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B1 Exclusion or bistability in a two-strain community. a The isoclines for the sensitive (blue) and producer (red) strains are shown in the p-s plane. The arrows give the flow of a point describing the densities of the two strains. When the producer is insufficiently toxic (g < g c ), the isoclines do not cross. From nearly all starting positions, the "community point" moves to the equilibrium on the s axis (given by the blue sphere). That is, the sensitive strain displaces the producer. b Here, we see the same dynamics expressed as the frequency of the sensitive strain over time. Despite the starting conditions, the sensitive type fixes (that is, it approaches a frequency of 1). c A symbolic representation of the community dynamics (see 2). The arrow pointing from the producer node to the sensitive node indicates that the sensitive strain will outcompete the producer under any starting conditions. d When the producer is sufficiently toxic (g > g c ), the isoclines cross and a new internal equilibrium (the gray sphere) is introduced. This new equilibrium is unstable. In this community, the initial strain densities become important-if the producer is sufficiently abundant relative to the sensitive strain, then the producer will displace the sensitive strain and vice versa. This is a bistable system where both edge equilibria (the red and blue spheres) are locally stable. e Now, the sensitive strain fixes only if frequent enoughotherwise, it goes extinct (and the producer fixes). f The symbolic representation shows arrows pointing to each node with an unstable internal node in between
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Summary The use of model laboratory communities, model organisms, and mathematical models has deeply enriched our understanding of the causes and consequences of toxin production in bacteria. In particular, such models have provided much insight into the dynamics of microbial communities with toxin producers. Both experimental and theoretical appro...
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... current state of the two-strain community can be expressed as a point (p, s) on the two-dimensional p-s plane (see Fig. 6 The arrows give the flow of a point describing the densities of the two strains. When the producer is insufficiently toxic (g < g c ), the isoclines do not cross. From nearly all starting positions, the "community point" moves to the equilibrium on the s axis (given by the blue sphere). That is, the sensi- tive strain displaces the ...
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... axis (given by the blue sphere). That is, the sensi- tive strain displaces the producer. b Here, we see the same dynamics expressed as the fre- quency of the sensitive strain over time. Despite the starting conditions, the sensitive type fixes (that is, it approaches a frequency of 1). c A symbolic representation of the commu- nity dynamics (see Fig. 6.2). The arrow pointing from the producer node to the sensitive node indicates that the sensitive strain will outcompete the producer under any starting conditions. d When the producer is sufficiently toxic (g > g c ), the isoclines cross and a new internal equilibrium (the gray sphere) is introduced. This new equilibrium is unstable. ...
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... assumptions (B1.3), (B1.4) and (B1.5), g c > 0. If g < g c (that is, the producer is not very toxic), then the isoclines do not cross in the positive quadrant of the p-s plane (see Fig. 6.B1a, where the sensitive isocline is in blue and the producer isocline is in red). The arrows in Fig. 6.B1a trace out the potential movement of a point giving the strain densities. Note that the arrows cut the blue line horizontally (because vertical movement of the point corresponds to changes in the sensitive strain, and the ...
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... assumptions (B1.3), (B1.4) and (B1.5), g c > 0. If g < g c (that is, the producer is not very toxic), then the isoclines do not cross in the positive quadrant of the p-s plane (see Fig. 6.B1a, where the sensitive isocline is in blue and the producer isocline is in red). The arrows in Fig. 6.B1a trace out the potential movement of a point giving the strain densities. Note that the arrows cut the blue line horizontally (because vertical movement of the point corresponds to changes in the sensitive strain, and the sensitive strain does not change its density on its isocline), and the arrows cut the red line vertically ...
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... the elevated deaths of sensitive cells) is disproportionately available to toxin-producing cells. In this way, small clumps of producers can "toxically clear-cut" sensi- tive cells at their periphery and radiate outward into a sea of sensitivity (see capacity would lead to the exclusion of producers by the invading sensi- tive strain). In Fig. 6.B1b, we see that the frequency of the sensitive strain approaches unity despite starting conditions ( Fig. 6.B1c shows this behav- ior schematically). Thus, without sufficient toxicity, the producer always goes extinct in head-to-head ...
