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"en": "*INTRODUCTION*\n\nModeling the complex interdependencies of climate, species distributions, land cover, soils, and other features of the Earth system has long been a focus of ecological research. While a growing compendium of Earth observation (EO) and other global datasets exists, differences in coordinate reference systems, spatial resolutions, geographic data models, and file formats, as well as the distortions induced by map projections, hinder integrated study in macroecological modeling.\n\nHere we introduce the Eco-ISEA3H database, built on the Icosahedral Snyder Equal Area (ISEA) aperture 3 hexagonal (3H) Discrete Global Grid System (DGGS). ISEA3H partitions the Earth's surface into a contiguous grid of *equal-area hexagonal* cells. These observational units have *uniform topology* with neighbors, sharing an edge with six adjacent cells, and are maximally *compact*, minimizing within-unit variability in expectation. See Sahr et al. (2003) for a comprehensive review:\n\nK. Sahr, D. White, and A. J. Kimerling. Geodesic discrete global grid systems. Cartography and Geographic Information Science, 30(2):121-134, 2003.\n\nThe Eco-ISEA3H database contains an extensive set of variables characterizing the Earth's terrestrial climate, geology, human geography, land cover, physical geography, and mammalian species ranges, derived from a number of frequently used, publicly available datasets. For each source dataset, variables are organized under one or more themes. Summary statistics for each ISEA3H grid cell include:\n\n*Mean*: The area-weighted mean of a continuous variable.\n\n*Fraction*: The fraction of the cell covered by each class of a categorical theme.\n\n*Mode*: The class of the categorical theme covering the greatest area; defined for cells in which the sum of fractions for the theme is greater than or equal to 0.2.\n\n*IDW*: The distance-weighted interpolated value of a continuous variable at the cell centroid.\n\n*Centroid*: The spot value of a continuous or discrete variable at the cell centroid.\n\nIn the Source Datasets section, the summary statistics calculated for each theme are specified, and the ISEA3H resolutions at which these statistics are available are listed in parentheses.\n\n*ISEA3H GRID SPECIFICATIONS*\n\nThe Eco-ISEA3H database contains variables at the following resolutions; for each, the number of ISEA3H grid cells, as well as the average cell area and centroid spacing, are listed below.\n\n*Resolution 5*: 2432 Cells, 209903.5 Km2, 452.5 Km\n\n*Resolution 6*: 7292 Cells, 69967.8 Km2, 261.2 Km\n\n*Resolution 7*: 21872 Cells, 23322.6 Km2, 150.8 Km\n\n*Resolution 8*: 65612 Cells, 7774.2 Km2, 87.1 Km\n\n*Resolution 9*: 196832 Cells, 2591.4 Km2, 50.3 Km\n\n*Resolution 10*: 590492 Cells, 863.8 Km2, 29.0 Km\n\n*DIRECTORY STRUCTURE & FILE NAMING CONVENTION*\n\nTo facilitate use in a wide range of applications, the Eco-ISEA3H database comprises plain text files, organized within a regular directory structure. The hexagon ID, or HID, serves as primary key, linking records associated with each ISEA3H grid cell among these files; HIDs are unique within each ISEA3H resolution.\n\nAt the root, each ISEA3H resolution has a folder; within these are folders for each source dataset, named following the format:\n\n[Source Dataset]-V[Version]\n\nWithin dataset folders are one or more text files, named following the format:\n\nISEA3H[Resolution]-[Source Dataset]-V[Version]-Y[Year]-[Theme]-[Summary Statistic]\n\nNote that files may have an additional Y[Year] specified, in which case the two Y[Year]s indicate the temporal range, inclusive, represented by the source dataset. A Y[Year] is included only when required; datasets without a specific temporal period (for example, GLiM), or averaged for a single, standard period (WorldClim), are not given a Y[Year] attribute.\n\nDiscrete, categorical variables within each file are generally named following the format:\n\n[Theme]-[Class]-[Summary Statistic]\n\nContinuous, real-numbered variables within each file are generally named following the format:\n\n[Variable]-[Summary Statistic]\n\nThe \"Spatial\" folder at the root contains accompanying spatial files in tabular latitude/longitude format (Text), ESRI shapefile point and polygon format (Vector), and GeoTIFF format (Raster). Tabular and vector files are named following the format:\n\n[Feature]-ISEA3H[Resolution]-[Projection]-[Raster Resolution, or \"V\"]-[Datum]\n\n\"V\" indicates a native vector dataset, and applies to centroid features. Polygon features were derived from a source raster, the resolution of which is specified in arc-seconds (AS) or arc-minutes (AM).\n\nFinally, raster files are named following the format:\n\n[Variable]-ISEA3H[Resolution]-[Raster Tile]-[Projection]-[Raster Resolution]-[Datum]\n\n*SOURCE DATASETS*\n\nSource datasets were processed primarily in R, using the *dggridR* and *raster* packages:\n\nR. Barnes. dggridR: Discrete global grids for R, version 2.0.4. https://CRAN.R-project.org/package=dggridR, 2020.\n\nR. J. Hijmans. raster: Geographic data analysis and modeling, version 3.0-12. https://CRAN.R-project.org/package=raster, 2020.