DOI: 10.6084/m9.figshare.30416476.v1
title: Global Fusarium wilt suitability and environmental predictors derived from remote sensing and Earth system models

description: This repository provides <b>global raster datasets</b> and <b>modelling outputs</b> used to assess the suitability and potential establishment of Fusarium wilt (FW) under current and future climate conditions.It includes:<b>Environmental and host-related variables</b> derived from <b>remote sensing (RS) data</b>.An <b>ensemble species distribution model (SDM)</b> output based solely on <b>RS data</b> for the present.<b>Suitability and range projections</b> derived from four <b>Coupled Model Intercomparison Project Phase 6 (CMIP6)</b> <b>Earth System Models (ESMs)</b> and their multi-model ensemble.<b>Repository contents</b><b>1) R_variables.7z</b>This file contains the global environmental and host-related predictor variables derived from remote sensing data that were used as inputs for the species distribution modelling. Each raster layer was harmonized to 1 km resolution and represents long-term bioclimatic, topographic, edaphic, or vegetation conditions.The dataset includes the following variables and their data sources:<b>Bio17 – Precipitation of the driest quarter (kg·m⁻²):</b> Computed from GloH2O MSWEP v2 monthly precipitation (0.1°; 1990–2020).<b>Bio18 – Precipitation of the warmest quarter (kg·m⁻²):</b> Derived from MSWEP v2 precipitation (0.1°; 1990–2020) combined with CHELSA v2.1 monthly temperature to identify the warmest quarter.<b>Bio24 – Annual mean soil moisture (m³·m⁻³):</b> Derived from ESA-CCI Surface Soil Moisture v06.1 product (1 km; 2000–2020).<b>Bio27 – Soil moisture seasonality (%):</b> Derived from ESA-CCI Surface Soil Moisture v06.1 product (1 km; 2000–2020).<b>Bio32 – Annual mean land surface temperature (K):</b> Derived from MODIS Terra/Aqua monthly land surface temperature products (MOD11C3 and MYD11C3, Collection 6.1; 0.05°; 2003–2022).<b>Bio33 – Annual mean land surface temperature diurnal range (K):</b> Computed from MODIS Terra/Aqua land surface temperature products (MOD11C3 and MYD11C3, Collection 6.1; 0.05°; 2003–2022).<b>Bio46 – Evapotranspiration seasonality (%):</b> Calculated from the MODIS/Terra 8-day gap-filled evapotranspiration product (MOD16A2GF, Collection 6.1; 500 m; 2000–2022).<b>Bio60 – Highest monthly wind speed (m·s⁻¹):</b> Derived from CHELSA v2.1 monthly near-surface wind speed data (1 km; 1990–2018).<b>Clay – Clay content (g·kg⁻¹):</b> Extracted from SoilGrids v2.0 at 250 m resolution, aggregated for 0–30 cm soil depth using weighted means.<b>Cropland – Fraction of cropland per pixel (%):</b> Obtained from Landsat-based 4-year average global cropland fraction maps (3 km; 2003–2019).<b>Mean SIF of the driest quarter – Solar-Induced Chlorophyll Fluorescence (SIF):</b> Derived from harmonized and downscaled SCIAMACHY and GOME-2 datasets (0.05°; 2002–2018), averaged for the driest quarter based on MSWEP v2 precipitation data.<b>Silt – Silt content (g·kg⁻¹):</b> Extracted from SoilGrids v2.0 at 250 m resolution, aggregated for 0–30 cm soil depth using weighted means.<b>Slope – Slope (°):</b> Derived from GMTED2010 global elevation dataset (1 km resolution).These data serve as global explanatory layers for developing SDM of FW risk and are intended to support future research on <b>plant disease modelling</b>, <b>environmental suitability analyses</b>, and <b>climate change impact assessments</b> on soilborne pathogens.<b>2) RS_modelling_output.7z</b>This file contains the <b>global suitability map</b> for FW derived solely from the <b>RS-based SDM</b> using the remote sensing variables described above. The raster represents suitability values (0–1) for the <b>present period</b>, providing an empirically derived baseline without climate model projections.<b>3–6) CMIP6 ESM-based projections</b>These four files contain the outputs of the ensemble SDM applied to environmental and host-related variables simulated by individual <b>CMIP6 ESMs:</b><b>GFDL-ESM4_modelling_output.7z</b><b>IPSL-CM6A-LR_modelling_output.7z</b><b>MPI-ESM1-2-HR_modelling_output.7z</b><b>UKESM1-0-LL_modelling_output.7z</b>Each archive includes:<b>Global suitability rasters</b> for the <b>historical baseline</b> and <b>three future periods</b> (2015–2040, 2041–2070, and 2071–2100), each for three <b>climate change scenarios</b>:<b>SSP126</b> (green development, low forcing),<b>SSP370</b> (regional rivalry, moderate forcing),<b>SSP585</b> (fossil-fueled development, high forcing).<b>Categorical range-change maps</b> showing areas of suitability <b>gain or loss</b> based on binary presence–absence transitions.In these <b>range-change maps</b>, each pixel value represents the <b>level of agreement among scenarios</b> on the direction of change:−6, −5, −4: Areas that lose suitability in three, two, or one scenario(s).−3: Areas of persistent presence, where all scenarios agree on maintaining suitability.0: Areas of persistent absence, where all scenarios agree on unsuitability.1, 2, 3: Areas that gain suitability in one, two, or three scenarios.<b>7) ESMs_ensemble_modelling_output.7z</b>This file contains the <b>multi-model ensemble</b> that integrates outputs from the four individual ESMs.<br>For each climate scenario and time window, suitability values were <b>averaged across the four models</b>, producing a comprehensive ensemble prediction.<br>The file also includes the <b>corresponding range-change maps</b> derived from the combined ensemble, indicating global consensus on projected <b>gains and losses</b> in FW suitability.<b>Data format and metadata</b><b>Format:</b> GeoTIFF, compressed in .7z archives.<b>Coordinate Reference System:</b> EPSG:4326 (WGS84 — World Geodetic System 1984).<b>Spatial resolution:</b> 1 km.<br>