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... this way, small clumps of producers can "toxically clear-cut" sensi- tive cells at their periphery and radiate outward into a sea of sensitivity (see capacity would lead to the exclusion of producers by the invading sensi- tive strain). In Fig. 6.B1b, we see that the frequency of the sensitive strain approaches unity despite starting conditions ( Fig. 6.B1c shows this behav- ior schematically). Thus, without sufficient toxicity, the producer always goes extinct in head-to-head ...
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... point is an unstable equilibrium (this can be shown locally using linear stability analysis; see the Appendix). From most starting posi- tions, either the sensitive strain displaces the producer or vice versa (see Fig. 6.B1d). Thus, initial community composition becomes important in determining which strain dominates. Generally, if sufficiently abundant, the producer displaces the sensitive strain, otherwise it goes extinct. This is shown in Fig. 6.B1e (and schematically in Fig. 6.B1f). This bistability was demonstrated in vitro with E. coli ( Adams et ...
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... the Appendix). From most starting posi- tions, either the sensitive strain displaces the producer or vice versa (see Fig. 6.B1d). Thus, initial community composition becomes important in determining which strain dominates. Generally, if sufficiently abundant, the producer displaces the sensitive strain, otherwise it goes extinct. This is shown in Fig. 6.B1e (and schematically in Fig. 6.B1f). This bistability was demonstrated in vitro with E. coli ( Adams et al. 1979;Chao and Levin ...
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... posi- tions, either the sensitive strain displaces the producer or vice versa (see Fig. 6.B1d). Thus, initial community composition becomes important in determining which strain dominates. Generally, if sufficiently abundant, the producer displaces the sensitive strain, otherwise it goes extinct. This is shown in Fig. 6.B1e (and schematically in Fig. 6.B1f). This bistability was demonstrated in vitro with E. coli ( Adams et al. 1979;Chao and Levin ...
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... Fig. 6.2a, we give the "boundary dynamics" on a de Finetti diagram for the rock-paper-scissors game (Frean and Abraham 2001;Czárán et al. 2002). We see that any community comprised of only "rock" and "paper" fixes for paper (since paper beats rock), any community of only "paper" and "scis- sors" fixes for scissors, and any community of only ...
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... on a de Finetti diagram for the rock-paper-scissors game (Frean and Abraham 2001;Czárán et al. 2002). We see that any community comprised of only "rock" and "paper" fixes for paper (since paper beats rock), any community of only "paper" and "scis- sors" fixes for scissors, and any community of only "scissors" and "rock" fixes for rock. In Fig. 6.2b, we show the dynamics when all three players are present -and we see continued cycles. In Fig. 6.2c, we give the boundary dynamics for the resistant-producer-sensitive game (when the producer is fairly toxic). Here, we see that we do not have a simple flow from one vertex to the next on the outside of the triangle. Rather, on the ...
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... 2002). We see that any community comprised of only "rock" and "paper" fixes for paper (since paper beats rock), any community of only "paper" and "scis- sors" fixes for scissors, and any community of only "scissors" and "rock" fixes for rock. In Fig. 6.2b, we show the dynamics when all three players are present -and we see continued cycles. In Fig. 6.2c, we give the boundary dynamics for the resistant-producer-sensitive game (when the producer is fairly toxic). Here, we see that we do not have a simple flow from one vertex to the next on the outside of the triangle. Rather, on the "sensitive-producer" edge we have flow going in both directions -both the sensitive pool and pro- ducer pool ...
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... producer is fairly toxic). Here, we see that we do not have a simple flow from one vertex to the next on the outside of the triangle. Rather, on the "sensitive-producer" edge we have flow going in both directions -both the sensitive pool and pro- ducer pool will exclude the other when they are sufficiently frequent in a two- strain community (see Fig. 6.B1f in Box 1). This is another appearance by the bistability described above. The hump (represented as a small gray point on the sensitive-producer edge in Fig. 6.2c) has reemerged. In Fig. 6.2d, we see that the dynamics are quite different than in the strict rock-paper-scissors game -the sensitive strain excludes the others from ...
