United States Geological Survey (Sayre et al. 2014) 20141209 Raster Digital Data Set http://gec.cr.usgs.gov/ Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. 20141209 Publication (Book) Washington, DC Association of American Geographers In response to the need and an intergovernmental commission for a high resolution and data-derived global ecosystem map, land surface elements of global ecological pattern were characterized in an ecophysiographic stratification of the planet. The stratification produced 3,923 terrestrial ecological land units (ELUs) at a base resolution of 250 meters. The ELUs were derived from data on land surface features in a three step approach. The first step involved acquiring or developing four global raster datalayers representing the primary components of ecosystem structure: bioclimate, landform, lithology, and land cover. These datasets generally represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. The second step involved a spatial combination of the four inputs into a single, new integrated raster dataset where every cell represents a combination of values from the bioclimate, landforms, lithology, and land cover datalayers. This foundational global raster datalayer, called ecological facets (EFs), contains 47,650 unique combinations of the four inputs. The third step involved an aggregation of the EFs into the 3,923 ELUs. This subdivision of the Earth’s surface into relatively fine, ecological land areas is designed to be useful for various types of ecosystem research and management applications, including assessments of climate change impacts to ecosystems, economic and non-economic valuation of ecosystem services, and conservation planning. It is difficult to describe the currency of the ELU product as it was developed from multiple sources from different dates representing different timeframes. Nevertheless, the ELUs can reasonably be considered as a baseline distribution of terrestrial ecological landscapes in the 2000 to 2010 timeframe. 2010 See Supplemental Info Complete As needed -180 180 83.625 -55.998 none ecosystems bioclimates landforms lithology land cover global See Data Use Constraints Although these data have been processed successfully on a computer system at the U.S. Geological Survey, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The U.S. Geological Survey shall not be held liable for improper or incorrect use of the data described and/or contained herein. We make every effort to provide and maintain accurate, complete, usable, and timely information on our Web sites. These data and information are provided with the understanding that they are not guaranteed to be correct or complete. Users are cautioned to consider carefully the provisional nature of these data and information before using them for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences. Conclusions drawn from, or actions undertaken on the basis of, such data and information are the sole responsibility of the user. Any use of trade, product or firm names is for descriptive purposes only and does not imply endorsement by the U. S. Government. U.S. Geological Survey, CLIMATE & LAND-USE Roger Sayre mailing address

Mail Stop 516, 12201 Sunrise Valley Dr

Reston VA 20192-0002 US 703-648-4529 703-648-5542 rsayre@usgs.gov Sayre, R., J. Dangermond, C. Frye, R. Vaughan, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, R. Nauman, W. Derrenbacher, D.Wright, C. Brown, C. Convis, J. Smith, L. Benson, D. Paco VanSistine, H. Warner, J. Cress, J. Danielson, S. Hamann, T. Cecere, A. Reddy, D. Burton, A. Grosse, D. True, M. Metzger, J. Hartmann, N. Moosdorf, H. Dürr, M. Paganini, P. DeFourny, O. Arino, S. Maynard, M. Anderson, and P. Comer. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. Environment as of Metadata Creation: Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; ESRI ArcCatalog 10.2 (Build 3552) Service Pack [N/A] (Build [N/A]) The primary accuracy assessment approach was to compare EFs at randomly generated points to their corresponding locations on high resolution imagery. This match of EFs to imagery was generally very high, with the following level of confirmation observed: Africa, 91%; California, 98%; Australia, 98%; and elsewhere in North America, 97%. The secondary accuracy assessment approaches included comparing ELUs to their corresponding ecosystem labels on the three GEO continental-scale ecosystem maps for South America, the conterminous United States, and Africa. The results for those comparisons were 88%, 87%, and 94%, for South America, the conterminous United States, and Africa, respectively. The comparison of EFs to other sources of thematic information yielded the following probable matches: Africa (81%), California (88%), Australia (96%) and elsewhere in North America (93%). Finally, for the Degree Confluence project points, a 100% match between the EFs and the VGI (photos and descriptions) was observed for Australia, and a 98% match was observed for elsewhere in North America. The quality of the data used in the global stratification will obviously influence the quality of the derived ecosystem products, and anomalous values were found in each of the input layers. While some of these data quality issues are discussed below, it is important to first note that both the input layers and the output products should be considered as collaborative best efforts and works in progress, rather than definitive, current, and complete representations of their themes. The production of any high resolution, globally comprehensive datalayer that characterizes a particular feature of the environment is an ambitious and sometimes very difficult undertaking. These efforts to develop and disseminate best available datasets are appreciated by the scientific community, and making the information broadly available is the best way to ensure it can be improved over time. Identification of anomalous values and other data quality issues in underlying data is important for both the understanding of unexpected results, and for the improvement of the input datasets. The bioclimates layer, as mentioned, represents an interpolated data surface from point observations obtained at meteorological stations. Some areas of the planet are not well-covered by weather stations, and the modeled climate regions in those areas (e.g. western Sahara Desert region) were developed from very little data. Moreover, we felt the original bioclimate regions were underrepresentative of aridity, and we modified the data accordingly. The landforms layer was built from a 250 m global DEM, and 250 m was the base resolution of the effort, given the big data nature of the effort and the difficulty of working at finer spatial resolutions. Nevertheless, 90 m and 30 m global DEMs do exist, and a finer spatial resolution global landforms layer could be developed. The global lithology layer, built as a compendium of a variety of best available regional and national scale lithology datasets, lacks complete attribution at all levels of the hierarchy, and does not attempt to reconcile or harmonize classes across maps from adjacent geographies produced by different organizations. The above-mentioned limitations in the data really represent opportunities for collective improvements in the characterization of ecologically important Earth surface features, and we anticipate working with these data providers and others in future collaborations to advance the quality, currency, resolution, and accessibility of Earth science data. The work is globally comprehensive, but Antarctica is omitted for lack of data representing the four input themes of bioclimates, landforms, lithology, and land cover. Not specified. The base resolution of the prohect is 250 m. A formal accuracy assessment of the vertical positional information in the data set is not applicable. Marc Metzger (Metzger et al.) 2013 Digital and/or Hardcopy Resources University of Edinburgh Unknown Digital and/or Hardcopy Resources 1950 2000 observed Bio_Val Bioclimate Values USGS 2014 Digital and/or Hardcopy Resources USGS USGS Digital and/or Hardcopy Resources Unknown ground condition LF_Val Landform values Jens Hartmann and Nils Moosdorf 2012 Digital and/or Hardcopy Resources University of Hamburg the Global Lithological map (GLiM) Digital and/or Hardcopy Resources Unknown ground condition Lith_Val Lithology values Arino et al., including the European Space Agency 2009 Digital and/or Hardcopy Resources Université Catholique de Louvain Globcover2009 Digital and/or Hardcopy Resources 2008 publication date LC_Val Landcover values The fundamental approach undertaken was to stratify the Earth into physically distinct areas with their