Dominance-discovery and discoveryexploitation trade-offs promote diversity in ant communities

RESEARCH ARTICLE Dominance-discovery and discoveryexploitation trade-offs promote diversity in ant communities Louise van OudenhoveID 1,2,3 *, Xim Cerda ´ 2 , Carlos Bernstein 3 1Universite ´Co ˆte d’Azur, INRA, CNRS, ISA, France, 2Estacio ´n Biolo ´gica de Doñana, CSIC, Sevilla, Spain, 3Universite ´de Lyon, Universite ´Claude Bernard Lyon 1, CNRS, Laboratoire de Biome ´trie et Biologie Evolutive, Villeurbanne, France *louise.vanoudenh[email protected] Abstract In ant communities, species coexist by using different foraging strategies. We developed an adaptive dynamics model to gain a better understanding of the factors that promote the emergence and maintenance of strategy diversity. We analysed the consequences of both interspecific competition and resource distribution for the evolutionary dynamics of social foraging in ants. The evolution of social foraging behaviour was represented using a stochastic mutation-selection process involving interactions among colonies. In our theoretical community, ant colonies inhabit an environment where resources are limited, and only one resource type is present. Colony interactions depend on colony-specific foraging strategies (defined as the degree of collective foraging), resource distribution patterns, and the degree of competition asymmetry. At the ecological timescale, we have created a model of foraging processes that reflects trade-offs between resource discovery and resource exploitation and between resource discovery and ant behavioural dominance. At the evolutionary timescale, we have identified the conditions of competition and resource distribution that can lead to the emergence and coexistence of both collective and individual foraging strategies. We suggest that asymmetric competition is an essential component in the emergence of diverse foraging strategies in a sympatric ant community. Introduction Niche partitioning plays a significant role in species coexistence in plant and animal communities [1,2,3,4,5]. Ecological theory predicts that species coexist when their niche differentiation is such that they utilise sufficiently distinct resources [6]. However, Andersen [7] suggests that there are “not enough niches” to explain the high species richness of many ant communities. He argues that the majority of ant species are generalists with overlapping requirements, and some field studies have confirmed the existence of niche inclusion [8,9,10]. Notwithstanding, within ant communities, co-occurring species often differ with regards to food type [11,12,13], food size [14,15], daily activity rhythms [16,17,18,19,20], species thermal PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 1 / 24 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: van Oudenhove L, Cerda ´X, Bernstein C (2018) Dominance-discovery and discoveryexploitation trade-offs promote diversity in ant communities. PLoS ONE 13(12): e0209596. https://doi.org/10.1371/journal.pone.0209596 Editor: Samir Suweis, Uniiversity of Padova, ITALY Received: October 15, 2018 Accepted: December 9, 2018 Published: December 31, 2018 Copyright: ©2018 van Oudenhove et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper. Funding: This work was supported by Spanish Ministry of Economy, Industry and Competitiveness (MINIECO) and FEDER (projects CGL2012-36181 and CGL2015-65807-P to XC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. tolerance [21,22,23], nesting site choice [8], and microhabitat use [24,25]. Additionally, ants display a wide spectrum of foraging strategies. Foragers range from being completely independent (solitary foraging) to retrieving food resources exclusively through cooperative recruitment [26,27], and sympatric ant species may use different foraging strategies [28]. Foraging strategies are the result of the complex set of behavioural and morphological traits that are best suited to gathering food resources in a particular environment [29]. In social foragers, the selective value of a given strategy strongly relies on the degree of interdependence among individual foragers [30], and elaborate communication mechanisms facilitate cooperative foraging [26,31,32]. For example, not all ant species are capable of displaying recruitment behaviour. This ability depends on the species’ morphology and ability to engage in chemical communication [31]. Individual or non-cooperative foraging does not require communication: each ant finds and harvests food on its own [33]. Recruitment, in contrast, requires that successful discoverers alert inactive nestmates. One of the most communication-intensive strategies is mass recruitment, which employs pheromone trails [34]. In the ant literature, individual-foraging species are commonly referred to as “primitive”, as compared to “advanced” mass-recruiting species [31]. However, the evolution of recruitment behaviour in ants remains unclear, and cladistic analysis suggests that an increase in recruitment efficiency has been repeatedly selected for within different ant clades [35,36]. A broader hypothesis that seems more biological sound is that foraging behaviour and its relative efficiency have been shaped by selection stemming from environmental constraints [36,37]. Ant foraging strategies appear to have evolved in response to both intrinsic factors, such as colony size (which seems to be positively correlated with the degree of foraging-related communication in many ant species [38,39,40]); and external factors, such as resource distribution patterns [41], competition [42, 40], predation [43], abiotic conditions (e.g. temperature [37,44,45], and vegetation [46]). In eusocial insects, social foraging occurs in two steps. First, a resource is discovered. Second, it is exploited. Scouts search for food, gather information about what they find [47], and, if possible, recruit foragers [31]. Recruits remain in the nest, ready to help exploit resources as soon as scouts relay their discoveries. The size of this pool of recruits determines the efficiency of the recruitment process [27,48]. At the colony level, the challenge is strike an optimal balance between investing in the discovery rate (high ratio of scouts) and investing in the exploitation rate (high ratio of recruits). We called this dilemma the “discovery-exploitation” tradeoff. Different theoretical studies on the scout-recruit constraint in social insects have predicted the optimum proportion of scouts needed to maximize a colony’s gross [49] or net gains [50, 51,52]. They have also indicated that the optimum strategy should strongly depend on resource characteristics. When food items are either small or easy to find, individual foraging is the most efficient strategy (i.e., all foragers are scouts), whereas other kinds of resources may promote social or cooperative foraging [49,53]. However, to date, theoretical work on this topic has focused on colony-level dynamics [54]. In contrast, empirical studies have emphasized the importance of both resource characteristics and competitive pressure for foraging strategies [55,56]. The evolution of foraging strategies might thus be strongly context dependent (i.e., depend on the strategies adopted by the various colonies in a given community). Yet, none of the current models takes into account frequency-dependent competition among species. Competitive trade-offs are often thought to promote diversity in communities [57,58,59], and competitive mechanisms can be distinguished according to the types of interactions involved. For example, interactions might be indirect and result from species harvesting the same limited resource, whereas interference competition involves direct physical interactions among organisms [60]. Ants exhibit both indirect and interference competition. Species that successfully defend food against direct competitors are said to be behaviourally dominant, Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 