WorldClim V1 Bioclim provides bioclimatic variables that are derived from the monthly temperature and rainfall in order to generate more biologically meaningful values. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation), seasonality (e.g., annual range in temperature and precipitation), and extreme or limiting environmental factors (e.g., …

This dataset contains BIOCLIM variables (plus huss, sfcwind, rsds) which have been prepared and calculated from the original ISIMIP3b bias-adjusted climate forcing data from 5 GCM models (obtained on 2023-08-07).

For more information on the original data and its properties, please see the ISIMIP3b modelling protocol and here specifically the climate forcing section https://protocol.isimip.org/#/ISIMIP3b/31-forcing-data and Frieler et al. (2024).

The original climate forcing data (global extent, daily temporal grain) were cropped to the European extent and spatial-temporally aggregated. Here 10 year (decadal) steps were chosen as target climatology.

For each time slot (e.g. 10 years) and scenario (historical or ssps) the following 22 variables were calculated:

bioclim01 = Annual Mean Temperature
bioclim02 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
bioclim03 = Isothermality (BIO2/BIO7) (×100)
bioclim04 = Temperature Seasonality (standard deviation ×100)
bioclim05 = Max Temperature of Warmest Month
bioclim06 = Min Temperature of Coldest Month
bioclim07 = Temperature Annual Range (BIO5-BIO6)
bioclim08 = Mean Temperature of Wettest Quarter
bioclim09 = Mean Temperature of Driest Quarter
bioclim10 = Mean Temperature of Warmest Quarter
bioclim11 = Mean Temperature of Coldest Quarter
bioclim12 = Annual Precipitation
bioclim13 = Precipitation of Wettest Month
bioclim14 = Precipitation of Driest Month
bioclim15 = Precipitation Seasonality (Coefficient of Variation)
bioclim16 = Precipitation of Wettest Quarter
bioclim17 = Precipitation of Driest Quarter
bioclim18 = Precipitation of Warmest Quarter
bioclim19 = Precipitation of Coldest Quarter
huss = Average (arithmetric mean) specific humidity
rsds = Average (arithmetric mean) Surface downwelling shortwave radiation
sfcwind = Average near-surface wind speed (arithmetric mean)

---
Data properties:

All files are provided in netCDF (nc) format. The preprocessed datasets are provided as it and the author takes no responsibility for errors or misuse.

Name and description of the bioclimatic variables used in this study. (XLSX)

Bold font indicates variables considered in initial model run;†superscript indicates the two variables included in final model.

A suite of 9s resolution BIOCLIM climate surfaces for the Australian continent. This collection represents a 30 year average centred on 1990 for the standard set of 35 BIOCLIM variables.

Data are provided as zipped ESRI float grids: Binary float grids (.flt) with associated ESRI header files (.hdr) and projection files (*.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file.

Additionally a short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files.

Lineage: BIOLCIM climate surfaces for the present were calculated in the ANUCLIM 6.1 (Xu and Hutchinson, 2011) 30 year average climate surfaces for Australia (1976-2005), with elevational lapse rate correction applied over the 9s GEODATA digital elevation model (Hutchinson et al , 2008).

WorldClim is a set of global climate layers (climate grids) with a spatial resolution of about 30 arc-second (1 square kilometer). The data layers were generated through interpolation of average monthly climate data from weather stations. Variables included are monthly total precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables. Bioclimatic variables are biologically meaningful variables that are often used in ecological niche modeling (e.g., BIOCLIM, GARP). The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). The WorldClim interpolated climate layers were made using: (1) major climate databases compiled by the Global Historical Climatology Network (GHCN), the FAO, the WMO, the International Center for Tropical Agriculture (CIAT), R-HYdronet, and a number of additional minor databases for Australia, New Zealand, the Nordic European Countries, Ecuador, Peru, Bolivia, among others. Where possible, input data were restricted to records from the 1950–2000 period. After removing stations with errors, the database consisted of precipitation records from 47,554 locations, mean temperature from 24,542 locations, and minimum and maximum temperature for 14,835 locations; (2) The SRTM elevation database (aggregeated to 30 arc-seconds, 1 km); and (3) The ANUSPLIN software for interpolating noisy multi-variate data using thin plate smoothing splines. Latitude, longitude, and elevation were used as independent variables. The data can be used for mapping and spatial modeling in a GIS or with other computer programs. For a complete description, see: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.

