Looking across Hayden Creek in the Lemhi Range, Lemhi County, Idaho

FEBRUARY 2015

Contact Travis Belote (tbelote@tws.org) for questions regarding the technical aspects of this assessment

I D A H O H I G H D I V I D E L A N D S C A P EW I L D L A N D V A L U E S

I D A H O O F F I C E

EXECUTIVE SUMMARY Effective conservation depends on assessing and mapping the values that we hope to sustain through natural resource management, ecological restoration, and land protection through permanent designations. Here, we describe an assessment using diverse spatial datasets to quantify and map the landscape values associated with human impacts and ecological conditions in Idaho’s High Divide (IHD) region. We standardized and summed a suite of indices related to landscape qualities associated with a “human footprint” (e.g., the control of hydrology through dams, level of remoteness, impacts of roads) and “ecological condition” (e.g., multi-biome connectivity, land cover, and wildlife habitat overlays) to map a composite of wildland values (Figure ES1). We also surveyed select staff at The Wilderness Society to calculate weights for each input, which were multiplied to each index for a weighted composite wildland value. We describe the relative wildland values of different ecoregions in IHD based on each quality individually, as well as the un-weighted and weighted composites. In general high elevation lands in IHD provide outstanding opportunities for wilderness recreation with minimal human footprint, but middle elevations possess greater ecological values – especially in the face of continued climate change (Figure ES2). This landscape assessment confirms other analyses and demonstrates the need to evaluate diverse wildland values individually and in combination with others. The report presented here serves as a starting point to shape conversations about landscape values and is not intended to direct prescriptions or designations.

Figure ES1. The general model and framework used in assessing wildland values in IHD. Various indices quantifying landscape qualities associated with “human footprints” and “ecological conditions” were standardized and summed to map composite values of each quality (only select indices are shown above). These indices were then summed for the unweighted composite wildland value and multiplied by a weight and summed for the weighted composite. A full page of the composite wildland values can be found in the following page along with a map of the existing ownership boundaries.

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Figure ES2. Final map of weighted wildland values in Idaho’s High Divide. Red areas represent high values associated with multiple indicators of “human footprints” and ecological conditions and properties. See text for details on the methods and interpretation.

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Figure ES3. Land ownership and management of Idaho’s High Divide is diverse and composed of a high proportion of federal lands.

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INTRODUCTION AND BACKGROUND Effective conservation depends on assessing and mapping the values that we hope to sustain through natural resource management, ecological restoration, and land protection through permanent designations. Knowing where values and vulnerabilities occur across landscapes and regions should be a first step in developing conservation strategies in the age of climate change (sensu Dickson et al. 2014). Spatial data depicting various environmental, climatic, vegetation, recreational, and land use characteristics are increasingly publicly available, which allows scientists, resource managers, and conservationists to overlay data and investigate gradients in multiple values simultaneously (e.g., Aplet et al. 2000, Leu et al. 2008, Theobald 2010, Dickson et al. 2014). Increasingly, sophisticated models are available that predict zones of connectivity where animals and plants are likely to move in response to natural population fluctuations (McRae et al. 2008, Carroll et al. 2011, Theobald et al. 2012), as well increases in human pressures including climate change (Nuñez et al. 2013).

At a coarse scale, lands between large protected areas that support core habitats of wide-ranging species have been identified as important regions to increase conservation focus. Conservation biology currently emphasizes the development networks of protected areas while implementing strategies focused on large landscapes spanning gradients of human land use and ecological conditions (Lindenmayer et al. 2008). A singular focus on designating core protected lands has given way to “stitching networks of protected areas together” while increasingly developing strategies where conservation in agricultural and working lands are coupled with efforts to sustain wildland values. A holistic conservation vision emerging from such strategies is vital to maintaining diverse land values in the era of climate change.

The high ridges and valleys of eastern Idaho lie directly between the large protected lands of the Greater Yellowstone Ecosystem (GYE) and Central Idaho’s Frank Church-River of No Return Wilderness (Figure 1). Both the GYE and central Idaho’s wilderness lands form 2 of the largest core areas of protected lands in the U.S. (Leu et al. 2008). At a coarse scale the region between the headwaters of the Salmon River and the GYE, known as Idaho’s High Divide (IHD), has been identified for its outstanding conservation value for current and potential connectivity (Carroll et al. 2011), sustaining irreplaceable ecosystems (Noss et al. 2002), and continuing to support anadromous fish migrations from the Pacific Ocean to the Rocky Mountains (Brannon et al. 2004). However, a finer scale analysis of the conservation values of the region have not yet been fully quantified and mapped to identify specific lands possessing exceptional wildland qualities. While typical conservation efforts focus on preservation of species diversity and ecological integrity, protected areas also serve as outstanding landscapes for primitive and wildland recreation. As human population increases, lands that offer visitors opportunities to separate themselves from sights and sounds of modern technology continue to dwindle. The Wilderness Society values the conservation of both ecological conditions (including connectivity of core areas) as well as primitive recreation, opportunities for solitude, and lands of “self-willed” nature (Aplet 1999). Here, we describe an assessment of wildland values of the IHD region that incorporates ecological and wildland recreational values, as well as existing and future vulnerabilities to those values. Our approach builds off of efforts by Aplet et al. (2000), Leu et al. (2008), Theobald (2010), Tricker (2012), and Dickson et al. (2014) by incorporating diverse landscape qualities into a single synthetic mapped index. The approach is flexible, transparent, and revisable as new data become available.

