Reason for Selection Large areas of intact natural habitat are favorable for conservation of numerous species, including reptiles and amphibians, birds, and large mammals. The Esri Green Infrastructure data covers the entire United States and has been used in other broad-scale conservation planning efforts, so using this existing data helps align the Blueprint with other conservation efforts and reduce duplication of effort. We chose to use “Core Size (acres)” as the metric for this indicator. Other evaluation attributes included in this index, such as the default “Core Score”, were less suitable because they were calculated using inputs that are duplicative of other indicators.Input Data2021 National Land Cover Database (NLCD)Southeast Blueprint 2024 extentEsri’s Intact Habitat Cores 2023, accessed 2-16-2024: Core Size (Acres); to download, select “Open in ArcGIS Desktop” and make a local copy. According to Esri’s data description for the 2023 intact habitat cores update: “This layer represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2019 National Land Cover Data. Cores were derived from all “natural” landcover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons.”Mapping StepsConvert the Esri Intact Habitat Cores 2023 polygons to a 30 m raster using the values in the “Acres” field. We used the feature layer map service as the input in the Polygon to Raster function in the code.Reclassify the above raster into 4 classes, seen in the final indicator values below.Use NLCD to remove zero values in deep marine areas, which are outside the scope of this terrestrial indicator. Use a conditional statement to assign NoData to any area with a pixel value >0 in the NLCD.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:3 = Large core (>10,000 acres)2 = Medium core (>1,000-10,000 acres)1 = Small core (>100–1,000 acres) 0 = Not a coreKnown IssuesThe core analysis for this indicator is based on the 2019 NLCD, not the more recent 2021 NLCD. Esri has shared the scripts and input data used to create this layer, which may also help update this indicator in the future.Even small dirt roads serve as hard boundaries for habitat cores. While this makes sense for some species, this indicator likely underestimates the effective size of the patch for some more mobile animals.Waterbodies like reservoirs are also considered part of habitat cores, so this layer likely overestimates the effective size of the habitat core for most species.Many intact habitat cores have a speckling of small altered areas inside of them. In some cases, like in areas of west TX with concentrated oil wells, there can be many alterations in a gridded pattern across the entire core. This indicator underestimates the cumulative impacts of interior alterations—especially when the small altered footprints are densely packed in a grid within a habitat core.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 (email hilary_morris@fws.gov).Literature CitedEsri Green Infrastructure Center. Data Description: Detailed Description and Methodology for Intact Habitat Cores. PDF. Last updated June 30, 2023. [https://nation.maps.arcgis.com/home/item.html?id=047d9b05e0c842b1b126bc0767acfd5e]. Esri Green Infrastructure Center, Inc. 2023. Intact Habitat Cores (2023). [https://www.arcgis.com/home/item.html?id=b404b86a079a48049cb50272df23267a].

Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.

This dataset features detailed maps of material stock and population for Germany on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.

Temporal extent

The data contains annual maps from 1985 to 2018.

Data format and units

Per German federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.

All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.

The dataset features

population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)

mass (in tons) of …

total material stock

… material stock in buildings

… in commercial and industrial buildings

… in multi-family residential buildings

… in single-family residential buildings

… in high-rise buildings

… in lightweight buildings

… material stock in road infrastructure

… material stock in rail infrastructure

… material stock in other infrastructure

Material stock in high-rise and lightweight buildings is not featured in the corresponding publication due to its overall negligible amount. It is, however, included here for completeness.

Further information

For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Corresponding publication

Schug, F., Frantz, D., Wiedenhofer, D., Virág, D., Haberl, H., van der Linden, S., Hostert, P. (in rev.): High-resolution mapping of 33 years of material stock and population growth in Germany. Journal of Industrial Ecology

Funding

This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

Reason for SelectionRiver networks with a variety of connected stream size classes are more likely to have a wide range of available habitat to support a greater number of species. This will help retain aquatic biodiversity in a changing climate by allowing species to access climate refugia and move between habitats. Input DataBase Blueprint 2022 extent Southeast Blueprint 2024 extentSoutheast Aquatic Resources Partnership’s Network Complexity metric The Southeast Aquatic Resources Partnership (SARP) developed metrics for their Southeast Aquatic Barrier Prioritization Tool. On June 7, 2023, Brendan Ward with Astute Spruce (software developer working on behalf of SARP) shared high resolution NHDPlus flowlines with attributes depicting the network complexity attribute for each functional network (see definition of “functional network” below). The network complexity attribute calculates the total number of different stream size classes within each functional network. SARP assigned stream and river reaches to size classes based on total drainage area:1a: Headwaters (<3.861 sq mi)1b: Creeks (≥3.861 and <8.61 sq mi)2: Small Rivers (≥38.61 and <200 sq mi)3a: Medium Tributary Rivers (≥200 and <1,000 sq mi)3b: Medium Mainstem Rivers (≥1,000 and <3,861 sq mi)4: Large Rivers (≥3,861 and <9,653 sq mi)5: Great Rivers (≥9,653 sq mi)Functional NetworkSARP compiles the Southeast Aquatic Barrier Inventory from national, regional, state, and local partner databases across the Southeast region. These include the National Inventory of Dams (2018), National Anthropogenic Barrier Dataset (2012), databases from state dam safety programs and other state agencies, information from local partners, and dam locations estimated by SARP. Waterfalls are compiled from national datasets and local partners. Dams and waterfalls are snapped to hydrologic networks extracted from the National Hydrography Dataset (NHD) - High Resolution Beta version. All dams and waterfalls are treated as “hard” barriers for network connectivity analysis. Aquatic networks are cut at the location of each barrier. All network “loops” (non-primary flowlines) are omitted from the analysis. An upstream functional network is constructed by traversing upstream from each barrier through all tributaries to the upstream-most origination point or upstream barrier, whichever comes first. Additional functional networks are defined from downstream-most non-barrier termination points, such as marine areas or other downstream termination points. The total length of all network segments within a functional network is summed to calculate the total network length of each functional network. Each flowline segment within the NHD is assigned to a size class based on total drainage area. This was used to calculate the number of unique size classes per functional network. Estimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018). National Hydrography Dataset Plus High Resolution (NHDPlus HR) National Release catchments, accessed 11-30-2022; download the data; view the user guideNHDPlus Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2); view the user guideCatchments A catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD productsThe glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key termsMapping StepsMerge the functional network lines from the 11 subregions delivered by SARP into one feature class. Convert the combined SARP network complexity values from the high resolution NHDPlus flowlines to a 30 m raster. Clip to the Base Blueprint 2022 extent.Apply the network complexity values to the NHDPlus HR catchments using the ArcPy Zonal Statistics “MAJORITY” function. This results in a raster where each catchment is assigned the majority network complexity value that intersects the catchment. Most catchments have only one intersecting line, but for catchments with interior dams, the analysis uses the majority network complexity value.To define the analysis extent of the indicator, make a copy of the NHDPlus HR catchments and convert it to raster, assigning it a value of 1.Clip the network complexity raster to the EPA floodplain layer. During this step, assign a value of 0 to areas outside the EPA floodplain. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Some areas of the floodplain are not scored in the resulting layer because they are missing SARP network complexity values. This is due to the fact that some small reaches, such as braids and loops in the stream network, are not assigned a network complexity value. SARP has to remove loops and braided streams in order to calculate network complexity because the analysis can only accommodate a one-way flow of water. Identify these holes in the floodplain and fill them in by looking at the network complexity value of the surrounding pixels and assigning the maximum value to the missing catchments in the floodplain. Note: This simplifies a complex series of analysis steps. For more specifics, please consult the code.Clip the network complexity raster to the NHDPlus V2.1 medium resolution catchments. This removes estuarine areas that are outside the intended scope of this indicator, particularly on the NC coast.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:7 = 7 connected stream classes6 = 6 connected stream classes5 = 5 connected stream classes4 = 4 connected stream classes3 = 3 connected stream classes2 = 2 connected stream classes1 = 1 connected stream class0 = Not identified as a floodplainKnown IssuesThis indicator does not include other smaller scale attributes of complexity (e.g., sinuosity, mixtures of riffles/pools/runs) that influence the habitat quality of the connections. The EPA Estimated Floodplain layer sometimes misses the small, linear connections made by artificial canals, especially when they go through areas that wouldn’t naturally be part of the floodplain. As a result, some areas (like lakes) that are connected via canals may appear to be disconnected, but still receive high scores.Small headwaters and creeks are not included in this indicator because the EPA estimated floodplain dataset does not include them.While this indicator generally includes the open water area of reservoirs, some open water portions of reservoirs (e.g., Kerr Lake in NC/VA) are missing from the estimated floodplain dataset.This indicator likely overestimates the number of connected stream classes in some areas due to missing barriers in the inventory, such as smaller dams or road-stream crossings. It could also underestimate the number of connected stream classes, given the extensive ongoing restoration work to improve aquatic connectivity across the SECAS geography. If you identify a missing barrier or a removed barrier, please let SARP know by emailing Kat Hoenke at kat@southeastaquatics.net. You can learn more about the current inventory of dams and road-stream crossings by visiting https://connectivity.sarpdata.com/.SARP did a lot of work to snap the dam locations to the line network, but there are likely still dams (including some large ones) that didn’t get snapped correctly due to the large distance between the centerpoint of the dam and the nearest flowline. If you see any of these cases when reviewing the data, please let SARP know (the giveaway is networks that look longer than they should on a map).In the area just south of Guadalupe Mountains National Park in West Texas, this indicator depicts the floodplain as a series of straight lines that poorly match the actual floodplain. This is due to an error in the EPA floodplain map used in this indicator.Due to issues with the national NHDPlus HR catchments layer, there are a handful of missing catchments (e.g., northwest TX, coastal LA, and eastern NC). These places receive a value of NoData in the indicator and are therefore underprioritized. We are investigating ways to resolve this in future updates.This indicator may slightly overvalue network complexity in WV compared to other Southeast states because the coverage of dams and barriers data is not as comprehensive. While the dams and barriers data coverage improved sufficiently for us to use the network complexity indicator across the entire state of WV in 2023 for the first time (whereas it was only used in the southern part of the state in 2022), there is still room for improvement and we anticipate significant

