Pilot Test of the Ecological Approaches to Environmental Protection Developed in Capacity Research Projects C06A and C06B (2013) / Chapter Skim
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Contents 1 CHAPTER 1 Overview and Context 2 Context 12 C21A Research Approach 12 Partner Involvement 13 Research Team Structure and Partner Collaboration 15 CHAPTER 2 Technical Analyses 15 IEF Step 2: Characterization of Resources 20 IEF Step 2: Species Distribution Modeling 29 IEF Step 2: Wetland Mapping 51 IEF Step 2: Conservation Value Summary 54 IEF Step 4: Impact Assessment 60 IEF Step 5: Establish and prioritize ecological actions 83 IEF Step 6: Develop Crediting Strategy 92 CHAPTER 3 TCAPP and IEF Assessment 92 TCAPP 93 Integrated Ecological Framework 101 Focus Group Interviews 106 CHAPTER 4 Lessons Learned and Recommendations 106 Partner Collaboration 107 Technical Analyses 111 Social Science Analyses 114 Recommendations 117 References 121 APPENDIX A Natural Heritage Methodology 128 APPENDIX B Data Sources used in Potential Habitat Distribution Models 140 APPENDIX C CNHP Potential Habitat Distribution Models 214 APPENDIX D Wetland Mapping Results by Quad 269 APPENDIX E Additional Wetland Mapping Summaries
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1 CHAPTER 1 Overview and Context Awareness of the need for more effective, streamlined, and integrated planning of transportation improvements has permeated all levels of government, and has become a top priority to advance the level of sophistication and integration for transportation planning. For example, the Federal Highways Administration (FHWA)
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2 transportation, land use, and conservation in practical and effective ways)
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3 Step Purpose Step 7: Develop Programmatic Consultation, Biological Opinion or Permit Develop MOUs, agreements, programmatic 404 permits or ESA Section 7 consultations for transportation projects in a way that documents the goals and priorities identified in Step 6 and the parameters for achieving these goals. Step 8: Implement Agreements and Deliver Conservation and Transportation Projects Design transportation projects in accordance with ecological objectives and goals identified in previous steps (i.e., keeping planning decisions linked to project decisions)
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4 stress from rapid growth and significant impacts from development. Thus, in many ways it represents an ideal proving ground for the methods developed in CO6A and CO6B.
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5 Between 1989 and 2003, Highway 285 was upgraded from a two-lane to a four-lane highway from Turkey Creek Canyon (milepost 248) to Foxton Road (milepost 235)
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6 Figure 1.2. Satellite imagery of study area.
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7 Figure 1.3. Land ownership in study area.
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8 Current CDOT Planning Process3 The current transportation planning process in Colorado includes the development of long-range multimodal Regional Transportation Plans (RTPs) in CDOT's 15 transportation planning regions, a long-range multimodal Statewide Transportation Plan that sets the vision for transportation in the state, and a Statewide Transportation Improvement Program (STIP)
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9 Natural Heritage Program, produced a programmatic biological assessment and conservation strategy for the Central Shortgrass Prairie ecoregion of Colorado. At the time, there were several prairie species proposed for federal listing, which would have greatly complicated CDOT's ability to complete its work.
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10 Planning, and 2) Integrating Land Use and Transportation Planning study (Tracey MacDonald, personal communication)
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11 Colorado Watershed Approach to Wetland Mitigation In 2008, U.S. Environmental Protection Agency (EPA)
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12 C21A Research Approach This research was designed to test select elements of Framework Steps 2-6. The objectives of this project were to: 1.
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13 partners, and to solicit preliminary feedback on approach and methods. During this discussion, a number of issues became clear: 1.
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14 CDOT staff from state headquarters, Region 1, and long-range planning participated in conference calls to provide background on current planning processes, meetings to review draft results from the technical analyses, and interviews to assess the IEF process. They also provided an assessment of the TCAPP website.
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15 CHAPTER 2 Technical Analyses IEF Step 2: Characterization of Resources Resource characterization focused on collecting and summarizing available spatial data pertaining to natural resources in the study area. In addition to compiling base data, the team identified species of interest in the area, produced models of suitable habitat, mapped wetlands, and investigated the potential to integrate these data with information about other classes of resources.
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16 • Historical land use and condition, including pre-development vegetation types, grazing history, logging or mining history, water diversion structures and hydrological modification through time, historic roads and trails, and similar data; • Detailed, frequently updated parcel ownership and assessed land value, such as is maintained by county assessor's offices, in a unified format; • Some kind of spatial representation of recreational or cultural values in an area, but not tied to exact point locations; and • Cumulative tracking of impacts and mitigation across all agencies and other land management entities, especially on a regional or watershed basis (for instance, water quality information in a watershed context)
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17 Table 2.1. Target Species That Were the Focus of GIS Analysis Scientific Name Common Name Global Status Ranka State Status Ranka Federal Listing Statusb State Listingc Federal Agency Sensitived AMPHIBIANS Bufo boreas boreas Western Toad - Southern Rocky Mountains population G4T1Q S1 SE Rana pipiens Northern Leopard Frog G5 S3 SC BLM / USFS BIRDS Accipiter gentilis Northern Goshawk G5 S3B BLM/ USFS Aegolius funereus Boreal Owl G5 S2 USFS Aquila chrysaetos Golden Eagle G5 S3 Athene cunicularia Burrowing Owl G4 S4B ST BLM/ USFS Buteo regalis Ferruginous Hawk G4 S3B,S4N BLM/ USFS Catharus fuscescens Veery G5 S3B Charadrius montanus Mountain Plover G2 S2B BLM/ USFS Empidonax trailii Willow Flycatcher G5 S4 Falco mexicanus Prairie Falcon G5 S4B,S4N Falco peregrinus anatum American Peregrine Falcon G4T4 S2B USFS Haliaeetus leucocephalus Bald Eagle G5 S1B,S3N USFS Lagopus leucurus White-tailed Ptarmigan G5 S4 USFS Melanerpes lewis Lewis's Woodpecker G4 S4 USFS Pelecanus erythrorhynchos American White Pelican G3 S1B BLM Seiurus aurocapilla Ovenbird G5 S2B Strix occidentalis lucida Mexican Spotted Owl G3T3 S1B, SUN LT FISH Onchorhynchus clarkii stomias Greenback Cutthroat Trout G4T2T3 S2 LT ST Phoxinus eos Northern Redbelly Dace G5 S1 USFS INSECTS Callophrys mossii schryveri Schryver's Elfin G4T3 S2S3 Celastrina humulus Hops Azure G2G3 S2 Cicindela nebraskana Prairie Long-lipped Tiger Beetle G4 S1?