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Figure 1. Idaho’s High Divide region (bold outline in map center) is located between two core protected areas of global significance: the Greater Yellowstone Ecosystem to the east and the Frank Church - River of No Return Wilderness area of central Idaho to the west and serves as a key area of regional connectivity of wildlands.

METHODS To begin we developed a conceptual

framework (Figure 2) that incorporated qualities and values of wildlands based on ecological conditions and the impact of human influence on natural processes and the ability for humans to experience solitude and primitive recreational opportunities (i.e., human footprint). We then developed core questions associated with these two primary qualities. From each question we proposed indicators used to address our questions, and metrics used to measure, quantify, and map our indicators.

Specific datasets used as metrics were obtained from diverse sources through

literature reviews, internet searches, agencies, and professional contacts (Appendix 1). A five person team at TWS was surveyed and asked to classify each value as essential, important, somewhat important, or not important. Survey results were then used to rank values and weighting schemes for the final wildland value index map (Appendix 2). Below we briefly describe the datasets used for each value represented in the final index of wildland qualities. We include descriptions of data manipulation and assumptions. While data are shown spatially through a series of maps, Appendix 3 also shows histograms of most of the input indices to more transparently display patterns in data.

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Figure 2. Conceptual framework used to develop our index and map of wildland values. For each quality, one example of several questions, indicators, and metrics are shown.

Human footprint qualities We considered qualities associated with human footprint to be those related to the ability of land to offer opportunities for solitude, primitive and unconfined recreation, and the degree to which natural processes are managed. For our analysis we quantified human footprint so that higher values were associated with areas with less impact of humans. Of the large list of questions and indicators that we identified, only seven were used because of data access and quality, and appropriateness of scale. Roads Roads impact wildlife habitat, hydrology, and other ecological values. While roads

provide access for visitors, they can also degrade wilderness recreation values.

We obtained road data from two primary sources: local agency contacts and the annual average daily traffic (AADT) database. Bureau of Land Management (BLM) and Forest Service data were separately merged and line density for 30 meter pixels was calculated using a moving window of 1000 km. We expanded the region by 1000 km on all sides, so that grid cells along the edge of the IHD polygon adequately “sampled” road networks and represent density of surrounding areas. We assumed that the resulting raster grid contains values that scale with impact of road density.

AADT were obtained from InsideIdaho and include estimates of daily traffic volume for

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major road networks throughout the region. We assumed that road segments with greater traffic volume would reduce the wildland quality of areas. We also assumed that segments of roads with high volume would impact regional connectivity more so than those with low traffic volume. We buffered values for these major roads by 2000 meters based on the methods of NatureServe landscape condition models (Comer and Hak 2012) and created a surface of the Euclidean distance from roads in the AADT data. We

then converted the buffered roads to a raster and assigned the new polygons values equal to the AADT values for the corresponding road segment. These were then standardized to scale traffic volume from 0 to 1 and added to the distance surface. This step was used to allow for the impact of road buffer to decay with distance, with impact decay varying with traffic volume estimates. The final AADT data were then combined with the agency road density data and rescaled from 0 to 1 (Figure 3).

Figure 3. An index of the impacts of roads based on road density and average traffic volume.

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Remoteness The quality of wildland recreation and “wildness” increases with remoteness. Remote lands (those far from human development) provide opportunities for visitors to experience landscapes away from the sights and sounds of modern civilization. We calculated an index of remoteness by combining distance from road networks and standardized slope derived from a digital elevation model (DEM). We assumed that distance from roads will increase remoteness

(Aplet et al. 2000), and that the steepness of the surrounding terrain will influence the ability of visitors to access places across the region. To more heavily weight distances from roads we doubled this index before summing it with slope. The justification for this was that roads leading to steep terrain may still experience high levels of visitor use. We assume that slope steepness limits travel away from roads. Figure 4 shows the final remoteness index.

Figure 4. An index of remoteness based on heavily weighted distance from roads combined with the steepness of terrain adjacent to each road.

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Night sky Light pollution is degrading the quality

of star-gazing worldwide (Cinzano et al. 2001). The degree of light pollution is also associated with significant ecological impacts (Longcore and Rich 2004). We downloaded light pollution data from the

National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NGDC) and clipped the data to IHD region, and rescaled the data from 0 to 1 by dividing the grid by the maximum value in IHD (Figure 5).

