Potential landscapes for conservation of the black-tailed prairie dog ecosystem
Data files
Jan 31, 2025 version files 7.65 GB
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Input_layers.zip
3.46 GB
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Output_layers.zip
4.19 GB
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README.md
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Abstract
Aim: To identify potential landscapes for the conservation of the black-tailed prairie dog (BTPD) ecosystem, across their historical geographic range within the United States.
Location: Central Grasslands of the United States.
Methods: We used a structured decision analysis approach to identify landscapes with high conservation potential (HCP) for the BTPD ecosystem. Our analysis incorporated ecological, political, and social factors, along with changing climate and land use to maximize long-term conservation potential. We created scenarios that involved current and future projected suitable BTPD habitat, across the BTPD range within the United States. These were our RANGEWIDE scenarios. Additionally, because conservation policies and funding decisions are often made by political entities, we also identified STATE-LEVEL conservation priorities, under both present and projected future climate. Our STATE-LEVEL analysis sought conservation solutions within each of the states’ boundaries only, so do not consider a rangewide perspective.
Results: The landscapes we identified with HCP (top 30% range-wide) represented 22% of the historical distribution of black-tailed prairie dogs and remained strongholds under projected climate change. We provide a suite of HCP area scenarios to help inform different conservation and management interests, including those that consider projected climate change and jurisdictional (state-level) boundaries. STATE-LEVEL conservation priorities differed considerably from RANGEWIDE priorities, under both current and future climate scenarios. The largest difference was among the southern states (Arizona, New Mexico, and Texas), where climate change reduces the conservation priorities across this region more when viewed from a RANGEWIDE perspective than when viewed from a STATE-LEVEL perspective. Additionally, from a RANGEWIDE perspective, the eastern states have fewer areas with HCP compared to the western states within the BTPD range, but when viewed from a STATE-LEVEL perspective there are considerably more areas with HCP. We expected such differences because this question was aimed at understanding the HCP areas within each state, so the analysis was seeking conservation solutions within each of the states’ boundaries. Identifying STATE-LEVEL conservation priorities is important because funding sources and management priorities are often focused at the state-level, and not range-wide. This way, each state has information on conservation priorities within their own jurisdictional boundaries. We suggest each state focus conservation efforts for the BTPD ecosystem in those areas that remain priorities into the future at the STATE-LEVEL, while also considering those priorities identified within their state under the RANGEWIDE perspective.
Main Conclusions: Our findings highlight the large conservation potential for BTPDs and associated species, and the maps we generated can be incorporated into other large-scale, multi-species conservation planning efforts being developed for the Central Grasslands of North America.
README
Description of the data and file structure
We compiled a suite of existing spatial data sets and converted them into the nested hexagon framework (NHF 2022). Once all files were converted into the hexagon framework, they were read into the program Zonation to run the conservation prioritization analysis. Here, we provide all of the datasets used in the Zonation analysis and the map products from the analysis that identify the landscapes with high conservation potential for the black-tailed prairie dog (BTPD) ecosystem.
https://doi.org/10.5061/dryad.wpzgmsbr5
Input Data
FINE SCALE HABITAT SUITABILITY SPATIAL DATA
File: ensemble_wave_Jan192021_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of an ensemble model of BTPD habitat potential, under current climate. Resolution: 90m. The data is from Davidson et al. 2023.
File: eensemble_Warmwet_90_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of an ensemble model of BTPD habitat potential, under the future climate (2100) warm and wet scenario. Resolution: 90m. The data is from Davidson et al. 2023.
File: eensemble_HotDry_90_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of an ensemble model of BTPD habitat potential, under the future climate (2100) hot and dry scenario. Resolution: 90m. The data is from Davidson et al. 2023.
File: HSM_Current_90_mask
Description: This set of files (.tfw, .tif, .tif.aux.xml, .tif.vat.cpg, .tif.vat.dbf) represents a raster file of BTPD non-habitat mask, which we created to mask out highly unsuitable habitat for the BTPD. We classified highly unsuitable habitat as those areas where suitability was in the 10th (lowest) percentile for the BTPD habitat suitability model generated under the current climate scenario. Resolution: 90m.
