The overall relationship between climatic variables and VIS-NIR
reflectance
Climate variables were the first two principal components (PCs) from a
Principal Components Analysis (PCA) of 9 climatic niche variables.
Climate PC1 was higher in species that inhabit in warmer, drier climatic
conditions and correlated most strongly with mean temperature (r= 0.98), solar radiation (r = 0.88) and water vapour pressure
(r = 0.87) (Fig. 1 left panel). Climate PC2 was higher in species
that inhabit areas with higher mean precipitation (r = 0.78) and
low precipitation seasonality (r = -0.77) (see Tables S1,2 for
the full PCA results).
Climate was a significant predictor of butterfly reflectance for all
body regions. In the multivariate phylogenetic regression, mean total
VIS-NIR reflectance was predicted by PC1 (F6,334= 12.58, P < 0.001), PC2 (F6,334= 3.66, P = 0.002), and size (F6,334 =
7.44, P < 0.001) (other variables excluded from the
model with P > 0.2). Phylogenetic signal was high
(λ = 0.76). Post-hoc PGLS models that used the mean
reflectance of each body region as a response variable showed that for
all body regions, species from colder environments (lower PC1) had lower
reflectance, which corresponded to a gradual decrease in the reflectance
of butterfly assemblages across latitudinal gradients (Table 1; Fig.
2,3). PC2 was also significant for all body regions (except for the
ventral thorax), with species from high precipitation environments
having lower reflectance than species from drier environments. Size
predicted the reflectance of both dorsal and ventral entire wing:
smaller species tended to show higher mean entire wing reflectance than
larger species. No other variables remained significant. The results
from the models excluding sexually dimorphic males showed the same
trends except that PC2 became non-significant for all ventral regions
(Supplementary materials, Tables S3,4). Phylogenetic signals were
consistently high for all body regions (all λ >
0.72).
The relationship between overall (VIS-NIR) reflectance and climate was
evident for both VIS and NIR wavelength ranges but driven more by NIR
reflectance for the thorax and basal wing. Results for VIS and NIR
reflectance separately were essentially consistent with the overall
reflectance models (see Supplementary materials, Tables S5,6, Figs. S3,4
for the full results). PC1 predicted VIS and NIR reflectance for all
body regions; however, the relationship was more robust (larger
coefficients) for NIR reflectance for the thorax and basal wing, but not
the entire wing (dorsal and ventral). PC2 also predicted the reflectance
of most body regions except for ventral thorax and basal regions in the
VIS range, and the ventral thorax in the NIR range. Size did not predict
VIS reflectance of butterflies but predicted NIR reflectance of the
entire wing (both dorsal and ventral); smaller butterflies showed higher
NIR reflectance than larger butterflies. The direction of this
relationship (i.e. whether coefficients had negative or positive values)
was always the same as the overall reflectance models for all
significant predictors.
We additionally examined whether NIR reflectance show adaptive variation
after accounting for its high correlation with VIS reflectance (see
methods). Analyses using residuals from the linear regression between
NIR and log(VIS) as response variables confirmed the importance of NIR
reflectance of the thorax and basal wing in the overall correlations
between reflectance and climate. The multivariate phylogenetic
regression model showed that PC1 (F6,334 = 4.63,P < 0.001), PC2 (F6,334 range=
2.34, P = 0.03, and size (F6,334 = 7.69,P < 0.001) were significant predictors of NIR residuals
(Table 1). Post-hoc PGLS revealed that the residuals of ventral
thorax and basal wing regions were higher in species from hotter
environments (with higher PC1; Fig. 4). PC2 predicted dorsal thorax,
basal wing, and ventral basal wing regions. Size predicted dorsal and
ventral entire wing regions. The trends of the significant relationships
were the same as the overall reflectance models (Table S7). All other
variables were non-significant with Padj> 0.1.