National Park Service

U.S. Department of the Interior

Natural Resource Program Center

A Natural Resource Condition Assessment for 

Rocky Mountain National Park

Natural Resource Report NPS/NRPC/WRD/NRR—2010/228
ON THE COVER

Rocky Mountain National Park

Photograph by: Billy Schweiger
A Natural Resource Condition Assessment for

Rocky Mountain National Park

Natural Resource Report NPS/NRPC/WRD/NRR—2010/228

David M. Theobald1,2

Jill S. Baron2,3

Peter Newman1

Barry Noon4

John B. Norman III1,2

Ian Leinwand1

Sophia E. Linn1

Richard Sherer4

Katherine E. Williams2,5

Melannie Hartman2

1

Department of Human Dimensions of Natural Resources, Colorado State University, Fort 

Collins, CO 80523-1480

2

Natural Resource Ecology Lab, Colorado State University, Fort Collins, CO 80523-1499

3

U.S. Geological Survey, Fort Collins, CO 80523

4

Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort 

Collins, CO 80523-1474

5

Current address: Department of Biology, University of Wyoming, Laramie, WY 82071

This report was prepared under Task Order J2380060103 

(Cooperative Agreement #H1200040001)

July 2010

U.S. Department of the Interior

National Park Service

Natural Resource Program Center

Fort Collins, Colorado
ii

The Natural Resource Publication series addresses natural resource topics that are of interest and 

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Printed copies of reports in these series may be produced in a limited quantity and they are only 

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(http://www.nature.nps.gov/publications/NRPM) on the Internet or by sending a request to the 

address on the back cover.

Please cite this publication as:

Theobald, D. M., J. S. Baron, P. Newman, B. Noon, J. B. Norman, III, I. Leinwand, S. E. Linn, 

R. Scherer, K. E. Williams, and M. Hartman. 2010. A natural resource condition assessment for 

Rocky Mountain National Park. Natural Resource Report NPS/NRPC/WRD/NRR—2010/228. 

National Park Service, Fort Collins, Colorado.

TIC Number: 121/105044

Authorship of individual indicators:

Air and Climate:

- Baron, J. S., K. E. Williams, and M. Hartman. 2010. Condition of alpine lakes and 

atmospheric deposition. In: A Natural Resource Condition Assessment for Rocky 

Mountain National Park. Natural Resource Report NPS/NRPC/WRD/NRR—

2010/228. National Park Service, Fort Collins, Colorado.
iii

Water:

- Theobald, D. M. and J. B. Norman, III. 2010. Extent and connectivity of wetland 

and riparian areas. In: A Natural Resource Condition Assessment for Rocky 

Mountain National Park. Natural Resource Report NPS/NRPC/WRD/NRR—

2010/228. National Park Service, Fort Collins, Colorado.

Biotic Integrity:

- Theobald, D. M. and J. B. Norman, III. 2010. Extent of exotic terrestrial plants. 

In: A Natural Resource Condition Assessment for Rocky Mountain National Park.

Natural Resource Report NPS/NRPC/WRD/NRR—2010/228. National Park 

Service, Fort Collins, Colorado.

- Theobald, D. M. and J. B. Norman, III. 2010. Extent and connectivity of fish

distributions. In: A Natural Resource Condition Assessment for Rocky Mountain 

National Park. Natural Resource Report NPS/NRPC/WRD/NRR—2010/228. 

National Park Service, Fort Collins, Colorado.

- Scherer, R., I. Leinwand, B. R. Noon, and D. M. Theobald. 2010. Extent of 

suitable beaver habitat. In: A Natural Resource Condition Assessment for Rocky 

Mountain National Park. Natural Resource Report NPS/NRPC/WRD/NRR—

2010/228. National Park Service, Fort Collins, Colorado.

Landscapes:

- Noon, B. R., D. M. Theobald, and J. Norman, III. 2010. Extent and pattern of 

major ecological systems. In: A Natural Resource Condition Assessment for 

Rocky Mountain National Park. Natural Resource Report 

NPS/NRPC/WRD/NRR—2010/228. National Park Service, Fort Collins, 

Colorado.

- Theobald, D. M., B. R. Noon, and J. Norman, III. 2010. Connectivity of natural 

landscapes. In: A Natural Resource Condition Assessment for Rocky Mountain 

National Park. Natural Resource Report NPS/NRPC/WRD/NRR—2010/228. 

National Park Service, Fort Collins, Colorado.

NPS 121/105044, July 2010
iv

Contents

Page

Contents................................................................................................................................. iv

Figures ................................................................................................................................. viii

Tables.................................................................................................................................... xii

Executive Summary............................................................................................................ xiv

Air and climate: Condition of Alpine Lakes and Atmospheric Deposition...................... xv

Water: Extent and Connectivity of Wetland and Riparian Areas.................................... xvi

Biotic Integrity: Exotic Terrestrial Plant Species ........................................................... xvii

Biotic Integrity: Extent and Connectivity of Fish Distributions.................................... xviii

Biotic Integrity: Extent of Suitable Beaver Habitat......................................................... xix

Landscapes: Extent and Pattern of Major Ecological Systems......................................... xx

Landscapes: Connectivity of Natural Landscapes........................................................... xxi

Acknowledgements............................................................................................................ xxii

Prologue .............................................................................................................................. xxii

Chapter 1. NRCA Background Information....................................................................... 1

1.1 NRCA Description............................................................................................................. 1

1.2 NRCA Purpose and Use .................................................................................................... 1

Chapter 2. Park Resource Setting/Stewardship Context................................................... 5

2.1 Park Resource Setting........................................................................................................ 5

2.1.1 Description and Characterization of Park Natural Resources.................................... 5

2.1.2 Overview of Resource Condition Issues.................................................................. 17

2.2 Resource Stewardship Context........................................................................................ 17

Chapter 3. Study Approach................................................................................................ 19

3.1 Preliminary Scoping ........................................................................................................ 19
v

3.1.1 Advisory/Oversight Team........................................................................................ 19

3.1.2 Scoping Workshop................................................................................................... 21

3.2 Reporting Areas............................................................................................................... 22

3.3 Assessment Frameworks Used in the Study.................................................................... 23

3.4 Reference Condition Framework..................................................................................... 26

3.5 Estimate of Concern and Degree of Confidence ............................................................. 31

Chapter 4. Natural Resource Conditions ......................................................................... 33

4.1 Regional Context and Issue Characterization.................................................................. 33

4.1.1 Estimating Visitor Use through Accessibility Modeling......................................... 38

Methods ..................................................................................................................... 39

Results........................................................................................................................ 42

4.1.2 Watersheds at Risk to Beetle Kill ............................................................................ 47

4.2 Analyses of Indicators ..................................................................................................... 48

4.2.1 Air and climate: Condition of Alpine Lakes and Atmospheric Deposition............. 48

Description/Purpose................................................................................................... 48

Approach/Methods .................................................................................................... 49

Reference Condition .................................................................................................. 51

Results/Discussion..................................................................................................... 52

Uses and Limitations ................................................................................................. 63

Summary: Air and climate – Condition of Alpine Lakes and Atmospheric Deposition

................................................................................................................................... 67

4.2.2 Water: Extent and Connectivity of Wetland and Riparian Areas............................ 68

Description/Purpose................................................................................................... 68

Approach/Methods .................................................................................................... 69

Reference Condition .................................................................................................. 72

Results/Discussion..................................................................................................... 73
vi

Uses and Limitations ................................................................................................. 80

Summary: Water - Extent and Connectivity of Wetland and Riparian Areas........... 81

4.2.3 Biotic Integrity: Extent of Exotic Terrestrial Plants................................................ 82

Description/Purpose................................................................................................... 82

Approach/Methods .................................................................................................... 83

Reference Condition .................................................................................................. 86

Results/Discussion..................................................................................................... 86

Uses and Limitations ................................................................................................. 91

Summary: Biotic Integrity – Extent of Exotic Terrestrial Plants .............................. 92

4.2.4 Biotic Integrity: Extent and Connectivity of Fish Distributions.............................. 93

Description/Purpose................................................................................................... 93

Approach/Methods .................................................................................................... 93

Reference Condition .................................................................................................. 98

Results/Discussion..................................................................................................... 98

Uses and Limitations ............................................................................................... 102

Summary: Biotic Integrity – Extent and Connectivity of Fish Distributions.......... 103

4.2.5 Biotic Integrity: Extent of Suitable Beaver Habitat............................................... 104

Description/Purpose................................................................................................. 104

Approach/Methods .................................................................................................. 106

Reference Condition ................................................................................................ 112

Results/Discussion................................................................................................... 113

Uses and Limitations ............................................................................................... 119

Summary: Biotic Integrity - Extent of Suitable Beaver Habitat.............................. 122

4.2.6 Landscapes: Extent and Proportion of Major Ecological Systems........................ 123

Description/Purpose................................................................................................. 123
vii

Approach/Methods .................................................................................................. 123

Reference Condition ................................................................................................ 125

Results/Discussion................................................................................................... 126

Uses and Limitations ............................................................................................... 132

Summary: Landscapes - Extent and pattern of major ecological systems............... 133

4.2.7 Landscapes: Connectivity of Natural Landscapes................................................. 134

Description/Purpose................................................................................................. 134

Approach/Methods .................................................................................................. 135

Reference Condition ................................................................................................ 137

Results/Discussion................................................................................................... 137

Uses and Limitations ............................................................................................... 143

Summary: Landscapes - Connectivity of natural landscapes .................................. 144

5. Natural Resource Condition Summaries..................................................................... 145

Discussion............................................................................................................................ 151

Data and Knowledge Gaps .................................................................................................. 152

Appendix 1. Literature Cited............................................................................................ 153

Technical Appendix 1: Stream Connectivity Methods................................................... 169

Technical Appendix 2. Life-zone Class Delineation Methods ....................................... 171

Technical Appendix 3: Beaver Suitable Habitat Modeling ........................................... 172
viii

Figures

Figure 1. Rocky Mountain National Park and other federal lands in Colorado. ............................ 6

Figure 2. Boundary changes of Rocky Mountain National Park, 1915 – 2003.............................. 7

Figure 3. Major land ownership types around Rocky Mountain National Park in 2008................ 8

Figure 4. Six major ecosystem or “life-zone” classes generated from park-specific vegetation.... 9

Figure 5. Roads and trails infrastructure inside ROMO. .............................................................. 10

Figure 6. Major hydrologic features within ROMO. .................................................................... 11

Figure 7. Names of 12-digit Hydrologic Unit Codes (HUC-12 catchments) derived from the 

major tributary within each HUC-12 unit......................................................................... 12

Figure 8. Management Zones found in ROMO as defined in the Backcountry/Wilderness 

Management Plan, 2001.................................................................................................... 14

Figure 9. Diagram of park specific scoping and a structured analysis. ........................................ 19

Figure 10. The top level of the Essential Ecological Attribute hierarchy (USEPA, 2002) .......... 24

Figure 11. Distribution of indicator values based on sampling numerous sites at various locations 

on the landscape................................................................................................................ 28

Figure 12. Cumulative distribution of indicator scores based on sampling numerous sites at 

various locations on the landscape.................................................................................... 29

Figure 13. Conceptual model depicting stages of change in biological conditions in response to 

an increasing stressor gradient.......................................................................................... 33

Figure 14. Accessibility within ROMO from main park entrances, assuming current 

transportation infrastructure (Scenario A) ........................................................................ 43

Figure 15. Accessibility within ROMO from park entrances, assuming no trails but roads as 

transportation infrastructure (Scenario B) ........................................................................ 44

Figure 16. Accessibility within ROMO from park entrances, assuming no trails or roads as 

transportation infrastructure (Scenario C). ....................................................................... 45

Figure 17. A cumulative distribution plot showing how the proportion of plots with cheatgrass 

relates to accessibility.. ..................................................................................................... 46

Figure 18. Wet deposition values over time for two NADP sites in ROMO, Beaver Meadows 

(CO19) and Loch Vale (CO98)......................................................................................... 53

Figure 19. A comparison of dry deposition from the ROM406 CASTNET site and wet deposition 

from CO98, the Loch Vale site......................................................................................... 54

Figure 20. Reconstructed dry plus wet atmospheric deposition to Loch Vale Watershed from 

1900 to 2000. .................................................................................................................... 54

Figure 21. a) Wet N deposition (light blue) in kgha-1yr-1for 2000, and median lake NO3

-

concentrations in mg L-1 (darker blue); b) Wet SO4

2- deposition (pink) in kgha-1

yr-1 for 
ix

2000, and median lake SO4

2- concentrations mg L-1 (orange), for sampled wilderness and 

park lakes of the U.S. Rocky Mountains. ......................................................................... 57

Figure 22. Ranked distributions from lowest to highest values of temperature and solutes from 

ROMO and Colorado Wilderness Areas........................................................................... 59

Figure 23. Median concentrations of ANC and NO3

-

from wilderness and national park lakes. 

Larger symbols depict higher concentration values.......................................................... 61

Figure 24. Modeled changes in Andrews Creek Acid Neutralizing Capacity (ANC, μeq L-1) over 

time using the DayCent-Chem model and reconstructed wet plus dry N and S deposition 

values. ............................................................................................................................... 63

Figure 25. Aerial photograph of Woods et al. (2006) Lost Creek study site................................ 69

Figure 26. Vegetation types identified as wetland and/or riparian in the ROMO vegetation 

dataset. T........................................................................................................................... 71

Figure 27. Hectares of riparian and wetland systems accumulated downstream, reflecting the 

“natural” scenario.............................................................................................................. 74

Figure 28. Hectares of riparian and wetland systems accumulated downstream, reflecting the 

historic (1900-1990s) scenario. ........................................................................................ 76

Figure 29. Area of riparian and wetland systems accumulated downstream, reflecting the current 

scenario.. ........................................................................................................................... 77

Figure 30. Change in connected stream systems between “natural” scenario and historic scenario, 

and between “natural” scenario and current scenario....................................................... 78

Figure 31. Graphs of cumulative frequency functions for total stream length, total 

riparian/wetland area (center), and average riparian/wetland area. .................................. 78

Figure 32. Percentage of each watershed (HUC-12) that is currently riparian or wetland 

vegetation.......................................................................................................................... 79

Figure 33. Distribution of known locations of exotic species from the Weed Control Database, 

Rocky Mountain National Park. ....................................................................................... 82

Figure 34. Distribution of known locations of exotic species from the USGS NIISS database... 83

Figure 35. Percentage of exotic vegetation for each of the 1,861 vegetation plots in and near 

Rocky Mountain National Park ........................................................................................ 87

Figure 36. Percentage of exotic vegetation summarizing the 1,861 vegetation plots in and near 

Rocky Mountain National Park by 12-digit Hydrologic Unit Code ................................. 88

Figure 37. The relationship between cheatgrass and accessibility using the ROMO vegetation 

plots................................................................................................................................... 89

Figure 38. ROMO vegetation plots that have cheatgrass shown with accessibility map behind. 90

Figure 39. Data from two additional datasets provide further support that cheatgrass occurs in 

more accessible areas........................................................................................................ 91

Figure 40. Total number of fish stocked in ROMO lakes from ~1920-1962. .............................. 94

Figure 41. The known hydrologic barriers that were in the park as of 1987................................ 95
x

Figure 42. Sections of stream reaches that are likely accessible to fish (i.e. no barrier present 

between the reach location and the fish sampling location) and that have been surveyed 

using electro-fishing by U.S. Fish & Wildlife Service personnel..................................... 97

Figure 43. Distribution of native and non-native fish based on the “natural” scenario................ 99

Figure 44. Distribution of native and non-native fish habitat based on the historic (1900-1990s) 

scenario. .......................................................................................................................... 100

Figure 45. Distribution of native and non-native fish habitat based on the current scenario, as of 

current conditions in 2010.. ............................................................................................ 101

Figure 46. Summary of current native fish distribution, in total km of stream per HUC12....... 102

Figure 47. Historic distribution of beaver based on park survey data from 1940 to 2000. Location 

of lodges and dams digitized from Mitchell 1999 to 2000 data. .................................... 105

Figure 48. Areas surveyed for beaver sign in 1999 to 2000. Data digitized from ROMO beaver 

survey reports by Mitchell et al. (2000).......................................................................... 106

Figure 49. Areas of beaver habitat suitability in Rocky Mountain National Park...................... 111

Figure 50. The distribution of geographic distances of locations of current and past beaver 

presence from the nearest patch...................................................................................... 112

Figure 51. Comparison of beaver densities from surveys in 1939-1940 and 1999-2000 in several 

drainages in ROMO. ....................................................................................................... 113

Figure 52. Map showing drainages surveyed in 1999 to 2000 classified based on impact of 

browsing on willow......................................................................................................... 115

Figure 53. Distribution of size (top) and quality (bottom) for the 536 patches in the study area.

