A demogenetic individual based model for the evolution of traits and genome architecture under sexual selection
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Abstract
Sexual selection has long been known to favor the evolution of mating behaviors such as mate preference and competitiveness, and to affect their genetic architecture, for instance by favoring genetic correlation between some traits. Reciprocally, genetic architecture can affect the expression and the evolvability of traits and mating behaviors. But sexual selection is highly context-dependent, making interactions between individuals a central process in evolution, governing the transmission of genotypes to the next generation. This loop between the genetic structure conditioning the expression and evolution of traits and behaviour, and the feedback of this phenotypic evolution on the architecture of the genome in the dynamic context of sexual selection, has yet to be thoroughly investigated. We argue that demogenetic agent-based models (DG-ABM) are especially suited to tackle such a challenge because they allow explicit modelling of both the genetic architecture of traits and the behavioural interactions in a dynamic population context. We here present a DG-ABM able to simultaneously track individual variation in traits (such as gametic investment, preference, competitiveness), fitness and genetic architecture throughout evolution. Using two simulation experiments, we compare various mating systems and show that behavioral interactions during mating triggered some complex feedback in our model, between fitness, population demography, and genetic architecture, placing interactions between individuals at the core of evolution through sexual selection. DG-ABMs can, therefore, relate to theoretical patterns expected at the population level from simpler analytical models in evolutionary biology, and at the same time provide a more comprehensive framework regarding individual trait and behaviour variation, that is usually envisioned separately from genome architecture in behavioural ecology.
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150 years after Darwin published ‘Descent of man and selection in relation to sex’ (Darwin, 1871), the evolutionary mechanism that he laid out in his treatise continues to fascinate us. Sexual selection is responsible for some of the most spectacular traits among animals, and plants, and it appeals to our interest in all things reproductive and sexual (Bell, 1982). In addition, sexual selection poses some of the more intractable problems in evolutionary biology: Its realm encompasses traits that are subject to markedly different selection pressures, particularly when distinct, yet associated, traits tend to be associated with males, e.g. courtship signals, and with females, e.g. preferences (cf. Ah-King & Ahnesjo, 2013). While separate, such traits cannot evolve independently of each other (Arnqvist & Rowe, 2005), and complex …
150 years after Darwin published ‘Descent of man and selection in relation to sex’ (Darwin, 1871), the evolutionary mechanism that he laid out in his treatise continues to fascinate us. Sexual selection is responsible for some of the most spectacular traits among animals, and plants, and it appeals to our interest in all things reproductive and sexual (Bell, 1982). In addition, sexual selection poses some of the more intractable problems in evolutionary biology: Its realm encompasses traits that are subject to markedly different selection pressures, particularly when distinct, yet associated, traits tend to be associated with males, e.g. courtship signals, and with females, e.g. preferences (cf. Ah-King & Ahnesjo, 2013). While separate, such traits cannot evolve independently of each other (Arnqvist & Rowe, 2005), and complex feedback loops and correlations between them are predicted (Greenfield et al., 2014). Traditionally, sexual selection has been modelled under simplifying assumptions, and quantitative genetic approaches that avoided evolutionary dynamics have prevailed. New computing methods may be able to free the field from these constraints, and a trio of theoreticians (Chevalier, De Coligny & Labonne 2020) describe here a novel application of a ‘demo-genetic agent (or individual) based model’, a mouthful hereafter termed DG-ABM, for arriving at a holistic picture of the sexual selection trajectory. The application is built on the premise that traits, e.g. courtship, preference, gamete investment, competitiveness for mates, can influence the genetic architecture, e.g. correlations, of those traits. In turn, the genetic architecture can influence the expression and evolvability of the traits. Much of this influence occurs via demographic features, i.e. social environment, generated by behavioral interactions during sexual advertisement, courtship, mate guarding, parental care, post-mating dispersal, etc.
The authors provide a lengthy verbal description of their model, specifying the genomic and behavioral parameters that can be set and how a ‘run’ may be initialized. There is a link to an internet site where users can then enter their own parameter values and begin exploring hypotheses. Back in the article several simulations illustrate simple tests; e.g. how gamete investment and preference jointly evolve given certain survival costs. One obvious test would have been the preference – courtship genetic correlation that represents the core of Fisherian runaway selection, and it is regrettable that it was not examined under a range of demographic parameters. As presented the author’s DG-ABM appears particularly geared toward mating systems in ‘higher’ vertebrates, where couples form during a discrete mating season and are responsible for most reproduction. It is not clear how applicable the model could be to a full range of mating systems and nuances, including those in arthropods and other invertebrates as well as plants.
What is the likely value of the DG-ABM for sexual selection researchers? We will not be able to evaluate its potential impact until readers with specialized understanding of a question and taxon begin exploring and comparing their results with prior expectations. Of course, lack of congruence with earlier predictions would not invalidate the model. Hopefully, some of these specialists will have opportunities for comparing results with pertinent empirical data.References
Ah-King, M. and Ahnesjo, I. 2013. The ‘sex role’ concept: An overview and evaluation Evolutionary Biology, 40, 461-470. doi: https://doi.org/10.1007/s11692-013-9226-7
Arnqvist, G. and Rowe, L. 2005. Sexual Conflict. Princeton University Press, Princeton. doi: https://doi.org/10.1515/9781400850600
Bell, G. 1982. The Masterpiece of Nature: The Evolution and Genetics of Sexuality. University of California Press, Berkeley.
Chevalier, L., De Coligny, F. and Labonne, J. (2020) A demogenetic individual based model for the evolution of traits and genome architecture under sexual selection. bioRxiv, 2020.04.01.014514, ver. 4 peer-reviewed and recommended by PCI Evol Biol. doi: https://doi.org/10.1101/2020.04.01.014514
Darwin, C. 1871. The Descent of Man and Selection in Relation to Sex. J. Murray, London.
Greenfield, M.D., Alem, S., Limousin, D. and Bailey, N.W. 2014. The dilemma of Fisherian sexual selection: Mate choice for indirect benefits despite rarity and overall weakness of trait-preference genetic correlation. Evolution, 68, 3524-3536. doi: https://doi.org/10.1111/evo.12542 -