Environmental variables used for BioClim GARP models.

Here, I provide a dataset of gridded climatic variables at a spatial resolution of 30 arc-seconds for 210 consecutive 100-year bins spanning the period from 21,000 to 0 BP. The dataset includes 19 bioclimatic and 16 ENVIREM variables (described by Title & Bemmels, 2018) commonly used in species distribution modelling. It covers the European continent and adjacent regions within the following boundaries: 32.5°W–70°E and 32.5°N–82.5°N.

For each 100-year bin, bioclimatic and ENVIREM variables were calculated based on the downscaled and debiased monthly temperature and precipitation simulations of the Community Climate System Model version 3 (CCSM3; Collins et al., 2006) as provided by the PaleoView software (Fordham et al., 2017). The downscaling procedure was based on the delta-change method (Ramirez Villejas & Jarvis, 2010). As a baseline climatic data, I used monthly temperature and precipitation grids from the CHELSA database for 1940–1989 (Karger et al., 2017).

A detailed description of the dataset, including the downscaling method applied, can be found in the Technical specification attached to this dataset.

This dataset provides a historical global reconstruction of bioclimatic indicators derived from ERA5 reanalysis on a latitude-longitude grid. These bioclimate indicators describe how the climate affects ecosystems, the services they deliver, and nature’s biodiversity. They are specifically relevant for applications within the biodiversity and ecosystem services community. The 78 indicators cover bioclimatic variables from both land and marine environments characterising surface energy, drought, soil moisture and the (near-)surface climate including wind as well as Essential Climate Variables (ECV) relevant to the biodiversity community and are based on hourly or monthly ERA5 reanalysis data. The bioclimatic indicators are widely used within the biodiversity community and have been chosen based on user requirements and consultation with stakeholders, in order to facilitate the direct use of climate information in screening analyses or in diverse downstream applications. The temporal resolution differs depending on the indicator varying between monthly, annual, and multi-annual averages. This dataset was produced on behalf of the Copernicus Climate Change Service.

Retirement Notice: This beta item will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer represents CMIP6 future projections of mean temperature during the three warmest months of the year. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: deg CCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 Bioclimate Climate ScenariosThe CMIP6 climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5.SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP2-4.5intermediate GHG emissions:CO2 emissions around current levels until 2050, then falling but not reaching net zero by 21002.0 °C2.7 °C2.1 – 3.5SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate DataWorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional InformationThe time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality IssuesEach model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica.Related LayersBioclimate 1 Annual Mean TemperatureBioclimate 2 Mean Diurnal RangeBioclimate 3 IsothermalityBioclimate 4 Temperature SeasonalityBioclimate 5 Max Temperature of Warmest MonthBioclimate 6 Min Temperature Of Coldest MonthBioclimate 7 Temperature Annual RangeBioclimate 8 Mean Temperature Of Wettest QuarterBioclimate 9 Mean Temperature Of Driest QuarterBioclimate 10 Mean Temperature Of Warmest QuarterBioclimate 11 Mean Temperature Of Coldest QuarterBioclimate 12 Annual PrecipitationBioclimate 13 Precipitation Of Wettest MonthBioclimate 14 Precipitation Of Driest MonthBioclimate 15 Precipitation SeasonalityBioclimate 16 Precipitation Of Wettest QuarterBioclimate 17 Precipitation Of Driest QuarterBioclimate 18 Precipitation Of Warmest QuarterBioclimate 19 Precipitation Of Coldest QuarterBioclimate Baseline 1970-2000

Bioclimatic parameters for Tyrol (BIOCLIM Tirol): - Annual mean temperature, unit: °C, resolution: 10m - Average sum of annual precipitation, unit: mm, resolution: 10m - Date of last frost, unit: Day of the year, dissolution: 10m - Potential evapotranspiration (calculated according to FAO56-Penmen Monteith), unit: mm, resolution: 10m Period 1991-2020 and three climate scenarios for the period 2071-2100: RCP4.5 - The 2-degree path (MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17_v1) RCP8.5 - The Middle Way (MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17_v1) RCP8.5 - The Fossil Way (ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4_v1)

