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Selection on M1 to decrease the fitness of M2, or vice versa, is evidence of fitness conflict. In principle, floral visitors might gain fitness benefits by robbing instead of pollinating, but at present there are virtually no data available to evaluate this assumption. For the most part, the concept of adultery is precisely what most people think it would be and is quite straightforward. Theory predicts that the potential for conflict between partners increases with environmental quality, since the relative importance of the partner decreases Hochberg et al.

Although no individual is being as cooperative as it could be, no individual is gaining a fitness advantage by being a worse partner either. How to identify cheaters When using other definitions of cheating, categorizing individuals as cheaters is only straightforward when the definition specifies a particular strategy or when there are just two strategies to compare. Ordered from least to most agreement, the definitions were A a cheater provides no reward, B a cheater takes more reward than other potential partners, C a cheater takes a benefit but does not provide a reward, D a cheater takes more than its fair share, E a cheater provides less than its fair share, F a cheater provides less reward than other potential partners and G a cheater has a higher reward taken: reward given ratio than other prospective partners.

Cheaters Never Prosper: Adultery and Affairs

Cheating is a focal concept in the study of mutualism, with the majority of researchers considering cheating to be both prevalent and highly damaging. However, current definitions of cheating do not reliably capture the evolutionary threat that has been a central motivation for the study of cheating. We then describe experiments required to conclude that cheating is occurring and to quantify fitness conflict more generally. Next, we discuss how our definition and methods can generate comparability and integration of theory and experiments, which are currently divided by their respective prioritisations of fitness consequences and traits. Introduction Mutualisms are defined by the reciprocal net benefit that heterospecific partners receive through the exchange of resources and services. However, while there is widespread agreement that cheating is a fundamental concept in the study of mutualism, there has been very little consensus on the definition of cheating itself Ghoul et al. There has been disagreement over what needs to be measured in order to determine whether cheating is happening, as well as which partner is the focus of such measurements. The literature also contains varying definitions regarding how distinct cheaters are from other partners and whether phylogenetic restrictions should be imposed on who can be considered a cheater Table. These differing definitions have led to inconsistency over how cheating is identified and modelled, hampering our ability to determine how common cheating is and to predict how mutualisms will evolve. As studies of cheating in mutualism have accumulated, so have definitions of cheating Table , Fig. To evaluate current perspectives in the community, we conducted an anonymous survey of professional ecologists and evolutionary biologists Supporting Information S1. Respondents were asked to evaluate seven potential definitions of cheating from the literature on a scale of 1 completely disagree to 5 completely agree. The level of agreement for each of the seven potential definitions is shown in Fig. Ordered from least to most agreement, the definitions were A a cheater provides no reward, B a cheater takes more reward than other potential partners, C a cheater takes a benefit but does not provide a reward, D a cheater takes more than its fair share, E a cheater provides less than its fair share, F a cheater provides less reward than other potential partners and G a cheater has a higher reward taken: reward given ratio than other prospective partners. Survey responses on the definition of cheating. Ordered from least to most agreement, the definitions were A a cheater provides no reward, B a cheater takes more reward than other potential partners, C a cheater takes a benefit but does not provide a reward, D a cheater takes more than its fair share, E a cheater provides less than its fair share, F a cheater provides less reward than other potential partners and G a cheater has a higher reward taken: reward given ratio than other prospective partners. Yet, the definition that has been proposed — that cheating increases individual fitness and reduces partner fitness Ghoul et al. Here, we provide a brief history of the development of the cheating concept in the mutualism literature. We then introduce a definition of cheating based on relative fitness that builds upon previous work and describe methods for measuring cheating and fitness conflict in future studies. Finally, we propose emerging research directions to move the field beyond its current focus on identifying cheaters and towards theoretical and empirical integration. Ultimately, this integration will enable us to make quantitative predictions about the benefits and ecosystem services generated by mutualisms. A Brief History of the Cheating Concept Individuals and species