2 / 24 whereas species that lose during such encounters are said to be behaviourally subordinate [61, 62,63]; however, the latter species have the ability to discover resources rapidly. Indeed, resource discovery and behavioural dominance are negatively correlated [61,64] and seem to be species-specific traits [65]. This so-called dominance-discovery trade-off might help promote ant species coexistence [66,67,68]. However, it is important to note that the dominance hierarchy is also affected by environmental factors such as temperature [28,23] and resource availability [11,69], which act to reduce competitive exclusion [70,7]. Both empirical and theoretical evidence show that the interplay between species-specific foraging behaviour and context-dependent asymmetric competition plays a fundamental role in shaping ant community structure [12,68]. Adler and colleagues [68] demonstrated that the dominance-discovery trade-off could promote the coexistence of species that differed in their foraging behaviour. Using a model of food patch occupancy dynamics, they characterised species according to resource discovery rate and worker body mass. They then imposed conditions of asymmetric interspecific competition by assuming that slower discoverers were totally dominant over faster discoverers. In this theoretical community, many species with different discovery rates could coexist. They also showed that when the intensity of the competitive trade-off was relaxed to better reflect realistic community dynamics, coexistence was still possible. However, the pioneering work by Adler et al. [68] left several important unanswered questions related to the proximate and ultimate causes of the dominance-discovery trade-off. First, from a proximate standpoint, what are the mechanisms underlying the trade-off between dominance and discovery abilities? Second, from a functional standpoint, how did evolution promote the establishment of strategy diversity? Third, how might the dominance-discovery trade-off have favoured the emergence of very different foraging strategies? In this study, we aimed to answer these questions by constructing a community-level demographic model that takes into account recruitment processes and in which foraging strategies structure competitive trade-offs. Indeed, since behavioral dominance is positively correlated with numerical dominance (i.e., forager density) [62,63], we assumed that success in interference competition depends on the number of foragers exploiting a food resource, which in turn depends on the recruitment ability displayed by the ant species. As in previous models of colony-level foraging in social insects, colony energy intake was constrained by the discovery-exploitation trade-off [50,51,54]. Since interspecific competition is a key structuring force in ant communities [31, 71], we likewise took frequency-dependent competition into account. We then used our model to examine how the interplay between resource characteristics and species interactions could determine the coexistence of foraging strategies at the ecological timescale and shape foraging strategy dynamics at the evolutionary timescale. To this end, we linked demographic dynamics and foraging processes. Our mathematical expressions were parametrised using field data. We then used adaptive dynamics to test to the extent to which the discovery-exploitation and dominance-discovery trade-offs could explain the emergence and maintenance of foraging strategy diversity in ant communities. Modeling Within colonies, some foragers search for food resources on their own (hereafter, scouts) while others wait in the nest to be recruited (hereafter, recruits). A colony might modulate its investment in scouts versus recruits. In our model, this investment defines the colony’s foraging strategy. The more a colony invests in scouts, the more efficiently it will discover food items. Conversely, a greater investment in recruits means a colony will more efficiently harvest resources (depending on food item size), which could translate into superiority in interference competition [68]. Our aim was to identify evolutionarily stable foraging strategies and to Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 3 / 24 determine the conditions under which such strategies could evolve and coexist, forming a strategy coalition [72]). We propose a simple model describing the evolution of foraging strategies in ants sharing the same environment. We start by using a set of differential equations to model interactions among ant colonies that are related to competition for food resources. Based on these interactions, we can establish the net rate of energy intake and the fitness of a single colony. Table 1 shows the main parameters used in the model. From this demographic model, and using an adaptive dynamics framework [73,74,75,76], we can deduce the evolution of foraging strategies. We develop functions that explicit the different foraging processes afterwards. Mathematical analyses were performed using Wolfram Mathematica 11.0. Demographic model We treat each colony as an individual, and we assume that all individuals are strictly equivalent except with regards to foraging strategy. In particular, colony size is constant and independent of foraging strategy. Each individual is characterised by its specific strategy (x i ), which represents its degree of collective foraging (e.g., the proportion of foragers that are recruits). Hereafter, we will refer to the set of colonies sharing the same strategy x i as species, and we will denote the number of colonies using this strategy using q i . In order to simplify computations, we made two main assumptions regarding food availability within the environment. First, food items are randomly distributed with mean density ρ. The pace at which new items appear (i.e., their renewal rate σ R ) is equivalent to mean food density: σ R =ρ. Second, the total amount of food or biomass available in the environment is fixed: the more numerous the food items (i.e., the higher their density), the smaller their size. The amount of energy available per item e R is inversely proportional to food density: e R =ρ −1 . The first step in understanding an ant community is to focus on resource fate [68]. Let nbe the number of species sharing the environment. We distinguish (n+ 1) types of food items Table 1. Parameters used in the model. Symbol Description x i Degree of collective foraging q i Number of colonies using a strategy x i R i Number of food items controlled by a colony using a given strategy x i D i Discovery rate of a colony using a given strategy x i E i Exploitation rate of a colony using a given strategy x i C i,j Probability that a colony iwill usurp a food item previously controlled by a colony j bFood item loss rate due to degradation or other organisms ρFood item density σ R =ρRenewal rate of food items e R =ρ −1 Amount of energy available per food item σ q Efficiency of converting energy into new colonies γColony mortality rate μRescaled parameter ¼g sq ϕDegree of competition asymmetry v D Search speed v E Speed of ants carrying food lLoading capacity ψDegree of preemptive competition https://doi.org/10.1371/journal.pone.0209596.t001 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 4 / 24 based on who controls them: Ris the number of undiscovered food items, and R i ,i2[1, n] is the number of food items that have been discovered and exploited (i.e., “controlled”) by a given colony of species i. A colony loses control of an item by fully harvesting it or by being driven away by a colony of a different species. We assume that the loss of a food item to a colony of the same species is irrelevant to the species-level success of a strategy. Below, we explicitly describe the dynamics of these (n+ 1) types of food items. Food-item dynamics are described in equation system (1). Eq (1.a) describes the dynamics of undiscovered food items. These items appear at rate σ R (because σ R =ρas stated above) and disappear at rate bas they are degraded or consumed by organisms other than ants. The disappearance rate of undiscovered food items (R) is the sum of the discovery rates D i of the q i colonies of each species. Eq (1.b) describes the dynamics of the food items controlled by a single colony of species i. The first term of Eq (1.b) deals with the discovery of new items: a colony of species idiscovers food items at discovery rate D i . It can discover either as-yet-undiscovered items, R, or items controlled by all colonies of species j,q j R j , according to probability C i,j . The second term of (1.b) corresponds to previously controlled food items that have been fully harvested by a colony or usurped by colonies of other species. Controlled items R i are harvested at exploitation rate E i and can be usurped by colonies of species jaccording to probability C j,i and the discovery rate of species j,D j . Controlled items also disappear because of non-ant competitors at rate b. ( dR dtR¼rRðPi2½1;n�qiDiþbÞ ð:aÞ dRi dtR¼DiðRþPj2½1;n� figCi;jqjRjÞ RiðEiþPj2½1;n� figCj;iqjDjþbÞ;8i2 ½1;n� ð:bÞð1Þ System (1) has a unique stable solution: R�and R� i;8i2 ½1;n�(the existence, uniqueness, and stability of this solution are shown in S1 Appendix). Food item dynamics are much faster than ant species dynamics. Food items change daily, whereas new colonies are established yearly [77]. We can thus consider the number of items controlled by each colony to be a function of foraging ability (D i ,E i ,8i2[1, n]), colony number (q i ,8i2[1, n]), and competitive interference between species (C i,j ,8(i,j)2[1, n] 2 ). The net rate of energy intake for a single colony of species iis given by the equilibrium number of food items the colony controls, R� i, multiplied by the species’ exploitation rate, E i , and the amount of available energy per item, e R (e R =ρ −1 , as mentioned above). This net rate of energy intake is a key component of the fitness Eq (2). The growth rate of a species depends on the amount of energy its colonies obtain (the net rate of energy intake) and the efficiency with which this energy is converted into new colonies (σ q ). Let parameter γbe colony mortality. The simplest differential equation describing the dynamics of species iis dqi dt ¼qiðsqr1EiR� igÞð2Þ In a community with a single resident species r, after performing the rescaling transformation T=σ q t,Eq (2) becomes _ qr¼qr DrEr ðbþqrDrÞðbþErÞm � � ð3Þ where m¼g sq. Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 5 / 24 If m<DrEr bðbþErÞ, the resident species reaches its asymptotically stable equilibrium number of colonies q� r¼ErðDrbmÞ b2m mDrðErþbÞ. For example, when there is no competition with non-ants (b= 0), μ −1 represents the stable equilibrium number of colonies. Evolutionary model An analysis was carried out at the evolutionary timescale in which the dynamics of a residentmutant community with two species (rand s) were characterised. Mutations are assumed to be rare enough to allow species dynamics to stabilise before mutants appear. The resident species rbegins at its equilibrium number of colonies q� r, and the mutant species sis initially rare. The resident-mutant system is defined by Eq (2) and system (1). It was used to address the following questions: (1) Will the mutant be able to establish itself in the community?; (2) Will the mutant supplant the resident?; (3) Will both species coexist?; and (4) Will a coalition of two coexisting species resist invasion by a new mutant? The answers to these questions depend on the invasion fitness, which represents the per capita growth rate of an initially rare mutant in a community of residents FrðsÞ: FrðsÞ ¼ mDsEsðmðbþErÞ2Cs;rðb2mþðbmDrÞErÞ DrErðmðbþEsÞðbþErÞ Cr;sðb2mþðbmDrÞErÞ1 ! ð4Þ The long-term evolution of the foraging strategy can be entirely predicted by FrðsÞ. The slope of the invasion fitness at xs¼xrð@FrðsÞ @xsjxs¼xrÞ, called the selection gradient, describes the direction and speed of evolutionary changes [73,74,75,76]. The values at which the selection gradient vanishes (i.e. the location of the evolutionarily singular strategies or SSs) are the equilibrium points of the evolutionary dynamics described by x�SS ,@FrðsÞ @xsjxs¼xr¼x�¼0. SSs are classified according to their evolutionary and convergence stability [75]. When they are evolutionarily stable (@FrðsÞ2 @x2 sjxs¼xr¼x�<0), no nearby mutants can invade the resident population, and thus the strategy constitutes an evolutionarily stable strategy [78]. SSs are said to be convergence stable (@FrðsÞ2 @x2 sjxs¼xr¼x�þ@FrðsÞ2 @xs@xrjxs¼xr¼x�<0) when they act as evolutionary attractors, which occurs when strategies in the vicinity of an SS evolve towards it [79]. Of particular interest are SSs that are convergence stable but not evolutionarily stable. At these branching points, nearby mutants can coexist with the resident population. The community is therefore composed of two resident species, each characterised by a specific strategy. Mutations might occur in both populations, and a mutant might either replace one of the residents (preserving dimorphism), replace both residents (eliminating dimorphism), or become a third resident (generating diversity). The likelihood that a polymorphic community will evolve depends on the invasion fitness of a scarce mutant in the dimorphic resident community (see [80,81] for recent developments in branching bifurcation). As in the monomorphic case, the first step is to find the pair of stable resident equilibria (qr1;qr2). Then both the number of food items exploited by a mutant in such an environment R� mand its invasion fitness are determined. Evolutionary scenarios are evaluated by analysing evolutionary and convergence stability [82]. Foraging processes To mathematically describe the discovery-exploitation trade-off and the dominance-discovery trade-off, we needed to explicitly define the functions D i ,E i , and C i,j . We aimed to establish simple functions that reflect the mechanisms involved in foraging processes and thus make Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 6 / 24 clear the contributions of the co-existing degrees of collective foraging x i and x j and food item density ρ. In the simplified environment described above, according to the first assumption, food items have a density of ρ(first assumption) and are randomly distributed. Therefore, the mean distance between the nest and the first item encountered is 1 2r1 2[83], which we felt was a good approximation of the mean distance between the nest and food items in general. The second assumption states that food item size is inversely proportional to food item density (ρ −1 ). If food items are treated as two-dimensional, then item diameter can be estimated using r1 2. The foraging functions were built using both logical and phenomenological arguments and parametrised using field data. The study site and species are described in detail in [22], and the fitting methods are explained in S2 Appendix. The data come from an individual forager: Cataglyphis cursor. The parameter estimates are listed in Table 2. Discovery rate (D i ). The discovery rate is the number of food items discovered by a given colony per unit time. Between time tand t+dt, a scout explores an area based on its search speed v D , search time dt, and food perception abilities [13]. We assume here that the long-distance sensory perception of ants is negligible [84] because most ant species perceive food items upon contact (but see [85]). So, the maximum distance at which an ant perceives a food item corresponds to item diameter: r1 2). The simplest approximation of the area explored by a single scout is therefore vDr1 2dt. Given that the number of food items per unit area is ρ, the number of items encountered by a single scout is vDr1 2dt. There are (1 −x i ) scouts searching for food. We assumed there is no interference among scouts, so the overall discovery rate for a given colony is Di¼vDr1 2ð1xiÞ. The