‐part interaction system of a mimicked organism or object (called model), a mimicking organism (called mimic), and one or more organisms as selecting agents. Bates gave two incongruous definitions of mimicry: one from the viewpoint of a natural agent that selects for, and in consequence is deceived by, the close resemblance of a toxic model's warning signal and the similar appearance of a palatable mimic, and another one from the viewpoint of a human taxonomist who under an evolutionary aspect focuses on convergent resemblance between model and mimic. Later definitions of Müllerian (F. Müller), arithmetic (A. Wallace) and social (M. Moynihan) mimicry abolish deception in the natural selecting agent, rely on the convergence criterion alone, fuse the roles of model and mimic but have to accept a mix of homologous and convergent resemblance amongst them for a functional explanation. The definition of vocal mimicry (E. Armstrong) refers to a learned resemblance between mimic and heterospecific model by character duplication (no convergence), so far without known (deceived or not deceived) natural selecting agents. It excludes Batesian vocal mimicry. The functional ethological understanding of mimicry as a tripartite communication system (W. Wickler) is consistent with Bates' concept and accepts deception as key element of Batesian mimicry beyond homologous and convergent resemblances. Deception is seen as caused by the divergence between a sign and its meaning for the natural selecting agent. This understanding covers mimicry in all behaviour domains, provides a generally applicable definition of mimic and model so far missing in any mimicry concept, and it distinguishes – still in line with Henry Bates – cultural from genetically determined model‐mimic‐resemblance; this applies to vocal mimicry in particular. Convergently evolved model‐mimic‐resemblance, not essential in Batesian mimicry but mandatory for its alternatives, marks a fundamental distinction between Batesian mimicry (including Mimesis) and all other conceptualized mimicries and accounts for the non‐existence of a unified meaning of the term mimicry. However, character convergence does not help to explain the mere existence of mimicry phenomena and is irrelevant for their permanence in nature. I therefore propose to remove the convergence argument from any mimicry definition.     

How Does Individual Recognition Evolve? Comparing Responses to Identity Information in Polistes Species with and Without Individual Recognition

A wide range of complex social behaviors are facilitated by the recognition of individual conspecifics. Individual recognition requires sufficient phenotypic variation to provide identity information as well as receivers that process and respond to identity information. Understanding how a complex trait such as individual recognition evolves requires that we consider how each component has evolved. Previous comparative studies have examined phenotypic variability in senders and receiver learning abilities, although little work has compared receiver responses to identity information among related species with and without individual recognition. Here, we compare responses to identity information in two Polistes paper wasps: P. fuscatus, which visually recognizes individuals, and P. metricus, which does not normally show evidence of individual recognition. Although the species differ in individual recognition, the results of this study show that receiver responses to experimentally manipulated identity information are surprisingly similar in both species. Receivers direct less aggression toward identifiable individuals than unidentifiable individuals. Therefore, the responses necessary for individual recognition may pre‐date its evolution in the P. fuscatus lineage. Additionally, our data demonstrate the apparent binary differences in a complex behavior between the two species, such as individual recognition, likely involve incremental differences along a number of axes.     

Single Cell Analysis of a Bacterial Sender-Receiver System

Monitoring gene expression dynamics on the single cell level provides important information on cellular heterogeneity and stochasticity, and potentially allows for more accurate quantitation of gene expression processes. We here study bacterial senders and receivers genetically engineered with components of the quorum sensing system derived from Aliivibrio fischeri on the single cell level using microfluidics-based bacterial chemostats and fluorescence video microscopy. We track large numbers of bacteria over extended periods of time, which allows us to determine bacterial lineages and filter out subpopulations within a heterogeneous population. We quantitatively determine the dynamic gene expression response of receiver bacteria to varying amounts of the quorum sensing inducer N-3-oxo-C6-homoserine lactone (AHL). From this we construct AHL response curves and characterize gene expression dynamics of whole bacterial populations by investigating the statistical distribution of gene expression activity over time. The bacteria are found to display heterogeneous induction behavior within the population. We therefore also characterize gene expression in a homogeneous bacterial subpopulation by focusing on single cell trajectories derived only from bacteria with similar induction behavior. The response at the single cell level is found to be more cooperative than that obtained for the heterogeneous total population. For the analysis of systems containing both AHL senders and receiver cells, we utilize the receiver cells as ‘bacterial sensors’ for AHL. Based on a simple gene expression model and the response curves obtained in receiver-only experiments, the effective AHL concentration established by the senders and their ‘sending power’ is determined.     