that are associated with mutualisms but do not confer benefits have been noted since biologists first began studying mutualisms. By the time the first lengthy review of mutualism was written Boucher et al. Through the 1980s, interpretations of the causes and consequences of cheating in mutualisms developed largely in the absence of any theoretical framework but see, e. This began to change with the publication of Axelrod and Hamilton's seminal 1981 paper on the evolution of cooperation. Although previous authors had made some of the same points e. Axelrod and Hamilton's game theory approach was rapidly adopted and applied to understanding how mutualism can persist evolutionarily. Since the 1980s, the theoretical and empirical literature on cheating in mutualism has proliferated Fig. Meanwhile, new empirical examples deemed to be cheating have continued to accumulate at a rapid pace, leading to several general reviews Bronstein , ; Yu ; Douglas ; Frederickson ; Ghoul et al. Prevalence of the cheating concept in studies of the evolution of mutualism. The number of articles on the evolution of mutualism per year with dark grey and without light grey a keyword relating to cheating are shown. The percentage of papers with a keyword about cheating is next to the bar. A search was conducted on Thomson Reuters Web of Science of all articles published between 1990 and 2014. Prevalence of the cheating concept in studies of the evolution of mutualism. The number of articles on the evolution of mutualism per year with dark grey and without light grey a keyword relating to cheating are shown. The percentage of papers with a keyword about cheating is next to the bar. A search was conducted on Thomson Reuters Web of Science of all articles published between 1990 and 2014. The main mechanisms that have been proposed to stabilise mutualism against cheating fall into two general categories. In contrast, sanctions may be used to punish inferior partners by decreasing rewards, inflicting additional costs, or terminating the interaction Denison ; West et al. A Standard Framework for Studying Cheating and Fitness Conflict As the studies on cheating in mutualism have multiplied, so have the definitions of cheating. Both the literature reviewed in Ghoul et al. Problematically, the different definitions have fundamental disparities Table that result from the frequent confounding of whether cheating is happening with how cheating can happen. At best, the multiplicity of definitions obscures insights that could be gained from synthesizing across studies. At worst, the failure to recognise that others are using incompatible definitions can lead to misunderstandings about the prevalence and threat of cheating. Here, we present our argument for defining cheating in terms of the relative fitness consequences of particular strategies to each partner. We complement our definition with a description of experimental methods to identify cheaters. Additionally, we suggest methods for measuring fitness conflict between mutualist species, which can give additional insight into the mutualism. Refining the definition of cheating We contend that a definition of cheating must do three things: be informative about the threat posed to the mutualism, explicitly prescribe how cheating can be identified and enable comparability across studies and systems. In order to pose a threat to the mutualism, cheating must both erode the benefit gained by the partner and also be evolutionarily favoured so that it can spread. These effects can only be captured by relative fitness criteria. Defining cheating in terms of relative fitness also meets the second two requirements by providing a clear, standardised unit of measurement. Cheating must therefore 1 increase the fitness of the actor above average fitness in the population and 2 decrease the fitness of the partner below average fitness in the partner population Fig. These individuals are defective, rather than defectors Friesen. When only criterion 1 is met, the strategy that increases actor fitness will spread, but it will not harm either the partner or the mutualism. Critically, measuring just the rewards exchanged between partners is not sufficient for identifying whether there is a threat to the mutualism, since the relationship between rewards and fitness is not straightforward. In particular, rewards may not be costly to produce i. Although it is possible in principle to infer fitness consequences from mutualist traits, we currently lack the quantitative understanding required to conclude that cheating is occurring from phenotypic data alone. Identifying cheaters by pairwise and population comparisons. In a pairwise comparison, one genotype is considered to be a cheater relative to another if it has higher fitness but causes lower partner fitness. Given overall fitness conflict c , pairwise comparisons result in a nested identification of cheaters d. Moreover, some genotypes identified as cheaters in pairwise comparisons would not fulfil the conditions to both increase in the population and decrease average partner fitness. Only genotypes that fall significantly into quadrant IV grey , i. Therefore, the only cheaters are genotypes F as shown in a and G, H, and I as shown in b. Identifying cheaters by pairwise and population