Moderators (predictors)
We investigated different categories of moderators, including absolute
latitude and elevation, as well as climate, productivity (annualPET),
topography (TRI, topoWET), leaf size and average temperature during the
experiments. Additional local moderators included available potential
prey (abundance of herbivores) for predators and abundance of predators
on experimental leaves for herbivores.
To create the climate gradients, we projected separately precipitation
and temperature variables in multivariate space using principal
component analysis (PCA) (Romero et al ., 2018). Scores of the
first axis of PCA for temperature variables
(PC1temperature, which explained 78% of the total
variance) represented a gradient spanning from higher temperature
variability to warming and lower temperature variability. Conversely the
first axis of PCA for precipitation variables
(PC1precipitation: 64% of the total variance)
represented a rainfall (dry to wet) gradient, at global (Fig. 1) and
local scales (Figs. S2-S3). PC1precipitation included
mean annual precipitation (bio12), precipitation of driest month
(bio14), index of the degree of water deficit below water need (aridity)
and precipitation seasonality (coefficient of variation) along the year
(bio15) (Table S1). The only variable denoting rainfall variability
(bio15) did not contribute much to the PCA1 variance (Fig. 1, Fig. S2).
Therefore, the PC1precipitation represented a rainfall
gradient, which varied from dry (negative values) to wet conditions
(positive values) (Fig. 1, Fig. S2). Sites defined as being climatically
more suitable were those characterized by higher
PC1precipitation scores. PC1temperatureincluded mean annual temperature (bio1), mean diurnal range (bio2),
temperature seasonality (bio4) and temperature annual range (bio7). The
signs of original PC1temperature scores were multiplied
by -1 to change their graphic presentation for the ease of
interpretation, matching with precipitation trends, thus producing a
gradient from temperature variability (negative values) to warming and
lower temperature variability (positive values). Sites defined as being
thermally more variable were those characterized by higher intra-annual
(e.g., bio4, bio7) or diurnal (e.g., bio2) variation in temperature
(Fig. 1, Fig. S3).
Some moderators were collinear (Fig. S4), and were thus removed from the
models based on their variance inflation factor (VIF) (Zuur et
al., 2010). The removed moderators were Latitude, topoWET and
AnnualPET. All remaining moderators had VIF < 2. The
best-fitting linear models were then obtained through backward
selection; the best models retained were those to present the smallest
Akaike Information Criteria corrected for small sample sizes, AICc
(∆AICc>2). Null models (intercept-only effects, i.e., no
moderators included) were contrasted with models containing moderators
plus the intercept.