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：过去几十年，宏观生态学已经识别出微生物群落丰度与多样性的宏观尺度模式，并提出了一些可能的解释。然而，这些进展并未同步带来对潜在动态过程的全面理解。特别是，在宏基因组样本中观察到的丰度波动存在相关性，但通过适当模型复现这些相关性仍然是一项未完成的任务。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。具体来说，我们探讨了在种群模型中引入相互作用的若干可能性，并确认Lotka-Volterra常数是一种成功的假设。我们设计了一种贝叶斯推断算法，以获取能够比现有最优方法更好地再现实验相关性分布的相互作用常数组。重要的是，该模型仍能复现文献先前报道的、涉及物种和群落间丰度波动的单物种实验宏观生态模式。基于与观测现象的一致性，我们的分析提供了对微生物相互作用特性的见解，并指出其稀疏性是平衡不同模式涌现的必要特征。