Reason for SelectionHardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Hardbottom is also sometimes associated with chemosynthetic communities that form around cold seeps or hydrothermal vents. In these unique ecosystems, micro-organisms that convert chemicals into energy form the base of complex food webs (Love et al. 2013). Hardbottom and associated species provide important habitat structure for many fish and invertebrates (NOAA 2018). Hardbottom areas serve as fish nursery, spawning, and foraging grounds, supporting commercially valuable fisheries like snapper and grouper (NCDEQ 2016).According to Dunn and Halpin (2009), “hardbottom habitats support high levels of biodiversity and are frequently used as a surrogate for it in marine spatial planning.” Artificial reefs arealso known to provide additional habitat that is quickly colonized to provide a suite of ecosystem services commonly associated with naturally occurring hardbottom (Wu et al. 2019). We did not include active oil and gas structures as human-created hardbottom. Although they provide habitat, because of their temporary nature, risk of contamination, and contributions to climate change, they do not have the same level of conservation value as other artificial structures.Input DataSoutheast Blueprint 2024 extentSoutheast Blueprint 2024 subregionsCoral & hardbottomusSEABED Gulf of America sediments, accessed 12-14-2023; download the data; view and read more about the data on the National Oceanic and Atmospheric Administration (NOAA) Gulf Data Atlas (select Physical --> Marine geology --> 1. Dominant bottom types and habitats)Bureau of Ocean Energy Management (BOEM) Gulf of America, seismic water bottom anomalies, accessed 12-20-2023The Nature Conservancy’s (TNC)South Atlantic Bight Marine Assessment(SABMA); chapter 3 of the final report provides more detail on the seafloor habitats analysisNOAA deep-sea coral and sponge locations, accessed 12-20-2023 on the NOAA Deep-Sea Coral & Sponge Map PortalFlorida coral and hardbottom habitats, accessed 12-19-2023Shipwrecks & artificial reefsNOAA wrecks and obstructions layer, accessed 12-12-2023 on the Marine CadastreLouisiana Department of Wildlife and Fisheries (LDWF) Artificial Reefs: Inshore Artificial Reefs, Nearshore Artificial Reefs, Offshore and Deepwater Artificial Reefs (Google Earth/KML files), accessed 12-19-2023Texas Parks and Wildlife Department (TPWD) Artificial Reefs, accessed 12-19-2023; download the data fromThe Artificial Reefs Interactive Mapping Application(direct download from interactive mapping application)Mississippi Department of Marine Resources (MDMR) Artificial Reef Bureau: Inshore Reefs, Offshore Reefs, Rigs to Reef (lat/long coordinates), accessed 12-19-2023Alabama Department of Conservation and Natural Resources (ADCNR) Artificial Reefs: Master Alabama Public Reefs v2023 (.xls), accessed 12-19-2023Florida Fish and Wildlife Conservation Commission (FWC):Artificial Reefs in Florida(.xlsx), accessed 12-19-2023Defining inland extent & split with AtlanticMarine Ecoregions Level III from the Commission for Environmental Cooperation North American Environmental Atlas, accessed 12-8-20212023 NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024National Oceanic and Atmospheric Administration (NOAA)Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic;read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (NCCOS) (matthew.poti@noaa.gov)Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S: unpublished NOAA data anddraft final report entitled Assessment of Benthic Habitats for Fisheries Managementprovided on 1-28-2021 by Matt Poti with NOAA NCCOS (matthew.poti@noaa.gov)Mapping StepsNote: Most of the mapping steps were accomplished using the graphical modeler in QGIS 3.34. Individual models were created to combine data sources and assign ranked values. These models were combined in a single model to assemble all the data sources and create a summary raster. Create a seamless vector layer to constrain the extent of the Atlantic coral and hardbottom indicator to marine and estuarine areas <1 m in elevation. This defines how far inland it extends.Merge together all coastal relief model rasters (.nc format) using the create virtual raster tool in QGIS.Save the merged raster to .tif format and import it into ArcPro.Reclassify the NOAA coastal relief model data to assign a value of 1 to areas from deep marine to 1 m elevation. Assign all other areas (land) a value of 0.Convert the raster produced above to vector using the raster to polygon tool.Clip to the 2024 Blueprint subregions using the pairwise clip tool.Hand-edit to remove terrestrial polygons (one large terrestrial polygon and the Delmarva peninsula).Dissolve the resulting data layer to produce a seamless polygon defining marine and estuarine areas <1 m in elevation.Hand-edit to select all but the main marine polygon and delete.Define the extent of the Gulf version of this indicator to separate it from the Atlantic. This split reflects the extent of the different datasets available to represent coral and hardbottom habitat in the Atlantic and Gulf, rather than a meaningful ecological transition.Use the select tool to select the Florida Keys class from the Level III marine ecoregions (“NAME_L3 = "Florida Keys"“).Buffer the “Florida Keys” Level III marine ecoregion by 2 km to extend it far enough inland to intersect the inland edge of the <1 m elevation layer.Reclassify the two NOAA Atlantic hardbottom suitability datasets to give all non-NoData pixels a value of 0. Combine the reclassified hardbottom suitability datasets to define the total extent of these data. Convert the raster extent to vector and dissolve to create a polygon representing the extent of both NOAA hardbottom datasets.Union the buffered ecoregion with the combined NOAA extent polygon created above. Add a field and use it to dissolve the unioned polygons into one polygon. This leaves some holes inside the polygon, so use the eliminate polygon part tool to fill in those holes, then convert the polygon to a line.Hand-edit to extract the resulting line between the Gulf and Atlantic.Hand-edit to use this line to split the <1 m elevation layer created earlier in the mapping steps to create the separation between the Gulf and Atlantic extent.From the BOEM seismic water bottom anomaly data, extract the following shapefiles: anomaly_confirmed_relic_patchreefs.shp, anomaly_Cretaceous.shp, anomaly_relic_patchreefs.shp, seep_anomaly_confirmed_buried_carbonate.shp, seep_anomaly_confirmed_carbonate.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_positives.shp, seep_anomaly_positives_confirmed_gas.shp, seep_anomaly_positives_confirmed_oil.shp, seep_anomaly_positives_possible_oil.shp, seep_anomaly_confirmed_corals.shp, seep_anomaly_confirmed_hydrate.shp.To create a class of confirmed BOEM features, merge anomaly_confirmed_relic_patchreefs.shp, seep_anomaly_confirmed_organisms.shp, seep_anomaly_confirmed_corals.shp, and seep_anomaly_confirmed_hydrate.shp and assign a value of 6.To create a class of predicted BOEM features, merge the remaining extracted shapefiles and assign a value of 3.From usSEABED sediments data, use the field “gom_domnc” to extract polygons: rock (dominant and subdominant) receives a value of 2 and gravel (dominant and subdominant) receives a value of 1.From the wrecks database, extract locations having “high” and “medium” confidence (positionQuality = “high” and positionQuality = “medium”). Buffer these locations by 150 m and assign a value of 4. The buffer distance used here, and later for coral locations, follows guidance from the Army Corps of Engineers for setbacks around artificial reefs and fish havens (Riley et al. 2021).Merge artificial reef point locations from FL, AL, MS and TX. Buffer these locations by 150 m. Merge this file with the three LA artificial reef polygons and assign a value of 5.From the NOAA deep-sea coral and sponge point locations, select all points. Buffer the point locations by 150 m and assign a value of 7.From the FWC coral and hardbottom dataset polygon locations, fix geometries, reproject to EPSG=5070, then assign coral reefs a value of 7, hardbottom a value of 6, hardbottom with seagrass a value of 6, and probable hardbottom a value of 3. Hand-edit to remove an erroneous hardbottom polygon off of Matagorda Island, TX, resulting from a mistake by Sheridan and Caldwell (2002) when they digitized a DOI sediment map. This error is documented on page 6 of the Gulf of Mexico Fishery Management Council’s5-Year Review of the Final Generic Amendment Number 3.From the TNC SABMA data, fix geometries and reproject to EPSG=5070, then select all polygons with TEXT_DESC = "01. mapped hard bottom area" and assign a value of 6.Union all of the above vector datasets together—except the vector for class 6 that combines the SABMA and FL data—and assign final indicator values. Class 6 had to be handled separately due to some unexpected GIS processing issues. For overlapping polygons, this value will represent the maximum value at a given location.Clip the unioned polygon dataset to the buffered marine subregions.Convert both the unioned polygon dataset and the separate vector layer for class 6 using GDAL “rasterize”.Fill NoData cells in both rasters with zeroes and, using Extract by Mask, mask the resulting raster with the Gulf indicator extent. Adding zero values helps users better understand the extent of this indicator and to make this indicator layer perform better in online tools.Use the raster calculator to evaluate the maximum value among

Reason for SelectionMigratory fish presence reflects uninterrupted connections between freshwater, estuarine, and marine ecosystems. Aquatic connectivity benefits diadromous fish and is considered a high priority for the integrity of aquatic ecosystems. Larger diadromous fish, like sturgeon, are often more sensitive to disruptions in aquatic connectivity. Smaller fish can make better use of fish ladders and other fish passage measures than larger fish. Input Data

  Southeast Aquatic Connectivity Assessment Project (SEACAP); see the final report for more information

SEACAP developed linear spatial data on the presence of priority diadromous species. These layers are modified versions of the NHDPlus Version 2. These data were altered to contain presence of Alabama shad using data from the Atlantic States Marine Fisheries Commission (produced for the ASMFC by the Biodiversity and Spatial Information Center at North Carolina State University, Alexa McKerrow), and expert knowledge of the SEACAP Workgroup.

