Community-wide herbivory measurements
We recorded foliar herbivory for each species following the methods
outlined in Chen et al. (2019). For each plant species in a
quadrat, we randomly sampled 25 leaves from at least five individuals.
For those species with less than 25 leaves, we sampled all leaves
available. To quantify herbivory severity, we predefined six damage
categories according to how much of the leaf was consumed: 0%,
<5%, 5–10%,
10–20%, 20–50% and >50% (Scherber et al. , 2010;
Ness et al. , 2011). For needlelike leaves (e.g. , fromKobresia myosuroides ), all incidences of leaf wounding were
placed into the <5% category as field observations revealed
that insect predators only ever consumed the tip of the leaves.
We calculated the overall herbivory percentage (ranging from 0% to
100%) for each plant species by first multiplying the number of leaves
in each damage class by the median removal value for that class
(i.e. , 0%, 2.5%, 7.5%, 15.0%, 35.0% and 75.0%). Obtained
values were then summed across all damage classes and the sum divided by
the total number of leaves (including undamaged ones) in a sample.
We calculated community-wide herbivory (hereafter, CWM herbivory) as the
summed species’ (herbivory) means weighted by their abundance (Chenet al. , 2019) using the following formula:
\begin{equation}
CWM\ herbivory=\frac{\sum_{i=1}^{S}{a_{i}h_{i}}}{\sum_{i=1}^{S}a_{i}}\nonumber \\
\end{equation}where S is the total number of plant species in a quadrat,ai is the aboveground biomass of plant speciesi , and hi is the herbivory on plant
species i .
Using the method proposed by Lepš et al . (2011) to decompose
effects into their direct and indirect components (i.e. ,
“intraspecific variability” and “turnover”, respectively), we
subtracted species’ turnover effects from the total CWM herbivory to
obtain the intraspecific variability in herbivory among sampling sites.
Turnover effects represent the expected CWM herbivory (based on
constituent host plant species) independent of the actual herbivory
measured in a given plot (Chen et al., 2019).