Figure 5. Degree of light pollution across IHD, and obstruction of night sky.

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Altered hydrological function Hydrological processes can be disrupted

by water diversions and dams and represent significant trammeling of ecological function (Aplet et al. 2000). We overlaid dam locations from Leppi et al. (2012) with HUC12 watersheds and calculated the number of dams per watershed by spatially joining the two layers. We created a new attribute called

“Count” and added a ‘1’ to each of 164 dams recorded in Idaho’s High Divide. We summed this field by watershed to calculate number of dams per watershed. We then created a raster that reflects HUC12 watersheds with the number of dams per watershed as the attributed value (i.e. red watersheds have more dams than greener watersheds in Figure 6).

Figure 6. Degree of watershed disruption from dams.

Campgrounds and transmission lines Campgrounds and other recreational development can erode opportunities for solitude and primitive recreation in landscapes. We obtained campground locations from the InsideIdaho data clearinghouse maintained by the Idaho Geospatial Office. We calculated the Euclidean distance away from each campground location. We then created a buffer around each campground that scaled with the

number of campsites at each campground. We assumed that the impact of campgrounds on solitude would be greater around larger campgrounds compared to smaller ones. We converted this buffer to a raster and multiplied the scaled buffer by the distance away from each campground, resulting in a raster layer that depicts an estimated impact of each campground around its surrounding area.

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Transmission lines can erode the values of the landscape that offer opportunities for visitors to separate themselves from modern civilization. We obtained transmission line data from the Human Footprint project maintained by USGS and calculated a surface representing the inverse of the distance away from lines. Data are not shown for campgrounds or transmission lines. Impact of airstrips

Air traffic impacts visitor uses and can erode wildland recreational values (Benfield at al. 2010). Impact of airstrips was calculated using two inputs: (1) distance to airstrip and (2) a modeled “footprint” of airstrips that increased with average annual aircraft use. We first calculated the Euclidean distance from each airstrip and created a 30 × 30 meter grid of distances for the entire IHD region. We then added one (to fill in the “zero” on grid cells directly over the airstrips) to this grid and took the inverse (divided by

one) to make smaller distances equate to larger relative impact values. This assumes impact was inversely proportional to effects of airstrip proximity. To estimate the footprint of each airstrip, we created a buffered polygon around each airstrip layer that scaled with the (1) number of flights squared (squaring this term was done to weight the number of air traffic heavily), (2) the addition of type of strip (gravel, grass, or asphalt), and (3) whether lights were used on airstrips. We corrected final footprint values so that they scaled with a meaningful distance by dividing by a “fudge factor” (1000). This method assumed that the impact of airstrips on solitude would be greatest (~12 miles) around more heavily used airstrips and airports in IHD. The distance from airstrip and impacted area were then multiplied and the resulting raster data were standardized so that values ranged from 0 (low impact) to 1 (high impact) (Figure 7).

Figure 7. Impact of airstrips as a function of number of flight operations per year.

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Ecological condition qualities We obtained data on (1) connectivity, wildlife habitat, animal species diversity, and (2) various ecological impacts to assess ecological condition, qualities, and values. We also factored in the level of representation of ecosystem types in the National Wilderness Preservation System based on data of Dietz et al. (in press). Finally, we quantified the amount of climate change that the landscape has experienced to date while accounting for the variability in potential climate change resilience of this region for coming decades. Multi-biome connectivity

Connectivity has emerged as a critical means to sustain ecological values, especially in the age of rapid anthropogenic climate change (Rudnick et al. 2012). We obtained connectivity data from the Western Governors’ Association efforts to map paths connecting areas of high ecological integrity for four biomes: forest, intermountain grassland, great plain grassland, and desert. These data were obtained from Dr. Meredith McClure of the Center for Large Landscape Conservation via John Pierce, chief wildlife scientist at Washington’s Department of Fish and Wildlife. Connectivity data for each biome were imported as polyline data. To convert the 4 polyline datasets into a surface

depicting predicted connectivity potential, we buffered each centerline by a value in meters based on its centrality × 100 so that lines with higher centrality scores would be depicted as thicker paths with greater weights than lines with lower centrality (e.g., centrality of 10 created a buffered corridor of 1000 meters wide). We then assigned the centrality value to each buffered line polygon. We set null values in the raster to 0 and summed all four biome models together. We also calculated the Euclidean distances from each line assuming that connectivity value of an area would decay with distance from the modeled centerlines. We then subtracted the combined buffered raster with the distance from centerline (i.e., as distances from centerlines increased the connectivity value decreased), and standardized the new multi-biome connectivity values by dividing by the maximum value resulting in a surface ranging from 0 (low connectivity) to 1 (high connectivity; Figure 8).

The resulting connectivity surface, while clipped to IHD, represents connectivity paths modeled for the entire northern rockies (including Montana and Wyomin). Therefore, connectivity values used here represent potential for providing connectivity throughout a much larger area beyond the IHD region.