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... edge we have flow going in both directions -both the sensitive pool and pro- ducer pool will exclude the other when they are sufficiently frequent in a two- strain community (see Fig. 6.B1f in Box 1). This is another appearance by the bistability described above. The hump (represented as a small gray point on the sensitive-producer edge in Fig. 6.2c) has reemerged. In Fig. 6.2d, we see that the dynamics are quite different than in the strict rock-paper-scissors game -the sensitive strain excludes the others from nearly every starting condition (see Nakamaru and Iwasa 2000, and the Appendix). Do note that these models assume infinite population sizes, and often the dynamical tra- ...
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... in both directions -both the sensitive pool and pro- ducer pool will exclude the other when they are sufficiently frequent in a two- strain community (see Fig. 6.B1f in Box 1). This is another appearance by the bistability described above. The hump (represented as a small gray point on the sensitive-producer edge in Fig. 6.2c) has reemerged. In Fig. 6.2d, we see that the dynamics are quite different than in the strict rock-paper-scissors game -the sensitive strain excludes the others from nearly every starting condition (see Nakamaru and Iwasa 2000, and the Appendix). Do note that these models assume infinite population sizes, and often the dynamical tra- jectory can come very close to ...
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... (the surface of an agar plate), Kerr et al. (2002) experimentally demonstrated that spatial structure can promote the maintenance of diversity in a bacte- riocin community. In a sense, spatial structure in these cases obliterates the "hump" on the sensitive-producer edge of the de Finetti diagram (Durrett and Levin 1997;Iwasa et al. 1998). In Fig. 6.2f, we see simulated dynamics from the lattice-based model described in Box 2. This behavior is much closer to the rock-paper-scissors game of Three-strain community dynamics. In this figure, the de Finetti diagram is used to rep- resent changes in community composition. Each vertex of the triangle is labeled with one of the three ...
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... this three-strain community is simulated using a Moore neigh- borhood, all three strains coexist under many different parameter settings. Because dispersal is local, clumps of the three strains form and these clumps chase one another at their boundaries -S clumps chase R clumps, R clumps chase P clumps, and P clumps chase S clumps (see Fig. 6.B2a, b). However, when a Global neighborhood is used, diversity is rapidly lost ...
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... Ecological and Evolutionary Dynamics of Model Bacteriocin Communities 125 (see Fig. 6.B2c, d). In the Global neighborhood, the toxic effects of pro- ducers are distributed globally. This can drive the sensitive strain to very low levels (unless the producer is not very toxic). Indeed, because our lat- tice is finite, the sensitive strain often goes extinct. Once one member of a non-transitive triplet is lost, the final ...
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... simulations with a Global neighborhood correspond closely to the dynamics given by the set of mean-field ordinary differential equations (see the Appendix and Fig. 6.2c, d). However, because such mathematical models assume infinite populations (and thus one can have an arbitrarily small density of sensitive cells), the sensitive strain is expected to "hang on" as the resistant displaces the producer, and eventually dominates the community. However, the outcome for the maintenance of diversity is the ...
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... is simulated without other competing strains, it evolves to minimize the cost of resistance (average ∆ R evolves to the minimum value in the range allowed). However, when evolution occurs in a three-strain community with local dispersal and interaction (using a Moore neighborhood), the cost of resistance does not evolve to its lowest level (see Fig. 6.3). It pays off to exercise competitive restraint in this non-hierarchical community because such restraint aids the enemy of your enemy (which, in turn, harms your enemy and thus aids you). An extremely interesting direction for future experimental work involves exploration of these counterintuitive spatial evolutionary dynamics within ...
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... Fig. 6.3, we see the maintenance of a non-minimal cost of resistance in a spatially structured three-strain community. On the other hand, if the resistant strain evolves alone in a spatially structured habitat, it does evolve to minimize its cost ( Fig. 6.3). In a structured non-transitive community, a higher cost of resistance retards ...
