manually checked and verified in reference to species known to exist at the site. Separate taxa tables were created based on trnL amplicons and each of the Asteraceae and Poaceae ITS amplicons. Samples with fewer than 550, 1000, and 100 sequences were removed from taxa tables based on trnL, Asteraceae ITS, and Poaceae ITS amplicons, respectively. We calculated the relative abundance of individual plant genera in each sample using the trnL sequence counts. Because the trnL gene yields limited taxonomic resolution for the Asteraceae and Poaceae, we replaced the total relative abundances of taxa (mostly unknown genera) within these two families with normalized relative abundances of genera determined using the ITS sequence data. Plant traits All leaf and root traits were measured using standard protocols (Cornelissen et al., 2003). Briefly, we measured specific leaf area, specific root length, leaf dry matter content and root dry matter content by weighing and scanning the fresh leaf and root samples. The samples were then oven dried at 60 °C for 48 h and their dry weights measured. The scanned digital images were analyzed in WinRhizo (Reagent Instruments Inc., Ville de Québec, QC, Canada) to determine leaf areas, root lengths and root diameters. Shoot and root N and C contents from the plants and the field sample plant communities were measured on an Elementar Vario elemental analyzer (Langenselbold, Germany). In both cases, plant material was freeze-dried and thoroughly homogenized prior to measurement. Soil characteristics Soil characteristics were measured as in Orwin et al. (2010). pH was measured using a ratio of 1 g fresh soil: 2.5 ml dH2O. Dissolved inorganic N, individual ions (NO3-N , NH4-N), and net N mineralization were assessed using 1 M KCl extracts, and dissolved organic N was assessed
as implemented in the ‘ecodist’ package and compared the explanatory power of the model with and without the addition of plant community dissimilarity as a predictor variable. In these models, each soil variable was transformed using log or inverse transformations where necessary to approximate a normal distribution, and they were standardized prior to calculating Euclidian distances. MRM was implemented with rank (i.e. Spearman) correlations, and the “best” models containing only soil variables were derived by first including all soil variables and using backwards elimination until all predictors explained significant levels of variation in the response dissimilarities. Results and Discussion The effect of plant species identity on soil communities Overall, the mesocosm soils contained expectedly diverse communities (Fig. S1A). Soil fungal communities were primarily composed of Ascomycota [43% of internal transcribed spacer (ITS) sequence reads, on average], Basidiomycota (31%), and Mucoromycota (21%); bacterial communities were primarily composed of Acidobacteria (31% of 16S rRNA gene reads, on average), Proteobacteria (20%), and Verrucomicrobia (16%); protistan communities were primarily composed of Rhizaria (26%), Amoebozoa (25%), Alveolata (22%), and Stramenopiles (16%); and metazoan communities were primarily composed of Nematoda (33%), Arthropoda (28%), and Annelida (15%; Fig. S1B). The structure of these communities was similar to those found in other temperate grasslands (Leff et al., 2015; Bates et al., 2013; Wu et al., 2011). Plant species identity explained differences in the overall composition of soil fungal (R2 = 0.33; P < 0.001), bacterial (R2 = 0.27; P = 0.02), protistan (R2 = 0.32; P < 0.001), and metazoan (R2 = 0.31; P < 0.001) communities (Fig. 1A). Further, these plant species effects
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