The Great Plains Landscape Conservation Cooperative (GPLCC, https://www.fws.gov/science/catalog) is a partnership that provides applied science and decision support tools to assist natural resource managers conserve plants, fish and wildlife in the mid- and short-grass prairie of the southern Great Plains. It is part of a national network of public-private partnerships — known as Landscape Conservation Cooperatives (LCCs, http://www.fws.gov/science/shc/lcc.html) — that work collaboratively across jurisdictions and political boundaries to leverage resources and share science capacity. The Great Plains LCC identifies science priorities for the region and helps foster science that addresses these priorities to support wildlife conservation throughout the Great Plains region. It also assists partners in building their own capacity to address scientific challenges associated with our rapidly changing environment.These data were compiled because the information did not previously exist as a single resource for the GPLCC area. They are intended to inform local and regional conservation and management strategies with a complete regional perspective. Abstract provided by original data sources: "The LANDFIRE vegetation layers describe the following elements of existing and potential vegetation for each LANDFIRE mapping zone: environmental site potentials, biophysical settings, existing vegetation types, canopy cover, and vegetation height. Vegetation is mapped using predictive landscape models based on extensive field reference data, satellite imagery, biophysical gradient layers, and classification and regression trees.DATA SUMMARYThe biophysical settings (BpS) data layer represents the vegetation that may have been dominant on the landscape prior to Euro-American settlement and is based on both the current biophysical environment and an approximation of the historical disturbance regime. It is a refinement of the environmental site potential map; in this refinement, we attempt to incorporate current scientific knowledge regarding the functioning of ecological processes - such as fire - in the centuries preceding non-indigenous human influence. Map units are based on NatureServe's Ecological Systems classification, which is a nationally consistent set of mid-scale ecological units (Comer and others 2003). LANDFIRES's use of these classification units to describe biophysical settings differs from their intended use as units of existing vegetation. As used in LANDFIRE, map unit names represent the natural plant communities that may have been present during the reference period. Each BpS map unit is matched with a model of vegetation succession, and both serve as key inputs to the LANDSUM landscape succession model (Keane and others 2002). The LANDFIRE BpS data layer is similar in concept to potential natural vegetation group layers produced in efforts related to fire regime condition class (Schmidt and others 2002; http://www.frcc.gov ).The first step in producing the LANDFIRE BpS data layer is the creation of an environmental site potential (ESP) layer. To create the ESP data layer, we first assign field plots to one of the ESP map unit classes (similar to BpS units in nomenclature but sometimes different in meaning). Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. Assignments are based on presence and abundance of the indicator plant species recorded on the plots and on the ecological amplitude and competitive potential of these species. We then intersect plot locations with a series of 30-meter spatially-explicit gradient layers. Most of the gradient layers used in the predictive modeling of ESP are derived using the WX-BGC simulation model (Keane and Holsinger, in preparation; Keane and others 2002). WX-BGC simulations are based largely on spatially extrapolated weather data from DAYMET (Thornton and others 1997; Thornton and Running 1999; http://www.daymet.org) and on soils data in STATSGO (NRCS 1994). Additional indirect gradient layers, such as elevation, slope, and indices of topographic position, are also used. We use data from plot locations to develop predictive classification tree models, using See5 data mining software (Quinlan 1993; Rulequest Research 1997), for each LANDFIRE map zone. These decision trees are applied spatially to predict the ESP for every pixel across the landscape.Next, map units in the ESP layer are evaluated and, in some cases, split to reflect different fire regimes. These splits are made within each LANDFIRE map zone using a combination of plot data, gradient data, input from vegetation dynamics models, and additional See5 classification tree models. We then merge split map units back into the original ESP layer to create a BpS data layer. Finally, pixel values in the BpS layer are, in some cases, modified based on a comparison with the LANDFIRE existing vegetation type (EVT) layer created with the use of 30-meter Landsat ETM satellite imagery. We make such modifications only in non-vegetated areas (such as water, rock, snow, or ice) and where information in the EVT layer clearly enables a better depiction of the biophysical settings concept.The BpS data layer is used in LANDFIRE to depict reference conditions of vegetation across landscapes. The actual time period for this data set is a composite of both the historical context provided by the fire regime and vegetation dynamics models and the more recent field and geospatial data used to create it. The weather data used in DAYMET were compiled from 1980 to 1997. Refer to spatial metadata for date ranges of field plot data and satellite imagery for each LANDFIRE map zone."Data were the best available at the time of compilation (2011) with current information represented by a combination of national-scale datasets and state or other regional data (e.g. soils) that could be reasonably aggregated.
