DOI: 10.5061/dryad.wpzgmsbr5
title: Potential landscapes for conservation of the black-tailed prairie dog ecosystem

description: 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.
description: Description of the data and file structure
 <em>Spatial Data Layers Used in Conservation Prioritization
 Analysis</em> 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.
 <em>Data Preparation</em> 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). <em>S</em>ome 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).    <em>Prioritization Analysis</em> <em> </em>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 (&gt;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).
description: ## 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](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/](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/](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](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/](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](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](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](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](http://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/pan3.10525](https://doi.org/10.1002/pan3.10525)1922–1936. ## Access information Other publicly accessible locations of the data: * [https://cnhp.colostate.edu/projects/hotr/](https://cnhp.colostate.edu/projects/hotr/) Interactive, publicly available web map (no software needed): * [https://ku.maps.arcgis.com/apps/mapviewer/index.html?webmap=0b801e76c7c948f5a155562db78e99e9](https://ku.maps.arcgis.com/apps/mapviewer/index.html?webmap=0b801e76c7c948f5a155562db78e99e9) ## **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. 1. .rrd (Reduced Resolution Dataset): Stores reduced-resolution versions of raster datasets. Viewable with QGIS or GDAL software. 2. .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. 3. .tfw (World File for Georeferencing Raster Data): Provides georeferencing information for raster images. Viewable with QGIS or GDAL software. 4. .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. 5. .cpg (Codepage File for Shapefiles): Specifies character encoding for .dbf files in shapefiles. Viewable with QGIS or GDAL software. 6. .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. 7. .prj (Projection File): Defines the coordinate reference system (CRS) for spatial data. Viewable with QGIS, GDAL, or Proj. 8. .shx (Shapefile Index File): Stores indexing information for shapefiles. Viewable with QGIS, GDAL, or MapServer. 9. .dbf (Database File for Shapefiles): Contains attribute data for vector datasets, typically in shapefiles. Viewable with QGIS, LibreOffice Calc, or DB Browser for SQLite.