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Figure 8. Multi-biome connectivity where green areas depict high levels of connectivity between areas of relatively high ecological integrity for forest, intermountain grassland, great plain grassland, and desert biomes. These paths represent a connectivity analysis for a much larger regional area that includes Montana and Wyoming.

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Overlay of charismatic wildlife habitat and predicted animal biodiversity

Large charismatic wildlife species can serve as umbrella species for ecosystem conditions (Simberloff 1998). We clipped GAP analysis habitat model data for grizzly bears, black bears, greater sage-grouse, cougar, lynx, bighorn sheep, and elk to IHD and set null values as 0. This created a grid with values and zeros reflecting the deductive habitat models produced by GAP. We then overlaid

these values by adding each grid resulting in a layer scaled from 0 to 7 representing the number of species with overlapping habitat for any grid cell, which we rescaled from 0 to 1 (Figure 9). We also obtained total predicted animal richness (all birds, reptiles and amphibians, and mammals) from GAP and rescaled the data by dividing by the maximum number of predicted species in the IHD region (Figure 10).

Figure 9. Overlapping habitat models of charismatic wildlife species indicative of high wildland values. Species included in this analysis were grizzly bears, black bears, greater sage-grouse, cougar, lynx, bighorn sheep, and elk.

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Figure 10. Total predicted animal species richness across the landscape including birds, reptiles and amphibians, and mammals.

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Land cover Land cover data are widely used to

estimate ecological integrity (Theobald 2010). Land cover data and ecosystem types and were obtained from the USGS GAP Analysis Program. Land cover types were assigned values based on their degree of human modification. For example grid cells classified as “developed - high intensity”

received a -3 and land covered classified as a native ecosystem received a value of 3. Other land cover types received values between -3 and 3, and were standardized by dividing by 3 to produce a map with grid cells ranging from -1 to 1 (Figure 11). This was done so that urban areas or lands with high human modification received lower final scores deriving from this layer.

Figure 11. Land cover scores from high positive values (native ecosystem types) to negative values (human modified).

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Ecosystem representation in wilderness The proportion of each land cover type

occurring in the National Wilderness Preservation System to the total amount occurring on federal lands was quantified in another project (Dietz et al. in press). These data were used to map the level of ecosystem representation in wilderness areas, using ecological groups from the GAP land cover data. The goal of this “representation analysis” was to investigate which ecosystems are current underrepresented in wilderness areas. Those underrepresented types could be prioritized for future designation to diversify the national

wilderness preservation system. Here, we assigned ecosystems a value based on their level of representation to prioritize those currently insufficiently represented in wilderness. Human modified cover types and open water were converted to 100, so that the standardized dataset would include these as the lowest priority in terms of representation (Figure 12). This is based on the assumption that agricultural land cover types are not priorities for inclusion in the national wilderness preservation system (NWPS) or other conservation strategies of land designation.

Figure 12. Level of ecosystem representation in the National Wilderness Preservation System.

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Historical disturbance regimes Disturbance regimes are vital

characteristics of ecosystems that maintain habitat, species diversity, and landscape-scale resilience of multiple ecological conditions (Pickett and White 1985). We obtained vegetation departure estimates from LANDFIRE, which estimates the level of departure a vegetation type has experiences as the result of logging, fire exclusion, or other disruptions to historical disturbance regimes. These data are widely used to estimate current departure, but have received

significant criticism (Aplet and Wilmer 2003). However, we know of no other comprehensive ‘wall-to-wall’ estimate of departure of vegetation conditions at landscape scales (e.g., while more robust, assessments of Hessburg et al. 1999 only characterized departure of sampled watersheds). Land cover types without an index of level of departure (e.g., rock) were assigned a zero value. Other values scaled from 0 (less departed) to 100 (most departed), and the values were standardize to scale from 0 to 1 (Figure 13).

Figure 13. Degree of departure from historical vegetation conditions through disruption of disturbance regimes.

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Nitrogen and sulfur pollution Atmospheric deposition of chemical compounds, especially those composed of nitrogen and sulfur, have significantly impacted both aquatic and terrestrial ecosystems throughout the globe by changing the biogeochemical cycling with resultant

impacts to individuals and populations of species within ecosystems (Dentener et al. 2006). We obtained sulfur and nitrogen deposition data from the National Atmospheric Deposition Program (NADP) and rescaled the values from 0 to 1 (Figure 14).

Figure 14. Total N and S deposition.

Risk of exotic plant species Invasive species are considered the second greatest threat to species and ecosystems and account for billions of dollars of economic loss per year (Vitousek 1994; Wilcove et al. 1998). We used the human footprint model of risk of exotic plant species to account for

risks of invasive plant impacts. Because these data were developed based on roads and land cover, we chose not to include this metric in the final calculation of wildland values. We present it here for reference (Figure 15) and in case future efforts decide to focus more intensively on this data product.

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Figure 15. Risk of exotic plant species invasion.

