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research article

Impact of complex spatial population structure on early and long-term adaptation in rugged fitness landscapes

Servajean, Richard  
•
Alexandre, Arthur Louis Mayeul  
•
Bitbol, Anne-Florence  
March 3, 2025
Evolution

We study how rugged fitness landscapes are explored by spatially structured populations with demes on the nodes of a graph, connected by migrations. In the weak mutation and rare migration regime, we find that, in most landscapes, migration asymmetries associated with some suppression of natural selection allow the population to reach higher fitness peaks first. In this sense, suppression of selection can make early adaptation more efficient. However, the time it takes to reach the first fitness peak is then increased. We also find that suppression of selection tends to enhance finite-size effects. Finite structures can adapt more efficiently than very large ones, especially in high-dimensional fitness landscapes. We extend our study to frequent migrations, suggesting that our conclusions hold in this regime. We then investigate the impact of spatial structure with rare migrations on long-term evolution by studying the steady state of the population with weak mutation, and introducing an associated steady-state effective population size. We find that suppression of selection is associated to small steady-state effective population sizes and thus to small average steady-state fitnesses.

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Type
research article
DOI
10.1093/evolut/qpaf025
Author(s)
Servajean, Richard  
Alexandre, Arthur Louis Mayeul  

EPFL

Bitbol, Anne-Florence  

EPFL

Editors
Jain, Kavita
•
Wolf, Josan
Date Issued

2025-03-03

Publisher

Oxford University Press (OUP)

Published in
Evolution
Article Number

qpaf025

Subjects

adaptation

•

population structure

•

models/simulations

•

deme-structured populations on graphs

•

fitness landscapes

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
UPBITBOL  
FunderFunding(s)Grant NumberGrant URL

European Research Council

European Union’s Horizon 2020

851173

Swiss National Science Foundation

Modeling evolution of bacteria in the gut

208196

https://data.snf.ch/grants/grant/208196
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RelationRelated workURL/DOI

IsSupplementedBy

[Dataset] fitness-landscapes

https://github.com/Bitbol-Lab/fitness-landscapes/
Available on Infoscience
April 14, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/249232
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