SEACAP also developed a functional river network layer (final SEACAP report, page 9). A functional river network is defined by those stream reaches that are accessible to a hypothetical fish within that network. The functional river network is defined by lines (streams). SEACAP also calculated “functional catchments,” which are polygons that represent the catchment area that is associated with each of those functional networks.

Note: A catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:

An article from USGS explaining the differences between various NHD products
The glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key terms

National Oceanic and Atmospheric Administration (NOAA) Gulf Sturgeon Critical Habitat
Estimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data 

The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018).

U.S. Geological Survey (USGS) Watershed Boundary Dataset (WBD), accessed 8-11-2020: HUC6s, HUC12s; download the data
Base Blueprint 2022 extent
Southeast Blueprint 2023 extent

Mapping Steps

Combine all the linework for Gulf Sturgeon using the ArcPy Data Management Merge function. This includes line data from SEACAP and the NOAA critical habitat. Add and calculate a field showing that these are sturgeon lines.
Combine all the linework for the Alabama shad, American shad, or striped bass from SEACAP using the ArcPy Data Management Merge function. Add and calculate a field showing these lines represent the above species. 
Assign the values from the two sets of linework above to HUC12s using two separate ArcPy Analysis Spatial Join functions.
Add and calculate a new field. If it intersects a sturgeon line, give it a value of 2. Otherwise, if it intersects the other species linework, give it a value of 1.
Covert the HUC12s from polygons to a 30 m raster using the field above.
Convert the polygon layers from the Gulf sturgeon critical habitat to 30 m rasters and give those pixels a value of 2.
Combine the two rasters above using the ArcPy Spatial Analyst Cell Statistic “MAX” function.
Clip the resulting layer to the EPA estimated floodplain. 
Use the HUC6 layer to remove from the resulting raster areas outside the Gulf drainage where those 4 species ranges occur. The Atlantic drainages are represented in the Blueprint by the Atlantic Migratory Fish Habitat Indicator.
Use the HUC6 layer to add zero values to the above raster representing the Gulf range of the 4 species listed above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.
Clip to the spatial extent of Base Blueprint 2023.
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 the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 2 = Presence of Gulf sturgeon 1 = Presence of Alabama shad, American shad, or striped bass 0 = Not identified as Gulf migratory fish habitat (east of the Mississippi River) Known Issues

This indicator does not account for smaller dams/culverts that serve as barriers to fish passage.
Where the SEACAP linear spatial data interests a dam, the indicator can extend to reservoirs that are not accessible to fish due to fish passage barriers (e.g., Ross R. Barnett Reservoir in MS).
The EPA Estimated Floodplain layer sometimes misses the small, linear connections made by artificial canals, especially when they go through areas that wouldn’t naturally be part of the floodplain. As a result, some areas (like lakes) that are connected via canals may appear to be disconnected, but still receive high scores.
While this indicator generally includes the open water area of reservoirs, some open water portions of reservoirs are missing from the estimated floodplain dataset.
Estuaries where Gulf sturgeon are not present are often underprioritized because data for the other species do not extend into the estuaries.
This indicator does not account for instream habitat quality, which can also be a barrier to fish passage.
This indicator likely underestimates the value of some areas for American eel. That species is not included in the indicator due to a lack of integrated regionwide data depicting how far upstream American eels have been observed.

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 (email hilary_morris@fws.gov). Literature Cited Martin, E. H, Hoenke, K., Granstaff, E., Barnett, A., Kauffman, J., Robinson, S. and Apse, C.D. 2014. SEACAP: Southeast Aquatic Connectivity Assessment Project: Assessing the ecological impact of dams on Southeastern rivers. The Nature Conservancy, Eastern Division Conservation Science, Southeast Aquatic Resources Partnership. [https://secassoutheast.org/pdf/SEACAP_Report.pdf].

EPA EnviroAtlas. 2018. Estimated Floodplain Map of the Conterminous U.S. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/Supplemental/EstimatedFloodplains.pdf].

Reason for SelectionThis indicator prioritizes places for river cane restoration near lands of federally recognized Tribes within the Southeast region. River cane (Arundinaria gigantea), is a bamboo native to the Southeast United States and historically formed large stands called canebrakes that could stretch for miles within the floodplain and riparian areas. While river cane is not a rare species, dense and intact patches of river cane habitat have declined to less than 2% of their historical extent (cite). River cane is an ecologically significant habitat for many native species including wood thrush, Swainson’s warbler, and pollinators. Often mistaken for invasive Asian bamboo, river cane is also considered a cultural keystone species for many Tribes and indigenous communities. Cultural keystone species represent species whose existence have significantly contributed to the cultural identity of a people (Garibaldi et al. 2004). For centuries, and possibly thousands of years, indigenous people used river cane extensively for a myriad of purposes including baskets, building materials, weaponry, religious practices, and more (Griffith 2025). Because of the decline of intact healthy canebrakes, lifeways and traditions that include or rely on rivercane have also declined. Canebrakes also serve as a critical habitat for a variety of fauna including threatened and endangered species, offer refugia to many animals, and support erosion control and a flood barrier along streams where frequent flooding may be an issue. River cane’s root structure helps maintain stability for streambanks while also efficiently taking up excess nutrients from runoff, protecting the water quality of freshwater habitats (cite).This indicator reflects the potential for relationships between Indigenous nations and land holders, particularly public lands, to build co-management practices that prioritize healthy rivercane ecosystems. This indicator also promotes consistency with the Tribal Trust responsibilities held by the federal government to Tribal nations to protect Indigenous resources and lands as well as the preservation goals of many Indigenous governments in the Southeast whose cultural survival and land health rely heavily on river cane ecosystems.Input DataEPA floodplainsGSSURGO 30 meter soils data CONUS (accessed March 2025)Flood Inundation TIGER/Line Shapefile, 2021, Nation, U.S., American Indian/Alaska Native/Native Hawaiian (AIANNH) AreasEPA US Level IV Ecoregions (without state boundaries) Conus 2021 C-Cap landcover (accessed April 2025)Stable Coastal Wetlands Indicator from the 2024 Southeast BlueprintSE Blueprint 2024 Continental ExtentGbif rivercane observationsCurrent 1/3 arc-second DEM downloaded from the national map using uGet https://apps.nationalmap.gov/downloader/ (accessed November 2024)Current 1 arc-second DEM downloaded from the national map using uGet https://apps.nationalmap.gov/downloader/ (accessed November 2024)Mapping Steps Identify potential restoration or management areas by creating an enhanced floodplain layer (EPA floodplains plus frequently flooded soil areas) and then removing areas that are too frequently flooded. Clip soils data to the 2024 SE Blueprint continental extent the extract by mask function Join the muaggatt table to the soils data using the add join function Make a copy of the raster to preserve the join Reclassify the raster using the flood frequency dominate condition field, giving values of Very frequent, Frequent, or Occasional a value of 1, and giving values of Very rare, Rare, or None a value of 0 Reclassify the raster using the flood frequency maximum field, giving values of Very frequent, Frequent, or Occasional a value of 1, and giving values of Very rare, Rare, or None a value of 0 Combine the two soils flood frequency rasters with the EPA floodplain raster using the cell statistics function with the statistics type of maximum. This enhances the EPA floodplain layer by adding additional potentially flooded areas Reproject the inundation data and convert it to 30 meters using the project raster function and a bilinear resampling type Pull out pixels from the resampled inundation layer that are greater than 10 using the spatial analyst conditional function. This is a threshold we are testing to pull out areas from the enhanced floodplain layer that may be too wet for rivercane. In the output raster, give pixels with a value greater than 10 a value of 0 and all other pixels a value of 1 Remove frequently inundated pixels from the enhanced floodplain layer using the spatial analyst times function Limit potential restoration or management areas to a rough estimate of the historic rivercane range Make a copy of the EPA ecoregions layer I reused some code here from the SE Blueprint subregions. It was fast to run, but is convoluted for these purposes. I’m going to rewrite it to simplify it But basically we selecting level III ecoregion that have rivercane observations in the Gbif database. We also added in a few level IV ecoregions to capture additional areas on the western side of the extent. This removes areas from our SE Blueprint geography on the western side and in peninsular Florida Remove potential or management areas that fall outside the rough extent using the spatial analyst extract by mask function Remove coastal wetlands from potential restoration or management areas Make a copy of the C-CAP raster Remove some coastal areas using the spatial analyst conditional function, giving the C-CAP estuarine wetland classes (16,17,18), barren land (20), and unconsolidated shore (21) pixels a value of 0, and all other pixels the value from the potential restoration or management areas created above Reclassify the stable coastal wetland indicator, giving NODATA or 0 pixels a value of 1 and 1 or 2 pixels a value of 0 Take reclassified stable coastal wetland indicator times the potential restoration or management areas created above, to further refine it Calculate a buffer around tribal lands, which will be used to rank potential restoration or management areas Make a copy of the tribal lands layer Remove tribal lands that are not federally recognized using the select function to pull out polygons with at AIANNHR value of F Remove some additional tribal lands that are in peninsular Florida. Discussions with Seminole GIS data managers indicated that rivercane is not a cultural priority, we need to double check with specific tribal nations to verify, but since the following areas are outside or on the edge of the historic rivercane extent, we are removing them from this analysis: Names IN ('Big Cypress', 'Brighton', 'Coconut Creek', 'Fort Pierce', 'Hollywood', 'Immokalee', 'Miccosukee', 'Seminole (FL)', 'Tampa')") Buffer the remaining federally recognized tribal lands by 30 miles Add a field to be used to convert to raster, then convert to raster and reclassify to assign a value of 100 to tribal lands and a buffer of 30 miles from tribal lands Identify protected areas, which will be used to rank potential restoration or management areasprepare the protected areas data, starting with the PAD-US 4.0 combined proclamation, marine, fee, designation, and easement layer. To exclude areas that do not meet the intent of this indicator, remove areas with location designations of ‘School Trust Land’, ‘School Lands’, ‘School Land’, ‘State Land Board’, or ‘3201’. These extensive lands are leased out and are not open to the public. Remove areas with the designation type of 'Military' or “Proclamation'. Military lands are not primarily managed for conservation. The proclamation category represents the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status. Remove areas with the owner name of 'BOEM'. These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM). Remove areas with a category of 'Proclamation' (see explanation above). PAD-US 4.0 is missing state wildlife management area boundaries in Oklahoma. Extract those from PAD-US 3.0 by using a combination of a state name of 'Oklahoma' and local designation of 'State Wildlife Management Area'. Merge the selected polygons from PAD-US 4.0 and PAD-US 3.0, then convert to raster and reclassify to assign a value of 10 to protected areas Rank potential rivercane restoration or management areas based on proximity to tribal lands and protected status Combine the following rasters using cell statistics maximum: potential restorable rivercane areas, tribal lands with a 30 mile buffer, and protected lands Reclassify the above raster to assign ranks based on proximity to tribal lands and protected status, as seen in the final legend values below As a final step, clip to the rough estimate of the historic rivercane extent. Final indicator valuesIndicator values are assigned as follows: 4 = potential rivercane restoration or management area on protected land that is within 30 miles of Tribal lands 3 = potential rivercane restoration or management area within 30mi of Tribal lands 2 = potential rivercane restoration or management area on protected land 1 = potential rivercane restoration or management area 0 = not identified as a rivercane restoration or management area