comparisons. In a pairwise comparison, one genotype is considered to be a cheater relative to another if it has higher fitness but causes lower partner fitness. Given overall fitness conflict c , pairwise comparisons result in a nested identification of cheaters d. Moreover, some genotypes identified as cheaters in pairwise comparisons would not fulfil the conditions to both increase in the population and decrease average partner fitness. Only genotypes that fall significantly into quadrant IV grey , i. Therefore, the only cheaters are genotypes F as shown in a and G, H, and I as shown in b. Linking rewards exchanged to fitness outcomes. If reward production is costly and there are no feedbacks in reward exchange, mutualist M1's fitness solid decreases as it produces more reward R1; meanwhile, partner M2's fitness dashed increases with R1 received a. Consequently, there is fitness conflict between M1 and M2 b. Any reduction in R1 production constitutes cheating by M1, as it gains a fitness benefit at a fitness cost to M2 though the cost is small if M2's fitness is saturating. Therefore, M1 cannot cheat by producing less R1, because there is no fitness advantage d. Finally, if there is a feedback between rewards exchanged, both partners have higher fitness when more R1 is produced e. Thus, there is fitness alignment f and M1 cannot cheat. Linking rewards exchanged to fitness outcomes. If reward production is costly and there are no feedbacks in reward exchange, mutualist M1's fitness solid decreases as it produces more reward R1; meanwhile, partner M2's fitness dashed increases with R1 received a. Consequently, there is fitness conflict between M1 and M2 b. Any reduction in R1 production constitutes cheating by M1, as it gains a fitness benefit at a fitness cost to M2 though the cost is small if M2's fitness is saturating. Therefore, M1 cannot cheat by producing less R1, because there is no fitness advantage d. Finally, if there is a feedback between rewards exchanged, both partners have higher fitness when more R1 is produced e. Thus, there is fitness alignment f and M1 cannot cheat. Reward exchange and cheating. Mutualists M1 and M2 produce rewards R1 and R2, respectively, and consume the reward produced by the partner a. The arrows show reward production black and reward consumption grey. Assuming that fitness is a monotonic function of each reward exchanged and that there are no feedbacks coupling these exchanges, the individual M1b can be a cheater relative to M1a by providing less reward to the partner, M2 b. However, M1b should not be considered a cheater if it not only gives a small reward but also receives a comparably small reward c. This case could result from M1b being defective and unable to take a larger reward or from mechanisms that thwart cheating by causing feedbacks between the amount of reward given and the amount of reward received. Modified from Jones et al. Reward exchange and cheating. Mutualists M1 and M2 produce rewards R1 and R2, respectively, and consume the reward produced by the partner a. The arrows show reward production black and reward consumption grey. Assuming that fitness is a monotonic function of each reward exchanged and that there are no feedbacks coupling these exchanges, the individual M1b can be a cheater relative to M1a by providing less reward to the partner, M2 b. However, M1b should not be considered a cheater if it not only gives a small reward but also receives a comparably small reward c. This case could result from M1b being defective and unable to take a larger reward or from mechanisms that thwart cheating by causing feedbacks between the amount of reward given and the amount of reward received. Modified from Jones et al. It is instructive to compare the definition of cheating given here with the standard definitions for mutualism and parasitism. We define cheating through comparison to the average fitness consequences of an interaction. Meanwhile, at the individual level, mutualism and parasitism are defined through comparison to the absence of an interaction. There is mutualism when each individual has higher fitness with the interaction than without the interaction. Parasitism entails a fitness increase for one individual and a decrease for the other, compared to their fitnesses in the absence of an interaction. As a consequence of the different comparison points, a cheater may be either a mutualist or a parasite. A cheater will still be a mutualist as long as it provides a net fitness benefit to the partner. Interacting with such a cheater is worse than interacting with the average partner, but it is still better than having no partner at all. Alternatively, cheaters might reduce partner fitness compared to no interaction and thus be parasites as well. It is also important to note that not all parasites should be considered cheaters. Implicit in the definition given here is the restriction of cheating to members of mutualistic lineages. The evolution of cooperation in general is considered a central problem in evolutionary biology because of selection for cheating within cooperative populations. Therefore, in agreement with some earlier treatments of cheating Bronstein ; Frederickson , our definition requires that cheaters must be derived from cooperators. Species that are not