rate of discovery thus increases with food item density and decreases with the degree of collective foraging (Fig 1). Exploitation rate (E i ). The exploitation rate represents the fraction of each controlled food item that is consumed per unit time. It is the inverse of the time needed to consume a single food item: T E . It depends on the round-trip travel time between the nest and the food item, T V ; the number of trips needed to fully harvest the food item, N V ; and the number of ants harvesting the food item, N A :TE¼TVNV NA. Travel time is distance divided by speed. The mean distance between a food item and the nest is approximated using 1 2r1 2. Consequently, TV¼1 2r1 2vE , where v E is the speed of ants carrying food. The number of trips needed to harvest a food item is inversely proportional to ant loading capacity, l. Assuming that only the smallest food items can be carried by a single ant in a single load, lhas a fixed value of 1. Since food item size is proportional to ρ −1 , the food item is fully harvested after NV¼r1 l¼r1trips. Regarding the number of foraging ants, we assume that individual foragers remember the position of a food item they have discovered and are able to return to it after delivering a food Table 2. Foraging parameters. Parameter Description Estimate v D Search speed (m.s −1 ) a 0.11 v E Speed of ants carrying food (m.s −1 ) a 0.02 lLoading capacity b 1 ψDegree of preemptive competition c 4 (a) = estimated from field data (see S2 Appendix for details). (b) = arbitrarily fixed. (c) = estimated from [22]. https://doi.org/10.1371/journal.pone.0209596.t002 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 7 / 24 load to the nest. There is always at least one ant harvesting a given food item. The other ants harvesting the food item have been recruited from the colony’s x i potential recruits. We consider that the recruits are uniformly distributed across the different food items being harvested. We used the number of food items encountered as a proxy for the number of food items being harvested. Since the number of food items discovered is proportional to r1 2, the number of recruits available to harvest a given item is proportional to xir1 2. However, not all the potential recruits are immediately engaged in resource harvesting upon resource discovery: the ant trail that allows recruitment requires the pheromone deposits of several ants to be efficient. Therefore, the number of recruits involved depends on the number of trips needed to harvest a given food item: if a large number of trips are needed, the trail has effectively been laid and xir1 2is a good approximation of the number of effective recruits per food item. If few or no trips are needed, the number of effective recruits also tends towards zero (the food item is fully harvested before recruitment has become efficient). The number of effective recruits per food item can then be modeled using the asymptotic function xir1 211 NV � �. To summarise, there are 1þxir1 2ð1rÞants harvesting a given food item: the ant that discovered the food item and that kept harvesting it and the ants that were recruited to harvest it. The colony-level exploitation rate is therefore Ei¼1 TE¼NA TVNV¼2vEr3 21þxir1 2ð1rÞ  �. Exploitation rate thus increases with food item density, and increases with the degree of collective foraging (Fig 2). Competition (C i,j ). We studied the impacts of two different kinds of competition: preemptive competition and inference competition. In preemptive competition, control over resources occurs on a “first come, first served” basis. Such was the case in our theoretical community where, once a colony has discovered a food item, it cannot be taken over by another colony (C i,j =C j,i = 0). In interference competition, the probability that a colony will win a direct contest over a food item (and thus usurp it) depends on the size of the food item and the degree of collective foraging of each colony. First, the probability that a colony will usurp a food item from another Fig 1. Discovery rate increases with food item density and decreases with the degree of collective foraging x.The different colors represent different degrees of collective foraging: light grey for x= 0, grey for x= 0.5, dark grey x= 0.8, and black for x= 0.9. https://doi.org/10.1371/journal.pone.0209596.g001 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 8 / 24 colony decreases with food item size. Indeed, very small items (ρ!1) can be carried by the ant discovering them and will not be usurped by other colonies. We thus assumed that C i,j is proportional to the linear function (1 −ρ), so it tends towards 0 when food size is very small. This probability also depends on the colony’s relative investment in collective foraging. The more a colony invests in recruits, the more workers that will be available to fight over contested food items, the higher the probability that the colony will win. We consider that a colony’s numerical advantage is proportional to its number of effective recruits (defined above), such that the physical competitive strength of a colony with strategy iis x i . Based on this relationship, C i,j tends towards 1 (resp. 0) for large food items, if x i −x j >0 (resp. x i −x j <0). The magnitude of the difference in foraging strategies is modulated by the coefficient ϕ, which signals the asymmetry of competition. If ϕ= 0, competition is symmetric and all colonies have the same probability of winning the item regardless of the degree of collective foraging. The higher the value of ϕ, the stronger the asymmetry and the greater the impact of the difference in strategies. In ants, the propensity to start a fight depends on the colony level of aggressiveness. This species-specific trait is positively correlated with investment in collective foraging [86]. The probability that two colonies fight thus depends on both degrees of collective foraging: the more collective their foraging strategies, the higher the probability they actually fight. To account for this fact, we multiplied the numerical advantage (x i −x j ) by the global aggressiveness (x i +x j ). To model colony contests, we used the inverse-logit function 1 1þcexp�ðxixjÞðxiþxjÞ, where ψrepresents the degree of preemptive competition. Indeed, when ψ!1, then C i,i !0. This parameter was estimated from field observations of Aphaenogaster senilis showing that about 20% of contests ended in intraspecific usurpation [22] (Table 2). Overall, the probability that a colony iwill usurp a food item controlled by colony jis Ci;j¼1r 1þcexp�ðx2 ix2 jÞ. The outcome depends on food item size, competition strength, and the degree of collective foraging employed (Fig 3). Fig 2. Exploitation rate increases with both food item density and the degree of collective foraging x.The different colors represent different degrees of collective foraging: light grey for x= 0, grey for x= 0.5, dark grey x= 0.8, and black for x= 0.9. https://doi.org/10.1371/journal.pone.0209596.g002 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 9 / 24 with those of previous models of the scout-recruit trade-off [50,49,51,52], despite our simpler foraging functions. (3) In the third case, where there was asymmetric competition with other ant colonies, three outcomes could be distinguished. (3.1) When resources are scarce and large, the investment in collective foraging should be great. However, the relative degree of investment depends on the degree of competition asymmetry. The greater the competitive advantage derived from collective foraging, the greater the investment should be. (3.2) When Fig 8. Values for coexistence criterion according to food item density and the degree of competition asymmetry. The red area is where coexistence criterion is zero (no possible coexistence for two different foraging area). The lighter areas indicate where criterion values are higher (i.e. the areas of possible coexistence for two different foraging strategies are larger). Here, b= 