Diagrammatic expressions for steady-state distribution and static responses in population dynamics

TL;DR

This paper develops diagrammatic methods to exactly compute steady-state distributions and static responses in population models, extending Markov chain theory to include trait reproduction loops.

Contribution

It introduces rooted 0/1 loop forests to generalize the Markov chain tree theorem, enabling exact calculation of static responses in population dynamics models.

Findings

Derived diagrammatic expressions for steady-state distributions.

Extended Markov chain tree theorem to include loops.

Provided numerical examples illustrating exact and approximate results.

Abstract

One of the fundamental questions in population dynamics is how biological populations respond to environmental perturbations. In population dynamics, the mean fitness and the fraction of a trait in the steady state are important because they indicate how well the trait and the population adapt to the environment. In this study, we examine the parallel mutation-reproduction model, which is one of the simplest models of an evolvable population. As an extension of the Markov chain tree theorem, we derive diagrammatic expressions for the static responses of mean fitness and the steady-state distribution of the population. For the parallel mutation-reproduction model, we consider self-loops, which represent trait reproduction and are excluded from the Markov chain tree theorem for the linear master equation. To generalize the theorem, we introduce the concept of rooted $0$/$1$ loop forests, which generalize spanning trees with loops. We demonstrate that the weights of rooted $0$/$1$ loop forests yield the static responses of mean fitness and the steady-state distribution. Our results provide exact expressions for the static responses and the steady-state distribution. Additionally, we discuss approximations of these expressions in cases where reproduction or mutation is dominant. We provide numerical examples to illustrate these approximations and exact expressions.