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‘Mimicry’ is used in the evolutionary and ecological literature to describe diverse phenomena. Many are textbook examples of natural selection's power to produce stunning adaptations. However, there remains a lack of clarity over how mimetic resemblances are conceptually related to each other. The result is that categories denoting the traditional subdivisions of mimicry are applied inconsistently across studies, hindering attempts at conceptual unification. This review critically examines the logic by which mimicry can be conceptually organized and analysed. It highlights the following three evolutionarily relevant distinctions. (i) Are the model's traits being mimicked signals or cues? (ii) Does the mimic signal a fitness benefit or fitness cost in order to manipulate the receiver's behaviour? (iii) Is the mimic's signal deceptive? The first distinction divides mimicry into two broad categories: ‘signal mimicry’ and ‘cue mimicry’. ‘Signal mimicry’ occurs when mimic and model share the same receiver, and ‘cue mimicry’ when mimic and model have different receivers or when there is no receiver for the model's trait. ‘Masquerade’ fits conceptually within cue mimicry. The second and third distinctions divide both signal and cue mimicry into four types each. These are the three traditional mimicry categories (aggressive, Batesian and Müllerian) and a fourth, often overlooked category for which the term ‘rewarding mimicry’ is suggested. Rewarding mimicry occurs when the mimic's signal is non-deceptive (as in Müllerian mimicry) but where the mimic signals a fitness benefit to the receiver (as in aggressive mimicry). The existence of rewarding mimicry is a logical extension of the criteria used to differentiate the three well-recognized forms of mimicry. These four forms of mimicry are not discrete, immutable types, but rather help to define important axes along which mimicry can vary.
‘Mimicry’ is used in the evolutionary and ecological literature to describe diverse phenomena (table 1) [1–21]. These impressive outcomes of natural selection are widely fêted in textbooks and documentaries. Despite their canonical status, there remains considerable lack of clarity over how these resemblances are related to one another and the extent to which they are products of the same evolutionary processes (reviewed in [22–24]). Just as recent papers have brought clarity to social evolution by defining and systematising terms used to describe social interactions [25,26], this review aims to facilitate research on mimicry by proposing a conceptual framework that contrasts and orders mimetic resemblances across sensory modalities and taxonomic groups. Rather than aiming to provide an entirely new classification scheme, this review instead critically examines the criteria by which mimicry is currently differentiated, and then explores them to their logical conclusions.
First, this review highlights an important (but largely overlooked) distinction between ‘signal mimicry’ and ‘cue mimicry’, and suggests an evolutionary pathway for the one form to transition to the other. Second, it examines the criteria which generate the three traditionally recognized mimicry forms (aggressive, Batesian and Müllerian). In uncovering the two key criteria that distinguish these forms, the review highlights the existence of a fourth form which is a logical extension of the criteria used to delineate the other three. In so doing, the review clarifies the conceptual relationships between these traditional forms of mimicry and shows that our current framework is incomplete unless this fourth form is included (figure 1).
The review's aim is to uncover the criteria by which examples of mimicry are conceptually organized, and not to re-define mimicry. Modern definitions of mimicry are based on the one proposed by Vane-Wright in 1980 , and which is widely used in the literature today (e.g. [28–30]). Modified versions of Vane-Wright's definition have been suggested, with recent papers on avian vocal mimicry  and mimicry more generally  suggesting that mimicry evolves if
a receiver perceives the similarity between a mimic and a model and as a result changes its behaviour in a manner that provides a selective advantage to the mimic. [24, p. 612]
This definition relaxes the condition of Vane-Wright's 1980 definition that fitness benefits to the mimic must arise from the receiver identifying the mimic as an example of the model. Instead, fitness benefits need only result from the receiver perceiving the similarity between mimic and model. This definition is adopted here and is consistent with the framework outlined in this review article for understanding the relationships between mimetic resemblances.
In studies of animal communication, a fundamental distinction is made between signals, which have evolved specifically to alter a receiver's behaviour, and cues, which are incidental sources of information detected by unintended receivers . An example of a signal is the warning colorations of many distasteful insects [33,34]. Selection by predators has led to the evolution of bright, conspicuous and memorable markings that convey information about the prey's toxicity to the predator . By contrast, the rustling sound produced by a mouse as it runs through the undergrowth is a cue. A predatory owl uses this sound to gain information about the mouse's location, but the trait has not evolved under selection to signal location to predators.
