Interior Southeast Grasslands (Southeast Blueprint Indicator ) [U.S. Fish and Wildlife Service] {2023} Full Details
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Full Details
- Title:
- Interior Southeast Grasslands (Southeast Blueprint Indicator ) [U.S. Fish and Wildlife Service] {2023}
- Description:
- Reason for Selection Native grasslands are important for many endemic species, provide critical habitat and food for pollinators, and are often hotspots for biodiversity. Once a predominant ecosystem type, grasslands have significantly declined from their historical extent and often occur in fragmented patches like fencerows, powerline corridors, roadsides, corners of old fields, and small clearings among forests. In part because of the regular disturbance (e.g., mowing, fire) typically required to maintain high-quality grasslands, they are difficult to detect through remote sensing and are not well-captured by other indicators. In addition, grassland birds are experiencing significant declines and arecurrently off-track for meeting the SECAS 10% goal, so it is important that the Blueprint capture known and potential grassland habitat. Input Data Base Blueprint 2022 subregions Central Hardwoods Ecological Potential Vegetation The Nature Conservancy's Resilient and Connected Network project:Geophysical settings Known grassland prairies dataset for the Middle Southeast subregion, provided by Toby Gray with Mississippi State University (available on request by emailingtoby@gri.msstate.edu); this is an improved version of theKnown Prairie Patches in the Gulf Coastal Plains and Ozarks (GCPO)layer Known Piedmont prairie locations in the South Atlantic subregion: We identified known prairie locations by requesting spatial data on known prairies from the 74 members of the Piedmont Prairie Partnership mailing list and other prairie managers (Wake County Open Space program and Prairie Ridge Ecostation in NC). We combined that information with known locations in Virginia aggregated by the Virginia Natural Heritage Program (available on request by emailingrua_mordecai@fws.gov).Southeastern Grasslands Institute polygons fromselected iNaturalist projects. We used only projects with polygons digitized at a fine resolution and did not include projects with more coarse polygons covering a large area. Specific projects used were: allegheny-mountains-riverscour-barrens, big-south-fork-riverscour-barrens-1, big-south-fork-riverscour-barrens-2, big-south-fork-riverscour-barrens-4-us, big-south-fork-riverscour-barrens-6, biodiversity-of-piedmont-granite-glades-outcrops, bluff-mountain-fen, caney-fork-sandstone-riverscour-barrens-and-glades, clear-creek-sandstone-riverscour-barrens, clear-fork-river-riverscour-barrens, craggy-mountains-mafic-outcrops-and-barrens, cumberland-plateau-escarpment-limestone-barrens, cumberland-river-limestone-riverscour-glades, daddy-s-creek-riverscour-barrens, dunbar-cave-prairie-restoration, eastern-highland-rim-limestone-riverscour-glade, emory-river-sandstone-riverscour-barrens, falls-of-the-ohio-river-limestone-riverscour-glade, flat-rock-cedar-glades-and-barrens-state-natural-area, grasshopper-hollow-fen, gunstocker-glade, hiwassee-river-phyllite-riverscour-glade, ketona-dolomite-barrens, laurel-river-riverscour-barrens-and-glades, lime-hills-limestone-barrens, limestone-barrens-of-the-western-valley-of-the-tennessee-river, little-mountains-limestone-barrens, little-river-canyon-riverscour-barrens-and-glades, moulton-valley-limestone-glades, mulberry-fork-of-black-warrior-river-riverscour-barrens-and-glades, muldraugh-s-hill-limestone-barrens, nashville- basin-limestone-glades, new-river-riverscour-barrens, obed-river-sandstone-riverscour-barrens, outer-bluegrass-dolomite-barrens, ridge-and-valley-sandstone-outcrops, rock-creek-sandstone-riverscour-barrens, rockcastle-river-sandstone-riverscour-barrens, shawnee-hills-sandstone-glades-and-outcrops, southern-blue-ridge-mountains-grass-balds, southern-blue-ridge-mountains-serpentine-barrens, southern-blue-ridge-phyllite-outcrops, southern-ridge-and-valley-limestone-glades, southern-ridge-and-valley-shale-barrens, southern-ridge-and-valley-siltstone-barrens, tennessee-ridge-and-valley-dolomite-barrens-and-woodlands-tn-us, the-farm-prairie-and-oak-savanna, tin-top-road-savanna, western-allegheny-escarpment-limestone-barrens, western-highland-rim-limestone-glade-and-barrens, western-valley-limestone-barrens-decatur-co-north-us, western-valley-limestone-barrens-hardin-wayne-cos, western-valley-limestone-barrens-perry-co, western-valley-silurian-limestone-barrens, white-s-creek-sandstone-riverscour-barrens-and-glades Esri U.S. Electric Transmission Line dataset (last updated 4-3-2022) Rangeland Analysis Platform (RAP) vegetation cover v3;download the data (vegetation-cover-v3-2021.tif, last updated 8-1-2022) Protected Areas Database of the United States (PAD-US)2.1 Combined Fee Easement 2019 National Land Cover Database (NLCD) Southeast Blueprint 2023 extent Mapping Steps Define the extent of this indicator using the Base Blueprint 2022 subregions layer. Include as part of the extent of this indicator