derived from cooperators may be parasites; however, these species pose an external threat to the mutualism, whereas cheaters pose a threat from within. How to identify cheaters When using other definitions of cheating, categorizing individuals as cheaters is only straightforward when the definition specifies a particular strategy or when there are just two strategies to compare. For example, strains of rhizobia vary continuously in the amount of nitrogen they provide to their legume hosts and they obtain varying amounts of carbon in return Thrall et al. We thus caution against the use of pairwise comparisons when identifying cheating. The most dangerous strategies from the perspective of mutualism stability will not necessarily be the worst partners in terms of quality; they will be those that lead to the greatest fitness conflict, i. Whether cheaters proceed to erode the mutualism or not depends on the relative fitness of other strategies as well as on the evolution of the partner. Individuals are only cheaters if they prosper, but that does not necessarily mean they will prosper for very long. A cheating strategy must 1 have increased relative fitness and 2 decrease partner relative fitness compared to the population mean fitnesses. An experimental approach would be required to determine the distribution of potential fitness benefits among interacting partner lineages due to mutualism. Alternatively, observational measurements of fitness under natural conditions could yield informative data for species not amenable to experimentation. If the rewards exchanged between partners are measured, they will be informative about cheating only if the relationship between rewards and each partner's fitness is known. Therefore, the fitness costs and benefits of the rewards must be measured. The same calculations below can then be used with fitnesses calculated using the known phenotype—fitness relationships. Because many mutualisms are context dependent, measurements taken under different ecological conditions may lead to different relationships between partner fitnesses Box. Finally, in systems where the individual is not the relevant unit of selection e. Step 1: Measure the fitness of each partner alone. This provides the baseline for determining whether their interaction is mutualistic i. Step 2: Measure the fitness of each partner genotype M1i and M2j in factorial pairings over a random sample of i's and j's in realistic environmental conditions or over multiple environments to explicitly study the contexts under which cheating might occur see Box. Step 3A: Calculate the correlation between the fitnesses of M1 and M2. The sign and magnitude of the correlation provide an estimate of fitness conflict or alignment between the species. Step 3B: Calculate the selection gradient for each partner to determine if there is asymmetrical selection for cheating. Treat the average fitness of M2 when M1i is the partner as a phenotype of M1i and conduct a standard selection analysis; similarly, treat the average fitness of M1 when M2j is the partner as a phenotype of M2j. Selection on M1 to decrease the fitness of M2, or vice versa, is evidence of fitness conflict. If experimentally controlled individual genotypes are available, a genetic selection analysis should be preferred. However, a phenotypic selection analysis with unknown genotypes would still yield insight into the patterns of selection. Two caveats with phenotypic selection analysis are 1 predicting the response to selection is not possible without knowledge of heritability and 2 environmental variation can lead to positive relationships between partner fitnesses since both partners may perform better in higher quality environments. Step 4: To categorise particular genotypes as cheaters, they must be compared to the population average. A cheater must have higher relative fitness than average and cause its partner to have lower relative fitness than average. We note that fitness conflict may be present even if no genotypes can be categorised as cheaters, especially when fitness variation has a large random component. Beyond cheating: measuring fitness conflict Cheating is fundamentally a manifestation of fitness conflict between partners Fig. Unless cheaters are just emerging, measuring the extent of fitness conflict is likely to be more informative about the evolutionary dynamics of the mutualism than identifying particular individuals as cheaters would be. Fitness conflict can be quantified in two ways: measuring fitness correlations or conducting selection analysis for each partner's effect on the other's fitness. Empirical procedures for these two approaches are outlined in Box , with additional consideration of context dependency discussed in Box. When fitness correlations are measured, a negative genetic correlation between actor and partner fitness suggests that cheating is widespread e. Meanwhile, in selection analyses, one partner's fitness can be treated as a trait of the other partner e. In a genotypic selection analysis, the selection gradient of the focal partner is determined using the fitness of each genotype of that species averaged across potential partners. Selection on the focal species to reduce its partners' fitness is evidence of fitness conflict. This conflict can be distinct