Pedology and plant provenance can improve predictions of species distributions of the Australian native flora: a calibrated and validated modelling exercise on 5,033 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 SDMs’ performance for 5,033 Australian plant species, selected to represent the largest phylogenetic diversity of native terrestrial vascular flora. 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 = 1,246), Temperate (n = 1,936), and Tropical (n = 1,211). 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 varia..., , # Pedology and plant provenance can improve predictions of species distributions of the Australian native flora: a calibrated and validated modelling exercise on 5,033 species

Dataset DOI: [10.5061/dryad.9cnp5hqwn](10.5061/dryad.9cnp5hqwn)

## File Descriptions

This dataset supports a species distribution modeling (SDM) study and contains 17 environmental predictor variables, including bioclimatic variables from the WorldClim database and soil variables from the Soil and Landscape Grid of Australia (SLGA). All raster files are provided in .tif (GeoTIFF) format and can be opened with standard GIS software such as QGIS or ArcGIS, or processed in R using packages like terra or raster. In addition, the dataset includes:

* A Supplementary Information (SI) PDF describing additional methodology and results.
* A README.md file that documents the file contents.
* R scripts used in the modeling workflow.

### Bioclimatic Variables (`bioXX.tif`)

* `bio01.tif` – Annual Mean Temperature  
* `bio...,