We assessed how the aboveground, belowground, and overall biomass differed between treatments, splitting the dataset into observations from each phytometer species. We ran a mixed-effects model (GLMM) relating biomass (transformed to the log₁₀ scale) as a function of the conditioning species, the sterilization status of the soil, and the interaction between the two. The pot ID number of the conditioned training soil was used as a random intercept with a fixed mean. Conditioned soils came from individual pots in the training stage that may differ in their abiotic and biotic features, so we chose to use mixed-effects models to account for the variance in the strength of feedback due to these differences. If the random effect was not significant (i.e. individual pots from the training stage did not differ in their effect on the feedback), we ran the same formula as a generalized linear model (GLM). For GLMMs or GLMs of aboveground, belowground, and overall biomass data, the most parsimonious model was selected through comparison of AIC between full and reduced models. The type of model, whether an interaction term was used, and the R² value for each model is indicated (Table 1). To determine if any of the simple main effects were significant, we ran the same formula as an ANOVA using the linear model to calculate degrees of freedom and sum of squares error. We were particularly interested in comparing the effects of live and sterilized eastern redcedar soil to live and sterile home soils for each phytometer species. To elucidate this relationship for each phytometer species, we performed post hoc pairwise comparisons to obtain the estimated marginal means (also called least-squares means) using the emmeans package (Russell 2021).

We visualized differences in phytometer biomass between live and sterile home and redcedar soils using effects plots that were derived from the linear model fit for each set of contrasts (Ho et al. 2019; Wilschut and van Kleunen 2021). These plots illustrate simple mean differences between contrasts of interest with 95% confidence intervals using the sample data. The second part of these plots shows the modeled means and 95% confidence intervals paired with raw data points (Figure 2).