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18 Scientific Name Common Name Global Status Ranka State Status Ranka Federal Listing Statusb State Listingc Federal Agency Sensitived Erynnis martialis Mottled Duskywing G3 S2S3 Hesperia leonardus montana Pawnee Montane Skipper G4T1 S1 LT MAMMALS Cynomys gunnisoni Gunnison's prairie dog G5 S5 PS:C BLM/ USFS Gulo gulo Wolverine G4 S1 C USFS Lynx canadensis Lynx G5 S1 LT SE Sorex nanus Dwarf Shrew G4 S2 Thomomys talpoides macrotis Northern pocket gopher macrotis subspecies G5T1 S1 Zapus hudsonius preblei Preble's Meadow Jumping Mouse G5T2 S1 LT PLANTS Aquilegia saximontana Rocky Mountain Columbine G3 S3 Arabidopsis salsuginea Saltwater Cress G4G5 S1 Armeria maritima ssp. sibirica Sea Pink G5T5 S1 USFS Astragalus molybdenus Molybdenum Milkvetch G3 S2 Astragalus sparsiflorus Front Range Milkvetch G3?
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19 Scientific Name Common Name Global Status Ranka State Status Ranka Federal Listing Statusb State Listingc Federal Agency Sensitived grass Draba oligosperma Few-seed Whitlow-grass G5 S2 Draba porsildii Porsild's Whitlow-grass G3G4 S1 Draba streptobrachia Colorado Divide Whitlow-grass G3 S3 Eriophorum altaicum var. neogaeum Altai Cotton-grass G4?
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20 Scientific Name Common Name Global Status Ranka State Status Ranka Federal Listing Statusb State Listingc Federal Agency Sensitived Sphagnum girgensohnii Girgensohn's Peatmoss G5 S1 Telesonix jamesii Jame's False Saxifrage G2 S2 Townsendia rothrockii Rothrock's Townsend-daisy G2G3 S2S3 Trichophorum pumilum Rolland's Leafless-bulrush G5 S2 BLM Viola pedatifida Prairie Violet G5 S2 BIG GAME Ovis canadensis Bighorn Sheep G4 S4 Ursus americanus Black Bear G5 S5 Cervus canadensis Elk G5 S5 Odocoileus hemionus Mule Deer G5 S4 Puma concolor Mountain lion G5 S4 Note: Species highlighted in bold have regulatory status, or, in the case of big game, are considered particular safety hazards for vehicle collision. a Global/State Status: 5 = demonstrably secure; 4 = secure; 3 = vulnerable; 2 = imperiled; 1 = critically imperiled; T = subspecies; B = breeding; N = non-breeding.
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21 or lack of precision in modeling assumptions, input data, or procedures may incorrectly predict suitable habitat where none exists. In addition, users should be aware that the true resolution of these distribution models is only as fine as the coarsest layer of input data.
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22 However, even simple potential habitat or range models seem to have utility as part of a preponderance-of-evidence approach to investigating the spatial patterns of biodiversity. Furthermore, these models serve as a coarse-filter surrogate for the ecosystem component of the study area, since there were enough species included to cover the majority of habitats in the area.
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23 Environmental Inputs Units Source Used Annual Growing Degree Days (average air temp above 0 °C) degreedays Daymet - Climatological summaries for the conterminous United States 19801997 www.daymet.org/ (1km)
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24 Environmental Inputs Units Source Used Soil pH pH Pennsylvania State University Conterminous United States Multi-Layer Soil Characteristics Data Set for Regional Climate and Hydrology Modeling.
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25 Table 2.4. Environmental Variables Used in Plant Models Environmental Inputs Units Source Al pi ne Cl iff W et la nd Ge ne ra l Annual Growing Degree Days (average air temp above 0 °C)
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26 Environmental Inputs Units Source Al pi ne Cl iff W et la nd Ge ne ra l Slope degrees Derived from USGS 30m Digital Elevation Model (DEM) for Colorado.
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27 meters may be reduced to an area of 1,800 square meters by the flattening effect of the digital mapping)
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28 Figure 1.4. MaxEnt model for boreal toad.
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29 IEF Step 2: Wetland Mapping Colorado's wetlands were mapped by USFWS's National Wetlands Inventory (NWI) program in the late 1970s and early 1980s.
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30 Figure 1.6. Study area with updated NWI wetland mapping for identified USGS 7.5-minute quads.
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31 "Wetlands are lands transitional between terrestrial and aquatic systems where the water table is usually at or near the surface or the land is covered by shallow water. For purposes of this classification wetlands must have one or more of the following attributes: (1)
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32 Subsystem: Systems are followed, when appropriate, by a subsystem. In the study area only the Riverine and Lacustrine systems require Subsystem division.
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33 Special modifier: Three special modifier codes were used in the study area. The modifiers present information about artificially and naturally modified wetlands.
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34 merged scans of USGS 1:24000 topographic maps, accessed through the ArcGIS Online Map Server, were used for estimates of relief and elevation. Scanned images of the original NWI maps available through the NWI Wetland Mapper were downloaded and used to identify previously mapped wetlands and areas which might require particular scrutiny during the mapping process.
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35 these seemed to be missed in the original mapping because of resolution limitation (i.e., the wetlands were too small)
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36 signatures and texture (e.g., leaf-on or leaf-off; following or prior to planting or harvest)
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37 Figure 1.8. Shape used to identify alteration to wetland or water body, seen with a straight line indicating a dam.
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38 Figure 1.10. Color intensity to determine relative wetness as shown with cattail marshes (PEMF)
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39 Figure 1.11.
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40 Quality Assurance/Quality Control (QA/QC) : CNHP uses a rigorous QA/QC procedure to ensure the highest possible data quality.
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41 portions are owned by the USFS and wetlands there remained relatively unchanged. During the office preparation and point selection for pre-mapping field visits, several habitats were noted as particularly confusing.
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42 Figure 1.14. Grassy swale (PEMC)
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43 Figure 1.16. Riverine feature (R3UBH)
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44 Results of GIS Mapping: Based on photo interpretation of 2009 aerial imagery, wetlands, riparian areas, and water bodies mapped within the study area total 84,987 acres. Of these, 71,401 acres (84%)
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45 Figure 1.19. Lower montane wetland complex with a shrubby stream corridor (R3UBG, PSSC)
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46 Table 2.7. Wetland Acreage in the C21A Study Area by NWI Hydrologic Regime NWI Code NWI Hydrologic Regime All NWI Acres % Wetlands & Waterbodies % Wetlands (excl.
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47 The Cowardin classification includes certain types of modification to wetlands and waterbodies. Within the study area, the most common human modifications were dammed/impounded (11,841 acres)
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48 Table 2.9. Wetland Acreage in the C21A Study Area by Grouped Land Owner Grouped Owner Total Land Area within Project Total NWI Acres within Project Total Acres % of Project Area Total Acres % of NWI Acres Federal Lands 1,035,902 51% 22,704 27% Arapaho-Roosevelt Nat'l Forest 62,291 3% 1,542 2% Bureau of Land Management 72,560 4% 1,382 2% Pike-San Isabel National Forest 865,337 43% 18,677 22% White River National Forest 29,565 1% 907 1% National Park Service 5,984 < 1% 196 < 1% State Lands 90,522 4% 4,292 5% Colorado Parks and Wildlife 27,564 1% 2,345 3% State Land Board 55,157 3% 1,734 2% State Parks 7,801 < 1% 213 < 1% Other 907,812 45% 57,991 68% Local Governments 56,921 3% 4,109 5% Non-Government Organizations 42,388 2% 11,011 13% Private 808,504 40% 42,871 50% Grand Total 2,034,237 100% 84,987 100%
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49 Figure 1.20. Study area wetlands before C21A mapping.