Reason for SelectionThis indicator captures the recreational value and opportunities to connect with nature provided by greenways and trails. Greenways and trails provide many well-established social and economic benefits ranging from improving human health, reducing traffic congestion and air and noise pollution, increasing property values, and generating new jobs and business revenue (ITRE 2018). The locations of greenways and trails are regularly updated through the open-source database OpenStreetMap, while data on condition are regularly updated through the National Land Cover Database (NLCD).Input DataBase Blueprint 2022 extentOpenStreetMap data “roads” layer, accessed 2-27-2023 A line from this dataset is considered a potential greenway/trail if the value in the “fclass” attribute is either bridleway, cycleway, footway, or path. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page.2019 National Land Cover Database (NLCD): Percent developed imperviousnessSoutheast Blueprint 2023 extentMapping StepsThe greenways and trails indicator score reflects both the natural condition and connected length of the greenway/trail. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code.Natural conditionNatural condition is based on the amount of impervious surface surrounding the greenway/trail. Since perceptions of a greenway’s “naturalness” are influenced both by the immediate surroundings adjacent to the path, and the greater viewshed, natural condition is calculated by averaging two measurements: local impervious and nearby impervious. Local impervious is defined as the percent impervious surface of the 30 m pixel that intersects the trail. Nearby impervious is defined as the average impervious surface within a 300 m radius circle surrounding the path (note: along a 300 m stretch of trail, we only count the impervious surface within a 45 m buffer on either side of the trail, since pixels nearer the trail have a bigger impact on the greenway/trail experience). The natural classes are defined as follows:3 = Mostly natural: average of local and nearby impervious is ≤1%2 = Partly natural: average of local and nearby impervious is >1 and <10%1 = Developed: average of local and nearby impervious is ≥10%Connected lengthThe connected length of the path is calculated using the entire extent of the potential greenways/trails dataset. A trail is considered connected to another trail if it is within 2 m of the other trail. Length thresholds are defined by typical lengths of three common recreational greenway activities: walking, running, and biking. The 40 km threshold for biking is based on the standard triathlon biking segment of 40 km (~25 mi). Because a 5K is the most common road race distance, the running threshold is set at 5 km (~3.1 mi) (Running USA 2017). The 1.9 km (1.2 mi) walking threshold is based on the average walking trip on a summer day (U.S. DOT 2002). Using the statistics software R, download the OpenStreetMap data for the continental Southeast area. Select all lines from the OpenStreetMap data that have a highway tag of either footway, cycleway, bridleway, or path. These are all considered potential trails. Removed all lines marked as private.Identify lines from the potential trails that are tagged as sidewalks. Assign them a value of 1 in the indicator.Final scoresIf the potential greenway/trail was tagged as a sidewalk in the “other tags” field, it is given a value of 1 to separate sidewalks from what most people think of as a trail or greenway. If a pixel does not intersect a potential greenway/trail but overlaps with a value that is not NoData in the 2019 NLCD impervious surface layer, it is coded with a value of 0. Then clip to the spatial extent of Base Blueprint 2022. As a final step, clip to the spatial extent of Southeast Blueprint 2023.Final indicator valuesIndicator values are assigned as follows:7 = Mostly natural and connected for ≥40 km6 = Mostly natural and connected for 5 to <40 km or partly natural and connected for≥40 km5 = Mostly natural and connected for 1.9 to <5 km, partly natural and connected for 5 to <40 km, or developed and connected for ≥40 km4 = Mostly natural and connected for <1.9 km, partly natural and connected for 1.9 to <5 km, or developed and connected for 5 to <40 km3 = Partly natural and connected for <1.9 km or developed and connected for 1.9 to <5 km2 = Developed and connected for <1.9 km1 = Sidewalk0 = Not identified as trail, sidewalk, or other pathKnown IssuesThis indicator sometimes misclassifies sidewalks as greenways and trails because they are not tagged as a sidewalk in the OpenStreetMap data. This indicator occasionally misclassifies driveways as “sidewalks and other paths” in places where they are not correctly tagged as private in OpenStreetMap. These typically appear as isolated pixels receiving a score of 1 on the indicator. OpenStreetMap does not provide a complete inventory of greenways and trails in the Southeast. Paths that are missing from the source data will be underprioritized in this indicator. For example, some trails are missing within National Wildlife Refuges.This indicator includes trails and sidewalks from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the path of a greenway) or incorrect tags (e.g., mislabeling a path as a footway that is actually a road for vehicles). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new greenways and trails to improve the accuracy and coverage of this indicator in the future. This indicator sometimes underestimates greenway length when connections route under bridges or along abandoned dirt roads. Some of these issues have been fixed through active testing and improvement, but some likely remain.When calculating nearby impervious for one greenway, if there’s another greenway within 300 m, impervious surface from the different but overlapping greenway buffer area is also used to compute natural condition. This is an unintended issue with the analysis methods. Investigation into potential fixes is ongoing.The indicator doesn’t currently include areas where future greenways are planned.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 (email hilary_morris@fws.gov).Literature CitedAmerican Planning Association. 2018. Recommendations for Future Enhancements to the Blueprint. [https://secassoutheast.org/pdf/Recommendations-for-Future-Enhancements-to-the-Blueprint-FINAL.pdf]. Institute for Transportation Research and Education (ITRE) & Alta Planning and Design. February 2018. Evaluating the Economic Impact of Shared Use Paths in North Carolina: 2015-2017 Final Report. [https://itre.ncsu.edu/wp-content/uploads/2018/03/NCDOT-2015-44_SUP-Project_Final-Report_optimized.pdf]. OpenStreetMap. Highways. Data extracted through Geofabrik downloads. Accessed February 23, 2022. [https://wiki.openstreetmap.org/wiki/Highways]. Running USA. 23 March 2017. U.S. Road Race Trends. Road race finisher total experiences slight year-over-year decline in 2016. [https://web.archive.org/web/20170404232619/https://www.runningusa.org/2017-us-road-race-trends]. 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]. U.S. Department of Transportation. National Highway Traffic Safety Administration and the Bureau of Transportation Statistics. 2002. National Survey of Pedestrian & Bicyclist Attitudes and Behaviors: Highlights Report. [https://www.bts.gov/sites/bts.dot.gov/files/docs/browse-statistical-products-and-data/bts-publications/archive/203331/entire-1.pdf]. Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George. 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, v. 146, p. 108–123. [https://doi.org/10.1016/j.isprsjprs.2018.09.006].

