Affiliation:
1. Department of Life Sciences, Ben‐Gurion University of the Negev Beer‐Sheva Israel
2. The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem Jerusalem Israel
3. National Institute of Biotechnology in the Negev, Ben‐Gurion University of the Negev Be'er Sheva Israel
4. The Goldman Sonnenfeldt School of Sustainability and Climate Change, Ben‐Gurion University of the Negev Be'er Sheva Israel
Abstract
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.