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50 Figure 1.21. Study area wetlands after C21A mapping.
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51 IEF Step 2: Conservation Value Summary Methods The first step in creating the Regional Ecosystem Framework step was to construct a biological conservation value summary (CVS) using the species distribution models and wetland map prepared earlier.
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52 Results The conservation value summary (Figure 1.22) indicates that the highest values in the study area are concentrated in alpine areas with limestone substrates and in areas associated with wetlands.
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53 Figure 1.22. Conservation Value Summary.
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54 IEF Step 4: Impact Assessment In order to address step 4 in the framework, the team developed methods to evaluate the impacts of various types of land use (including transportation effects) on resource conservation objectives identified in the conservation value summary.
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55 world – i.e., does the effect drop sharply near the source but then fade gradually, or perhaps maintain a noticeable effect for some distance away from the source before decreasing, or is the rate of decrease constant? The landscape integrity model incorporated a family of sigmoid (sshaped)
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56 Figure 1.23. As an example, for a total distance of 2,000 meters, different values of a and b produce the following curve types.
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57 Low intensity development 300 gradual SWReGAP low intensity development types Roads primary & secondary 500 moderate 2006 TIGER/Line roads (A1-A3) Roads - local & rural, 4WD etc.
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58 Figure 1.24. Landscape Integrity model.
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59 anthropogenic sources, including transportation development, and can be adapted to track impacts over time. In the study area, there were no major projects planned with which to test the hypothetical assessment process, so draft methods are presented.
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60 be identified, probably on a resource-specific basis. The raster datasets for both the individual project impact and the additive impacts over time may be maintained on a statewide or regional scale, but the datasets must be of equal extents and correctly aligned.
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61 populations into the future. How "cost" is defined depends on the project objectives and available data.
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62 are ongoing. Ideally, a land value dataset would be compiled from the parcel-specific information maintained by county assessor's offices in the study area.
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63 and/or manage for conservation and then transformed to a scale of 0 (no cost) to approximately 1,000 (high cost)
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64 Figure 1.26. Landscape Integrity as "cost" input for Marxan analysis.
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65 Figure 1.27. Land Value model as "cost" input for Marxan analysis.
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66 Analysis Targets and Conservation Goals Target species were represented by CNHP EOs and all species distribution models and activity maps listed in Table 2.2. Because wetland mapping was being completed simultaneously with this analysis, the updated wetland map was not available in time to include here.
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67 Table 2.14. Goal Scheme Rules for Marxan Analysis Initial Goal Scheme Rules for Targets & PCAs (high-risk | low-risk)
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68 Table 2.15. Final Species Goals for Marxan AnalysisScientific Name Common Name Data Source Amount in Areaa Low-Risk Goal High-Risk Goal Lagopus leucurus White-tailed Ptarmigan Model 216,270 75% 50% Melanerpes lewis Lewis's Woodpecker Model 178,760 75% 50% Pelecanus erythrorhynchos American White Pelican Model 111,050 60% 30% EOs 1.0 100% 100% Seiurus aurocapilla Ovenbird Model 132,880 90% 100% Strix occidentalis lucida Mexican Spotted Owl Model 269,610 75% 75% FISH Onchorhynchus clarkii stomias Greenback Cutthroat Trout Model 40,680 b 90% 90% Phoxinus eos Northern Redbelly Dace Modelc 2,490 ‡ 90% 90% INSECTS Callophrys mossii schryveri Schryver's Elfin Model 43,490 20% 10% EOs 1.3 100% 77% Celastrina humulus Hops Azure Model 23,070 20% 10% EOs 1.0 100% 100% Cicindela nebraskana Prairie Long-lipped Tiger Beetle EOs 1.0 100% 100% Erynnis martialis Mottled Duskywing Model 58,070 20% 10% EOs 2.9 100% 69% Hesperia leonardus Montana Pawnee Montane Skipper Model 14,690 20% 10% EOs 2.0 100% 100% MAMMALS Cynomys gunnisoni Gunnison's prairie dog Model 147,470 66% 33% Gulo gulo Wolverine Model 318,210 60% 30% EOs 1.6 100% 100% Lynx canadensis Lynx Model 262,940 90% 90% Sorex nanus Dwarf Shrew Model 1,191,790 20% 10% EOs 1.0 100% 100% Thomomys talpoides macrotis Northern pocket gopher macrotis subspecies Model 130 90% 90% Zapus hudsonius preblei Preble's Meadow Jumping Mouse Model 7,250 90% 90% PLANTS Aquilegia saximontana Rocky Mountain Columbine Model 220,030 20% 10% EOs 6.0 83% 50% Arabidopsis salsuginea Saltwater Cress Model 79,890 20% 10% EOs 3.0 100% 100%
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69 Table 2.15. Final Species Goals for Marxan AnalysisScientific Name Common Name Data Source Amount in Areaa Low-Risk Goal High-Risk Goal Armeria maritima ssp.
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70 Table 2.15. Final Species Goals for Marxan AnalysisScientific Name Common Name Data Source Amount in Areaa Low-Risk Goal High-Risk Goal Draba globosa Rockcress Draba Model 5,320 20% 10% EOs 1.0 100% 100% Draba grayana Gray's Peak Whitlow-grass Model 45,490 20% 10% EOs 2.0 100% 100% Draba oligosperma Few-seed Whitlow-grass Model 14,540 20% 10% EOs 3.0 100% 75% Draba porsildii Porsild's Whitlow-grass Model 40,930 20% 10% EOs 0.04 100% 100% Draba streptobrachia Colorado Divide Whitlow-grass Model 19,510 20% 10% EOs 2.0 100% 50% Eriophorum altaicum var.
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71 Table 2.15. Final Species Goals for Marxan AnalysisScientific Name Common Name Data Source Amount in Areaa Low-Risk Goal High-Risk Goal EOs 3.0 100% 100% Potentilla ambigens Southern Rocky Mountain Cinquefoil Model 119,570 20% 10% EOs 1.0 100% 100% Potentilla rupincola Rocky Mountain Cinquefoil Model 322,410 20% 10% EOs 1.0 100% 100% Primula egaliksensis Greenland Primrose Model 73,690 20% 10% EOs 16.0 100% 100% Ptilagrostis porteri Porter's Feathergrass Model 260,860 20% 10% EOs 19.0 100% 100% Ranunculus karelinii Arctic Buttercup Model 9,430 20% 10% EOs 3.0 100% 100% Ribes americanum Wild Black Currant Model 36,140 90% 75% Rubus arcticus ssp.
