Data analysis
All data manipulations and statistical analyses were performed in R
version 4.1.3 (R Core Team, 2021). Every analysis was carried out for
two groups: for the total ruderals and for the non-native ruderals.
Using the historical Rallarvägen surveys, we analyzed 1) region-wide
species richness as a function of the observational year, and 2) the
mean EIV-T as a function of the observational year and first year of
observation. Using the 2021 Rallarvägen survey, we analyzed 3) species
richness as a function of distance to the railroad, distance to the E10,
and soil temperature variables and 4) the Z -score abundances as a
function of the ruderal species richness. Finally, using the MIREN trail
survey, we modeled 5) species richness as a function of elevation and 6)
the elevational maximum as a function of the first year of observation.
Models consisting of a dependent variable with count data (number of
species) were analyzed using generalized linear models (functionglm , poisson or quasipoisson distribution),
otherwise linear models were used (function lm ). We identified
the best fitting models using the Akaike Information Criterion with a
correction for smaller sample sizes (AICc) from the AICcmodavg package
(Mazerolle, 2020). For significant interactions consisting of two
continuous variables, we centered one independent variable at its sample
mean to make interpretation easier (Schielzeth, 2010). In multiple
regression analyses, we checked for possible multicollinearity of
independent variables by calculating the variance inflation factor (vif)
using the vif function from the car package (Fox & Weisberg,
2011). We considered results to be significant when p ≤ .05 and
marginally significant when p < .10.
To visualize the dissimilarities in vegetation composition between
subregions and observational years, we conducted a Principal Coordinates
Analysis (PCoA, = Multidimensional scaling, MDS). PCoA is an ordination
technique to explore and visualize dissimilarities in species
composition data by focusing on distances. The more similar the
compositions are, the closer together they occur in the plot. Distances
were calculated with the function vegdist from the Vegan package
(Oksanen, 2022), and from this distance matrix the principal coordinate
scaling was computed with the pcoa function from the ape package
(Paradis, 2022). We used the Jaccard distance which is defined as:Jaccard distance = 2B/(1+B) , where B is the Bray-Curtis
dissimilarity. Bray-Curtis dissimilarity usually focuses on the
dissimilarity of abundance, but by specifying binary = TRUE in
the function it calculates distances based on presence-absence data. The
obtained dissimilarity is a number between 0 and 1 – this value is 0
when two communities share all the same species, and 1 when they do not
share any species.