DOI: 10.5061/dryad.9cnp5hqwn
title: 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

description: 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
 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 overfitting. In soil-only models, Clay content
 (CLY), Nitrogen Total Organic (NTO), and Soil Organic Carbon (SOC) were
 important across modelled species, with their relevance varying by
 ecoregion. Our findings have significant implications for understanding
 the interplay between climate, soil, and plant distribution within diverse
 ecoregions. By highlighting the crucial role of climate in large-scale
 models, this study serves as a foundation for developing more accurate
 predictions of plant distributions, ultimately improving model accuracy
 for biodiversity assessments.
description: # 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   * `bio02.tif` – Annual Mean Diurnal Range   * `bio04.tif` –
 Temperature Seasonality   * `bio05.tif` – Maximum Temperature of Warmest
 Month   * `bio06.tif` – Minimum Temperature of Coldest Month   *
 `bio12.tif` – Annual Precipitation   * `bio13.tif` – Precipitation of
 Wettest Month   * `bio14.tif` – Precipitation of Driest Month   *
 `bio15.tif` – Precipitation Seasonality   ### Soil Variables *
 `BDW_000_005_EV_N_P_AU_TRN_N_20230607.tif` – Bulk Density *
 `CEC_000_005_EV_N_P_AU_TRN_N_20220826.tif` – Cation Exchange Capacity   *
 `CLY_000_005_EV_N_P_AU_TRN_N_20210902.tif` – Clay Content   *
 `DES_000_200_EV_N_P_AU_TRN_C_20190901.tif` – Depth of Soil *
 `SOC_000_005_EV_N_P_AU_TRN_N_20220727.tif` – Soil Organic Carbon   *
 `NTO_000_005_EV_N_P_AU_NAT_C_20140801.tif` – Nitrogen Total Organic *
 `PTO_000_005_EV_N_P_AU_NAT_C_20140801.tif` – Total Phosphorus *
 `Relief_twi_3s.tif` – Topographic Wetness Index (TWI) ## Code/Software
 Modeling and data processing were conducted using **R version 4.3.0**. The
 script includes steps for: * Preprocessing environmental rasters *
 Preparing occurrence and background data * Calibrating individual SDMs *
 Creating ensemble models Key packages used in the workflow: `terra`,
 `raster`, `sf`, `MASS`, `covsel`, `dismo`, `flexsdm`, `biomod2`