Data from: North America defies a global trend of reduced vascular plant diversity in extreme environments: Are Asteraceae responsible?
Data files
Sep 22, 2026 version files 1.38 GB
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Dryad_DataPackage_Edwards_et_al_AJB.zip
1.38 GB
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README.md
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Abstract
Premise of the study. It is generally assumed that more extreme environments are less habitable for a majority of organisms, leading to a decrease in biodiversity. However, definitions of extremeness are typically based on the abiotic tolerances of specific lineages, and thus may be partly circular and poorly applicable across global biodiversity. Here we test this assumption agnostic to biological definitions of extremeness.
Methods. We use upper and lower extremes of environmental variables on six continents to derive a set of 30 extreme abiotic stress layers that fall within nine extreme stress categories, each expected to affect the survival of terrestrial organisms. We compare these to vascular plant species richness.
Key results. While plant species richness decreases globally where extremes co-occur, the reverse is true in North America, where increased species richness is associated with regions where diverse types of extreme environmental conditions overlap. Data for the largest family of plants (Asteraceae) support this finding in North America, and suggest that the diversity of this family by itself contributes much to this pattern.
Conclusions. Our results demonstrate how insights may be gained through approaches that define environmental extremes agnostic of organismal tolerances. While extreme environments do restrict plant diversity overall, by identifying regions where multiple extreme stressors co-occur, we can detect lineages that prosper under such conditions. Understanding of how extreme conditions have shaped plant diversity across the globe provides a historical context for shifting geographic distributions of extreme environmental conditions in the coming decades and centuries.
Dataset DOI: 10.5061/dryad.s1rn8pkmm
USGS Data Release Title: Plant Species Richness in Extreme Environments (1970-2000)
Description of the data and file structure
A set of 30 environmental layers was derived from WorldClim2 (climate) and SoilGrids1km (substrate and topographic variables), representing statistical extremes in the distribution of the underlying environmental variable within each continent, defined as either high-end extremes (>95th percentile) or low-end extremes (<5th percentile). Global combined raster layers representing the presence/absence of the extreme condition were generated at a grid cell resolution of ~1km2, along with a layer representing the total number of extreme layers present per cell.
These environmental layers were further sorted into nine classes according to the type of stress conferred: energy limitation, water limitation, nutrient limitation, heat stress, cold stress, extreme variability, ultraviolet radiation exposure, high moisture stress, and soil physical stress. A derived global raster layer was generated representing the total number of extreme class layers present per cell.
To test the relationship between the number of extreme stress layers present and plant biodiversity, data on vascular plant species richness were obtained from Ellis, Antill & Kreft (2012) for equal-area polygons of size 7800 km2. The number of single layers (0-30) and number of classes (0-9) present at the centroids of these polygons were calculated, corresponding to a given species richness value. These datasets were then used for analysis of species richness against the number of extreme classes or layers through permutation. Additionally, to assess the impact of the Latitudinal Diversity Gradient, a set of residuals from species richness regressed against latitude were generated to correct for any association with latitude.
Files and variables
File: Dryad_DataPackage_Edwards_et_al_AJB.zip
Description: File directory containing two folders:
(1) The "Data_Spreadsheets" folder contains tabular CSV files relating species richness to the presence or absence of single layers, or presence or absence of stress classes. All have the same format, with OBJECTID representing a grid cell, the grid cell's centroid latitude and longitude (or easting/northing for Asteraceae), followed by a list of either single-layer variable names or stress class identifiers (where data 1=present, 0=absent), followed by a SUMofLayers or SUMofClasses column with the total number present, and the species Richness value for the grid cell. Single-layer files also contain the residuals of regression against latitude, and global files contain the continent in which the grid cell is found. Three metadata CSV files define each variable for single-layer files, class breakdown files, and the North American Asteraceae files, respectively.
