A new global map of ecological land units

Background/Question/Methods

We report on a massive biophysical stratification of the planet at a finest yet-attempted spatial resolution (250 m) to produce a new global map of distinct physical environments and their associated land cover (hence an example of "ecological science at the frontier"). Production of the map and associated data was commissioned by the Group on Earth Observations (GEO), a consortium of almost 100 nations collaborating to build the Global Earth Observation System of Systems (GEOSS). The global ecosystem-mapping task, as defined here, is a key program within the GEO Biodiversity Observation Network and the GEO Ecosystems Initiative. We define ecosystems as distinct physical environments and their associated vegetation, and used the best available datasets of global bioclimates, global landforms, global geology, and global land cover.

Results/Conclusions

A combination of these four in a rubric both classification-neutral and data-driven, resulted in over 47,000 "ecological facets." These were then aggregated to nearly 4000 distinct terrestrial "ecological land units." The intended use of the data and map is to provide scientific support for planning and management, and to enable understanding of impacts to ecosystems from climate change and other disturbances. The map and data should also prove useful as an ecologically meaningful spatial accounting framework for assessments of the economic and social values of ecosystem goods and services, thus fulfilling one of the main recommendations of the White House in the President's Council of Advisors on Science and Technology Report on Sustainable Environmental Capital. The data are available in both the public domain and as ArcGIS Online content with considerable value-added analytical and visualization functionality. Exploration, testing, and use cases are welcomed by the ecological community, as well as the delineation of additional ecological regions using the ecophysiographic stratification approach with local, finer resolution datasets.