Reason for Selection Habitat near rivers and streams is strongly linked to water quality and instream flow (Naiman 1997). Intact vegetated buffers within the floodplain of rivers and streams provide aquatic habitat, improve water quality, reduce erosion and flooding, recharge groundwater, and more (WeConservePA 2014). Natural floodplain landcover is often described as “the first line of defense” for aquatic systems. Input Data2021 National Land Cover Database (NLCD): Land coverSoutheast Blueprint 2024 extentEstimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018). National Hydrography Dataset Plus (NHDPlus) Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2); view the user guideCatchmentsA catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD productsThe glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key termsMapping StepsClip the 2021 NLCD to the EPA estimated floodplain layer.Reclassify the clipped NLCD to identify natural landcover using the following classes: open water, barren land, deciduous forest, evergreen forest, mixed forest, scrub/shrub, grassland/herbaceous, woody wetlands, and emergent wetlands.Calculate the percent of riparian natural landcover inside each NHDPlus catchment using ArcPy Spatial Analyst Zonal Statistics “MEAN” function.Take the resulting raster times 100 to convert from a decimal to whole number percent.Reclassify the above raster into the 1-5 classes seen in the final indicator values below.Clip the resulting raster back to the EPA estimated floodplain layer. It is necessary to do this again since the Zonal Statistics function outputs pixel values for the entire catchment. During this step, assign a value of 0 to areas outside the EPA floodplain. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows:5 = >90% natural landcover within the estimated floodplain, by catchment4 = >80-90%3 = >70-80%2 = >60-70%1 = ≤60% natural landcover within the estimated floodplain, by catchment0 = Not identified as a floodplainKnown IssuesSmall headwaters and creeks are not included in this indicator because the EPA estimated floodplain dataset does not include them.This indicator does not account for the accumulated impacts of upstream riparian buffers. Buffers at the headwaters are treated the same as those downstream.This indicator does not consider the river or stream size in relation to the estimated floodplain. Aquatic habitat needs may differ based on the river size class. For example, smaller headwater streams may need more natural landcover than larger rivers to maintain aquatic health. It also does not account for variation in buffer quality within the floodplain at a scale below the catchment. This means that within the estimated floodplain, loss of natural habitat adjacent to the river is treated the same as loss farther away.While this indicator generally includes the open water area of reservoirs, some open water portions of reservoirs (e.g., Kerr Lake in NC/VA) are missing from the estimated floodplain dataset.The catchment boundaries are inconsistent in how far they extend toward the ocean. As a result, this indicator does not consistently apply to estuaries, coastal areas, and barrier islands.In the area just south of Guadalupe Mountains National Park in West Texas, this indicator depicts the floodplain as a series of straight lines that poorly match the actual floodplain. This is due to an error in the EPA floodplain map used in this indicator.The catchment boundaries cross the United States/Mexico border, but the NLCD impervious data does not; as a result, the values along the United States/Mexico border are only based on the portion of the catchment where there are NLCD impervious values.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 (email hilary_morris@fws.gov)Literature CitedDewitz, J., 2023, National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey data release. [https://doi.org/10.5066/P9JZ7AO3]. EPA EnviroAtlas. 2018. Estimated Floodplain Map of the Conterminous U.S. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/Supplemental/EstimatedFloodplains.pdf]. Naiman, Robert J., and Henri Decamps. “The Ecology of Interfaces: Riparian Zones.” Annual Review of Ecology and Systematics 28 (1997): 621–58. [https://www.nativefishlab.net/library/textpdf/19487.pdf]. U.S. Environmental Protection Agency (USEPA) and the U.S. Geological Survey (USGS). 2012. National Hydrography Dataset Plus 2. [https://www.horizon-systems.com/nhdplus/]. WeConservePA. 2014. ConservationTools.org: The Science Behind the Need for Riparian Buffer Protection. [https://conservationtools.org/guides/131-the-science-behind-the-need-for-riparian-buffer-protection]. 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].

Reason for Selection According to the Southeastern Freshwater Biodiversity Conservation Strategy, “the Southeastern United States is a global hotspot of freshwater biodiversity, supporting almost two-thirds of the country’s fish species, over 90% of the US total species of mussels and nearly half of the global total for crayfish species. More than a quarter of this region’s species are found nowhere else in the world. Unfortunately, this region is also a hotspot for imperilment. The number of imperiled freshwater fish species in the Southeast has risen 125% in the past 20 years” (RBC and TNACI 2023). This indicator identifies areas with abundant rare and endemic aquatic species that would benefit from conservation action. It captures patterns of rare and endemic species diversity not well-represented by other freshwater indicators. The Southeast Aquatic Resources Partnership (SARP) tracks the number of aquatic animal species in different conservation categories, including Species of Greatest Conservation Need (SGCN), Regional SGCN (RSGCN), and threatened or endangered. To depict the aquatic priorities of state fish and wildlife agencies, this indicator previously used SGCN, which are identified in each State Wildlife Action Plan (SWAP) as most in need of conservation action. In 2024, this indicator switched to using RSGCN. RSGCN are regional priority species derived from all Southeast SWAPs using a set of consistent criteria, such as level of conservation concern, regional stewardship responsibility, and biological or ecological significance (Terwilliger Consulting 2019). Using RSGCN provides more clarity and focus for regional conservation and enhanced opportunities for multi-state collaboration. It also accounts for differences in the ways aquatic species are identified as SGCN in each SWAP, which caused the indicator to overprioritize some states because they had a more comprehensive SGCN list, but not necessarily more aquatic biodiversity.Input DataSoutheast Blueprint 2024 extent Southeast Aquatic Resources Partnership (SARP) RSGCN HUC12 summariesSARP provided these summaries on May 8, 2024 as a spreadsheet containing the ID number for each HUC12 watershed and an attribute for the number of aquatic animal RSGCN observed in that watershed. This dataset is based on state Natural Heritage Program occurrence records for fishes, mussels, snails, crayfish, and amphibians. It was last updated in April 2024. More information on this dataset is available in the appendix of the Blueprint development process pdf.Estimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018).National Hydrography Dataset Plus (NHDPlus) Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2); view the user guideCatchmentsA catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD products The glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key termsMapping StepsJoin the RSGCN count table to the HUC12 spatial data.Convert to a 30 m raster, where the values represent the number of RSGCN.Reclassify the species count values to the 1-9 indicator values below.Mask the resulting raster to the EPA estimated floodplain. Assign a value of 0 to all areas outside the EPA floodplain. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Mask the resulting floodplain-masked raster to the NHDPlus medium resolution catchments layer to remove values in the nearshore environment.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:9 = 8+ aquatic animal Regional Species of Greatest Conservation Need (RSGCN) observed 8 = 7 aquatic animal RSGCN observed7 = 6 aquatic animal RSGCN observed6 = 5 aquatic animal RSGCN observed5 = 4 aquatic animal RSGCN observed4 = 3 aquatic animal RSGCN observed3 = 2 aquatic animal RSGCN observed2 = 1 aquatic animal RSGCN observed1 = 0 aquatic animal RSGCN observed0 = Not identified as a floodplain Known IssuesAs this indicator is based on occurrence records, poorly surveyed areas may be scored too low. Therefore, this data does not imply absence of species.While this indicator generally includes the open water area of reservoirs, some open water portions of reservoirs (e.g., Kerr Lake in NC/VA) are missing from the estimated floodplain dataset.Small headwaters and creeks are not included in this indicator because the EPA estimated floodplain dataset does not include them.This indicator may underprioritize areas important for aquatic plants. A list of Southeastern plant Regional Species of Greatest Conservation Need has recently been developed, but at this point SARP is only collecting information on aquatic animal species. We will explore ways to incorporate aquatic plant species in the future.In the area just south of Guadalupe Mountains National Park in West Texas, this indicator depicts the floodplain as a series of straight lines that poorly match the actual floodplain. This is due to an error in the EPA floodplain map used in this indicator.The catchment boundaries are inconsistent in how far they extend toward the ocean. As a result, this indicator does not consistently apply to estuaries, coastal areas, and barrier islands.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 (email hilary_morris@fws.gov).Literature CitedEPA EnviroAtlas. 2018. Estimated Floodplain Map of the Conterminous U.S. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/Supplemental/EstimatedFloodplains.pdf]. Southeast Aquatic Resources Partnership. Species Summaries by HUC12. Accessed May 2024. Terwilliger Consulting, Inc. Regional Species of Greatest Conservation Need in the Southeastern United States. July 2019. [https://secassoutheast.org/pdf/SEAFWA_RSGCN_Final_Report_20190715.pdf]. The River Basin Center and The Tennessee Aquarium Conservation Institute. The Southeastern Freshwater Biodiversity Conservation Strategy. Accessed July 31, 2023. [https://southeastfreshwater.org/]. U.S. Environmental Protection Agency (USEPA) and the U.S. Geological Survey (USGS). 2012. National Hydrography Dataset Plus 2. [https://www.horizon-systems.com/nhdplus/].

This indicator measures the average percent of non-impervious cover within each catchment. It originates from the 2019 National Land Cover Database percent developed impervious layer.

Reason for Selection

Impervious cover is easy to monitor and model, and is widely used and understood by diverse partners. It is also strongly linked to water quality, estuary condition, eutrophication, and freshwater inflow. The 90% permeable surface threshold (i.e., 10% impervious) is a well-documented signal of major, negative changes to aquatic ecosystems (Schueler et al. 2009). The 95% permeable surface threshold (i.e., 5% impervious) has been documented to impact Piedmont fish tricolor shiner (Cyprinella trichroistia), bronze darter (Percina palmaris), Etowah darter (Etheostoma etowahae) and estuarine species blue crab (Callinectes sapidus), white perch (Morone americana), striped bass (M. Saxatilis) and spot (Leiostomus xanthurus).

Input Data

• Base Blueprint 2022 extent
• 2019 National Land Cover Database (NLCD): Percent developed imperviousness

• National Hydrography Dataset Plus (NHDPlus) Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2 - view the user guide for more information)

  NHDPlus V2.1 Medium Resolution Catchments

  A catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics.

  More specifically, the NHDPlus V2.1 medium resolution catchment dataset used in this indicator incorporates snapshots of a) surface water features from the medium-resolution (1:100K scale) National Hydrography Dataset b) watershed boundaries from the Watershed Boundary Dataset, and c) the National Elevation Dataset 30 m digital elevation model.

  To learn more about catchments and how they’re defined, check out these resources:

  • Basic information on NHDPlus from the EPA
  • An article from USGS explaining the differences between various NHD products
  • The NHDPlus V2.1 User Guide, which explains in detail how the dataset was produced
  • The glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key terms

Mapping Steps

• Calculate percent impervious for each NHDPlus catchment using the NLCD 2019 impervious surface layer and the ArcPy Spatial Analyst Zonal Statistics "MEAN" function. The Zonal Statistics Mean calculates the average of the impervious surface values in each catchment, and assigns that value to all the cells inside that catchment.
• Convert percent impervious to percent permeable using the formula [percent permeable = 100 - percent impervious] to maintain consistent scoring across Southeast indicators (high values indicate better ecological condition).
• Reclassify the above raster into 4 classes, seen in the final indicator values below.
• As a final step, clip to the spatial extent of Base Blueprint 2022.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2022 Data Download under BlueprintInputs > BaseBlueprint2022 > 6_Code.

