ArticleScientific reports2018
Stirring does not make populations well mixed.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Pattern Formation in Agent-Based and PDE Models for Evolutionary Games with Payoff-Driven Motion.Bulletin of mathematical biology · 2026Article
- Frequent asymmetric migrations suppress natural selection in spatially structured populations.PNAS nexus · 2023Article
- Hydrodynamic flow and concentration gradients in the gut enhance neutral bacterial diversity.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Article
- The Moran process on 2-chromatic graphs.PLoS computational biology · 2020Article
- Ocean currents promote rare species diversity in protists.Science advances · 2020Article
- Turbulent coherent structures and early life below the Kolmogorov scale.Nature communications · 2020Article
- Motion, fixation probability and the choice of an evolutionary process.PLoS computational biology · 2019Article
- Fixation probabilities in weakly compressible fluid flows.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
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Authors and funding
3 authors.
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Abstract
In evolutionary dynamics, the notion of a 'well-mixed' population is usually associated with all-to-all interactions at all times. This assumption simplifies the mathematics of evolutionary processes, and makes analytical solutions possible. At the same time the term 'well-mixed' suggests that this situation can be achieved by physically stirring the population. Using simulations of populations in chaotic flows, we show that in most cases this is not true: conventional well-mixed theories do not predict fixation probabilities correctly, regardless of how fast or thorough the stirring is. We propose a new analytical description in the fast-flow limit. This approach is valid for processes with global and local selection, and accurately predicts the suppression of selection as competition becomes more local. It provides a modelling tool for biological or social systems with individuals in motion.
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