from overall fitness conflict or alignment e. Heath since it is not necessarily symmetrical between the partners. Due to these different forms of context dependency, assigning a single measure of partner quality for a particular genotype is often not possible, or more precisely is only valid in a particular abiotic and biotic environment and for a given partner. More complex experimental designs are required to properly account for context dependency when assessing cheating. Environmental context dependence and phenotypic plasticity: Some variation in performance can always be expected from the environment E. The environment can affect an individual's overall physiological state and ability to invest in its partner. The environmental context may also affect the costs and benefits of mutualist rewards themselves, such as a lack of benefit to protection mutualists if there are no enemies present. In many cases, there is also genetic variation in phenotypic plasticity, with genotypes having different responses to the environment G × E. The best examples of these interactions come from the legume—rhizobia and plant—mycorrhizal literature, which typically find evidence of genetically based compatibility. Similarly, the same strain of mycorrhizal fungus can be beneficial to one plant genotype but parasitic on another e. Especially when these interactions are simultaneous e. Standard variance decomposition methods can then be used to estimate the effects of G, E, G × E, G × G, and G × G × E on each partner's fitness e. Genetic correlations can be estimated within this statistical framework to determine whether there is fitness conflict within the interaction e. If the relative frequency of the focal partner increases after the interaction, we would infer that it has higher relative fitness than other partners. If, furthermore, the host's fitness decreases with increasing frequency of the focal partner, we could infer that the partner is indeed a cheater. Genotypic selection analysis gives stronger evidence for fitness conflict or alignment than phenotypic selection analysis, since it eliminates the confounding effect of environmentally induced covariance between each partner's fitness. However, the link between a potential cheating action and these fitness outcomes can be broken in two ways. For example, if individuals vary in how many resources they have, individuals that provide less reward do not necessarily end up with a fitness advantage over individuals that provide more reward. Second, the outcome of the interaction depends on more than just one individual's strategy. As soon as stabilizing mechanisms such as partner fidelity feedback, partner choice or sanctions Box are implemented, cheating actions are no longer guaranteed to result in a fitness advantage e. Figs e, f and c. Turning off the empirical mechanisms that maintain cooperation to see whether actions that would be cheating are being thwarted is much more difficult, although it is sometimes possible to prevent partner choice from occurring by restricting access to alternative partners. However, it is typically unknown whether uncooperative actions could have paid off if not for the stabilizing mechanism. Since potential cheating actions do not always lead to cheating outcomes, it is critical to recognise that phenotypic and fitness perspectives of cheating are not interchangeable. Cheating must be defined on either the basis of phenotype or fitness. Acknowledging the distinction between cheating actions and cheating outcomes will help close the gap between theoretical and empirical perspectives over the prevalence of cheating in nature. Yet, empirical studies have revealed that there can be fitness conflict over other aspects of the mutualism, especially the extraction of benefits from the partner. As described in more detail in our review of empirical evidence below, individuals may vary in both how much reward they take and in the kinds of rewards they take. However, few models have considered cheating by extracting a greater benefit e. Variation in providing benefits and extracting benefits are not mutually exclusive and can be investigated simultaneously, but this has rarely been done but see Ferdy et al. By promoting recognition of these other strategies as forms of cheating, we can open the way for studies comparing types of cheating. Cheating should be rare Models of cheating in mutualism assume the existence of fitness conflict, i. When selection leads to a monomorphic equilibrium at which all partners provide the same net benefit e. Although no individual is being as cooperative as it could be, no individual is gaining a fitness advantage by being a worse partner either. When variation is maintained at a stable equilibrium by selection alone, then by definition, all phenotypes must have equal fitness on average. Meanwhile, fluctuations in environmental conditions can lead to variation in the costs and benefits of mutualism and thus to varying selection on partner quality and the maintenance of multiple partner quality strategies e. The general theoretical expectation that cheating should be rare is at odds with the many empirical descriptions of cheating. Next, we turn to empirical data on mutualisms and highlight how our definition of cheating resolves much