WorldClim 2.1 provides downscaled estimates of climate variables as monthly means over the period of 1970-2000 based on interpolated station measurements. Here we provide analytical image services of precipitation for each month along with an annual mean. Each time step is accessible from a processing template.Time Extent: Monthly/Annual 1970-2000Units: mm/monthCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 16 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim v2.1Using Processing Templates to Access TimeThere are 13 processing templates applied to this service, each providing access to the 12 monthly and 1 annual mean precipitation layers. To apply these in ArcGIS Online, select the Image Display options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left-hand menu. From the Processing Template pull down menu, select the version to display.What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides a graph of the time series along with the calculated annual mean value.This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and an area count of precipitation may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from month to month to show seasonal patterns.To calculate precipitation by land area, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Source Data: The datasets behind this layer were extracted from GeoTIF files produced by WorldClim at 2.5 minutes resolution. The mean of the 12 GeoTIFs was calculated (annual), and the 13 rasters were converted to Cloud Optimized GeoTIFF format and added to a mosaic dataset.Citation: Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

WorldClim 2.1 Data https://www.worldclim.org/data/worldclim21.html

Two scales (1km and 10km)

Bioclimatic Variables https://www.worldclim.org/data/bioclim.html

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

The data covers sampling sites over all of the province of Québec, with more data in the southern regions of the province. Data are from Hydro-Québec and the Québec Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP). These datasets include a heterogeneous mix of standardized and non-standardized fish surveys by government biologists and by environmental consulting firms sub-contracted by Hydro-Québec, collected between 1973 and 2021. Overall, 6498 unique sites (3087 sites in lakes, 3412 sites in rivers; reservoirs excluded), were included in the database. For each site, the data consisted of species counts (adult and juvenile life stages), location (latitude and longitude), sampling date, habitat type (lake or river) and fishing gear (three categories: electrofishing, gillnet, or seine). Climate data (means over 1970 to 2000) and elevation were extracted for each site according to the site’s location from the WorldClim website (https://www.worldclim.org/) in 2023; a site which provides global weather and climate data at high spatial resolution. All 19 ‘bioclimatic variables’ provided by WorldClim were obtained from WorldClim raster files (10-min. resolution) using the function extract from the package raster (Hijmans and van Etten 2012). Five weakly-correlated bioclimatic variables were retained: annual means for temperature and precipitation, mean diurnal temperature range, annual temperature range, and precipitation seasonality (coefficient of variation of monthly total precipitation).

This layer represents CMIP6 future projections of the variation in monthly precipitation totals over the course of the year. This index is the ratio of the standard deviation of the monthly total precipitation to the mean monthly total precipitation (also known as the coefficient of variation) and is expressed as a percentage. The larger the percentage, the greater the variability of precipitation. In some regions the CV values exceed 100%. These regions, such as deserts, may have such little rainfall that any variation creates an extreme percentage. 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: mmCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim CMIP6 BioclimateClimate 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

The data covers sampling sites over all of the province of Québec, with more data in the southern regions of the province. Data are from Hydro-Québec and the Québec Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs (MELCCFP). These datasets include a heterogeneous mix of standardized and non-standardized fish surveys by government biologists and by environmental consulting firms sub-contracted by Hydro-Québec, collected between 1973 and 2021. Overall, 6498 unique sites (3087 sites in lakes, 3412 sites in rivers; reservoirs excluded), were included in the database. For each site, the data consisted of species counts (adult and juvenile life stages), location (latitude and longitude), sampling date, habitat type (lake or river) and fishing gear (three categories: electrofishing, gillnet, or seine). Climate data (means over 1970 to 2000) and elevation were extracted for each site according to the site’s location from the WorldClim website (https://www.worldclim.org/) in 2023; a site which provides global weather and climate data at high spatial resolution. All 19 ‘bioclimatic variables’ provided by WorldClim were obtained from WorldClim raster files (10-min. resolution) using the function extract from the package raster (Hijmans and van Etten 2012). Five weakly-correlated bioclimatic variables were retained: annual means for temperature and precipitation, mean diurnal temperature range, annual temperature range, and precipitation seasonality (coefficient of variation of monthly total precipitation).