Mimics can simulate both signals (‘signal mimicry’) and cues (‘cue mimicry’) of models to alter receiver behaviour. However, this distinction is often overlooked. It is briefly noted by Maynard Smith & Harper [32, p. 86] but not explored further. An example of signal mimicry is the similarity in begging calls between some nestling brood-parasitic birds and their hosts. Horsfield's bronze-cuckoos (Chalcites basalis) are brood-parasitic birds that lay their eggs in the nests of a variety of other species. The young cuckoo develops begging calls that closely match those of the nestlings of the host species it is being raised by . The trait being copied is the model's begging call, which has evolved specifically under selection to transfer information to the host parent.
An example of cue mimicry comes from spiders. A predatory jumping spider (Portia fimbriata) attracts orb-web spiders (Zygiella x-notata and Zosis geniculatus) by vibrating the latter's web to resemble a fly struggling . The jumping spider is the mimic, the fly is the model, and the orb-web spider is the receiver. The model's traits being copied (the web vibrations of a struggling fly) are cues, as they have not evolved under selection to signal information to the orb-web spider.
To summarize, in signal mimicry, the mimic's signal comes to resemble the model's signals, whereas in cue mimicry, the mimic's signal comes to resemble the model's cues (for examples see table 1). Importantly, in both cue mimicry and signal mimicry, the trait of the mimic is a signal to its intended receiver, as it has evolved specifically to alter that receiver's behaviour.
A key difference between signal mimicry and cue mimicry is that mimic and model share the same intended receiver in signal mimicry, but do not in cue mimicry. This is evolutionarily relevant, because the receiver is not just the passive recipient of a signal, but the agent of selection whose perception of and response to that signal determines its adaptive value. Therefore, in signal mimicry, mimic and model have the same agent of selection, whereas in cue mimicry, mimic and model do not.
The distinction between shared and unshared receivers is important, first, because different receivers may vary in their sensory systems and cognition . Therefore, different receivers select for the mimic to simulate the model's trait in different ways. When one organism converges on the traits of another organism/object through mimicry, it does not perfectly simulate all aspects of the model's trait. Rather, it mimics those aspects of the trait necessary to make the mimic's receiver perceive the similarity between mimic and model . This is a useful attribute of mimicry as it highlights the aspects of the model's traits the mimic's receiver uses to identify the model. Therefore, in cue mimicry, mimic and model's traits may closely resemble one another from the perspective of the mimic's intended receiver, but seem quite different from the perspective of the model's intended receiver. The extent of this discrepancy will depend on the divergence in selection pressures exerted by mimic and model's receivers.
A second reason to distinguish signal and cue mimicry is that when mimic and model share the same intended receiver (signal mimicry), the reliability of the mimic's signal can serve to reinforce or undermine the reliability of the model's. If the mimic's signal is non-deceptive, the mimic's signal will reinforce the reliability of the model's signal to their shared receiver. If the mimic's signal is deceptive, it will undermine the reliability of the model's traits. The more deceptive signal mimics in the population, the less reliable, on average, the signal is to the receiver . This may lead to selection for the receiver to no longer avoid/approach the model's phenotype, with detrimental consequences for both model and mimic . By contrast, if mimic and model have different receivers (cue mimicry), the reliability of the mimic's signal should have no impact on the perceived reliability of the model's trait to the model's receiver. An exception would be if the model's intended receiver were also an unintended receiver of the mimic's signal; for example, superb lyrebirds mimic other bird species' calls , presumably to attract mates and defend territory, but their vocalizations might also be heard by individuals of the species it is mimicking. This might lead to selection for members of the mimicked species not to respond to the call, as it would often be unreliable.
The distinction between signal and cue mimicry can be illustrated with empirical examples (table 1). In the Horsfield's bronze-cuckoo begging call example , the nestling cuckoo (mimic) and host (model) have the same intended receiver (the host parent). Therefore, the model's trait being copied are signals to the mimic's intended receiver, making this signal mimicry. By contrast, anglerfish draw in prey using a fleshy extension on their head as a lure that resembles the shape and movement of a worm or fish . The intended receiver of the model's traits being mimicked differs from the mimic's intended receiver, making them cues (not signals) to the mimic's intended receiver.
