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Dridi S.,UNIL Sorge | Dridi S.,University of Pennsylvania | Lehmann L.,UNIL Sorge
Behavioral Ecology | Year: 2016

Learning is a fundamental biological adaptation that is widespread throughout the animal kingdom. According to previous research, 2 conditions are necessary for learning to be adaptive: between-generation environmental variability and within-generation environmental predictability. In this article, we show that between-generation variability is not necessary and that instrumental learning can provide a selective advantage in a complex environment, where an individual is exposed to a large number of different challenges during its lifespan. We construct an evolutionary model where individuals have a memory with limited storage capacity, and an evolving trait determines the fraction of that memory that should be allocated to innate responses to the environment versus learning these responses. The evolutionarily stable level of learning depends critically on the features of the environmental process, but generally increases with environmental complexity. We conclude by emphasizing that the specific advantages of learning should be distinguished from the general advantages of phenotypic plasticity, and we discuss possible routes to empirically test our claims. © 2015 The Author.


Wakano J.Y.,Meiji University | Wakano J.Y.,Japan Science and Technology Agency | Lehmann L.,UNIL Sorge
Journal of Theoretical Biology | Year: 2014

Adaptive dynamics shows that a continuous trait under frequency dependent selection may first converge to a singular point followed by spontaneous transition from a unimodal trait distribution into a bimodal one, which is called "evolutionary branching". Here, we study evolutionary branching in a deme-structured population by constructing a quantitative genetic model for the trait variance dynamics, which allows us to obtain an analytic condition for evolutionary branching. This is first shown to agree with previous conditions for branching expressed in terms of relatedness between interacting individuals within demes and obtained from mutant-resident systems. We then show this branching condition can be markedly simplified when the evolving trait affect fecundity and/or survival, as opposed to affecting population structure, which would occur in the case of the evolution of dispersal. As an application of our model, we evaluate the threshold migration rate below which evolutionary branching cannot occur in a pairwise interaction game. This agrees very well with the individual-based simulation results. © 2014 Elsevier Ltd.


Aoki K.,University of Tokyo | Wakano J.Y.,Meiji University | Lehmann L.,UNIL Sorge
Theoretical Population Biology | Year: 2012

Individual learning (e.g., trial-and-error) and social learning (e.g., imitation) are alternative ways of acquiring and expressing the appropriate phenotype in an environment. The optimal choice between using individual learning and/or social learning may be dictated by the life-stage or age of an organism. Of special interest is a learning schedule in which social learning precedes individual learning, because such a schedule is apparently a necessary condition for cumulative culture. Assuming two obligatory learning stages per discrete generation, we obtain the evolutionarily stable learning schedules for the three situations where the environment is constant, fluctuates between generations, or fluctuates within generations. During each learning stage, we assume that an organism may target the optimal phenotype in the current environment by individual learning, and/or the mature phenotype of the previous generation by oblique social learning. In the absence of exogenous costs to learning, the evolutionarily stable learning schedules are predicted to be either pure social learning followed by pure individual learning ("bang-bang" control) or pure individual learning at both stages ("flat" control). Moreover, we find for each situation that the evolutionarily stable learning schedule is also the one that optimizes the learned phenotype at equilibrium. © 2012 Elsevier Inc.


Buckleton J.,Environmental and Scientific Research Ltd. | Buckleton J.,U.S. National Institute of Standards and Technology | Curran J.,University of Auckland | Goudet J.,UNIL Sorge | And 4 more authors.
Forensic Science International: Genetics | Year: 2016

The interpretation of matching between DNA profiles of a person of interest and an item of evidence is undertaken using population genetic models to predict the probability of matching by chance. Calculation of matching probabilities is straightforward if allelic probabilities are known, or can be estimated, in the relevant population. It is more often the case, however, that the relevant population has not been sampled and allele frequencies are available only from a broader collection of populations as might be represented in a national or regional database. Variation of allele probabilities among the relevant populations is quantified by the population structure quantity FST and this quantity affects matching proportions. Matching within a population can be interpreted only with respect to matching between populations and we show here that FST, can be estimated from sample allelic matching proportions within and between populations. We report such estimates from data we extracted from 250 papers in the forensic literature, representing STR profiles at up to 24 loci from nearly 500,000 people in 446 different populations. The results suggest that theta values in current forensic use do not have the buffer of conservatism often thought. © 2016 Elsevier Ireland Ltd. All rights reserved.


Jiricny N.,University of Oxford | Diggle S.P.,University of Nottingham | West S.A.,University of Oxford | Evans B.A.,University of Edinburgh | And 3 more authors.
Journal of Evolutionary Biology | Year: 2010

There is growing awareness of the importance of cooperative behaviours in microbial communities. Empirical support for this insight comes from experiments using mutant strains, termed 'cheats', which exploit the cooperative behaviour of wild-type strains. However, little detailed work has gone into characterising the competitive dynamics of cooperative and cheating strains. We test three specific predictions about the fitness consequences of cheating to different extents by examining the production of the iron-scavenging siderophore molecule, pyoverdin, in the bacterium Pseudomonas aeruginosa. We create a collection of mutants that differ in the amount of pyoverdin that they produce (from 1% to 96% of the production of paired wild types) and demonstrate that these production levels correlate with both gene activity and the ability to bind iron. Across these mutants, we found that (1) when grown in a mixed culture with a cooperative wild-type strain, the relative fitness of a mutant is negatively correlated with the amount of pyoverdin that it produces; (2) the absolute and relative fitness of the wild-type strain in the mixed culture is positively correlated with the amount of pyoverdin that the mutant produces; and (3) when grown in a monoculture, the absolute fitness of the mutant is positively correlated with the amount of pyoverdin that it produces. Overall, we demonstrate that cooperative pyoverdin production is exploitable and illustrate how variation in a social behaviour determines fitness differently, depending on the social environment. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.


PubMed | UNIL Sorge, University of Washington, U.S. National Institute of Standards and Technology, Flinders University and 2 more.
Type: | Journal: Forensic science international. Genetics | Year: 2016

The interpretation of matching between DNA profiles of a person of interest and an item of evidence is undertaken using population genetic models to predict the probability of matching by chance. Calculation of matching probabilities is straightforward if allelic probabilities are known, or can be estimated, in the relevant population. It is more often the case, however, that the relevant population has not been sampled and allele frequencies are available only from a broader collection of populations as might be represented in a national or regional database. Variation of allele probabilities among the relevant populations is quantified by the population structure quantity FST and this quantity affects matching proportions. Matching within a population can be interpreted only with respect to matching between populations and we show here that FST, can be estimated from sample allelic matching proportions within and between populations. We report such estimates from data we extracted from 250 papers in the forensic literature, representing STR profiles at up to 24 loci from nearly 500,000 people in 446 different populations. The results suggest that theta values in current forensic use do not have the buffer of conservatism often thought.


Lehmann L.,UNIL Sorge | Rousset F.,Montpellier University
Philosophical Transactions of the Royal Society B: Biological Sciences | Year: 2014

We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multidimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments. © 2014 The Author(s) Published by the Royal Society. All rights reserved.


Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation-selection-drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamilton's inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system. © 2012 The Authors. Journal of Evolutionary Biology © 2012 European Society For Evolutionary Biology.


PubMed | UNIL Sorge
Type: Journal Article | Journal: Journal of evolutionary biology | Year: 2012

Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation-selection-drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamiltons inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system.

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