Modes of competition and the fitness of evolved populations

Rogers, Tim; McKane, Alan J.

doi:10.1103/physreve.92.032708

Cited by 2 publications

(3 citation statements)

References 42 publications

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“…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.…”

Section: Discussionmentioning

confidence: 93%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Bhat,

Guttal

2024

Preprint

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Theoretical studies from diverse areas of population biology have shown that demographic stochasticity can substantially impact evolutionary dynamics in finite populations, including scenarios where traits that are disfavored by natural selection can nevertheless increase in frequency through the course of evolution. Historically, most general analytic frameworks have either restricted themselves to models with constant or deterministically varying total population size or have resorted to dynamically insufficient formulations. Here, we analytically describe the eco-evolutionary dynamics of finite populations from demographic first principles to investigate how noise-induced effects can alter the evolutionary fate of populations in which total population size may vary stochastically over time. Starting from a generic birth-death process describing a finite population of individuals with discrete traits, we derive a set of stochastic differential equations (SDEs) that recover well-known descriptions of evolutionary dynamics such as the replicator-mutator equation, the Price equation, and Fisher’s fundamental theorem in the infinite population limit. For finite populations, our SDEs reveal how stochasticity can induce a directional evolutionary force termed ‘noise-induced selection’ via two distinct mechanisms, one that operates over relatively faster (ecological) timescales and another that is only apparent over longer (evolutionary) timescales. Despite arising from the stochasticity of finite systems, the effects of noise-induced selection are predictable and may oppose natural selection. In some cases, noise-induced selection can even reverse the direction of evolution predicted by natural selection. By extending and generalizing some standard equations of population genetics, we thus describe how noise-induced selection appears alongside and interacts with the more well-understood forces of natural selection, neutral drift, and transmission effects (mutation/migration) to determine the eco-evolutionary dynamics of finite populations of non-constant size.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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

Bhat,

Guttal

2024

Preprint

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show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Xu¹,

Zhang²,

Yang³

et al. 2019

IJPE

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Node importance ranking of complex networks is of great significance to the study of network robustness. The classical centrality measure degree can reflect the number of neighbors of a node, but it ignores the information between its neighbors. In order to mine the important nodes in the network accurately and efficiently, a method of ranking the node importance of complex networks based on multiattribute evaluation and node deletion is proposed in this paper. Based on the degree attributes of the target node and its neighbors, this method introduces two attributes, which are the local network density centered on the target node and the assortativity coefficient. It takes into account the characteristics of the scale, tightness, and topology of the local area network where the node and its neighbors are located. This paper conducts deliberate attack experiments on four real networks. Through a comparison between the experimental results of the maximal connected coefficient and network efficiency, our approach is proven to be valid and feasible.

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Modes of competition and the fitness of evolved populations

Cited by 2 publications

References 42 publications

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

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

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

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