Importantly, traits are not inherently ‘cues’ or ‘signals’ but can only be classified as such with respect to a given receiver. For example, a male Túngara frog's (Physalaemus pustulosus) vocalizations are signals to attract females, but also cues the predatory fringe-lipped bat (Trachops cirrhosus) uses to locate frog prey [39,40]. In the context of mimicry, the receiver with respect to which the model's trait is judged as signal or cue is the intended receiver of the mimic's signal. Therefore, if mimic and model share the same intended receiver, then the model's trait is viewed as a signal and the system can be classed as signal mimicry. If, however, mimic and model differ in their intended receiver (or the model's trait is not a signal in any context), the model's trait is a cue, and the system can be classed as cue mimicry. This emphasizes the importance of identifying the receivers driving the evolution of both mimic's and model's traits.
Cue mimicry and signal mimicry do not necessarily have disparate evolutionary trajectories. Instead, cue mimicry can transition to signal mimicry when the mimic's presence has a fitness consequence on the model, either directly or via the mimic's effects on the receiver. These fitness consequences mean that, once the mimic has reached a sufficient frequency in the environment, there will be selection for the model to alter the trait that is being mimicked either towards or away from that of the mimic. Once this has occurred, the model is now signalling information to the mimic's receiver (i.e. it has become a signal to the mimic's receiver) and the system transitions from cue to signal mimicry. So, becoming the model in a cue mimicry system can set the stage for an evolutionary shift in a trait from being a cue to a signal. Interestingly, if the response of the model is to converge on the mimic's signal, the model itself will become a mimic.
To illustrate the transition from cue to signal mimicry, I use mimicry of host eggs by brood-parasitic birds as an example [3,41]. Ancestrally, prior to being parasitized, the colour and pattern of host eggs may have evolved for camouflage or thermoregulation [42,43]. In copying these host egg features, parasites were initially mimicking the model's cues (cue mimicry). Subsequently, a coevolutionary arms race ensued in which hosts responded by altering their eggs' appearance to signal information about maternal identity, so hosts can better distinguish their own eggs from parasite eggs [4,41,44,45]. Parasites have tracked this change, altering their own eggs' appearance to deceptively signal the same information as the host's eggs [4,44]. Following the framework proposed here, the phenomenon has shifted from cue mimicry to signal mimicry. Thus, cue mimicry can be the first step on a coevolutionary path to signal mimicry.
When the model is inanimate, cue mimicry is stable and will not transition to signal mimicry via a coevolutionary arms race. For example, if the model is a rock (whose appearance is apparently copied by stone plants, Lithops ), it cannot evolve to alter its appearance.
How does ‘masquerade’ fit within the framework proposed here? The modern definition of masquerade stems from Endler , who considered masquerade as the adaptive resemblance of an organism to an inanimate or inedible object. This definition of masquerade was updated by Skelhorn et al. . They considered Endler's 1981 definition to exclude certain resemblances that might intuitively be considered masquerade and suggested the following formal definition:
one whose appearance causes its predators or prey to misclassify it as a specific object found in the environment, causing the observer to change its behaviour in a way that enhances the survival of the masquerader. Any change in the population/ evolutionary dynamics of the model caused by the presence of the masquerader will not be as a result of the signal receiver changing its behaviour towards the model. [48, p. 4]
In many publications since, the second part of the definition of masquerade—that the masquerader must not influence the population/evolutionary dynamics of the model by changing the receiver's behaviour—is left out. Instead the focus is on the ‘inanimate’, ‘inedible’ or ‘uninteresting’ nature of the model [16,49,50]. It has also been formulated as situations in which the model is ‘ignored’ by the receiver .
The ‘inanimate’ and ‘uninteresting’ aspects to models in masquerade systems place them in the category of cue mimicry. If the models being copied are inanimate, their traits cannot evolve to become signals and, if they are uninteresting, they have not evolved to signal information to a receiver. From the perspective of shared (signal mimicry) versus unshared (cue mimicry) receivers, masquerade also falls within cue mimicry. If the model is ‘uninteresting’ to the mimic's intended receiver, then the model must have a different intended receiver from the mimic (or have no intended receiver at all).