Based on extremes in the elevation of plot locations.Rainfall appears high, but is probably reasonable, considering nearby Mt Howick rainfall of 379 mm (1994–2012, DAFWA) at approximately the same distance from the coast.The abbreviation for GO in parenthesis is as in Figure 1.

This nc file contains F-IDF values at streamflow gage sites over the CONUS.

The header of this netcdf file is shown below:

----------------------------------------------------------------------------------------------------------

netcdf flashiness_dataset {
dimensions:
stnid = 6987 ;
duration = 6 ;
frequency = 6 ;
variables:
double flashiness(stnid, duration, frequency) ;
flashiness:description = "It is calculated with the slope of a given time window (cfs/15min*len(window)), divided by the drainage area (sqkm) and convert to a standardized unit" ;
flashiness:units = "mm/h^2" ;
flashiness:_FillValue = NaN ; area (sqkm)" ;
string stnid(stnid) ;
int64 frequency(frequency) ;
int64 duration(duration) ;
double lon(stnid) ;
lon:_FillValue = NaN ;
double lat(stnid) ;
lat:_FillValue = NaN ;
double area(stnid) ;
area:_FillValue = NaN ;
area:units = "sqkm" ;
double data_length(stnid) ;
data_length:_FillValue = NaN ;
data_length:units = "years" ;
data_length:description = "years of available data from USGS 15-min observation" ;
double dor_pc_pva(stnid) ;
dor_pc_pva:_FillValue = NaN ;
dor_pc_pva:units = "percent" ;
dor_pc_pva:long_name = "degree of regulation" ;
dor_pc_pva:description = "degree of regulation retrieved from hydrobasin V10 level 12" ;
double slp_dg_uav(stnid) ;
slp_dg_uav:_FillValue = NaN ;
slp_dg_uav:units = "degree" ;
slp_dg_uav:long_name = "Terrain slope" ;
slp_dg_uav:description = "Terrain slope of total watershed upstream of a pour point" ;
double sgr_dk_sav(stnid) ;
sgr_dk_sav:_FillValue = NaN ;
sgr_dk_sav:units = "degree" ;
sgr_dk_sav:long_name = "Stream gradient" ;
sgr_dk_sav:description = "the stream gradient was calculated as the ratio between the elevation drop within the river reach (i.e. the difference between min. and max. elevation along the reach) and the length of the reach." ;
double tmp_dc_uyr(stnid) ;
tmp_dc_uyr:_FillValue = NaN ;
tmp_dc_uyr:units = "degree celsius" ;
tmp_dc_uyr:long_name = "Annual mean air temperature" ;
tmp_dc_uyr:description = "Annual mean air temperature retrieved from WorldClim, station-based monitoring network" ;
double pre_mm_uyr(stnid) ;
pre_mm_uyr:_FillValue = NaN ;
pre_mm_uyr:units = "mm" ;
pre_mm_uyr:long_name = "Annual mean precipitation" ;
pre_mm_uyr:description = "Annual mean precipitation retrieved from WorldClim, station-based monitoring network and interpolated by the thin-plate smoothing spline algorithm" ;
double pet_mm_uyr(stnid) ;
pet_mm_uyr:_FillValue = NaN ;
pet_mm_uyr:units = "mm" ;
pet_mm_uyr:long_name = "Annual mean potential evaporation" ;
pet_mm_uyr:description = "Annual mean PET based on termperature inputs from WorldClim and a simple temperature-based transfer model" ;
double aet_mm_uyr(stnid) ;
aet_mm_uyr:_FillValue = NaN ;
aet_mm_uyr:units = "mm" ;
aet_mm_uyr:long_name = "Annual mean actural evaporation" ;
aet_mm_uyr:description = "Annual mean AET based on the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;
double ari_ix_uav(stnid) ;
ari_ix_uav:_FillValue = NaN ;
ari_ix_uav:units = "" ;
ari_ix_uav:long_name = "Global aridity index" ;
ari_ix_uav:description = "The Global Aridity Index (Global-Aridity) is modeled using data from WorldClim as input parameters" ;
double cmi_ix_uyr(stnid) ;
cmi_ix_uyr:_FillValue = NaN ;
cmi_ix_uyr:units = "" ;
cmi_ix_uyr:long_name = "Global climate moisture index" ;
cmi_ix_uyr:description = "The Climate Moisture Index (CMI) was derived from the annual precipitation (P) and potential evapotranspiration (PET) datasets as provided by the WorldClim v1.4" ;
double snw_pc_uyr(stnid) ;
snw_pc_uyr:_FillValue = NaN ;
snw_pc_uyr:units = "%" ;
snw_pc_uyr:long_name = "snow cover extent" ;
snw_pc_uyr:description = "data obtained from The MODIS/Aqua Snow Cover Daily L3 Global 500m Grid (MYD10A1)" ;
double cly_pc_uav(stnid) ;
cly_pc_uav:_FillValue = NaN ;
cly_pc_uav:units = "%" ;
