COS 123-5 - Ecological network analysis of peatland microbial communities

Thursday, August 10, 2017: 9:20 AM
B117, Oregon Convention Center
Sheryl L. Bell1,2, Montana Smith1,2, Erik A. Hobbie3 and Kirsten S. Hofmockel2,4, (1)Pacific Northwest National Laboratory, Richland, WA, (2)Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, (3)Earth Systems Research Center, University of New Hampshire, Durham, NH, (4)Pacific Northwest National Laboratory

Soil microorganisms are the drivers of organic matter decomposition and the organic material stored in boreal wetlands is considered particularly vulnerable to atmospheric and climatic change. Soil bacteria, archaea and fungi interact and mediate the cycling of carbon (C) and nitrogen (N) through their metabolism and extracellular enzymes, but studies often link only the abundance of individual taxa to nutrient pools and chemical parameters. The co-occurrence of diverse microbial assemblages could be useful in understanding their role in ecosystem function. To address the community interactions of peatland microbial communities, fresh peat was collected in June and September 2013 at the Marcell Experimental Forest, Minnesota, USA from six replicate locations adjacent to the Spruce and Peatland Responses Under Climatic and Environmental Change Experiments (SPRUCE). Cores were collected from paired hummocks and hollows to address the effect of topography and sectioned into 10-cm depth fractions. The microbial composition was analyzed by 16S and ITS amplicon sequencing, and assayed for extracellular enzymes, microbial biomass and ergosterol.


Microbial community composition differed with depth (p= 0.001), and therefore samples were grouped into “Surface” and “Deep” communities to capture overall variability when calculating the co-occurrence of taxa. The Surface and Deep networks were constructed from strong significant correlations. The two resulting networks were similar in overall size, density, average node degree, and betweenness. Modularity and transitivity were also similar, and were higher for both, than for random networks of the same size, indicating strong hierarchical clustering. However, the most highly connected families in each network differed greatly. Of the top 25 most highly connected families in each network, only three were in common [uncultured bacteria 4-29 (order Nitrospirales), unidentified phylum Parcubacteria members, and TM146 (order Solirubrobacterales)]. Archaea (Methanomicrobiales, Methanobacteriaceae, and unidentified Crenarchaeota) were highly connected only in the Deep network. No fungi were among the top-connected nodes in either network and the most highly connected fungi in each network differed. Nodes were clustered into modules for each network, and ongoing research aims to identify highly interrelated sub-communities and will correlate family abundances with biogeochemical data.