Statistical analyses were performed with R version 3.1.3 (R Core Team 2015) using the nlme (Pinheiro et al. 2012), lme4 (Bates et al. 2012), effects (Fox 2003) and multcomp (Hothorn et al. 2008) packages. Appropriate models were determined by the nature of the response variables. For the impact of P. marginata exposure on continuous plant measurements, ANOVA was used with change in the plant metric as the response variable and treatment, species and their interaction as explanatory variables. Oviposition choice data were analysed using a linear-mixed effect model (LMM). Other data were analysed with GLMMs using a Poisson distribution for count data and a binomial distribution for binary response variables. Each analysis began by fitting all relevant explanatory variables, interactions and random factors (block or plant IDs) in a maximal model. Model simplification then proceeded by a backwards deletion of non-significant terms until further removals led to a significant (p < 0.05) increase in deviance. This was assessed by comparing the model with and without the term in question using log-likelihood ratio tests for LMMs and χ2 values for GLMMs. Plots of model residuals against fitted values were visually inspected for normal distribution, homogeneity of variance and the presence of influential outliers. Results showing significant treatment effects were further investigated using Tukey HSD (Honest Significant Differences) post-hoc tests to identify differences between treatment means.

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