of the disagreement between the theoretical and empirical literatures on the prevalence of cheating. A challenge of analysing the empirical literature is that empirical claims of cheating until now have been based on differing definitions of cheating. We note that these examples are representative but by no means exhaustive. Ants and plants Hundreds of plant species in the Asian, African and American tropics have convergently evolved domatia structures that ants can live inside and often also extrafloral nectaries or food bodies. These rewards attract ants, which defend the plant from herbivores or other enemies and thus increase plant growth. Thus, these ants may have evolved to extract more benefits from their host plants via sterilisation. Surprisingly, this ant behaviour may not reduce plant lifetime fitness; a demographic model by Palmer et al. However, whether sterilisation per se or other C. Thus, whether or not plant sterilisation by ants qualifies as cheating depends critically on whether this behaviour reduces plant lifetime fitness, and the current evidence is equivocal. Since it is rare for all seeds to be destroyed Janzen ; Keeley et al. There are essentially two ways that the insects could cheat the plant: by pollinating less or by consuming more seeds. However, it is not clear whether any fitness advantage is derived from neglecting to pollinate the host plant. It has been argued that the cost of pollination is small Pellmyr ; meanwhile, pollination benefits the pollinator itself by increasing the chance that the fruit containing the pollinator's eggs will be retained and that there will be resources seeds in yuccas and ovules in figs for the maturing larvae to consume. However, variation in the number of fig wasps that emerge from a fig is influenced by many factors, including the number of wasps that visit the fig, their behaviours inside the fig and the resources allocated by the tree to the developing fig. Therefore, though it is plausible that insects cheat by consuming more seeds, we are lacking data on whether this behaviour increases insect relative fitness and decreases plant lifetime fitness. Nectar larceny can occur via 1 puncturing the flower to reach the nectar, 2 feeding through holes made by others or 3 entering the floral opening but not transferring pollen due to morphological mismatch Inouye. Although the behaviours of pollination and larceny are discrete, the distinction between cooperators and cheaters is surprisingly hazy. The floral visitor's behaviour can depend on the partner, as most animals that have been identified as nectar larcenists of one plant species are known to be effective pollinators of other plant species. The behaviour can also depend on the individual within the species, with some individuals acting as pure nectar larcenists and others behaving solely as legitimate pollinators. Finally, the behaviour can vary among visits; floral visitors commonly have multiple foraging behaviours within their repertoires Bronstein ; Irwin et al. Depending on the system, these effects can occur through both direct mechanisms e. Thus, the fitness consequences of nectar larceny for the plants are sometimes, but not always, consistent with larceny being a form of cheating. In principle, floral visitors might gain fitness benefits by robbing instead of pollinating, but at present there are virtually no data available to evaluate this assumption. Plants and arbuscular mycorrhizal fungi Arbuscular mycorrhizal fungi AMF often benefit their host plant by increasing nutrient and water uptake in exchange for plant carbon. However, it is also fairly common to observe them reducing host plant performance Hoeksema et al. It remains unclear whether specific AMF strains always reduce performance of a particular host plant or whether their effects vary depending on the abiotic or biotic context. A study inoculating 10 plant species with each of 11 AMF strains found that each AMF had both positive and negative effects that depended on which plant species it was interacting with, while every plant species benefited from at least one AMF species and had reduced fitness in the presence of at least one AMF species Klironomos. They conclude, however, that it is possible this potential cheater would be more beneficial to its hosts under different conditions. Clearly, particular strains of mycorrhizal fungi can reduce host fitness relative to other strains and even relative to no partner under some conditions. However, the connection between a decrease in host benefit and an increase in fungal performance has only rarely been tested. Moreover, spatial separation of the two AMF reverses this outcome to favour the more beneficial AMF, implying that plants have mechanisms for regulating symbiont fitness that may prevent cheating, provided there is sufficient spatial structure in the symbiont community. Furthermore, Kiers et al. While this variation in stabilizing mechanisms could allow AMF to cheat, we largely lack coupled estimates of host and symbiont fitness to assess the prevalence of cheating in these interactions. Each partner might cheat by offering few or no resources while still accruing resources from the other, and there is substantial variation in plant growth with different microbial partners Thrall et