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... Fig. 6.3, we see the maintenance of a non-minimal cost of resistance in a spatially structured three-strain community. On the other hand, if the resistant strain evolves alone in a spatially structured habitat, it does evolve to minimize its cost ( Fig. 6.3). In a structured non-transitive community, a higher cost of resistance retards replacement of producers by resistant cells. is the average death rate of the evolving resistant strain. The minimum value that CR can obtain is n. A proxy for cost of resist- ance is CR′ = CR-n. All else being equal, the resistant strain is expected to ...
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... face a dynamical hump to get over ( Adams et al. 1979;Chao and Levin 1981;Levin 1988;Durrett and Levin 1997;Iwasa et al. 1998). In unstructured habitats, the signature of this hump is present in the resistant-producer-sensitive community, changing the dynamics from a straightforward "rock-paper- scissors" to a "one-winner" outcome (compare Fig. 6.2a, b to c, d). Population structure (e.g., spatial structure) can effectively eliminate the hump ( Chao and Levin 1981;Durrett and Levin 1997;Iwasa et al. 1998) and restore the game of rock-paper-scissors. In this spatial game, players stably chase each other around a structured arena as clumps, with balanced gains and losses occurring at the ...
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... However, when the environment is spatially structured (e.g. agar plate) the two species form microcolonies, which results in local interactions where bacteriocins only affect cells that are physically close to the producing cells, as well as resources made available by killing (Chao & Levin, 1981;Kerr, 2007). This allows the producer to preferentially gain a fitness benefit, an increase in population size, from the available resources and space. ...
... As bacteriocin production in 1:1 co-inoculation did not result in a fitness benefit for Psy, we sought to assess whether the population frequency influenced competition, as has been previously shown in other systems (Chao & Levin, 1981;Inglis et al., 2009;Kerr, 2007). We altered the inoculation frequency between Psy strains and Pph to either 1:9 or 9:1. ...
... Previous bacteriocin antagonism studies have been performed using computer models or laboratory systems, showing that a fitness benefit for the toxinproducing population is dependent on the environment (Chao & Levin, 1981;Kerr, 2007;Majeed et al., 2011). Our results suggest that when at parity or in the majority there was no bacteriocin-mediated fitness benefit in vitro for Psy compared to Psy ΔRrbp across 8 dpi. ...
Bacteria compete for resources in diverse environments using an array of antagonistic strategies, including the production of narrow‐spectrum protein antibacterials termed bacteriocins. Although significant research has focused on bacteriocin‐mediated dynamics in culture environments, little research has explored bacteriocin‐mediated dynamics within a host context, particularly in plant environments. Here, we show that a bacterial plant pathogen, Pseudomonas syringae pv. syringae (Psy), expresses a bacteriocin both in culture and in leaf apoplast when co‐inoculated with a bacteriocin‐sensitive competitor, P. syringae pv. phaseolicola (Pph). Although there is an observable negative effect of the bacteriocin on the Pph population at most time points both in culture and in the leaf apoplast, a bacteriocin‐mediated benefit to Psy was only observed when the producing strain was co‐infiltrated at a low population frequency (1:9) into the leaf apoplast. At 6 days post‐infiltration, Psy achieved an eightfold population increase compared to a bacteriocin‐deficient mutant in the apoplast. No bacteriocin‐mediated benefit for Psy was observed under the culture conditions tested. Additionally, we found that the bacteriocin‐mediated benefit for Psy was dependent on the Type III Secretion System. Taken together, our results demonstrate that the fitness benefit of bacteriocin‐mediated antagonism is influenced by interactions within the host plant.
... Thus, in terms of proliferation, S outcompetes R and R outcompetes K. This model, which is able to sustain bacterial populations, gave rise to new studies aimed at figuring out how microorganisms use and evolve bacteriocin and antagonistic molecules to sustain biodiversity in structured ecosystems (Kerr, 2007). The shallow water system of Churince, located in Cuatro Cienegas, Mexico, sustains an impressive microorganism biodiversity, mainly due to its geological history . ...