......................................................................................................................................... 117

Figure 54. Minimum patch size of 0.01 ha and a minimum average suitability score of 0.4 for 

locations of current beaver activity................................................................................. 118

Figure 55. Beaver restoration potential by HUC12. ................................................................... 119

Figure 56. Weighted mean patch size (log10 transformed) by land cover type, by watershed, 

Rocky Mountain National Park. ..................................................................................... 127

Figure 57. Cumulative distribution functions comparing patches found inside the park vs. outside 

(but in the southern Rockies Ecoregion): Alpine/Tundra, High Elevation Forest, Midelevation Forest, and Low Elevation Forest. .................................................................. 129

Figure 58. Cumulative distribution functions comparing patches found inside the park vs. outside 

(but in the southern Rockies Ecoregion): Aspen, Lodgepole Pine, Riparian/Wetland, and 

Savannah......................................................................................................................... 130

Figure 59. Natural landscapes (NL) metric values in 1992 for Rocky Mountain National Park, 

Colorado.......................................................................................................................... 139

Figure 60. Natural landscapes (NL) metric values in 2001 for Rocky Mountain National Park, 

Colorado.......................................................................................................................... 140
xi

Figure 61. Natural landscapes (NL) metric values in 2030 for Rocky Mountain National Park, 

Colorado.......................................................................................................................... 141

Figure 62. Natural landscapes (NL) values in 2001 for the Southern Rocky Mountain Ecoregion 

(boundary shown in thick grey line).. ............................................................................. 142

Figure 63. Watershed summary of riparian/wetland condition, calculated as the percent of 

connected area currently, normalized by “natural” conditions....................................... 146

Figure 64. The ratio of patch size in ROMO to the weighted mean patch size for the Southern 

Rockies Ecoregion (the reference distribution), averaged within each HUC12 watershed.

......................................................................................................................................... 147

Figure 65. The ratio of patch size in ROMO to the weighted mean patch size for the Southern 

Rockies Ecoregion (the reference distribution), averaged within each HUC12 watershed.

......................................................................................................................................... 148
xii

Tables

Table 1. Catchment names associated with hydrologic 12-digit unit codes (HUC-12)................ 13

Table 2. Definitions of management zones from Rocky Mountain National Park 

Backcountry/Wilderness Management Plan and Environmental Assessment.................. 15

Table 3. Members of the Project Advisory Team for the ROMO assessments............................ 20

Table 4. Summary of meetings with advisory team. .................................................................... 21

Table 5. A listing of the Rocky Mountain Network high-priority vital signs (from Britten et al. 

2007) used to “front-load” the discussion at the scoping workshop................................. 22

Table 6. Listing of existing assessment approaches reviewed for this study................................ 23

Table 7. List of indicators identified during scoping workshop, organized by NPS Inventory & 

Monitoring Ecological Monitoring Framework................................................................ 25

Table 8. The final list of indicators selected for the assessment, paired with anticipated questions 

and issues of decision makers that each indicator is intended to address......................... 26

Table 9. Unified Classification of Direct Threats, first two class levels. ..................................... 34

Table 10. Synthesis of park issues of concern, placed within the classification of issues, with the 

indicator that addresses a particular issue......................................................................... 36

Table 11. Listing of the datasets, attributes, and scale of data used to generate the accessibility 

maps. ................................................................................................................................. 39

Table 12. Weights used to reflect the proportion of park visitors entering ROMO from each of 

the four main park “entrance” locations. .......................................................................... 40

Table 13. Travel time modifier for computing off-trail hiking speed........................................... 41

Table 14. Travel time in hours to locations within ROMO from four entrances for Scenario A 

(current road and trails infrastructure). ............................................................................. 42

Table 15. Summary of average travel time in hours for different accessibility scenarios in Rocky 

Mountain National Park.................................................................................................... 42

Table 16. Average one-way travel time to Research Natural Areas in Rocky Mountain National 

Park reflecting different infrastructure scenarios.............................................................. 42

Table 17. Average one-way travel time to different management zones in Rocky Mountain 

National Park. ................................................................................................................... 46

Table 18. Sources of lake chemistry data used for inorganic solute comparisons. ...................... 51

Table 19. Summary Statistics for Select Alpine Lakes................................................................. 55

Table 20. Summary Statistics for ROMO Sampled Lakes East and West of Continental Divide.

........................................................................................................................................... 60

Table 21. Water Quality Thresholds............................................................................................. 66

Table 22. Barrier features that were identified by park resource managers that alter movement 

and connectivity along the freshwater system in the park. ............................................... 68
xiii

Table 23. Land cover types identified as wetland and/or riparian types in the ROMO vegetation 

map.................................................................................................................................... 70

Table 24. Three scenarios reflecting natural, historic, and current conditions that affect 

connectivity between wetland and riparian areas along stream corridors. ....................... 73

Table 25. Results for the connected hydrologic sub-networks, created by human-barriers 

fragmenting the network (and incorporating natural barriers as well), averaged by 12 digit 

HUC for the “natural”, historic, and current scenarios..................................................... 73

Table 26. Known exotic plant species in Rocky Mountain National Park (Rutledge and 

McLendon 1996). ............................................................................................................. 84

Table 27. Proportion of plots comprised by exotic plants, summarized by ecological system class 

(sorted by elevation). ........................................................................................................ 86

Table 28. Features that affect connectivity of fish scenarios........................................................ 96

Table 29. Total stream length for scenarios and fish distributions............................................... 98

Table 30. Cost weights assigned to stream and terrestrial pixels of different gradients............. 108

Table 31. The original vegetation classes of Salas et al. (2005) and our reclassification with 

respect to each class’s utility and suitability to beaver. .................................................. 109

Table 32. Summary statistics for the patches of suitable beaver habitat in Rocky Mountain 

National Park.. ................................................................................................................ 116

Table 33. Listing of the eight major ecological systems from LANDFIRE existing vegetation 

types, for which we measured the extent and proportion. .............................................. 124

Table 34. Landscape metrics computed for the Big Thompson, Cache la Poudre, Saint Vrain, and 

Colorado River watersheds, Rocky Mountain National Park......................................... 125

Table 35. Summary of class-level landscape metrics by watershed for the areas within ROMO..

......................................................................................................................................... 126

Table 36. Weighted mean patch size values between SRE and ROMO..................................... 131

Table 37. Assignment of natural/human-dominated values for each National Land Cover Dataset 

(retrofit) class. T.............................................................................................................. 136

Table 38. A summary of indicator condition by watershed (HUC-12). ..................................... 149

Table 39. Summary of raw indicator values by management zones outlined in the ROMO 

Backcountry Wilderness Management Plan (BWMP). .................................................. 150

Table 40. Main findings of the watershed condition assessment................................................ 151
xiv

Publisher’s Note: Some or all of the work done for this project preceded the revised guidance 

issued for this project series in 2009/2010. See Prologue (p. xxii) for more information.

Executive Summary

We conducted a natural resource assessment of Rocky Mountain National Park (ROMO) to 

provide a synthesis of existing scientific data and knowledge to address the current conditions for 

a subset of important park natural resources. The intent is for this report to help provide park 

resource managers with data and information, particularly in the form of spatially-explicit maps 

and GIS databases, about those natural resources and to place emerging issues within a local, 

regional, national, or global context. With an advisory team, we identified the following 

condition indicators that would be useful to assess the condition of the park:

• Air and Climate: Condition of alpine lakes and atmospheric deposition

• Water: Extent and connectivity of wetland and riparian areas

• Biotic Integrity: Extent of exotic terrestrial plant species, extent of fish distributions, 

and extent of suitable beaver habitat

• Landscapes: Extent and pattern of major ecological systems and natural landscapes 

connectivity

These indicators are summarized in the following pages. We also developed two maps of 

important issues for use by park managers: visitor use (thru accessibility modeling) and 

proportion of watersheds affected by beetle kill.

Based on our analysis, we believe that there is a high degree of concern for the following 

indicators: condition of alpine lakes; extent and connectivity of riparian/wetland areas; extent of 

exotic terrestrial plants (especially below 9,500’); extent of fish distributions; extent of suitable 

beaver habitat; and natural landscapes and connectivity. We found a low degree of concern for:

the extent and pattern of major ecological systems.

The indicators and issues were also summarized by the 34 watershed units (HUC12) within the 

park. Generally, we found six watersheds to be in “pristine” condition: Black Canyon Creek, 

Comanche Creek, Middle Saint Vrain Creek, South Fork of the Cache la Poudre, Buchanan 

Creek, and East Inlet. Four watersheds were found to have strong restoration opportunities: Big 

Thompson River West, Cache la Poudre South, Colorado River North, and Onahu Creek. Ten 

watersheds were found to have substantial near-term issues: Aspen Brook, Big Thompson River 

West, Black Canyon Creek, Cabin Creek, Cache la Poudre South, Fall River, Hague Creek, La 

Poudre Pass Creek, North Fork Big Thompson (East), and Colorado River North.
xv

Air and climate: Condition of Alpine Lakes and Atmospheric Deposition

What: Measures atmospheric deposition of nutrients and pollutants in high elevation lakes.

Why: High elevation lakes are bellwethers of ecological change and the extent of human-caused disturbance.

Stressors: Land use change, climate change.

Confidence: High degree of concern; moderate evidence, high agreement.

Atmospherically-deposited nitrogen has caused east-side ROMO lakes to have higher than background nitrate concentrations and 

lower than expected acid-neutralizing capacity (ANC). East-side concentrations of dieldrin and DDT are also elevated, while westside mercury in fish tissue is at or above concentrations that are harmful for some birds and mammals. West-side nitrate and ANC are 

consistent with reference conditions, and lake pH and chemical concentrations of calcium, chloride, sulfate, ammonium for all ROMO 

lakes are consistent with reference conditions. 

*Additional data sources: Western Airborne Contaminants Study 

(WACAP 2008).

Measure Reference 

Condition

Current condition Confidence Level of 

Concern

Lake pH 6.5-7.5 6.6 (east)

7.0 (west)

Moderate High

Lake ANC 80-100 μeq L-1 50 μeq L-1(east)

80 μeq L-1(west)

Moderate High

Lake 

ammonium

Below detection 

limits

0.01 mg L-1 (east)

0.01 mg L-1 (west)

Moderate High

Lake nitrate ≤0.1 mg L-1 0.57 mg L-1 (east)

0.06 mg L-1 (west)

High High

Lake sulfate 0.2-109.0 mg L-1

(all lakes)

1.3 mg L-1 (east)

1.5 mg L-1 (west)

High High

Lake chloride 0.03-1.16 mg L-1

(all lakes)

0.08 mg L-1 (east)

0.06 mg L-1 (west)

Moderate Low

Lake calcium 0.1-43.9 mg L-1 1.20 mg L-1 (east)

1.65 mg L-1 (west)

Moderate Low

Lake 

conductivity

2.2-341.8 μS cm-1 9.8 μS cm-1 (east)

13.0 μS cm-1 (west)

High Low

Fish Mercury <0.01μg g-1 0.03-0.09 μg g-1 (east)

0.04-1.10 μg g-1 (west)

High High

Dieldrin 0.0 ng g-1 snow >1.0 ng g-1 snow High High

DDT 0.0 ng g-1 fish ≥4.0 ng g-1 fish High High
xvi

Water: Extent and Connectivity of Wetland and Riparian Areas

What: Measures the extent and connectivity of riparian and 

wetlands* along the hydrologic network.

Why: To understand potential impacts on riparian and wetland 

communities as a result of hydrologic fragmentation due to 

human barriers (e.g., dams, ditches).

Stressors: Infrastructure, visitor use, climate change.

Confidence: High degree of concern; low evidence, moderate 

agreement.

Restoration of four dams and four ditches has improved the connectivity 

of riparian and wetland communities somewhat from 1900-1990s 

conditions, but current conditions (shown right) remain substantially 

fragmented compared to “natural” hydrological conditions.

Measure “Natural” Historic 

(1900-1990)

Current

Average connected stream 

length (km)

11.5

SD=36.5

8.7

SD=22.3

9.6

SD=26.2

Average connected 

riparian/wetland area (ha)

146.6

SD=410.2

112.1

SD=288.6

124.2

SD=322.6

*As mapped in ROMO vegetation map (Salas et al. 2005) and 1:24K hydrology.
xvii

Biotic Integrity: Exotic Terrestrial Plant Species

What: Measures the proportion and abundance of nonnative, exotic plant species weighted by percent 

cover in each plot of the ROMO vegetation 

database (Salas et al. 2005).

Why: Exotic species can displace native species, impact 

wildlife habitat by reducing forage and altering 

natural processes such as fire.

Stressors: Visitor use, land use change, climate change.

Confidence: High concern; moderate evidence, moderate 

agreement.

The distribution of known locations of exotic plants (especially 

cheatgrass) is strongly correlated with accessibility, with roughly 

80% of known locations in highly accessible areas (<1 hour). 

East-side watersheds have higher rates of invasive species (2.8% 

vs. 1.8%), and lower montane and riparian ecological systems are 

particularly affected. 

Measure Current condition

Proportion of exotic species* 1.7% ROMO

4.6% outside ROMO

Proportion by ecological system*

Alpine

Upper montane

Lower montane

Riparian

Savannah

0.1% (0.015 SE) 

2.4% (0.036 SE)

5.2% (0.051 SE)

4.6% (0.048 SE)

1.5% (0.028 SE)

*Total of 1,861 plots: 1,279 within ROMO boundary, 571 outside of park. 
xviii

Biotic Integrity: Extent and Connectivity of Fish Distributions

What: Measures the distribution of native and non-native 

fish, by incorporating observation data at streams 

and lakes with hydrologic connectivity (and barriers 

to up-stream movement).

Why: Non-native species displace native species.

Stressors: Riparian vegetation, visitor use, climate change.

Confidence: High degree of concern; low evidence, moderate 

agreement.

Currently the proportion of streams that are “secure” (native only or 

fishless above a barrier) is 10% and there are 54 known human 

barriers, and native-only streams have increased slightly from 7.0% 

to 7.3% in the past 50 years. 

Measure Reference 

Condition

Current 

Condition

Proportion of streams with native-only fish 97.3% 7.3%

Proportion of streams with native fish 100% 75.8%

Proportion of streams* in ROMO above a 

barrier

100% 54%

Mean length of native-only fish streams 

(km)

228.5 76.1

*Total length of streams in ROMO is 1,074.2 km (at 1:24K).
xix

Biotic Integrity: Extent of Suitable Beaver Habitat

What: Measures the distribution and proportion of potentially suitable beaver habitat and compares it to historical surveys.

Why: Important to understand the historical role beaver had in riparian vegetation and is critical to understand elk/willow 

dynamics.

Stressors: Over-abundance of elk, Grand Ditch, climate change.

Confidence: High concern; moderate evidence, moderate agreement.

The current distribution of beaver is far below historic levels (bottom map). 

Highest areas for restoration potential (right map) include Colorado River 

(north); Big Thompson River (west), Onahu Creek, Cache la Poudre (south), 

Fall River, and Glacier Creek watersheds.
xx

Landscapes: Extent and Pattern of Major Ecological Systems

What: Landscape metrics that characterize the extent and pattern of 

major terrestrial ecological systems using LANDFIRE 

existing vegetation types.

Why: These provide the landscape context of major ecological 

systems.

Stressors: Visitor use, land use change, climate change, exotics.

Confidence: Low degree of concern; low evidence, moderate agreement.

Generally the extent and pattern of patches within the park are similar to 

those found within the broader Southern Rockies Ecoregion (SRE), although 

there was some indication that the park contains more large patches of upper 

elevation forest, but smaller patches of alpine tundra, mid- and lowelevation forest. 

Measure Vegetation Type

Reference 

Condition 

(SRE)

Current 

Condition 

(ROMO)

Weighted 

mean patch 

size (ha)

Alpine tundra

Upper elevation forest

Mid-elevation forest

Low-elevation forest

Aspen

Lodgepole

Riparian/wetland

Savannah

654

1,868

2,873

3,140

1,311

2,114

44

3,146

180

2,600

1,205

443

262

1,293

48

15

1

Patches are defined by 12-digit HUC boundaries and contiguous (8 neighbor)
xxi

Landscapes: Connectivity of Natural Landscapes

What: Measures the natural landscape context of ROMO 

within the Southern Rockies Ecoregion (SRE).