cly_pc_uav:long_name = "clay fraction in soils" ;
cly_pc_uav:description = "Data obtained from SoilGrids1km" ;
double slt_pc_uav(stnid) ;
slt_pc_uav:_FillValue = NaN ;
slt_pc_uav:units = "%" ;
slt_pc_uav:long_name = "silt fraction in soils" ;
slt_pc_uav:description = "Data obtained from SoilGrids1km" ;
double snd_pc_uav(stnid) ;
snd_pc_uav:_FillValue = NaN ;
snd_pc_uav:units = "%" ;
snd_pc_uav:long_name = "sand fraction in soils" ;
snd_pc_uav:description = "Data obtained from SoilGrids1km" ;
double swc_pc_uyr(stnid) ;
swc_pc_uyr:_FillValue = NaN ;
swc_pc_uyr:units = "%" ;
swc_pc_uyr:long_name = "Soil water content" ;
swc_pc_uyr:description = "Soil water content is provided as part of the Global High-Resolution Soil-Water Balance dataset which contains gridded estimates of actual evapotranspiration and soil water deficit" ;
double kar_pc_use(stnid) ;
kar_pc_use:_FillValue = NaN ;
kar_pc_use:units = "%" ;
kar_pc_use:long_name = "Karst area extent" ;
kar_pc_use:description = "The World Map of Carbonate Rock Outcrops represents an upper limit of the area of exposed karst terrain." ;
double ero_kh_uav(stnid) ;
ero_kh_uav:_FillValue = NaN ;
ero_kh_uav:units = "kg/hectare per year" ;
ero_kh_uav:long_name = "Soil erosion" ;
string ero_kh_uav:description = "GloSEM erosion estimates were produced with a high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data" ;
double pop_ct_usu(stnid) ;
pop_ct_usu:_FillValue = NaN ;
pop_ct_usu:units = "count" ;
pop_ct_usu:long_name = "Population count" ;
pop_ct_usu:description = "The Gridded Population of the World (GPW) database." ;
double urb_pc_use(stnid) ;
urb_pc_use:_FillValue = NaN ;
urb_pc_use:units = "%" ;
urb_pc_use:long_name = "Urban extent" ;
urb_pc_use:description = "The Global Human Settlement (GHS) framework produces global spatial information about the human presence on the planet over time" ;
double rdd_mk_uav(stnid) ;
rdd_mk_uav:_FillValue = NaN ;
rdd_mk_uav:units = "meters per km^2" ;
rdd_mk_uav:long_name = "Road density" ;
rdd_mk_uav:description = "The Global Roads Inventory Project (GRIP) dataset" ;
double dis_m3_pyr(stnid) ;
dis_m3_pyr:_FillValue = NaN ;
dis_m3_pyr:units = "cms" ;
dis_m3_pyr:long_name = "Natural discharge" ;
dis_m3_pyr:description = "Simulated discharge by WaterGAP" ;
double run_mm_syr(stnid) ;
run_mm_syr:_FillValue = NaN ;
run_mm_syr:units = "mm" ;
run_mm_syr:long_name = "Land surface runoff" ;
run_mm_syr:description = "Simulated land surface runoff by WaterGAP" ;
double inu_pc_umx(stnid) ;
inu_pc_umx:_FillValue = NaN ;
inu_pc_umx:units = "%" ;
inu_pc_umx:long_name = "Annual maximum inundation extent " ;
inu_pc_umx:description = "GIEMS-D15 is a high-resolution global inundation map at a pixel size of 15 arc-seconds" ;
double ria_ha_usu(stnid) ;
ria_ha_usu:_FillValue = NaN ;
ria_ha_usu:units = "hectares" ;
ria_ha_usu:long_name = "River area" ;
ria_ha_usu:description = "River area was calculated using the the HydroSHEDS database at 15 arc-second resolution. It is based on a rating curve" ;
double riv_tc_usu(stnid) ;
riv_tc_usu:_FillValue = NaN ;
riv_tc_usu:units = "1000 m^3" ;
riv_tc_usu:long_name = "River volume" ;
riv_tc_usu:description = "River volume was calculated using the the HydroSHEDS database at 15 arc-second resolution." ;
double gwt_cm_sav(stnid) ;
gwt_cm_sav:_FillValue = NaN ;
gwt_cm_sav:units = "cm" ;
gwt_cm_sav:long_name = "Groundwater table depth" ;
gwt_cm_sav:description = "Fan et al. (2013) compiled global observations of water table depth from government archives and literature" ;
double ele_mt_uav(stnid) ;
ele_mt_uav:_FillValue = NaN ;
ele_mt_uav:units = "m" ;
ele_mt_uav:long_name = "Elevation" ;
ele_mt_uav:description = "Elevation above mean sea level based on EarthEnv-DEM90" ;
double glc_pc_u01(stnid) ;
glc_pc_u01:_FillValue = NaN ;
glc_pc_u01:units = "%" ;
glc_pc_u01:long_name = "Land cover area extent" ;
glc_pc_u01:description = "Land Cover extent for the drainage system. Data from GLC2000 Global Land Cover in year 2000" ;
double glc_pc_u02(stnid) ;
glc_pc_u02:_FillValue = NaN ;
glc_pc_u02:units = "%" ;
glc_pc_u02:long_name = "Land cover area extent" ;
glc_pc_u02:description = "Land