File: HSM_HotDry_90_mask
Description: This set of files (.tfw, .tif, .tif.aux.xml, .tif.vat.cpg, .tif.vat.dbf) represents a raster file of BTPD non-habitat mask, which we created to mask out highly unsuitable habitat for the BTPD. We classified highly unsuitable habitat as those areas where suitability was in the 10th (lowest) percentile for the BTPD habitat suitability model generated under the future Hot and Dry climate scenario and where soils were comprised of 90% or greater of sand. Resolution: 90m.
File: HSM_WarmWet_90_mask
Description: This set of files (.tfw, .tif, .tif.aux.xml, .tif.vat.cpg, .tif.vat.dbf) represents a raster file of BTPD non-habitat mask, which we created to mask out highly unsuitable habitat for the BTPD. We classified highly unsuitable habitat as those areas where suitability was in the 10th (lowest) percentile for the BTPD habitat suitability model generated under the future Warm and Wet climate scenario. Resolution: 90m.
File: Sand_90_mask
Description: This set of files (.tfw, .tif, .tif.aux.xml, .tif.vat.cpg, .tif.vat.dbf) represents a raster file of BTPD non-habitat mask, which we created to mask out highly unsuitable habitat for the BTPD. We classified highly unsuitable habitat as those areas where soils were comprised of 90% or greater of sand. Resolution: 90m.
LANDSCAPE SCALE LAND USE/LAND COVER SPATIAL DATA
File: Fragmentation_mean_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of grassland fragmentation (modified from Augustine et al. 2021), used in the Zonation analysis. Resolution: 90m. To create this layer, we mapped the degree of rangeland fragmentation across the historic BTPD range following the methods of Augustine et al. (2021), except that we used the 2016 National Land Cover Database as the source data layer, rather than a combination of the 2011 NLCD and USDA Cropland Data Layers. Every pixel was classified as (1) rangeland, which we defined as grassland, shrubland, and improved pasture/hay cover types, (2) a fragmenting land cover type, which we defined as forest, cropland, or developed lands, or (3) neutral land cover types which were not rangeland, but also did not fragment adjacent rangelands. In the final fragmentation map, we set all pixels mapped as either a fragmenting or a neutral land cover type to a value of zero, and then calculated the distance to the nearest fragmenting land cover type for each rangeland pixel (e.g., Figure 3 of Augustine et al. 2021).
File: PctGrass_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of percent cover grassland/shrubland based on 2016 National Land Cover Database (land cover classes: 52, 71, 81; USGS 2019). The percent cover was calculated based on 1 sq km hexagons of the nested hexagon framework, that were then converted to a raster layer. Resolution: 90m.
File: PctEmgWet_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of percent cover of emergent wetland, based on the 2016 National Land Cover Database (land cover class: 95; USGS 2019). The percent cover was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
File: PctTrees_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of percent cover of forests/woodlands, based on data from: the National Land Cover Database (USGS 2019), USFS % tree cover (United States Forest Service 2016) + Playa Lakes Joint Venture data on cedar and mesquite. The percent cover was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
File: PctGrass1Mile_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of percent cover of grassland/shrubland within the six adjacent hexagons (1 mile), based on the 2016 National Land Cover Database (land cover classes: 52, 71, 81; USGS 2019). The percent cover was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
File: NmbrActiveWells_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of oil/gas wells (well count), within the BTPD geographic range. Data from Welldatabase (2021). The number of wells was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
File: TransmissionDistance_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of distance to transmission lines, within the BTPD geographic range. Data from Homeland Security Infrastructure Program (2020). The distance to a transmission line was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
File: TurbinePresence_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of wind turbine count, within the BTPD geographic range. Data from Federal Aviation Administration obstruction database (Federal Aviation Administration 2021). The presence of turbines was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
File: GP_roads_Primary_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represent a raster of primary road density within the BTPD geographic range. Data from the United States Census Bureau (2020). The total length of roads was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
File: GP_roads_secondary_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of secondary road density within the BTPD geographic range. Data from the United States Census Bureau (2020). The total length of roads was calculated based on 1 sq km hexagons of the nested hexagon framework, which were then converted to a raster layer. Resolution: 90m.
RISK OF FUTURE HABITAT LOSS SPATIAL DATA
File: GP_A2y2050pctGrass_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of Scenario A2, projected land cover change in 2050 within the BTPD geographic range. Data from Sohl et al. (2018). Resolution: 90m.