Economic models of animal communication

Many models of animal signal evolution fail to incorporate an explicit strategy for receivers prior to the evolution of signals. When reasonable assumptions are made for such strategies, we have shown that there is a minimal accuracy of signal coding that is required before receivers should attend to signals (Bradbury & Vehrencamp 1998, Principles of Animal Communication). Depending upon the relative payoffs of correct and incorrect decisions by receivers, this minimal accuracy can be quite high. Here we use this result to explain why so many signals appear to be traits that provided useful information to receivers before becoming ritualized into signals. Our model also supports one prediction of sensory drive models: that latent preferences may selectively favour some signal precursors over others. However, it imposes a serious constraint on sensory drive by requiring that there be sufficient benefits to a receiver to compensate for the costs of disrupting the optimal receiver strategy used before exploitation. Finally, we discuss the overlap between signal honesty and accuracy and show how senders that completely disagree with receivers about appropriate receiver decisions may still benefit by providing moderately honest and accurate signals. Copyright 2000 The Association for the Study of Animal Behaviour.     

The evolution of mimicry under constraints

The resemblance between mimetic organisms and their models varies from near perfect to very crude. One possible explanation, which has received surprisingly little attention, is that evolution can improve mimicry only at some cost to the mimetic organism. In this article, an evolutionary game theory model of mimicry is presented that incorporates such constraints. The model generates novel and testable predictions. First, Batesian mimics that are very common and/or mimic very weakly defended models should evolve either inaccurate mimicry (by stabilizing selection) or mimetic polymorphism. Second, Batesian mimics that are very common and/or mimic very weakly defended models are more likely to evolve mimetic polymorphism if they encounter predators at high rates and/or are bad at evading predator attacks. The model also examines how cognitive constraints acting on signal receivers may help determine evolutionarily stable levels of mimicry. Surprisingly, improved discrimination abilities among signal receivers may sometimes select for less accurate mimicry.     

Cleaner wrasse mimics inflict higher costs on their models when they are more aggressive towards signal receivers

Aggressive mimics are predatory species that resemble a 'model' species to gain access to food, mating opportunities or transportation at the expense of a signal receiver. Costs to the model may be variable, depending on the strength of the interaction between mimics and signal receivers. In the Indopacific, the bluestriped fangblenny Plagiotremus rhinorhynchos mimics juvenile cleaner wrasse Labroides dimidiatus. Instead of removing ectoparasites from larger coral reef fish, fangblennies attack fish to feed on scales and body tissue. In this study, juvenile cleaner wrasse suffered significant costs when associated with P. rhinorhynchos mimics in terms of reduced cleaning activity. Furthermore, the costs incurred by the model increased with heightened aggression by mimics towards signal receivers. This was apparently because of behavioural changes in signal receivers, as cleaning stations with mimics that attacked frequently were visited less. Variation in the costs incurred by the model may influence mimicry accuracy and avoidance learning by the signal receiver and thus affect the overall success and maintenance of the mimicry system.     

Becoming a Sign: The Mimic’s Activity in Biological Mimicry

From a semiotic perspective biological mimicry can be described as a tripartite system with a double structure that consists of ecological relations between species and semiotic relations of sign. In this article the focus is on the mimic who is the individual benefiting from its resemblance to the cues or signals of other species or to the environment. In establishing the mimetic resemblance the question of mimic’s activity becomes crucial, and the activity can range from the fixed bodily patterns to fully dynamic behavioural displays. The mimic’s activity can be targeted at two other participants of the mimicry system—either at the model or at the receiver. The first possibility is quite common in camouflage and there are several possibilities for mimic’s activity to occur: selecting a resting place or habitat based on conformity with the environment, changing body coloration to correspond to the surrounding environment, covering oneself with particles of the soil. In its activity aimed at the model, the mimic develops a strong semiotic connection with its specific perceptual environment or part of it and obtains a representational character. In the second possibility the activity of a mimetic organism is aimed at the receiver who is confused by the resemblance, and between the two participants an active communicative interaction is established. Such type of mimicry can be exemplified by abstract threat displays found in various groups of animals, for instance a toad’s upright posture as a response to the presence of a snake. From the semiotic viewpoint it can be interpreted as the motive of fear in the predator’s Umwelt being entered into the mimic’s subjective world and manifested in its behaviour. The mimetic organism ends up in an ambiguous position, where it needs to pretend to be something other than it is. In the final part of the article it is argued that the mimetic sign is basically a false designator as the mimic’s activity to become a sign is aimed at a specific type of signs. Rather than signifying belonging to its own species or group, a mimetic sign indicates that its carrier belongs to the type of some other species. The tension between the form and behaviour of mimetic organisms arises from the discrepancy between the type of organism that it essentially is and the type of organism that the mimetic sign it carries imposes on it.     