0.1, μ= 0.001. https://doi.org/10.1371/journal.pone.0209596.g008 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 16 / 24 resources are highly abundant and tiny, colonies should invest entirely in individual foraging. (3.3) In intermediate situations (i.e, where food items are medium-sized or somewhat small), individual and collective foraging are evolutionarily stable and can coexist. This last finding fits with those from a previous study examining the association between foraging strategies and spatiotemporal resource attributes, in which both individual and trunk-trail foragers (e.g., leaf-cutting ants) were seen collecting small, common resources [41]. We propose that the investment in collective foraging is the key mechanism underlying the dominance-discovery trade-offs. To make this definition more concrete, we suggest that this investment manifests itself via the trade-off between scouts and recruits, where scouts are workers that search for food by themselves and recruits are workers that harvest food discovered by scouts. However, this vision of foraging can be criticized in two ways. First, ant colonies may display some flexibility in their responses to environmental conditions [43,41]. They are known to adjust task allocation according to both internal and external factors [88]. In particular, species capable of collective foraging behaviour can efficiently match their efforts to Fig 9. Simulated evolutionary trees for: a) strategies emerging from small-scale mutation-selection processes (var G = 0.01), where food item density ρ= 0.986 and b) strategies emerging from introduction (var G = 1), where food item density ρ= 0.8. Line darkness reflects the relative number of colonies in the population using the corresponding strategy. All the simulations started with a monomorphic community of mass recruiting species (x r = 0.8). The degree of competition asymmetry ϕ= 5. The ecological timescale was assumed to be 10 5 times faster than the evolutionary timescale. Here, b= 0.1 and μ= 0.001. https://doi.org/10.1371/journal.pone.0209596.g009 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 17 / 24 variable environmental characteristics, notably resource distribution [89], resource size [56], resource quality [90], and community composition [91]. Second, this rather black-and-white definition of scouts versus recruits could fail to capture the highly dynamic nature of foraging [92]. In particular, it fails to account for “lost” recruits. Indeed, not all recruits succeed in helping to harvest a given food resource: around 30% of recruits in mass-recruiting species Tapinoma erraticum and up to 80% of recruits in group-recruiting species Tetramorium impurum fail to reach the target food resource [93]. These “errors” appear to be advantageous since they enhance discovery, especially when resources are highly dispersed and of poor quality [93]. However, here we defined foraging strategy as the relative investment made in collective foraging by a given species, which allows us to avoid these criticisms specifically related to the scout-recruit trade-off. For example, our approach accounts for chemical communication ability. This is a specific trait that would not be modified by plastic behaviours and that would thus preserve the shape of the foraging functions. There is no direct link between our theoretical model and ant systematics. However, our modeling results underscore that individual foraging would be evolutionarily stable in many different contexts. One such interesting context is when communities are characterised by highly competitive interspecific interactions. It is an outcome that reflects reality since individual-foraging species occur throughout the ant phylogenetic tree [37,41]. Moreover, in our model, emergence of diversity by means of small-scale mutation-selection processes depends on the ancestral strategy. Unexpectedly, polymorphism could evolve from ancestral collectiveforaging strategies but not from ancestral individual-foraging strategies. Even though collective-foraging species are ecologically dominant in ant communities [40], they always coexist with subordinate individual-foraging species [68]. There appears to be enough niche space for both strategies. Furthermore, according to experimental research, the individual-foraging niche is far from being “suboptimal” [94]. Adler et al. [68] demonstrated that the dominance-discovery trade-off could maintain diversity in ant communities. With our evolutionary approach, we have shown that this tradeoff coupled with the discovery-exploitation trade-off can also generate diversity. In particular, our results emphasize the role played by asymmetric competition. Diversity was neither generated nor maintained in communities with symmetric competition. Conversely, different strategies were able to evolve in communities with asymmetric competition. Our exploration of the emergence and coexistence criteria revealed that the conditions promoting diversity generally broadened as competition asymmetry increased. The latter reflects the intensity of the dominance-discovery trade-off, which is known to shaped by different selective pressures. First, abiotic conditions such as temperature might influence the outcome of interference competition [14,23], even though such effects may be context dependent [95]). Second, predators such as parasitoid flies may affect community structure [96,15,67]. Experimental research has shown that parasitoids are attracted to the pheromones of their hosts [97]. The trail pheromones laid down by recruits are used as olfactory cues by parasitoids. Both theoretical and empirical evidence indicate that the trade-off between mass recruitment and parasitoid vulnerability reinforces the dominance-discovery trade-off [67]. This so-called “balance of terror” might play a determinant role in shaping competition asymmetry in natural ant communities [98]. The results of this study are consistent with other theoretical predictions underscoring the importance of trade-offs in reducing the potential for competitive exclusion. Negative interactions between life-history traits can occur at different spatial scales and may involve many different factors (see [99] for a review). The trade-offs considered in our model are not specific to ants: their role in community diversity has been discussed in theoretical studies of different organisms. First, the interference-exploitation trade-off seems to allow the coexistence of competitors, regardless of specific resource dynamics [100]. Second, the dominance-discovery Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 18 / 24 trade-off, in which the ability to successfully compete for food stands in opposition to the ability to “colonise” food, is essentially equivalent to the better known competition-colonisation trade-off. The latter is known to promote species coexistence [57] even when competitive intensity is relaxed [59]. Third, the interaction between competitive trade-offs, local dispersal, and species-specific enemies is a mechanism maintaining coexistence in sessile organisms [58]. In short, under conditions of frequency-dependent selection, trade-off shape is crucial in the evolution of life-history traits [101]. It thus seems fundamentally important to understand the allocation constraints that give rise to such trade-offs [102]. Our model involved three strong simplifying assumptions: (i) the environment contains a single resource type whose size is inversely proportional to its abundance; (ii) ant species differ only in their foraging strategies; and (iii) the fitness of ant colonies is proportional to their energy intake. If those assumptions had been relaxed, it probably would have been possible to explain a wider range of foraging strategies. First, a more spatially heterogeneous environment would probably lend itself to a higher degree of diversity, especially for small animals like ants [103]. Models that incorporate environmental