Bottlenecks, population dynamics and antibiotic resistance evolution

Abstract

Antibiotic resistance is a growing global health threat, driving experimental and theoretical studies to identify factors that prevent or slow its emergence. Studies often compare the effi-cacy of treatment strategies but rarely consider population bottlenecks—events that drastical-ly reduce population size. In pathogen infections, bottlenecks occur due to pathogen trans-mission, immune responses, or antibiotic treatment. Despite their known impact on evolution, their role in resistance evolution, especially alongside other infection-related factors, remains largely unexplored. In our study, we used mathematical models informed by data to explore the effect of population bottlenecks on antibiotic resistance evolution. As a first step, we focused on the interplay between antibiotic pressure and bottleneck size. We built a mathematical model based upon experimental results from Mahrt et al. 2021, ex-ploring trait adaptation and the effect of demographic fluctuations. Our results show that dif-ferent bottleneck sizes can favour the selection of different resistance traits—for example, small bottlenecks promote the adaptation of the maximum growth rate, while large bottlenecks promote the adaptation of lag time and carrying capacity. These findings provide insight into how different treatment conditions can steer resistance evolution through distinct adaptive pathways, potentially informing the design of more effective antibiotic therapies. As a second step, we focused on the interplay between migration, bottlenecks, and competi-tion on evolutionary dynamics. This study was motivated by recent experimental work show-ing that mixing of within-species strains and bacterial interactions can influence resistance evolution in polymicrobial infections (Batra et al., submitted to Nature Ecology and Evolution). Using a mathematical approach, we developed a meta-population model to explore how mi-gration between demes (isolated subpopulations) affects adaptive outcomes. We compared two extreme regimes: full isolation, where demes evolve independently, and full migration, where demes are well-mixed. Our study identifies the key factors that amplify differences be-tween these regimes, highlighting the relevance of spatial structure and stochastic effects in resistance evolution. These findings have broader implications, extending beyond antibiotic resistance to various ecological contexts.