File: GP_A2y2100pctGrass_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of Scenario A2, projected land cover change in 2100 within the BTPD geographic range. Data from Sohl et al. (2018). Resolution: 90m.
File: GP_B2y2050pctGrass_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .img.vat.cpg, .img.vat.dbf, .rrd) represents a raster file (GeoTIFF) of Scenario B2, projected land cover change in 2050 within the BTPD geographic range. Data from Sohl et al. (2018). Resolution: 90m.
File: GP_B2y2100pctGrass_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .img.vat.cpg, .img.vat.dbf, .rrd) represents a raster file (GeoTIFF) of Scenario B2, projected land cover change in 2100 within the BTPD geographic range. Data from Sohl et al. (2018). Resolution: 90m.
SOCIAL ENVIRONMENT SPATIAL DATA
File: LCV_median90_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster file (GeoTIFF) of the League of Conservation Voters Conservation Score Card within the BTPD geographic range (on a per county basis). Data from the League of Conservation Voters (2022). Resolution: 90m.
File: HOTR_Inc900_clip
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd, .ige) represents a raster file of the probability that a region would support increases in prairie dog populations based on survey responses from over 29,000 North American residents. Census tract level estimates were generated using a Bayesian multi-level regression with post stratification wherein the demographics of survey respondents are used to map the probability to Census geographies based on the demographic composition of the Census tracts. Data from Williamson et al., 2023(a)(b). Resolution: 900m.
File: PctCRP_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster of the percent of Conservation Reserve Program grasslands at the county-level, within the BTPD geographic range. Data from USDA Farm Service Agency (2020). Resolution: 90m.
File: LWCF_count_HOTR
Description: This set of files (.img.aux.xml, .img.xml, .img, .rrd) represents a raster file of the count of Land and Water Conservation Fund projects (on a per county basis), within the black-tailed prairie dog geographic range. Data from The Wilderness Society (2015). Resolution: 90m.
Output: Priority Area Maps
MAPS OF RANGE-WIDE PRIORITY AREAS
File: FullModel_Present_FullRange.tif
Description: This file (.tif) represents a raster of the top priority areas for the BTPD ecosystem across the BTPD range within the United States, under the current climate. Resolution: 900m.
File: FullModel_Warm_Wet_FullRange.tif
Description: This file (.tif) represents a raster of the top priority areas for the BTPD ecosystem across the BTPD range within the United States, under the future climate (2100) warm & wet scenario. Resolution: 900m.
File: FullModel_Hot_Dry _FullRange.tif
Description: This file (.tif) represents a raster of the top priority areas for the BTPD ecosystem across the BTPD range within the United States, under the future climate (2100) hot & dry scenario. Resolution: 900m.
File: FullModel_Present_FullRange_90m.tif
Description: This file (.tif) represents a raster of the top priority areas for the BTPD ecosystem across the BTPD range within the United States, under the current climate. Resolution: 90m.
File: FullModel_Warm_Wet_FullRange_90m.tif
Description: This file (.tif) represents a raster of the top priority areas for the BTPD ecosystem across the BTPD range within the United States, under the future climate (2100) warm & wet scenario. Resolution: 90m.
File: FullModel_Hot_Dry _FullRange_90m.tif
Description: This file (.tif) represents a raster of the top priority areas for the BTPD ecosystem across the BTPD range within the United States, under the future climate (2100) hot & dry scenario. Resolution: 90m.
File: overlap_top10_fullrange.tif
Description: This set of files (.tif, .tif.aux.xml, .tif.ovr) represents a raster of the overlap of the top 10% of lands with high conservation potential for the BTPD ecosystem across the present and future climate scenarios, from a range-wide perspective. Resolution: 900m.
File: overlap_top30_fullrange.tif
Description: This set of files (.tif, .tif.aux.xml, .tif.ovr) represents a raster of the overlap of the top 30% of lands with high conservation potential for the BTPD ecosystem across the present and future climate scenarios, from a range-wide perspective. Resolution: 900m.
MAPS OF STATE-LEVEL PRIORITY AREAS
File: FullModel_Present_ByState.tif
Description: This file (.tif) represents a raster of the top state-level priority areas for the BTPD ecosystem across the BTPD range within the United States, under the current climate. Resolution: 900m.
