The development of microbial networks is central to ecosystem functioning and is the hallmark of complex natural systems. Characterizing network development over time and across environmental gradients is hindered by the millions of potential interactions among community members, limiting interpretations of network evolution. We developed a feature selection approach using data winnowing that identifies the most ecologically influential microorganisms within a network undergoing change. Using a combination of graph theory, leave-one-out analysis, and statistical inference, complex microbial communities are winnowed to identify the core organisms responding to external gradients or functionality, and then network development is evaluated against these externalities. In a plant invasion case study, the winnowed microbial network became more influential as the plant invasion progressed as a result of direct plant-microbe links rather than the expected indirect plant–soil–microbe links. This represents the first use of structural equation modeling to predict microbial network evolution, which requires identification of keystone taxa and quantification of the ecological processes underpinning community structure and function patterns.
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Processed sequencing data have been deposited into the Dryad Digital Repository at https://doi.org/10.5061/dryad.00b1d. These data include abundance and taxonomy for archaea, bacteria, and fungi, as well as environmental data. Steps 1–3 of the winnowing pipeline have been automated online at https://winnowing.usask.ca/ and the R and Python code for steps 4–10 are available at https://github.com/sua474/winnowing-pipeline-merged. A full port of the winnowing pipeline into a Python library is currently underway.
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We thank Carl Gutwin and Gurjot Bhatti for help with the infrastructure that we used to process the data. Candace Piper aided in the experimental design along with Tristrom Winsley for bioinformatic analysis. Syed Umair Aziz compiled the R and python code onto GitHub. This work is supported by a grant from the Plant Phenotyping and Imaging Research Centre (P2IRC) to BLH, EGL, KS, and SDS. P2IRC is a digital agriculture research center funded by the Canada First Research Excellence Fund (CFREF) from the Natural Sciences and Engineering Research Council (NSERC), managed by the Global Institute for Food Security (GIFS), and located at the University of Saskatchewan (U of S).
Conflict of interest
The authors declare that they have no conflict of interest.
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