complexity, such as variable resource quality, can explore the behavioural flexibility of generalist species, which is known to expand the conditions allowing the coexistence of specialists and generalists within communities [104,105]. Second, more than 15,916 ant species have been identified [106], and they vary greatly in their biology and ecology. In particular, ant worker body size show substantial intra- (worker polymorphism) and inter-specific variations [26]. The load individual ant workers can transport might thus vary across species, and might also be linked to the competitive ability [40]. More importantly, foraging strategies are associated with colony nest number, activity rhythm, and worker number [40]. Indeed, there is a strong positive correlation between the investment in collective foraging and colony size [38]. As workers are the basis for colony productivity, their numbers are a key part of the population growth rate [26,107], and they might also acquire adaptations over the course of evolution [39]. Third, there is not necessarily a correlation between foraging activity and reproductive success, especially in ecosystems where abiotic conditions are stressful [108]. In stressful environments, the net rate of foraging intake should be balanced by risk (e.g., desiccation costs in deserts), making it even more probable that different behaviours evolve and coexist [109]. Here, we have identified asymmetric competition as being the cornerstone upon which diversity in social foraging strategies is based in ant communities. Our model is novel because it links small-scale processes, such as the the discovery and exploitation of food resources by a colony to large-scale processes, such as macroscopic evolution at the community level. This approach allowed us to assess the evolutionary dynamics of recruitment strategies and to identify how ant diversity is impacted by such simple factors as food size, food density, and the asymmetry of interspecific competition. Supporting information S1 Appendix. Stability of food-item dynamics. (PDF) S2 Appendix. Fitting the model using field data. (PDF) Acknowledgments Many colleagues gave useful comments on early versions of this manuscript: we thank F. Adler, I. Amat, V. Calcagno, J.-L. Deneubourg, E. Desouhant, L. Guegen, L. Mailleret, J. Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 19 / 24 Pearce-Duvet, P. Pelosse, C. Rueffler, and N. Sanders. We appreciated the comments of two anonymous reviewers that have considerably contributed to improve the clarity of the manuscript. We dedicate this work to our beloved colleague and friend R. Boulay, who left us far too early. Author Contributions Conceptualization: Louise van Oudenhove. Formal analysis: Louise van Oudenhove. Funding acquisition: Xim Cerda ´. Supervision: Xim Cerda ´, Carlos Bernstein. Writing – original draft: Louise van Oudenhove. Writing – review & editing: Louise van Oudenhove, Xim Cerda ´, Carlos Bernstein. References 1. Schoener TW. Resource partitioning in ecological communities. Science. 1974; 185(4145):27–39. https://doi.org/10.1126/science.185.4145.27 PMID: 17779277 2. Colwell RK, Fuentes ER. Experimental studies of the niche. Annual Review of Ecology and systematics. 1975; 6(1):281–310. https://doi.org/10.1146/annurev.es.06.110175.001433 3. Pianka ER. Competition and niche theory. Theoretical ecology principles and applications. 1981; p. 167–196. 4. Kukami T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annual Review of Ecology, Evolution and Systematics. 2015; 46:1–23. https://doi.org/10. 1146/annurev-ecolsys-110411-160340 5. Letten AD, Ke PJ, Fukami T. Linking modern coexistence theory and contemporary niche theory. Ecological Monographs. 2017; 87:161–177. https://doi.org/10.1002/ecm.1242 6. Tilman D. Resource competition and community structure. Princeton University Press; 1982. 7. Andersen AN. Not enough niches: non-equilibrial processes promoting species coexistence in diverse ant communities. Austral Ecology. 2008; 33:211–220. https://doi.org/10.1111/j.1442-9993. 2007.01810.x 8. Torres JA. Niches and coexistence of ant communities in Puerto Rico: repeated patterns. Biotropica. 1984; 16:284–295. https://doi.org/10.2307/2387937 9. Andersen AN, Arnan X, Sparks K. Limited niche differentiation within remarkable co-occurrences of congeneric species: Monomorium ants in the Australian seasonal tropics. Austral Ecology. 2013; 38(5):557–567. https://doi.org/10.1111/aec.12000 10. Fowler D, Lessard JP, Sanders NJ. Niche filtering rather than partitioning shapes the structure of temperate forest ant communities. Journal of Animal Ecology. 2014; 83(4):943–952. https://doi.org/10. 1111/1365-2656.12188 PMID: 24289457 11. Sanders NJ, Gordon D. Resource-dependent interactions and the organization of desert ant communities. Ecology. 2003; 84:1024–1031. https://doi.org/10.1890/0012-9658(2003)084%5B1024:RIATOO %5D2.0.CO;2 12. Blu¨thgen N, Fiedler K. Competition for composition: lessons from nectar-feeding ant communities. Ecology. 2004; 85:1279–1485. 13. Pearce-Duvet JMC, Feener DH. Resource discovery in ant communities: do food type and quantity matter? Ecological Entomology. 2010; 35:549–556. https://doi.org/10.1111/j.1365-2311.2010.01214.x 14. Cerda ´X, Retana J, Manzaneda A. The role of competition by dominants and temperature in the foraging of subordinate species in Mediterranean ant communities. Oecologia. 1998; 117:404–412. https:// doi.org/10.1007/s004420050674 PMID: 28307920 15. Lebrun EG. Who is the top dog in ant communities? Resources, parasitoids, and multiple competitive hierarchies. Oecologia. 2005; 142:643–652. https://doi.org/10.1007/s00442-004-1763-4 PMID: 15688219 16. Albrecht M, Gotelli NJ. Spatial and temporal niche partitioning in grassland ants. Oecologia. 2001; 126:134–141. https://doi.org/10.1007/s004420000494 PMID: 28547432 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 20 / 24 17. Stuble KL, Rodriguez-Cabal MA, McCormick GL, JurićI, Dunn RR, Sanders NJ. Tradeoffs, competition, and coexistence in eastern deciduous forest ant communities. Oecologia. 2013; 171(4):981–992. https://doi.org/10.1007/s00442-012-2459-9 PMID: 23242423 18. Houadria M, Salas-Lopez A, Orivel J, Blu¨thgen N, Menzel F. Dietary and Temporal Niche Differentiation in Tropical Ants. Can They Explain Local Ant Coexistence? Biotropica. 2015; 47(2):208–217. 19. Houadria M, Blu¨thgen N, Salas-Lopez A, Schmitt MI, Arndt J, Schneider E, et al. The relation between circadian asynchrony, functional redundancy, and trophic performance in tropical ant communities. Ecology. 2016; 97(1):225–235. https://doi.org/10.1890/14-2466.1 PMID: 27008791 20. Żmihorski M, Ślipiński P. The importance of diurnal and nocturnal activity and interspecific interactions for space use by ants in clear-cuts. Ecological Entomology. 2016; 41(3):276–283. https://doi.org/10. 1111/een.12297 21. Vepsa ¨la ¨inen K, Savolainen R. The effect of interference by Formicine ants on the foraging of Myrmica. Journal of Animal Ecology. 1990; 59:643–654. https://doi.org/10.2307/4886 22. Cerda ´X, Retana J, Cros S. Prey size reverses the outcome of interference interactions of scavenger ants. Oikos. 1998; 82:99–110. https://doi.org/10.2307/3546920 23. Bestelmeyer BT. The trade-off between thermal tolerance and behavioural dominance in a subtropical south American ant community. Journal of Animal Ecology. 2000; 69:998–1009. 24. Savolainen R, Vepsa ¨la ¨inen K. Niche differentiation of ant species within territories of the wood ant Formica polyctena. Oikos. 1989; 56:3–16. https://doi.org/10.2307/3566082 25. Sanders NJ, Gotelli NJ, Wittman SE, Ratchford JS, Ellison AM, Jules ES. Assembly rules of groundforaging ant assemblages are contingent on disturbance, habitat and spatial scale. Journal of Biogeography. 2007; 34:1632–1641. https://doi.org/10.1111/j.1365-2699.2007.01714.x 26. Oster GF, Wilson EO. Caste and ecology in the social insects. Princeton University Press; 1978. 