To summarize, masquerade can be considered a special case of cue mimicry in which the model is inanimate, uninteresting and inedible.
Section 2 emphasized the importance of the signal versus cue mimicry distinction. This section now revisits the traditional subdivisions within mimicry. It aims to find clear and evolutionarily relevant criteria that separate mimicry types from one another and take them to their logical conclusions (figure 1). Clear criteria not only help us to draw comparisons between seemingly disparate examples of mimicry, but also highlight how the different forms of mimicry can evolve from one to another.
Efforts to subdivide mimicry were first formalized with Vane-Wright , who separated mimicry types according to the interactions between the three parties: receiver, mimic and model. These depended on three distinctions: (i) whether the mimic's presence had a positive effect on the model's fitness or a negative one; (ii) whether the receiver's ‘biological roles’ with respect to the model and to the mimic are ‘aggressive’ or ‘protective’; and (iii) whether the model, mimic and receiver are all of the same species, all of different species, or only two of the same species. The various permutations of these criteria result in him identifying 40 different types of mimicry .
Here, I suggest a conceptual organization of mimicry based on the information content of the mimic's signal to the receiver. This framework accommodates the three general types of mimicry commonly recognized today (aggressive, Batesian, Müllerian), and highlights a fourth, often overlooked form, for which I suggest the term ‘rewarding mimicry’. Here mimicry is organized according to two axes: information content and deceptiveness.
First, information content: does the mimic signal a fitness cost (punishment) or benefit (reward) to manipulate receiver behaviour? Organisms can manipulate receiver behaviour by either promising a reward or a punishment. For example, an inedible butterfly species uses aposematic coloration to signal its distastefulness to receivers and avoid being eaten. By contrast, a nectar-containing flower signals its rewarding nature through a conspicuous flower to encourage pollinators to visit it. Similarly, in copying the traits of models, mimics manipulate receiver behaviour by presenting the potential of a reward or punishment.
Second, deceptiveness: is the mimic's signal deceptive? In some situations, the perceived punishment is ‘real’, such as when multiple distasteful butterfly species evolve to resemble one another. In others, it is ‘false’, such as when an edible butterfly species has evolved to resemble an inedible one. The degree of discrepancy between the mimic's advertised reward/punishment and the actual levels of reward/punishment is a measure of how deceptive the mimic's signal is.
The framework presented here is not hierarchical and the two criteria can be applied in either order with neither having priority over the other. The framework can be visualized as a two-dimensional graph divided into four quadrants (figure 1). The four quadrants on the graph do not signify discrete categories of mimicry. Instead, they help to define two important axes along which examples of mimicry vary. It is useful to think of them as the four points on a compass that can help us to position mimicry systems relative to one another across a ‘mimicry landscape’. Clear criteria defining these extremes facilitate comparisons between examples of mimicry and clarify the mechanisms through which they can transition from one type to another.
In aggressive mimicry, the mimic signals a fitness benefit to the receiver and the mimic's signal is deceptive. More generally, a system can be classified as aggressive mimicry when the advertised benefits to the receiver are greater than the actual benefits.
An example of aggressive signal mimicry is a praying mantis that has evolved to resemble a flower to attract insect prey . The mantis deceptively signals a fitness benefit to the receiver, exploiting the flower's attractive signals to the pollinator to gain access to prey. Other examples include Bolas spiders mimicking the sexual attractant pheromones of female moths to attract male moths as prey items , or sexually deceptive plants attracting male pollinators [53,54].
The anglerfish system referred to earlier  is an example of aggressive cue mimicry. Anglerfish draw in prey using a fleshy extension on their head as a lure. This is an example of cue rather than signal mimicry, because the model's traits being copied are cues (not signals) to the anglerfish's intended receiver. It is classified as aggressive mimicry because the mimic is deceptively signalling a fitness benefit to manipulate receiver behaviour.
In Batesian mimicry, the mimic signals a fitness cost to the receiver and the mimic's signal is deceptive. More generally, a mimicry system can be classified as Batesian mimicry when the advertised costs to the receiver are greater than the actual costs.