Climate change Ongoing and expected changes in climate regimes will alter ecosystems globally and has been described as the most important conservation issue of our generation (Parmesan and Yohe 2003). We used data quantifying the amount of climate change already experienced, as well as a measure of spatial gradients in existing climate conditions, topographic relief, and geological parent material to create an index of climate resilience. Variability in climate regimes associated with topographic complexity (e.g., steepness of slope and elevation relief) and geological parent material may allow species and ecosystems greater opportunities to find suitable habitat and climate niches compared to less topographically complex landscapes (sensu Beier and Brost 2010). In other words, we assumed that if there are steep gradients in climatic, topographic, and geological diversity, there will be more options for an ecological community to reshuffle while

keeping its parts (i.e., species) as the climate changes. We calculated an index of climate resilience by adding standardize climate gradient data from Dobrowski et al. (2013) with a standardized calculated range of elevation diversity using a 30 meter DEM. We also added this to a geological diversity index by calculating the number of unique geological parent materials present within a 900 m grid surrounding each 30 m pixel. We then subtracted an index of existing measured climate change velocity (Dobrowski et al. 2013). In sum, we define the potential climate resilience as climatic diversity + elevation range + geological diversity – climate velocity. The final value was standardized so that the maximum value was 1, but the minimum value was <0, because some pixels experienced levels of climate change in regions with little climatic, topographic, or geological diversity (Figure 16).

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Figure 16. Climate resilience index calculated from climatic, topographic, and geological diversity and retrospective climate change.

Calculated indices Human footprint index was calculated

using the inputs described above as: Rmt – Rd – Lt – Dms – Cg – Tr – As

where Rmt is remoteness, Rd is road impact, Lt is light pollution, Dms is dam density for watersheds, Cg is our estimated impact of campgrounds, Tr is impact of transmission lines and As is impact of airstrips. This human footprint index scales from high values where the impact of humans on landscape condition are lower to low values where the impacts are higher. Ecological condition index was calculated using inputs described above as:

Cn + CR + LC + WL – VD – SN – AR where Cn is connectivity, CR is climate resilience, LC is land cover scores, WL is charismatic wildlife habitat overlay, VD is the index of vegetation departure, SN is the index of sulfur and nitrogen deposition, and AR is

the index of predicted animal richness. We did not include ecosystem representation in the ecological condition index, as it does not necessarily represent an enhancement or erosion of ecological value. We reserved this index for the final index of wildland values. We calculated an unweighted and weighted version of the wildland value index by combining the human footprint and ecological condition indices above. Specifically, the unweighted index of wildland values was calculated as: Cn + CR + LC + WL – VD – SN + AR + Rmt – Rd –

Lt – Dms – Cg – Tr – As – EcRep where variables are as above and EcRep is ecosystem representation. We subtract this value to give weight to ecosystems with little representation in the NWPS, because lower values are less well represented. The weighted version of the index used the same formula as above, but multiplied each input

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index by a value representing a weighted score based on a survey of five TWS staff (Appendix 2). Summarization by ecoregions, administrative zones, and watersheds Following calculation of the human footprint, ecological condition qualities, and their combined unweighted and weighted indices, we summarized values by ecoregions to describe variation in wildland values among recognized zones in IHD. Level 4

ecoregions were obtained from EPA and clipped to the IHD (Figure 17). Mean and standard deviation of human footprint, ecological condition, and final weighted and unweighted indices were summarized for each ecoregion. The final weighted value was also summarized forest service ranger districts and several protected areas (National Parks) to evaluate variability in existing administrative zones. We also summarize data by HUC12 watersheds.

Figure 17. Level 4 ecoregions of Idaho’s High Divide.

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RESULTS All four indices (human footprint, ecological condition, unweighted and weighted final wildland value) were correlated among ecoregions. In other words, despite the fine-scaled differences in various combinations of values, there was general agreement among relative values among ecoregions (Table 1). The ecoregions with the highest scores for the final indices and the ecological index were the same. The top 2 ecoregions with the highest human footprint index (lowest impact of humans) differed from the other indices. We discuss these results below.

Human footprint index As noted above, the human footprint quality index is highest where the impact of humans of natural processes or erosion of solitude was lowest. Human footprint index values was on average highest in the alpine and High Idaho Batholith ecoregions of IHD, and lowest in the Upper Snake River Plain (Figure 18; Table 1). The patterns were driven primarily driven by road densities, which influenced our index of remoteness of land. Generally speaking, elevation drives these qualities with higher lands being less influenced by human footprint.

Figure 18. Final index of human footprint qualities in IHD.

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Ecological condition index The ecological condition index was highest in the Hot Dry Canyons, Barren Mountains, and Dry Partly Wooded Mountains in IHD. The Hot Dry Mountains ecoregions border the core wildland of central Idaho, are important for connectivity, are a hotspot of predicted animal diversity, and are characterized by

vegetation types that are less altered while also having lower atmospheric deposition pollution (Figure 19; Table 1). The ecoregion’s steep topography and relatively high diversity of geological parent material also likely offer species better opportunities for coping with climate change (i.e., climate resilience score is high).