27. Deneubourg JL, Aron S, Goss S, Pasteels JM, Duerinck G. Random behaviour, amplification processes and number of participants: how they contribute to the foraging properties of ants. Physica D. 1986; 22:176–186. https://doi.org/10.1016/0167-2789(86)90239-3 28. Cerda ´X, Retana J, Cros S. Thermal disruption of transitive hierarchies in Mediterranean ant communities. Journal of Animal Ecology. 1997; 66:363–374. https://doi.org/10.2307/5982 29. Schoener TW. Theory of feeding strategies. Annual Review of Ecology and Systematics. 1971; 2: 369–404. https://doi.org/10.1146/annurev.es.02.110171.002101 30. Giraldeau LA, Caraco T. Social foraging theory. Princeton, NJ: Princeton University Press; 2000. 31. Ho ¨lldobler B, Wilson EO. The Ants. Harvard University Press; 1990. 32. Seeley TD. The wisdom of the hive: the social physiology of honey bee colonies. Harvard University Press; 2009. 33. Goss S, Deneubourg JL, Pasteels JM, Josens G. A model of noncooperative foraging in social insects. American Naturalist. 1989; 134:273–287. https://doi.org/10.1086/284980 34. Bonabeau E, Theraulaz G, Deneubourg JL. Group and mass recruitment in ant colonies: the influence of contact rates. Journal of Theoretical Biology. 1998; 195:157–66. https://doi.org/10.1006/jtbi.1998. 0789 PMID: 9822561 35. Baroni Urbani C. Phylogeny and behavioural evolution in ants, with a discussion of the role of behaviour in evolutionary processes. Ethology, Ecology and Evolution. 1989; 1:137–168. https://doi.org/10. 1080/08927014.1989.9525520 36. Baroni Urbani C. The diversity and evolution of recruitment behaviour in ants, with a discussion of the usefulness of parsimony criteria in the reconstruction of evolutionary histories. Insectes Sociaux. 1993; 40:233–260. https://doi.org/10.1007/BF01242361 37. Ruano F, Tinaut A, Soler JJ. High surface temperatures select for individual foraging in ants. Behavioral Ecology. 2000; 11:396–404. https://doi.org/10.1093/beheco/11.4.396 38. Beckers R, Goss S, Deneubourg JL, Pasteels JM. Colony size, communication and ant foraging strategy. Psyche. 1989; 96:239–256. https://doi.org/10.1155/1989/94279 39. van Oudenhove L, Cerda ´X, Bernstein C. An evolutionary dynamics model adapted to eusocial insects. PloS one. 2013; 8(3):e55159. https://doi.org/10.1371/journal.pone.0055159 PMID: 23469162 40. Retana J, Arnan X, Cerda ´X. A multidimensional functional trait analysis of resource exploitation in European ants. Ecology. 2015; 96(10):2781–2793. https://doi.org/10.1890/14-2326.1 PMID: 26649398 41. Lanan M. Spatiotemporal resource distribution and foraging strategies of ants (Hymenoptera: Formicidae). Myrmecological news. 2014; 20:53–70. PMID: 25525497 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 21 / 24 42. Cerda X, Arnan X, Retana J. Is competition a significant hallmark of ant (Hymenoptera: Formicidae) ecology. Myrmecological News. 2013; 18:131–147. 43. Traniello JFA. Foraging strategies of ants. Annual Review of Entomology. 1989; 34:191–210. https:// doi.org/10.1146/annurev.en.34.010189.001203 44. van Oudenhove L, Billoir E, Boulay R, Bernstein C, Cerda ´X. Temperature limits trail following behaviour through pheromone decay in ants. Naturwissenschaften. 2011; 98(12):1009–1017. https://doi. org/10.1007/s00114-011-0852-6 PMID: 22038287 45. van Oudenhove L, Boulay R, Lenoir A, Bernstein C, Cerda X. Substrate temperature constrains recruitment and trail following behavior in ants. Journal of Chemical Ecology. 2012; 38(6):802–809. https://doi.org/10.1007/s10886-012-0130-x PMID: 22573108 46. Arnan X, Cerda ´X, Retana J. Ant functional responses along environmental gradients. Journal of Animal Ecology. 2014; 83(6):1398–1408. https://doi.org/10.1111/1365-2656.12227 PMID: 24720700 47. Gordon DM. The relation recruitment rate to activity rhythms in the harvester ant, Pogonomyrmex barbatus. Journal of Kansas Entomological Society. 1983; 56:277–285. 48. Beekman M, Sumpter DJT, Ratnieks FLW. Phase transition between disordered and ordered foraging in Pharaoh’s ants. Proceedings of the National Academy of Sciences. 2001; 98:9703–9706. https:// doi.org/10.1073/pnas.161285298 49. Jaffe K, Deneubourg JL. On foraging, recruitment systems and optimum number of scouts in eusocial colonies. Insectes Sociaux. 1992; 39:201–213. https://doi.org/10.1007/BF01249295 50. Johnson LK, Hubbell SP, Feener DH. Defense of food supply by eusocial colonies. American Zoologist. 1987; 27:347–358. https://doi.org/10.1093/icb/27.2.347 51. Anderson C. The adaptive value of inactive foragers and the scout-recruit system in honey bee (Apis mellifera) colonies. Behavioral Ecology. 2001; 12:111–119. https://doi.org/10.1093/oxfordjournals. beheco.a000372 52. Dechaume-Moncharmont FX, Dornhaus A, Houston AI, McNamara JM, Collins EJ, Franks NR. The hidden cost of information in collective foraging. Proceedings of the Royal Society B: Biological Sciences. 2005; 272:1689–95. https://doi.org/10.1098/rspb.2005.3137 PMID: 16087424 53. Edelstein-Keshet L. Simple models for trail-following behaviour; Trunk trails versus individual foragers. Journal of Mathematical Biology. 1994; 32:303–328. https://doi.org/10.1007/BF00160163 54. Sumpter D, Pratt S. A modelling framework for understanding social insect foraging. Behavioral Ecology and Sociobiology. 2003; 53:131–144. 55. Tanner CJ. Resource characteristics and competition affect colony and individual foraging strategies of the wood ant Formica integroides. Ecological Entomology. 2008; 33:127–136. https://doi.org/10. 1111/j.1365-2311.2007.00950.x 56. Cerda ´X, Angulo E, Boulay R, Lenoir A. Individual and collective foraging decisions: a field study of worker recruitment in the gypsy ant Aphaenogaster senilis. Behavioral Ecology and Sociobiology. 2009; 63:551–562. https://doi.org/10.1007/s00265-008-0690-5 57. Tilman D. Competition and biodiversity in spatially structured habitat. Ecology. 1994; 75:2–16. https:// doi.org/10.2307/1939377 58. Chave J, Muller-Landau HC, Levin SA. Comparing classical community models: theoretical consequences for patterns of diversity. American Naturalist. 2002; 159:1–23. https://doi.org/10.1086/ 324112 PMID: 18707398 59. Calcagno V, Mouquet N, Jarne P, David P. Coexistence in a metacommunity: the competition-colonization trade-off is not dead. Ecology Letters. 2006; 9:897–907. https://doi.org/10.1111/j.1461-0248. 2006.00930.x PMID: 16913929 60. Park T. Beetles, competition and population. Science. 1962; 138:1369–1375. https://doi.org/10.1126/ science.138.3548.1369 PMID: 17753855 61. Fellers JH. Interference and exploitation in a guild of woodland ants. Ecology. 1987; 68:1466–1478. https://doi.org/10.2307/1939230 62. Savolainen R, Vepsa ¨la ¨inen K. A competition hierarchy among boreal ants: impact on resource partitioning and community structure. Oikos. 1988; 51:135–155. https://doi.org/10.2307/3565636 63. Davidson D. Resource discovery versus resource domination in ants: a functional mechanism for breaking the trade-off. Ecological Entomology. 1998; 23:484–490. https://doi.org/10.1046/j.13652311.1998.00145.x 64. Holway DA. Competitive mechanisms underlying the displacement of native ants by the invasive Argentine ant. Ecology. 1999; 80:238–251. https://doi.org/10.1890/0012-9658(1999)080%5B0238: CMUTDO%5D2.0.CO;2 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 22 / 24 65. Jordan R, Blu¨thgen N. No evidence for a dominance-discovery trade-off among Formica pratensis colonies (Hymenoptera: Formicidae). Myrmecological News. 2007; 10:7–10. 66. Perfecto Y. Foraging behavior as a determinant of asymmetric competitive interaction between two ant species in a tropical agroecosystem. Oecologia. 1994; 98:184–192. https://doi.org/10.1007/ BF00341471 PMID: 28313976 67. Lebrun EG, Feener DH Jr. When trade-offs interact: balance of terror enforces dominance discovery trade-off in a local ant assemblage. Journal of Animal Ecology. 2007; 76:58–64. https://doi.org/10. 1111/j.1365-2656.2006.01173.x PMID: 17184353 68. Adler FR, Lebrun EG, Feener DH Jr. Maintaining diversity in an ant community: modeling, extending, and testing the dominance-discovery trade-off. American Naturalist. 