al. However, plants can preferentially direct rewards to better rhizobia and can also choose which strains to interact with e. One rhizobium strain that has been referred to as a cheater provides no nitrogen to the plant and proliferates in nodules Sachs et al. Cleaning results in improved hygiene for the clients, in one case with demonstrated fitness benefits Waldie et al. Although some cleaner species prefer ectoparasites Barbu et al. When a client is bitten by the cleaner, it responds with an involuntary physical jolt; individual L. In contrast, a related species, L. Unlike its relative, L. However, it is not known how costly it is for a client to be bitten, or how much of a fitness increase cleaners receive from biting their clients, so we cannot conclude that cleaners cheat without additional data. How common is cheating? In both specialised and generalised pollination systems, there is variation in whether the flower visitors provide benefits by pollinating. Similarly, both mycorrhizae and rhizobia vary in how many resources they contribute to their host plants. Meanwhile, ants and cleaner fish vary in whether they perform behaviours that potentially harm their partners in order to extract greater benefits for themselves. Yet, despite all these observations, there is almost no evidence for the fitness costs and benefits necessary to establish conclusively that cheating is happening. In most cases, there is simply a paucity of fitness information, making it inconclusive whether the variation observed represents ongoing cheating. Future Research Directions Moving forward, it is imperative that theoretical and empirical studies of mutualisms occur under a unified framework, so that data collected can be used in a predictive rather than a descriptive manner. To spur these activities, we outline challenges for future empirical and theoretical work. Identifying the genes underlying mutualistic traits can give insight into the evolutionary history of mutualisms, the mechanisms currently producing and maintaining partner quality variation, and the likely future evolution of the mutualism. Once genes that contribute to partner quality and stabilizing mechanisms have been identified, comparative and population genomic approaches can be used to make inferences about the forces driving mutualism evolution. For example, did sanctions against uncooperative partners arise as innovations favoured by cheating, or are they exaptations that originally evolved to optimise resource allocation and that subsequently favoured the evolution of cooperation Frederickson? Has there been balancing selection on mutualistic traits, as expected with negative frequency dependence, or stabilizing selection e. Experimental manipulations could also take advantage of known genetic bases of mutualist traits. Individuals could be genetically engineered to enable precise manipulation of partner strategies. Manipulation of reward production and extraction traits would allow the fitness effects of strategy variation to be measured without the confounding effects of overall genetic quality. Finally, elucidating how the genes underlying variation in partner quality interact is necessary in order to develop predictive evolutionary models. Context dependency of strategies and outcomes: mutualisms in changing environments The costs and benefits of mutualism are well known to depend on the abiotic and biotic environment Bronstein ; Chamberlain et al. Environmental change intensifies the need for a mechanistic understanding of how the environment affects both the expression of mutualism traits and the relationship between mutualism traits and fitness. To predict mutualism performance and evolution in changing environments, we must develop mechanistic models that connect the physiological and ecological dimensions of the interactions to fitness. We first require data on the costs and benefits of mutualism in the context of specific environments; furthermore, these costs and benefits should be considered relative to alternative methods of obtaining rewards e. Environmental quality, as well as the presence or absence of antagonists or alternate mutualists, may affect whether rewards are needed, how readily available rewards are from different sources and how costly rewards are to offer. Environmental change may in fact facilitate the acquisition of a mechanistic understanding of mutualistic interactions in two ways. Our conceptual framework suggests that cheating and fitness conflict are most likely to be detected away from evolutionary equilibrium. Thus, sources of fitness conflict could be revealed by studying mutualisms that are currently responding to environmental changes or to novel partners Mayer et al. Environmental perturbations also enable tests of the role of the environment in mutualism evolution. Theory predicts that the potential for conflict between partners increases with environmental quality, since the relative importance of the partner decreases Hochberg et al. Some contemporary changes have led to increased environmental quality. In the case of soy, older cultivars obtain more benefit from a mixture of rhizobia strains than newer cultivars, which suggests the decay of host mechanisms that align partner fitness Kiers et al. Other types of global change cause