File: FullModel_Warm_Wet_ByState.tif
Description: This file (.tif) represents a raster of the top state-level priority areas for the BTPD ecosystem across the BTPD range within the United States, under the future climate (2100) warm & wet scenario. Resolution: 900m.
File: FullModel_Hot_Dry_ByState.tif
Description: This file (.tif) represents a raster of the top state-level priority areas for the BTPD ecosystem across the BTPD range within the United States, under the future climate (2100) hot & dry scenario. Resolution: 900m.
File: FullModel_Present_ ByState_90m.tif
Description: This file (.tif) represents a raster of the top state-level priority areas for the BTPD ecosystem across the BTPD range within the United States, under the current climate. Resolution: 90m.
File: FullModel_Warm_Wet_ByState_90m.tif
Description: This file (.tif) represents a raster of the top state-level priority areas for the BTPD ecosystem across the black-tailed prairie dog range within the United States, under the future climate (2100) warm & wet scenario. Resolution: 90m.
File: FullModel_Hot_Dry_ByState_90m.tif
Description: This file (.tif) represents a raster of the top state-level priority areas for the BTPD ecosystem across the BTPD range within the United States, under the future climate (2100) hot & dry scenario. Resolution: 90m.
File: overlap_top10_states.tif
Description: This file (.tif) represents a raster of the overlap of the top 10% of lands with high conservation potential for the BTPD ecosystem at the state-level, across the present and future climate scenarios. Resolution: 900m.
File: overlap_top30_states.tif
Description: This file (.tif) represents a raster of the overlap of the top 30% of lands with high conservation potential for the BTPD ecosystem at the state-level, across the present and future climate scenarios. Resolution: 900m.
Citations within this ReadMe
Augustine, D.J., Davidson, A.D., Dickinson, K. & Van Pelt, B. (2021). Thinking like a grassland: challenges and opportunities for biodiversity conservation in the Great Plains of North America. Rangel. Ecol
Davidson, A.D., Fink, M., Menefee, M., Sterling-krank, L., Van Pelt, W.E. & Augustine, D.J. (2023). Present and future suitable habitat for the black-tailed prairie dog ecosystem. Biol. Conserv., 286, 110241.
Ducks Unlimited & The Trust for Public Land. (2021). National Conservation Easment Database (NCED) [WWW Document]. URL https://www.conservationeasement.us/
Federal Aviation Administration. (2021). Digital Obstruction File [WWW Document]. 2021. URL https://www.faa.gov/air_traffic/flight_info/aeronav/digital_products/dof/
Homeland Security Infrastructure Program. (2020). Transmission Lines [WWW Document]. URL https://hifld-geoplatform.opendata.arcgis.com/datasets/electric-power-transmission-lines/explore?location=6.862172%2C-7.477918%2C2.00
League of Conservation Voters. (2022). National Environmental Scorecard [WWW Document]. URL https://scorecard.lcv.org/
NHF. (2022). Kansas Biological Survey and Center for Ecological Research, University of Kansas. https://kars-geoplatform-ku.hub.arcgis.com/pages/nhf-lsdb.
Sohl, T.L., Sayler, K.L., Bouchard, M.A., Reker, R.R., Freisz, A.M., Bennett, S.L., Sleeter, B.M., Sleeter, R.R., Wilson, T., Soulard, C., Knuppe, M. & Van Hofwegen, T. (2018). Conterminous United States Land Cover Projections - 1992 to 2100 [WWW Document]. U.S. Geol. Surv. data release, https//doi.org/10.5066/P95AK9HP. URL https://www.sciencebase.gov/catalog/item/5b96c2f9e4b0702d0e826f6d
The Wilderness Society. (2015). Land and Water Conservation Fund database [WWW Document]. URL
United States Census Bureau. (2020). TIGER/Line Shapefiles [WWW Document]. URL https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html
USDA Farm Service Agency. (2020). Conservation Reserve Program Enrollment by County [WWW Document].