Files contained:
Africa_Extremes_ClassBreakdown.csv
Africa_Extremes_SingleLayers.csv
Australia_Extremes_ClassBreakdown.csv
AustraliaExtremesSingleLayers.csv
Eurasia_Extremes_ClassBreakdown.csv
Eurasia_Extremes_SingleLayers.csv
Global_CombinedContinents_Extremes_ClassBreakdown.csv
Global_CombinedContinents_Extremes_SingleLayers.csv
METADATA_ASTERACEAE_Extremes_SingleLayers.csv
METADATA_Extremes_ClassBreakdown.csv
METADATA_Extremes_SingleLayers.csv
NorthAmerica_ASTERACEAE_Extremes_ClassBreakdown.csv
NorthAmerica_ASTERACEAE_SingleLayers.csv
NorthAmerica_Extremes_ClassBreakdown.csv
NorthAmerica_Extremes_SingleLayers.csv
SouthAmerica_Extremes_ClassBreakdown.csv
SouthAmerica_Extremes_SingleLayers.csv
Variable descriptions for SingleLayers files:
| Variable | Description |
|---|---|
| OBJECTID | Identification number for each geographic pixel, defined arbitrarily within this analysis |
| Latitude | Latitude of pixel centroid (decimal degrees) |
| Longitude | Longitude of pixel centroid (decimal degrees) |
| BIO1_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO1 high-end extreme layer? (>95th percentile for Annual Mean Temperature within the continent) 0=no, 1=yes |
| BIO1_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO1 low-end extreme layer? (<5th percentile for Annual Mean Temperature within the continent) 0=no, 1=yes |
| BIO12_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO12 high-end extreme layer? (>95th percentile for Annual Precipitation within the continent) 0=no, 1=yes |
| BIO12_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO12 low-end extreme layer? (<5th percentile for Annual Precipitation within the continent) 0=no, 1=yes |
| BIO13_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO13 high-end extreme layer? (>95th percentile for Precipitation of the Wettest Month within the continent) 0=no, 1=yes |
| BIO14_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO14 low-end extreme layer? (<5th percentile for Precipitation of the Driest Month within the continent) 0=no, 1=yes |
| BIO15_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO15 high-end extreme layer? (>95th percentile for Precipitation Seasonality within the continent) 0=no, 1=yes |
| BIO2_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO2 high-end extreme layer? (>95th percentile for Mean Diurnal Temperature Range within the continent) 0=no, 1=yes |
| BIO4_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO4 high-end extreme layer? (>95th percentile for Temperature Seasonality within the continent) 0=no, 1=yes |
| BIO5_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO5 high-end extreme layer? (>95th percentile for Maximum Temperature of the Warmest Month within the continent) 0=no, 1=yes |
| BIO6_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO6 low-end extreme layer? (<5th percentile for Minimum Temperature of the Coldest Month within the continent) 0=no, 1=yes |
| BIO7_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO7 high-end extreme layer? (>95th percentile for Temperature Annual Range within the continent) 0=no, 1=yes |
| MA_srad_high_end_extremes | Binary classification - does the pixel centroid fall within the MA_SRAD high-end extreme layer? (>95th percentile for Mean Annual Incident Solar Radiation [Mean Annual Radiation] within the continent) 0=no, 1=yes |
| MA_srad_low_end_extremes | Binary classification - does the pixel centroid fall within the MA_SRAD low-end extreme layer? (<5th percentile for Annual Mean Incident Solar Radiation [Mean Annual Radiation] within the continent) 0=no, 1=yes |
| MA_vapr_high_end_extremes | Binary classification - does the pixel centroid fall within the MA_VAPR high-end extreme layer? (>95th percentile for Mean Annual Water Vapor Pressure [Mean Annual Humidity] within the continent) 0=no, 1=yes |
| MA_vapr_low_end_extremes | Binary classification - does the pixel centroid fall within the MA_VAPR low-end extreme layer? (<5th percentile for Mean Annual Water Vapor Pressure [Mean Annual Humidity] within the continent) 0=no, 1=yes |
| MA_wind_high_end_extremes | Binary classification - does the pixel centroid fall within the MA_WIND high-end extreme layer? (>95th percentile for Mean Annual Wind Speed within the continent) 0=no, 1=yes |