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72 Table 2.15. Final Species Goals for Marxan AnalysisScientific Name Common Name Data Source Amount in Areaa Low-Risk Goal High-Risk Goal Ursus americanus Black Bear SAMd 248,210 50% 10% Cervus canadensis Elk SAMd 243,000 50% 10% Odocoileus hemionus Mule Deer SAMd 575,170 66% 33% Puma concolor Mountain lion SAMd 479,120 66% 33% Notes: Regulatory and safety concern species are in bold.
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73 significantly exceeded, because Marxan attempts to meet all goals for all species, which can potentially select more area than is needed for any one species. A way to determine which subset of species may be largely driving the final solution is to examine which goals were met at no more than 100% with at least 1,000 planning units.
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74 • Areas requiring a Protection Strategy are believed to be in good condition, and would require only some form of legal protection from land-use conversion to preserve their ecological value. These areas are obvious candidates for conservation easements or similar mitigation efforts.
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75 Figure 1.28. Marxan solution for low-risk goal set, landscape integrity cost layer, and full target list.
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76 Figure 1.29. Marxan solution for low-risk goal set, landscape integrity cost layer, and regulatory species only.
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77 Figure 1.30. Marxan solution for low-risk goal set, land value cost layer, and full target list.
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78 Figure 1.31. Marxan solution for low-risk goal set, land value cost layer, and regulatory species only.
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79 Figure 1.32. Marxan solution for high-risk goal set, landscape integrity cost layer, and full target list.
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80 Figure 1.33. Marxan solution for high-risk goal set, landscape integrity cost layer, and regulatory species only.
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81 Figure 1.34. Marxan solution for high-risk goal set, land value cost layer, and full target list.
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82 Figure 1.35. Marxan solution for high-risk goal set, land value cost layer, and regulatory species only.
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83 IEF Step 6: Develop Crediting Strategy Ecosystem Services Ecosystem services are the benefits that people derive from nature that support and fulfill human life (Millennium Ecosystem Assessment 2005)
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84 20, 2011; (3) a literature review of documents related to the study region and ecological data compiled by the Colorado Natural Heritage Program; and (4)
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85 minor. Consultation with CDOT confirmed that impacts to carbon are not currently a primary consideration in transportation projects, though ongoing state, national, and international policy discussions on climate change should be monitored to see if regulatory requirements change in the future.
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86 are two wetland and stream banks with confirmed service areas applicable to the study region. These banks are the Middle South Platte River Wetland Mitigation Bank and the Mile High Wetland Bank.
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87 impacts from CDOT projects in Douglas County to the federally listed threatened species Preble's Meadow Jumping Mouse (Zapus hudsonius preblei)
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88 tax credit is also transferable. This means that the landowner may sell the unused portion of the tax credit and receive cash from the tax credit purchaser.
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89 impacts to Preble's Meadow Jumping Mouse from transportation projects in Douglas County. Accordingly, CDOT already has initial experience working with these market-based approaches that can advance "progressive" approaches to ecosystem-based mitigation (Cambridge Systematics 2011)
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90 into transportation planning. There are wetland and stream mitigation banks in operation (as described above)
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91 investment in watershed stewardship to improve forest health conditions. This is part of a nationwide Forests-to-Faucets effort, of which one of the most high profile projects to date has been a $33 million, 5-year partnership between the USFS and Denver Water.
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92 CHAPTER 3 TCAPP and IEF Assessment TCAPP CDOT headquarters environmental and long-range planning staff tested the TCAPP website. Comments reflect the version of TCAPP that was online during the late February to early March, 2012 time frame.
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93 • Can timelines be provided for each step? For example, LRP-1, Scope of the LRTP would take six to eight months.
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94 present state, given the mission of CDOT, which is to provide transportation infrastructure, not to conserve natural resources. Effecting a transition within CDOT to an IEF-based approach will likely require a sustained and concerted effort aimed at all levels of their hierarchy, including top level managers, mid-level managers, and planners and staff within state headquarters and the regions.
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95 Data Issues Issues revolving around "lack of data" appear to be more closely related to the lack of resources needed to collect, house, and maintain comprehensive datasets than to an actual lack of existing data per se. On one level, there is definite interest in having access to data (especially GIS data)
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96 CDOT perspective seems to be that a resource agency or a non-governmental organization should take charge of leading those efforts. In addition, there is a sense of "here we go again" related to implementing the IEF.
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97 batch-produced potential habitat or range models appear to have utility as part of a preponderance-of-evidence approach to investigating the spatial patterns of biodiversity. It is difficult to calculate a standard price per model that accounts for the variability inherent in knowledge of species of interest, and the consequent variable levels of effort required.
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98 after products in conservation planning. However, the invariable response of stakeholders involved in planning and project decision making is to ask what precise data is behind a particular high- or low-priority area identified by an analysis.
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99 A quick search of online resources devoted to presenting the type of geospatial data in question indicates that methods for addressing these issues continue to be developed. Some county assessor websites are able to present ownership and other information that is accessed by the user clicking on a parcel map.
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100 document – it does not contain information on specific transportation improvement projects. A corridor study was available as a reference, but its findings were comprised of proposed improvements only.
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101 nervous about how to know if they are "saving enough." The commitment to specific conservation goals is a component of the IEF and of all decision-support tools with which the team is familiar. This is, without doubt, the biggest hurdle for partners working on this type of project.
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102 conservation planning process might be used. Diversity of expression regarding resource values and descriptions was encouraged throughout each session.
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103 focus groups were held with 31 participants involved in conservation or transportation planning. Demographics of the six focus groups are listed below: Group 1: Teleconference with five transportation planners, environmental engineers, and CDOT conservation specialists.
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104 for practical use. The group also emphasized that the process did not seem useful for the permitting process, which is a large part of their day-to-day activities.
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105 Conclusions In conclusion, it is apparent that more social science research is necessary to address the barriers to adoption for the IEF Process. A meta-analysis of the social science piece showed that the species and habitat mapping (quantitative)
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106 CHAPTER 4 Lessons Learned and Recommendations The insights obtained by the research team in the course of completing this pilot are summarized in this section. Lessons learned are organized under three themes: Partner Collaboration, Technical Analyses, and Social Science Analysis.
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107 state, and local laws (e.g., NEPA)
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108 Specific Species Distribution Models Effort: This task took the most time (~6 months) of all the technical tasks except wetland mapping, which was comparable, because it required literature research, extensive data collection and processing, output evaluation, and soliciting and incorporating expert feedback.
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109 Value: The conservation value summary provided an at-a-glance snapshot of current understanding of the distribution of biological values across the study area that was very simple for partners to understand. It was very useful for focusing discussion around priority conservation areas.
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110 Landscape Integrity Model Effort: A statewide model had been previously created for another project. After determining that the study area would benefit from a slightly revised version, the team only had to recombine previously created input components.
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111 Value: Given the number of assumptions and judgment calls required by this program, its best utility is likely to be in long-range planning exercises for which relatively coarse-scale data are appropriate. Social Science Analyses General 1.
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112 7. With respect to systematically collecting information about highway user preferences, it is important to obtain a representative sample from the population of highway users.
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From page 118... ...
113 to work with transportation professionals to develop policy options to merit stakeholder input. In other words, when the mapping process (quantitative research)
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From page 119... ...
