Analysis of leaf trait variation
We first evaluated if traits were either normally or log-normally
distributed using the ‘fitdist’ function in the ‘fitdistrplus’ R package
(Delignette-Muller & Dutang, 2015), with the highest log-likelihood
scores indicating the best-fit data distribution. For traits that were
normally distributed, we calculated descriptive statistics as means and
standard deviations (SD), while medians, median and SD values were
calculated for log-normal traits. We also calculated and present trait
ranges and coefficients of variation (CV) for each trait. In addition we
tested for differences in mean trait values as a function of planting
rows, using analysis of variance (ANOVA). These descriptive statistics
and ANOVAs were complemented by variance partitioning analyses, which
were used to identify the primary sources of trait variation in our
dataset. These analyses entailed fitting linear mixed models (using the
‘lme’ function in the ‘nlme’ R package (Pinheiro, Bates, DebRoy, Sarkar,
& Team, 2017)) to each trait individually (where n =45 in all
cases), where an intercept was the only fixed effect, and plant identity
within sample row identity were included as nested random effects. The
proportion of trait variation explained by each nested random effect, as
well as the proportion of variation unexplained by the factors
considered here, was then estimated using the ‘varcomp’ function in the
‘ape’ R package (Paradis, Claude, & Strimmer, 2004).
We then assessed the influence of soil compaction on both individual
traits and multivariate trait syndromes. First, we fit separate mixed
models for each trait individually, where trait values were predicted as
a function of soil bulk density (included as a fixed effect), while
accounting for plant identity nested within sampling row (included as
nested random effects). We then used a Principal Component Analysis
(PCA) to test if multivariate leaf trait syndromes of ‘Chardonnay’
varied as a function of planting row/compaction or vine identity. Our
PCA included four LES traits (i.e., A mass,R mass, leaf N, and LMA), two traits derived from
light response curves (i.e., Φ and LLCP), and one trait related
to light interception (i.e., leaf area). The relationship between
individual traits and each PCA axis was evaluated using the ‘dimdesc’
function in the ‘FactoMineR’ R package (Le, Josse, & Husson, 2008).
Then, we used a permutational multivariate analysis of variance
(PerMANOVA) based on Euclidean distances and 10,000 permutations, to
test if multivariate trait syndromes vary as a function of planting row
(reflecting the soil bulk density gradient), plant identity, and a
row-by-plant interaction term. Based on our PerMANOVA results, we also
visualized our PCA biplot with 95% confidence ellipses surrounding the
data points within each sample row. All multivariate analyses were
implemented in the ‘vegan’ R package (Oksanen et al., 2017).
Finally, we evaluated bivariate trait relationships among ‘Chardonnay’
leaves using Pearson correlation tests. However, one of the main goals
of our analysis was to test for the presence of an intragenotype LES in
Chardonnay, and evaluate if these relationships differed from the global
LES defined by Wright et al. (2004). For this, we used standardized
major axis (SMA) regressions to evaluate the slope of the bivariate
trait relationships that exist among four LES traits measured in our
study vines, including A mass,R mass, LMA, and leaf N. All of these SMA
regressions had a sample size of n =45, and were implemented with
the ‘sma’ function in the ‘smatr’ R package (Warton, Duursma, Falster,
& Taskinen, 2012). Then, we evaluated if these SMA slopes of the
intragenotype ‘Chardonnay’ LES differed from the trait relationships
found across a functionally and phylogenetically diverse set of plants
species globally. This analysis entailed merging our ‘Chardonnay’ data
with the GLOPNET dataset (i.e., the dataset used in the original LES
analysis of Wright et al. (2004)), and testing for statistical
differences in the SMA slope of each trait-trait relationship between
the ‘Chardonnay’ vs. GLOPNET, using the ‘slope.test’ function of the
‘smatr’ R package (Warton et al., 2012).