| XtH_BLDFIE_M_sl1_1km | Binary classification - does the pixel centroid fall within the BLDFIE_sl1 high-end extreme layer? (>95th percentile for Soil Bulk Density of the Fine Earth Fraction at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_CLYPPT_M_sl1_1km | Binary classification - does the pixel centroid fall within the CLYPPT_sl1 high-end extreme layer? (>95th percentile for Soil Weight Percentage of Clay particles (< 0.002 mm) in the Fine Earth Fraction at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_CRFVOL_M_sl1_1km | Binary classification - does the pixel centroid fall within the CRFVOL_sl1 high-end extreme layer? (>95th percentile for Soil Volumetric Percentage of Coarse Fragments [>2mm] at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_DEMSRE3a | Binary classification - does the pixel centroid fall within the DEMSRE3a high-end extreme layer? (>95th percentile for Elevation within the continent) 0=no, 1=yes |
| XtH_PHIHOX_M_sl1_1km | Binary classification - does the pixel centroid fall within the PHIHOX_sl1 high-end extreme layer? (>95th percentile for Soil pH Index measured in Water Solution at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_SNDPPT_M_sl1_1km | Binary classification - does the pixel centroid fall within the SNDPPT_sl1 high-end extreme layer? (>95th percentile for Soil Weight Percentage of Sand Particles [0.5-2mm] in the Fine Earth Fraction at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_TWISRE3a | Binary classification - does the pixel centroid fall within the TWISRE3a high-end extreme layer? (>95th percentile for Topographic Wetness Index within the continent) 0=no, 1=yes |
| XtL_AWCtS_M_sl1_1km | Binary classification - does the pixel centroid fall within the AWCtS_Sl1 low-end extreme layer? (<5th percentile for Available Water Capacity of the Soil at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_BDRICM_M_1km | Binary classification - does the pixel centroid fall within the BDRICM low-end extreme layer? (<5th percentile for Depth to Bedrock [R horizon] within the continent) 0=no, 1=yes |
| XtL_CECSOL_M_sl1_1km | Binary classification - does the pixel centroid fall within the CECSOL_sl1 low-end extreme layer? (<5th percentile for Cation Exchange Capacity of Soil at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_ORCDRC_M_sl1_1km | Binary classification - does the pixel centroid fall within the ORCDRC_sl1 low-end extreme layer? (<5th percentile for Soil Organic Carbon Content at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_PHIHOX_M_sl1_1km | Binary classification - does the pixel centroid fall within the PHIHOX_sl1 low-end extreme layer? (<5th percentile for Soil pH Index measured in Water Solution at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_TWISRE3a | Binary classification - does the pixel centroid fall within the TWISRE3a low-end extreme layer? (<5th percentile for Topographic Wetness Index within the continent) 0=no, 1=yes |
| SUMofLayers | Sum of the number of extreme layers present at the pixel centroid |
| Richness | Native vascular plant species richness (via Ellis, Antill, and Kreft, 2012) for pixel centroid |
| residuals | Residuals of plant species richness after regression on latitude (to assess the impact of the Latitudinal Diversity Gradient). Note: not calculated for the "Global_CombinedContinents_Extremes_SingleLayer.csv" file. |
| Continent | For the global combined dataset, the continent from which the pixel originates (or glaciated Greenland for North America analyses with and without Greenland) |
Variable descriptions for ClassBreakdown files:
| Variable | Description |
|---|---|
| OBJECTID | Identification number for each geographic pixel, defined arbitrarily within this analysis |
| Latitude | Latitude of pixel centroid (decimal degrees) |
| Longitude | Longitude of pixel centroid (decimal degrees) |
| EnergyLim | Binary classification - does the pixel centroid fall within an extreme layer within the Energy Limitation class? 0=no, 1=yes |
| ExtremeVariability | Binary classification - does the pixel centroid fall within an extreme layer within the Extreme Variability class? 0=no, 1=yes |
| ColdStress | Binary classification - does the pixel centroid fall within an extreme layer within the Cold Stress class? 0=no, 1=yes |
