Speaker
Description
Microbial communities display striking macroecological regularities in their temporal abundance fluctuations across very different biomes \cite{grilli2020}. In particular, previous studies reported that species abundances follow a gamma-like distribution, often interpreted as evidence of exogenous stochastic drivers such as environmental noise. In this work, we show \cite{arroyo2026} that these patterns can also emerge from endogenous ecological dynamics. We study generalized Lotka–Volterra models with many interacting species, first including constant migration and then extending the framework to a spatial metacommunity with dispersal among patches. Our results show that deterministic species interactions alone can generate persistent, irregular fluctuations and reproduce the observed distribution of pairwise abundance correlations, a key empirical feature that stochastic models fail to capture. However, neither the open local model nor single patches of the metacommunity recover the empirical gamma abundance fluctuation distribution. Remarkably, this pattern appears only after aggregating abundances across spatial patches. This suggests that the gamma law may arise from spatial structure and regional-scale averaging rather than from a specific microscopic biological mechanism. Our findings highlight the central role of space in microbial ecology and suggest that macroecological patterns may reflect emergent effects of spatial aggregation as much as local community dynamics.
Bibliography
@article{grilli2020,
title={Macroecological laws describe variation and diversity in microbial communities},
author={Grilli, Jacopo},
journal={Nat. Commun.},
volume={11},
number={1},
pages={4743},
year={2020},
publisher={Nature Publishing Group UK London}
}
@misc{arroyo2026,
title={The role of space in the macroecological patterns of microbial abundances},
author={Guti{\'e}rrez-Arroyo, Adri{\'a}n and Lampo, Aniello and Cuesta, Jos{\'e} A.},
year={2026},
eprint={XXXX.XXXXX},
archivePrefix={arXiv},
primaryClass={q-bio.PE},
}
