Drivers of assemblage composition
We tested which factors, namely host identity, spatial distance between
sampled trees, abiotic conditions (maximum and minimum temperature,
temperature standard deviation, light intensity and soil moisture
measured per BC) and biotic factors (tree basal area per BC, tree
height, distance to forest edge and the tree composition per BC),
affected fungal endophyte community composition.
To do this we used a generalized dissimilarity modelling (GDM) approach.
This flexible approach enabled us to simultaneously incorporate
categorical (i.e. host), linear (e.g. climate), compositional (i.e. tree
compositional dissimilarity), and spatial data (i.e. geographic
distance) predictors of endophyte composition into one analysis (Ferrieret al. , 2007). GDM is a nonlinear extension of matrix regression,
which has specifically been designed to deal with two types of
nonlinearity commonly encountered in biological data: 1) the curvilinear
relationship between ecological or spatial separation and the observed
compositional dissimilarity, and 2) non-stationarity, i.e. differences
in the rate of compositional turnover along environmental or spatial
gradients (Ferrier et al. , 2007; Fitzpatrick et al. ,
2013).
The default of three I-spline basis functions (knots) per predictor
variable was used in all GDM analyses (Ferrier et al. , 2007); and
backwards selection was used to determine how many variables to retain
in each of the final models (Williams et al. , 2012). The sum of
the coefficients per I-spline represents the maximum amount of variation
explained by a particular variable, and can be used to determine
variable importance (Ferrier et al. , 2007). Since host identity
explained the majority of the endophyte compositional dissimilarity for
the full dataset (see Results), we repeated the GDM analyses per
individual host species using the same approach as above.