polygons with the following values in the SubRgn_II field: ‘Central Gulf Coastal Plain', ‘Interior Plateau', ‘Mid East Gulf Coastal Plain', ‘North Appalachians', ‘North Piedmont', ‘Ozarks and Plains', ‘South Appalachians', ‘South Piedmont'. Merge into a single feature class the individual known grassland polygons from the Piedmont Prairie Partnership, Middle Southeast subregion, and Southeastern Grasslands Institute. For the known grassland polygons that were originally .kml files, convert to feature class using the Batch Import Data function. Use a Repair Geometry function to clean up the converted .kml files. Then, combine all feature classes using the Union function. From the PAD-US 2.1 Combined Fee Easement layer, select the polygons that are part of the Grassland Reserve Program using the following selection: Loc_Ds = ‘GRP' Or Unit_Nm LIKE ‘%Grasslands Reserve Program%' Or Unit_Nm LIKE ‘%Grassland Reserve Program%'. Combine the known grassland and Grassland Reserve Program polygons using a Union function. Convert to a raster and assign known grasslands a value of 5. Buffer the full known grassland layer by 800 m to capture the habitat needs of bumble bees for foraging, nesting, and overwintering (Schweitzeret al. 2012). Convert the buffered areas to a raster and assign them a value of 4. Remove pixels that identified in the NLCD as developed high intensity, developed medium intensity, or open water. Identify classes in the TNC geophysical settings raster associated with grassland geology. We started by using the following classes in the CL_GEOSOIL field: ‘Calcareous Sedimentary', ‘Mafic/Intermediate Granitic', ‘Silt/Clay over Limestone', ‘Ultramafic'. This did not capture everything we needed, so we also used the following classes in the Full_Name class: ‘Calcareous Sedimentary', ‘Moderately Calcareous Sedimentary', ‘Mid Elevation Acidic Sedimentary'. Classify all pixels that meet the above conditions as 1 and classify everything else as 0. In the Central Hardwoods region, the Ecological Potential Vegetation layer provides a better representation of grassland geology than TNC geophysical settings. Use as grassland geology pixels with the following values in the VEG_ORDER field: 1 = Prairie/Grassland, 2 = Prairie/Savanna (< 20% canopy), 3 = Prairie/Savanna (Barrens) (< 20% canopy), 4 = Glade/Savanna Mosaic (< 20% canopy), 5 = Oak Open Woodland (20-50% canopy), 7 = Pine/Bluestem Open Woodland (20-50% canopy). Classify all pixels that meet the above conditions as 1 and classify everything else as 0. In the area where Central Hardwoods Potential Ecological Vegetation layer overlaps with TNC resilient land settings, use only the Central Hardwoods layer. Use the Mosaic to New Raster function, using "first" as the mosaic operator, making sure that the Central Hardwoods layer is listed first. In the resulting raster, values of 1 are considered "grassland geology." Use the transmission lines to identify areas of potentially compatible grassland management. Convert these lines from vector to a 30 m raster. Use the RAP vegetation cover perennial forbs and grasses layer to identify additional areas of potentially compatible grassland management. Extract areas classified as >30% perennial forb and grass cover. Combine the transmission line and perennial forbs and grasses layers created above into single raster representing areas of potentially compatible grassland management. From the combined potentially compatible grassland management layer, remove some pixels using the NLCD. Expand by 1 pixel areas classified as the 3 highest urban classes (developed high intensity, developed medium intensity, or developed low intensity). Remove all areas that overlap or are within one 30 m pixel of these urban classes. Next, further limit these potentially compatible management areas to landcover types in the NLCD we identified as restorable (developed open space, barren land, deciduous forest, evergreen forest, mixed forest, scrub/shrub, herbaceous, hay/pasture). In this reduced potentially compatible grassland management area, there are some isolated pixels. Remove all pixel clumps (using an 8-neighbor rule) that contain only 1 or 2 pixels. Where the potentially compatible management layer intersects the grassland geology layer, create a raster and assign those areas a value of 3. Where the potentially compatible management layer does not intersect the grassland geology layer, create a raster and give those areas a value of 2. Combine all 5 rasters produced above using a Cell Statistics "MAX" function that retains the maximum value for each pixel across all inputs. This results in a raster with 5 classes, seen in the final indicator values below. Clip the resulting layer to the unique indicator extent defined in the first step. As a final step, clip to the spatial extent of Southeast Blueprint 2023. Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator values Indicatorvaluesareassignedasfollows:5 = Known grassland4 = Known grassland buffer3 = Potentially compatible management within grassland geology (undeveloped powerlineright-of-way or perennial forbs and grasses)2 = Potentially compatible management outside of grassland geology (undeveloped powerline right-of-way or perennial forbs and grasses)1 = Grassland geology 0 = Grassland less likely Known Issues Some known grassland locations are omitted due to confidentiality concerns around potential poaching and disturbance of rare plant species. The powerline dataset does not include smaller powerline rights-of-way that can also be important grassland habitat. Important roadside grassland habitat often occurs in these smaller power line rights-of-way. We are continuing to look for data sources that depict these smaller power line rights-of-way across the full Southeast region. Other locations of disturbance that can help maintain grasslands, like sewer lines and pipelines, are not included in this indicator. We have been unable to find a multi-state data layer that depicts sewer line locations. A national dataset on pipeline locations does exist, but its terms of use prevent us from using it in an indicator. Locations of many managed grasslands, especially in the Central Hardwoods subregion, are not yet included in the known grassland class. We are working to obtain more spatial data on the locations of managed grasslands within the region scored in this indicator. While other grassland-specific indicators cover important areas not scored in this indicator, there is a gap in Northeast Oklahoma between the area scored in this indicator and where the complementary indicator, Great Plains Perennial Grasslands, takes over. Neither the powerline data nor the RAP perennial forbs and grasses layer differentiate between native and non-native grassland plant species. They also don't account for other non-compatible management practices that may occur, like non-selective herbicide application. Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (emailhilary_morris@fws.gov). Literature Cited Allred, B.W., B.T. Bestelmeyer, C.S. Boyd, C. Brown, K.W. Davies, M.C. Duniway, L.M. Ellsworth, T.A. Erickson, S.D. Fuhlendorf, T.V. Griffiths, V. Jansen, M.O. Jones, J. Karl, A. Knight, J.D. Maestas, J.J. Maynard, S.E. McCord, D.E. Naugle, H.D. Starns, D. Twidwell, and D.R. Uden. 2021. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution. [https://dx.doi.org/10.1111/2041-210x.13564]. Oak Ridge National Laboratory, Los Alamos National Laboratory, Idaho National Laboratory, and National Geospatial-Intelligence Agency Homeland Security Infrastructure Program Team. Last updated April 2022. Accessed June 23, 2022. Homeland Infrastructure Foundation-Level Data: Electric Power Transmission Lines. [https://fws.maps.arcgis.com/home/item.html?id=d4090758322c4d32a4cd002ffaa0aa12]. Schweitzer, D.F., N.A. Capuano, B.E. Young, and S.R. Colla. 2012. Conservation and management of North American bumble bees. NatureServe, Arlington, Virginia, and USDA Forest Service, Washington, D.C. [https://www.natureserve.org/sites/default/files/publications/files/cons-mgmt-na-bumblebees-web-rev.pdf]. Southeast Conservation Adaptation Strategy. 2021. Recent Trends in Southeastern Ecosystems (2021): Measuring Progress toward the SECAS Goal. [https://secassoutheast.org/pdf/SECAS-goal-report-2021.pdf]. U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2020, Protected Areas Database of the United States (PAD-US) 2.1: U.S. Geological Survey data release. [https://doi.org/10.5066/P92QM3NT]. U.S. Geological Survey (USGS). Published June 2021. National Land Cover Database (NLCD) 2019 Land Cover Conterminous United States. Sioux Falls, SD. [https://doi.org/10.5066/P9KZCM54]. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [https://doi.org/10.1016/j.isprsjprs.2018.09.006].
- Creator:
- Department of the Interior
- Provider:
- U.S. Fish and Wildlife Service Open Data
- Resource Class:
- Imagery and Web services
- Temporal Coverage:
- 2023
- Date Issued:
- 2022-09-06
- Place:
- Rights:
- The United States Fish and Wildlife Service (Service) shall not be held liable for improper or incorrect use of the data described and/or contained herein. While the Service makes every reasonable effort to ensure the accuracy and completeness of data provided for distribution, it may not have the necessary accuracy or completeness required for every possible intended use. The Service recommends that data users consult the associated metadata record to understand the quality and possible limitations of the data. The Service creates metadata records in accordance with the standards endorsed by the Federal Geographic Data Committee. As a result of the above considerations, the Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of the data. It is the responsibility of the data user to use the data in a manner consistent with the limitations of geospatial data in general and these data in particular. Although these data have been processed successfully on a computer system at the Service, no warranty, expressed or implied, is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This applies to the use of the data both alone and in aggregate with other data and information.
- Access Rights:
- Public
- Format:
- Imagery
- Language:
- English
- Date Added:
- 2023-08-11