Spatial variations of leaf trichomes and their relationships
with climatic factors across Eastern Asia
The mean value of trichome density in situ was 459.78 trichome
mm-2 within the range of from 325.79 to 552.38
trichome mm-2, and the coefficient of variations of
trichome density was 0.16 (Fig. 1). The first two of PCA axes of 3
geographic factors explained 57% and 27% of the total variation in
geographic gradients of 44 populations across eastern Asia (Fig. 2). The
first axis of PCA (GeoPC1) generally explained longitude
and elevation, and the second axis (GeoPC2) mainly
related to latitude. The PCA axes of 5 environmental factors accounted
for 72% and 16% of the total variation in climate. The first axis of
PCA (ClimPC1) mainly explained the variability of
precipitation and temperature-related climatic factors, including MAP,
AI, and MAT. The second axis of PCA (ClimPC2) best
explained the variability of solar radiation (i.e. MMSR).
The piecewise SEM explained 36% of the spatial variation in trichome
density. GeoPC1 and GeoPC2 showed
indirect effects on trichome density through ClimPC1 and
other leaf functional traits, including LMA, SD, SS, and VD.
ClimPC1 had both direct and indirect effects on trichome
density through four pathways (Fig. 2): one direct from
ClimPC1 to trichome density (path coefficient 0.21),
three indirect including from ClimPC1 via LMA to
trichome density (path coefficient 0.13, 0.64×0.20), from
ClimPC1 via SD to trichome density (path coefficient
0.06, 0.17×0.35), and from ClimPC1 through LMA and SD to
trichome density (path coefficient 0.08,0.64×0.37×0.35).
Moreover, leaf functional traits could have both direct and indirect
effects on trichome density. We found that LMA, SD, and SS had direct
effects on trichome density (path coefficient 0.20, 0.35, and -0.15,
respectively), whereas LMA and VD had indirect effects on trichome
density through SD (path coefficient 0.13, 0.37×0.35) and SS (path
coefficient 0.05, -0.31×-0.15), respectively (Fig. 2).
ClimPC2 and LA had no significant effects on trichome
density resulting in the removal of these pathways from the model. In
addition, we further evaluated the correlation of trichome density with
climatic factors in the ClimPC1, showing leaf trichome
density was positively correlated with AI and negatively with MAT and
MAP (Fig. 3).