During the last decades macroecology has identified broad-scale patterns of abundances and diversity of microbial communities and put forward some potential explanations for them. However, these advances are not paralleled by a full understanding of the underlying dynamical processes. In particular, abundance fluctuations over metagenomic samples are found to be correlated, but reproducing these through appropriate models remains still an open task. The present paper tackles this problem and points to species interactions as a necessary mechanism to account for them. Specifically, we discuss several possibilities to include interactions in population models and recognize Lotka-Volterra constants as successful ansatz. We design a Bayesian inference algorithm to obtain sets of interaction constants able to reproduce the experimental correlation distributions much better than the state-of-the-art attempts. Importantly, the model still reproduces single-species, experimental, macroecological patterns previously detected in the literature, concerning the abundance fluctuations across both species and communities. Endorsed by the agreement with the observed phenomenology, our analysis provides insights on the properties of microbial interactions, and suggests their sparsity as a necessary feature to balance the emergence of different patterns.

翻译：近几十年来，宏观生态学识别了微生物群落的丰度与多样性的广域模式，并提出了若干潜在解释。然而，这些进展并未伴随对背后动态过程的充分理解。特别是，宏基因组样本中丰度波动被发现具有相关性，但通过适当模型重现这一现象仍是一项未决课题。本文针对这一问题展开研究，指出物种相互作用是解释该现象的必要机制。具体而言，我们探讨了在种群模型中引入相互作用的多种可能性，并确认Lotka-Volterra常数是一种成功的假设形式。我们设计了一种贝叶斯推断算法，通过获取相互作用常数集合，使模型能够比现有最优方法更好地复现实验相关性分布。重要的是，该模型仍能重现此前文献中检测到的、关于物种与群落层面丰度波动的单物种实验性宏观生态模式。基于与观测现象的一致性，我们的分析揭示了微生物相互作用的特性，并提出其稀疏性是平衡不同模式涌现的必要特征。