Nineteen bioclimatic variables retrieved from WorldClim database using principal coordinates for each sampling site.

Understanding the response of Betula ermanii populations to climate change is crucial for conservation efforts. Range-wide provenance trials provide valuable insights into local adaptation and phenotypic plasticity, aiding in the maintenance of productivity in boreal and alpine forest ecosystems. This study aimed to evaluate the impact of climate change on survival and productivity of B. ermanii, and to formulate conservation strategies for future climates. Using survival and growth data from provenance trials, models were developed and applied to projected climate scenarios obtained from WorldClim. Results indicated that populations at the southern edge and thermal limit experienced more pronounced declines in survival and productivity compared to others. Particularly, the southern-edge population struggled to survive in situ under severe climate warming, while the high-altitude edge population faced challenges in surviving ex situ. These findings emphasize the necessity of integrating both in situ and ex situ conservation measures tailored to source populations and the severity of climate change. Range-wide provenance trial data provide valuable insights into how climatic responses affect populations, guiding conservation efforts for Betula ermanii in the face of changing environmental conditions. Methods Betula ermanii, a wind-pollinated deciduous tree species, is a significant component of Japan's deciduous broad-leaved forests, particularly prevalent in subalpine regions and forest margins, where it often forms pure forests. Seeds of B. ermanii were gathered during the autumns of 2016 and 2017 from 11 natural populations across its distribution zones. These seeds were grown in the nursery at the University of Tokyo Hokkaido Forest (UTHF) in April 2018. Following two growing seasons, containerized saplings were planted at 11 sites across Japan in autumn 2019 and spring 2020. Each planting site received 20 seedlings per population, with exceptions for Akkeshi (AKS), Goyo-San (GYS), and Choukai-San (CKS). This endeavor resulted in a total of 2013 seedlings (183 seedlings × 11 planting sites). Random planting designs were implemented for each site to mitigate spatial autocorrelation issues. Survival counts, height, and diameter measurements were conducted in autumn from 2020 to 2023. The productivity index was calculated by multiplying the survival rate by the average height of each site. Relative differences in climatic parameters between the planting site and the seed source population were analyzed. To forecast future climate, Mean Annual Temperature (MAT - Bio1) and Annual Precipitation (PRT - Bio12) data were obtained from the WorldClim database (Hijmans et al., 2005). The differences in MAT and PRT between the provenance trials and source populations from 2011 to 2022 were calculated using the equations: ΔMAT = MAT_s – MAT_p, ΔMAT2 = (ΔMAT) ^2, ΔPRT = (PRT_s – PRT_p)/PRT_p, where MAT_s represents the MAT of the planting site, MAT_p denotes the MAT of the seed source population, PRT_s signifies the PRT of the planting site, and PRT_p stands for the PRT of the seed source population.