associated land cover. The stratification was executed as a geospatial combination of the four input layers (bioclimate, landform, lithology, and land cover) to produce a single raster datalayer where every cell represented a unique combination of the four inputs. Following the production of the foundational raster datalayer, a data reduction step was undertaken to reduce the large number of combinations produced from the union of the input datalayers. The approach was undertaken in three steps. Step One involved acquiring or developing the four input raster base layers (bioclimates, landforms, lithology, and land cover), and reconciling them to a standard, 250 meter global raster framework. The choice of 250 m as the base resolution for the project was based on the availability of a global 250 m digital elevation model (Danielson and Gesch, 2011) whose raster framework could be used as the geospatial reference standard, as well as the desire to improve over the typical square kilometer resolution associated with many global data products (e.g. Gesch et al., 1999; Hijmans et al., 2005). Step Two involved combining all four raster inputs into a single master 250 m global raster datalayer where each cell was the resulting combination of the values from the four input rasters. This foundational raster dataset was called the ecological facets (EFs) layer. Finally, Step Three involved reducing the many classes of EFs resulting from the spatial combination into a more manageable and cartographically approachable number of ecological land units (ELUs). The aggregation was achieved by generalizing the input layer attribute classes. This approach to developing global ELUs can be considered as classification neutral in the sense that no a priori ecosystem classification was used to label the mapped entities. 2014 Raster Grid Cell 67019 172800 1 0.00208333333333 0.00208333333333 Decimal seconds D_WGS_1984 WGS_1984 6378137.0 298.257223563 Attribute Table Table containing attribute information associated with the data set. Producer defined Pixel value Integer value used to attribute the raster dataset as an object identifier and key field Producer defined 1 56908 none Count Count (number) of 250m resolution pixels Producer defined 1 91928869 pixels Bio_Val Bioclimate code for the 37 climate classes Producer defined 0 37 none LF_Val Landform code for the 10 landform classes Producer defined 0 9 none Lit_Val Lithology code for the 16 lithology classes Producer defined 0 16 none Land cover code Land cover code for the 23 land cover classes Producer defined 0 230 EF_Bio_Des Ecological Facet climate class Producer defined Classes include: Arctic, Very Cold Very Wet, Very Cold Wet, Very Cold Moist, Very Cold Semi-Dry, Very Cold Dry, Very Cold Very Dry, Cold Very Wet, Cold Wet, Cold Moist, Cold Semi-Dry, Cold Dry, Cold Very Dry, Cool Very Wet, Cool Wet, Cool Moist, Cool Semi-Dry, Cool Dry, Cool Very Dry, Warm Very Wet, Warm Wet, Warm Moist, Warm Semi-Dry, Warm Dry, Warm Very Dry, Hot Very Wet, Hot Wet, Hot Moist, Hot Semi-Dry, Hot Dry, Hot Very Dry, Very Hot Very Wet, Very Hot Wet, Very Hot Moist, Very Hot Semi-Dry, Very Hot Dry, Very Hot Very Dry EF_LF_Desc Ecological Facet landform class Producer defined Landform classes include: Flat Plains, Smooth Plains, Irregular Plains, Escarpments, Low Hills, Hills, Breaks, Low Mountains, HighMountains/Deep Canyons, Surface Water EF_Lit_Des Ecological Facet lithology class Producer defined Lithology classes include: Siliciclastic Sedimentary Rock, Carbonate Sedimentary Rock, Mixed Sedimentary Rock, Unconsolidated Sediments, Evaporites, Metamorphic Rock, Acidic Plutonics, Intermediate Plutonics, Basic Plutonics, Acidic Volvcanics, Intermediate Volcanics, Basic Volcanics, Pyroclastics, Ice and Glaciers, Water, Undefined EF_GLC_Des Ecological Facet land cover class Producer defined Land cover classes include: Bare Areas; Artificial Surfaces and Urban Areas (>50% pixel composition); Shrubland, Closed to Open (>15%), Broadleaved or Needleleaved, Evergreen or Deciduous, <5m Canopy Height; Herbaceous Vegetation, Closed to Open (>15%) Grassland, Savannas, or Lichens/Mosses; Mosaic Forest or Shrubland (50-70%) with Grassland (20-50%); Mosaic Grassland (50-70%) with Forest or Shrubland (20-50%); Mosaic Vegetation (Grallsland/Shrubland/Forest) (50-70%) with Cropland (20-50%); Rainfed