Co-occurrence networks offer insights into the complexity of microbial interactions, particularly in highly diverse environments where direct observation is challenging. However, identifying the scale at which local and non-local processes structure co-occurrence networks remains challenging because it requires simultaneously analyzing network structure within and between local networks. In this context, the rumen microbiome is an excellent model system because each cow contains a physically confined microbial community, which is imperative for the host's livelihood and productivity. Employing the rumen microbiome of 1012 cows across seven European farms as our model system, we constructed and analyzed farm-level co-occurrence networks to reveal underlying microbial interaction patterns. Within each farm, microbes tended to close triangles but some microbial families were over-represented while others under-represented in these local interactions. Using stochastic block modeling we detected a group structure that reflected functional equivalence in co-occurrence. Knowing the group composition in one farm provided significantly more information on the grouping in another farm than expected. Moreover, microbes strongly conserved co-occurrence patterns across farms (also adjusted for phylogeny). We developed a meta-co-occurrence multilayer approach, which links farm-level networks, to test scale signatures simultaneously at the farm and inter-farm levels. Consistent with the comparison between groups, the multilayer network was not partitioned into clusters. This result was consistent even when artificially disconnecting farm-level networks. Our results show a prominent signal of processes operating across farms to generate a non-random, similar (yet not identical) co-occurrence patterns. Comprehending the processes underlying rumen microbiome assembly can aid in developing strategies for its manipulation. More broadly, our results provide new evidence for the scale at which forces shape microbe co-occurrence. Finally, the hypotheses-based approach and methods we developed can be adopted in other systems to detect scale signatures in species interactions.

中文翻译：

---

区域过程塑造瘤胃微生物共生网络的结构


共现网络提供了对微生物相互作用复杂性的见解，尤其是在直接观察具有挑战性的高度多样化的环境中。然而，确定本地和非本地过程构建共现网络的规模仍然具有挑战性，因为它需要同时分析本地网络内部和之间的网络结构。在这种情况下，瘤胃微生物组是一个很好的模型系统，因为每头奶牛都包含一个物理限制的微生物群落，这对于宿主的生计和生产力至关重要。采用 7 个欧洲农场的 1012 头奶牛的瘤胃微生物组作为我们的模型系统，我们构建并分析了农场层面的共生网络，以揭示潜在的微生物相互作用模式。在每个农场中，微生物往往呈紧密三角形，但一些微生物家族在这些局部相互作用中的代表性过高，而另一些微生物家族的代表性不足。使用随机块建模，我们检测到一个组结构，该结构反映了共现中的功能等效性。了解一个服务器场中的组组成后，有关另一个服务器场中分组的信息比预期的要多得多。此外，微生物在农场之间高度保守的共现模式（也针对系统发育进行了调整）。我们开发了一种元共现多层方法，该方法将场级网络连接起来，以同时在场级和场间级测试规模特征。与组之间的比较一致，多层网络没有被划分为集群。即使在人为断开服务器场级网络时，此结果也是一致的。 我们的结果表明，跨农场运行的过程会产生非随机、相似（但不相同）的共现模式。理解瘤胃微生物组组装背后的过程有助于制定其操作策略。更广泛地说，我们的结果为力塑造微生物共存的规模提供了新的证据。最后，我们开发的基于假设的方法和方法可以用于其他系统来检测物种相互作用中的尺度特征。