... Of the previous studies that have investigated the role of bacteriocins on invasion outcomes (Frank, 1994;Durrett and Levin, 1997;Kerr, 2007), only a handful have examined trajectories of invasions (Gordon and Riley, 1999;Nakamaru and Iwasa, 2000) and none have explicitly considered the role of toxin characteristics, for example size and mode/efficacy of killing. Strictly in terms of theoretical work, the majority of existing literature has focused on a few select topics: the role of bacteriocins in maintaining bacterial population coexistence (Frank, 1994;Durrett and Levin, 1997;Kerr, 2007) and establishing microbial diversity (Lenski and Riley, 2002;Czárán et al., 2002), costbenefit analyses of bacteriocin production by bacteria (Blanchard et al., 2016), and the environmental conditions favoring bacteriocin evolution (Gardner et al., 2004;Czárán and Hoekstra, 2007). ...
... Of the previous studies that have investigated the role of bacteriocins on invasion outcomes (Frank, 1994;Durrett and Levin, 1997;Kerr, 2007), only a handful have examined trajectories of invasions (Gordon and Riley, 1999;Nakamaru and Iwasa, 2000) and none have explicitly considered the role of toxin characteristics, for example size and mode/efficacy of killing. Strictly in terms of theoretical work, the majority of existing literature has focused on a few select topics: the role of bacteriocins in maintaining bacterial population coexistence (Frank, 1994;Durrett and Levin, 1997;Kerr, 2007) and establishing microbial diversity (Lenski and Riley, 2002;Czárán et al., 2002), costbenefit analyses of bacteriocin production by bacteria (Blanchard et al., 2016), and the environmental conditions favoring bacteriocin evolution (Gardner et al., 2004;Czárán and Hoekstra, 2007). The few studies that have examined invasion rates have done so either in the context of serial dilutions (Gordon and Riley, 1999) or with moving population boundaries (Nakamaru and Iwasa, 2000). ...
The theory of invasions and invasion speeds has traditionally been studied in macroscopic systems. Surprisingly, microbial invasions have received less attention. Although microbes share many of the features associated with competition between larger-bodied organisms, they also exhibit distinctive behaviors that require new mathematical treatments to fully understand invasions in microbial systems. Most notable is the possibility for long-distance interactions, including competition between taxa mediated by diffusible toxins and cooperation among individuals of a single taxon using quorum sensing. In this paper, we model bacterial invasion using a system of coupled partial differential equations based on Fisher's equation. Our model considers a competitive system with diffusible toxins that, in some cases, are expressed in response to quorum sensing. First, we derive analytical approximations for invasion speeds in the limits of fast and slow toxin diffusion. We then test the validity of our analytical approximations and explore intermediate rates of toxin diffusion using numerical simulations. Interestingly, we find that toxins should diffuse quickly when used offensively, but that there are two optimal strategies when toxin is used as a defense mechanism. Specifically, toxins should diffuse quickly when their killing efficacy is high, but should diffuse slowly when their killing efficacy is low. Our approach permits an explicit investigation of the properties and characteristics of diffusible compounds used in non-local competition, and is relevant for microbial systems and select macroscopic taxa, such as plants and corals, that can interact through biochemicals.
... In cells that contain the plasmid, the immunity protein, which is constitutively expressed, prevents cell death (Riley and Wertz, 2002b;Cascales et al., 2007). Thus, active producers kill sensitive competitors, allowing their latent immune clones to utilize liberated resources (Riley and Gordon, 1999;Kerr, 2007). ...
... Under induction, colicins and phage are produced, and released into the environment after cell lysis. of microbes are often structured spatially (e.g., in biofilms). Given such spatial structure, only a bacterium that releases bacteriocin allows its clones to expand into the territory held by sensitive neighbors (Chao and Levin, 1981;Kerr, 2007). Due to spatially restricted dispersal of cells and limited diffusion of toxins, the potential for a cheater to free ride off the releasing strain is diminished. ...