USGS. (2019). NLCD 2016: Land Cover Conterminous United States, 2016 edition. Remote-sensing image. [WWW Document]. U.S. Geol. Surv. URL ttps://www.mrlc.gov/data
Welldatabase. (2021). Welldatabase [WWW Document]. URL Welldatabase.com
Williamson, M.A., Lischka, S.A., Olive, A., Pittman, J. & Ford, A.T. (2023a). Who Should Govern Wildlife? Examining Attitudes Across the Country. In: Transform. Polit. Wild Underst. Overcoming Barriers to Conserv. Canada (ed. Olive, A., Finnegan, C., and Beazley, K.F.). University of Toronto Press, Toronto.
Williamson, M.A., Parrott, L., Carter, N.H. & Ford, A.T. (2023b). Implementation resistance and the human dimensions of connectivity planning. People Nat., 5, https://doi.org/10.1002/pan3.105251922–1936.
Access information
Other publicly accessible locations of the data:
Interactive, publicly available web map (no software needed):
File Extensions and Open-Source Software
Below is a list of the file extensions associated with the datasets herein, along with open-source software that can be used to open and interact with the files provided.
- .rrd (Reduced Resolution Dataset): Stores reduced-resolution versions of raster datasets. Viewable with QGIS or GDAL software.
- .img, .img.xml (Erdas Imagine Image File and Metadata): Raster format used in GIS applications, often accompanied by metadata files. Viewable with QGIS, GDAL, or GRASS GIS.
- .tfw (World File for Georeferencing Raster Data): Provides georeferencing information for raster images. Viewable with QGIS or GDAL software.
- .tif.aux.xml, .tif.ovr (Auxiliary Metadata and Overviews for .tif Files): Auxiliary files containing metadata or reduced-resolution overviews for .tif files. Viewable with QGIS or GDAL software.
- .cpg (Codepage File for Shapefiles): Specifies character encoding for .dbf files in shapefiles. Viewable with QGIS or GDAL software.
- .tif.vat.dbf, .img.vat.dbf, .img.vat.cpg (Value Attribute Tables): Tables containing attribute data for raster datasets. Viewable with QGIS or GRASS GIS.
- .prj (Projection File): Defines the coordinate reference system (CRS) for spatial data. Viewable with QGIS, GDAL, or Proj.
- .shx (Shapefile Index File): Stores indexing information for shapefiles. Viewable with QGIS, GDAL, or MapServer.
- .dbf (Database File for Shapefiles): Contains attribute data for vector datasets, typically in shapefiles. Viewable with QGIS, LibreOffice Calc, or DB Browser for SQLite.
Methods
Description of the data and file structure
Spatial Data Layers Used in Conservation Prioritization Analysis
We used the spatial conservation prioritization method and Zonation software (Moilanen et al. 2005) to evaluate how landscapes varied in their potential for prairie dog ecosystem conservation and restoration across the full range of the species in the United States. Our analysis included a total of 23 environmental input datasets for the full study area, based on the data sources described in Table 1. The most important layer we used to inform our analysis was the BTPD habitat suitability model, as it provided the basis for where, ecologically, the best places are to conserve and restore the BTPD ecosystem (Davidson et al. 2023). This habitat suitability model (HSM) was based on presence and absence data for BTPD occurrences across their geographic range within the United States (McDonald et al. 2015), and quantified how prairie dog occurrences related to climate, soils, topography, and land cover (see Davidson et al. 2023 for details). We also utilized HSMs for BTPDs under two future climate scenarios: 1) warm and wet and 2) hot and dry, to inform where the most ecologically suitable habitat will likely be located under a warming climate (Davidson et al. 2023).
