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Boutillon, N.

Publications and source records attributed to Boutillon, N..

2 recordsLinked to original sources

How Demographic Noise Shapes Phenotypic Clusters in Environmental Gradients

2Although resources are typically distributed continuously in space, the distribution of species is often organized in discrete clusters. Such clusters have been shown to spontaneously arise in population densities, even in environments with continuously varying conditions, under the phenomenon known as Turing instability. In this work, we consider two models grounded in population dynamics: a one-dimensional model based on the nonlocal Fisher-KPP equation, and a two-dimensional model involving an environmental gradient. We show that phenotypic clusters emerge in these models, and we prove that they do not emerge because of Turing instability, but because of stochasticity. We first consider initial populations that are uniformly distributed in the state space. We show that phenotypic clusters quickly emerge, and that the distances between them depend on population size, that is, on the degree of stochasticity. In addition to the effect of population size, we provide quantitative estimates of the various parameters of the model with an environmental gradient on the equilibrium distance between phenotypic clusters. Next, we start from clearly defined phenotypic clusters and modify the distance between those. We identify three regimes in the connection between population size, the initial distances between clusters, and the distances between clusters at equilibrium. Last, on the two-dimensional model, we relax the hypothesis of complete clonality by varying the effective recombination rate. We explore its effect on phenotypic clustering, and show that phenotypic clustering decays drastically with slight recombination. 1 Non-specialist summaryWe explored the origin of discrete phenotypic clusters in populations living within a continuous space. Unlike previous studies, our research highlights the importance of stochasticity in the emergence of discreteness. We demonstrate this in two types of model: the first considers a single dimension corresponding either to the phenotype or to the resource spectrum, while the second considers a geographic dimension and a phenotypic dimension. We demonstrate how population size and the other model parameters affect the stable minimum distance between coexisting clusters, as well as how the initial distance between the phenotypes influences this distance. Finally, we relax the hypothesis of complete clonality by introducing sexual reproduction and varying an effective recombination rate.

ecology↗

Time-resolved bottleneck analysis in bacterial infection dynamics

Within-host pathogen dynamics are shaped by successive host-imposed barriers that generate bottlenecks and ultimately determine infection outcome, yet these processes remain difficult to study in vivo. Here, we use neutral genetic barcoding to quantify the root-entry bottleneck during infection of tomato by the bacterial soil-borne phytopathogen Ralstonia pseudosolanacearum. We establish an updated theoretical framework showing that the temporal structure of pathogen entry, often overlooked, is a key determinant of root colonisation. We introduce the concept of time-resolved bottleneck, capturing infection scenarios in which entry occurs continuously while the pathogen population simultaneously expands within the host. We develop quantitative tools that exploit barcode dynamics to disentangle the respective roles of motility, growth, and spatial competition, enabling direct inference of in vivo pathogen fluxes and growth rates and linking host susceptibility to mechanistic features of colonisation. Extending beyond plants, reanalysis of two independent animal infection datasets reveals previously unrecognised within-host development patterns. Together, these results show that time-resolved bottleneck is a general principle of bacterial infection dynamics and propose a new standard for barcoding-based studies: a host- and pathogen-agnostic framework that is directly applicable to a majority of existing STAMP(-R)-style datasets, distributed as an open, annotated R/Python notebook to facilitate community reuse.

microbiology↗