Why: Movement of animals and ecological processes connect 

the park to adjacent landscapes beyond the park 

boundary. 

Stressors: Land use change, climate change.

Confidence: High degree of concern; low evidence, moderate 

agreement.

Rocky Mountain National Park maintains natural landscapes that are 

placed within a key location to maintain connections to adjacent 

natural areas, especially to the north and west.

Time period

Natural Landscapes 

Metric

Reference (SRE)

Natural Landscapes 

Metric

ROMO

1992

2001

2030

0.931678

0.924163

0.916705

0.957785

0.955563

0.947110

* Data sources: NLCD retrofit product, SERGoM (Theobald 2005); 

National Transportation Atlas
xxii

Acknowledgements

This assessment was prepared by the team from Colorado State University, but with the 

assistance of many people. We are very appreciative of the substantive and technical 

contributions from Jeff Connor and Billy Schweiger, and strong guidance and assistance from 

the rest of the Technical Advisory Committee, including Mike Britten, John Reber, Jen Burke, 

and Rick Wilson. Other key assistance from Rocky Mountain National Park included Ron 

Thomas who provided GIS data and technical insight; Ben Bobowski, Judy Visty, Mary Kay 

Watry, and Nathan Williamson. Jeff Albright served as the project manager, and we appreciate 

his careful management and guidance of this assessment.

We also thank a number of outside scientists who contributed their expertise to help guide this 

assessment, including: David Cooper and Mike Cougenour, Colorado State University; Jessica 

Salo, Colorado State University, for assistance with landscape metric data and atmospheric 

deposition data; Jim Graham and Tom Stohlgren, Natural Resource Ecology Lab, Colorado State 

University, for sharing data on exotic species; Dan Manier, USGS; Scott Sowa from MoRAP; 

and Brian R. Parker, University of Alberta; Ann-Kristin Bergström, Umeå University, Mark 

Story, USFS, Jasmine Saros, University of Maine, and M. Alisa Mast, USGS for lake chemistry 

data

Prologue

Publisher’s Note: This was one of several projects used to demonstrate a variety of study 

approaches and reporting products for a new series of natural resource condition assessments in 

national park units. Projects such as this one, undertaken during initial development phases for 

the new series, contributed to revised project standards and guidelines issued in 2009 and 2010 

(applicable to projects started in 2009 or later years). Some or all of the work done for this 

project preceded those revisions. Consequently, aspects of this project’s study approach and

some report format and/or content details may not be consistent with the revised guidance, and 

may differ in comparison to what is found in more recently published reports from this series.
1

Publisher’s Note: Some or all of the work done for this project preceded the revised guidance 

issued for this project series in 2009/2010. See Prologue (p. xxii) for more information.

Chapter 1. NRCA Background Information

1.1 NRCA Description

A natural resource condition assessment (NRCA) is a spatially explicit multi-disciplinary 

synthesis of existing scientific data and knowledge, from multiple sources, that helps answer the 

question: What are current conditions for important park natural resources? NRCAs strive to 

provide a mix of new insights and useful scientific documentation about current resource 

conditions and some of the factors influencing those conditions (i.e., issues and stressors). A 

successful NRCA has practical value to park managers for their ongoing efforts to:

1) develop near-term strategies and priorities (given limited park staff and funding, what are 

some park areas and resources deserving their greatest attention right now?);

2) engage in watershed or landscape scale resource partnerships and education efforts;

3) conduct formal planning to describe and quantify desired conditions for their most 

important resources, and to develop comprehensive strategies for how to best 

protect/restore those same resources; and

4) report to “resource condition status” performance/accountability measures as instructed 

by the Department of Interior and the Office of Management and Budget.

Typically NRCAs share standard elements related to study design and reporting products. Within 

those general sideboards many important study details remain flexible, to be decided on a park 

by park (individual project) basis. NRCAs are multi-disciplinary (ecological) in scope, though 

breadth and number of resources/indicators evaluated remains a project level decision; report on 

current conditions across the entire park, though for practical reasons some park areas will be 

excluded from consideration; rely on existing data from NPS and other sources, but field-based 

rapid assessment techniques can be used with prior approval; use hierarchical study frameworks

that include the following components: natural resource indicators; reference conditions; current 

condition reporting by indicators, by ecological characteristics or attributes, and by park areas; 

use the standard NRCA report outline as the template to report key study findings; and 

emphasize spatial analyses and reporting products which are especially helpful for the primary 

types of expected uses (outlined above). 

1.2 NRCA Purpose and Use

This document reports on an ecological assessment of natural resource conditions in and adjacent 

to Rocky Mountain National Park (ROMO)1

 1 A companion assessment provides a report of conditions for Florissant Fossil Beds National Monument 

in Colorado and follows the general framework laid out in this report. The report citation is: Theobald, 

D.M., S.E. Linn, I. Leinwand, and J.B. Norman, III. 2010. A Natural Resource Condition Assessment for 

Florissant Fossil Beds National Monument. Natural Resource Report NPS/NRPC/WRD/NRR—

2010/0XX. National Park Service, Fort Collins, Colorado.

. The broad project objective was to evaluate the 

conditions for a subset of important park natural resources—that is, a set of ecological attributes 

and resource condition indicators most relevant to ROMO. This is a broad, overview assessment 
2

that is comprehensive in scope and places the park within its regional context. It considers biotic 

and abiotic resources, and evaluates conditions for both aquatic and terrestrial components of the 

park. It identifies critical data and knowledge gaps, and provides a preliminary examination of 

the identified resources in the face of selected issues and stressors facing the park.

The report has relied on evaluation and synthesis of existing scientific data and information from 

multiple sources, combined with best professional judgment from an interdisciplinary team of 

specialists. To the extent possible, we have made use of quantitative data and analyses, but the 

report also recognizes the practical need to use expert opinion for many of the indicators—and

reports on gaps in data and knowledge. We placed our assessment within an existing assessment 

framework to ensure a careful and explicit examination of the range of biotic and abiotic 

resources. The report emphasizes spatial analyses and products, including condition status for 

each indicator, some preliminary identification of pristine and “stressed” watersheds, and 

identification of most at-risk watersheds of the park. Although each indicator was developed 

independently based on the availability of relevant data, we summarize each indicator at a 

common scale—the HUC 12, or Hydrologic Unit Code, level 12—for consistency and 

comparability.

The three primary audiences for the assessment are: decision makers such as park 

superintendents, resource managers at the park, and scientists and technicians engaged to assist 

parks (e.g., Inventory & Monitoring Network ecologists and data managers). The assessment 

findings are designed to assist and inform these audiences for, among other things: 

• Near-term strategic planning, to allocate limited staff and budget resources toward high 

priority (relatively more significant or vulnerable) park-managed watersheds and habitats;

• General Management Plan and Resource Stewardship Strategy development, which represent 

the planning process that formalizes park management zones, Desired Condition 

management objectives, and associated measurement indicators and targets;

• Park reporting to the Department of Interior’s “land health goals” and to an Office of 

Management and Budget “resource condition scorecard”;

• Park efforts to communicate and partner with other stakeholders, in order to address 

watershed- or landscape-scale resource management issues. 

The specific objectives of this project were:

1. To provide park superintendents and managers with initial, science-based judgments about 

resource condition status, and to provide data, information, and recommendations that will be 

useful to park managers in their work to define the park’s management zones and desired 

conditions.

2. To provide assessment statistics and summaries to allow park superintendents and managers 

to develop reports that meet GPRA and OMB reporting requirements.

3. To develop an assessment framework and process that can be repeated in the future and can 

serve as a template for resource assessments at other park units. 
3

To address the project objectives, we conducted the study in three phases.

Phase 1. We developed a scientifically-credible assessment and analytical framework for

assessing and reporting on the current condition of resources within the park, and identied the 

principal near-term (~5-20 year) issues and stressors.

Phase 2. We compiled a list of datasets and products that would be most pertinent and useful 

for assessing conditions within the park and that could reasonably be developed given the 

financial and time constraints of the project.

Phase 3. We provided a multi-disciplinary assessment of resource condition to inform NPS 

about scientific significance, functional status, and current and emerging issues/challenges 

associated with park-managed resources and habitats. The assessment incorporates a strong 

geospatial component, uses a watershed framework to summarize and compare various 

indicators, and is presented as both a written report and a set of GIS databases. 

Because the assessment is broad and integrative, a strong emphasis was placed on conducting 

spatially-explicit analyses using Geographic Information Systems (GIS) techniques. As a 

consequence, we developed numerous maps and visualizations of indicators and findings in this 

report, including a technical appendix, as well as a full suite of GIS datasets. We are planning to 

conduct a technical workshop for NPS staff on how to use and interpret the spatial datasets that 

we produced.

A main sign of success of this report will be the extent to which it provides park resource 

managers data and information that help them to see “the big picture” and relationships among 

critical issues, and to help place emerging issues within a local, regional, national, or global 

context.

5

Chapter 2. Park Resource Setting/Stewardship Context

2.1 Park Resource Setting

2.1.1 Description and Characterization of Park Natural Resources

This section provides a general discussion of the setting of Rocky Mountain National Park, 

including the regional and historic context, as well as unique and significant park resources and 

designations. This includes a number of reference maps on basic physiographic, biological, and 

cultural resources. The park, which straddles the Continental Divide, preserves some of the finest 

examples of physiographic, biologic, and scenic features of the southern Rocky Mountains. The 

park contains the headwaters of several river systems including the Colorado River. Geologic 

processes, including glaciation, have resulted in a varied and dramatic landscape. Elevations 

span from 7,630 feet to 14,259 feet—atop Longs Peak, a landmark feature. The park's varied 

elevations encompass diverse ecosystems where wilderness qualities dominate. Varied plant and 

animal communities and a variety of ecological processes prevail.

Threats from nutrient enrichment, eutrophication, sedimentation, invasive species, water and air 

pollution, and development on adjoining private lands are management concerns.

ROMO includes 14,259 foot Longs Peak which dominates the surrounding landscape. Trail 

Ridge Road, the highest continuous paved road in the nation, provides easy access to alpine 

tundra for many American and international visitors. Wildlife viewing, especially for elk during 

the fall rut, can be spectacular. Most of ROMO, just miles from the largest urban area in the 

Rocky Mountain region, is designated and/or managed as Wilderness, giving many Coloradans 

and other visitors an opportunity for solitude and wilderness recreation. Its complex topography 

and wide range of elevation result in a remarkable biological diversity.

ROMO’s purpose and significant resources include its wild landscape and scenery, opportunities 

for solitude and tranquility, wilderness recreational and wildlife viewing opportunities, the scenic 

and scientific values of alpine tundra, and biodiversity (especially in three important ecosystems: 

tundra, forests, and aquatic and associated riparian systems). The park is a premier example of 

the southern Rocky Mountain region.

Important habitats include alpine tundra, montane habitats (especially ponderosa pine and upland 

shrub communities), lodgepole pine, riparian and wetland habitats, and aspen woodlands. 

Important species include elk, bighorn sheep, mule deer, mountain goat (exotic), moose (a recent 

arrival), beaver, black bear, wolverine (confirmed observation June 26, 2009), wolves (they may 

recolonize ROMO eventually), mountain lion (as an important large predator and a threat to 

human safety), pine marten, river otter, greenback cutthroat trout, Colorado River cutthroat trout, 

non-native fish (and other non-native aquatic species), boreal toad, raptors, white-tailed 

ptarmigan, songbirds, butterflies (there is extensive information on ROMO butterflies and 

evidence for their recent declines), capshell snail, exotic invasive plants, and rare plants.
6

Important ecological processes include fire, herbivory (especially by elk and moose) and its 

effects on aspen and willow communities, stream hydrology, other herbivores such as beaver and 

ptarmigan, exotic plant animal invasion, and erosion.

The following maps and tables provide general reference information about Rocky Mountain 

National Park (Figure 1 - Figure 8; Table 1 -Table 2).

Figure 1. Rocky Mountain National Park and other federal lands in Colorado (Colorado Ownership, 

Management and Protection v7).
7

Figure 2. Boundary changes of Rocky Mountain National Park, 1915 – 2003. See Animation.
8

Figure 3. Major land ownership types around Rocky Mountain National Park in 2008. (Data source: 

Colorado Ownership Management and Protection v7, Theobald et al. 2008).
9

Figure 4. Six major ecosystem or “life-zone” classes generated from park-specific vegetation (Salas et al. 

2005).
10

Figure 5. Roads and trails infrastructure inside ROMO. (Data source: http://www.nps.gov/gis/).
11

Figure 6. Major hydrologic features within ROMO. (Data source: National Hydrologic Dataset, 1:100K; 

http://www.nps.gov/gis/). Note that these data are for reference only; we used 1:24K scale maps for our 

indicator analyses below. Note that Eureka ditch was removed in 1996, Mac ditch still exists but is on 

private land.
12

Figure 7. Names of 12-digit Hydrologic Unit Codes (HUC-12 catchments) derived from the major tributary 

within each HUC-12 unit. Note 1:24K-scale hydrology shown.
13

Table 1. Catchment names associated with hydrologic 12-digit unit codes (HUC-12).

Hydrologic 12 digit 

unit code (HUC-12) Catchment Name

101900060203 Aspen Brook

101900060207 Big Thompson River East

101900060202 Big Thompson River West

101900060205 Black Canyon Creek

140100010307 Buchanan Creek

101900050203 Cabin Creek

101900070205 Cache La Poudre North

101900070202 Cache La Poudre South

140100010302 Colorado River North

140100010308 Colorado River West

101900060101 Cow Creek

140100010304 East Inlet

101900060204 Fall River

101900060206 Fish Creek

101900060201 Glacier Creek

101900070203 La Poudre Pass Creek

101900060401 Little Thompson River

101800010503 Michigan River

101900050102 Middle Saint Vrain Creek

101900060103 Miller Fork

101900060104 North Fork Big Thompson (E)

101900060102 North Fork Big Thompson (W)

140100010305 North Inlet

101900050202 North Saint Vrain Creek (East)

101900050204 North Saint Vrain Creek (West)

140100010303 Onahu Creek

101900070103 Pennock Creek

101900050201 Rock Creek

101900070102 South Fork Cache La Poudre

101800010501 South Fork Michigan River

101900060402 West Fork Little Thompson River
14

Figure 8. Management Zones found in ROMO as defined in the Backcountry/Wilderness Management 

Plan, 2001. Potential conservation areas are mapped by Colorado Natural Heritage Program. (Data 

source: http://www.nps.gov/gis/; CO State Parks).
15

Table 2. Definitions of management zones from Rocky Mountain National Park Backcountry/Wilderness Management Plan and Environmental 

Assessment (BWMP), 2001. Backcountry/Wilderness Management Plan, July 2001 (Chart contents were copied from metadata of 

managezones2001.shp).

Management Class 1 

Areas Class 2: Cross Country Class 3: Trail Corridors Class 4: Front Country

Approximately 170,236 acres, 

generally includes Research 

Natural Areas, tundra areas, 

and other areas not in 

Management Classes 2, 3 or 4.

Approximately 36,832 acres, 

generally includes cross country 

routes, cross country camping 

areas, and bivouac areas

Approximately 27,474 acres, 

generally includes formal trail 

corridors (100 feet on either side of 

trail) and designated campsite 

areas (100 feet from edge of 

campsite)

Approximately 23,313 acres, generally 

includes formal trail corridors (200 

feet on either side of trail), specific 

day use areas (200 feet from edge of 

area) and backcountry areas not 

recommended as Wilderness

Use

• Day use only

• No overnight camping 

(except approved

management activities and 

winter area camping with 

restrictions)

• Group size of seven (7) or 

less desirable

• Low day use

• No stock use

• Area camping allowed, seven 

(7) or fewer people; no fires

• No designated camp areas 

(except Little Rock Lake)

• Low to moderate use

• No stock use

• Moderate - high

• Day group size of no larger 

than 20 recommended

• Designated campsites - group 

size 1-7 or 8-12

• Campfires in specific 

campsites only

• Stock use allowed on stock 

designated trails and camp sites 

only

• High

• No group size recommendation; 

however, large groups encouraged 

to split up

• Day use only, no camping 

(except Moore Park, Rabbit Ears, 

Peregrine, Cub Lake, Arch Rocks, 

Mill Creek Basin and Upper Mill 

Creek designated camp areas)

• Stock use allowed on stock 

designated trails only

Access and 

Challenge

• Generally moderate to 

difficult

• Challenge/risk/freedom and 

self reliance are primary 

goals of the visitor

• Moderate to difficult

• Challenge/risk/freedom and 

self reliance are primary goals 

of visitors

• Low to high

• Broad spectrum of expected 

challenge level

• High access

• Low to moderate challenge

Opportunity 

for solitude

• Outstanding opportunity 

for solitude

• Chance of seeing other 

visitors/park staff is low

• Natural sounds prevail

• Opportunity for solitude high 

most of the year, moderate 

during summer months

• Chance of seeing other 

visitors/park staff is low to 

moderate

• Some noise interferes with 

natural sounds

• Broad spectrum, low to high, 

depending on time of year, day 

of week, time of day, weather 

etc.