Final Indicator Values

Indicator values are assigned as follows:

• 4 = >95% of catchment permeable (likely high water quality and supporting most sensitive aquatic species)
• 3 = >90-95% of catchment permeable (likely declining water quality and supporting most aquatic species)
• 2 = >70-90% of catchment permeable (likely degraded water quality and not supporting many aquatic species)
• 1 = ≤70% of catchment permeable (likely degraded instream flow, water quality, and aquatic species communities)

Known Issues

• This indicator may not account for differences in permeability between different types of soils and land uses.
• The catchment boundaries are inconsistent in how far they extend toward the ocean. As a result, this indicator does not consistently apply to estuaries, coastal areas, and barrier islands in different parts of the Southeast.
• The catchment boundaries cross the United States/Mexico border, but the NLCD impervious data does not; as a result, the values along the United States/Mexico border are only based on the portion of the catchment where there are NLCD impervious values.
• The NLCD percent impervious layer contains classification inaccuracies that may cause this indicator to overestimate or underestimate the amount of permable surface in some catchments.

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 (email hilary_morris@fws.gov).

Literature Cited

Schueler, T., Fraley-McNeal, L., and Cappiella, K. 2009. ”Is Impervious Cover Still Important? Review of Recent Research.” J. Hydrol. Eng. 14, SPECIAL ISSUE: Impervious Surfaces in Hydrologic Modeling and Monitoring, 309–315.

Uphoff Jr. JH, McGinty M, Lukacovic R, Mowrer J, Pyle B. 2011. Impervious surface, summer dissolved oxygen, and fish distribution in Chesapeake Bay subestuaries: linking watershed development, habitat conditions, and fisheries management. North American Journal of Fisheries Management 31:554-566.

U.S. Environmental Protection Agency (USEPA) and the U.S. Geological Survey (USGS). 2012. National Hydrography Dataset Plus 2. http://www.horizon-systems.com/nhdplus/].

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].

Wenger, S. J., J. T. Peterson, M. C. Freeman, B. J. Freeman, D. D. Homans. 2008. Stream fish occurrence in response to impervious cover, historic land use and hydrogeomorphic factors Canadian Journal of Fisheries and Aquatic Sciences 65, 1250-1264.

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].

We used version 5 of Zonation for the entire Southeast Blueprint. We ran Zonation within 6 zones. Zonation ranks the pixels in each zone according to their indicator scores, using a modeling approach that tries to conserve high-value representations of all indicators collectively. Pixels that rank higher in Zonation become higher priority in the Blueprint. INLAND CONTINENTAL REMOVING RESERVOIRS Reasoning Though reservoirs are highly altered systems, they still have conservation value. Unfortunately, the current set of indicators used in the Blueprint do not do a good job of capturing important parts of reservoirs or distinguishing the relative value of different reservoirs. As a result, we remove reservoirs from the zones used to define the boundaries of each Zonation run so that they are not eligible to be prioritized in the Blueprint. However, the indicators do capture the value of areas surrounding reservoirs, and those areas are not removed. The areas around reservoirs are also where most conservation actions occur to improve reservoir condition. Input Data

USGS National Hydrography Database (High Resolution) in FileGDB 10.1 format (published 08-30-2021) - NHDWaterbody and NHDFlowlines, accessed 10-14-2021
National Inventory of Dams (NID), accessed 10-15-2021; download the data
2019 National Land Cover Database (NLCD) 
Floodplain Inundation Frequency Southeast version: available on request by emailing yvonne_allen@fws.gov
Base Blueprint 2022 extent

Mapping Steps

Make copies of the NHDWaterbody and NHDFlowlines layers for editing. 
Extract features identified as either “LakePond” or “Reservoir” from the NHDWaterbody layer. Most reservoirs in the Southeast region are coded as “LakePond” in this dataset. 
Make a geospatial layer of the National Inventory of Dams (NID) from the source .csv file.
Select NHD waterbodies that are within 200 m of NID locations.
Add to the selection all NHD waterbodies that are within 5 m of the selection generated in the previous step to ensure that all parts of a single waterbody are selected.
Select NHD flowlines that are within 50 m of NID locations.
Select NHD waterbodies that are within 50 m of the selection generated in the previous step. 
At this stage, we did some hand-editing to add in obvious large reservoirs (especially in Texas) that were omitted from the above selections because the NID did not capture the dam locations. We used Inundation Frequency and the 2019 NLCD to assist in this step. In addition, the NHD contains some misclassified reservoirs (e.g., reservoirs classified as swamp/marsh or stream/river) that we manually added in. Note: The NID is also missing many dam locations associated with small farm ponds, which are too numerous to add by hand.
Convert to raster using the ArcPy Polygon to Raster function and clip to the spatial extent of Base Blueprint 2022.
Create a mask to use for Zonation runs by creating a raster where everything in the SECAS extent is 1 except for the reservoirs, which are NoData. For rebalancing later, we also create a raster where everything in the SECAS extent is 1 and the reservoirs have a value of 0. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. ZONATION INPUTS To create the inputs for each inland continental Zonation run:

Clip the reservoirs mask defined above to the 4 inland continental zones. This effectively removes reservoirs from each zone so that reservoirs are not included in each Zonation run. This step is not required in the continental marine or the Caribbean areas because the reservoir layer does not extend there. 
Clip the inland continental indicators to the 4 inland continental zones.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. The zones with reservoirs masked out serve as the boundaries of each Zonation run. Each Zonation run in the inland continental geography includes:

All indicators that occur in a given zone, clipped to that zone
All subregions that occur in a given zone

ZONATION RUN SETTINGS Determining Indicator Weights We use Zonation to identify a network of priorities that includes the areas most important for all indicators collectively. Since the goal is to create a balanced portfolio where all indicators are represented, in a perfect world, we would weight each indicator equally. However, in some cases, we have to downweight indicators that would otherwise have an outsized impact on the Blueprint priorities.

The way Zonation ranks pixels on the landscape can be influenced by factors like an indicator's spatial rarity within a given zone and the distribution of high and low values (i.e., is the indicator’s distribution top-heavy or bottom-heavy). In addition, some indicators are based on coarse-scale data. Other indicators have a wide spread of high to low values, but the data provider only intends the highest values to be considered good candidates for conservation action. In these situations, we use weighting to limit the disproportionate influence of certain indicators.

We developed four standard indicator weighting rules to ensure that:

Coarse-scale indicators have less of an influence
Spatially limited (i.e., rare) indicators were not overprioritized
Subregional indicators that don’t cover most of their target ecosystem were not overprioritized
Indicators with limited overlap of below-average values in a given zone were not overprioritized 

In a handful of edge cases, we developed additional exceptions to those four rules. The section below describes the application of the standard rules and additional exceptions. Coarse-scale indicators Two indicators (amphibian and reptile areas and estuarine coastal condition) have a much coarser spatial scale than the other indicators in the Blueprint. Amphibian and reptile areas uses generalized, expert-defined polygons, while estuarine coastal condition interpolates data from a relatively small number of sampling points into broad condition scores. These datasets oversimplify more site-specific variation in ecosystem health and habitat value, so we reduce their weight by 0.5 across all zones to limit their influence on the Blueprint priorities. This allows other finer-scale indicators to play a stronger role in teasing out key places within broad areas of importance for these coarser indicators. Spatially limited indicators With equal weights, the entire area of many indicators that cover a limited part of a zone (e.g., South Atlantic beach birds, greenways and trails) is identified as a high priority. To address this issue, we weighted indicators based on the proportion of their total area with values ≥1 in a given zone.

For indicators where the proportion of ≥1 values was between 0.1 and 0.5, we set their weights equal to that proportion. For example, we set the imperiled aquatic species indicator weight to approximately 0.26 in the Coastal Zone because that was the proportion of indicator pixels analyzed by Zonation in that zone with a value ≥1. For indicators where the proportion of ≥1 values was ≤0.1, we set their weights equal to that proportion multiplied by 3. We found that indicators in this range began to be underprioritized when only using the unadjusted proportion.

For indicators where the proportion of ≥1 values was 0.5 and higher, we set their weight to 1.0 unless they were covered by the exceptions discussed below. Subregional indicators that don’t cover most of their target ecosystem Nine indicators use source datasets that only cover specific subregions and do not apply to the majority of their target ecosystem (Mississippi Alluvial Valley forest birds - protection, Mississippi Alluvial Valley forest birds - reforestation, West Coastal Plain and Ouachitas forested wetland birds, West Coastal Plain and Ouachitas open pine birds, West Gulf Coast mottled duck nesting, South Atlantic forest birds, South Atlantic beach birds, South Atlantic low-urban historic landscapes, South Atlantic maritime forest). For example, South Atlantic forest birds does not capture important forest bird habitat that occurs elsewhere in the Southeast. While these indicators help the Blueprint identify important areas within these subregions, they can also unfairly penalize areas that aren’t covered by the models that would otherwise score highly due to their habitat or ecosystem value. This can cause Zonation to overprioritize the subregions that happen to be covered by these indicators. To reduce these unintended negative impacts, we reduce the weight of these indicators by 0.5. Indicators with limited overlap of below-average values in a given zone In a few cases, an indicator that targets a specific ecosystem will occur primarily in one zone, but spill over the boundary slightly into a neighboring zone. When this handful of overlapping pixels scores relatively low for that indicator, Zonation will often overprioritize these low-scoring areas. This occurs because Zonation can only see the range of indicator values that occur in a given zone—so while they score low overall, they are the highest values in that zone, which makes these areas seem more important than they actually are. To address this, we multiply the weight of the four indicators that have a small amount of overlapping below-average values in a given zone by 0.1. This rule applies to East Coastal Plain open pine birds and resilient coastal sites in the Greater Appalachians, Gulf coral and hardbottom in the Central West, and imperiled aquatic species in the Marine. Other exceptions Landscape condition & intact habitat cores in the Arid West The Arid West Zone is composed of much drier ecosystems that are climatically distinct from the rest of the SECAS

