Statistical analysis
We used mixed-effect ANOVA to
examine the effects of region of origin (Italy vs. Fennoscandia),
maternal environment (greenhouse vs. Italy field site) and their
interaction (fixed effects), and population nested within region of
origin and its interaction with maternal environment (random effects) on
germination proportion 12 weeks after seed maturation. The analysis was
based on line means. We included in the analysis only populations with
germination proportions available for both the greenhouse and the
Italian maternal environments (43 populations in total). Seeds from the
Swedish field site could not be included due to low seed production of
Italian populations. The response variable was arcsine square-root
transformed prior to analysis to obtain a normal distribution of
residuals. Because of heteroscedasticity, variance was allowed to vary
among regions of origin. Statistical significance of fixed explanatory
variables was determined by F-tests with type III Sums of Squares and
Kenward-Rogers adjustment for degrees of freedom, while significance of
random factors was tested using Likelihood Ratio Tests between full and
reduced models. The analysis was performed using the R package ‘nlme’.
Additionally, we tested the effect of maternal environment, population
and their interaction on germination proportions of Fennoscandian
populations collected at the two field sites. We included in the
analysis only populations with germination proportions available for
both field maternal environments (18 populations in total). The response
variable was arcsine square-root transformed and statistical
significance of the effect variables was determined by F-tests with type
III Sums of Squares.
To assess the association between
climate at the site of origin and germination proportion up to 12 weeks
after seed maturation, we first
conducted a scaled and centered principal component analysis (PCA) among
the five climatic variables for each of the two regions separately using
the R function prcomp from the ‘base’ package. The first three
Principal Components (PCs) cumulatively explained 99% of the total
variation in both regions (Table S3 ). We then used these PCs as
independent variables in multiple regressions with mean germination
proportion of populations (based on line means) as response variable. We
chose to use principal components rather than the original climatic
variables as predictors in the multiple regression to avoid the problem
of collinearity among the chosen climatic variables. In addition, to
assess spatial variation in seed dormancy within regions, we tested the
relationship between population mean germination proportions, and
latitude, longitude and their interaction using linear models analyzed
separately by region. These analyses were restricted to estimates of
seed germinability up to 12 weeks after seed maturation. Estimates of
dormancy obtained soon after seed maturation are arguably the estimates
most directly related to dormancy of seeds in natural environments due
to post-dispersal environmental effects and their interaction with the
maternal environment on seed dormancy release (Postma et al.2016; Coughlan et al. 2017; Buijs et al. 2020). Hence, the
correlation between environmental conditions at the site of origin and
estimates of seed dormancy is likely to decrease with time after seed
maturation in our experiments.
Seeds produced in the greenhouse by plants in cohort 1 had markedly
higher germination proportions than seeds produced by the other cohorts,
most likely due to absence of the vernalization treatment. However,
removal of cohort 1 from the dataset did not affect the statistical
significance nor markedly change effect sizes, and the analyses
presented below include cohort 1.
All statistical analyses were conducted in R version 3.4.0 (R Core Team,
2017).