114 goals. This prolongs the conservation planning process, when they inevitably "meet" during a NEPA or zoning meeting.
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From page 120... ...
115 4. Consider the implementation of incentives and processes within the IEF that would encourage DOTs to investigate the cumulative effects of projects that fit under NEPA's categorical exclusion, which do not require regulatory action and represent the majority of transportation infrastructure improvements undertaken by CDOT today.
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From page 121... ...
116 6. Realign agency jurisdictional boundaries (e.g., Forest Service Ranger Districts, BLM Resource Areas)
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From page 122... ...
117 References Ardron, J.A., Possingham, H.P., and Klein, C.J.
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From page 123... ...
118 Fern, E.F. 2001 Advanced Focus Group Research.
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From page 124... ...
119 Millennium Ecosystem Assessment.
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From page 125... ...
120 Wilcox, G., D
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From page 126... ...
121 APPENDIX A Natural Heritage Methodology NatureServe, a non-profit conservation organization whose mission is to provide the scientific basis for effective conservation action of rare and endangered species and threatened ecosystems, represents an international network of biological inventories-known as natural heritage programs or conservation data centers-operating in all 50 U.S. states, Canada, Latin America, and the Caribbean.
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From page 127... ...
122 Status is assessed and documented at both the global (G) , and state/provincial (S)
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From page 128... ...
123 G/SH Historically known, but not verified for an extended period of time. G#T# Trinomial rank (T)
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From page 129... ...
124 Register. PDL Proposed for delisting.
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From page 130... ...
125 Potential Conservation Areas In order to successfully protect populations or occurrences CNHP designs Potential Conservation Areas (PCAs)
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From page 131... ...
126 Table A.3. Natural Heritage Program Biological Diversity Ranks and their Definitions B1 Outstanding Significance (indispensable)
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From page 132... ...
127 NatureServe.
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From page 133... ...
128 APPENDIX B Data Sources used in Potential Habitat Distribution Models Table B.1. Habitat Groups Used for Plant Species Models General Group Astragalus sparsiflorus Oligoneuron album Carex oreocharis Potentilla ambigens Cypripedium parviflorum Townsendia rothrockii Machaeranthera coloradoensis Viola pedatifida Mentzelia speciosa Alpine Group Armeria maritima ssp.
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From page 134... ...
129 Annual Growing Degree Days Peter E Thornton, National Center for Atmospheric Research.
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From page 135... ...
130 resolution. Raster was downsampled to 30m, re-projected and snapped to be compatible with other environmental inputs.
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From page 136... ...
131 CNHP Observations The Colorado Natural Heritage Program, Colorado State University.
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132 Online link: http://nhd.usgs.gov/index.html Other citation details: NHDFlowline NHDWaterbody NHDPoint Source scale denominator: 12,000 - 24,000 Source contribution: Habitat model environmental input. USGS High Resolution National Hydrography Dataset (NHD)
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From page 138... ...
133 LandFire Current Veg: "SYSTMGRPNA" LIKE '%Riparian%' OR "SYSTMGRPNA" LIKE '%Wet%' Elevation U.S. Geological Survey.
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134 Peter E Thornton, National Center for Atmospheric Research.
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From page 141... ...
136 Relative Forest Cover Derived from Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey.
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From page 142... ...
137 Soil Texture Derived from Miller, D.A.
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From page 143... ...
138 SNODAS snow depth (mm) data for March, April, and May were averaged over the years 2004 - 2011.
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From page 144... ...
139 Source contribution: Environmental Input – categorical. Vegetation type (LANDFIRE)
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From page 145... ...
140 APPENDIX C CNHP Potential Habitat Distribution Models Aquilegia saximontana habitat model Predictive species distribution model of potential habitat for Aquilegia saximontana in Colorado.
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From page 146... ...
141 Arabidopsis salsuginea habitat model Predictive species distribution model of potential habitat for Arabidopsis salsuginea in Colorado. Species also known as Thellungiella salsuginea.
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From page 147... ...
142 Armeria maritima ssp. sibirica habitat model Predictive species distribution model of potential habitat for Armeria maritima ssp.
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From page 148... ...
143 Astragalus molybdenus habitat model Predictive species distribution model of potential habitat for Astragalus molybdenus in Colorado.
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From page 149... ...
144 Astragalus sparsiflorus habitat model Predictive species distribution model of potential habitat for Astragalus sparsiflorus in Colorado.
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From page 150... ...
145 Braya glabella ssp. glabella habitat model Predictive species distribution model of potential habitat for Braya glabella ssp.
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From page 151... ...
146 Braya humilis habitat model Predictive species distribution model of potential habitat for Braya humilis in Colorado.
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From page 152... ...
147 Carex limosa habitat model Predictive species distribution model of potential habitat for Carex limosa in Colorado.
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From page 153... ...
148 Carex livida habitat model Predictive species distribution model of potential habitat for Carex livida in Colorado.
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From page 154... ...
149 Carex oreocharis habitat model Predictive species distribution model of potential habitat for Carex oreocharis in Colorado.
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From page 155... ...
150 Carex scirpoidea habitat model Predictive species distribution model of potential habitat for Carex scirpoidea in Colorado.
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From page 156... ...
151 Carex viridula habitat model Predictive species distribution model of potential habitat for Carex viridula in Colorado.
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From page 157... ...
152 Castilleja puberula habitat model Predictive species distribution model of potential habitat for Castilleja puberula in Colorado.
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From page 158... ...
153 Crepis nana habitat model Predictive species distribution model of potential habitat for Crepis nana in Colorado. Species also known as Askellia nana.
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From page 159... ...
154 Cypripedium parviflorum habitat model Predictive species distribution model of potential habitat for Cypripedium parviflorum in Colorado. Species also known as Cypripedium calceolus ssp.
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From page 160... ...
155 Draba borealis habitat model Predictive species distribution model of potential habitat for Draba borealis in Colorado.
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From page 161... ...
156 Draba crassa habitat model Predictive species distribution model of potential habitat for Draba crassa in Colorado.
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From page 162... ...
157 Draba exunguiculata habitat model Predictive species distribution model of potential habitat for Draba exunguiculata in Colorado.
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From page 163... ...
158 Draba fladnizensis habitat model Predictive species distribution model of potential habitat for Draba fladnizensis in Colorado.
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From page 164... ...
159 Draba globosa habitat model Predictive species distribution model of potential habitat for Draba globosa in Colorado.
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From page 165... ...
160 Draba grayana habitat model Predictive species distribution model of potential habitat for Draba grayana in Colorado.
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From page 166... ...
161 Draba oligosperma habitat model Predictive species distribution model of potential habitat for Draba oligosperma in Colorado.
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From page 167... ...
162 Draba porsildii habitat model Predictive species distribution model of potential habitat for Draba porsildii in Colorado.
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From page 168... ...
163 Draba streptobrachia habitat model Predictive species distribution model of potential habitat for Draba streptobrachia in Colorado.
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From page 169... ...
164 Eriophorum altaicum var. neogaeum habitat model Predictive species distribution model of potential habitat for Eriophorum altaicum var.
