Pedology and Plant Provenance Can Improve Species Distribution Predictions of Australian Native Flora: A Calibrated and Validated Modeling Exercise on 5033 Species

Species distribution models (SDMs) are valuable tools for assessing species' responses to environmental factors and identifying areas suitable for their survival. The careful selection of input variables is critical, as their interactions and correlations with other environmental factors can affect model performance. This study evaluates the influence of climate and soil variables on the performance of SDMs for 5033 Australian terrestrial vascular plant species, representing the largest phylogenetic diversity of native flora assessed in such an analysis. Using an ensemble of correlative models, we assessed the predictive performance of climate and soil variables, individually and in combination, across four distinct ecoregions: Desert (n = 640 species), Mediterranean (n = 1246), Temperate (n = 1936), and Tropical (n = 1211). Our results demonstrate that on a continental scale, climate variables have a greater influence on plant distributions than soil variables. Although incorporating soil and climate variables enhanced model performance in some ecoregions, our results indicate that relying solely on small-scale variables such as soil may increase the likelihood of underfitting. The most influential predictor variables in the models varied across ecoregions and between specialist and generalist species. Mean annual rainfall (bio1) was consistently a strong climate predictor variable across ecoregions, but other climate variables became more important when analyses were restricted to ecoregion-specific species (i.e., specialists). Soil organic carbon (SOC) was the most important soil variable in the Temperate and Tropical ecoregions across generalist and specialist species. In the Mediterranean ecoregion, clay content (CLY) became more important than SOC when analyses were restricted to ecoregion-specific species, whereas nitrogen total organic (NTO) was consistently the strongest predictor soil variable for plants in the Desert. Our findings have significant implications for understanding the interplay between climate, soil, and plant distribution within diverse ecoregions. This study serves as a foundation for developing more accurate SDM predictions.