... Examples of allelopathy abound in the microbial world, as numerous antibiotics and other secondary metabolites have been found to have inhibitory effects [15]. A key class of allelopathic compounds, bacteriocins, target even closely related strains of the same species and are likely a key mechanism maintaining withinspecies diversity [16,17]. Critically, allelopathy is only beneficial in the presence of a susceptible competitor (figure 1d). ...
Variation among parasite strains can affect the progression of disease or the effectiveness of treatment. What maintains parasite diversity? Here I argue that competition among parasites within the host is a major cause of variation among parasites. The competitive environment within the host can vary depending on the parasite genotypes present. For example, parasite strategies that target specific competitors, such as bacteriocins, are dependent on the presence and susceptibility of those competitors for success. Accordingly, which parasite traits are favoured by within-host selection can vary from host to host. Given the fluctuating fitness landscape across hosts, genotype by genotype (G×G) interactions among parasites should be prevalent. Moreover, selection should vary in a frequency-dependent manner, as attacking genotypes select for resistance and genotypes producing public goods select for cheaters. I review competitive coexistence theory with regard to parasites and highlight a few key examples where within-host competition promotes diversity. Finally, I discuss how within-host competition affects host health and our ability to successfully treat infectious diseases.
© 2015 The Author(s) Published by the Royal Society. All rights reserved.
... Thus, in terms of proliferation, S outcompetes R and R outcompetes K. This model, which is able to sustain bacterial populations, gave rise to new studies aimed at figuring out how microorganisms use and evolve bacteriocin and antagonistic molecules to sustain biodiversity in structured ecosystems (Kerr, 2007). The shallow water system of Churince, located in Cuatro Cienegas, Mexico, sustains an impressive microorganism biodiversity, mainly due to its geological history (Souza et al., 2006). ...
Most of the studies in Ecology have been devoted to analyzing the effects the environment has on individuals, populations, and communities, thus neglecting the effects of biotic interactions on the system dynamics. In the present work we study the structure of bacterial communities in the oligotrophic shallow water system of Churince, Cuatro Cienegas, Mexico. Since the physicochemical conditions of this water system are homogeneous and quite stable in time, it is an excellent candidate to study how biotic factors influence the structure of bacterial communities. In a previous study, the binary antagonistic interactions of 78 bacterial strains, isolated from Churince, were experimentally determined. We employ these data to develop a computer algorithm to simulate growth experiments in a cellular grid representing the pond. Remarkably, in our model, the dynamics of all the simulated bacterial populations is determined solely by antagonistic interactions. Our results indicate that all bacterial strains (even those that are antagonized by many other bacteria) survive in the long term, and that the underlying mechanism is the formation of bacterial community patches. Patches corresponding to less antagonistic and highly susceptible strains are consistently isolated from the highly-antagonistic bacterial colonies by patches of neutral strains. These results concur with the observed features of the bacterial community structure previously reported. Finally, we study how our findings depend on factors like initial population size, differential population growth rates, homogeneous population death rates, and enhanced bacterial diffusion.
... Canonical examples of social evolution, such as the altruism described by Hamilton (1964aHamilton ( , 1964b, can be attributed to kin selection even without quantitative analysis of costs and benefits, because the persistence of individually costly traits can be explained only by benefits to relatives. Examples include alarm calling (Mateo 1996), reproductive restraint (Strassman et al. 2000), and suicidal antibiotic production (Gardner et al. 2004;Kerr 2007). In contrast, some behaviors may benefit the focal individual as well as nearby kin. ...
Abstract Disentangling individual selection from kin selection is one of the greatest challenges of evolutionary biology. Even solitary organisms that do not interact directly with conspecifics may interact indirectly with them through competition for resources. As a result, traits that appear to affect individual fitness alone can also modify the fitness of relatives nearby and thus may evolve partially through these cryptic indirect fitness effects. Here we develop a method to quantitatively separate direct and indirect fitness consequences when some microbes become dormant, while neighbors of the same genotype remain active. Dormant microbes typically survive stresses that kill metabolically active cells, but dormancy also has a social side effect, sparing resources that may be used by nondormant individuals for growth. In structured populations, spared resources may be preferentially consumed by nondormant clonemates, providing an indirect benefit. Without population structure, however, exploitation by a never-dormant competitor imposes an indirect fitness cost on dormant cells. Cryptic indirect fitness effects may play a significant role in the evolution of many ostensibly asocial traits.