Retirement Notice: This beta item will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer represents CMIP6 future projections of temperature variation over an entire year. This layer can be used to compare with recent climate histories to better understand the potential impacts of future climate change.WorldClim produced this projection as part of a series of 19 bioclimate variables identified by the USGS and provides this description:"Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year)."Time Extent: averages from 2021-2040, 2041-2060, 2061-2080, 2081-2100Units: deg CCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 Bioclimate Climate ScenariosThe CMIP6 climate experiments use Shared Socioeconomic Pathways (SSPs) to model future climate scenarios. Each SSP pairs a human/community behavior component with the traditional RCP greenhouse gas forcing from the previous CMIP5. Three SSPs were chosen by Esri to be included in the service based on user requests: SSP2 4.5, SSP3 7.0 and SSP5 8.5.SSPScenarioEstimated warming(2041–2060)Estimated warming(2081–2100)Very likely range in °C(2081–2100)SSP2-4.5intermediate GHG emissions:CO2 emissions around current levels until 2050, then falling but not reaching net zero by 21002.0 °C2.7 °C2.1 – 3.5SSP3-7.0high GHG emissions:CO2 emissions double by 21002.1 °C3.6 °C2.8 – 4.6SSP5-8.5very high GHG emissions:CO2 emissions triple by 20752.4 °C4.4 °C3.3 – 5.7While the 8.5 scenario is no longer generally considered likely, SSP3 7.0 has been included and is considered the high end of possibilities. SSP5 8.5 has been retained since many organizations report to this threshold. The warming associated with SSP2 4.5 is equivalent to the global targets set at the 2021 United Nations COP26 meetings in Glasgow. Processing the Climate DataWorldClim provides 20-year averaged outputs for the various SSPs from 24 global climate models. A selection of 13 models were averaged for each variable and time based on Mahony et al 2022. These models included ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CNRM-ESM2-1, EC-Earth3-Veg, GFDL-ESM4, GISS-E2-1-G, INM-CM5-0, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL. GFDL-ESM4 was not available for SSP2 4.5 or SSP5 8.5. Accessing the Multidimensional InformationThe time and SSP scenario are built into the layer using a multidimensional raster. Enable the time slider to move across the 20-year average periods. In ArcGIS Online and Pro, use the Multidimensional Filter to select the SSP (SSP2 4.5 is the default). What can you do with this layer?These multidimensional imagery tiles support analysis using ArcGIS Online or Pro. Use the Bioclimate Baseline layer to see the difference in pixels and calculate change from the historic period into the future. Use the Multidimensional tab in ArcGIS Pro to access a variety of useful tools. Each layer or variable can be styled using the Image Display options. Known Quality IssuesEach model is downscaled from ~100km resolution to ~5km resolution by WorldClim. Some artifacts are inevitable, especially at a global scale. Some variables have distinct transitions, especially in Greenland. Also, SSP2 4.5 has missing data for several variables in Antarctica.Related LayersBioclimate 1 Annual Mean TemperatureBioclimate 2 Mean Diurnal RangeBioclimate 3 IsothermalityBioclimate 4 Temperature SeasonalityBioclimate 5 Max Temperature of Warmest MonthBioclimate 6 Min Temperature Of Coldest MonthBioclimate 7 Temperature Annual RangeBioclimate 8 Mean Temperature Of Wettest QuarterBioclimate 9 Mean Temperature Of Driest QuarterBioclimate 10 Mean Temperature Of Warmest QuarterBioclimate 11 Mean Temperature Of Coldest QuarterBioclimate 12 Annual PrecipitationBioclimate 13 Precipitation Of Wettest MonthBioclimate 14 Precipitation Of Driest MonthBioclimate 15 Precipitation SeasonalityBioclimate 16 Precipitation Of Wettest QuarterBioclimate 17 Precipitation Of Driest QuarterBioclimate 18 Precipitation Of Warmest QuarterBioclimate 19 Precipitation Of Coldest QuarterBioclimate Baseline 1970-2000