A global map of 5 land use types at 30s (approx. 1km) resolution for 2005. The data set was generated through the statistical downscaling of the Land-use Harmonisation data set (Hurt et al 2011) at http://luh.umd.edu/. Five land use types (primary, secondary, pasture, crop, urban) are provided as separate raster layers, with the value of each cell representing the proportion of the grid cell occupied by that land use type. An additional layer representing cells defined as permanent ice (value of 1) is also provided. Lineage: Statistical downscaling was based on the following global raster layers:

Coarse Scale Land -Use: 2005 data layer of five land-use classes from the world Harmonised Land Use database.

Input covariates:- ACC.flt : Global Accessibility Index. The travel time to the nearest population centre of 50,000 or more. EARS.flt : MOD16 data set gap filled with Annual Actual Evaporations calculated as the sum of monthly EA derived using the Budkyo framework based on WorldClim climatic data, using PAWHC calculated from 1km Soil Depth from www.soilgrids.org combined with AWC from the Harmonised World Soil Database. MAT.flt: Mean Annual Temperature with maximum and minimum temperature corrected for radiation differences due to variation in terrain based on Danielson and Dean (2011) following Wilson and Gallant (2000). PTA.flt: Annual precipitation. Sum of monthly precipitation from WorldClim. TWI.flt: Topographic Wetness Index. Calculated at 9 s and upscaled to 1 km. ICE.flt: Presence of permanent ice. SLP.flt: Slope calculated at 9 s and upscaled to 1 km. SOC.flt: Soil Organic Carbon content. Weighted average of all depth classes. WATER.flt: Presence of permanent water bodies. POP.flt: Population density. CLC.flt: Consensus land-cover. 1 km land-cover product made by harmonising multiple products.

description: The data we used for this study include species occurrence data (n=15 species), climate data and predictions, an expert opinion questionnaire, and species masks that represented the model domain for each species. For this data release, we include the results of the expert opinion questionnaire and the species model domains (or masks). We developed an expert opinion questionnaire to gather information regarding expert opinion regarding the importance of climate variables in determining a species geographic range. The species masks, or model domains, were defined separately for each species using a variation of the target-group approach (Phillips et al. 2009), where the domain was determine using convex polygons including occurrence data for at least three phylogenetically related and similar species (Watling et al. 2012). The species occurrence data, climate data, and climate predictions are freely available online, and therefore not included in this data release. The species occurrence data were obtained primarily from the online database Global Biodiversity Information Facility (GBIF; http://www.gbif.org/), and from scientific literature (Watling et al. 2011). Climate data were obtained from the WorldClim database (Hijmans et al. 2005) and climate predictions were obtained from the Center for Ocean-Atmosphere Prediction Studies (COAPS) at Florida State University (https://floridaclimateinstitute.org/resources/data-sets/regional-downscaling). See metadata for references.; abstract: The data we used for this study include species occurrence data (n=15 species), climate data and predictions, an expert opinion questionnaire, and species masks that represented the model domain for each species. For this data release, we include the results of the expert opinion questionnaire and the species model domains (or masks). We developed an expert opinion questionnaire to gather information regarding expert opinion regarding the importance of climate variables in determining a species geographic range. The species masks, or model domains, were defined separately for each species using a variation of the target-group approach (Phillips et al. 2009), where the domain was determine using convex polygons including occurrence data for at least three phylogenetically related and similar species (Watling et al. 2012). The species occurrence data, climate data, and climate predictions are freely available online, and therefore not included in this data release. The species occurrence data were obtained primarily from the online database Global Biodiversity Information Facility (GBIF; http://www.gbif.org/), and from scientific literature (Watling et al. 2011). Climate data were obtained from the WorldClim database (Hijmans et al. 2005) and climate predictions were obtained from the Center for Ocean-Atmosphere Prediction Studies (COAPS) at Florida State University (https://floridaclimateinstitute.org/resources/data-sets/regional-downscaling). See metadata for references.