Individual-Based models for adaptive diversification in high-dimensional phenotype spaces
Iaroslav Ispolatov, Vaibhav Madhok, and Michael Doebeli

TL;DR
This paper develops a method to construct individual-based models that replicate complex adaptive dynamics in high-dimensional phenotype spaces and demonstrates how frequency-dependent competition can induce diversification.
Contribution
It introduces a novel approach to build individual-based models from adaptive dynamics and shows how to induce diversification through Gaussian competition terms.
Findings
Constructed models faithfully reproduce adaptive dynamics attractors.
Adding Gaussian competition induces frequency-dependent diversification.
Strong competition leads to diversification orthogonal to the selection gradient.
Abstract
Most theories of evolutionary diversification are based on equilibrium assumptions: they are either based on optimality arguments involving static fitness landscapes, or they assume that populations first evolve to an equilibrium state before diversification occurs, as exemplified by the concept of evolutionary branching points in adaptive dynamics theory. Recent results indicate that adaptive dynamics may often not converge to equilibrium points and instead generate complicated trajectories if evolution takes place in high-dimensional phenotype spaces. Even though some analytical results on diversification in complex phenotype spaces are available, to study this problem in general we need to reconstruct individual-based models from the adaptive dynamics generating the non-equilibrium dynamics. Here we first provide a method to construct individual-based models such that they faithfully…
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