An extension of the classification of evolutionarily singular strategies in Adaptive Dynamics

Boldin, Barbara; Diekmann, Odo

doi:10.1007/s00285-013-0725-z

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…They show that the assumption made on the super-infection function has important consequences for whether the continuously stable strategy at the within-host and at the between-host level match or not. They also show that super-infection can lead to evolutionary branching depending on the shape of the transmission-virulence trade-off function (see also box 1 and [71] for further details).…”

Section: Nested Models With Multiple Infectionsmentioning

confidence: 93%

Co-infection and super-infection models in evolutionary epidemiology

Alizon

2013

Interface Focus.

109

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One contribution of 11 to a Theme Issue 'Modelling biological evolution: recent progress, current challenges and future direction'. Multiple infections are intensively studied because of their consequences for the health of the host but also because they can radically alter the selective pressures acting on parasites. I discuss how multiple infections have been modelled in evolutionary epidemiology. First, I briefly mention within-host models, which are at the root of these epidemiological models. Then, I present the super-infection framework, with an original focus on how the definition of the super-infection function can lead to evolutionary branching. There are several co-infection models and, for each of them, I briefly go through the underlying mathematics (especially the invasion fitness of a mutant strain) and I discuss the biological assumptions they make and the questions they consequently may ask. In particular, I show that a widely used co-infection model should not be invoked for invasion analyses because it confers a frequency-dependent advantage to rare neutral mutants. Finally, I present more recent frameworks, such as the Price equation framework in epidemiology, that can account for increased parasite diversity. To conclude, I discuss some perspectives for the study of multiple infections in evolutionary epidemiology.

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Section: Nested Models With Multiple Infectionsmentioning

confidence: 93%

Co-infection and super-infection models in evolutionary epidemiology

Alizon

2013

Interface Focus.

109

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“…It was recognized that some ESS can be unreachable by evolution ("Gardens of Eden") 30 , and therefore the notion of evolutionary convergence was introduced to indicate ecological attractors towards which all nearby attractors evolve. Strikingly, evolutionary stability and evolutionary convergence are independent notions that can occur in all possible combinations 27,31 . States that are both convergent and stable are end--points of the eco--evolutionary dynamics.…”

Section: Main Textmentioning

confidence: 99%

Emergence of evolutionarily stable communities through eco-evolutionary tunnelling

Kotil

Vetsigian

2018

Nat Ecol Evol

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Ecological and evolutionary dynamics of communities are inexorably intertwined. The ecological state determines the fate of newly arising mutants, and mutations that increase in frequency can reshape the ecological dynamics. Evolutionary game theory and its extensions within adaptive dynamics have been the mathematical frameworks for understanding this interplay, leading to notions such as evolutionarily stable states (ESS) in which no mutations are favoured, and evolutionary branching points near which the population diversifies. A central assumption behind these theoretical treatments has been that mutations are rare so that the ecological dynamics has time to equilibrate after every mutation. A fundamental question is whether qualitatively new phenomena can arise when mutations are frequent. Here, we describe an adaptive diversification process that robustly leads to complex ESS, despite the fact that such communities are unreachable through a step-by-step evolutionary process. Rather, the system as a whole tunnels between collective states over a short timescale. The tunnelling rate is a sharply increasing function of the rate at which mutations arise in the population. This makes the emergence of ESS communities virtually impossible in small populations, but generic in large ones. Moreover, communities emerging through this process can spatially spread as single replication units that outcompete other communities. Overall, this work provides a qualitatively new mechanism for adaptive diversification and shows that complex structures can generically evolve even when no step-by-step evolutionary path exists.

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“…The analysis of the discontinuous model can be interesting from a theoretical point of view (e.g. [137]), but this falls outside the scope of this paper and is thus left for future research.…”

Section: Appendix a Model And Methodsmentioning

confidence: 99%

Variability in life-history switch points across and within populations explained by Adaptive Dynamics

Landi

Vonesh

Hui

2018

J. R. Soc. Interface.

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Understanding the factors that shape the timing of life-history switch points (SPs; e.g. hatching, metamorphosis and maturation) is a fundamental question in evolutionary ecology. Previous studies examining this question from a fitness optimization perspective have advanced our understanding of why the timing of life-history transitions may vary across populations and environments. However, in nature we also often observe variability among individuals within populations. Optimization theory, which typically predicts a single optimal SP under physiological and environmental constraints for a given environment, cannot explain this variability. Here, we re-examine the evolution of a single life-history SP between juvenile and adult stages from an Adaptive Dynamics (AD) perspective, which explicitly considers the feedback between the dynamics of population and the evolution of life-history strategy. The AD model, although simple in structure, exhibits a diverse range of evolutionary scenarios depending upon demographic and environmental conditions, including the loss of the juvenile stage, a single optimal SP, alternative optimal SPs depending on the initial phenotype, and sympatric coexistence of two SP phenotypes under disruptive selection. Such predictions are consistent with previous optimization approaches in predicting life-history SP variability across environments and between populations, and in addition they also explain within-population variability by sympatric disruptive selection. Thus, our model can be used as a theoretical tool for understanding life-history variability across environments and, especially, within species in the same environment.

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An extension of the classification of evolutionarily singular strategies in Adaptive Dynamics

Cited by 6 publications

References 15 publications

Co-infection and super-infection models in evolutionary epidemiology

Co-infection and super-infection models in evolutionary epidemiology

Emergence of evolutionarily stable communities through eco-evolutionary tunnelling

Variability in life-history switch points across and within populations explained by Adaptive Dynamics

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