Spatial heterogeneity shapes microbial eco-evolutionary dynamics of soil carbon
- 1ENS-PSL
Abstract
The study of reciprocal influences between ecological and evolutionary processes has advanced considerably, yet integration between evolutionary biology and ecosystem-level ecology remains limited. Here we contribute to this integration by advancing the theory of eco-evolutionary feedbacks between soil microbial adaptation and soil-atmosphere carbon fluxes in a warming climate. We develop a spatially structured model of soil organic matter decomposition that represents microbial populations in microsites embedded in a bulk-soil matrix and focuses on exoenzyme production as a key resource-acquisition trait. The evolutionarily adapted investment in exoenzyme production is shaped by opposing selective forces: negative selection within microsites, where lower-investing mutants exploit exoenzymes as public goods, and positive selection in the soil matrix, where exoenzyme production directly benefits individual cells. Microsite density emerges as a critical determinant of microbial adaptation to warming and its consequences for soil carbon loss. Even small changes in microsite density across a threshold can reverse the ecosystem-level effect of adaptation, from buffering to amplifying carbon loss. High microsite density generally promotes buffering, whereas low microsite density has little effect in cool ecosystems but can strongly amplify carbon loss in warm ecosystems, especially when microbial mobility is low. These results identify soil spatial structure at microsite scale as a key mediator of microbial evolutionary adaptation and soil carbon-climate feedback under global environmental change, with implications for quantitatively improving Earth system models.
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BibTeX
@misc{chavelucas2026spatial,
title = {{Spatial heterogeneity shapes microbial eco-evolutionary dynamics of soil carbon}},
author = {A. Chave-Lucas and R. Ferriere},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.03.749071},
url = {https://www.biorxiv.org/content/10.64898/2026.09.03.749071v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Spatial heterogeneity shapes microbial eco-evolutionary dynamics of soil carbon
AU - A. Chave-Lucas
AU - R. Ferriere
PY - 2026
DA - 2026/09/03
PB - bioRxiv
DO - 10.64898/2026.09.03.749071
UR - https://www.biorxiv.org/content/10.64898/2026.09.03.749071v1
AB - The study of reciprocal influences between ecological and evolutionary processes has advanced considerably, yet integration between evolutionary biology and ecosystem-level ecology remains limited. Here we contribute to this integration by advancing the theory of eco-evolutionary feedbacks between soil microbial adaptation and soil-atmosphere carbon fluxes in a warming climate. We develop a spatially structured model of soil organic matter decomposition that represents microbial populations in microsites embedded in a bulk-soil matrix and focuses on exoenzyme production as a key resource-acquisition trait. The evolutionarily adapted investment in exoenzyme production is shaped by opposing selective forces: negative selection within microsites, where lower-investing mutants exploit exoenzymes as public goods, and positive selection in the soil matrix, where exoenzyme production directly benefits individual cells. Microsite density emerges as a critical determinant of microbial adaptation to warming and its consequences for soil carbon loss. Even small changes in microsite density across a threshold can reverse the ecosystem-level effect of adaptation, from buffering to amplifying carbon loss. High microsite density generally promotes buffering, whereas low microsite density has little effect in cool ecosystems but can strongly amplify carbon loss in warm ecosystems, especially when microbial mobility is low. These results identify soil spatial structure at microsite scale as a key mediator of microbial evolutionary adaptation and soil carbon-climate feedback under global environmental change, with implications for quantitatively improving Earth system models.
ER -