CHELSA_prec_1_landCHELSA_prec_2_landCHELSA_prec_3_landCHELSA_prec_4_landCHELSA_prec_5_landCHELSA_prec_6_landCHELSA_prec_7_landCHELSA_prec_8_landCHELSA_prec_9_landCHELSA_prec_10_landCHELSA_prec_11_landCHELSA_prec_12_landCHELSA_temp10_1_landCHELSA_temp10_2_landCHELSA_temp10_3_landCHELSA_temp10_4_landCHELSA_temp10_5_landCHELSA_temp10_6_landCHELSA_temp10_7_landCHELSA_temp10_8_landCHELSA_temp10_9_landCHELSA_temp10_10_landCHELSA_temp10_11_landCHELSA_temp10_12_landCHELSA_tmin10_1_landCHELSA_tmin10_2_landCHELSA_tmin10_3_landCHELSA_tmin10_4_landCHELSA_tmin10_5_landCHELSA_tmin10_6_landCHELSA_tmin10_7_landCHELSA_tmin10_8_landCHELSA_tmin10_9_landCHELSA_tmin10_10_landCHELSA_tmin10_11_landCHELSA_tmin10_12_landCHELSA_tmax10_1_landCHELSA_tmax10_2_landCHELSA_tmax10_3_landCHELSA_tmax10_4_landCHELSA_tmax10_5_landCHELSA_tmax10_6_landCHELSA_tmax10_7_landCHELSA_tmax10_8_landCHELSA_tmax10_9_landCHELSA_tmax10_10_landCHELSA_tmax10_11_landCHELSA_tmax10_12_landCHELSA_bio10_1_landCHELSA_bio10_2_landCHELSA_bio10_3_...

Standard (19) WorldClim Bioclimatic variables for South Africa from WorldClim version 2 (see https://worldclim.org/data/worldclim21.html) at 10 minute spatial resolution. They are the average for the years 1970-2000. BIO1 = Annual Mean Temperature BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO3 = Isothermality (BIO2/BIO7) (×100) BIO4 = Temperature Seasonality (standard deviation ×100) BIO5 = Max Temperature of Warmest Month BIO6 = Min Temperature of Coldest Month BIO7 = Temperature Annual Range (BIO5-BIO6) BIO8 = Mean Temperature of Wettest Quarter BIO9 = Mean Temperature of Driest Quarter BIO10 = Mean Temperature of Warmest Quarter BIO11 = Mean Temperature of Coldest Quarter BIO12 = Annual Precipitation BIO13 = Precipitation of Wettest Month BIO14 = Precipitation of Driest Month BIO15 = Precipitation Seasonality (Coefficient of Variation) BIO16 = Precipitation of Wettest Quarter BIO17 = Precipitation of Driest Quarter BIO18 = Precipitation of Warmest Quarter BIO19 = Precipitation of Coldest Quarter For further information see: https://worldclim.org/data/bioclim.html The file format is stacked raster, and can be read into R using the raster::stack("filename"). CMIP5 climate projections for South Africa from GCMs downscaled and calibrated (bias corrected) using WorldClim 1.4 as baseline climate (see https://worldclim.org/data/v1.4/cmip5_10m.html). The file format is stacked raster, and can be read into R using the raster::stack("filename"). Projections are for 2050 assuming two representative concentration pathways (RCPs) - 4.5 and 8.5 - for the following GCMs: HadGEM2-ES ("he45bi50", "he85bi50") CNRM-CM5 ("cn45bi50", "cn85bi50") MPI-ESM-LR ("mp45bi50", "mp85bi50")

Bioclim variables.

Total Annual precipitation was derived from the WorldClim bio-climatic variable: BIO12. Bio-climatic variables are derived from the monthly temperature and rainfall values in order to generate meaningful variables. These are often used in ecological niche modelling (e.g., BIOCLIM, GARP). The bio-climatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environment factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of the three months (1/4 of the year).The WorldClim is a set of global climate layers (climate grids). The data can be used for mapping and spatial modeling in a GIS or with other computer programs.Further Information:Very high resolution interpolated climate surfaces for global land areasDownload data at: WorldClim - Global Climate Data

