Modes of competition and the fitness of evolved populations

Abstract

Competition between individuals drives the evolution of whole species. Although the fittest individuals survive the longest and produce the most offspring, in some circumstances the resulting species may not be optimally fit. Here, using theoretical analysis and stochastic simulations of a simple model ecology, we show how the mode of competition can profoundly affect the fitness of evolved species. When individuals compete directly with one another, the adaptive dynamics framework provides accurate predictions for the number and distribution of species, which occupy positions of maximal fitness. By contrast, if competition is mediated by the consumption of a common resource then demographic noise leads to the stabilization of species with near minimal fitness.

Eco-evolutionary dynamics for finite populations and the noise-induced reversal of selection

“…31. This observation could generically help explain why finite populations frequently exhibit a lower tendency or take a longer time to undergo evolutionary branching compared to infinite population models (Johansson and Ripa, 2006; Claessen et al, 2007; Wakano and Iwasa, 2013; Rogers and McKane, 2015; Débarre and Otto, 2016). Indeed, a special case of Eq.…”

“…In ecology and evolution, stochastic models need not exhibit phenomena predicted by their deterministic analogues (Proulx and Day, 2005; Johansson and Ripa, 2006; Black and McKane, 2012; Débarre and Otto, 2016). They may also exhibit novel phenomena not predicted by deterministic models (Constable et al, 2016; Rogers and McKane, 2015; Joshi and Guttal, 2018; DeLong and Cressler, 2023).…”

Node Importance Ranking of Complex Network based on Degree and Network Density

“…Therefore, it is instructive to evaluate the importance of network nodes and effectively excavate important nodes according to their quantitative data, which are of theoretical significance to the complex network applications in reality. For example, inhibiting the spread of the virus [4][5], locating the leaders of terrorist organizations, avoiding the cascade failure of power networks [6][7], determining the sorting of search results of search engines, and mining community centers in complex network community structure involve the calculation of node importance assessment.…”