\n\n*CLIMATE*\n\nNote: The ETCCDI climate extremes indices (also referred to as Climdex indices) were derived from the outputs of a number of global climate models and atmospheric reanalyses by Sillmann et al. (2013):\n\nJ. Sillmann, V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh. Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. Journal of Geophysical Research: Atmospheres, 118(4): 1716–1733, 2013.\n\nJ. Sillmann, V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh. Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. Journal of Geophysical Research: Atmospheres, 118(6):2473–2493, 2013.\n\n*Community Climate System Model Version 4 (CCSM4)* | Versions: Historical, Representative Concentration Pathway 8.5 (RCP8.5) | Dataset Type: Model Output\n\nTheme: Expert Team on Climate Change Detection and Indices (ETCCDI) Climate Extremes Indices | Periods: 1950-2000 (Historical), 2061-2080 (RCP8.5) | Variables: 27 | Summary Statistics: IDW (9)\n\nCitation: P. R. Gent, G. Danabasoglu, L. J. Donner, M. M. Holland, E. C. Hunke, S. R. Jayne, D. M. Lawrence, R. B. Neale, P. J. Rasch, M. Vertenstein, P. H. Worley, Z.-L. Yang, and M. Zhang. The Community Climate System Model version 4. Journal of Climate, 24(19):4973–4991, 2011.\n\n*Environmental Rasters for Ecological Modeling (ENVIREM)* | Version: 1.0 | Dataset Type: Continuous Raster, 30 Arc-Second Resolution\n\nTheme: Climate | Variables: 16 | Summary Statistics: Mean (8-9) | NoData Value: -1000\n\nCitation: P. O. Title and J. B. Bemmels. ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography, 41(2):291-307, 2018.\n\n*European Centre for Medium-Range Weather Forecasts (ECMWF)* | Version: ERA-40 | Dataset Type: Model Output\n\nTheme: Expert Team on Climate Change Detection and Indices (ETCCDI) Climate Extremes Indices | Period: 1958-2001 | Variables: 27 | Summary Statistics: IDW (5-9)\n\nCitation: S. M. Uppala, P. W. Kållberg, A. J. Simmons, U. Andrae, V. Da Costa Bechtold, M. Fiorino, J. K. Gibson, J. Haseler, A. Hernandez, G. A. Kelly, X. Li, K. Onogi, S. Saarinen, N. Sokka, R. P. Allan, E. Andersson, K. Arpe, M. A. Balmaseda, A. C. M. Beljaars, L. Van De Berg, J. Bidlot, N. Bormann, S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes, S. Hagemann, E. Hólm, B. J. Hoskins, L. Isaksen, P. A. E. M. Janssen, R. Jenne, A. P. McNally, J. F. Mahfouf, J. J. Morcrette, N. A. Rayner, R. W. Saunders, P. Simon, A. Sterl, K. E. Trenberth, A. Untch, D. Vasiljevic, P. Viterbo, and J. Woollen. The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society, 131(612):2961–3012, 2005.\n\n*GloH2O* | Version: 1.0 | Dataset Type: Categorical Raster, 30 Arc-Second Resolution\n\nTheme: Köppen-Geiger Climate Classification (KoppenGeiger) | Classes: 30 | Summary Statistics: Fraction, Mode (9) | NoData Value: NA\n\nCitation: H. E. Beck, N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg, and E. F. Wood. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5:180214, 2018.\n\n*WorldClim* | Version: 1.4 | Dataset Type: Continuous Raster, 30 Arc-Second Resolution\n\nTheme: Bioclimatic Variables (BIO) | Periods: 1950-2000, 2061-2080 (CCSM4, RCP8.5) | Variables: 19 | Summary Statistics: Mean (9) | NoData Value: -1000\n\nCitation: R. J. Hijmans, S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15):1965–1978, 2005.\n\n*WorldClim* | Version: 2.0 | Dataset Type: Continuous Raster, 30 Arc-Second Resolution\n\nTheme: Bioclimatic Variables (BIO) | Variables: 19 | Summary Statistics: Centroid (5-10), Mean (6-9) | NoData Value: -100\n\nTheme: Monthly Precipitation (PREC) | Variables: 12 | Summary Statistics: Centroid (5-10) | NoData Value: -1\n\nTheme: Monthly Solar Radiation (SRAD) | Variables: 12 | Summary Statistics: Centroid (5-10) | NoData Value: -1\n\nTheme: Monthly Mean Temperature (TAVG) | Variables: 12 | Summary Statistics: Centroid (5-10) | NoData Value: -100\n\nTheme: Monthly Minimum Temperature (TMIN) | Variables: 12 | Summary Statistics: Centroid (5-10) | NoData Value: -100\n\nTheme: Monthly Maximum Temperature (TMAX) | Variables: 12 | Summary Statistics: Centroid (5-10) | NoData Value: -100\n\nTheme: Monthly Vapor Pressure (VAPR) | Variables: 12 | Summary Statistics: Centroid (5-10) | NoData Value: -1\n\nTheme: Monthly Wind Speed (WIND) | Variables: 12 | Summary Statistics: Centroid (5-10) | NoData Value: -1\n\nCitation: S. E. Fick and R. J. Hijmans. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12):4302-4315, 2017.\n\n*GEOLOGY*\n\n*Digital Soil Map of the World (DSMW)* | Version: 3.6 | Dataset Type: Vector\n\nTheme: Soil Units | Classes: 123 | Summary Statistics: Fraction, Mode (5-9) | NoData Value: -1\n\nCitation: Land and Water Development Division, Food and Agriculture Organization (FAO) of the United Nations. The digital soil map of the world, version 3.6, 2003.\n\n*Global Lithological Map (GLiM)* | Version: 1.1 | Dataset Type: Vector\n\nTheme: Lithology | Classes: 16, 12, 14 | Summary Statistics: Centroid (9) | NoData Value: ------\n\nCitation: J. Hartmann and N. Moosdorf. The new global lithological map database GLiM: A representation of rock properties at the Earth surface. 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