Examples of Batesian signal mimics include Papillio swallowtail butterflies resembling defended butterfly species  and harmless hoverfly species (family Syrphidae) resembling defended wasps and bees (order Hymenoptera) [13,56].
By contrast, an example of ‘Batesian cue mimicry’ would be an undefended caterpillar that resembles a bird dropping. This is cue rather than signal mimicry because the traits of the model (a bird dropping) being copied have not (as far as is known) evolved to signal information to the caterpillar's intended receiver (probably an avian predator). It is classified as Batesian because the mimic is deceptively signalling a fitness cost to the receiver.
In Müllerian mimicry, the mimic signals a fitness cost to the receiver, and the mimic's signal is non-deceptive.
An example of Müllerian signal mimicry comes from Heliconius butterflies, in which multiple toxic species converge on the same phenotype under selection to signal their distastefulness to predators [17,57,58]. The mimic non-deceptively signals a fitness cost to the receiver to manipulate its behaviour, making it Müllerian. It is signal mimicry because mimic and model share an intended receiver (avian predators) of the mimicked trait (wing patterns). There are numerous other examples of Müllerian signal mimicry, such as in catfish , birds  and velvet ants (Mutilidae) .
A possible example of Müllerian cue mimicry would be a distasteful organism that resembles a distasteful inanimate object under selection to more effectively signal its distastefulness to predators. For example, if a caterpillar that looked like a bird dropping was itself distasteful, then this would be an example of Müllerian cue mimicry. It would be interesting to review known resemblances of organisms to animal droppings and see whether, in any of these cases, the mimic is itself unpalatable to its intended receiver.
Examples of both Müllerian cue and Müllerian signal mimicry come from fork-tailed drongos (Dicrurus adsimilis) depending on who the intended receiver of the drongo's call is. Drongos produce a variety of alarm calls while foraging alongside other species. Sometimes these alarm calls are produced when a predator is present (‘true’ alarm calls), and sometimes they are produced when there is no predator (‘false’ alarm calls). The calls can either be drongo-specific or mimic calls of a range of other species it forages alongside. By using a mix of honest and deceptive alarm calls, drongos cause heterospecific foragers to drop their prey in response to the perceived risk of attack by predators and the drongo is then able to seize the deserted prey [19,62]. When the drongo uses mimicry to direct alarm calls at other foraging birds when a predator is really present, the signal is reliable and the drongo is alerting the receiver to a real danger, but uses the ‘voice’ of another species to do so. This constitutes Müllerian signal mimicry in the case where model and receiver are the same species (mimic and model share a receiver), and Müllerian cue mimicry when model and receiver are different species (mimic and model have different intended receivers of their calls).
The fourth permutation, in which the mimic signals a fitness benefit to manipulate receiver behaviour and the mimic's signal is non-deceptive, is one that is rarely identified and for which the term ‘rewarding mimicry’ is proposed here. While the possibility of Müllerian-like systems based on profitability rather than unprofitability has previously been acknowledged [20,63,64], it has always been classified within Müllerian mimicry.
Plant–pollinator interactions could provide the best systems in which to look for examples of rewarding signal mimicry. Here, multiple species of plants may gain a benefit by using the same flower phenotype to signal to shared pollinators. The mimic's signal is reliable to the receiver (the pollinator) as both mimic and model plants reward the pollinator with nectar. Such interactions have been noted by other authors, but they have generally classified them under Müllerian mimicry [20,63,64] or more broadly under ‘non-deceptive’ mimicry . An example of reliable mimicry in plant–pollinator interactions has been identified between plants of the families Turneraceae and Malvaceae . A rewarding species of Turneraceae (Turnera sidoides) was shown to resemble co-flowering species of Malvaceae and to gain higher pollination levels when growing together with the model plant than when growing alone . This example is classified as Müllerian mimicry by the paper's authors; however, given that the mimic signals rewards rather than punishment to manipulate receiver behaviour, this is better classed as rewarding mimicry. This is rewarding signal mimicry, because mimic and model share the same intended receivers (pollinating insects).
