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Genes as Cues of Relatedness and Social Evolution in Heterogeneous Environments.

Leimar O, Dall SR, Hammerstein P, McNamara JM - PLoS Comput. Biol. (2016)

Bottom Line: We analyze a model of social evolution in a two-habitat situation with limited dispersal between habitats, in which the average relatedness at the time of helping and other benefits of helping can differ between habitats.An important result from the analysis is that alleles at a polymorphic locus play the role of genetic cues, in the sense that the presence of a cue allele contains statistical information for an organism about its current environment, including information about relatedness.Another important result is that the genetic linkage between a cue locus and modifier loci influences the evolutionary interest of modifiers, with tighter linkage leading to greater divergence between social traits induced by different cue alleles, and this can be understood in terms of genetic conflict.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, Stockholm University, Stockholm, Sweden.

ABSTRACT
There are many situations where relatives interact while at the same time there is genetic polymorphism in traits influencing survival and reproduction. Examples include cheater-cooperator polymorphism and polymorphic microbial pathogens. Environmental heterogeneity, favoring different traits in nearby habitats, with dispersal between them, is one general reason to expect polymorphism. Currently, there is no formal framework of social evolution that encompasses genetic polymorphism. We develop such a framework, thus integrating theories of social evolution into the evolutionary ecology of heterogeneous environments. We allow for adaptively maintained genetic polymorphism by applying the concept of genetic cues. We analyze a model of social evolution in a two-habitat situation with limited dispersal between habitats, in which the average relatedness at the time of helping and other benefits of helping can differ between habitats. An important result from the analysis is that alleles at a polymorphic locus play the role of genetic cues, in the sense that the presence of a cue allele contains statistical information for an organism about its current environment, including information about relatedness. We show that epistatic modifiers of the cue polymorphism can evolve to make optimal use of the information in the genetic cue, in analogy with a Bayesian decision maker. Another important result is that the genetic linkage between a cue locus and modifier loci influences the evolutionary interest of modifiers, with tighter linkage leading to greater divergence between social traits induced by different cue alleles, and this can be understood in terms of genetic conflict.

No MeSH data available.


Elements of the model.Panel (A) shows the population cycles in habitat 1 (color coded blue) and habitat 2 (red), including formation of social groups and playing the public goods game, resulting in the production of dispersing offspring, some of which go to the dispersal pool in their birth habitat and some go the pool in the other habitat. New social groups are then formed from the pool in each habitat. (B) The expected payoff Eq (2) for mutant trait z′ in habitat 1 (blue) and habitat 2 (red) in the limit of no between-habitat dispersal. The resident traits in habitats 1 and 2 are z1 and z2 (blue and red vertical lines). The gray curve shows mutant payoff when there is random dispersal, with the same two resident traits. (C) Illustration of group formation for two groups in habitat 1 with N1 = 3. First founding group members are randomly drawn from the dispersal pool, followed by asexual reproduction forming N1 offspring, each of which is a copy of a randomly selected parent in the founding group. (D) For a rare mutant (darker blue), founding groups with mutants will predominantly contain a single mutant. The offspring groups can contain from 0 to N1 mutants, and in expectation contain one mutant.
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pcbi.1005006.g001: Elements of the model.Panel (A) shows the population cycles in habitat 1 (color coded blue) and habitat 2 (red), including formation of social groups and playing the public goods game, resulting in the production of dispersing offspring, some of which go to the dispersal pool in their birth habitat and some go the pool in the other habitat. New social groups are then formed from the pool in each habitat. (B) The expected payoff Eq (2) for mutant trait z′ in habitat 1 (blue) and habitat 2 (red) in the limit of no between-habitat dispersal. The resident traits in habitats 1 and 2 are z1 and z2 (blue and red vertical lines). The gray curve shows mutant payoff when there is random dispersal, with the same two resident traits. (C) Illustration of group formation for two groups in habitat 1 with N1 = 3. First founding group members are randomly drawn from the dispersal pool, followed by asexual reproduction forming N1 offspring, each of which is a copy of a randomly selected parent in the founding group. (D) For a rare mutant (darker blue), founding groups with mutants will predominantly contain a single mutant. The offspring groups can contain from 0 to N1 mutants, and in expectation contain one mutant.

