Predicting pathogen susceptibility
As a demonstration of how these data can be applied, we used the data
collected on pathogen impacts on invasive plants in Table 2 and applied
a multivariate model to associate plant traits with pathogen effects
(Figure 2). We compiled trait data from the TRY database for each
invader and then selected all the traits that were available for at
least 70% of species. We then ran principal component analysis (PCA)
with imputed data for missing values using the ‘FactoMineR’ and
‘missMDA’ packages in R
(Lê et al. 2008;
Josse et al. 2016). We calculated the pathogen effect as the
relative difference in performance (e.g. survival, reproductive output,
growth) between the control and pathogen infected plants averaged across
all available studies of each plant-pathogen combination (Table S1). For
studies that tested multiple pathogens on a single invader only the most
pathogenic was used in the analysis. The pathogen effect was included as
a supplementary variable (i.e. not used in the calculation of the axes
but projected over the trait axes) to the PCA. For three species in our
dataset a quantitative pathogen effect was not available (Ambrosia
artemisiifolia, Asclepias syriaca, and Centaurea
diffusa ), so we added these as supplemental species to the PCA based on
their trait values as a demonstration of how trait data can be used to
predict pathogen susceptibility. From this framework, we can begin to
determine the trait axes that are most associated with pathogen
susceptibility. The first two axes of the PCA explained 75.9% of the
total variation. Axis 1 was significantly associated with SLA (r = 0.91;
P < 0.01) and leaf nitrogen (r = 0.66; P = 0.03). Axis 2 was
significantly associated with leaf mass (r = 0.90; P < 0.01),
vegetative height (r = 0.90; P < 0.01), and seed mass (r =
0.82; P < 0.01). Pathogen susceptibility was most strongly
associated with Axis 2 of the PCA (r = 0.55; P = 0.08). Therefore, we
can hypothesize that taller invasive plants with larger leaves and seeds
are more susceptible to pathogens, and recommend methods to test this
hypothesis by expanding and improving the model in the future (Box 2).
Increasing the availability of both trait data and pathogen
susceptibility data will help to refine this model. There is a
particular need for additional data on root traits, for which there were
none available for enough species to use in the analysis, despite the
recent creation of a root specific trait database incorporated into the
TRY database (Iversenet al. 2017). It is also important to note that this model does
not necessarily imply direct causation of the traits correlated with
pathogen effect but instead is meant to be predictive.