• Broad spectrum, low to 

moderate, depending on time of 

year, day of week, time of day, 

weather, etc.

• Chance of seeing other visitors 

and staff high during summer 

months
16

Management Class 1 

Areas Class 2: Cross Country Class 3: Trail Corridors Class 4: Front Country

Acceptable 

Resource 

Condition

• Natural environment with 

little evidence of recent 

impacts by humans

• Evidence of management is 

extremely rare

• Resource impacts are nondiscernable

• Resource impacts are 

restricted to minor losses of 

vegetation where camping 

occurs and along use routes

• Predominately unmodified 

natural environment

• Resource impacts are limited 

to the immediate trail corridor 

(100 feet either side of center 

line of trail) and campsites (100 

foot radius from metal 

arrowhead)

• Resource impacts are limited to 

the immediate trail corridor (200 

feet either side of center line of 

trail) and day use areas (200 foot 

radius from attraction)

Management 

Use

• No designated or 

maintained trails

• Routes are generally nondiscernable

• No signs, cairns

• No facilities

• No aircraft or motorized 

equipment (except during 

emergency operations or 

absolutely critical for the 

protection of natural or 

cultural resources as 

determined on a case-bycase basis through a 

Minimum Requirement 

Analysis and approved by 

the Superintendent)

• No designated trails, but 

some designated routes

• No formal maintained 

treadway (erosion and 

drainage control techniques 

allowed}

• Minimum cairns as necessary 

to provide for resource 

protection and visitor safety

• No facilities (cabins, 

hitchrails, privies)

• No motorized equipment 

(except when approved via 

Minimum Requirement 

Analysis)

• Only those signs necessary to 

protect resources and public 

safety

• Facilities: privies, hitchrails, 

corrals, cabins, tent pads, food 

protection devices, signs, 

research equipment etc. as per 

the Minimum Requirement 

Concept o Use of 

aircraft/motorized 

equipment/blasting, requires

minimum Requirement

Analysis (programmatically in 

an approved management plan 

or on an individual basis)

• Use of stock for facility/trail 

maintenance

• Designated, formally 

constructed and maintained 

trails (Standards D, E and F -

see Section 2.1.4.7.2)

• Facilities: privies, hitchrails, 

signs, hardened areas at 

attractions etc. as per the 

Minimum Requirement Concept

• Use of aircraft/motorized 

equipment/blasting, requires 

Minimum Requirement Analysis 

(programmatically in an approved 

management plan or on an 

individual basis)

• Use of stock for facility/trail 

maintenance

• Designated, formally
17

2.1.2 Overview of Resource Condition Issues

Important natural resource issues include visitor impacts (social trailing, crowding, back-country 

sanitation, trampling in alpine tundra, traffic and hiker effects on wildlife); aircraft overflights 

and impacts to wilderness values; adjacent land use development; air and water pollution 

(particulates and haze impacts to visibility and scenery, nitrogen deposition effects on terrestrial 

and aquatic systems, and mercury deposition); fire; erosion associated with failures of man-made 

irrigation ditches and impoundments; and over-abundance of elk.

2.2 Resource Stewardship Context

The following park designations set the legal and policy context for the park. The purpose, 

mission and significance statements are taken directly from park documents received from the 

manager of the ROMO park information office (personal correspondence, Katy Sykes, 2/23/09)

Rocky Mountain National Park (ROMO) was established by Act of Congress in 1915, and "is 

dedicated and set apart as a public park for the benefit and enjoyment of the people of the United 

States." (Jan. 26, 1915, Ch. 19, Sec. 1, 38 Stat. 798). The purpose of ROMO is to preserve the 

park's natural conditions and scenic beauties, its natural and historic objects and wildlife, and to 

provide the freest recreational uses consistent with this purpose. The National Park Service's 

mission at ROMO is to care for, protect, manage, improve, understand and interpret park 

resources and to provide for a high-quality visitor experience.

Rocky Mountain National Park provides exceptional accessibility to a wild landscape with 

dramatic scenery, opportunities for solitude and tranquility, wildlife viewing and a variety of 

recreational opportunities. The fragile alpine tundra encompasses one-third of the park and is one 

of the main scenic and scientific features for which the park was established. This is one of the 

largest examples of alpine tundra ecosystems preserved in the National Park System in the lower 

48 states.

19

Chapter 3. Study Approach

3.1 Preliminary Scoping

There were three general steps in the process to conduct this assessment: forming an oversight 

team, holding a scoping workshop, and conducting the structured analyses (Figure 9).

Figure 9. Diagram of park specific scoping and a structured analysis.

3.1.1 Advisory/Oversight Team

The first step in the process was to form a Project Advisory Team composed of scientists from 

Colorado State University and the National Park Service to provide general oversight to the 

project (Table 3). The charge of this team was to provide guidance on how to conduct the 

scoping workshop and provide general advice on the assessment. 
20

Table 3. Members of the Project Advisory Team for the ROMO assessments. *Designates Technical 

Advisory role as well.

Individual Affiliation Primary Area(s) of Expertise

*David Theobald (lead P.I.) Natural Resource Ecology Lab, 

Department of Human 

Dimensions, Colorado State 

University

Landscape ecology, terrestrial 

and freshwater spatial 

assessments, GIS.

Jill Baron (co-PI) Natural Resource Ecology Lab, 

Colorado State University

Air and water quality; 

ecological indicators

Peter Newman (co-PI) Department of Human 

Dimensions, Colorado State 

University

Visitor experience and

standards and indicators

Barry Noon (co-PI) Department of Fishery, Wildlife, 

and Conservation Biology, 

Colorado State University

T&E species, wildlife habitat

*John Norman Natural Resource Ecology Lab, 

Colorado State University

GIS analysis, soil scientist

Jeff Albright (NPS Project 

Manager)

NPS-Water Resources Division Hydrology, resource 

management

Mike Britten NPS-I&M Rocky Mountain 

Network

Ecology

*Jeff Connor NPS-Rocky Mountain National 

Park

Resource management

John Reber NPS-IMR Resource management

*Billy Schweiger NPS-I&M Rocky Mountain 

Network

Ecology

Rick Wilson NPS-Florissant Fossil Beds 

National Monument

Resource management

Members of the team met on a number of occasions to determine the process for developing and 

conducting the analysis (Table 4). Numerous smaller meetings and discussions were held but not 

summarized here.
21

Table 4. Summary of meetings with advisory team.

Task Who When/where

Formed Advisory/Oversight Team CSU

Field visit to parks CSU, ROMN to ROMO, FLFO ROMO

FLFO: December 13, 2006

Advisory team meeting CSU, ROMO, ROMN

Scoping meeting CSU, ROMO, FLFO, ROMN, 

IWR

December 18 - 19, 2006. 

ROMO visitor center

Update meetings February 15 - 16, 2007

Technical workshops: FLoWS CSU, ROMN December 7, 2007

Synthesis meeting w/technical team December 16, 2008

GIS technical dissemination 

workshop Technical team May 2008

3.1.2 Scoping Workshop

The second step in our project was to hold a scoping workshop that engaged not only the 

investigators from CSU and the NPS staff who are members of the project oversight team (Table

3), but also NPS staff with management responsibilities at the park, and individuals from other 

agencies or groups that may be able to provide input on the availability of existing data or 

information sources.

The specific goals of the scoping workshop were to:

• briefly review the indicator framework and establish assessment goals at the park;

• elicit management issues faced by park managers;

• develop and prioritize a list of important indicators to inform assessment of 

ecological condition;

• identify data and information sources to generate indicators.

Because the Rocky Mountain Network (ROMN) had just released their draft report (Britten et al. 

2007) and had developed a prioritized list of indicators for their vital signs that asked very 

similar questions, with many of the same people, we decided to “front-load” the scoping 

workshop by providing a preliminary listing of indicators that built on those identified in the 

Inventory and Monitoring (I&M) report as high-priority vital signs (Table 5). We believed that 

this would help to avoid developing yet another “laundry list” of indicators that was not 

comprehensive or potentially unbalanced. Note that we explicitly provided the opportunity at the 

workshop to nominate additional indicators not identified in the preliminary list. 
22

Table 5. A listing of the Rocky Mountain Network high-priority vital signs (from Britten et al. 2007) used to 

“front-load” the discussion at the scoping workshop.

3.2 Reporting Areas

The ideal use of the findings of this report and the spatial datasets provided with the assessment 

is to inform and support park managers and scientists in developing recommendations about 

overall conditions in the park. That is, our intent is to provide a platform to assist ROMN and 
23

ROMO scientists to make a judgment of resource condition through quick and ready access to 

data on important indicators. To do this, we summarized the ecological condition of ROMO as 

represented by the indicators for two reporting areas. First, we provide a general discussion and 

summary of each indicator by HUC-12 watershed. We also provide a well-documented summary 

dataset that can be used by park staff to further explore the comprehensive, summary dataset. 

Note that in addition to the raw value for each indicator for a given watershed, we also provide a 

normalized score. Our intent is to help provide a decision support system that facilitates park 

managers and scientists to explore the spatial pattern of high and low scores that reflect different 

incorporation of and weighting of different indicators. We also found it useful to summarize the 

condition indicators by management zones (outlined in Table 2).

3.3 Assessment Frameworks Used in the Study

In addition to forming the Advisory Team, the CSU investigators compiled existing approaches 

and conducted a brief literature review to identify a suite of different frameworks and approaches 

that have been developed and might be useful for the assessment. These existing approaches can 

be characterized generally as ad hoc, geomorphologic, or hierarchical (Table 6).

Table 6. Listing of existing assessment approaches reviewed for this study.

Type Name Citation

Ad hoc Ecosystem analysis at the watershed scale Federal Guide for 

Watershed Analysis 1995

An ecological assessment of the U.S. Mid-Atlantic 

Region: A landscape atlas

Jones et al. 1997

Natural habitat integrity Tiner 2004

CrEAM White and Maurice 2004

Geomorphologic Hydrogeomorphic Hauer & Smith 1998

Process domains Montgomery 1999

Hierarchical Hierarchical classification of drainage basins Jensen et al. 2001

A Framework for Assessing and Reporting on 

Ecological Condition

EPA 2002

Watershed Analysis and Management Guide for 

States and Communities

EPA 2003

Northwest Forest Plan - Preliminary assessment of 

the condition of watersheds

Reeves et al. 2004

The Nature Conservancy’s freshwater planning 

approach

Higgins et al. 2005

Ecological Integrity Assessment & Performance 

Measures for Wetland Mitigation

NatureServe 2006

We held an initial Advisory Team meeting to review and evaluate potential assessment and 

threat analysis frameworks. Based on our preliminary literature review, we narrowed the list of 

candidate frameworks to the EPA’s A Framework for Assessing and Reporting on Ecological 

Condition – Essential Ecological Attributes (EEA); Ecosystem analysis at the watershed scale; 

Ecological Monitoring Framework (EMF); Northwest Forest Plan; and the Ecological Integrity 

Assessment & Performance Measures for Wetland Mitigation. We selected the EPA’s Essential 

Ecological Attribute (EEA) framework because it provides a useful checklist of EEAs, is 

comprehensive, has been used by others for watershed condition assessments so that we could 

benefit from their experience, distinguishes indicators of ecological condition as separate from 
24

stressors, and is flexible. We used it as a guide, but did not constrain discussion of resource 

condition by it (Figure 10).

Figure 10. The top level of the Essential Ecological Attribute hierarchy (USEPA, 2002)

We used the Ecological Monitoring Framework (EMF) as the framework for our assessment 

indicators (Fancy et al. 2009). The EMF was developed to organize and promote a systems-based 

approach to monitoring, and itself is based on a variety of frameworks, including the EPA’s 

Essential Ecological Attributes (EPA 2002). Organizing the assessment indicators into this 

framework helps ensure complementarity and provides a direct coupling with the NPS’ 

Inventory & Monitoring program (Table 7).

For each indicator we collected data and conducted a separate analysis. Because the amount, 

quality, and geographic coverage of data and knowledge about each indicator ranged 

considerably, the approach to developing each indicator was unique. Rather than limiting any 

one particular indicator to adhere to some minimum level of data or consistent approach, we 

decided to allow the analysis of each indicator to be pursued somewhat independently. To 

provide a means to examine the distribution of values of an indicator for specific parts of the 

park, as well as to provide a tool for park staff to investigate combinations of indicators by 

geographic location, we generated a summary database that contains both the raw values and 

condition values for each 12-digit HUC watershed. This approach ensures that each indicator is 

referenced to a common theme—that is, the map of ROMO—because the ability to spatially 

reference indicator values is essential for a management response. Because of the limited 

resources available for this project, we focused on assessing landscape patterns, dynamics, and 
25

structure as derived from existing land cover data and other existing geospatial datasets, rather 

than conducting new site-specific analyses.

Table 7. List of indicators identified during scoping workshop, organized by NPS Inventory & Monitoring 

Ecological Monitoring Framework.

NPS Level 1 

Vital Sign Indicators Decision Maker Questions and Issues

Air & 

Climate

Condition of alpine lakes How does water quality of park lakes compare to 

other lakes?

Wet and dry atmospheric 

deposition

What are trends in deposition of pollutants to the 

park?

Water Monthly & annual discharge 

in streams

How do diversions and the Grand Ditch affect wetland 

areas?

Biotic 

Integrity

Extent of invasive terrestrial 

plants

Where are areas with low occurrence of exotics to 

monitor?

Extent and proportion of 

non-native fish

Where are refugia? Where are re-introductions most 

likely to be successful?

Extent of potentially suitable 

beaver habitat

How many beaver could ROMO support? Where 

would their habitat be? How is that important for 

elk/willow dynamics?

Extent of major stream, 

lake, wetland and riparian 

types

Where are most important riparian/wetland areas? 

How connected are they to the stream network? 

Connectivity/isolation of 

freshwater system types

Where are the most important riparian/wetland areas? 

How connected are they to the stream system? 

Landscapes

Disturbance-dependent 

vegetation and 

seral/successional state

Where are forest fuels? Which watersheds are most 

susceptible to insect/fire/erosion cycle?

Major ecological systems Are Research Natural Areas useful for reference 

condition?

Land use change 

What are trends, rates, and patterns of land use 

change surrounding parks (ROMO, FLFO)?

What is the affect of WUI–related treatments on other 

park resources?

Landscape connectivity

How are changes in habitat and connectivity related to 

bighorn sheep? Possible introduction of wolves? Role 

of park for lynx?

Note that early in our project we began to pursue developing a simple conceptual model for each 

indicator. A conceptual model

“...outlines the interconnections among ecosystem components, the strength, and direction of those 

linkages, and the attributes that characterize the state of the system. The model should demonstrate how the 

system “works,” with particular emphasis on anticipated system responses to human-induced stresses. The 

model should also indicate the pathways by which the system accommodates natural disturbances and what 

system attributes provide resilience to disturbance. These processes could be portrayed by illustrating the 

acceptable bounds of variation of system components, and normal patterns of variation in input and output 

among the model elements” (Noon & McKelvey 2006: 946).

After attempting to work through a couple indicators, it was clear that there were insufficient 

project resources to complete this task in a reasonable way, although we believe that ultimately 

deeper understanding and communication of these indicators to managers and the public is best 
26

founded on explicit representation of the system in the form of conceptual models. Some useful 

resources are available on communication with conceptual models (Heemskerk et al. 2003) and a 

primer for developing conceptual models (Gross 2003).

Table 8 provides a finalized list of the indicators and anticipated questions and issues that each 

indicator is intended to address. Note that the indicators listed here are a slight re-organization of 

the list of indicators from the scoping workshop included in Table 7. We made these 

modifications to better reflect changes (subsequent to the workshop) to the EMF and further 

discussions among the PIs.

Table 8. The final list of indicators selected for the assessment, paired with anticipated questions and 

issues of decision makers that each indicator is intended to address.