| HeatStress | Binary classification - does the pixel centroid fall within an extreme layer within the Heat Stress class? 0=no, 1=yes |
| HighMoisture | Binary classification - does the pixel centroid fall within an extreme layer within the High Moisture Stress class? 0=no, 1=yes |
| NutrientLim | Binary classification - does the pixel centroid fall within an extreme layer within the Nutrient Limitation class? 0=no, 1=yes |
| SoilPhysical | Binary classification - does the pixel centroid fall within an extreme layer within the Soil Physical Stress class? 0=no, 1=yes |
| UVDamage | Binary classification - does the pixel centroid fall within an extreme layer within the UV Damage class? 0=no, 1=yes |
| WaterLimitation | Binary classification - does the pixel centroid fall within an extreme layer within the Water Limitation class? 0=no, 1=yes |
| Presence/Absence | Binary classification - does the pixel centroid fall within an extreme layer within any of the nine extreme classes? 0=no, 1=yes |
| SUMofClasses | Sum of the number of extreme classes present at the pixel centroid |
| Richness | Native vascular plant species richness (via Ellis, Antill, and Kreft, 2012) for pixel centroid |
| Continent | For the global combined dataset, the continent from which the pixel originates (or glaciated Greenland for North America analyses with and without Greenland) |
Variable descriptions for North American ASTERACEAE files:
| Variable | Description |
|---|---|
| OBJECTID | Identification number for each geographic pixel, defined arbitrarily within this analysis |
| Easting | Easting of pixel centroid (in meters) in NAD83 / North America Albers Equal Area Conic (ESRI:102008); lat?=40°, lon?=?96°, SP1=20°, SP2=60°, FE/FN=0) |
| Northing | Northing of pixel centroid (in meters) in NAD83 / North America Albers Equal Area Conic (ESRI:102008); lat?=40°, lon?=?96°, SP1=20°, SP2=60°, FE/FN=0) |
| BIO1_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO1 high-end extreme layer? (>95th percentile for Annual Mean Temperature within the continent) 0=no, 1=yes |
| BIO1_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO1 low-end extreme layer? (<5th percentile for Annual Mean Temperature within the continent) 0=no, 1=yes |
| BIO12_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO12 high-end extreme layer? (>95th percentile for Annual Precipitation within the continent) 0=no, 1=yes |
| BIO12_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO12 low-end extreme layer? (<5th percentile for Annual Precipitation within the continent) 0=no, 1=yes |
| BIO13_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO13 high-end extreme layer? (>95th percentile for Precipitation of the Wettest Month within the continent) 0=no, 1=yes |
| BIO14_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO14 low-end extreme layer? (<5th percentile for Precipitation of the Driest Month within the continent) 0=no, 1=yes |
| BIO15_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO15 high-end extreme layer? (>95th percentile for Precipitation Seasonality within the continent) 0=no, 1=yes |
| BIO2_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO2 high-end extreme layer? (>95th percentile for Mean Diurnal Temperature Range within the continent) 0=no, 1=yes |
| BIO4_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO4 high-end extreme layer? (>95th percentile for Temperature Seasonality within the continent) 0=no, 1=yes |
| BIO5_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO5 high-end extreme layer? (>95th percentile for Maximum Temperature of the Warmest Month within the continent) 0=no, 1=yes |
| BIO6_low_end_extremes | Binary classification - does the pixel centroid fall within the BIO6 low-end extreme layer? (<5th percentile for Minimum Temperature of the Coldest Month within the continent) 0=no, 1=yes |
| BIO7_high_end_extremes | Binary classification - does the pixel centroid fall within the BIO7 high-end extreme layer? (>95th percentile for Temperature Annual Range within the continent) 0=no, 1=yes |
| MA_srad_high_end_extremes | Binary classification - does the pixel centroid fall within the MA_SRAD high-end extreme layer? (>95th percentile for Mean Annual Incident Solar Radiation [Mean Annual Radiation] within the continent) 0=no, 1=yes |