... In the simplest ecological scenario containing two genotypes, one a toxin producer and the other a sensitive strain, it has been shown that it cannot. One strain always wins, although which strain wins depends on the environment in which the strains interact as well as on their relative frequencies [12,13]. When the killer strain is rare in a mass-action environment such as a shaken flask, it loses because these cells pay a cost to produce their toxin, yet benefit only minimally from its production and secretion. ...
... More recently, Kerr [13] modelled a situation where the metabolic burden of the immune strains can evolve. In the case where the immune strain entirely occupies one environment, if a mutant with lower metabolic burden arises, then it easily eliminates all the other bacteria. ...
Bacteriocins are usually viewed as the effective weapons of bacterial killers. However, killing competitors with bacteriocins may be not only a means of eliminating other strains, but also a crucial unappreciated mechanism promoting bacterial diversity. In the present short review, we summarize recent empirical and theoretical studies examining the role bacteriocins that may play in driving and maintaining diversity among microbes. We conclude by highlighting limitations of current models and suggest directions for future studies.
... A broad strand of theoretical work has explored the role of bacteriocins in maintaining microbial diversity and investigated the evolution of bacteriocin production (Gardner et al. 2004;Kerr 2007). These theoretical studies focus on bacteriocin production and sensitivity as the key difference among strains, where "all else is equal," and thus can be viewed as models of intraspecific evolution. ...
... Our data suggest that bacteriocin-mediated interactions between conspecific may play a similar role in maintenance of species diversity. Furthermore, the high frequency of bacteriocin-mediated intraspecific interactions may explain the great genetic variability observed within bacterial species (e.g., Vos and Velicer 2006) due to non-transitive interactions between bacteriocin producers, bacteriocin sensitives, and bacteriocin resistant strains (Kerr 2007). ...
Bacteriocins are bacteriocidal toxins released by almost all bacteria. They are thought to have a narrow range of killing, but as bacteriocin-mediated interactions have been rarely studied at biologically relevant scales, whether this narrow range of action falls mostly within or mostly between coexisting species in natural communities is an open question with important ecological and evolutionary implications. In a previous study, we systematically sampled Xenorhabdus bacteria along a hillside and found evidence for genotypic variability and bacteriocin-mediated interactions within Xenorhabdus bovienii and X. koppenhoeferi colonies that were collected only a few meters apart. In contrast, colonies that were isolated from the same soil sample were always genetically similar and showed no inhibitions. Here, we conducted pairwise growth-inhibition assays within and between seven X. bovienii and five X. koppenhoeferi colonies that were isolated from different soil samples; all seven X. bovienii colonies and at least three of the X. koppenhoeferi have been distinguished as distinct genotypes based on coarse-grain genomic markers. We found signatures for both conspecific and heterospecific bacteriocin inhibitions in this natural community of Xenorhabdus bacteria, but intraspecific inhibitions were significantly more common than interspecific inhibitions. These results suggest that bacteriocins have a major role in intraspecific competition in nature, but also suggest that bacterocins are important in mediating interspecific interactions among coexisting species in natural communities.
... Altering the chemical nature of ones surroundings, via bacteriocin production, can also be considered a form of niche construction, which feeds back to affect the rest of the community (e.g. Kerr 2007). This process is ultimately frequency-dependent as the invasion of bacteriocin producers is constrained by the cost to an individual of successfully modifying the environment. ...
... Bacteriocins are an important biological phenomenon, with an estimated 99% of all bacteria thought to produce at least one bacteriocin (Klaenhammer 1988;Riley and Wertz 2002), and they play an important role in interspecific competition, virulence and social evolution (Riley and Wertz 2002;Kerr 2007). Here we show that horizontal gene transfer can help bacteriocinproducing genes to spread and established themselves in the population. ...
