Analysis
We calculated productivity as the aboveground plant biomass for each
plant community. A small proportion (~4%) of plants
died during the experiment, of which almost all (~94%)
were Asclepias syriaca . To determine if the death of plants was
influenced by inoculation treatments, we modeled the survival ofA. syriaca as a function of soil and leaf microbe inoculation. We
found no effect of inoculation treatments on survival. We excluded the
plant communities with dead individuals, leaving 316 out of 360 plant
communities that were included in further analyses. During the sampling,
we found a different phenotype of Solidago canadensis in 5 of out
the 316 pots, the analysis with and without the 5 pots show consistent
results. Therefore, we present here the results of analyses including
these phenotypically distinct individuals.
We used linear mixed-effect models and analysis of variance (ANOVA)
(packages ‘lme4’ and ‘lmeTest’ in R version 4.2.0; ) to test effects of
plant diversity, soil microbial inoculation, leaf microbial inoculation
and all interactions among these three explanatory variables on plant
productivity. To control the effect of plant community composition on
plant productivity, we included the composition as a random term in the
model . To specifically test the effect of microbial inoculations on
plant productivity and plant diversity-productivity relationships, we
contrasted the marginal means of plant productivity between microbial
treatments using Tukey’s HSD post hoc test; and compared the slopes of
diversity-productivity relationships between microbial treatments using
the emtrend function in the ‘emmeans’ package (Lenth et al.
2018). We further calculated net biodiversity effects, also known as
overyielding, for all pots with more than two plant species and
partitioned the biodiversity effect into selection and complementarity
components according to Loreau and Hector (2001). We tested the effect
of microbial inoculations on net biodiversity effects, selection
effects, and complementarity effects using Tukey’s HSD post hoc test.
Since we did not observe any significant effect of low-concentration
leaf microbial inoculum on productivity, we only present comparisons
regarding high-concentration microbial inoculum when discussing leaf
microbial effect although all data were included in our analysis.
We used DADA2 to identify the amplicon sequence variants (ASV) of
bacteria and fungi . For bacterial 16S sequences, we calculated the
quality scores (Q) of the reads to inspect the sequencing quality, then
we removed 30 and 28 nucleotides from the start of reads and kept total
sequence length at 180 and 220 for forward and reverse sequences
respectively. This trim method removed the primers and low-quality
nucleotides resulting in reads of fixed length. However, the ITS2 region
of fungi is highly variable in length and this variation reflects the
biological differences between fungal taxonomic groups. Thus, we only
removed the primers from the ITS amplicon sequences but did not trim
them to fixed length. After that, we inferred the error rates of
nucleotide substitution and assigned sequences to ASVs following DADA2
default parameters . The paired read ends were merged with a minimum
overlap of 12 nucleotides, and non-target sequences and chimeras were
removed. Bacterial and fungal taxonomy was assigned by comparison with
the SILVA SSU r138 and ITS v8.3 databases respectively .
The total number of reads of both bacterial and fungal ASVs varied
greatly among samples which could bias the estimation of sample
diversity. Thus, we rarefied the community data using the ‘vegan’ R
package (Oksanen, J., et al. 2022). For soil microbial communities
collected at the end of the experiment, we firstly used generalized
linear models (Poisson family with log link function) to test for
effects of plant diversity, soil and leaf treatments on microbial ASV
richness (alpha diversity) and then applied distance-based redundancy
analysis on the Euclidean distance matrix of Hellinger-transformed
community data to test the effects of these treatments on microbial
community composition (beta diversity). Additionally, we used
Permutational multivariate analysis of variance (PERMANOVA) to quantify
the variation in microbial community composition explained by plant
diversity, soil, and leaf inoculation respectively. We did not consider
the interactions among plant diversity, soil and leaf inoculation in
microbial analysis due to a limited number of microbial samples.
Finally, we conducted ANCOM-BC analysis to identify the microbial taxa
with differential abundance between inoculated and non-inoculated soils
using the ‘ANCOMBC’ R package .