increased stress, such as heavy metal contamination, higher temperatures and increased or decreased precipitation Kiers et al. However, most models focus on only one dimension of the interaction — the amount of reward produced. Therefore, many theoretical questions remain about how traits affecting reward extraction evolve and what mechanisms can curb cheating along this alternate path. Fixation on the idea of cheating as investing less in the partner may also have led to the neglect of other potential forms of cheating in empirical investigations. Currently, theoretical predictions about cheating derive mostly from cheating as giving less reward; we know very little about other aspects of partner quality or the relationships among partners along additional trait axes. There has also been no direct comparison made between different forms of cheating. In particular, theoretical studies could determine whether types of cheating differ in the threat they pose to the persistence of mutualism and the mechanisms needed to oppose them. Future empirical studies investigating conflict in mutualism should measure the control that each partner has over each step in the production and consumption of the rewards exchanged Fig. Such studies are necessary in order to determine whether cheating is occurring through routes other than producing less reward. In particular, we expect that conflict over reward consumption may be common in mutualisms derived from host—parasite interactions. In these mutualisms, former parasites have evolved to benefit their hosts; however, they may not have completely lost the original parasitic traits used to extract benefits from their hosts. Studies of this nature should also consider whether or not different types of conflict over reward exchange are stabilised by the same mechanisms. The scope of both theoretical and empirical studies on cheating and conflict should also be broadened beyond the resources and services traditionally recognised as mutualism rewards. There are a growing number of examples of conflict between partners over other aspects of the interaction. For example, there can be conflict between plants and their fungal endophytes over pollen vs. In addition, figs and fig wasps may experience conflict over wasp sex ratio, as male wasps do not provide any benefit to the fig e. Thus, even if the primary rewards exchanged between partners exhibit fitness alignment, there may be traits for which fitness conflict arises from different trait optima for each partner. Conclusion Although cheating is currently held to be an important facet of mutualism, the claims of cheating in empirical systems are based on disparate definitions of cheating. To move forward as a unified field, we must adopt a common theoretical and empirical framework for investigating cheating and fitness conflict in mutualism. We propose that cheating can be defined as increasing the fitness of the actor above average fitness and decreasing the fitness of the partner below average partner fitness. This definition focuses on fitness outcomes rather than on specific strategies employed by mutualists, since only fitness outcomes are informative about the evolutionary threat to the mutualism. Standardizing the measurement of cheating will facilitate comparisons between systems and enable a critical assessment of the degree of fitness conflict in mutualisms. These models will be crucial as we seek to predict the consequences of cheating and conflict on mutualism ecosystem services under changing environments. Such models will also provide the ultimate test of our understanding of mutualistic interactions. MEF acknowledges support from an NSERC Discovery Grant and the University of Toronto. We thank our anonymous reviewers for their helpful comments. All other authors contributed substantially to discussions leading to the conceptual framework described here, writing sections of the first draft and revisions of the manuscript.

In addition, the children may repeat that behavior when they are adults. Maybe later on in my career, LOL. So, the man could be a highway tout, the woman could be a prostitute — if I believe in their love, I am content. In both specialised and generalised pollination systems, there is variation in whether the flower visitors provide benefits by pollinating. You have definitely come to the right website. cheaters prosper definition According to a survey carried out by the glad's newspaper, a tenth of people admitted to cheating on an exam, with athletics cheaters prosper definition most likely to cheat. A Standard Framework for Studying Cheating and Fitness Conflict As the studies on cheating in mutualism have multiplied, so have the definitions of cheating. We then describe experiments required to conclude that cheating is occurring and to quantify fitness conflict more generally. Selection on the focal species to reduce its partners' fitness is evidence of fitness conflict. We propose that cheating can be defined as increasing the fitness of the actor above average fitness and decreasing the fitness of the met below average partner fitness. Thus, even if the primary rewards exchanged between partners exhibit fitness alignment, there may be traits for which fitness conflict arises from different trait optima for each partner.