Analyses of trait variation in greenhouse and field
conditions
We used data from three vegetative and two reproductive traits to
analyse the drivers of intraspecific variation in greenhouse and field
conditions. Vegetative traits were biomass, SLA and RSR (the latter only
measured in greenhouse conditions), and reproductive traits were
probability of flowering and fecundity. Biomass was estimated for all
greenhouse and field individuals using leaf measurements and an equation
obtained for a subset of plants (Appendix S3). Probability of flowering
was modelled as a binary variable with data from the flowering vs.
non-flowering plant status. Total inflorescence length was used as a
proxy for fecundity, as we found a strong correlation between total
inflorescence length and seed production (conditional
R2 = 0.77; Appendix S3). In a preliminary analysis of
field data, we found generally weak correlations among traits (Appendix
S3). Thus we did not systematically consider trait covariation when
analysing the sources of trait variation. However, the correlation
between biomass and fecundity was moderately strong, so reproductive
traits were analysed by controlling for biomass. This allowed us to
assess size-independent reproductive investment (see below).
To analyse the effects of source and exposure environment on traits in
the greenhouse, we applied 1) Linear Mixed Models (LMM) to plant
biomass, SLA, fecundity and RSR and 2) Generalized Linear Mixed Models
(GLMM) with a binomial error for probability of flowering (see details
on Appendix S3). For each trait, we constructed a full model with four
source environmental drivers (rotated components for Aridity,
Temperature and Vegetation cover, and the binary variable Mowing), Water
and Light treatments, interactions between environmental drivers and
treatments, and Population as a random effect (Table S3). Full models
for biomass, probability of flowering and fecundity included control
biomass as a covariate. For a comparison of the role of genetic
differentiation vs. plasticity, we assessed whether the effects of two
source environmental drivers (Aridity and Vegetation Cover) were higher,
similar to, or lower than the effects from their corresponding exposure
treatments (Water and Light) and their interactions.
To test for the effects of environmental drivers on traits in field
populations, we applied 1) LMMs to biomass, SLA and fecundity, and 2)
GLMM with a binomial error distribution for probability of flowering
(see details on Appendix S3). We constructed full models including the
four source environmental drivers. To account for the possible influence
of range (native vs. non-native), the models included the effect of
range and its interaction with each environmental driver (Table S4). We
added Population and Plot nested within Population as random effects.
For probability of flowering and fecundity, we included biomass as a
covariate.
Full models of the analyses with either greenhouse or field data were
compared with all possible model subsets using the Akaike Information
Criterion corrected for finite sample sizes (AICc) and
the AICc weights (Burnham & Anderson 2002, Johnson &
Omland 2004). We focused on the best AICc models, since
they had high support and parameter values were overall consistent
across competing models (see Appendix S3, Table S5, S6). Finally, we
evaluated the utility of observational datasets to predict genetic
differentiation. Genetic differentiation was considered predictable if
the presence and direction of source environment effects on traits were
the same in greenhouse and field conditions, and unpredictable
otherwise. For probability of flowering and fecundity, we also assessed
whether excluding the covariate biomass from the original analyses
modified our evaluation of the predictability of genetic
differentiation.