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From page 170... ...
165 Eriophorum gracile habitat model Predictive species distribution model of potential habitat for Eriophorum gracile in Colorado.
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From page 171... ...
166 Eutrema penlandii habitat model Predictive species distribution model of potential habitat for Eutrema penlandii in Colorado. Species also known as Eutrema edwardsii ssp.
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From page 172... ...
167 Ipomopsis globularis habitat model Predictive species distribution model of potential habitat for Ipomopsis globularis in Colorado.
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From page 173... ...
168 Machaeranthera coloradoensis habitat model Predictive species distribution model of potential habitat for Machaeranthera coloradoensis in Colorado.
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From page 174... ...
169 Mentzelia speciosa habitat model Predictive species distribution model of potential habitat for Mentzelia speciosa in Colorado. Species also known as Nuttallia speciosa.
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From page 175... ...
170 Mimulus gemmiparus habitat model Predictive species distribution model of potential habitat for Mimulus gemmiparus in Colorado.
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From page 176... ...
171 Oligoneuron album habitat model Predictive species distribution model of potential habitat for Oligoneuron album in Colorado. Species also known as Unamia alba.
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From page 177... ...
172 Packera pauciflora habitat model Predictive species distribution model of potential habitat for Packera pauciflora in Colorado.
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From page 178... ...
173 Parnassia kotzebuei habitat model Predictive species distribution model of potential habitat for Parnassia kotzebuei in Colorado.
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From page 179... ...
174 Physaria alpina habitat model Predictive species distribution model of potential habitat for Physaria alpina in Colorado.
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From page 180... ...
175 Potentilla ambigens habitat model Predictive species distribution model of potential habitat for Potentilla ambigens in Colorado.
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From page 181... ...
176 Potentilla rupincola habitat model Predictive species distribution model of potential habitat for Potentilla rupincola in Colorado.
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From page 182... ...
177 Primula egaliksensis habitat model Predictive species distribution model of potential habitat for Primula egaliksensis in Colorado.
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From page 183... ...
178 Ptilagrostis porteri habitat model Predictive species distribution model of potential habitat for Ptilagrostis porteri in Colorado.
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From page 184... ...
179 Ranunculus karelinii habitat model Predictive species distribution model of potential habitat for Ranunculus karelinii in Colorado. Species also known as Ranunculus gelidus ssp.
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From page 185... ...
180 Ribes americanum habitat model Predictive species distribution model of potential habitat for Ribes americanum in Colorado.
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From page 186... ...
181 Salix candida habitat model Predictive species distribution model of potential habitat for Salix candida in Colorado.
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From page 187... ...
182 Salix serissima habitat model Predictive species distribution model of potential habitat for Salix serissima in Colorado.
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From page 188... ...
183 Saussurea weberi habitat model Predictive species distribution model of potential habitat for Saussurea weberi in Colorado.
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From page 189... ...
184 Sisyrinchium pallidum habitat model Predictive species distribution model of potential habitat for Sisyrinchium pallidum in Colorado.
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From page 190... ...
185 Sphagnum girgensohnii habitat model Predictive species distribution model of potential habitat for Sphagnum girgensohnii in Colorado.
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From page 191... ...
186 Telesonix jamesii habitat model Predictive species distribution model of potential habitat for Telesonix jamesii in Colorado.
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From page 192... ...
187 Townsendia rothrockii habitat model Predictive species distribution model of potential habitat for Townsendia rothrockii in Colorado.
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From page 193... ...
188 Trichophorum pumilum habitat model Predictive species distribution model of potential habitat for Trichophorum pumilum in Colorado.
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From page 194... ...
189 Viola pedatifida habitat model Predictive species distribution model of potential habitat for Viola pedatifida in Colorado.
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From page 195... ...
190 American Peregrine Falcon habitat model Predictive species distribution model of potential nesting and roosting habitat for American Peregrine Falcon (Falco peregrinus anatum) in Colorado.
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From page 196... ...
191 Bald Eagle habitat model Predictive species distribution model of potential habitat for Bald Eagle (Haliaeetus leucocephalus) in Colorado.
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From page 197... ...
192 Boreal Owl habitat model Predictive species distribution model of potential habitat for Boreal Owl (Aegolius funereus) in Colorado.
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From page 198... ...
193 Boreal toad habitat model Predictive species distribution model of potential habitat for boreal toad (Anaxyrus boreas) in Colorado.
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From page 199... ...
194 Burrowing Owl habitat model Predictive species distribution model of potential habitat for Burrowing Owl (Athene cunicularia) in Colorado.
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From page 200... ...
195 Ferruginous Hawk habitat model Deductive model of potential habitat for Ferruginous Hawk (Buteo regalis) in Colorado.
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From page 201... ...
196 Greenback cutthroat trout habitat model Predictive species distribution model of potential habitat for greenback cutthroat (Oncorhynchus clarkii stomias) trout in Colorado.
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From page 202... ...
197 Gunnison's prairie dog habitat model Predictive species distribution model of potential habitat for Gunnison's prairie dog (Cynomys gunnisoni) in Colorado.
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From page 203... ...
198 Hops feeding azure habitat model Predictive species distribution model of potential habitat for hops feeding azure in Colorado. The species is also known as hops azure (Celastrina humulus)
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From page 204... ...
199 Lynx habitat model Deductive model of potential habitat for lynx (Lynx canadensis) in Colorado.
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From page 205... ...
200 Mexican Spotted Owl habitat model Predictive species distribution model of potential habitat for Mexican Spotted Owl (Strix occidentalis lucida) in Colorado.
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From page 206... ...
201 Moss's elfin habitat model Predictive species distribution model of potential habitat for Moss's elfin in Colorado. The species is also known as Schryver's elfin (Callophrys mossii schryveri)
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From page 207... ...
202 Mottled dusky wing habitat model Predictive species distribution model of potential habitat for mottled dusky wing (Erynnis martialis) in Colorado.
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From page 208... ...
203 Mountain Plover habitat model Predictive species distribution model of potential habitat for Mountain Plover (Charadrius montanus) in Colorado.
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From page 209... ...
204 Northern leopard frog habitat model Predictive species distribution model of potential habitat for northern leopard frog (Rana pipiens) in Colorado.
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From page 210... ...
205 Northern pocket gopher ssp macrotis habitat model Predictive species distribution model of potential habitat for northern pocket gopher ssp macrotis (Thomomys talpoides macrotis) in Colorado.
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From page 211... ...
206 Northern redbelly dace habitat model Predictive species distribution model of potential habitat for Northern redbelly (Phoxinus eos) dace in Colorado.
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From page 212... ...
207 Ovenbird habitat model Deductive model of potential habitat for Ovenbird (Seiurus aurocapilla) in Colorado.
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From page 213... ...
208 Prairie Falcon habitat model Attempts were made to create an inductive predictive species distribution model for this species using MaxEnt, but a good fit could not be obtained with existing data. The SWReGAP Vertebrate Habitat Model for the species was also reviewed and deemed too inaccurate.