Mentions: There are two habitats, each containing a large number of groups. They are formed and dissolved by colonization followed by social interaction and the production of offspring that disperse, and again colonization. A group in habitat i, where i = 1, 2, is founded by Ni haploid individuals, randomly derived from a pool of dispersers in that habitat. To implement variation between habitats in average within-group relatedness, group members reproduce asexually following founding, forming Ni haploid offspring group members, such that each founding group member has an equal and independent chance of producing each of the Ni offspring (model details are given in S1 Text). A smaller Ni thus corresponds to higher relatedness. For a pair of group members, the probability of being identical by descent since founding isri=1Ni,(1)which follows [27] and [28]. The offspring group members engage in a social interaction, for instance a public goods game [29], and produce dispersing offspring in proportion to the payoff in the game. An individual’s phenotype z represents an investment (strategy) in the game, and we assume 0 ≤ z ≤ 1. The payoff to an individual with phenotype z in habitat i is a function of z and the average investment of the individual’s group. As a convenient example we will use , where the benefit is proportional to the average investment and the cost ciz2 is assumed to increase quadratically with the individual’s investment. For polymorphic populations the group compositions will vary, and we are particularly interested in the expected payoff in habitat i to a randomly chosen rare mutant player of the game with phenotype z′, in a population where the resident phenotypes z1 and z2 occur with frequencies pi1 and pi2 (where pi1 + pi2 = 1). We write this asw¯i′=E[wi(z′,z¯)/z1,z2,pi1,pi2].(2)Because a new group is founded by random dispersers, those groups containing mutant strategies will predominantly be founded by one mutant and Ni − 1 resident types. Some basic aspects of the model are illustrated in Fig 1.


Genes as Cues of Relatedness and Social Evolution in Heterogeneous Environments.

Leimar O, Dall SR, Hammerstein P, McNamara JM - PLoS Comput. Biol. (2016)

Elements of the model.Panel (A) shows the population cycles in habitat 1 (color coded blue) and habitat 2 (red), including formation of social groups and playing the public goods game, resulting in the production of dispersing offspring, some of which go to the dispersal pool in their birth habitat and some go the pool in the other habitat. New social groups are then formed from the pool in each habitat. (B) The expected payoff Eq (2) for mutant trait z′ in habitat 1 (blue) and habitat 2 (red) in the limit of no between-habitat dispersal. The resident traits in habitats 1 and 2 are z1 and z2 (blue and red vertical lines). The gray curve shows mutant payoff when there is random dispersal, with the same two resident traits. (C) Illustration of group formation for two groups in habitat 1 with N1 = 3. First founding group members are randomly drawn from the dispersal pool, followed by asexual reproduction forming N1 offspring, each of which is a copy of a randomly selected parent in the founding group. (D) For a rare mutant (darker blue), founding groups with mutants will predominantly contain a single mutant. The offspring groups can contain from 0 to N1 mutants, and in expectation contain one mutant.
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Related In: Results  -  Collection