Figure 19. Final index of ecological conditions and values.

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Final wildland value index The final combined weighted and unweighted wildland values indices were strongly correlated among ecoregions (Figures 20-21; Table 1). Similar to the ecological condition index the Hot Dry Canyons were ranked the highest with the

Barren Mountains and Dry Partly Wooded Mountains being ranked 2nd and 3rd, respectively. These latter ecoregions occupy intermediate elevation ranges more overlapping species’ habitats, multiple connectivity zones, and more underrepresented ecosystems in the NWPS.

Figure 20. Unweighted wildland value index.

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Figure 21. Final weighted wildland value map. An estimated density probability function is in Figure 22. See Figure ES2 on page 2 for a map of the final weighted wildland values using a different color ramp and including landmarks.

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TABLE 1. Mean and standard deviation (stdev) of values for indices of human footprint, ecological condition, and unweighted and weighted wildland values among ecoregions in Idaho’s High Divide. Data are ranked by the final unweighted wildland value and the top 5 ecoregions for each index is highlighted in grey (several ecoregions had ties for the human footprint index). Maps of indices are in Figures 3-16.

Human footprint

Ecological condition

Wildland value

Weighted wildland value

Ecoregion Mean stdev Mean stdev Mean stdev Mean stdev

Hot Dry Canyons 0.20 0.14

2.00 0.31

2.03 0.41

27.78 4.13

Barren Mountains 0.13 0.18

1.87 0.32

1.81 0.44

26.77 4.32

Dry Partly Wooded Mountains 0.13 0.19

1.77 0.34

1.76 0.44

25.92 4.79

Dry Gneissic-Schistose-Volcanic Hills -0.01 0.19

1.83 0.35

1.72 0.49

24.50 5.15

Western Beaverhead Mountains 0.12 0.20

1.78 0.29

1.71 0.43

25.16 4.35

South Clearwater Forested Mountains 0.13 0.14

1.74 0.30

1.62 0.48

24.60 4.59

Gneissic-Schistose Forested Mountains 0.19 0.12

1.53 0.33

1.50 0.39

25.03 3.76

Foothill Shrublands-Grasslands -0.03 0.20

1.61 0.33

1.50 0.49

22.33 5.23

Southern Forested Mountains 0.07 0.19

1.72 0.30

1.49 0.50

24.02 4.85

Partly Forested Mountains -0.01 0.14

1.68 0.34

1.49 0.53

23.95 5.30

Alpine Zone 0.30 0.16

1.60 0.34

1.41 0.47

23.12 4.90

High Idaho Batholith 0.23 0.16

1.47 0.35

1.37 0.49

23.30 4.83

Eastern Batholith 0.01 0.12

1.64 0.25

1.35 0.41

22.30 4.10

Forested Beaverhead Mountains -0.02 0.12

1.56 0.27

1.34 0.31

21.25 3.45

High Glacial Drift-Filled Valleys -0.06 0.12

1.50 0.33

1.24 0.52

20.80 5.50

Yellowstone Plateau -0.20 0.21

1.63 0.37

1.24 0.57

22.53 5.60

Lava Fields -0.12 0.16

1.35 0.35

1.15 0.43

17.94 4.71

Dry Intermontane Sagebrush Valleys -0.20 0.18

1.46 0.73

1.02 1.15

17.06 11.71

Eastern Snake River Basalt Plains -0.18 0.12

1.07 0.50

0.70 0.79

14.29 8.05

Camas Prairie -0.28 0.17

0.62 0.82

-0.28 1.26

4.43 12.87

Dissected Plateaus and Teton Basin -0.23 0.23

0.33 0.74

-0.69 1.14

0.87 11.73

Upper Snake River Plain -0.30 0.17 0.11 0.60 -1.09 0.86 -3.99 9.15

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Weighted wildland values for the iconic protected areas such as Yellowstone (23.6) and Craters of the Moon (21.8) National Parks within IHD were near the estimated medianvalue (22.3, Figure 22) across the region. Ranger districts of Forest Service lands varied near the median value with the mean value among ranger districts (22.2) nearly equaling the overall median. The Salmon-Colbart Ranger District of the Salmon-Challis forest ranked the highest in weighted wildland value (Table 2).

We also summarized data by HUC12 watersheds to quantify patterns of associated with specific location in IHD. Data are not

shown here, but the top five watersheds with highest wildland value were North Creek, Hurst Creek, Taylor Creek-Cedarvill Canyon, Falls Creek, and Furnace Creek. These were scattered across the mountainous portions of IHD. In contrast the watersheds with the lowest wildland value were clustered in the southeastern portion of IHD and include (from lowest): South Fork Teton River, Birch Creek (not the one draining the southeastern portion the Valley between the Beavehead and Lemhi Mountains), Farmers Friend Canal-Lower Teton River, Lewisville Knolls, and Texas Slough-Lower Henry’s Fork.