Croplands; Mosaic Cropland (50-70%) with Mixed vegetation (Grassland/Shrubland/Forest) (20-50%); Post-flooding or Irrigated Croplands (or Aquatic); Forest/Woodland, Open (15-40%), Broadleaved Deciduous, >5m Canopy Height;Forest, Closed (>40%), Broadleaved Deciduos, >5m Canopy Height; Forest, Closed to Open (>15%) Broadleaved Evergreen of Semi-Deciduous, >5m Canopy Height; Forest, Closed to Open (>15%) Mixed Broadleaved and Needleleaved, >5m Canopy Height; Forest, Open (15-40%), Needleleaved Deciduous or Evergreen, >5m Canopy Height; Forest, Closed (>40%) Needleleaved Evergreen, >5m Canopy Height; Snow and Ice; Sparse (<15%) Vegetation; Water Bodies; Forest, Closed to Open (>15%) Broadleaved, Regularly Flooded (Semi-Permanently orTemporarily), Fresh or Brackish Water; Grassland or Woody Vegetation, Closed to Open (>15%), Regularly Flooded or Warerlogged Soil, Fresh, Brackish, or Saline Water; Forest or Shrubland, Closed (>40%), Broadleaved, Permanently Flooded, Saline or Brackish Water; No Data (Burnt Areas, Clouds, etc.) EF Ecological Facet (EF) label Producer defined There are 47,560 EFs in the dataset, each one representing a unique combination of bioclimate, landform, lithology, and land cover. ELU_Bio_De Ecological Land Unit bioclimate class Producer defined Aggregated bioclimate classes, including: Arctic, Cold Wet, Cold Moist, Cold Semi-Dry, Cold Dry, Warm Wet, Warm Moist, Warm Semi-Dry, Warm Dry, Hot Wet, Hot Moist, Hot Semi-Dry, and Hot Dry ELU_LF_Des Ecological Land Unit landform class Producer defined Aggregated landform classes, including: Plains, Hills, and Mountains ELU_Lit_De Ecological Land Unit lithology class Producer defined Aggregated lithology classes, including: Pyroclastics, Unconsolidated Sediment or Surface Water, Non-Carbonate Sedimentary Rock, Carbonate Sedimentary Rock, Mixed Sedimentary Rock, Metamorphics, Evaporites, Acidic Volcanics, Acidic Plutonics, Non-Acidic Volcanics, and Non-Acidic Plutonics. ELU_GLC_De Ecological Land Unit land cover class Producer defined Aggregated land cover classes, including: Swampy or Often Flooded Vegetation, Sparse vegetation, Mostly Needleleaf/Evergreen Forest, Mostly Deciduous Forest, Mostly Cropland, Grassland/Sage/Scrub, Bare Areas, Artificial Surface or Urban Area, and Surface Water ELU Ecological Land Unit label Producer defined The ELUs are an aggregation of the EFs by first aggreagting the classes of the 4 input layers, and then combining them. There are 3,923 ELUs mapped. The ELUs are the core product in this dataset, describing almost 4,000 ecologically distinct areas based on land surface characteristics. ELU_Site ELU climate and landform only groupings Producer defined Unknown Eco_Pot EF climate, landform, and lithology only groupings Producer defined Unknown ELU_Pot ELU climate, landform, and lithology only groupings Producer defined Unknown The entity and attribute information provided here describes the tabular data associated with the data set. Please review the detailed descriptions that are provided (the individual attribute descriptions) for information on the values that appear as fields/table entries of the data set. The entire dataset colludes to the integral ELU attribute field; and can be seen as a visual construction of the ELU attribute field. ELU stands for Ecological Land Units; and is a combination of Bioclimate, Landcover, Lithology, and Landforms within a 250m resolution area. The entity and attribute information was generated by the individual and/or agency identified as the originator of the data set. Please review the rest of the metadata record for additional details and information. U.S. Geological Survey, Southwest Region Jill Janene Cress Computer Scientist mailing address

Mail Stop 980, West 6th Ave. & Kipling St., DFC Bldg. 25

Lakewood CO 80225-0046 US 303-236-1248 303-236-5349 jjcress@usgs.gov Distributor assumes no liability for misuse of data; any other information has been covered in data use constraints. Raster Digital Data Set http://rmgsc.cr.usgs.gov/outgoing/ecosystems/Global/ None. No fees are applicable for obtaining the data set. 20141204 Devon M Burton U.S. Geological Survey, Climate and Land-Use Change Student Intern mailing address

Mail Stop 519, 12201 Sunrise Valley Dr

Reston VA 20192 US 703-648-4561 703-648-5542 dburton@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998