An example of rewarding cue mimicry comes from the brood-parasitic Vidua finches of Africa. Both male and female Vidua imprint on their host species with males growing up to mimic the songs of their host and females acquiring a mate preference for males who sing like the host she was raised by [65,66]. Here, the mimic is the adult male Vidua, the model is an adult of its host species and the receiver is the adult female Vidua. Male Vidua use mimicry to reliably signal to female Vidua information about their early natal environment (in which species's nest he was raised). Females perceive the similarity between the male Vidua's song and that of their own host and alter their behaviour accordingly. The model's trait being mimicked (its song) has not evolved under selection from female Vidua; instead, the intended receiver of the hosts song is other members of its own species. For these reasons, this example shows characteristics of rewarding cue mimicry.
Shifts in the levels of deceptiveness shown by mimics can result in transitions from one mimicry type to another. Mimics vary in the degree to which their signals are deceptive . The mimic's signal is deceptive in Batesian and aggressive mimicry, but not so in Müllerian and rewarding mimicry.
Both Müllerian and rewarding mimicry are susceptible to cheating. In rewarding mimicry, cheats may reduce investment in the reward for the receiver, making their signal deceptive. In so doing, the system would transition from rewarding towards aggressive mimicry. Aggressive mimicry is found in non-rewarding plants that look like a rewarding species, thus duping pollinators to visit them [64,67]. While some authors classify this resemblance as Batesian mimicry , it is classified as aggressive mimicry here, because the mimic is signalling a reward to the receiver. By contrast, if a Müllerian mimic were to cheat by reducing investment in toxicity, the system would transition to Batesian mimicry (figure 1).
If co-mimics differ from one another in their degree of toxicity, the system is sometimes termed ‘quasi-Batesian’ mimicry as the mimic is not entirely undefended, just less so than the model . Similarly, if rewarding mimics were to cheat by decreasing investment in rewards relative to co-mimics, this system could be classified as ‘quasi-aggressive’ mimicry.
Finally, I consider some cases that may seem difficult to position within the mimicry framework outlined in figure 1, focusing on masquerade systems and avian vocal mimicry.
In masquerade, it can be difficult to know whether the mimic is signalling a fitness cost or benefit to the receiver. For example, consider a praying mantis that resembles dead leaves to allow the mantis closer access to prey before striking. Here, the model is the dead leaf, the mimic is the mantis and the receiver is the insect prey. There are two ways to look at this situation. One is to take the absolute levels of reward/punishment being advertised by the mimic's signal, which, in this situation, is effectively neutral. When these examples are plotted in figure 1, they fall at the border between Batesian and aggressive mimicry as the mimic's signal is deceptive, but the mimic is signalling neither fitness benefits nor punishment to the receiver. If the relationship between the receiver and the model changes for any reason, such that the receiver now has reason to avoid or engage with the model, the mimicry system would transition from masquerade to Batesian or aggressive mimicry.
A second way to classify these masquerade examples would be to compare the advertised fitness benefits/costs to the receiver of the mimic's signal with the actual fitness costs/benefits. In aggressive mimicry the advertised fitness costs to the receiver are less than the actual costs, whereas in Batesian mimicry the advertised fitness costs are greater than the actual costs. In the case of a praying mantis that has evolved to resemble a dead leaf, under selection to allow closer access to prey items, it can be considered aggressive mimicry as the advertised costs (an inconsequential, non-predatory dead leaf) are lower than the actual costs to the receiver (it gets eaten). However, if selection to resemble a dead leaf has been driven by selection from predators, then the system can be thought of as Batesian mimicry as the advertised costs to the receiver (wasted time and energy trying to eat a dead leaf) are greater than the actual costs (getting a meal). While this might seem an arbitrary distinction given that the resulting mimetic phenotype of the mantis is similar in both scenarios, it has been generated by different selection pressures (different receivers) and employed in different contexts. This latter way of classifying masquerade systems is preferable as it is an extension of the logic which classifies a less toxic butterfly species mimicking a more toxic species as a (quasi-)Batesian mimic , or a less rewarding plant species mimicking a more rewarding one as a (quasi-)aggressive mimic.
