AUTHOR=Farrer Emily C. , Porazinska Dorota L. , Spasojevic Marko J. , King Andrew J. , Bueno de Mesquita Clifton P. , Sartwell Samuel A. , Smith Jane G. , White Caitlin T. , Schmidt Steven K. , Suding Katharine N. TITLE=Soil Microbial Networks Shift Across a High-Elevation Successional Gradient JOURNAL=Frontiers in Microbiology VOLUME=10 YEAR=2019 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2019.02887 DOI=10.3389/fmicb.2019.02887 ISSN=1664-302X ABSTRACT=

While it is well established that microbial composition and diversity shift along environmental gradients, how interactions among microbes change is poorly understood. Here, we tested how community structure and species interactions among diverse groups of soil microbes (bacteria, fungi, non-fungal eukaryotes) change across a fundamental ecological gradient, succession. Our study system is a high-elevation alpine ecosystem that exhibits variability in successional stage due to topography and harsh environmental conditions. We used hierarchical Bayesian joint distribution modeling to remove the influence of environmental covariates on species distributions and generated interaction networks using the residual species-to-species variance-covariance matrix. We hypothesized that as ecological succession proceeds, diversity will increase, species composition will change, and soil microbial networks will become more complex. As expected, we found that diversity of most taxonomic groups increased over succession, and species composition changed considerably. Interestingly, and contrary to our hypothesis, interaction networks became less complex over succession (fewer interactions per taxon). Interactions between photosynthetic microbes and any other organism became less frequent over the gradient, whereas interactions between plants or soil microfauna and any other organism were more abundant in late succession. Results demonstrate that patterns in diversity and composition do not necessarily relate to patterns in network complexity and suggest that network analyses provide new insight into the ecology of highly diverse, microscopic communities.