Figure 22. Density function of the final weighted wildland value for IHD.

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TABLE 2. Mean wildland (standard deviation, Stdev) values for IHD Forest Service Ranger Districts ranked highest to lowest by National Forest.

National Forest Ranger District Mean Weighted Wildland Value Stdev

Caribou-Targhee Dubois Ranger District 23.01 5.26

Ashton/Island Park Ranger District 22.69 4.03

Teton Basin Ranger District 22.07 6.43

Palisades Ranger District 22.05 11.82

Salmon-Challis Salmon-Cobalt Ranger District 24.27 4.78

North Fork Ranger District 23.02 5.93

Middle Fork Ranger District 22.62 8.23

Lost River Ranger District 22.53 8.33

Leadore Ranger District 22.22 5.56

Challis Yankee Ranger District 22.21 8.61

Sawtooth Ketchum Ranger District 22.24 8.93

Sawtooth National Recreation Area 20.46 10.12

Fairfield Ranger District 19.41 9.29

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DISCUSSION This assessment quantifies and maps wildland values in Idaho’s High Divide using datasets that characterize two broad qualities of wildlands: the impact of human footprint and the ecological conditions of the landscape. Calculations and maps of combinations of indicators for these qualities provide a coarse assessment of wildland values and could be used to communicate conservation values of lands to agencies, policy makers, and the public. Our assessment is very similar to other similar efforts with some minor distinctions, which we have quantified and serves as a source of uncertainty in conservation priorities in the region (Appendix 4). We caution against prescribing any management or conservation strategy based solely on this assessment. Rather, we see this assessment as a starting point and first draft of an effort to characterize land values. The human footprint has impacted much of the globe, and we view areas with minimal human influence as having higher wildland qualities compared to areas where humans dominate the landscape. In the IHD, the high peaks and alpine zones of the Lost River and Lemhi Mountain Ranges are characterized by the lowest amount of human footprint. From the perspective of mapping recreational opportunities where visitors can experience solitude away from the sites and sounds of modern civilization, the high mountain peaks are some of the most wild not only in IHD, but also nationally (base do on data from Aplet et al. 2000). Ecological values associated with connectivity, wildlife habitat, and ecosystem function were highest in mid-elevations of the mountain ranges. In these mixes of forests, sagebrush, and grassland many wildlife species’ habitats are predicted to overlap, and connectivity potential is also maximized in mid-elevation regions. These ecosystems -compared to higher elevation zones - also

tend to be less well represented in the NWPS and are less impacted by sulfur and nitrogen deposition. Diversity of topography, geology, and climate also provide greater predicted resilience capacity under a changing climate in mid-elevation zones. It is worth noting that ecological condition in this case refers both to the relative integrity of ecosystems, but also to gradients in existing and predicted ecological values. We have described patterns in the data from a regional or ecoregional perspective, but the output of this analysis could reasonably be scaled down to more local scales to highlight variability in land values at larger map scales (i.e., smaller ecological scales). Figure 23 below shows 2 examples of the patterns in the index of wildland values at finer scales.

Watersheds with the lowest level of wildland values may still possess important conservation values and we caution against triaging watersheds based on this assessment. Rather, we suggest that this assessment be used to stimulate further investigation, which could eventually be used in assigning broad conservation strategies of climate adaptation, restoration, and land protection. Conclusions

By separately assessing landscape qualities associated with our conceptualized “human footprint” and “ecological conditions” we are able to transparently demonstrate two components of wildness and the ways in which they can be considered distinct (Aplet 1999). High elevation lands in IHD may provide outstanding opportunities for wilderness recreation, but middle elevations possess greater ecological values – especially in the face of continued climate change. Conservation of wildland values should consider both qualities and aim to maximize both.

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Figure 23. Left shows the final weighted wildland value for Upper Lemhi River Valley. Right shows the Donkey Hill area. Both maps illustrate the fine (local) scaled patterns.

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APPENDIX 1. Table of data used for inputs, their resolution, sources, and websites, as well as general comments.

Dataset Resolution Source Download available Comments

WGA biome connectivity Polylines WGA Not available at this time. Data likely available by contacting John Pierce or through DataBasin in the future. Converted to 30 m raster.

Overlapping habitat models 30m GAP gapanalysis.usgs.gov/species Overlay of key species calculated

Land cover and ecosystem representation

30m GAP gapanalysis.usgs.gov/gaplandcover Calculated representation in NWPS from Dietz et al. (in revision); available upon request from R.T. Belote

Vegetation departure 30m LANDFIRE landfire.cr.usgs.gov

Sulfur and Nitrogen deposition 2624m NADP nadp.isws.illinois.edu/NTN/maps.aspx Coarser data from other inputs, but final indices were calculated as 30m grids

Night sky 400m NOAA - NGDC ngdc.noaa.gov/eog/viirs/download_monthly.html Coarser data from other inputs, but final indices were calculated as 30m grids

Climate resilience 800m UM Landscape Ecology Lab

Not available publicly at this time. Geological and elevation diversity calculated using national geology layers and a 30m DEM. Climate gradient and velocity from Dobrowski et al. 2013.

