Principal Component Analysis
To examine the association between plant infochemicals and host location
and selection in closely related host-flexible parasitoids a principal
component analysis was performed using the relative area of the
chromatogram peaks, a measurement of abundance for each compound,
obtained from the GC/MS and parasitoid-host association data. Four
separate principal component analyses were run in R using the package
“FactoMineR” (Lê, Josse, & Husson, 2008) and visualized using the
package “Factoextra” (Kassambara & Mundt, 2017). The first principal
component analysis included all Eucalyptus leaves collected to
examine differences in phytochemistry between the two species ofEucalyptus sampled (globulus vs. sp. 1). The second
principal component analysis included damaged and undamaged flushEuc. globulus leaves(the age class preferred by the beetles) to
explore variation in plant chemoprofiles when damaged, suggesting a
specific herbivore-induced response. Only Euc . globuluswere examined because there were not enough samples to adequately assess
herbivore-induced responses in the other Eucalyptus species. The
third principal component analysis included damaged leaves corresponding
to unparasitized beetles, with beetles identified by their distinctive
larval coloration: yellow body with a prominent black dorsal strip inParopsisterna cloelia ; and black body with orange/yellow lateral
stripes in Pst. agricola . This subset of samples permitted
examination of the relationships between infochemicals and the observed
herbivore assemblage. The fourth principal component analysis included
damaged leaves corresponding to parasitized beetles from which different
species of Eadya were reared, in order to examine the
relationship between infochemicals from the plant and Eadya host
selection
To minimize the effects of pseudo-replication in the principal component
analysis in the parasitoids and beetles, the dataset was collapsed to
only include one sample per Eadya species for each tree. To
confirm separation in each of the four principal component analyses,
one-way ANOVAs were performed using the first and second principal
components, with Tukey post-hoc tests for the parasitoids and beetle
analyses. To determine phytochemical compounds which may be utilized as
infochemicals by Eadya , compounds were ranked in descending order
by the absolute value of PC1 – PC2 variable loadings. The R script for
the PCA and ANOVAs, as well as all data files, can be found on Figshare
(www.figshare.com10.6084/m9.figshare.17105768).