| MA_srad_low_end_extremes | Binary classification - does the pixel centroid fall within the MA_SRAD low-end extreme layer? (<5th percentile for Annual Mean Incident Solar Radiation [Mean Annual Radiation] within the continent) 0=no, 1=yes |
| MA_vapr_high_end_extremes | Binary classification - does the pixel centroid fall within the MA_VAPR high-end extreme layer? (>95th percentile for Mean Annual Water Vapor Pressure [Mean Annual Humidity] within the continent) 0=no, 1=yes |
| MA_vapr_low_end_extremes | Binary classification - does the pixel centroid fall within the MA_VAPR low-end extreme layer? (<5th percentile for Mean Annual Water Vapor Pressure [Mean Annual Humidity] within the continent) 0=no, 1=yes |
| MA_wind_high_end_extremes | Binary classification - does the pixel centroid fall within the MA_WIND high-end extreme layer? (>95th percentile for Mean Annual Wind Speed within the continent) 0=no, 1=yes |
| XtH_BLDFIE_M_sl1_1km | Binary classification - does the pixel centroid fall within the BLDFIE_sl1 high-end extreme layer? (>95th percentile for Soil Bulk Density of the Fine Earth Fraction at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_CLYPPT_M_sl1_1km | Binary classification - does the pixel centroid fall within the CLYPPT_sl1 high-end extreme layer? (>95th percentile for Soil Weight Percentage of Clay particles (< 0.002 mm) in the Fine Earth Fraction at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_CRFVOL_M_sl1_1km | Binary classification - does the pixel centroid fall within the CRFVOL_sl1 high-end extreme layer? (>95th percentile for Soil Volumetric Percentage of Coarse Fragments [>2mm] at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_DEMSRE3a | Binary classification - does the pixel centroid fall within the DEMSRE3a high-end extreme layer? (>95th percentile for Elevation within the continent) 0=no, 1=yes |
| XtH_PHIHOX_M_sl1_1km | Binary classification - does the pixel centroid fall within the PHIHOX_sl1 high-end extreme layer? (>95th percentile for Soil pH Index measured in Water Solution at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_SNDPPT_M_sl1_1km | Binary classification - does the pixel centroid fall within the SNDPPT_sl1 high-end extreme layer? (>95th percentile for Soil Weight Percentage of Sand Particles [0.5-2mm] in the Fine Earth Fraction at standard depth 1 within the continent) 0=no, 1=yes |
| XtH_TWISRE3a | Binary classification - does the pixel centroid fall within the TWISRE3a high-end extreme layer? (>95th percentile for Topographic Wetness Index within the continent) 0=no, 1=yes |
| XtL_AWCtS_M_sl1_1km | Binary classification - does the pixel centroid fall within the AWCtS_Sl1 low-end extreme layer? (<5th percentile for Available Water Capacity of the Soil at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_BDRICM_M_1km | Binary classification - does the pixel centroid fall within the BDRICM low-end extreme layer? (<5th percentile for Depth to Bedrock [R horizon] within the continent) 0=no, 1=yes |
| XtL_CECSOL_M_sl1_1km | Binary classification - does the pixel centroid fall within the CECSOL_sl1 low-end extreme layer? (<5th percentile for Cation Exchange Capacity of Soil at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_ORCDRC_M_sl1_1km | Binary classification - does the pixel centroid fall within the ORCDRC_sl1 low-end extreme layer? (<5th percentile for Soil Organic Carbon Content at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_PHIHOX_M_sl1_1km | Binary classification - does the pixel centroid fall within the PHIHOX_sl1 low-end extreme layer? (<5th percentile for Soil pH Index measured in Water Solution at standard depth 1 within the continent) 0=no, 1=yes |
| XtL_TWISRE3a | Binary classification - does the pixel centroid fall within the TWISRE3a low-end extreme layer? (<5th percentile for Topographic Wetness Index within the continent) 0=no, 1=yes |
| SUMofLayers | Sum of the number of extreme layers present at the pixel centroid |
| Richness | Asteraceae species richness calculated using Biodiverse v2.1 from GBIF, iDigBio, and BISON occurrence records |
| residuals | Residuals of Asteraceae species richness after regression on latitude (to assess the impact of the Latitudinal Diversity G |
(2) The "Global_GeoTIFFs" folder contains two subfolders.