Artificial neural networks in models of specialization, guild evolution and sympatric speciation

Sympatric speciation can arise as a result of disruptive selection with assortative mating as a pleiotropic by-product. Studies on host choice, employing artificial neural networks as models for the host recognition system in exploiters, illustrate how disruptive selection on host choice coupled with assortative mating can arise as a consequence of selection for specialization. Our studies demonstrate that a generalist exploiter population can evolve into a guild of specialists with an 'ideal free' frequency distribution across hosts. The ideal free distribution arises from variability in host suitability and density-dependent exploiter fitness on different host species. Specialists are less subject to inter-phenotypic competition than generalists and to harmful mutations that are common in generalists exploiting multiple hosts.When host signals used as cues by exploiters coevolve with exploiter recognition systems, our studies show that evolutionary changes may be continuous and cyclic. Selection changes back and forth between specialization and generalization in the exploiters, and weak and strong mimicry in the hosts, where non-defended hosts use the host investing in defence as a model. Thus, host signals and exploiter responses are engaged in a red-queen mimicry process that is ultimately cyclic rather then directional. In one phase, evolving signals of exploitable hosts mimic those of hosts less suitable for exploitation (i.e. the model). Signals in the model hosts also evolve through selection to escape the mimic and its exploiters. Response saturation constraints in the model hosts lead to the mimic hosts finally perfecting its mimicry, after which specialization in the exploiter guild is lost. This loss of exploiter specialization provides an opportunity for the model hosts to escape their mimics. Therefore, this cycle then repeats.We suggest that a species can readily evolve sympatrically when disruptive selection for specialization on hosts is the first step. In a sexual reproduction setting, partial reproductive isolation may first evolve by mate choice being confined to individuals on the same host. Secondly, this disruptive selection will favour assortative mate choice on genotype, thereby leading to increased reproductive isolation.     

Avian vocal mimicry: a unified conceptual framework

Mimicry is a classical example of adaptive signal design. Here, we review the current state of research into vocal mimicry in birds. Avian vocal mimicry is a conspicuous and often spectacular form of animal communication, occurring in many distantly related species. However, the proximate and ultimate causes of vocal mimicry are poorly understood. In the first part of this review, we argue that progress has been impeded by conceptual confusion over what constitutes vocal mimicry. We propose a modified version of Vane‐Wright's (1980) widely used definition of mimicry. According to our definition, a vocalisation is mimetic if the behaviour of the receiver changes after perceiving the acoustic resemblance between the mimic and the model, and the behavioural change confers a selective advantage on the mimic. Mimicry is therefore specifically a functional concept where the resemblance between heterospecific sounds is a target of selection. It is distinct from other forms of vocal resemblance including those that are the result of chance or common ancestry, and those that have emerged as a by‐product of other processes such as ecological convergence and selection for large song‐type repertoires. Thus, our definition provides a general and functionally coherent framework for determining what constitutes vocal mimicry, and takes account of the diversity of vocalisations that incorporate heterospecific sounds. In the second part we assess and revise hypotheses for the evolution of avian vocal mimicry in the light of our new definition. Most of the current evidence is anecdotal, but the diverse contexts and acoustic structures of putative vocal mimicry suggest that mimicry has multiple functions across and within species. There is strong experimental evidence that vocal mimicry can be deceptive, and can facilitate parasitic interactions. There is also increasing support for the use of vocal mimicry in predator defence, although the mechanisms are unclear. Less progress has been made in explaining why many birds incorporate heterospecific sounds into their sexual displays, and in determining whether these vocalisations are functionally mimetic or by‐products of sexual selection for other traits such as repertoire size. Overall, this discussion reveals a more central role for vocal mimicry in the behavioural ecology of birds than has previously been appreciated. The final part of this review identifies important areas for future research. Detailed empirical data are needed on individual species, including on the structure of mimetic signals, the contexts in which mimicry is produced, how mimicry is acquired, and the ecological relationships between mimic, model and receiver. At present, there is little information and no consensus about the various costs of vocal mimicry for the protagonists in the mimicry complex. The diversity and complexity of vocal mimicry in birds raises important questions for the study of animal communication and challenges our view of the nature of mimicry itself. Therefore, a better understanding of avian vocal mimicry is essential if we are to account fully for the diversity of animal signals.     

The potential for floral mimicry in rewardless orchids: an experimental study

More than one-third of orchid species do not provide their pollinators with either pollen or nectar rewards. Floral mimicry could explain the maintenance of these rewardless orchid species, but most rewardless orchids do not appear to have a rewarding plant that they mimic specifically. We tested the hypothesis that floral mimicry can occur through similarity based on corolla colour alone, using naive bumble-bees foraging on arrays of plants with one rewarding model species, and one rewardless putative mimic species (Dactylorhiza sambucina) which had two colour morphs. We found that when bees were inexperienced, they visited both rewardless morphs randomly. However, after bees had gained experience with the rewarding model, and it was removed from the experiment, bees resampled preferentially the rewardless morph most similar to it in corolla colour. This is the first clear evidence, to our knowledge, that pollinators could select for floral mimicry. We suggest that floral mimicry can be a selective force acting on rewardless orchids, but only under some ecological conditions. In particular, we argue that selection on early-flowering rewardless orchids that receive visits from a large pool of naive pollinators will be weakly influenced by mimicry.     

Deception and the origin of honest signals

Deceptive signals are a challenge to explain because on average, signals should be reliable. When being deceived is costly to the receiver, a coevolutionary struggle between senders and receivers can ensue. Recent work by Macías Garcia and Ramirez raises the intriguing possibility that through such a coevolutionary process, cheats can become honest.