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).