First, the "Single_Layer_GeoTIFFs" folder contains global raster maps for the spatial extent of extremes for the 30 environmental variables in GeoTIFF format, with a metadata CSV file defining the variable represented in each file. Each GeoTIFF represents a binary classification: does the pixel fall within the respective extreme layer? 0=no, 1=yes, -1.7e+308=NoData
Files contained:
BIO1_high_end_extremes.tif
BIO1_low_end_extremes.tif
BIO12_high_end_extremes.tif
BIO12_low_end_extremes.tif
BIO13_high_end_extremes.tif
BIO14_low_end_extremes.tif
BIO15_high_end_extremes.tif
BIO2_high_end_extremes.tif
BIO4_high_end_extremes.tif
BIO5_high_end_extremes.tif
BIO6_low_end_extremes.tif
BIO7_high_end_extremes.tif
MA_srad_high_end_extremes.tif
MA_srad_low_end_extremes.tif
MA_vapr_high_end_extremes.tif
MA_vapr_low_end_extremes.tif
MA_wind_high_end_extremes.tif
METADATA_GeoTIFFS_SingleLayers.csv
XtH_BLDFIE_M_sl1_1km.tif
XtH_CLYPPT_M_sl1_1km.tif
XtH_CRFVOL_M_sl1_1km.tif
XtH_DEMSRE3a.tif
XtH_PHIHOX_M_sl1_1km.tif
XtH_SNDPPT_M_sl1_1km.tif
XtH_TWISRE3a.tif
XtL_AWCtS_M_sl1_1km.tif
XtL_BDRICM_M_1km.tif
XtL_CECSOL_M_sl1_1km.tif
XtL_ORCDRC_M_sl1_1km.tif
XtL_PHIHOX_M_sl1_1km.tif
XtL_TWISRE3a.tif
Second, the "Derived_GeoTIFFs" folder contains global raster maps for the number of layers (0-17) or number of classes (0-9) present for each grid cell, with a metadata CSV file defining the variable represented in each file. The NoData value is (-3.4e+38). Auxiliary files (.tif.aux) contain histogram and statistical data for visualization in GIS software.
Files contained:
METADATA_Derived_GeoTIFFS.csv
number_of_classes.tif
number_of_classes.tif.aux
number_of_layers.tif
number_of_layers.tif.aux
NOTE: A USGS-formatted XML Metadata file is also included
Code/software
CSV files (.csv) can be opened with any suitable software (e.g., Excel)
GeoTIFFs (.tif, .tif.aux) should be opened with GIS software (e.g., QGIS, ArcGIS, etc)
Access information
Environmental data were derived from the following source before analysis:
- WorldClim2 (https://www.worldclim.org/)
Fick, S. E. & Hijmans, R. J. (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315.
https://doi.org/10.1002/joc.5086 - SoilGrids1km (soilgrids.org)
Hengl, T. et al. (2014) SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS One 9, e105992.
https://doi.org/10.1371/journal.pone.0105992
Global plant species richness data were derived from the following source before analysis:
- Ellis, E. C., Antill, E. C. & Kreft, H. (2012) All is not lost: plant biodiversity in the Anthropocene. PLoS One, 7. https://doi.org/10.1371/journal.pone.0030535
Zenodo Supplement Description
The supplemental directory archived with Zenodo contains original database files of North American Asteraceae occurrence records as obtained from the three source databases: the Global Biodiversity Information Facility (GBIF), Integrated Digitized Biocollections (iDigBio), and Biodiversity Information Serving Our Nation (BISON). These occurrence records were used to generate the North American Asteraceae species richness datasets contained within the core Dryad repository and are provided here to document the original source information.
