Data analysis
We evaluated changes in plant, insect, and soil microbial community and
functional composition in Wyoming in 2019 and Montana in 2020 in several
ways. First, we assessed community level richness across plant species,
insect families, and soil microbial OTUs. To calculate richness, we used
the codyn package (Hallett et al. 2016) in R version 3.6.2 (R Core Team
2019) for plants and insects, and Qiime2 for soil microbial OTUs
(level-7 in Qiime2). Second, we used Bray-Curtis dissimilarity matrices
(calculated for plants and insects with the vegan package (Dixon 2003)
and for microbes with Qiime2) and non-metric multidimensional scaling
(NMDS) with two axes to visualize multidimensional community and
functional composition of plants, insects, and microbes. For plants, we
focused on changes in non-brome composition by excluding B.
arvensis from B. arvensis gradients and B. tectorum fromB. tectorum gradients. We chose plots with very little cover ofB. arvensis in B. tectorum plots, and B. tectorumcover in B. arvensis plots. For functional level responses across
plants, insects, and microbes, we assigned each plant species to a
functional group (C3 annual grass, C3perennial grass, C4 perennial grass, cactus, forb,
sub-shrub/shrub), each insect family to a feeding guild (leaf chewing
herbivore, parasitoid, pollen/nectar eating herbivore, predator, sap
sucking herbivore, other herbivore) (La Pierre and Smith 2016), and each
soil microbial OTU to an ecological function (Faprotax prokaryotic
environmental function database; OTUs not found in the database were
left as unassigned and OTUs could be assigned to multiple functional
groups (Louca et al. 2016)). Third, we determined how each plant,
insect, and microbial functional group associated with invasion.
For all analyses, we modeled each site, year, and invasive species
separately. To assess invasion gradient establishment, we evaluated the
relationship between absolute percent brome cover and relative percent
brome cover using Type III mixed-model ANOVAs with a random block effect
(lme4 and lmerTest package (Bates et al. 2015, Kuznetsova et al. 2017),
using Satterthwaite’s method (Satterthwaite 1941)). We used the same
model set-up to compare invasion cover across sites within each invasion
level, using Tukey’s pairwise comparisons (Tukey 1977) with
Benjamini-Hochberg’s correction for multiple comparisons (Benjamini and
Hochberg 1995). We then used the same model set-up to assess how
community richness and each functional group related to invasion
abundance. To assess compositional differences among invasion levels for
both community and functional level analyses, we used permutational
multivariate ANOVA (PERMANOVA) with a random effect of block included.
For significant effects, we calculated pairwise comparisons
(RVAideMemoire package (Hervé 2022)).
We then determined how insect herbivory and total biomass changed with
invasion in Montana in 2021 and 2022. For this, we tested the effect of
invasion level, species status (native or invasive), and their
interaction on total plant herbivory using Type III ANOVA with a random
effect of block included. We also tested this for leaf level herbivory
damage, but the results were very similar, so we present only on total
plant herbivory. Last, we used the same model set-up to assess the
relationship between invasion abundance and total insect biomass for all
4 study years.
For all regression analyses, we visually assessed plots of the residuals
and Autocorrelation Function/Partial Autocorrelation Function to look
for evidence of nonlinearity and autocorrelation, respectively, but we
did not find evidence of violations of either assumption in any of our
results. To test the assumption of homoscedasticity, we used Levene’s
test for equality of variances (Levene 1960). We assessed normality of
the residuals of all response variables using Shapiro-Wilk,
Anderson-Darling, Cramer-von Mises, and Kolmogorov-Smirnov tests as part
of the Olsrr package (Hebbali 2020), transforming data when necessary to
achieve approximate normality and homoscedasticity. Throughout this
study, we use α = 0.05, but we report results with 0.05 < p
< 0.1 as marginally significant.