Road density and traffic volume Polylines Various fs.usda.gov/main/r4/landmanagement/gis (FS data) cloud.insideidaho.org (for 2012 AADT data)

Search: Idaho 2012 Average Annual Daily Traffic at Inside Idaho for AADT data; line data from BLM and FS obtained through

Native animal species diversity (biodiversity hotspots)

30m GAP gapanalysis.usgs.gov/species Richness values obtained from GAP contacts.

Remoteness 30m DEM DEM obtained from GAP, but available here: ned.usgs.gov or here: nationalmap.gov/viewer.html

-

Stream flow impacts HUC12 Leppi et al. (2012) Similar dataset available at cloud.insideidaho.org -

Campsite locations and capacity Points Inside Idaho cloud.insideidaho.org Search: Campgrounds of Idaho. A better dataset may be available, but this index has little impact on final outputs.

Airstrips Line features National Transportation Atlas Database (NTAD)

rita.dot.gov Distance from airstrips and volume of air traffic were used to calculated index.

Transmission lines Polyline Human footprint sagemap.wr.usgs.gov/HumanFootprint.aspx -

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APPENDIX 2. Survey results from questionnaire with 5 TWS respondents asking participants to rate criteria on a scale from ‘not important’ to ‘essential.’ This scale was assigned a value from 0 (not important) to 3 (essential), and responses where multiplied by their score to calculate a final weight. We multiplied indices by the final weight to calculate the weighted wildland value index.

Index Not

important Somewhat important

Important Essential Total Final

Weight

Connectivity of biomes 0 0 0 5 5 15

Land cover 0 0 1 3 4 13.75

Roads 0 1 0 4 5 13

Presence of charismatic megafauna habitat 0 0 2 3 5 13

Native animal species diversity (biodiversity hotspots) 0 1 2 2 5 11

Disturbance regimes (departure from historical?) 0 1 3 1 5 10

Remoteness 1 1 1 2 5 9

Climate change 1 2 0 2 5 8

Stream flow impacts (dam density) 0 3 1 1 5 8

Ecosystem representation 1 2 2 0 5 6

Atmospheric deposition of nitrogen and sulfur 2 1 2 0 5 5

Campsite locations and use 2 2 1 0 5 4

Airstrips 2 2 1 0 5 4

Light pollution 2 2 1 0 5 4

Transmission lines 1 4 0 0 5 4

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APPENDIX 3. Histograms of rescaled key input indices based on 5,000 random samples from the IHD region. Index codes used in calculations are listed along the x-axis and general index feature is listed in panels.

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APPENDIX 4. Comparisons with other evaluations of landscape condition Other efforts have recently evaluated landscape conditions values using similar methods, inputs, and values (Leu et al. 2008; Theobald 2010; Comer and Hak 2012; Dickson et al. 2014). After calculating our weighted wildland index we evaluated the level of agreement our index has with another similar analysis (Comer and Hak 2012). We obtained the “landscape condition” data from WGA of Comer and Hak 2012. This effort quantified landscape condition based on a series of similar criteria to identify “relative effects of land uses on natural ecosystems and habitats.” We rescaled both the WGA landscape condition data and our weighted wildland value index by subtracting each cell with the mean value and divided by the standard deviation, a method for standardizing differently scaled datasets (Zuur et al. 2010). We resampled our data to match the spatial resolution of WGAs and regressed our wildland value data against the WGA

model. We then calculated and mapped the residuals.

In this analysis more positive residuals scores indicate areas that we identified as having high values, but WGA’s show relative low values, and negative residuals indicate where WGA identified high value, but our wildland value was relatively low. Interestingly, the two methods show high levels of agreement (Figures 24-25). The main difference between our effort and those of the WGA landscape condition occurs in the Snake River plan where the WGA scored areas relatively higher compared to our evaluation. WGA also tended to more highly value high elevations compared to our final wildland assessment. In contrast, the red areas near the Hot Dry Canyon ecoregion and in some mid elevation zones were identified as areas of higher value by our method compared to the landscape condition. Overall, both efforts were significantly correlated (p<0.0001; Figure 25), and differences were relatively subtle when mapped across the IHD.

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Figure 24. Map of residuals from a linear model relating the wildland value and WGA’s landscape condition. The high degree of agreement between relative values is indicated by the high area of yellow color.

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Figure 25. Scatter plot between relativized TWS wildland value index and WGA landscape condition. Linear model used to calculated residuals is shown in red (top). Histograms of residuals of the linear model are shown on the bottom and zero is heighted in red.

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