2007; 169:323–333. https://doi. org/10.1086/510759 PMID: 17230402 69. Lester PJ, Stringer LD, Haywood J. The role of resource dispersion in promoting the co-occurrence of dominant and subordinate species. Oikos. 2010; 119:659–668. https://doi.org/10.1111/j.1600-0706. 2009.18012.x 70. Palmer TM, Young TP, Stanton ML, Wenk E. Short-term dynamics of an acacia ant community in Laikipia, Kenya. Oecologia. 2000; 123:425–435. https://doi.org/10.1007/s004420051030 PMID: 28308598 71. Parr CL, Gibb H. Competition and the role of dominant ants. In: Lach L, Parr CL, Abbott KL, editors. Ant Ecology. Oxford University Press, Oxford; 2010. p. 77–96. 72. Geritz SAH, Kisdi E, Mesze ´na G, Metz JAJ. Adaptive Dynamics of Speciation: Ecological Underpinnings. In: Dieckmann U, Doebeli M, Metz JAJ, Tautz D, editors. Adaptive Speciation. Cambridge University Press; 2004. p. 54–75. 73. Dieckmann U, Law R. The dynamical theory of coevolution: a derivation from stochastic ecological processes. Journal of Mathematical Biology. 1996; 34:579–612. https://doi.org/10.1007/BF02409751 PMID: 8691086 74. Metz J, Geritz S, Meszena G, Jacobs F, van Heerwaarden J. Adaptive dynamics: A geometrical study of the consequences of nearly faithful reproduction. In: van Strien SJ, Verduyn Lunel SM, editors. Stochastic and Spatial Structures of Dynamical Systems. North-Holland Publishing Co., Amsterdam, Netherlands.; 1996. p. 183–231. 75. Geritz SAH, Metz JAJ, Kisdi E, Mesze ´na G. Dynamics of adaptation and evolutionary branching. Phys Rev Lett. 1997; 78(10):2024–2027. https://doi.org/10.1103/PhysRevLett.78.2024 76. Dercole F, Rinaldi S. Analysis of evolutionary processes: the adaptive dynamics approach and its applications: the adaptive dynamics approach and its applications. Princeton University Press; 2008. 77. Gordon DM. Behavioral flexibility and the foraging ecology of seed-eating ants. American Naturalist. 1991; 138:379–411. https://doi.org/10.1086/285223 78. Maynard Smith J. The theory of games and the evolution of animal conflicts. Journal of theoretical Biology. 1974; 47:209–221. https://doi.org/10.1016/0022-5193(74)90110-6 79. Christiansen FB. On the conditions for evolutionary stability for a continuously varying character. American Naturalist. 1991; 138:37–50. https://doi.org/10.1086/285203 80. Della Rossa F, Dercole F, Landi P. The branching bifurcation of Adaptive Dynamics. International Journal of Bifurcation and Chaos. 2015; 25:1540001. https://doi.org/10.1142/S0218127415400015 81. Dercole F, Della Rossa F, Landi P. The transition from evolutionary stability to branching: A catastrophic evolutionary shift. Scientific reports. 2016; 6:26310. https://doi.org/10.1038/srep26310 PMID: 27215588 82. Geritz SA, van der Meijden E, Metz JA. Evolutionary dynamics of seed size and seedling competitive ability. Theoretical Population Biology. 1999; 55:324–43. https://doi.org/10.1006/tpbi.1998.1409 PMID: 10366556 83. Moore PG. Spacing in plant populations. Ecology. 1954; 35:222–227. https://doi.org/10.2307/1931120 84. Gordon DM. The expandable network of ant exploration. Animal Behaviour. 1995; 50:995–1007. https://doi.org/10.1016/0003-3472(95)80100-6 85. Buehlmann C, Graham P, Hansson BS, Knaden M. Desert ants locate food by combining high sensitivity to food odors with extensive crosswind runs. Current Biology. 2014; 24(9):960–964. https://doi. org/10.1016/j.cub.2014.02.056 PMID: 24726153 86. Arnan X, Cerda ´X, Retana J. Distinctive life traits and distribution along environmental gradients of dominant and subordinate Mediterranean ant species. Oecologia. 2012; 170:489–5000. https://doi. org/10.1007/s00442-012-2315-y PMID: 22476711 87. Landi P, Dercole F, Rinaldi S. Branching scenarios in eco-evolutionary prey-predator models SIAM Journal on Applied Mathematics. 2013; 73:1634–1658. Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 23 / 24 88. Gordon DM. The organization of work in social insect colonies. Nature. 1996; 380:121–124. https:// doi.org/10.1038/380121a0 89. Bernstein RA. Foraging strategies of ants in response to variable food density. Ecology. 1975; 56:213–219. https://doi.org/10.2307/1935314 90. Detrain C, Deneubourg JL. Complexity of environment and parsimony of decision rules in insect societies. Biological Bulletin. 2002; 202:268–74. https://doi.org/10.2307/1543478 PMID: 12086999 91. Putyatina TS. The choice of foraging strategy as a mechanism for the coexistence of Myrmica species (Hymenoptera, Formicidae) in a multispecific ant association. Zoologicheskii Zhurnal. 2007; 86: 701–708. 92. Biesmeijer JC, de Vries H. Exploration and exploitation of food sources by social insect colonies: a revision of the scout-recruit concept. Behavioral Ecology and Sociobiology. 2001; 49:89–99. https:// doi.org/10.1007/s002650000289 93. Deneubourg JL, Pasteels JM, Verhaeghe JC. Probabilistic Behaviour in Ants: A Strategy of Errors? Journal of theoretical Biology. 1983; 105:259–271. https://doi.org/10.1016/S0022-5193(83)80007-1 94. Tanner CT, Adler FR. To fight or not to fight: context-dependent interspecific aggression in competing ants. Animal Behaviour. 2009; 77:297–305. https://doi.org/10.1016/j.anbehav.2008.10.016 95. Wiescher PT, Pearce-Duvet J, Feener DH. Environmental context alters ecological trade-offs controlling ant coexistence in a spatially heterogeneous region. Ecological Entomology. 2011; 36(5): 549–559. https://doi.org/10.1111/j.1365-2311.2011.01301.x 96. Feener DH. Is the assembly of ant communities mediated by parasitoids? Oikos. 2000; 90:79–88. https://doi.org/10.1034/j.1600-0706.2000.900108.x 97. Feener DH, Jacobs LF, Schmidt JO. Specialized parasitoid attracted to a pheromone of ants. Animal Behaviour. 1996; 51:61–66. https://doi.org/10.1006/anbe.1996.0005 98. Adler FR. The balance of terror: an alternative mechanism for competitive trade-offs and its implications for invading species. American Naturalist. 1999; 154:497–509. https://doi.org/10.1086/303258 PMID: 10561123 99. Kneitel JM, Chase JM. Trade-offs in community ecology: linking spatial scales and species coexistence. Ecology Letters. 2004; 7:69–80. https://doi.org/10.1046/j.1461-0248.2003.00551.x 100. Amarasekare P. Interference competition and species coexistence. Proceedings Royal Soc., Biological sciences. 2002; 269:2541–50. https://doi.org/10.1098/rspb.2002.2181 101. de Mazancourt C, Dieckmann U. Trade-off geometries and frequency-dependent selection. American Naturalist. 2004; 164:765–778. https://doi.org/10.1086/424762 PMID: 29641922 102. Amarasekare P, Hoopes MF, Mouquet N, Holyoak M. Mechanisms of coexistence in competitive metacommunities. American Naturalist. 2004; 164:310–326. https://doi.org/10.1086/422858 PMID: 15478087 103. Hutchinson GE. Homage to Santa Rosalia or Why are there so many kinds of animals? American Naturalist. 1959; 93:145–159. 104. Abrams PA. The prerequisites for and likelihood of generalist-specialist coexistence. American Naturalist. 2006; 167:329–342. https://doi.org/10.1086/499382 PMID: 16673342 105. Abrams PA. The effects of switching behavior on the evolutionary diversification of generalist consumers. American Naturalist. 2006; 168:645–59. https://doi.org/10.1086/507878 PMID: 17080363 106. AntWeb. Available from http://www.antweb.org.; Accessed 12 October 2018. 107. Sumpter DJT, Pratt SC. A modelling framework for understanding social insect foraging. Behavioral Ecology and Sociobiology. 2003; 53:131–144. 108. Gordon DM. The rewards of restraint in the collective regulation of foraging by harvester ant colonies. Nature. 2013; 498(7452):91–93. https://doi.org/10.1038/nature12137 PMID: 23676676 109. Wolf M, Van Doorn GS, Leimar O, Weissing FJ. Life-history trade-offs favour the evolution of animal personalities. Nature. 2007; 447(7144):581–584. https://doi.org/10.1038/nature05835 PMID: 17538618 Trade-offs promote diversity in ant communities PLOS ONE | https://doi.org/10.1371/journal.pone.0209596 December 31, 2018 24 / 24