NPS Level 1 

Vital Sign Indicators Decision Maker Questions & Issues

Air & 

Climate

Condition of alpine lakes 

and atmospheric deposition

How does water quality of select park lakes compare to 

other lakes? What are trends in deposition of pollutants 

to park?

Water Extent and connectivity of 

wetland and riparian areas

Where are most important riparian/wetland areas? How 

connected are they to stream systems? 

Biotic 

Integrity

Extent of exotic terrestrial 

plants

Where are exotic plant species located? Where are 

areas with low occurrence of exotics that should be 

monitored?

Extent of fish distributions Where are refugia? Where are re-introductions most 

likely to be successful?

Extent of suitable beaver 

habitat

How many beaver could ROMO support? Where would 

their habitat be and what is their importance for 

elk/willow dynamics?

Landscapes

Extent and pattern of major 

ecological systems

Are RNAs useful for reference condition? Where are 

forest fuels? Which watersheds are most susceptible to 

insect/fire/erosion cycle?

Natural landscapes and 

connectivity

How are ranges in habitat and connectivity related to 

bighorn sheep? Possible introduction of wolves? Role of 

park for lynx?

3.4 Reference Condition Framework

Here we distinguish an assessment from a more general characterization study. An assessment

attempts to measure condition relative to some benchmark; it is placed within a context of 

expected or desired conditions that are described by a set of reference distributions.

Ecosystems are complex and multidimensional, yet their management and measurement require 

simplification to a small number of indicator variables. An ecological indicator is any 

measurable attribute that provides insights into the state of the environment and provides 

information beyond its own measurement (Noon 2003). Indicators are usually surrogates for 

properties or system responses that are too difficult or costly to measure directly (Leibowitz et al.

1999). Indicators differ from estimators in that functional relationships between the indicator and 

the various ecological attributes are generally unknown (McKelvey and Pearson 2001). Not all 

indicators are equally informative—one of the key challenges to assessment and monitoring is to 
27

select for measurement those attributes whose values (or trends) provide insights into ecological 

integrity at the scale of the ecosystem. In developing the list of indicators and specific measures, 

we considered some basic criteria for useful ecological indicators as provided by Harwell et al. 

(1999). Useful indicators need to be understandable to multiple audiences, including scientists, 

policy makers, managers and the public; they need to show status and/or condition over time; 

and there should be a clear, transparent scientific basis for the assigned condition.

An essential component of an assessment or monitoring program is the generation of expected 

values (e.g., baselines) or expected time trends of the ecological indicators. Only by comparing 

observed with expected values or trends can a determination be made about the current state of 

the ecosystem or the effectiveness of management practices. The close approach to, or the 

passing of, an expected value is the threshold point that triggers a change in management 

practices. In the ecological literature, these expected values have been referred to as reference 

conditions—“the condition in the absence of human disturbance which is used to describe the 

standard, or benchmark, against which the current condition is compared” (Sanchez-Montoya et 

al. 2009:41). It is often difficult, however, to standardize reference conditions across indicators. 

This is because different ecosystem indicators vary in the degree to which they reflect a pristine 

state. Some ecosystem types are more degraded than others. Stoddard et al. (2006) have usefully 

categorized reference conditions along a continuum from minimally disturbed, to historical 

condition, to least disturbed, to best attainable. Reference conditions are also subtly different 

from desired future conditions, which are typically a simple narrative statement. In a world 

dominated by human behavior, reference conditions may reflect what is realistically attainable, 

not necessarily is what is desirable. We also caution that benchmarks are needed for monitoring 

programs even though “steady-state” and “the balance of nature” are antiquated paradigms.

Estimating reference conditions is difficult, and imprecise for several reasons: (1) the limited 

availability of pristine, undisturbed ecosystems to provide insights to benchmark conditions; (2) 

an incomplete understanding of the relationship between the value of an indicator and the desired 

ecosystem state(s); (3) inadequate knowledge of the expected variability, over time and space, of 

the indicator of ecosystem state (or species status); (4) the non-linear relationships between 

indicator values and ecosystem processes, including the existence of sharp threshold regions – so 

called “ecological thresholds” (Groffman et al. 2006); and (5) the fact that indicator benchmarks 

may be best represented by probability distributions rather than single target values (Noon 2003).

Expected values and thresholds as discrete targets implicitly assume that an ecosystem will 

evolve to (or was historically at) a dynamic steady state with regard to its structure, composition, 

and processes. This assumption is often false. The dynamic nature of ecosystems argues for 

specifying a distribution of indicator values rather than an expected value at a single point in 

time or space. That is, the state(s) of an ecosystem is best described by a probability distribution

rather than a specific target value. In practice, this makes the management decision process less 

certain, but the probability concept is a more realistic characterization of nature. The challenge to 

the manager is two-fold—to assess the state of the system, and to determine if the current state is 

under an improbable natural disturbance regime. If the system is degraded, or in an improbable 

state, some management response is triggered.

Determining threshold values and reference distributions (Stoddard et al. 2006) first requires the 
28

selection of both a spatial and temporal scale to observe the ecosystem. If the spatial scale is a 

point in space—for example, when measuring stream temperature at a single location—an 

indicator threshold may be specified as a single value. However, if the spatial scale includes 

entire parks, complete watershed(s), or the geographic range of a species, specifying an expected 

distribution of indicator values over the area would be more appropriate. Thus, there are two 

different categories of indicators—those that lend themselves to threshold values at local 

temporal and spatial scales (e.g., water temperature for some fish and amphibians), and those 

best categorized by a reference distribution (e.g., mean and variance of the number of snags per 

hectare). In practice, few indicators will be characterized by a single target value. Because the 

physical and biological processes and structural/compositional elements that characterize 

ecosystems vary in space and time, most indicators are best considered random variables. That 

is, when integrated across space (time), at a given point in time (space), a specific process or 

landscape element is characterized by a dynamic reference distribution. This distribution, often 

referred to as the historic (or natural) range of variation (Landres et al. 1999; Parson et al. 1999), 

is critically dependent on the spatial scale and temporal window over which the distribution is 

estimated.

Once the scale of observation has been determined, it is possible to aggregate indicator values 

into a frequency distribution. The observed distribution of indicator values would then be 

compared to the expected distribution to detect both the magnitude and pattern of deviation from 

desired conditions. The concept of a spatial distribution of indicator values as the appropriate 

evaluative statistic is critical to the monitoring and assessment of ecological systems (Figure 11; 

Stoddard et al. 2006).

A useful assessment and monitoring program must address two distinct questions: (1) Is the 

observed value of the indicator at a specific location at a specific time within acceptable bounds 

of the expected probability distribution?; and (2) When indicator values are combined, across 

space or time, does the expected distribution of indicator values result?

Figure 11. Distribution of indicator values based on sampling numerous sites at various locations on the 

landscape. Individual sites can be interpreted as representing degraded, marginal, or nominal ecological 

conditions, but the overall inference is to the ecological state at a landscape scale (from Noon 2003).

For a given resource on the landscape (e.g., a segment of stream, a forest stand, a riparian 

corridor), it may be appropriate to establish a target value for a given indicator. However, when 
29

evaluating deviation from the desired ecosystem state at the landscape scale, inferences drawn 

from the indicator’s value at a site are of limited use without considering that signal in the 

broader context of values from neighboring landscape locations. A useful way to portray the 

distribution data is as a cumulative frequency distribution, which allows an estimate of the 

proportion of the sample locations below a certain indicator score (Figure 12).

Figure 12. Cumulative distribution of indicator scores based on sampling numerous sites at various 

locations on the landscape. The proportion of sites below a certain indicator score can be readily inferred 

(from EPA 1993).

Despite the importance of establishing benchmark distributions, the process of establishing 

reference conditions is subject to some degree of arbitrariness. For example, there is uncertainty 

on how benchmark conditions are to be estimated (Stoddard et al. 2006). There is no clear 

guidance on how far back in time, or to what spatial extent, one should go to find an appropriate 

point of reference or what constitutes pristine conditions. There may be no current analogs for 

past conditions. In addition, it is clear that the concept of benchmarks can be reconciled with the 

dynamical nature of ecosystems only in terms of frequency distributions, especially when viewed 

over long temporal or broad spatial scales. Thus, all decisions about the proximity of an indicator 

to its expected value must be made in probabilistic terms. 

The process of developing reference conditions as expected distributions for assessment or 

monitoring requires two key questions to be addressed: (1) What set of environmental features 

(i.e., indicators) should be selected to best characterize the state of an ecosystem?; and (2) What 

temporal and spatial scales should be assumed for estimating the historic range of variations or 

benchmark conditions? One useful distinction for indicators is based on scale and discriminates 

among so-called “coarse-filter" and “fine-filter” measures of ecosystems. Coarse-filter measures 

include, for example, assessments based on the area and spatial distribution of dominant 

vegetation types and their successional stages at the scale of large watersheds. Fine-filter 

measures could include vegetation elements such as downed wood and snags, and population 

measures for individual plant or animal species.
30

An exhaustive set of coarse- and fine-filter indicators should possess the properties of 

complementarity and comprehensiveness (Noon 2003; Noon and McKelvey 2006). Individual 

indicators should be minimally redundant and provide insights that complement other 

measurements. Collectively, a set of non-redundant measures should span the temporal and 

spatial dimensions that characterize an ecosystem. These properties are difficult to achieve in a 

minimal indicator set but are useful goals to strive for. Given the complexity and dimensionality 

of an ecosystem, a surrogate-based approach to ecosystem assessment is a pragmatic 

requirement. The challenge is in the selection of the set of indicators—the collection of 

indicators that, collectively, have the properties of complementarity and comprehensiveness 

(Noon and McKelvey 2006). Indicators are complementary if their information content is largely 

independent of other indicators, and the set of indicators is comprehensive if they collectively 

span the full dimensionality of the ecosystem. Pragmatic considerations alone dictate that only a 

small number of indicators can possibly be measured. However, strategies and processes for 

selecting ecological indicators are complex and poorly developed (Barber 1994, NRC 1995).

We have provided this discussion of ecological indicators and reference condition as a “best 

practices” for assessments. However, we recognize that because of limitations of data, 

knowledge, and resources (time and money), we were forced to take practical, first steps in this 

assessment. That is, our estimation of reference condition for our indicators presented here were 

based primarily on best professional judgment and data available, not based on more rigorous 

approaches discussed above. We pursued these simplified approaches because of practical 

limitations, but we placed our work explicitly into this broader reference condition framework 

and encourage more conceptually grounded and rigorous assessments in the future.

A reference condition describes

“... a distribution rather than a single absolute value. The range of values (for any given index or metric) 

results from sampling error and natural variability, both in time and in space. It is the degree of spatial or 

temporal variance, removed from sampling error, that is of interest. At any point in time, a set of sites, all in 

undisturbed condition, will exhibit a range of biological attributes. In addition, single sites in a natural state 

will vary over time, due to the influences of climate and natural disturbance” (Stoddard et al. 2006: 1269). 

The specific terms used to characterize the reference condition to which current conditions are 

compared are (Stoddard et al. 2006):

• “minimally disturbed condition” (MDC); 

• “historical condition” (HC); 

• “least disturbed condition” (LDC); and

• “best attainable condition” (BAC). 

For each indicator in this assessment, a reference condition was determined using the most 

rigorous means possible. However, limited availability of data, research or historic accounts 

prevented a high level of rigor for developing some of the indicators. 

In practice there are generally five methods to estimate a reference condition. We place our 

indicators in this context, following Stoddard et al. (2006). These are ordered, roughly, from 

most to least rigorous, and are referred to throughout the remainder of this report:
31

I. Reference-site. This relies on quantifying the biological condition at a set of minimally, 

least-disturbed, or best-attainable sites. 

II. Interpreting historical condition. For some indicators, it may be possible to examine 

datasets recorded at earlier (historic) times. One way we employed this method was to 

generate a series of scenarios that reflect the condition of the park at different time 

frames, especially with respect to significant natural events that were important for the 

park, or important management actions that have occurred.

III. Extrapolation from empirical models. This condition can be inferred by extrapolating 

from an empirical model that develops a functional relationship of condition (response) 

and predictive (explanatory) variables at known locations.

IV. Best professional judgment. This relies on the qualitative estimation of conditions from 

experienced biologists of the conditions in an area. It is desirable to the extent possible 

that the judgment considers the following: consistency with ecological theory and other 

experts, and documentation and clear description of the “rules” by which the expert came 

to his or her conclusions (after Stoddard et al. 2006).

V. Ambient distributions. This relies on using a distribution of indicator values that are 

currently observed over some region (e.g., 5% or 25% percentile value). One way to use 

this method is to place a park in its broader ecoregional context by developing the 

distribution of values from the full ecoregion and then examining what percentiles the 

park’s values are with respect to the full distribution. Important limitations to note are 

that this method often involves an a priori decision about a threshold (e.g., 5 or 25%?)

that is commonly arbitrary, it assumes that higher indicator values represent better 

conditions (or if lower values indicate better conditions, then ranking is reversed), and 

depends on the distribution of sites relative to the range of the indicator. 

3.5 Estimate of Concern and Degree of Confidence

For each indicator, we provide an estimate of the degree of concern, using the reference 

condition where possible. This estimate is intended to provide guidance to park staff to 

distinguish those indicators that we believe should be of relatively low concern from those of 

relatively high concern. Although ideally our estimates would be based on quantitative data and 

a sound monitoring design, in practice we made rough estimates of park condition using general 

scientific knowledge and, for the most part, qualitative data. For each indicator we estimated the 

degree to which the available evidence and understanding of an indicator justified concern, 

placing it into one of three general categories: low concern, high concern, or insufficient data 

and/or knowledge (qualitative data and/or understanding were not sufficient to make even a 

general determination; more research and data collection are warranted.)

We also provide our estimates with the level of confidence we have in an indicator in terms of its 

ability to provide reliable insights to a particular aspect of ecological condition. We considered 

two separate but related elements of confidence based on a framework developed by the U.S.

Climate Change Science Program (USCCSP 2008). The first element is the amount of evidence 

that was available to assess whether an indicator provided strong guidance of ecological 
32

condition (e.g., if high then it would indicate that the topic is well-studied and understood). The 

second element is the level of agreement or consensus across the different lines of evidence 

regarding an indicator, including literature, expert opinion, and datasets. We rated the level 

confidence according to the following criteria: 

High/moderate/low amount of evidence - Is this indicator well-studied and understood, or 

instead is it mostly experimental or theoretical and not well-studied? Does your 

experience in the field, your analyses of data, and your understanding of the literature 

indicate that there is a high/low amount of information on the degree to which this 

indicator provides guidance about ecological condition? 

High/moderate/low amount of agreement - Do the studies, reports, and your experience in 

the field, analyzing data, or use of this indicator reflect a high degree of agreement on the 

effectiveness of this approach, or does it lead to competing interpretations?
33

Chapter 4. Natural Resource Conditions

The goal in developing the indicators of condition was to provide a suite of indicators that would 

help managers and scientists interpret the condition of important park resources—incorporating 

as many resources and as much of the park as practical. Because this is an initial, comprehensive 

assessment, it was important throughout the project to document our assumptions and logic for 

findings, describe the level of confidence, identify critical data gaps, and to recommend 

approaches to further refine/quantify reference conditions over time.

4.1 Regional Context and Issue Characterization

The goal of this section is to provide a general discussion of park issues and stressors, 

particularly in reference to how well ecological systems are functioning or facing risks that 

currently impact resources, habitats, and/or watersheds. We also provide a brief analysis of two 

major issues for which we are able to readily generate a spatially-explicit map: visitor use (using 

accessibility) and beetle kill.

Conceptually, issues and stressors are typically aligned along a continuum from low to high, 

where areas with low stress generally have a relatively natural condition (Figure 13; Davies and 

Jackson 2006). This conceptual model is helpful to understand the relationship of biological 

condition to stressor gradient. However, we believe that it is not the place for the natural resource 

condition assessment to provide a synthetic summary score for the park. The scope of the 

assessment, the variety of ecological systems, and the complex interactions do not warrant a 

combined, synthetic score. Rather, the best use of this assessment is to provide a baseline of 

indicators with spatially-explicit data that can be further analyzed and synthesized by park 

scientists and managers to develop a deeper understanding of condition.

