|Title||Plant functional group drives the community structure of saprophytic fungi in a grassland biodiversity experiment|
|Author(s)||Francioli, Davide; Rijssel, Sophie Q. van; Ruijven, Jasper van; Termorshuizen, Aad J.; Cotton, Anne; Dumbrell, Alex J.; Raaijmakers, Jos M.; Weigelt, Alexandra; Mommer, Liesje|
|Source||Plant and Soil (2020). - ISSN 0032-079X|
Plant Ecology and Nature Conservation
Laboratory of Nematology
|Publication type||Refereed Article in a scientific journal|
|Keyword(s)||Decomposition - Fungal saprophytes - Grasslands - Plant functional group - Plant species richness - Root traits|
Aims: Saprophytic fungi are important agents of soil mineralization and carbon cycling. Their community structure is known to be affected by soil conditions such as organic matter and pH. However, the effect of plant species, whose roots provide the litter input into the soil, on the saprophytic fungal community is largely unknown. Methods: We examined the saprophytic fungi in a grassland biodiversity experiment with eight plant species belonging to two functional groups (grasses and forbs), combining DNA extraction from plant roots, next-generation sequencing and literature research. Results: We found that saprophyte richness increased with plant species richness, but plant functional group richness was the best predictor. Plant functional group was also the main factor driving fungal saprophytic community structure. This effect was correlated with differences in root lignin content and C:N ratio between grasses and forbs. In monocultures, root traits and plant functional group type explained 16% of the variation in community structure. The saprophyte taxa detected in mixed plant communities were to a large extent subsets of those found in monocultures. Conclusions: Our work shows that the richness and community structure of the root-associated saprophytic fungi can largely be predicted by plant functional groups and their associated root traits. This means that the effects of plant diversity on ecosystem functions such as litter decomposition may also be predictable using information on plant functional groups in grasslands.