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 dynamical processes behind them. In particular, abundance fluctuations of different species are found to be correlated, both across time and across communities in metagenomic samples. Reproducing such correlations through appropriate population models remains an open challenge. The present paper tackles this problem and points to sparse 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 a successful ansatz. For this, we design a Bayesian inference algorithm to extract sets of interaction constants able to reproduce empirical probability distributions of pairwise correlations for diverse biomes. Importantly, the inferred models still reproduce well-known single-species macroecological patterns concerning abundance fluctuations across both species and communities. Endorsed by the agreement with the empirically observed phenomenology, our analyses provide insights on the properties of the networks of microbial interactions, revealing that sparsity is a crucial feature.

翻译：过去数十年间，宏观生态学已识别出微生物群落在丰度与多样性方面的宏观格局，并提出了若干潜在解释。然而，这些进展并未伴随对其背后动态过程的充分理解。具体而言，在不同物种的丰度波动中，跨时间维度及宏基因组样本跨群落之间均存在相关性。如何通过恰当的种群模型重现此类相关性，仍是一项有待解决的前沿挑战。本文针对该问题展开研究，指出稀疏的物种相互作用是解释此类现象的必要机制。具体地，我们探讨了将相互作用纳入种群模型的多种可能性，并确认Lotka-Volterra常数可作为有效理论框架。为此，我们设计了贝叶斯推断算法，用于提取能够重现不同生物群落中成对相关性经验概率分布的相互作用常数集合。重要的是，所推断模型仍能准确重现涉及物种间与群落间丰度波动的经典单物种宏观生态学格局。基于与经验观测现象的一致性，本研究揭示了微生物相互作用网络的特性，表明稀疏性是其中的关键特征。