Reason for SelectionImpervious cover is strongly linked to water quality, estuary condition, eutrophication, and freshwater inflow. Impervious surface affects not only aquatic habitats and biodiversity, but also human communities. High levels of impervious surface cause more frequent flooding by increasing the volume of stormwater runoff, reduce the amount of available drinking water by preventing groundwater recharge, and pollute waterways where people swim and fish (Chesapeake 2023, USGS 2018, EPA 2018). The 90% permeable surface threshold (i.e., 10% impervious) is a well-documented signal of major negative changes to aquatic ecosystems (Schueler et al. 2009). The 95% permeable surfac e threshold (i.e., 5% impervious) has been documented to impact Piedmont fish tricolor shiner (Cyprinella trichroistia), bronze darter (Percina palmaris), Etowah darter (Etheostoma etowahae) and estuarine species blue crab (Callinectes sapidus), white perch (Morone americana), striped bass (M. Saxatilis) and spot (Leiostomus xanthurus).Input Data2021 National Land Cover Database (NLCD): Percent developed imperviousnessSoutheast Blueprint 2024 extentNational Hydrography Dataset Plus (NHDPlus) Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2); view the user guideCatchmentsA catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD productsThe glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key termsMapping StepsThe NLCD impervious surface raster uses a value of 127 as NoData. Use the Set Null function to change these values to NoData so they won’t impact the analysis.Calculate percent impervious for each NHDPlus catchment using the NLCD 2021 impervious surface layer and the ArcPy Spatial Analyst Zonal Statistics “MEAN” function. The Zonal Statistics Mean calculates the average of the impervious surface values in each catchment and assigns that value to all the cells inside that catchment.Convert percent impervious to percent permeable using the formula [percent permeable = 100 - percent impervious] to maintain consistent scoring across Southeast indicators (high values indicate better ecological condition).Reclassify the above raster into 4 classes, seen in the final indicator values below.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:4 = >95% of catchment permeable (likely high water quality and supporting most sensitive aquatic species)3 = >90-95% of catchment permeable (likely declining water quality and supporting most aquatic species)2 = >70-90% of catchment permeable (likely degraded water quality and not supporting many aquatic species)1 = ≤70% of catchment permeable (likely degraded instream flow, water quality, and aquatic species communities)Known IssuesThis indicator may not account for differences in permeability between different types of soils and land uses.The catchment boundaries are inconsistent in how far they extend toward the ocean. As a result, this indicator does not consistently apply to estuaries, coastal areas, and barrier islands in different parts of the Southeast.The catchment boundaries cross the United States/Mexico border, but the NLCD impervious data does not; as a result, the values along the United States/Mexico border are only based on the portion of the catchment where there are NLCD impervious values. The NLCD percent impervious layer contains classification inaccuracies that may cause this indicator to overestimate or underestimate the amount of permeable surface in some catchments.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 (email hilary_morris@fws.gov).Literature CitedChesapeake Bay Program. 2023. Stormwater Runoff. Accessed September 7, 2023. [https://www.chesapeakebay.net/issues/threats-to-the-bay/stormwater-runoff]. Dewitz, J., 2023, National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey data release. [https://doi.org/10.5066/P9JZ7AO3]. Environmental Protection Agency. EnviroAtlas. Data Fact Sheet. January 2018. Percent of Stream and Shoreline with 15% or More Impervious Cover within 30 Meters. Accessed September 7, 2023. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/Percstreamw15percentimperviousin30meters.pdf].

Reason for SelectionThis indicator represents aquatic connectivity between fresh and salt water in Atlantic drainages. It incorporates both physical barriers to connectivity and indirect barriers related to habitat quality. It also promotes consistency with the priorities of the Atlantic Coast Fish Habitat Partnership.Input DataAtlantic Coast Fish Habitat Partnership (ACFHP) Fish Habitat Conservation Area Mapping and Prioritization Project: South Atlantic and Mid-Atlantic Diadromous AnalysisBase Blueprint 2022 extentSoutheast Blueprint 2023 extentMapping StepsConvert the South Atlantic Diadromous Analysis from vector to 30 m raster using the FINALSCORE field.Convert the Mid-Atlantic Diadromous Analysis from vector to 30 m raster using the TotalPoints field.Combine the above rasters using the ArcPy Spatial Analyst Cell Statistics “MAX” function.Reclassify the above raster into 8 classes, seen in the final indicator values below.Clip to the spatial extent of Base Blueprint 2022.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 the Southeast Blueprint Data Download under > 6_Code.Final indicator valuesIndicator values are assigned as follows:8 = Final score of 80 (areas of excellent fish habitat)7 = Final score of 70 (areas of excellent fish habitat)6 = Final score of 60 (restoration opportunity areas)5 = Final score of 50 (restoration opportunity areas)4 = Final score of 40 (restoration opportunity areas)3 = Final score of 30 (restoration opportunity areas)2 = Final score of 20 (restoration opportunity areas)1 = Final score of 10 (degraded areas of opportunity)0 = Final score of 0 (degraded areas of opportunity)Known IssuesThis indicator under and overrepresents migratory fish habitat in some areas. The South Atlantic and Mid-Atlantic Diadromous Analysis did not include fish presence and fishing data because of inconsistent sampling methods across the study area and because this data was unavailable in many shallow water habitats.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 (email hilary_morris@fws.gov).Literature CitedMartin, Erik, Kat Hoenke, and Lisa Havel. Atlantic Coast Fish Habitat Partnership. Fish Habitat Conservation Area Mapping and Prioritization Project: A Prioritization of Atlantic Coastal, Estuarine, and Diadromous Fish Habitats for Conservation. August 2020. [https://www.atlanticfishhabitat.org/wp-content/uploads/2020/08/ACFHP-Mapping-and-Prioritization-Final-Report.pdf].

