Appendix S1 Null models are used to generate artificial distributions of a metric (here PD and FD) under a well-defined null hypothesis in order to highlight deviations from random patterns. Here we used a null model to control for trends in TD and standardize PD and FD by highlighting deviations from randomly expected values, given the observed taxonomic diversity of each sampled vegetation plot. The observed metric was calculated in each plot, following which 9999 independent randomizations were performed in order to breaking down the phylogenetic and functional relationships among species. Specifically randomization was applied on the inter-species phylogenetic/functional distance matrix, leaving the species by site matrix unchanged (Null Model 1s in Hardy 2008). This manipulation allows one to maintain both the species list and the abundance structure of the community constant (i.e., TD), but effectively breaks any link between trait values/phylogeny and abundances. The metrics were computed at each null model run, creating a null distribution of 10,000 values (including the observed value). Finally, each observed metric was compared to its null distribution by calculating the probability P that the observed value is lower/greater than expected. We then calculated the Standardized Effect Size (SES; e.g., Swenson and Enquist, 2009; Jung et al., 2010) for each site and metric as follows: (Observed value – Mean of randomized values) / Standard deviation of randomized values Positive SES values indicate greater FD/PD than expected by chance, while negative SES values suggest smaller FD or PD than expected. The randomization algorithm we used allows deviations of FD and PD from random expectations unrelated to TD patterns to be quantified. As expected, this approach yielded almost identical results to those obtained using regression residuals (Spearman’s rank correlation coefficients (ρ) = 0.95 and 0.94 respectively; Fig. S1). Fig. S1 | Scatterplot showing the correlation between regression residuals (RES) and standardized effect sizes (SES) for (A) FD and (B) PD. Hardy, O. J. (2008). Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. Journal of Ecology, 96, 914-926. Swenson, N.G. & Enquist, B.J. (2009). Opposing assembly mechanisms in a Neotropical dry forest: Implications for phylogenetic and functional community ecology. Ecology, 90, 2161–2170. Jung, V., Violle, C., Mondy, C., Hoffmann, L. & Muller, S. (2010) Intraspecific variability and trait-based community assembly. Journal of Ecology, 98,1134–1140. Appendix S2 In a “random loss scenario” in which all species have an equal probability of being excluded from the community, low-abundance or rare groups are more likely to disappear from the species pool through chance alone. We therefore tested whether species more sensitive to Iceplant invasion in our study tended to be rare or have low vegetation cover. We calculated two metrics of species rarity using data from non-invaded plots - mean vegetation cover and presence frequency (proportion of plots in which the species is found) – and then related these two metrics to the species sensitivity values (slope parameter β from linear models relating individual species abundances to iceplant cover). We found no evidence to support the “random loss scenario”, as both rarity metrics showed no correlation with sensitivity (Fig. S2). Fig. S2 | Relationship between species sensitivity to invasion (β from linear models relating individual species abundances to iceplant cover) and two metrics of species rarity - mean vegetation cover (A) and frequency of plots in which the species is found (B). Both metrics were calculated using data from non-invaded plots. Appendix S3 A high degree of correlation (Spearman’s rank correlation coefficients (ρ) = 0.89) was found between the two metrics used to assess differences in community phylogenetic structure: Mean Distance to Native Species (MDNS) and Weighted Mean Distance to Native Species (WMDNS). Similarly to MDNS, WMDNS also showed a clear positive trend in relation to iceplant abundance (Fig. S2), although in this case the relationship was non-significant. Fig. S3 | Scatterplot showing the relationship between Weighted Mean Distance to Native Species (WMDNS; log transformed) and iceplant vegetation cover (%). Circles represent mean WMDNS values for each level of iceplant cover, with bars corresponding to standard errors. The solid line shows the fit of a linear model. Non-invaded plots (left of dotted line) are shown as an empty circle, while filled circles represent mean values for invaded plots. The standard error for non-invaded plots was calculated on a random subsample of 30 sites, and then averaged across 1000 iterations. This was done in order to provide comparable estimates of uncertainty between invaded (58) and non-invaded plots (191). Figure S4 Fig. S4 | Plot comparing the number (mean ± SE) of therophyte species in plots characterized by varying degrees of iceplant abundance: none (open circle), low (<50% iceplant cover) and high (>50% iceplant cover). There were significantly more therophyte species in non-invaded plots (left of dotted line) than in plots with high iceplant abundance (letters refer to independent groups according to Tukey HSD test).