Figure 13. Conceptual model depicting stages of change in biological conditions in response to an 

increasing stressor gradient (Davies and Jackson 2006).
34

Similar to the ecological indicators, we have found it important to make reference to a 

comprehensive, logical structure or framework to examine issues and stressors. To characterize 

the issues to the park, we chose to use the Unified Classification of Direct Threats framework, 

because it is a comprehensive, hierarchical classification of threats. In this framework, a direct 

threat is the “...proximate (human) activities or processes that have caused, are causing or may 

cause the destruction, degradation and/or impairment of biodiversity and natural processes. This 

is synonymous with sources of stress and proximate pressures.” This is a hierarchical framework 

containing three levels of classes with full definitions (the first two class levels are provided in 

Table 9). It is important to note that we considered a number of other approaches, such as the 

EPA’s Ecological Risk Assessment framework (USEPA 1998), the Ecological Risk Index 

(Mattson and Angermeier 2007), and the Human Threat Index (Sowa et al. 2008), but we did not 

pursue these because these indices are based on arbitrary assignment of qualitative risk, which is 

fraught with difficulty. 

Table 9. Unified Classification of Direct Threats, first two class levels.

Level 1 Level 2

1. Residential & commercial 

development

1.1 Housing & urban areas

1.2 Commercial & industrial

1.3 Tourism & recreation

2. Agriculture & aquaculture 2.1 Annual & perennial non-timber crops

2.2 Wood & pulp plantations

2.3 Livestock farming & ranching

2.4 Marine & freshwater aquaculture

3. Energy production & mining 3.1 Oil & gas drilling

3.2 Mining & quarrying

3.3 Renewable energy

4. Transportation & service corridors 4.1 Roads & railroads

4.2 Utility & service lines

4.3 Shipping lanes

4.4 Flight paths

5. Biological resource use 5.1 Hunting & collecting terrestrial animals

5.2 Gathering terrestrial plants

5.3 Logging & wood harvesting

5.4 Fishing & harvesting aquatic resources

6. Human Intrusions & disturbance 6.1 Recreational Activities 

6.2 War, Civil Unrest & Military Exercises

6.3 Work & Other Activities

7. Natural system modifications 7.1 Fire & fire suppression

7.2 Dams & water management/use

7.3 Other ecosystem modification

8. Invasive & other problematic species 

& genes

8.1 Invasive non-native/alien species

8.2 Problematic native species

8.3 Introduced genetic material

9. Pollution 9.1 Household sewage & urban waste water

9.2 Industrial & military effluents

9.3 Agricultural & forestry effluents

9.4 Garbage & solid waste

9.5 Air-borne pollutants

9.6 Excess energy

10. Geological events 10.1 Volcanoes

10.2 Earthquakes/tsunamis

10.3 Avalanches/landslides

11. Climate change & severe weather 11.1 Habitat shifting & alteration

11.2 Droughts

11.3 Temperature extremes

11.4 Storms & flooding
35

At the scoping workshop, we elicited a list of major park issues for ROMO managers and 

scientists (recall Table 7). Using the unified classification of threats, we cross-walked the issues 

raised by park managers with the ROMO indicators. This resulting table (Table 10) is intended to 

provide a way to: a) organize the issues identified by park scientists and managers; b) examine 

which indicators we anticipate will be sensitive to an issue – that is, that will change in response 

to a change in issue; c) identify potential additional or future sources of issues to the park; and d) 

identify data and knowledge gaps. Note that the intent of this assessment was not to provide an 

indicator for every park issue.
36

Table 10. Synthesis of park issues of concern, placed within the classification of issues, with the indicator that addresses a particular issue.

Threat (Level I) Park Issues of Concern Indicator

1. Residential & commercial 

development

Wildland Urban Interface and adjacent high fire risk areas Landscapes  Natural landscapes metric

Prescribed fires and wildland fires in wilderness

2. Agriculture & aquaculture Nitrogen emissions from Front Range row crops and feedlots are 

sources of atmospheric N deposition

Condition of alpine lakes and atmospheric deposition

3. Energy production & 

mining

Nitrogen and mercury emissions are sources of atmospheric 

deposition

Condition of alpine lakes and atmospheric deposition

4. Transportation & service 

corridors

Nitrogen emissions are sources of atmospheric N deposition Condition of alpine lakes and atmospheric deposition

Increased visitation related to park shuttle system

Shuttle impacts and related increased impact to resources

*

5. Biological resource use None listed.

6. Human Intrusions & 

Disturbance

Visitor impacts to wildlife (e.g., bighorn stress at Sheep Lakes, 

introduction of pathogens, habituation of wildlife – bears and coyotes 

with food rewards)

Visitor impacts in backcountry areas (noise, increase use in remote 

areas of the park, social trails, human waste and water quality)

Water  Extent & connectivity of wetland/riparian areas

VERP/carrying capacity/crowding **

Shifts in visitor use due to aging baby boomers, and increasing thrill 

seekers with no or little environmental ethics?

7. Natural system 

modifications

Lack of beaver Biotic integrity  Extent of suitable beaver habitat

Lack of native predators (wolf and grizzly bear) Landscapes  Natural landscapes metric

Diminishing populations of certain species (e.g., boreal toad, bighorn 

sheep, river otter, mink, peregrine falcon)

Water diversions (e.g., Grand Ditch) Water  Extent & connectivity of wetland/riparian areas

Fire in the montane and sub-alpine zones: inside or outside the 

natural range of variability?***

Changes in landscape dynamics due to bark beetles and forest 

diseases

Landscapes  Extent & proportion of major ecosystem 

types

Jurisdiction over park species ranges and the challenge of 

coordination with CDOW in cross boundary wildlife issues.

8. Invasive & other 

problematic species & 

genes

Overabundant ungulate species (elk, moose and possibly mule deer) 

and impacts on herbivory, habitat, and hydrologic processes

Disease pathogens that may lead to population declines (chytrid, 

CWD, whirling disease)

Nonnative wildlife threatening native populations (mountain goats, 

rainbow trout, brook trout, brown trout, rock dove, European starling, 

and a gray line species – moose)

Biotic integrity  Extent of native and non-native fish 

distributions

Species not yet in the park but nearby – Quagga mussel (in Shadow 
37

Threat (Level I) Park Issues of Concern Indicator

Mountain Lake), New Zealand mudsnail

Invasive exotic plants and their impacts - are new species becoming 

established and existing species moving into higher elevation areas 

due to climate change and drought?

Biotic integrity  Extent of exotic terrestrial plants

Black bear conflicts with campers

Landscape impacts from bark beetle, blister rust, mistletoe, especially 

in high-value tree areas such as campgrounds, visitor centers, picnic 

areas, national register site districts and structures

9. Pollution Poor visibility

Atmospheric deposition and changes in micro-organisms in water 

and soil due to atmospheric deposition

Air and climate  Condition of alpine lakes and 

atmospheric deposition

Rigidity of state air quality regulations and how those regulations 

greatly limit any future prescribed fire operations.

10. Geological events

11. Climate change & 

severe weather

Diminishing water and water quality due to climate change, drought 

and reduction in snowpack

Uncertainty of long-term sustainability and sensitivity to climate 

change for native species such as the pika, rosy finches, white-tailed 

jackrabbit, white-tailed ptarmigan, black swift and others

*See related work by P. Newman on soundscapes and visitor use.

**This is not an indicator per se, but see maps of accessibility that approximate visitor use below.

***See Sibold et al. 2007.
38

4.1.1 Estimating Visitor Use through Accessibility Modeling2

One of the main issues identified by park managers is visitor use within the park. Consequently, 

we estimated visitor use through accessibility modeling to assist in identifying watersheds and 

ecological systems that are particularly vulnerable to visitor use. 

Understanding the degree to which visitor use affects park resources is a critical need, and is 

considered by the park managers to be an important issue. Estimating visitor use is critical to the 

management of natural areas in order to inform how visitor use might affect natural resources 

and social conditions. However, many land management agencies have insufficient visitor 

monitoring programs and little baseline data about visitor use patterns and trends (Watson et al.

2000). A survey of wilderness managers reported that 63% of managers relied on best guesses to

estimate visitor use. Some reasons why wilderness use has not been examined adequately include 

lack of funding and logistical problems resulting from size of area, number of access points, lack 

of personnel time, and lack of knowledge and training about available methods to collect and 

analyze data (Watson et al. 2000). 

A few methods have been developed to estimate the spatial distribution of visitor use, though 

many of these are applicable to urban land cover types (e.g., Brown and Vivas 2005) or rely on 

incremental buffers from roads (e.g., Lesslie et al. 1988; Aplet et al. 2000; Sanderson et al. 2002; 

Theobald 2003) or distance (as a continuous variable) from roads (e.g., Riitters and Wickham 

2003; Watts et al. 2007). One notable example of estimating visitor use specifically for protected 

areas is Shumacher et al. (2002) who developed a “human use intensity” model. This is 

computed as an equal-weighting of population density and road/trail density rasters (weighted 

using 3 for improved roads, 2 for unimproved roads, and 1 for trails) using a 1 km radius moving 

window. Rather than using road and trail density, which does not differentiate back-country from 

front-country locations, we developed an estimate of visitor use based on accessibility (Geertman 

and van Eck 1995; Nelson 2008) or remoteness (Carver et al. 2002) –defined as the one way 

travel time for an average visitor to any location within the park. Accessibility is a strong 

surrogate of visitor use, which has been found to be related to travel time and distance from a 

trailhead (van Wagtendonk et al. 1980; Pettebone et al. 2006). Note that this does not estimate 

the distribution of visitors (e.g., a density map of visitors); however, a useful next step would be 

to estimate of the number of park visitors moving across the landscape.

To understand how the issue has changed from previous “natural” conditions to the current state, 

we developed accessibility surfaces to reflect three scenarios:

- Scenario A. Current roads and trails: This scenario is based on the current transportation 

infrastructure (i.e., roads and trails), and travel time is the weighted average from the four 

main park entrances. 

- Scenario B. Current roads without trails: This scenario reflects the road transportation 

infrastructure, but does not include the trail system. This allows us to examine how the 

current trail infrastructure modifies the “natural” accessibility to ecological systems in the 

park.

 2 Note that parts of this section are published in Theobald et al. (2010).
39

- Scenario C. “Natural” conditions without roads or trails: This scenario reflects no 

transportation infrastructure (i.e., roads or trails) in the interior of the park. This allows us 

to examine how the “natural” accessibility to ecological systems varies within in the park.

Methods

We used cost-distance techniques in ArcGIS to create “accessibility” models to estimate travel 

time to all locations within ROMO (Theobald 2005; Frakes et al. 2008). We conducted this 

analysis using 10 m resolution raster datasets that included data on roads, trails, slope, streams, 

and land cover (vegetation) types (Table 11). 

Table 11. Listing of the datasets, attributes, and scale of data used to generate the accessibility maps.

Factor Source Attribute variables Scale (grain)

Roads ESRI StreetMap (1:100K)* Posted speed limit 1:100,000

Trails Colorado Trails dataset (Linn et 

al. 2008)*

Surface type, mode of travel, 

trail gradient, trail heads, 

parking lots

1:1,000-24,000

Topography USGS National Elevation 

Dataset Elevation (m), off-trail slope 10 m

Hydrography USGS National Hydrography 

dataset, ROMO hydrography Streams, lakes, stream order 1:24,000

Land cover ROMO vegetation (Salas et al. 

2004)

Reclassified to major land 

cover types 10 m

*We used these datasets because some trails in the Bear Lake corridor and outside of the park were not mapped

correctly, and these provide infrastructure outside of the park (e.g., north access from Pingree Park area). 

Generally, there are four steps to develop an accessibility surface to compute the time it would 

take a person to travel (one-way) to a given location: 1) from a starting location; 2) along road 

infrastructure to a trailhead; 3) from a trailhead along the trail infrastructure; and 4) off-trail 

across the landscape to all locations in the park. 

First, we defined the starting (initial) locations to be the main entrances to the park (Table 12). 

There are three formal entrances: two on the east side (Beaver Meadows and Fall River) and one

on the west (Kawauneechee). We added an additional “informal” trail entrance on the north side 

of the park near Long Draw Reservoir. Also note that there are four additional informal 

“entrances” accessible via trailheads on the east side of the park: Lily Lake, Twin Sisters, Lumpy 

Ridge-McGraw Ranch, and Dunraven. On the west side there are also four informal trailhead 

entrances: Tonahutu, Summerland Park, East Inlet, and Shadow Mountain Dam.

We developed a separate accessibility surface (in minutes of travel time) from each of the four 

starting locations. We combined these by finding the weighted average travel time—weighted to 

reflect the approximate distribution of visitors entering each gate, based on the 1996 park 

visitation survey. We also adjusted the weights for each layer spatially so that locations close to 

each entrance were more important in determining the overall travel time (using inverse distance 
40

as a relative weight). This is necessary so that extremely long (and unrealistic) travel times do 

not outweigh nearby estimates.

Table 12. Weights used to reflect the proportion of park visitors entering ROMO from each of the four 

main park “entrance” locations. (Unpublished data 1996). *Estimated by authors.

Entrance No. of respondents Weight

Beaver Meadows 1,388 0.43

Kawaneeche 721 0.22

Fall River 1,134 0.34

Northern* 40 0.01

The second step was to incorporate travel time along the road infrastructure. We assumed that 

visitors would travel via automobile along the roads at the assigned speed limit, and stop only at 

parking lots and designated trailheads. This is an important distinction from most accessibility 

models (e.g., Theobald 2005; Frakes et al. 2008) that model travel time off-trail directly adjacent 

to roads—which assumes that a visitor can stop at any location along a road, leave the car, and 

begin hiking away from the road. This was accomplished by weighting off-road and off-trail 

cells that were directly adjacent to road cells a modifier weight of 0.01. For gravel/4-wheel drive 

roads, we assumed a speed limit of 15 mph. Note that we assume automobile travels are 

unaffected by the steepness of roads.

The third step was to model travel time from trailheads and parking lots, along trails assuming 

visitors would be hiking (walking). Typical walking velocity for a hiker is 5 km/hr on flat terrain 

but diminishes on steeper terrain. We used the hiking velocity equation of (Imhof 1951) to reflect 

changes in travel speed as a function of trail gradient (or slope):

w=6*exp(-3.5* |S + 0.05|)

where w = walking velocity and S = slope = tan (theta). Note that we do not distinguish travel 

direction (i.e., walking up a steep trail might take longer than down a steep trail).

The fourth step was to incorporate travel time due to off-trail hiking. Travel was assumed to be 

the same speed as the on-trail, but with an additional cost-weight applied to reflect the difficulty 

of moving through vegetation and crossing rivers and lakes. We assumed that hikers who plan to 

travel off-trail will use an existing (marked) trail to access the closest point to their off-trail 

destination. Areas where vegetation is denser—shrublands, for example—were weighted higher 

than areas where hiking velocity is less restricted, such as grasslands. Lakes and marshes were 

given an extremely high value to make travel through these areas virtually impossible. We 

generalized the 46 vegetation associations from the ROMO vegetation dataset (Salas et al. 2004) 

to 25 classes and estimated a modifier (1.0 no effect to 0.0001 nearly impassable) to reduce the 

travel speed (increase the cost weight or resistance). We also assigned a travel time modifier to 

large streams, rivers, and water bodies (Table 13).
41

Table 13. Travel time modifier for computing off-trail hiking speed. These values are multiplied by the ontrail travel time, so smaller modifier values mean slower travel speeds. These are estimates consistent 

with Frakes et al. (2008), which developed weights for coarser USGS National Land Cover Dataset cover 

types. Also, our weights for crossing larger streams (3rd order and greater) is 3-10 smaller (and assumes 

it takes more time to cross).

Vegetation Class Modifier

Barren 1.00

Blue Spruce 0.60

Cottonwood 0.90

Dead and Down woodland 0.30

Glacier 0.50

Herbaceous Upland Alpine 0.85

Herbaceous Upland Alpine Fell field 0.65

Herbaceous Upland Montane 0.85

Herbaceous Wetland Cross Zone - Marsh 0.45

Herbaceous Wetland Cross Zone - Wetland 0.45

Herbaceous Wetland Subalpine 0.45

Juniper 0.75

Krummholz 0.40

Lodgepole Pine 0.50

Mixed Conifer with Aspen 0.65

Montane Douglas Fir 0.65

Ponderosa Pine 0.75

Riparian Aspen 0.70

Riparian Montane Mixed Conifer 0.65

Shrub Riparian Cross Zone 0.35

Shrub Upland Alpine 0.45

Shrub Upland Lower Montane 0.45

Sub-alpine Conifer 0.50

Upper Montane Aspen 0.65

Water (lakes & reservoirs)

Tributaries (1st order)

Small streams (2nd order)

Large streams (3rd order)

Rivers (>3rd order)

0.0001

0.80

0.65

0.20

0.02
42

Results

Given the current road and trail infrastructure (Scenario A), the average one-way travel time for 

ROMO is 3.5 hours (Table 14). This means that the travel time from entrances to all locations in 

the park is, on average, 3.5 hours with a standard deviation of 2.3 hours. Roughly 7% of the park 

is within 1 hour; 50% is within 4 hours; and 80% of the park is within 8 hours. The west side of 

the park (west of the Continental Divide) is slightly less accessible, with an average one-way 

travel time of 3.8 hours, while it is 3.4 hours on the east side (Figure 14). The most accessible 

ecological system zones were savannah (2.0 hrs) and lower montane (2.3 hrs; Table 15). Note 

that these values are averages for the entire zones; there are some parts of each of these systems

that are much more (and less) accessible than the mean value.

