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Microbial populations hardly ever grow logistically and never sublinearly

J. Camacho-Mateu, A. Lampo, M. Castro, José A. Cuesta

Physical Review E Vol. 111, nº. 4, pp. 044404-1 - 044404-8

Summary:

We investigate the growth dynamics of microbial populations, challenging the conventional logistic model.
By analyzing empirical data from various biomes, we demonstrate that microbial growth is better described by a generalized logistic model, the θ-logistic model. This accounts for different growth mechanisms and environmental fluctuations, leading to a generalized gamma distribution of abundance fluctuations. Our findings reveal that microbial growth is never sublinear, so they cannot endorse—at least in the microbial world—the recent proposal of this mechanism as a stability enhancer of highly diverse communities. These results have significant implications for understanding macroecological patterns and the stability of microbial ecosystems.


Spanish layman's summary:

Hemos demostrado que los microbios crecen de una manera que desafía los modelos tradicionales, mostrando que su crecimiento siempre está por encima de un cierto nivel y no apoya la idea de que un crecimiento más lento ayuda a estabilizar comunidades diversas.


English layman's summary:

We have found that microbes grow in a way that challenges traditional models, showing that their growth is always above a certain level and doesn't support the idea that slower growth helps stabilize diverse communities.


Keywords: Microbial Growth, Logistic Model, Macroecological Patterns, Environmental Fluctuations


JCR Impact Factor and WoS quartile: 2,200 - Q1 (2023)

DOI reference: DOI icon https://doi.org/10.1103/PhysRevE.111.044404

Published on paper: April 2025.

Published on-line: April 2025.



Citation:
J. Camacho-Mateu, A. Lampo, M. Castro, José A. Cuesta, Microbial populations hardly ever grow logistically and never sublinearly. Physical Review E. Vol. 111, nº. 4, pp. 044404-1 - 044404-8, April 2025. [Online: April 2025]


    Research topics:
  • Mathematical Models and Artificial Intelligence in Healthcare