DOI: 10.5281/zenodo.17651613
title: CHELSA Bioclimatic and Environmental Predictors for Species Distribution Models (OneSTOP Project – Task 5.1): SSP1-2.6 Scenario (2041–2070)

description: This dataset provides bioclimatic and environmental predictor variables used in Species Distribution Models (SDMs) developed within the OneSTOP Project (Task 5.1). The original climatic data are based on the CHELSA v2.1 BIOCLIM+ dataset, which provides high-resolution (~1 km) bioclimatic variables derived from downscaled and bias-corrected climate data.

For future projections, CHELSA v2.1 climate layers were obtained for all five available CMIP6 Global Circulation Models (GCMs): GFDL-ESM4, UKESM1-0-LL, MPI-ESM1-2-HR, IPSL-CM6A-LR, and MRI-ESM2-0) and corresponding to the relevant SSP scenario. To produce a consistent climatic baseline that matches land-cover projections in the Chen et al. (2022) dataset, all available GCMs were averaged to generate a single ensemble mean for each time period and scenario. This ensemble approach reduces individual model biases and aims to provide a robust representation of mid- and late-century climatic conditions for SDMs. Raster data has been internally scaled and reprojected (bilinear method) in the terra R package.

All raster layers are provided as GeoTIFF (float) files in the coordinate reference system EPSG:6933, WGS 1984/NSIDC EASE-Grid 2.0 Global (Cylindrical Equal Area projection). The datasets correspond to one of the following temporal windows and SSP scenarios:

- Historical Baseline: 1981–2010 (used for model training)
- Future Mid-Century (2041–2070): SSP1–2.6, SSP3–7.0, SSP5–8.5 (used for model projection)
- Future Late-Century (2071–2100): SSP1–2.6, SSP3–7.0, SSP5–8.5 (used for model projection)

These predictor/projection datasets are intended for use in SDM workflows to assess climatic suitability and potential species distributions under changing environmental conditions. They were prepared to support the OneSTOP project's modeling of Invasive Alien Species (IAS) and integration with land-cover projections (Chen et al., 2022).

Climatic variables were obtained from CHELSA v2.1 (https://www.chelsa-climate.org/datasets), including the full set of bioclimatic variables describing temperature and precipitation regimes. In addition to the standard BIOCLIM variables, the predictor set includes BIOCLIM+ metrics such as growing-season length, growing-season precipitation, growing-season mean temperature, growing-degree days above 0 °C, 5 °C, and 10 °C, and net primary productivity, providing an expanded representation of climate conditions relevant to species' ecological requirements.

---

Chen, G., Li, X., & Liu, X. (2022). Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios. Scientific Data, 9, 125. https://doi.org/10.1038/s41597-022-01208-6
description: List of variables in the dataset




Variable
Name
Description




BIO1
Annual Mean Temperature
Mean annual air temperature.


BIO2
Mean Diurnal Range
Mean of monthly (Tmax − Tmin).


BIO3
Isothermality
BIO2 / BIO7 × 100.


BIO4
Temperature Seasonality
Standard deviation of temperature × 100.


BIO5
Max Temperature of Warmest Month
Highest monthly mean temperature.


BIO6
Min Temperature of Coldest Month
Lowest monthly mean temperature.


BIO7
Annual Temperature Range
BIO5 − BIO6.


BIO8
Mean Temperature of Wettest Quarter
Average temperature during the wettest quarter.


BIO9
Mean Temperature of Driest Quarter
Average temperature during the driest quarter.


BIO10
Mean Temperature of Warmest Quarter
Average temperature during the warmest quarter.


BIO11
Mean Temperature of Coldest Quarter
Average temperature during the coldest quarter.


BIO12
Annual Precipitation
Total annual precipitation.


BIO13
Precipitation of Wettest Month
Highest monthly precipitation.


BIO14
Precipitation of Driest Month
Lowest monthly precipitation.


BIO15
Precipitation Seasonality
Coefficient of variation of monthly precipitation.


BIO16
Precipitation of Wettest Quarter
Total precipitation in the wettest quarter.


BIO17
Precipitation of Driest Quarter
Total precipitation in the driest quarter.


BIO18
Precipitation of Warmest Quarter
Total precipitation in the warmest quarter.


BIO19
Precipitation of Coldest Quarter
Total precipitation in the coldest quarter.


GDD0
Growing Degree Days >0 °C
Annual sum of degree-days above 0 °C.


GDD5
Growing Degree Days >5 °C
Annual sum of degree-days above 5 °C.


GDD10
Growing Degree Days >10 °C
Annual sum of degree-days above 10 °C.


GSL
Growing Season Length
Number of days with mean temperature above the growing-season threshold.


GSP
Growing Season Precipitation
Total precipitation during the growing season.


GST
Growing Season Temperature
Mean temperature during the growing season.


NPP
Net Primary Productivity
Modelled net primary productivity (vegetation biomass production).