Reason for SelectionBeaches in Puerto Rico and the U.S. Virgin Islands support a wide array of shorebirds, colonial seabirds, and sea turtles (ACJV et al. 2015, USFWS 2022). However, their limited spatial extent makes beaches an ecosystem of special concern for conservation (ACJV et al. 2015). In addition, coastal dunes and beaches are some of the Caribbean ecosystems facing the greatest threat from disturbance and development (ACJV et al. 2015). This indicator focuses on a suite of bird and sea turtle species that nest on beaches, though it includes beach habitat used for other activities like foraging and breeding, in addition to nesting.Input DataPuerto Rico Gap Analysis Project predicted vertebrate species distributions: data provided by Dr. Bill Gould with the Caribbean Climate Hub on 4-4-2022 (contact william.a.gould@usda.gov for more information); read the final report In Puerto Rico, we used the following GAP species models:Wilson’s plover Puerto Rico Gap Analysis Project landcover; download the data; read the final report U.S. Virgin Islands Gap Analysis Project predicted vertebrate species distributions and landcover; data and report appendices provided by Dr. Bill Gould with the Caribbean Climate Hub on 2-6-2023 (contact william.a.gould@usda.gov for more information); read the final report In the USVI, we used the following GAP species models:Wilson’s ploverAmerican oystercatcherHawksbill sea turtleLeatherback sea turtleGreen sea turtle State of the World’s Sea Turtles (SWOT) nest locations for hawksbill, leatherback, green, and loggerhead sea turtles; download the data using the download icon on the left side of the SWOT mapping application. Note: loggerhead was only observed in USVI, while the other species were observed in both PR and USVI. OpenStreetMap data, accessed 6-28-2023 A polygon from this dataset is considered a beach if the value in the “nature” attribute is beach. OpenStreetMap describes natural areas as “a wide variety of physical geography, geological and landcover features”. Data were downloaded in .pbf format using R code. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page. U.S. Virgin Islands beaches layer (separate vector layers for St. Croix, St. Thomas, and St. John) provided by Joe Dwyer with Lynker/the NOAA Caribbean Climate Adaptation Program on 3-3-2023 (contact jdwyer@lynker.com for more information)Southeast Blueprint 2023 subregions: Caribbean Southeast Blueprint 2023 extentMapping StepsBuffer by 15 m the beach polygons from OpenStreetMap and the USVI beaches layer. This is consistent with the methodology used in the urban park size indicator to avoid the loss of narrow beaches when converting to raster. Project and convert to raster.Extract from GAP landcover the relevant beach classes and resample to a 30 m pixel. In Puerto Rico, the beaches include two classes: “Gravel beaches and stony shoreline” and “Fine to coarse sandy beaches, mixed sand and gravel beaches”. In the U.S. Virgin Islands, the beaches include three classes: “Fine to Medium Grained Sandy Beaches,” “Gravel Beaches” and “Mixed Sand and Gravel Beaches”. Extract the predicted habitat class from the GAP predicted habitat rasters for species that nest on beaches. These include Wilson’s plover, American oystercatcher, and hawksbill, leatherback, and green sea turtles. Note: Only Wilson’s plover was predicted in Puerto Rico. Project and resample the rasters to 30 m. Extract from the SWOT data nest all point locations for hawksbill, leatherback, green and loggerhead sea turtles and convert them to 30 m rasters. Merge together the SWOT and GAP predicted habitat rasters for each species and identify each pixel that contains at least one species. Then clip the resulting raster to the beach extent.To define individual beaches, run a region group on beach extent.Run the Zonal Statistics “MAX” function to apply species presence to the entirety of each beach.Reclassify to 0 the beach extent layer created above.To create the final indicator values seen below, mosaic together three rasters: the beaches containing at least one species, the beach extent, and the Caribbean Blueprint 2023 extent. This adds back in a 0 value for areas not identified as beaches and a 1 value for beaches that did not contain any species predictions or observations. Zero values better represent the extent of the source data and make the indicator perform better in online tools.Clip to the Caribbean Blueprint 2023 subregion.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 the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:2 = Beach with 1+ nesting species predicted or observed1 = Other beach0 = Not identified as a beachKnown IssuesThis indicator likely underprioritizes beaches in Puerto Rico due to disparities in both beach extent and species data coverage between Puerto Rico and the U.S. Virgin Islands. USVI has a comprehensive hand-digitized beach layer that is not available in Puerto Rico. GAP models only one beach-nesting species in Puerto Rico, compared to five in the U.S. Virgin Islands (though the additional species are known to occur in Puerto Rico as well). In addition, the SWOT sea turtle observations better aligned with the beach polygons in USVI. We will explore additional datasets and methods for addressing these disparities in future revisions. To help mitigate this issue for this year, we set the maximum species richness as 1+ rather than using the full range of species richness values, since Puerto Rico had a maximum species richness of 3 in the available data, compared to a maximum value of 6 in USVI.This indicator includes beaches from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the boundary of a beach) or incorrect tags (e.g., labelling an area as a beach that is not actually a beach). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new parks to improve the accuracy and coverage of this indicator in the future.This indicator may exclude some small beaches that aren’t captured in the source data. We encourage interested partners and citizens to add any missing beaches to OpenStreetMap so we can better capture them in future updates.This indicator does not account for other factors that influence the quality of beach habitat, such as distance to roads, light pollution, and vulnerability to erosion and sea-level rise.Other Things to Keep in MindThe species chosen for this indicator are birds and sea turtles that nest on beaches. However, the indicator also includes beach habitat used for activities other than nesting, like foraging and breeding.This indicator does not always align with Caribbean coastal shoreline condition. Some areas identified as important beach habitat in this indicator, especially those coming from the GAP Wilson’s plover model, are scored as armored in coastal shoreline condition (e.g., the Hyatt Regency Grand Hotel in Río Grande, Puerto Rico). This often occurs where riprap is present along narrow beaches, or occasionally near bulkheads. There is often a section of beach present behind the riprap or bulkhead that could still provide habitat, or the riprap is sporadically placed on a long stretch of beach to protect inland structures. In these cases, the mismatch reflects the different intent of these complementary indicators. In some cases, hardened structures may be actually misclassified as beach. Inconsistencies in alignment and classification likely result from the older age and coarser resolution of the GAP data (10 m raster based on 2001 landcover) compared to the more recent and fine-scale CUSP shorelines (vectors dating primarily from 2014-2021) and challenges in distinguishing the unique remote sensing signature of beach vs. riprap and other hardened structures. Because of the 30 m resolution of the Blueprint and underlying data, a single pixel may contain a mix of beach habitat and hardened structures and be reflected differently in each of these two indicators due to their different functions.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 (email hilary_morris@fws.gov).Literature CitedAtlantic Coast Joint Venture, Caribbean Landscape Conservation Cooperative, and U.S. Fish and Wildlife Service. Avian Conservation Planning Priorities for Puerto Rico and the U.S. Virgin Islands (BCR 69). February 2015. [https://acjv.org/documents/PRUSVI_plan.pdf].Gould, William A.; Alarcón, Caryl; Fevold, Brick; Jiménez, Michael E.; Martinuzzi, Sebastián; Potts, Gary; Quiñones, Maya; Solórzano, Mariano; Ventosa, Eduardo. 2008. The Puerto Rico Gap Analysis Project. Volume 1: Land cover, vertebrate species distributions, and land stewardship. Gen. Tech. Rep. IITF-GTR-39. Río Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 165 p. [https://data.fs.usda.gov/research/pubs/iitf/iitf_gtr39.pdf].Gould WA, Solórzano MC, Potts GS, Quiñones M, Castro-Prieto J, Yntema LD. 2013. U.S. Virgin Islands Gap Analysis Project – Final Report. USGS, Moscow ID and the USDA FS International Institute of Tropical Forestry, Río Piedras, PR. 163 pages and 5 appendices. [https://www.thinkamap.com/share/IndividualGISdata/PDFs/USVI_FINAL_REPORT.pdf].Halpin, P.N., A.J. Read,

Reason for SelectionMany deep-sea corals form tree-like shapes and complex reefs that provide valuable three-dimensional habitat structure for many fish and invertebrate species. The presence of more coral genera typically creates more complex habitats that support more species. In tropical coral reef communities, higher levels of coral diversity and topographic complexity have been shown to promote higher diversity of fish species (Komyakova et al. 2013). Deep-sea corals support commercially important fisheries such as grouper, snapper, sea bass, rockfish, shrimp, and crab. Because most deep-sea corals grow very slowly, they are highly vulnerable to damage from trawling and energy development, as well as ocean acidification due to climate change (NOAA 2018).Input DataSoutheast Blueprint 2023 subregions: Marine (combined Atlantic & Gulf of America)Southeast Blueprint 2023 extentNational Oceanic and Atmospheric Administration (NOAA) Characterizing Spatial Distributions of Deep-sea Corals and Hardbottom Habitats in the U.S. Southeast Atlantic; read the final report; data shared prior to official release on 2-4-2022 by Matt Poti with the NOAA National Centers for Coastal Ocean Science (matthew.poti@noaa.gov)This dataset provides probability models for 24 deep-sea coral genera (Eunicella, Enallopsammia, Cladocora, Chrysogorgia, Callogorgia, Bathypathes, Antipathes, Anthothela, Acanthogorgia, Acanella, Thesea, Tanacetipathes, Stylasteridae, Stichopathes, Solenosmilia, Plumarella, Paramuricea, Paragorgia, Oculina, Nicella, Muricea, Madrepora, Lophelia, Leiopathes),as well as a combined genus richness layer that counts the average number of unique genera predicted to occur in each 100 m pixel. Matt Poti provided a summarized vector version of the continuous genus richness raster layer that mirrors the way the Bureau of Safety and Environmental Enforcement displays the Gulf version of these data. This classification is based on natural breaks in the data distribution and used 10 bins. We further collapsed the bins from 10 to 5 for simplicity.Mapping StepsConvert the categorical text descriptions in the provided genus richness shapefile into the indicator values.Reclassify to collapse the 10 classes into 5 by combining the “0-0.5” and “0.5-1” classes and assigning a value of 1, combining the “1-1.5” and “1.5-2” classes and assigning a value of 2, combining the “2-2.5” and “2.5-3” classes and assigning a value of 3, combining the “3-3.5” and “3.5-4” classes and assigning a value of 4, and combining the “4-5” and “>5” classes and assigning a value of 5.Convert the shapefile to a raster.Clip to the Southeast Blueprint 2023 marine subregion.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 valuesIndicator values are assigned as follows:5 = Highest predicted average genus richness (>4)4 = High predicted average genus richness (>3-4)3 = Medium predicted average genus richness (>2-3)2 = Low predicted average genus richness (>1-2)1 = Lowest predicted average genus richness (0-1)Known IssuesWhile this layer has a 30 m resolution, the NOAA deep-sea coral models were coarser than that. We downsampled 100 m pixels to 30 m.This indicator underprioritizes areas with low survey effort for the variables used to predict deep-sea corals. This is especially true for seafloor geology, curvature, and aspect. Sharp linear features and shapes in this indicator, when not aligned with a shelf break, are a symptom of places with low survey effort for these variables.The source data does not consistently cover all areas deeper than 50 m within the Blueprint marine subregion. According to the final report for the NOAA project, “The study area included waters between 50–3,500 m depth within BOEM’s Straits of Florida, South Atlantic, and Mid-Atlantic Planning Areas and extended from Florida to Delaware. Locations of underwater visual surveys used to compile the presence-absence database for this study did not span this entire depth range across the study area. Therefore, the depth range of the study area extent varied with latitude. Offshore of south Florida (the Straits of Florida, the Miami and Pourtalès Terraces, and the adjacent escarpment) presence-absence data were not located shallower than approximately 150 m, so the study area was restricted to waters from 150–3,500 m depth south of 26.5 °N latitude. Similarly, the study area was restricted to continental slope waters from 200–3,500 m depth north of 34.5 °N because presence-absence data north of Cape Lookout, North Carolina, were located only on the continental slope (in submarine canyons and inter-canyons areas) and not on the continental shelf (<200 m depth).”Other Things to Keep in MindAtlantic and Gulf deep-sea coral richness are intended to serve as complementary indicators and are based on very similar NOAA source data. Because of the different deep-sea coral communities present in the Atlantic and the Gulf, the data provider recommended different thresholds for what level of genus richness qualifies as highest, high, medium, etc. to ensure the Blueprint captures the most important deep-sea coral areas within each region.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedKomyakova V, Munday PL, Jones GP. Relative importance of coral cover, habitat complexity and diversity in determining the structure of reef fish communities. PLoS One. 2013 Dec 13;8(12):e83178. [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3862682/#B11]. National Oceanographic and Atmospheric Administration. Deep Sea Coral Research and Technology Program 2018 Report to Congress. December 2018. [https://www.fisheries.noaa.gov/resource/document/deep-sea-research-and-technology-program-2018-report-congress]. Poti M, Goyert HF, Salgado EJ, Bassett R, Coyne M, Winship AJ, Etnoyer PJ, Hourigan TF, Coleman HM, Christensen J. 2022. Data synthesis and predictive modeling of deep-sea coral and hardbottom habitats offshore of the southeastern US: guiding efficient discovery and protection of sensitive benthic areas. New Orleans (LA): US Department of the Interior, Bureau of Ocean Energy Management. 224 p. Contract No.: M16PG00010. Report No.: OCS Study BOEM 2022- 038. [https://espis.boem.gov/final%20reports/BOEM_2022-038.pdf].