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Nematodes are an active part of complex soil food webs on golf courses, with some members promoting plant growth, while others are pathogenic or neutralists. The artificial, sand-based rootzone mixtures of putting greens, the most intensely managed areas of a golf course, are especially prone to nematode damage. A better understanding of the interactions of nematodes with soil microbes is key to developing improved turf management strategies. The coupling of amplicon sequencing with network analysis provides a way of better understanding which taxa may be closely associated, allowing hypothesis generation to learn more about how nematodes interact with soil microbes. We performed weighted gene correlation network analyses on bacteria, fungi, and bacteria with nematodes and fungi with nematodes collected from the soil of roughs, fairways, and putting greens of three cool-season turfgrass golf courses on Martha's Vineyard, Massachusetts. Rhodoplanes spp. were found in many bacterial modules, suggesting they may be a common species. Many nematodes formed positive correlations with known nematode antagonizing microbes. Among five nematode trophic groups, the carnivorous nematodes were most connected to both bacteria and fungi, suggesting these nematodes may have previously overlooked interactions with soil microbes. Consensus eigengene networks were highly preserved among management areas on each golf course for both the bacteria and fungi, showing conserved meta-modules despite management differences. The results of this work provide deeper insight into a unique, complex perennial ecosystem on golf courses that could be leveraged for future investigations on these relationships and eventually to improved turf health and disease management in the future. To our knowledge this study is the first use of network analysis to explore the relationship of the turf-associated bacterial and fungal phytobiomes with nematodes.

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Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.