Some instances of avian vocal imitations where birds imitate the vocalizations of other species in mate attraction and/or territory defence may also seem difficult to situate in the framework. Returning to the definition of mimicry stated in the introduction, avian vocal imitation is only considered mimicry if ‘the receiver perceives the similarity between a mimic and a model and as a result changes its behaviour in a manner that provides a selective advantage to the mimic’ [24, p. 612]. According to this, only those instances of avian vocal mimicry in which the receiver perceives the similarity between mimic and model vocalizations can be considered mimicry. In many instances of vocal imitation for mate attraction, the bird may just be using the sounds in the surrounding environment to help direct the development of its own call [31,69,70]. The receiver is not necessarily perceiving the resemblance of the call to a locally occurring species. Receivers may instead just be selecting for large vocal repertoires rather than mimicry per se, with imitation of other species just providing a fruitful source of new vocal material for displaying birds. This hypothesis makes the testable prediction that there would be no different fitness outcomes if the repertoire of a mimetic species was expanded using non-local or local species. For example, male marsh warblers (Acrocephalus palustris) imitate African bird species while vocalizing on their European breeding grounds . Female marsh warblers are unlikely to be perceiving similarity between the male's calls and that of the African species given that the female may have wintered in a different area to the male and not encountered those African species. If this is true, then these examples fall outside the definition of mimicry.
By contrast, in those examples of avian vocal imitation where the receiver alters its behaviour as a result of perceiving a similarity between mimic and model vocalizations the framework can be applied as described in several examples throughout this review to position these systems within the mimicry landscape (e.g. drongo alarm call mimicry, Vidua host mimicry, cuckoo begging call mimicry). Again, the information content of the mimic's signal can be used to position it in the framework by considering whether it is deceptive, and the extent to which it advertises rewards or punishment in order to manipulate receiver behaviour.
The conceptual framework presented in this paper provides a set of criteria to categorize and compare examples of mimicry across sensory modalities. The close focus on definitions is not just semantic, but instead draws attention to the commonalities and differences in the processes underlying the evolution of mimicry. It is hoped it will act as a guide with which to conceptually link and differentiate trait similarity in nature, organizing the huge diversity of adaptive resemblances in nature explicitly according to the processes that generate them.
The framework highlights the following key features of mimicry/masquerade systems, which must be characterized in order to allow the evolutionary processes driving them to be clearly distinguished: (i) whether or not the intended receivers of the mimic and model are shared, and therefore whether the model's traits being mimicked are cues or signals to the mimic's receiver; (ii) the deceptiveness of the mimic's signal; and (iii) whether the mimic manipulates receiver behaviour through advertising fitness benefits or costs. In so doing, this framework draws attention to important gaps in our knowledge and suggests some areas where future research efforts would be revealing.
The framework identifies ‘rewarding’ mimicry as a fourth type of mimicry that is a logical extension of the criteria used to separate the three commonly recognized mimicry forms (aggressive, Batesian and Müllerian). In rewarding mimicry, the mimic's signal is non-deceptive and the mimic manipulates receiver behaviour by signalling a fitness benefit. Given that researchers make a fundamental distinction between Batesian and aggressive mimicry, by the same logic, rewarding should also be differentiated from Müllerian mimicry. Currently, the best examples of rewarding mimicry are found in pollinator–plant interactions.
The reason for focusing solely on these four mimicry types, rather than the many other forms of mimicry sometimes recognized, is that these four are not restricted to a certain modality (unlike ‘visual’ or ‘vocal’ mimicry), behavioural interaction (unlike ‘protective’ or ‘competitive’ ) or taxonomic group (unlike ‘egg mimicry’). This makes them very general categories that can be applied broadly across mimetic phenomena and used to make comparisons between seemingly disparate cases of mimicry.
The ‘signal’ versus ‘cue’ criterion for distinguishing signal and cue mimicry allows masquerade to be categorized as a special case of cue mimicry, in which the model is inanimate/uninteresting. The same criteria used to subdivide signal mimicry can be used to differentiate types of cue mimicry and, by extension, masquerade. This provides a clear conceptual niche for masquerade within the broader framework of mimicry, and provides internally consistent guidelines by which to explore the diversity of mimicry systems in nature.
I thank Neeltje Boogert, Anastasia Dalziell, Nick Davies, Nicholas Horrocks, Rebecca Kilner, Peter Lawrence, Claire Spottiswoode and Justin Welbergen for discussion and for reading earlier drafts of this manuscript. They have greatly helped to improve its contents.
I declare I have no competing interests.
This research is supported by a Research Project Grant from The Leverhulme Trust.