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From page 214... ...
209 Preble's meadow jumping mouse habitat model Predictive species distribution model of potential habitat for Preble's meadow jumping mouse (Zapus hudsonius preblei) in Colorado.
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From page 215... ...
210 Veery habitat model Predictive species distribution model of potential habitat for Veery (Catharus fuscescens) in Colorado.
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From page 216... ...
211 Willow Flycatcher habitat model Predictive species distribution model of potential habitat for Willow Flycatcher (Empidonax traillii) in Colorado.
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From page 217... ...
212 Wolverine habitat model Predictive species distribution model of potential habitat for wolverine (Gulo gulo) in Colorado.
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From page 218... ...
213 Additional species distribution and habitat data not created by CNHP Species Activity Maps. Colorado Parks and Wildlife, last updated 6/2011.
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From page 219... ...
214 APPENDIX D Wetland Mapping Results by Quad Agate Mountain Figure D.1. Wetland polygons mapped within the Agate Mountain quad shown over 2009 true-color aerial photography (left)
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From page 220... ...
215 Alma Figure D.2. Wetland polygons mapped within the Alma quad shown over 2009 true-color aerial photography (left)
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From page 221... ...
216 Antero Reservoir Figure D.3. Wetland polygons mapped within the Antero Reservoir quad shown over 2009 true-color aerial photography (left)
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From page 222... ...
217 Antero Reservoir NE Figure D.4. Wetland polygons mapped within the Antero Reservoir NE quad shown over 2009 true-color aerial photography (left)
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From page 223... ...
218 Bailey Figure D.5. Wetland polygons mapped within the Bailey quad shown over 2009 true-color aerial photography (left)
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From page 224... ...
219 Boreas Pass Figure D.6. Wetland polygons mapped within the Boreas Pass quad shown over 2009 truecolor aerial photography (left)
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From page 225... ...
220 Castle Rock Gulch Figure D.7. Wetland polygons mapped within the Castle Rock Gulch quad shown over 2009 true-color aerial photography (left)
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From page 226... ...
221 Cheesman Lake Figure D.8. Wetland polygons mapped within the Cheesman Lake quad shown over 2009 true-color aerial photography (left)
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From page 227... ...
222 Climax Figure D.9. Wetland polygons mapped within the Climax quad shown over 2009 true-color aerial photography (left)
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From page 228... ...
223 Como Figure D.10. Wetland polygons mapped within the Como quad shown over 2009 true-color aerial photography (left)
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From page 229... ...
224 Conifer Figure D.11. Wetland polygons mapped within the Conifer quad shown over 2009 truecolor aerial photography (left)
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From page 230... ...
225 Deckers Figure D.12. Wetland polygons mapped within the Deckers quad shown over 2009 truecolor aerial photography (left)
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From page 231... ...
226 Dick's Peak Figure D.13. Wetland polygons mapped within the Dicks Peak quad shown over 2009 truecolor aerial photography (left)
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From page 232... ...
227 Divide Figure D.14. Wetland polygons mapped within the Divide quad shown over 2009 true-color aerial photography (left)
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From page 233... ...
228 Eagle Rock Figure D.15. Wetland polygons mapped within the Eagle Rock quad shown over 2009 truecolor aerial photography (left)
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From page 234... ...
229 Elevenmile Canyon Figure D.16. Wetland polygons mapped within the Elevenmile Canyon quad shown over 2009 true-color aerial photography (left)
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From page 235... ...
230 Elkhorn Figure D.17. Wetland polygons mapped within the Elkhorn quad shown over 2009 truecolor aerial photography (left)
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From page 236... ...
231 Evergreen Figure D.18. Wetland polygons mapped within the Evergreen quad shown over 2009 truecolor aerial photography (left)
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From page 237... ...
232 Fairplay East Figure D.19. Wetland polygons mapped within the Fairplay East quad shown over 2009 true-color aerial photography (left)
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From page 238... ...
233 Fairplay West Figure D.20. Wetland polygons mapped within the Fairplay West quad shown over 2009 true-color aerial photography (left)
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From page 239... ...
234 Farnum Peak Figure D.21. Wetland polygons mapped within the Farnum Peak quad shown over 2009 true-color aerial photography (left)
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From page 240... ...
235 Garo Figure D.22. Wetland polygons mapped within the Garo quad shown over 2009 true-color aerial photography (left)
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From page 241... ...
236 Glentivar Figure D.23. Wetland polygons mapped within the Glentivar quad shown over 2009 truecolor aerial photography (left)
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From page 242... ...
237 Green Mountain Figure D.24. Wetland polygons mapped within the Green Mountain quad shown over 2009 true-color aerial photography (left)
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From page 243... ...
238 Guffey NW Figure D.25. Wetland polygons mapped within the Guffey NW quad shown over 2009 truecolor aerial photography (left)
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From page 244... ...
239 Hackett Mountain Figure D.26. Wetland polygons mapped within the Hackett Mountain quad shown over 2009 true-color aerial photography (left)
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From page 245... ...
240 Harris Park Figure D.27. Wetland polygons mapped within the Harris Park quad shown over 2009 true-color aerial photography (left)
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From page 246... ...
241 Hartsel Figure D.28. Wetland polygons mapped within the Hartsel quad shown over 2009 truecolor aerial photography (left)
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From page 247... ...
242 Idaho Springs Figure D.29. Wetland polygons mapped within the Idaho Springs quad shown over 2009 true-color aerial photography (left)
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From page 248... ...
243 Indian Hills Figure D.30. Wetland polygons mapped within the Indian Hills quad shown over 2009 truecolor aerial photography (left)
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From page 249... ...
244 Jefferson Figure D.31. Wetland polygons mapped within the Jefferson quad shown over 2009 truecolor aerial photography (left)
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From page 250... ...
245 Jones Hill Figure D.32. Wetland polygons mapped within the Jones Hill quad shown over 2009 truecolor aerial photography (left)
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From page 251... ...
246 Lake George Figure D.33. Wetland polygons mapped within the Lake George quad shown over 2009 true-color aerial photography (left)
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From page 252... ...
247 Marmot Peak Figure D.34. Wetland polygons mapped within the Marmot Peak quad shown over 2009 true-color aerial photography (left)
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From page 253... ...
248 McCurdy Mountain Figure D.35. Wetland polygons mapped within the McCurdy Mountain quad shown over 2009 true-color aerial photography (left)
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From page 254... ...
249 Meridian Hill Figure D.36. Wetland polygons mapped within the Meridian Hill quad shown over 2009 true-color aerial photography (left)
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From page 255... ...
250 Milligan Lakes Figure D.37. Wetland polygons mapped within the Milligan Lakes quad shown over 2009 true-color aerial photography (left)
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From page 256... ...
251 Montezuma Figure D.38. Wetland polygons mapped within the Montezuma quad shown over 2009 true-color aerial photography (left)
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From page 257... ...
252 Morrison Figure D.39. Wetland polygons mapped within the Morrison quad shown over 2009 truecolor aerial photography (left)
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From page 258... ...
