Adaptive dynamics in logistic branching populations
Nicolas Champagnat, Amaury Lambert (FESE)

TL;DR
This paper extends adaptive dynamics models by incorporating genetic drift into a finite population framework, deriving a diffusion process that combines stochastic and deterministic evolutionary forces, with explicit fixation probability formulas.
Contribution
It introduces the canonical diffusion of adaptive dynamics, blending genetic drift and selection, and provides explicit formulas for fixation probabilities in finite populations.
Findings
Derived explicit fixation probability formulas using invasibility coefficients.
Formulated the canonical diffusion as a combination of drift and diffusion terms.
Presented numerical simulations illustrating the diffusion process.
Abstract
We consider a trait-structured population subject to mutation, birth and competition of logistic type, where the number of coexisting types may fluctuate. Applying a limit of rare mutations to this population while keeping the population size finite leads to a jump process, the so-called `trait substitution sequence', where evolution proceeds by successive invasions and fixations of mutant types. The probability of fixation of a mutant is interpreted as a fitness landscape that depends on the current state of the population. It was in adaptive dynamics that this kind of model was first invented and studied, under the additional assumption of large population. Assuming also small mutation steps, adaptive dynamics' theory provides a deterministic ODE approximating the evolutionary dynamics of the dominant trait of the population, called `canonical equation of adaptive dynamics'. In this…
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