Data analysis
We used generalized linear mixed-effects models (GLMMs, with the package lme4, version 1.1-23 (Bates et al. 2014)) in R, version 3.2.3 (R Core Team 2019) for all models, unless specified otherwise (see Table 1 for final models). We used the emmeans package (Lenth 2020) in R to calculate estimated marginal means and conduct post-hoc tests. Chi-square tests and p-values were calculated using the Anova function in the package car (Fox & Weisberg 2019).
We calculated species richness (i.e. alpha-diversity) of fungal pathogens in plant roots and plant communities. First, total fungal richness was calculated as the sum of OTUs present in a taxa/sample matrix of all fungi that was rarefied to the minimum size per sample (1,500 sequences). Rarefaction ensures that all samples can be compared at the same sequencing depth, but did result in the loss of 15 individual plants (out of 490) from the dataset (those which had <1,500 sequences). Pathogen richness was calculated by subsetting the rarefied fungal taxa/sample matrix to include only the taxa identified by FUNGuild as pathogenic to plants. To calculate pathogen relative abundance in plant roots, we summed the total number of fungal pathogen sequences found in each individual plants’ roots and divided that by the total number of fungal sequences in that root. To calculate pathogen relative abundance at the community level, we took the ratio of the sum of the total number of fungal pathogen sequences and the sum of all fungal sequences found in the roots in each community.
To study partner sharing, we calculated the species-level interaction network metrics normalized degree and closeness centrality (weighted closeness) using the R package bipartite (Dormann et al. 2011). Normalized degree measures the proportion of all possible partners in the community that an organism interacts with, which we used as an indicator of generality. Normalized degree was calculated for each 1) plant and 2) pathogen inhabiting the plant roots. Closeness centrality measures the degree to which a plant species mediates sharing/indirect interactions among the community. It was calculated on a unipartite (plant only) projection of the bipartite (plant-fungal) network, whereby plant species are connected if they share a fungal partner. High closeness centrality indicates that a plant species has a short path to any other plant species in the unipartite network, such that it tends to share partners with many species or with particular species that share with many others. Given this sharing, we used closeness here as an indicator of how much influence a plant could have in a community in terms of its ability to indirectly affect other species by spreading pathogens; a negative equivalent to previous findings that closeness confers indirect benfits in belowground mutualisms (Tylianakis et al. 2018). Normalized degree of plants was modeled using a normal error distribution, pathogen relative abundance with a binomial distribution and normalized degree of pathogens and closeness centrality with a Gamma distribution. We used the logit link function in the binomial models and the log link function in all other models.
To test whether exotic plants accumulate fungal taxa that are known to cause plant disease (Question 1), we quantified how pathogen richness and relative abundance changed as a function of the proportion of exotics planted into the community (ranging from 0-100%), our soil treatment (home or away) and their interaction as fixed effects. To account for non-independence of plants in the same mesocosm community, we added mesocosm (pot 1-80) nested in plant community (community 1-20) in the models as random effects. To test how the generality of interactions and the potential for interactions to be shared changed along the exotic plant gradient (Questions 1 & 2), we modeled normalized degree and closeness centrality (respectively) as a function of the same predictors described above. As an additional check, we ran all models again, replacing the proportion of exotics planted with the proportion realized at the end of the experiment to be sure both model results corroborated. We did this for each model that tested the effects of the proportion of exotics planted, and results were consistent between these two approaches (Table S2).
To understand differences in the way native vs. exotic plants interact with pathogens, independently of the extent of invasion, we ran a separate analysis focusing on only the 40 plant communities containing 100% natives or 100% exotic plants. We tested how pathogen richness and relative abundance, the generality of interactions (i.e. normalized degree) and the relative efficiency with which plants could potentially spread pathogens (i.e. closeness centrality) varied as a function of plant provenance, our soil treatment and their interaction as fixed effects. We added mesocosm nested in plant community and plant species as random effects in these models.
To test whether natives vs. exotics differ in the proportion of fungal pathogens they share (Question 2), we modeled pathogen sharing between native and exotic plants in each mesocosm community as a function of the proportion of exotics planted, the soil treatment and their interaction, with random factors as described above (and in Table 1). We excluded communities containing both 100% native and 100% exotic plants from this analysis (i.e. retained communities with 25, 50 and 75% exotic) because there could be no sharing in those communities. To calculate the proportion of pathogens that were shared (or not) between native and exotic plants in a community, we calculated the number of pathogenic fungal taxa that were identified from 1) only native plant roots, 2) only exotic plant roots, or 3) both native and exotic plant roots in each mesocosm pot. We then calculated the proportion of pathogenic fungal taxa that were shared (between natives and exotics) in a mesocosm by dividing the number of shared pathogen OTUs by the total pathogen OTU richness in each mesocosm.
To determine whether exotics shared pathogens with natives more than would be expected by chance in each community, we simulated a null model showing how much sharing (as calculated above) we would expect along the full realized exotic gradient (i.e. proportion of exotic plants at the end of the experiment) if native and exotic plants were equivalent in their potential to share pathogens. We ran that model 1,000 times, randomizing the provenance of plants and randomly shuffling pathogen reads across plant individuals, to calculate a distribution of expected sharing. We then used a Monte Carlo simulation to compare that null model data to our observed molecular data of shared pathogens along the realized exotic gradient (more details about the null model can be found in Supplementary methods). We used polynomial regression and fitted a curvilinear line to model the relationship between sharing and exotic dominance and modeled the deviation in sharing from the mean of the null expectation along the realized exotic gradient for each mesocosm.
To test whether pathogen sharing between native and exotic plants correlates with exotic plant impact (Question 3), we calculated the relative interaction intensity index (RII, Armas, et al. 2004) for native and exotic plants and quantified its relationship with the proportion of pathogens shared between native and exotic plants in the community. The relative interaction intensity index quantifies the relative change in plant biomass from what was initially planted to what was realized at the end of the experiment, using the formula ((realized proportional biomass-planted proportional biomass)/(realized proportional biomass+planted proportional biomass)). We used this formula to measure changes in total native and total exotic biomass separately in each mesocosm. RII is bounded by 1 and -1, so it controls for extreme values, with values above zero reflecting realized proportional growth that was higher than what was originally planted and values below zero reflecting realized proportional growth that was lower than what was originally planted.
To test whether plant-soil feedbacks in monoculture predict feedbacks in a community (Question 4), we evaluated whether plant-soil feedbacks (i.e. the difference between biomass of a given species in home vs. away soils) differed between native vs. exotic plants (i.e. interaction effect between soil treatment and plant provenance) and whether this difference was influenced by whether a plant was grown in monoculture or a community. To test this, we modeled 1) plant biomass from the monoculture experiment as a function of plant provenance, the soil treatment and their interaction as fixed effects with plant species as a random effect and 2) plant biomass from the communities as a function of plant provenance, the soil treatment and their interaction as fixed effects with plant species and mesocosm nested in plant community as random effects. While others have presented response ratios to evaluate plant-soil feedbacks (Brinkman et al. 2010), we were unable to calculate a response ratio in communities due to differential plant mortality across treatments.