Habitat stability is important for maintaining biodiversity by preventing species extinction, but this stability is being challenged by climate change. The tropical alpine ecosystem is currently one of the ecosystems most threatened by global warming, and the flora close to the permanent snow line is at high risk of extinction. The tropical alpine ecosystem, found in South and Central America, Malesia and Papuasia, Africa, and Hawaii, is of relatively young evolutionary age, and it has been exposed to changing climates since its origin, particularly during the Pleistocene. Estimating habitat loss and gain between the Last Glacial Maximum (LGM) and the present allows us to relate current biodiversity to past changes in climate and habitat stability. In order to do so, 1) we developed a unifying climate-based delimitation of tropical alpine regions across continents, and 2) we used this delimitation to assess the degree of habitat stability, i.e. the overlap of suitable areas between the ..., The dataset consists of a set of script developed for the corresponding publication using CHELSA v.1.2 (https://chelsa-climate.org/downloads/) to delimit tropical alpine regions based on bioclimatic variables. Using the scripts and setting the limits of the respective bioclimatic variables, will result in the GIS shapefiles with the delimitied region. Here, we provide the corresponding GIS shapefiles and figures for the above mentioned publication, that were the outcome of running the R scripts. The shapefiles and figures are based on the mean temperature of the coldest and warmest quarter (bioclim 10 and bioclim 11) of -3 to +10/+18°C respectively, plus a restriction to the tropics based on bioclim 3, the ratio of diurnal variation to annual variation in temperatures, ranging from 50 to 300 °C/10., , # ClimateAnalyzer

ClimateAnalyzer is a set of script written in R to delimit areas based on bioclimatic variables.

The scripts have been developed to delimit tropical alpine areas based on bioclimatic variables from CHELSA (). The work has been presented in Kandziora et al (under review) "The ghost of past climate acting on present-day plant diversity: lessons from a climate-based delimitation of the tropical alpine ecosystem".

Description of the data and file structure

The uploaded GIS shapefiles and figures are based on a delimitation based on the mean temperature of the coldest and warmest quarter (bioclim 10 and bioclim 11) of -3 to +10 °C, plus a restriction to the tropics based on bioclim 3, the ratio of diurnal variation to annual variation in temperatures, ranging from 50 to 300 °C/10.

The delimitation was done for current climatic conditions as well as two reconstructions of the climate during the last glacial maximum, based on MPI-M_MPI-ESM-P (abbreviated to MPI) and...

TreeGOER (Tree Globally Observed Environmental Ranges) is a database that documents the environmental ranges (minimum, maximum, median, mean and 5%, 25%, 75% and 95% quantiles) for 48,129 tree species and for 51 environmental variables, including 38 bioclimatic variables, 8 soil variables and 3 topographic variables. These ranges were calculated after cleaning occurrence records and standardizing species names with the WorldFlora R package to World Flora Online or the World Checklist of Vascular Plants for a global GBIF occurrence download of 44,267,164 occurrences (GBIF.org 2021 GBIF Occurrence Download https://doi.org/10.15468/dl.77gcvq). The 5% and 95% quantiles were calculated separately for two methods of outlier detection and for the full data set. The process of compilation of TreeGOER with 30 arc-seconds global grid layers, two examples of BIOCLIM applications that investigated the effects of climate change on global tree diversity patterns and R scripts to repeat these analyses have been described by Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914.

TreeGOER can be used in combination with the CitiesGOER database (https://doi.org/10.5281/zenodo.8175429) that documents the conditions for the same environmental variables (except elevation) for 52,602 cities with a human population ≥ 5000. TreeGOER could also be used with the TreeGOER Global Zones atlas that can be obtained from https://doi.org/10.5281/zenodo.8252756. This high resolution atlas includes sheets with global zones for the Climatic Moisture Index (CMI) and the number of months with average temperature > 10 degrees C (Tmo10); these are zones for which presence of the 48,129 species was documented by TreeGOER.

Changes between different versions of the databases are documented in a specific sheet in the metadata file.

The development of TreeGOER was supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, and by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project. When using TreeGOER in your work, cite the publication (Kindt 2023) as well as this repository using the DOI (https://doi.org/10.5281/zenodo.7922927).

This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios.

Contents:

Historical NDVI Data (1982-2018): Mean NDVI values derived from remote sensing data.

Future NDVI Predictions: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.

Methodology:

Model Training:

A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.

The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.

Future Predictions:

Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.

Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).

Data Specifications:

Extent: Covers the geographical area of Türkiye and adjacents.

Sources:

NDVI Data:

Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.

CHELSA Bioclim Data:

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4

WorldClim V1 Bioclim, daha biyolojik olarak anlamlı değerler oluşturmak için aylık sıcaklık ve yağıştan elde edilen biyoklimatik değişkenler sağlar. Biyoklimatik değişkenler yıllık eğilimleri (ör. yıllık ortalama sıcaklık, yıllık yağış), mevsimselliği (ör. sıcaklık ve yağıştaki yıllık aralık) ve aşırı veya sınırlayıcı çevresel faktörleri (ör. …