253 Mount Evans Figure D.40. Wetland polygons mapped within the Mount Evans quad shown over 2009 true-color aerial photography (left)
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From page 259... ...
254 Mount Logan Figure D.41. Wetland polygons mapped within the Mount Logan quad shown over 2009 true-color aerial photography (left)
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From page 260... ...
255 Mount Sherman Figure D.42. Wetland polygons mapped within the Mount Sherman quad shown over 2009 true-color aerial photography (left)
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From page 261... ...
256 Observatory Rock Figure D.43. Wetland polygons mapped within the Observatory Rock quad shown over 2009 true-color aerial photography (left)
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From page 262... ...
257 Pine Figure D.44. Wetland polygons mapped within the Pine quad shown over 2009 true-color aerial photography (left)
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From page 263... ...
258 Platte Canyon Figure D.45. Wetland polygons mapped within the Platte Canyon quad shown over 2009 true-color aerial photography (left)
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From page 264... ...
259 Shawnee Figure D.46. Wetland polygons mapped within the Shawnee quad shown over 2009 truecolor aerial photography (left)
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From page 265... ...
260 South Peak Figure D.47. Wetland polygons mapped within the South Peak quad shown over 2009 truecolor aerial photography (left)
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From page 266... ...
261 Spinney Mountain Figure D.48. Wetland polygons mapped within the Spinney Mountain quad shown over 2009 true-color aerial photography (left)
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From page 267... ...
262 Squaw Pass Figure D.49. Wetland polygons mapped within the Squaw Pass quad shown over 2009 truecolor aerial photography (left)
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From page 268... ...
263 Sulphur Mountain Figure D.50. Wetland polygons mapped within the Sulphur Mountain quad shown over 2009 true-color aerial photography (left)
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From page 269... ...
264 Tarryall Figure D.51. Wetland polygons mapped within the Tarryall quad shown over 2009 truecolor aerial photography (left)
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From page 270... ...
265 Thirtynine Mile Mountain Figure D.52. Wetland polygons mapped within the Thirtynine Mile Mountain quad shown over 2009 true-color aerial photography (left)
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From page 271... ...
266 Topaz Mountain Figure D.53. Wetland polygons mapped within the Topaz Mountain quad shown over 2009 true-color aerial photography (left)
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From page 272... ...
267 Windy Peak Figure D.54. Wetland polygons mapped within the Windy Peak quad shown over 2009 true-color aerial photography (left)
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From page 273... ...
268 Witcher Mountain Figure D.55. Wetland polygons mapped within the Witcher Mountain quad shown over 2009 true-color aerial photography (left)
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From page 274... ...
269 APPENDIX E Additional Wetland Mapping Summaries Table E.1. Wetland Acreage in the C21A Study Area by Ecoregion and NWI System / Class Level III / IV Ecoregiona Total Land Area within Project Total NWI Acres within Project Wetland Acres by NWI System/Class Total Acres % of Project Area Total Acres % of NWI Acres R2/3/4 L1/2 PAB PUB PUS PEM PSS PFO Rp1 25: High Plains 19,544 1.0% 492 0.6% 18 191 2 49 1 54 59 108 11 25d: Flat to Rolling Plains 323 < 0.1% 12 < 0.1% 0 11 0 2 0 0 0 0 0 25l: Front Range Fans 19,221 0.9% 480 0.6% 18 180 2 47 1 54 59 108 11 21: Southern Rockies 2,014,692 99.0% 84,495 99.4% 2,622 10,525 706 908 2,300 41,428 25,021 795 189 21a: Alpine Zone 192,145 9.4% 8,429 9.9% 113 359 77 104 16 1,010 6,699 51 0 21c: Crystalline Mid-Elevation Forests and Shrublands 675,717 33.2% 15,503 18.2% 1,041 1,186 225 442 61 6,369 5,855 194 129 21b: Crystalline Subalpine Forests 366,510 18.0% 8,854 10.4% 204 430 56 55 10 2,007 5,930 151 10 21d: Foothills and Shrublands 38,156 1.9% 570 0.7% 63 145 5 56 < 1 31 122 98 49 21j: Grassland Parks 461,232 22.7% 41,507 48.8% 968 8,239 126 166 2,166 28,017 1,577 248 0 21f: Sedimentary Mid-Elevation Forests and Shrublands 169,859 8.4% 5,979 7.0% 183 135 136 28 41 2,819 2,604 33 0 21e: Sedimentary Subalpine Forests 39,781 2.0% 3,054 3.6% 49 32 62 33 3 684 2,172 18 0 21h: Volcanic Mid-Elevation Forests and Shrublands 52,090 2.6% 552 0.7% 0 0 17 24 2 479 29 2 0 21g: Volcanic Subalpine Forests 19,202 0.9% 48 0.1% 0 0 < 1 1 < 1 13 33 0 0 Grand Total 2,034,236 100% 84,987 100% 2,640 10,716 708 957 2,301 41,483 25,080 904 200 Note: See Table 2.7 within the report for explanation of NWI codes.
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From page 275... ...
270 Table E.2. Wetland Acreage in the C21A Study Area by Ecoregion and NWI Hydrologic Regime.
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From page 276... ...
271 Table E.3. Wetland Acreage in the C21A Study Area by Ecoregion and the Nine Largest Grouped Land Owners Level III/IV Ecoregion Grand Total BLM CPW CITY NGO PRIVATE SLB USFS - ARNF USFS - PIKE USFS - WHITE RIVER 25: High Plains 492 0% 0% 66% 0% 27% 0% 0% 0% 0% 25d: Flat to Rolling Plains 12 0% 0% 0% 0% 100% 0% 0% 0% 0% 25l: Front Range Fans 480 0% 0% 67% 0% 25% 0% 0% 0% 0% 21: Southern Rockies 84,495 2% 3% 4% 12% 50% 2% 2% 22% 1% 21a: Alpine Zone 8,429 3% 0% 1% 0% 16% < 1% 13% 61% 6% 21c: Crystalline Mid-Elevation Forests and Shrublands 15,503 1% 2% 1% 8% 57% < 1% < 1% 26% < 1% 21b: Crystalline Subalpine Forests 8,854 0% 1% 1% < 1% 19% < 1% 5% 72% 2% 21d: Foothills and Shrublands 570 0% 0% 23% < 1% 63% < 1% 0% 3% 0% 21j: Grassland Parks 41,507 2% 4% 7% 22% 59% 3% 0% 1% 0% 21f: Sedimentary Mid-Elevation Forests and Shrublands 5,979 1% 6% 0% 1% 58% 4% 0% 29% 0% 21e: Sedimentary Subalpine Forests 3,054 7% 0% 0% 0% 62% < 1% 0% 25% 5% 21h: Volcanic Mid-Elevation Forests and Shrublands 552 0% 0% 0% 0% 94% < 1% 0% 5% 0% 21g: Volcanic Subalpine Forests 48 0% 0% 0% 0% 8% 2% 0% 90% 0% Grand Total 84,987 1,382 2,344 3,448 10,523 42,871 1,734 1,542 18,676 907
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Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.