However, the goal of our analysis was to not only determine potential landscapes for conservation based on local habitat suitability, but also to examine how the distribution and connectivity of native grassland habitat at broad spatial scales, the distribution of threats to prairie dog habitat (such as development and conversion to cropland), and the political and social landscape collectively influence opportunities to conserve and restore the BTPD ecosystem (Table 1; Fig. S1). We used the 2016 National Land Cover Database (NLCD) to inform on the location, extent, and connectivity of favorable habitat (grassland/shrubland), versus unfavorable habitat (forest/woodland and emergent wetland) for prairie dogs (USGS 2019a). We also created a landscape fragmentation layer by mapping the degree of rangeland fragmentation across the historical BTPD range. To do this, we followed the methods of Augustine et al. (2021), except that we used the 2016 NLCD as the source data layer rather than a combination of the 2011 NLCD and USDA Cropland Data Layers. Briefly, every pixel was classified as either (1) rangeland, which we defined as grassland, shrubland, and improved pasture/hay cover types, (2) a fragmenting land cover type, which we defined as forest, cropland, or developed lands, or (3) neutral land cover types which were not rangeland, but also did not fragment adjacent rangelands. In the final fragmentation map, we set all pixels mapped as either a fragmenting or a neutral land cover type to a value of zero, and then calculated the distance to the nearest fragmenting land cover type for each rangeland pixel (e.g., Figure 3 of Augustine et al. 2021). Additionally, we incorporated spatial data on land use: oil and gas well locations, distance to transmission lines, wind turbine count, and road density (Homeland Security Infrastructure Program (HSIP) 2020; United States Census Bureau 2020; Federal Aviation Administration 2021; Welldatabase 2021). These land use data layers provide information on anthropogenic activity that reflect the presence of humans and habitat quality. Areas that have higher levels of human activity may be less favorable for the BTPD ecosystem because of the increased potential for shooting of prairie dogs, impacts on associated species through behavioral modification, and habitat degradation. We also included spatial layers on projected habitat loss. The tillage risk layer (Olimb & Robinson 2019) informs where habitat is most likely to be lost to cropland. Further, we included scenarios of overall landcover change projected into the future (Sohl et al. 2018), with a focus on areas that would retain the greatest amount of favorable grassland habitat. We then obtained PAD-US (USGS 2019b), National Conservation Easement Database (NCED; (Ducks Unlimited & The Trust for Public Land 2021)), and other private conservation land data to determine the landownership of identified HCP areas (Table 1). We also obtained data from Carlson et al. 2022 (Wildlife Model in Figure 1) to relate HCP areas to plague risk.
We also included social and political spatial data in our analysis. We collated percent of Conservation Reserve Program (CRP) grasslands per county and the League of Conservation Voters Conservation Score Card (LCVCSC) to reflect political and social support for the environment (on a per county basis) (USDA Farm Service Agency 2020; League of Conservation Voters 2022). We also included data from a novel survey of wildlife governance preferences delivered to Canadian, Mexican, and American residents (Anonymized et al. 2023(a), 2023(b)) to determine the probability that a region would support increases in prairie dog populations or support federal or private incentives for prairie dog conservation. Census tract level estimates were generated using a Bayesian multi-level regression with post stratification wherein the demographics of survey respondents are used to map the probability to census geographies based on the demographic composition of the Census tracts (Anonymized et al. 2023(b); Gelman 2007; Hanretty 2020). Finally, we created a spatial layer of the count of Land and Water Conservation Fund (LWCF) projects (The Wilderness Society 2015) to reflect a regions’ institutional capacity to actualize conservation.
Data Preparation
The data layers were integrated into a nested hexagon framework (NHF). A NHF grid is based around a 1 km2 hexagon unit that is aggregated up by units of 7 to generate coarser scale cells of 7 km2 (cogs), 49 km2 (wheels), and 343 km2 (rings), allowing for cross-scale multidisciplinary analysis while obscuring precise sensitive location data.
A total of 31 data layers representing point, polygon, and raster formats were processed and summarized into the NHF for consideration in the Zonation analysis (Table S1). While the exact process used to integrate the data layers into the NHF and subsequently into raster files for the Zonation analysis was slightly different for each data layer, the general process was the same. All GIS data processing was done using ESRI ArcMap 10.7 software. Input data layers were intersected with the NHF and the data layers were summarized per NHF hexagon cell using Zonal Statistics, Tabulate Area, or other similar geoprocessing tools to generate a summary of the source layer data per hexagon. Examples of the resulting tabular summaries conveyed the area of each landcover class per hexagon cell (later converted to a percent), the mean tillage risk, majority landscape condition, the sum of the meters of road or number of wells within a cell, or the presence of wind turbines within each 1 km2 hexagon cell.
