Statistical Analysis
Collinearity between predictor variables (soil and habitat structure) was accounted for by doing a principle component analysis of the two sets of variables respectively. The first two axes of the PCA for soils explained 66.55 % of the variation, the first axis 51.98 %, and the second 14.57 %. PC1 was positively associated with more sandy soil and negatively associated with clay soils (Figure S1). The first two axes of the PCA for habitat structure explained 69.28 % of the variation, first axis, 41.92, and the second 27.36 %. The first axis was positively related to increased canopy cover, leaf litter, and more complex structure.
These principle components were used to model gradients in ant diversity in response to habitat structure and soil respectively. Two further categorical variables, habitat (open vs closed) and season (hot-wet and hot-dry) were also included. Species richness and effective number of species for Shannon diversity and Simpson’s diversity (Jost, 2006) were modelled using Generalized Linear Mixed Models (GLMM), with site as the random factor and Poisson error distributions, a loglink function for richness and Gaussian error distributions, and an identity link function for Shannon and Simpson’s effective number of species. The best model was identified using a Alkaike Information Criterion, the lowest being the best. Models that were < Δ2AIC were also included. The relative contribution of marginal (fixed, R2m) and conditional (fixed and random, R2c) in the variation explained were calculated for each model (Nakagawa & Schielzeth, 2013).
Compositional variation was modelled using a model-based multivariate approach where we fitted multivariate generalized linear models (GLMs) to ant species abundance data in the R package “mvabund” (Wang, Naumann, Wright, & Warton, 2012) . Predictors included were similar to those included into the univariate GLMMs. This model-based approach deals with confounding mean–variance relationships typical of count data that are zero inflated (Warton, Thibaut, & Wang, 2017). Likelihood ratio statistics for each taxon were summed, that results in a community-level measure for each predictor. Correlation across species was accounted for by using the PIT-residual bootstrap method to derive p-values by resampling 999 rows of the data set (Warton et al., 2017). Predictors were included in the model individually to explore the marginal (variation explained by the predictor on its own) deviance explained.
Model fit for both univariate and multivariate models were evaluated by visually inspecting residual plots for deviations from normality, heteroscedasticity, systematics patterns, and autocorrelation. Ordination of ant assemblages was done using Bayesian ordination and regression (Hui, 2016). To aid visualization, samples for the two seasons were pooled.
Responses of ant assemblages to gradients in soil and habitat structure were modelled using Threshold Indicator Taxa Analysis (TITAN) from the “TITAN2” package (Baker & King, 2010). This method uses standardizedz -scores obtained from indicator species analysis (Indicator Value) to detect the species-specific change points, and the direction of response along a gradient (Baker & King, 2010; Costas, Pardo, Mendez-Fernandez, Martinez-Madrid, & Rodriguez, 2018). Increasing responses (z+) are distinguished from those decreasing (z- ) at a specific change point (Baker & King, 2010). TITAN also estimates indicator reliability and the proportion of times that a taxon is given the same classification through bootstrapping, as well as uncertainty around the location of individual taxa and community change points (Baker & King, 2010).