Table 14. Travel time in hours to locations within ROMO from four entrances for Scenario A (current road 

and trails infrastructure).

Averaging Unit Mean (hr) STD (hr)

ROMO 3.5 2.3

Continental divide East 3.4 2.2

West 3.8 2.4

Ecological 

systems

Alpine 4.8 1.7

Upper Montane 3.2 1.8

Lower Montane 2.3 1.2

Riparian 3.0 2.6

Savanna 2.0 1.0

We also examined how travel time has changed as a function of the transportation infrastructure 

that has developed over time to its current extent. Figure 15 shows travel time without trails and 

shows travel time without roads or trails. Table 15. shows how average travel time increases 

from 3.5 hours (with roads and trails) to 3.9 (no trails but roads) to 7.4 (no roads or trails), and 

Table 16 shows travel times to RNAs.

Table 15. Summary of average travel time in hours for different accessibility scenarios in Rocky Mountain 

National Park.

Accessibility Scenario Average (hr) Standard 

Deviation (hr)

A. Roads and trails (current) 3.5 2.4

B. Roads only 3.9 2.6

C. No roads or trails (“natural”) 7.4 3.4

Table 16. Average one-way travel time to Research Natural Areas in Rocky Mountain National Park 

reflecting different infrastructure scenarios.

Research 

Natural Area

A. Roads and Trails B. Roads only C. No roads or trails

Avg. (hr) STD (hr) Avg. (hr) STD (hr) Avg. (hr) STD (hr)

Specimen 

Mountain 3.2 1.4 3.6 1.6 10.5 1.2

West Creek 4.8 1.0 5.2 1.1 5.8 1.5

Paradise Park 6.8 3.1 7.3 3.1 9.6 2.7
43

Figure 14. Accessibility within ROMO from main park entrances, assuming current transportation 

infrastructure (Scenario A)—shown as one-way travel time assuming shortest travel time along roads, 

trails, and off-trail.
44

Figure 15. Accessibility within ROMO from park entrances, assuming no trails but roads as transportation 

infrastructure (Scenario B)—shown as one-way travel time assuming shortest travel time along roads.
45

Figure 16. Accessibility within ROMO from park entrances, assuming no trails or roads as transportation 

infrastructure (Scenario C)—shown as one-way travel time.

Because the stated management goal for backcountry and Research Natural Areas is low impact, 

we compared the average accessibility of these areas to the rest of the park (Table 17).
46

Table 17. Average one-way travel time to different management zones in Rocky Mountain National Park.

Management zone A. Roads and Trails

Avg. (hr) STD (hr)

Research Natural Areas 6.5 27.2

Alpine tundra 4.8 18.2

Cross-country 4.2 11.1

Corridors 3.1 1.5

Front country 3.4 4.9

Not covered in BCMP 1.5 3.5

Another objective of this effort was to illustrate how accessibility could be used to inform the 

spread of exotic plant species. The average time to plots that contain cheatgrass was 0.86 hrs 

(SD=0.432), while the plots without cheatgrass were 3.24 hours away (SD=9.1; Figure 17). 

Cheatgrass occurs in more accessible areas—roughly 80% of plots with cheatgrass are within 

one hour’s travel time.

Figure 17. A cumulative distribution plot showing how the proportion of plots with cheatgrass relates to 

accessibility. The majority (80%) of the plots that have cheatgrass are within 1 hour travel time, whereas 

the majority (80%) of locations throughout the park are much less accessible (~3 hours travel time).

A few limitations to our approach should be noted. Most data we used were fine-scale (1:24K), 

but the spatial position of the roads in particular (from 1:100K scale) should be refined in future 
47

efforts. The goal of accessibility modeling is not to estimate the distribution of visitors (e.g., a 

density map of visitors); rather it is to estimate how accessible any location in a park is to an 

average visitor, with the explicit assumption that more accessible areas tend to have more visitor 

use. A useful next step would be to estimate of the number of park visitors moving across the 

landscape by incorporating destinations/attractions (e.g., peaks, lakes, waterfalls, etc.), perhaps

using recreation choice modeling (e.g., Termansen et al. 2004). This might also include more 

nuanced aspects of visitor use such as type of use (day/overnight), mode (hike, bike, horse, 

motorized, etc.), duration, and values orientation to use. Finally, although we have verified 

accessibility times in the field in an informal way, a rigorous study that develops empirical 

estimates of travel time for different users, mode of use, and permeability modifiers as a function 

of different land cover types would make estimates of travel time more precise.

4.1.2 Watersheds at Risk to Beetle Kill

We briefly examined the risk of watersheds to beetle kill. We used data from the U.S. Forest 

Service Region 2’s Forest Insect and Disease Aerial Survey 

(www.fs.fed.us/r2/resources/fhm/aerialsurvey/download). This provides data on the location and 

type of vegetation disturbance associated with beetle kill. We overlaid the relatively coarse 

polygons drawn from the aerial survey with the ROMO vegetation data and removed cover types 

that would not likely be infested by beetles (e.g., herbaceous, rock/ice, water, etc.). We then 

summarized these refined infested polygons by computing their percentage of the full watershed 

(HUC-12) area. These summaries are provided Table 35, located in Chapter 5 (Natural Resource 

Condition Summaries).
48

4.2 Analyses of Indicators

4.2.1 Air and climate: Condition of Alpine Lakes and Atmospheric Deposition

Authors: Jill S. Baron, Katherine E. Williams, and Melannie D. Hartman

Description/Purpose

Freshwater aquatic ecosystems, due to their location at the bottom of catchments and watersheds, 

are literally the “sinks” into which terrestrial landscapes drain, making them both responsive to 

upstream disturbance, and good indicators of human-caused change (Baron et al. 2002). High 

elevation lakes serve as bellwethers of ecological change from climate change as well as the 

long-range transport and subsequent deposition of nutrients and pollutants that result from 

human activity. We compared the chemical solutes of alpine lakes across the Rocky Mountain 

chain that could serve as indicators of the influence of warming temperatures, land use change 

and production of dust, and industrial or agricultural emissions of nutrients such as nitrogen and 

pollutants such as organochlorines. We used the distribution of chemical parameters from 244 

alpine lakes to establish a reference condition by which to evaluate the state of Rocky Mountain 

National Park lakes during the period 1997-2006. We used an ecosystem model to estimate 

changes in alpine acid neutralizing capacity since 1900. We also recount the history of 

atmospheric deposition of nutrients and pollutants to Rocky Mountain National Park derived 

from measured and EPA emissions reconstructions. 

The effects of acid deposition on lake and stream aquatic ecosystems have been a concern in 

North America and Europe since the 1970s, due to well-documented effects of acidification on 

aquatic organisms (Charles 1991). Acid rain from sulfate (SO4

2-

) or sulfuric acid inputs can 

increase the acidity of lake waters to the point where concentrations of monomeric aluminum

solution increases. Monomeric aluminum (Al) is toxic to fish and invertebrates (Charles 1991). 

Concentrations of SO4

2-

, acidity, and Al can reflect inputs of acid rain, along with measurable 

responses in fish health and viability, and invertebrate abundance and assemblages. Atmospheric 

nitrogen, mercury, and soluble organochlorines have more recently been shown to alter aquatic 

food webs and productivity, fish reproductive health and behavior, and even the health of 

organisms that consume fish, such as birds, small mammals, and humans (Vitousek et al. 1997, 

Fitzgerald et al. 1998, Blais et al. 1998). The deposition of dust can serve to import nutrients 

such as phosphorus to alpine systems (Sickman et al. 2003, Neff et al. 2008). Evidence of 

possible climate warming effects on high elevation lakes is beginning to emerge, as well. Several 

studies have noted unusually high concentrations of heavy metals, weathering products, and 

nitrate in alpine lakes below retreating glaciers and rock glaciers (Lafreniere and Sharp 2005, 

Williams et al. 2007, Thies et al. 2007, Baron et al. 2009). 

The east side of Rocky Mountain National Park has received elevated atmospheric nitrogen 

deposition since about 1950, and there has been a strong connection between atmospheric 

nitrogen deposition and measurable east-side ecosystem effects at select lakes (Baron et al. 2000, 

Nanus et al. 2008, Elser et al. 2009a, 2009b). In these aquatic systems, those changes have been 

increased nitrate (NO3

-

) concentrations, possibly lowered acid-neutralizing capacity (ANC), 

altered algal assemblages, and increased primary productivity (Baron et al. 2000, Wolfe et al. 

2003, Enders et al. 2008). 
49

In this report we begin with a description of measured atmospheric deposition trends to ROMO 

and modeled deposition trends from 1900 to 2000. We follow with a spatial comparison of 

nutrients, physical parameters, and pollutant concentrations in alpine lakes in the Rocky 

Mountains. We also summarize the results from modeling studies to infer changes in chemical 

parameters in a few ROMO lakes over time. Our objective was to place select lakes of ROMO in 

a geographic and temporal perspective. This is especially helpful for exploring change brought 

about by atmospheric deposition, as regions with elevated N deposition have alpine lakes with 

high NO3

- concentrations, while regions receiving acid rain often show a chemical response of 

low ANC and pH (Stoddard et al. 1999, Bergström and Jansson 2006, Nanus et al. 2008). We 

used the recent WACAP study(Landers et al. 2008) to address the condition of ROMO alpine 

lakes with respect to atmospherically deposited mercury and organic contaminants.

Changes in chemical and biological condition over time provide critical information about lake 

condition, particularly in response to changing inputs or conditions. Long-term monitoring 

programs, such as the Loch Vale watershed program, or the Niwot Ridge LTER provide longterm records for select lakes. There are few, if any, time series in national parks, however, that 

have directly measured a change in water quality of alpine lakes away from a pristine condition. 

Proxy information from lake sediment records and ecosystem models can be used to infer 

changes for some, but not all, chemical and biological conditions (Baron et al. 1986, Wolfe et al. 

2003, Landers et al. 2008). The NPS Vital Signs program will also contribute information about 

the causes and consequences of change over time. In a rapidly changing world it becomes 

important to establish a scientifically defensible reference condition of environmental quality in 

order to be able to evaluate the current state of natural resources and chart excursions toward or 

away from the reference. This is particularly true for U.S. national parks, whose significance as 

representatives of naturally functioning ecosystems becomes more important as their surrounding 

landscapes become increasingly altered by human activities (Baron 2004). 

We mention here, but do not discuss further, an additional force for ecological change in alpine 

lakes. Resource management to enhance recreational opportunities has influenced the biological 

integrity of some alpine lakes in ROMO. Stocking of non-native trout has been pervasive in the 

Rockies (Pister 2001; Freshwater connectivity indicator, this report). Fish introductions 

dramatically alter native communities and extirpate native fishes, zooplankton, and benthic 

invertebrates (Knapp et al. 2001a and b). Stocking practices were eliminated in the 1960s, and 

non-native trout now persist only where there are reproducing populations. In a national park 

whose mission is protection of naturally-functioning native ecosystems, non-native trout 

represent a departure from the minimally disturbed condition.

Approach/Methods

Measured Deposition Chemistry and Estimated Historical Trends

ROMO has two National Atmospheric Deposition Program/National Trends Network 

(NADP/NTN) sites, both located on the east side of the park. The lower elevation (2490 m) 

Beaver Meadows (CO19) site has been operating since 1980, and the higher elevation (3159 m) 

Loch Vale (CO98)site since 1983. Wet deposition trend data for major ions from both sites were 

taken from the NADP website. 
50

Dry deposition of select gases and aerosols has been measured at the Clean Air Status and 

Trends (CASTNET) site ROM406 located on the eastern boundary of ROMO (2743 m) since 

1995. Dry deposition trend data for nitrogen and sulfur were taken from the CASTNET website. 

Estimated historical emissions of NO3

-

, NH4

+ and SO42- deposition to Loch Vale Watershed were 

based on emissions (EPA NEI 2004, http://www.epa.gov/air/data/) of NOx and SO2. Using an 

estimated background nitrogen deposition value of 0.5 kg N ha-1 yr-1 in 1900, and a 19 year 

record of measured values from Loch Vale (NADP site CO98), N deposition history was 

reconstructed using exponential equations that correlated well with EPA-reported NOx emissions 

from Colorado and from the sum of emissions of 11 western states. Full methods are described in 

Baron (2006). The 1984-2001 measured SO4

2-

concentrations at Loch Vale Watershed 

(NADP/NTN site CO98) were correlated with Western U.S. SO2 EPA-reported emissions 

(R2

=0.55). The relation between emissions and measured concentrations was used to hindcast

annual SO4

2-

concentrations back to 1900. As with N deposition, precipitation values from a 

climate reconstruction model (VEMAP 1996) were multiplied by concentrations to derive annual 

SO4

2-

deposition values for Loch Vale Watershed. 

Sources of Lake Data 

We gathered lake data from federally-protected lands of the Rocky Mountains of Alberta, 

Montana, Idaho, Wyoming, Utah, and Colorado (Table 18). Eighty of the lakes were in 

Colorado. There are thousands of alpine lakes in the Rocky Mountains. A subset of these lakes 

has been sampled for water quality parameters, so the following analysis is not unbiased as much 

as opportunistic. Our selection criteria for inclusion of a lake in the analysis were:

• lake or lake outlet sample collection and analysis using established protocols for low-ionic 

strength waters and adequate quality assurance protocols similar to the Loch Vale Watershed 

Long-term ecological research and monitoring program and the Western Lake Survey;

• location above treeline;

• sampled one or more times during late summer season (July 15-October 31) during the years 

1997-2007.

Lakes were not screened based on parent material. Although bedrock composition is a common 

criterion for identifying lakes susceptible to acidification, it does not pertain to other types of 

disturbance that could degrade lake condition, such as deposition of nutrients (N, P), 

contaminants (organochlorines, metals), or climate change. The data sources, described in Table 

18, represent most of the alpine lake samples collected in the Rocky Mountain region during the 

1997-2007 period. 

Statistical treatment of lakes

For lakes that had more than one sample event, means and medians of data values were 

calculated so that each lake had only one value within the set. There was no significant 

difference between mean versus median values (Student’s T-Tests p<0.05), and means and 

medians are used interchangeably in this document. Physical and chemical information in 

common at all lakes included major ions, alkalinity, conductivity, and pH. Most lakes also 

reported late summer surface temperatures. 
51

Once assembled, summary statistics were performed on the data set, followed by rank order 

graphing of each chemical attribute. 

Table 18. Sources of lake chemistry data used for inorganic solute comparisons.

Data 

Source

Number of lakes from 

which lake data were 

assembled*

Lakes used 

for analysis** Reference Comments

USGS 

NWIS 21 21 http://waterdata.usgs.gov/nwis

Resample of lakes initially sampled in 

1985 WLS

USFS 251 230 http://www.fs.fed

.us/ARMdata/

Lakes selected for long-term 

monitoring from Wilderness level 

reconnaissance surveys in MT, ID, 

WY, UT, CO

CSU 22 22

Lafrancois et al. 

2003, Nydick et 

al. 2004

CO and WY lakes collected for 

research

*includes lakes above and below treeline, multiple sample dates per lake

** some lakes were sampled by multiple groups, so the numbers do not add to 244

Models of pre-measurement stream chemistry

We used DayCent-Chem, a daily time-step ecosystem-hydrogeochemical model to investigate 

the changes in stream water chemistry that may have occurred in Andrews Creek, Loch Vale 

Watershed, over the past century. DayCent-Chem is a daily time-step ecosystem model that

couples ecosystem nutrient cycling and plant dynamics with geochemical equilibrium equations 

(Hartman et al. 2007).We ran the model from the known ecosystem state in 1999 backwards to 

1900 using daily historic VEMAP climate (VEMAP 1996) and hindcasts of N and S deposition 

described above.