Within the attribute table of the hexagon feature class, a series of new attribute fields were created to convey the newly summarized data (e.g., % grassland, number of wells). Using the unique hexagon ID’s, the data tables of the summarized information were joined with the feature class attribute table, and the summarized data was copied into the newly created hexagon attribute fields using the “calculate field” process. Due to the number of hexagons (over 2 million record rows) being calculated, this process often took several days so researchers later began using a python script to “update cursor” that proved much faster than join/calculate field process. The resulting attribute table of the NHF one-kilometer cells provided a summary of the datasets integrated, all pre-summarized to the same framework for compatibility and easy use (Table S1). Some source data layers like percent of CRP and the political voting data were originally in coarse (county/voting district) spatial resolutions. As a result of summarizing these datasets to the hexagons, the results displayed a false level of spatial precision regarding the data values conveyed. In cases where coarse data was summarized and displayed at a higher spatial resolution, many individual hexagons share the same value that originally represented the district/county as a whole, not a specific hexagon.
The hexagon feature class data was exported to a series of raster layers using the ArcMap Feature to Raster function to accommodate the conservation prioritization software requirements that all input data be in a raster format. Output raster layers were specified to have a 90 m resolution, were snapped to the same 90 m pixels as the ensemble habitat suitability models, and the raster values were derived from the values in each of the feature class attribute fields representing the 1 km2 hexagon summarized data. The intersect, calculate field, and convert to raster processes were done in batches using the 5x5 degree NHF tile or by regional groupings of 7 tiles for the northern half of the range and 9 tiles for the southern half of the range for efficient processing. After each tile was converted to a raster layer, they were mosaiced together to create a series of range-wide raster layers, and then clipped to the BTPD range boundary (Fig. S2).
Prioritization Analysis
We used Zonation, an approach and software for spatial conservation prioritization, to select HCP areas for the conservation of the prairie dog ecosystem. Zonation produces a hierarchical spatial priority ranking of the study region, accounting for complementarity by considering local representation of the biodiversity features (species, ecosystem types, etc.; Moilanen et al. 2005). Zonation iteratively removes cells whose removal causes the smallest loss in feature representation across the overall remaining region until no cells are left in the region. The hierarchical conservation rank of the region is based on the order of cell removal, which is recorded and can be used later to select any given top fraction (e.g., best 25%) of the region. We used the additive benefit function (ABF) removal rule, which is based on the sum of the features representation in each cell, favoring places containing high habitat quality for a large number of biodiversity features.
The relative weighting of data layers is an important component of the Zonation algorithm and impacts the order in which cells are removed from the prioritization landscape. Cells that contain a high-weight feature are kept longer in the analysis than cells with only low-weight features. Features with a negative weight are considered undesirable. Consequently, they are found among the cells with low conservation priority and removed from the landscape early in the analysis. To identify those areas with the highest potential for prairie dog ecosystem conservation, we used a weight of 10 for spatial layers describing habitat suitability for BTPDs, a weight of 1 for landscape-scale land use/land cover features that have a positive influence on conservation potential and a weight of 1 for social environment layers with a positive influence on conservation potential. The spatial layers were considered as features in the analysis with positive values (i.e., higher values indicated favorable places for BTPD conservation). Because suitable habitat is ultimately the most important variable for conservation, we assigned habitat suitability features with the highest weighting among all positive features. We also considered land use in the selection of priorities, aiming to avoid places with high intensity of anthropogenic activities and potential conservation conflicts. Those layers within the landscape-scale land use/land cover and risk of habitat loss categories that negatively affect conservation potential were given negative weights (-4). These areas consequently had low values of conservation priority and were removed from the study region early in the analysis. Details on each feature used can be found in Table 1. Areas with low habitat suitability or high sandy soil (>90%) were masked out of the analysis using an area mask file, where cells with value “1” were included in the analysis, while cells with value “0” were excluded (Table 1).
We used Zonation to evaluate conservation potential under various scenarios. First, we evaluated HCP areas across the geographic range of BTPDs using suitable habitat under the current climate. Next, we created scenarios that involved current and future projected suitable BTPD habitat, across the BTPD range within the United States. To do this, we used the interaction function that induces connectivity of suitable sites for the interacting features to account for distribution shifts due to climate change. Additionally, because conservation policies and funding decisions are often made by political entities, we also identified conservation priorities within each state, under both present and projected future climate. For this, we used the Administrative Units (ADMU) function in Zonation to also select state priorities in the final conservation ranking (Moilanen & Arponen 2011).