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pcbi.1005006.g001: Elements of the model.Panel (A) shows the population cycles in habitat 1 (color coded blue) and habitat 2 (red), including formation of social groups and playing the public goods game, resulting in the production of dispersing offspring, some of which go to the dispersal pool in their birth habitat and some go the pool in the other habitat. New social groups are then formed from the pool in each habitat. (B) The expected payoff Eq (2) for mutant trait z′ in habitat 1 (blue) and habitat 2 (red) in the limit of no between-habitat dispersal. The resident traits in habitats 1 and 2 are z1 and z2 (blue and red vertical lines). The gray curve shows mutant payoff when there is random dispersal, with the same two resident traits. (C) Illustration of group formation for two groups in habitat 1 with N1 = 3. First founding group members are randomly drawn from the dispersal pool, followed by asexual reproduction forming N1 offspring, each of which is a copy of a randomly selected parent in the founding group. (D) For a rare mutant (darker blue), founding groups with mutants will predominantly contain a single mutant. The offspring groups can contain from 0 to N1 mutants, and in expectation contain one mutant.
Mentions: There are two habitats, each containing a large number of groups. They are formed and dissolved by colonization followed by social interaction and the production of offspring that disperse, and again colonization. A group in habitat i, where i = 1, 2, is founded by Ni haploid individuals, randomly derived from a pool of dispersers in that habitat. To implement variation between habitats in average within-group relatedness, group members reproduce asexually following founding, forming Ni haploid offspring group members, such that each founding group member has an equal and independent chance of producing each of the Ni offspring (model details are given in S1 Text). A smaller Ni thus corresponds to higher relatedness. For a pair of group members, the probability of being identical by descent since founding isri=1Ni,(1)which follows [27] and [28]. The offspring group members engage in a social interaction, for instance a public goods game [29], and produce dispersing offspring in proportion to the payoff in the game. An individual’s phenotype z represents an investment (strategy) in the game, and we assume 0 ≤ z ≤ 1. The payoff to an individual with phenotype z in habitat i is a function of z and the average investment of the individual’s group. As a convenient example we will use , where the benefit is proportional to the average investment and the cost ciz2 is assumed to increase quadratically with the individual’s investment. For polymorphic populations the group compositions will vary, and we are particularly interested in the expected payoff in habitat i to a randomly chosen rare mutant player of the game with phenotype z′, in a population where the resident phenotypes z1 and z2 occur with frequencies pi1 and pi2 (where pi1 + pi2 = 1). We write this asw¯i′=E[wi(z′,z¯)/z1,z2,pi1,pi2].(2)Because a new group is founded by random dispersers, those groups containing mutant strategies will predominantly be founded by one mutant and Ni − 1 resident types. Some basic aspects of the model are illustrated in Fig 1.

Bottom Line: We analyze a model of social evolution in a two-habitat situation with limited dispersal between habitats, in which the average relatedness at the time of helping and other benefits of helping can differ between habitats.An important result from the analysis is that alleles at a polymorphic locus play the role of genetic cues, in the sense that the presence of a cue allele contains statistical information for an organism about its current environment, including information about relatedness.Another important result is that the genetic linkage between a cue locus and modifier loci influences the evolutionary interest of modifiers, with tighter linkage leading to greater divergence between social traits induced by different cue alleles, and this can be understood in terms of genetic conflict.

View Article: PubMed Central - PubMed

Affiliation: Department of Zoology, Stockholm University, Stockholm, Sweden.

ABSTRACT
There are many situations where relatives interact while at the same time there is genetic polymorphism in traits influencing survival and reproduction. Examples include cheater-cooperator polymorphism and polymorphic microbial pathogens. Environmental heterogeneity, favoring different traits in nearby habitats, with dispersal between them, is one general reason to expect polymorphism. Currently, there is no formal framework of social evolution that encompasses genetic polymorphism. We develop such a framework, thus integrating theories of social evolution into the evolutionary ecology of heterogeneous environments. We allow for adaptively maintained genetic polymorphism by applying the concept of genetic cues. We analyze a model of social evolution in a two-habitat situation with limited dispersal between habitats, in which the average relatedness at the time of helping and other benefits of helping can differ between habitats. An important result from the analysis is that alleles at a polymorphic locus play the role of genetic cues, in the sense that the presence of a cue allele contains statistical information for an organism about its current environment, including information about relatedness. We show that epistatic modifiers of the cue polymorphism can evolve to make optimal use of the information in the genetic cue, in analogy with a Bayesian decision maker. Another important result is that the genetic linkage between a cue locus and modifier loci influences the evolutionary interest of modifiers, with tighter linkage leading to greater divergence between social traits induced by different cue alleles, and this can be understood in terms of genetic conflict.

No MeSH data available.