Reference Condition

We define the reference condition assuming that alpine lakes of national parks and wilderness 

areas are minimally disturbed, after the Minimally Disturbed Condition (MDC) definition of 

Stoddard and colleagues (2006). The MDC describes the condition of waters in the absence of 

significant human disturbance, and Stoddard and colleagues state that once established, the 

distribution created by a group of sites in MDC should vary little over time and can serve as a 

nearly invariant anchor by which to judge current conditions (Stoddard et al. 2006). A lake could 

receive atmospheric contaminants but still fall in the minimally disturbed condition if the inputs 

fall below the threshold for observed biological effects. A second type of reference condition, the 

Historical Condition (HC), can be an accurate estimator of the true reference condition defining 

biotic integrity if the historical point chosen is before the start of any human disturbance 

(Stoddard et al. 2006). We use a combination of interpreting historical condition and ambient 

distributions to develop reference distributions.

There is abundant literature describing low concentrations for minimally polluted lakes and 

increasing concentrations with increasing atmospheric deposition inputs, but just how low is the 

true MDC (Bergström et al. 2005, Bergström and Janssen 2006, Elser et al. 2009a, 2009b)? We 

addressed this by comparing lake concentrations across the sample area, and matching low 

concentrations with low N deposition inputs. 
52

Spatial comparisons are not useful for other analytes that have variable concentrations due to 

different parent materials. Conductivity, calcium, sulfate, and chloride concentrations can 

weather from certain bedrock materials, but their concentrations in a lake can also change over 

time due to inputs of dust or pollution. Our presentation of these values for ROMO therefore 

represents a HC against which future change can be identified. Acid neutralizing capacity and 

pH can be representative of parent material, but can also change with inputs of atmospheric 

deposition of acids or strong acid anions. Differences in concentrations of these chemicals 

between the east and west sides of ROMO, and the reconstructed water chemistry based on SOx

and NOx emissions records are suggestive that deviation away from the MDC has occurred on 

the east side; this is discussed below. We infer the HC for some heavy metals, contaminants, and 

nutrients from lake sediment proxies of past conditions. 

Results/Discussion

Measured wet and dry deposition trends

Long-term wet deposition monitoring shows changes and trends in the pattern of precipitation 

chemistry, with some solutes clearly increasing in deposition (and concentration; data not 

shown) and others clearly decreasing (

Figure 18). Both the low elevation and the high elevation site, Beaver Meadows (CO19) and 

Loch Vale (CO98), displayed similar trends for wet deposition, although the total deposition was 

greater at Loch Vale, where precipitation amounts were higher. Calcium deposition has increased 

at Loch Vale at a rate of 0.03 kg ha1 yr-1, while calcium at Beaver Meadows showed little 

change. Clear spikes in years such as 2005 suggest dust events. Total inorganic N increased at 

both sites, but the rate of change, 0.04 kg ha1 yr-1 at Beaver Meadows, was greater than at Loch 

Vale (0.01 kg ha1 yr-1). Acidity and sulfate have both declined strongly at both sites, in keeping 

with regional trends of reduced SO2 emissions. Chloride deposition has also declined. 

Concentration values for both sites had the same trend patterns as deposition (data not shown).
53

Figure 18. Wet deposition values over time for two NADP sites in ROMO, Beaver Meadows (CO19) and 

Loch Vale (CO98). Note that nitrate and ammonium concentrations are molecular, and not their nitrogen 

component, and thus are not directly comparable to the inorganic N values on the same figure. Inorganic 

N represents the sum of only the nitrogen portions of nitrate and ammonium. Linear trend lines are 

shown. 
54

Dry deposition data (http://www.epa.gov/castnet/charts/rom406_wdn.gif), presented in Figure 

19, suggest about a third of the total N and S deposition (wet plus dry) is deposited as dry 

species. Although statistical trend analysis is not possible since not all data are reported, there 

appears to have been a slight decline in dry deposited species since 1996. Gaseous ammonia is 

not reported, but contributes to the total N inputs. 

Figure 19. A comparison of dry deposition from the ROM406 CASTNET site and wet deposition from 

CO98, the Loch Vale site.

Estimated Historical Trends in Deposition

Hindcast deposition trends show an exponential increase in both nitrate and ammonium 

commensurate with increasing population growth in the West (Figure 20; Baron 2006). Sulfate 

concentrations were more variable with time, and seem to reflect major economic or social 

events, such as a depression in 1920-1921, the Great Depression from 1930-1940, an increase in 

industrial production during World War II, and the decline in regional copper smelting and 

implementation of the Clean Air Act beginning in the 1970s (Oppenheimer et al. 1985). 

Figure 20. Reconstructed dry plus wet atmospheric deposition to Loch Vale Watershed from 1900 to 

2000. SO2 emissions from EPA were calibrated to measured SO4

2-

deposition to ROMO for 1984-2000. 

The relation was hindcast back to 1900. NO3

-

and NH4

+ deposition was hindcast with an exponential 

regression from measured depositions values 1984-2000 back to estimated background values in 1900.
55

Rocky Mountain Regional Lake Comparison

Comparison with Western Lake Survey 

Most of the lakes in our sample were selected for long-term monitoring by the U.S. Forest 

Service because of their suspected sensitivity to acid rain, thus they represent low ANC lakes on 

crystalline bedrock (http://www.fs.fed.us/ARMdata/). Thus it makes sense that the 224 alpine 

lakes in our survey were more dilute and with lower mean concentrations of most solutes than 

those from the 1985 Western Lake Survey (Landers et al. 1987; Table 19). Western Lake Survey 

lakes were selected through a stratified random sample to describe surface water chemistry on a 

regional scale. Concentrations of NH4

+

, NO3

-

, and SO4

2-

, were higher in our Rocky Mountain 

sample than Western Lake Survey means and median values. This type of comparison is 

important for context, and a comparison of minimum values for our sample with the values from 

the first quartile of WLS lakes (representing concentrations for the lowest 25% of lakes in their 

population) can help in the selection of values for the MDC. 

Table 19. Summary Statistics for Select Alpine Lakes. Values presented are the mean and range of 

select physical and chemical characteristics of 244 alpine lakes included in the study. Lakes were 

included in the study if sampled between mid-July and late October, 1997-2007. <DL signifies the sample 

was below the analytical detection limits. Values to the right of the double line are the mean (median) and 

minimum values from the 1985 Western Lake Survey (Landers et al. 1987) for the entire 719 lakes in the 

WLS and for the 139 lakes in the Southern Rockies Region, for comparison.

Attribute Mean Minimum Maximum

All WLS

Mean 

(median)

All WLS

1st Quartile

WLS 

Southern 

Rockies

Mean 

(median)

WLS 

Southern 

Rockies

1st

Quartile

Elevation (m) 3260 1921 4000 2466(2613) 1746 3164(3264) 2659

Temperature 

( ̊C) 9.5 1.7 19

-- -- -- --

ANC (μeqL-1) 87.3 -3 1200 328(119) 54.5 598(317) 113.9

pH 6.7 5.7 8.3 7.3(7.2) 6.8 7.7(7.6) 7.1

Conductivity 

(μScm-1

) 21.0 2.0 342.0 38.0(16.5) 8.4 70.4(37.1) 14.2

Ca2+ (mg L-1) 2.7 0.1 44.0 4.9(1.9) 0.9 10.2(4.7) 1.6

NH4

+ (mg L-1

) 0.03 <DL 0.6 0.01(0.0) 0.00 0.01(0.0) 0.00

NO3

- (mg L-1

) 0.5 <DL 7.4 0.1(0.02) 0.01 0.1(0.03) 0.01

SO4

2- (mg L-1

) 2.7 0.2 109.0 2.2(0.9) 0.3 5.8(1.7) 0.8

Cl- (mg L-1) 0.1 0.03 1.2 0.4(0.2) 0.1 0.5(0.2) 0.1
56

As stated above, a spatial comparison of solutes is valuable for comparing chemicals deposited 

in atmospheric deposition, namely NO3

-

and NH4

+

. The mean concentration for all WLS lakes 

and Southern Rockies region of WLS lakes is 0.1 and 0.01 mg L-1 for NO3

-

and NH4

+

, 

respectively. The median and 1stquartile concentrations for NO3

- were 0.03 and 0.01 mg L-1 for 

the Southern Rockies, while the median and 1st quartile concentrations for NH4

+ were 0.01 and 

0.00 mg L-1. The MDC therefore is a value ≤0.1 mg L-1 for NO3

- and ≤0.01 mg L-1 for NH4+

. 

In a comparison of median lake concentrations with wet atmospheric N deposition values from 

the year 2000 (NADP; http://nadp.sws.uiuc.edu/), one can see that high lake NO3

-

concentrations 

often, but not always, co-occur with the highest wet N deposition (Figure 21 top). The highest N 

deposition occurred in Colorado at Front Range NADP sites located near Indian Peaks, Mount 

Evans, ROMO, Rawah, as well as La Garita, South San Juan, and Mount Zirkel wildernesses. 

The highest median lake NO3

-

concentrations occurred in the Rawah, ROMO, Indian Peaks, 

Absaroka, Maroon Bells, Mount Evans, La Garita, and High Uintahs. Caution needs to be taken 

in interpreting wet deposition data from CO02, Niwot Saddle, since an overcatch problem from 

wind-blown snow produces abnormally high values (Williams et al. 1998). 

Sampled wilderness lakes with the lowest median lake SO4

2- concentrations are among the sites 

that also have the lowest wet SO4

2- deposition values, but low wet SO4 deposition values also 

occur where the median lake SO4

2- concentrations are somewhat higher (Figure 21 bottom). The 

highest lake SO4

2- concentrations occurred in the Holy Cross, Weimenuche, Sangre de Cristo, 

Collegiate Peaks, and Sawtooth Wildernesses samples, where the values may reflect sulfurbearing minerals in the bedrock. With the exception of deposition values from CO02, Niwot 

Saddle, for the Indian Peaks and Mt. Evans Wildernesses, which may be abnormally high 

(Williams et al. 1998), wet SO4

2- deposition seems to be uniformly low with values less than 2.0 

kgha-1

yr-1, or moderate, with values around 6.0 kgha-1yr-1. 

We suggest caution in interpreting these figures. The density of NADP sites in the western U.S. 

is low, and most NADP locations are lower in elevation than alpine lakes. Weather patterns may 

differ between high and low elevations (Weathers et al. 2006) And even though lakes may be 

above treeline, if they are surrounded by tundra or wetland vegetation terrestrial N uptake may 

limit the movement of N from deposition to lakes (Sickman et al. 2002). 
57

Figure 21. a) Wet N deposition (light blue) in kgha-1yr-1for 2000, and median lake NO3

-

concentrations in 

mg L-1 (darker blue); b) Wet SO4

2-

deposition (pink) in kgha-1yr-1 for 2000, and median lake SO4

2-

concentrations mg L-1 (orange), for sampled wilderness and park lakes of the U.S. Rocky Mountains. 

Deposition values were taken from the closest NADP site.
58

Comparison of ROMO lake sample with Colorado Wilderness Area sampled lakes

Even when compared with other Colorado lakes, ROMO lakes stand out as colder and more 

dilute (Figure 22). The lowest median pH (most acidic) and the second lowest ANC values were 

found in ROMO lakes. The concentrations of Ca2+ and Cl- were low in ROMO lakes and the 

median sulfate concentrations were similar to most of the other lakes sampled. Median NO3

-

concentrations in the park were among the highest third of the ranked areas; wilderness areas 

with similar or higher nitrate concentrations included Rawah, Indian Peaks, Mount Evans, 

Maroon Bells, and La Garita. Median NH4

+ concentrations were low, but measurable, in ROMO, 

unlike nine other wilderness areas that had values below detection limits. 

The ranges of solute concentrations in ROMO lakes were narrower than those from many of the 

wilderness areas, suggesting that the ROMO lakes included in our sample were located on 

similar parent material and subject to similar atmospheric inputs. The wilderness areas vary 

greatly in size and the different mountain ranges of Colorado have many different types of 

bedrock materials; this is reflected in the range of concentrations especially in weathering 

products such as sulfate and calcium. 
59

Figure 22. Ranked distributions from lowest to highest 

values of temperature and solutes from ROMO and 

Colorado Wilderness Areas. Blue bars delineate 

maximum and minimum values; horizontal lines are the 

median value of all samples. ROMO is distinguished by 

a yellow circle for the median. Temperature units are oC, 

conductivity is in μS cm-1

, pH is in pH units, acid 

neutralizing capacity is in μeq L-1

, and all other values 

are in mg L-1

.
60

East vs. West ROMO Comparison

Within ROMO 25 lakes were sampled east of the continental divide and 7 on the west side 

(Table 20). ANC was about 30 μeq L-1 lower in the west side sites, while pH was about 0.4 units 

higher. Calcium, Cl-, and NH4

+ were similar in sampled lakes from east and west sides;

conductivity was slightly higher in west-side lakes; and NO3

- was 0.5 mg L-1 higher in east-side 

lakes. The values for NO3

- and NH4+ defined above suggest that east side lakes have 

concentrations greater than the MDC, while the west side lakes can be classified as in their 

MDC. Knowing that there is a history of elevated atmospheric N deposition to the east side lakes 

(Baron et al. 2000), we infer that ANC and pH have been reduced by inputs on the east side, but 

since historical reconstructions of regional SO4

2-

deposition, described below, would have 

affected both east and west side ROMO lakes, it is not possible to simply use the east-west 

comparison for defining and ANC or pH MDC. 

Table 20. Summary Statistics for ROMO Sampled Lakes East and West of Continental Divide. ANC is in 

μeq L-1

, Cond is conductivity, μS cm-1, pH is in pH units and all other solutes are reported in mg L-1.

ANC Ca2+ Cl- Cond NH4

+ NO3- pH SO42-

East

mean 

(stdev)

49.7 

(21.2)

1.2

(0.6)

0.08

(0.05)

9.8

(2.8)

0.01

(0.01)

0.57

(0.33)

6.6

(0.3)

1.3

(0.7)

West 

mean 

(stdev)

80.3

(23.4)

1.7

(0.7)

0.06

(0.01)

13.0

(3.3)

0.01

(0.025)

0.06

(0.08)

7.0

(0.2)

1.5

(0.9)

East 

median 44.2 1.1 0.07 9.1 0.01 0.54 6.6 1.2

West 

median 84.1 1.5 0.05 12.6 0.009 0.00 7.0 1.0

Median concentrations of ANC and NO3

- for each Colorado wilderness area and ROMO show 

geographic differences reflective of atmospheric deposition, and bedrock composition (Figure 

23). We plotted median values for east and west sides of all the wilderness areas that are bisected 

by the continental divide to see whether similar patterns existed outside of ROMO. In addition to 

ROMO, lakes were sampled on east and west sides of the Mount Zirkel, Indian Peaks, 

Weimenuche, and Holy Cross wilderness areas. 
61

Figure 23. Median concentrations of ANC and NO3

-

from wilderness and national park lakes. Larger 

symbols depict higher concentration values. Medians were calculated across all sites in a park or 

wilderness boundary.
62

The lowest median ANC values (20-49 ueqL-1) were found in sampled lakes from the east side of 

ROMO and on both sides of the divide in the Weimenuche. Other than ROMO, however, there 

was no difference in ANC based on location east or west of the continental divide. Higher NO3

-

concentrations, however, appeared to be geographically clustered closer to source areas. The 

highest sampled median lake NO3

-

concentrations occurred in the Colorado Front Range east of 

the continental divide in ROMO, Indian Peaks, and Mount Evans wilderness areas, as well as 

Maroon Bells-Snowmass and La Garita wilderness areas. The lowest (<0.1 mgL-1) median NO3

-

concentrations in lakes of Mt Zirkel (east and west), South San Juan, Collegiate Peaks, Holy 

Cross (east), Weimenuche (east), and the west side of ROMO indicate the end-member median 

reference condition for alpine lakes is ≤0.1 mg NO3

-

L-1. These data again suggest the MDC value 

is ≤0.1 mg NO3

-

L-1.

Organic Contaminants and Metals

Measurable concentrations of manufactured contaminants, by definition, represent deviation 

away from the Historic Condition (HC), since they represent pollutants absent in natural

conditions. The Western Area Contaminant Assessment Project (Landers et al. 2007) measured 

contaminant levels in select sites in western national parks, including Rocky Mountain, and

compared concentrations across parks and within parks geographically and by elevation. The 

concentrations of contaminants in snow, air, vegetation, fish, and lake sediments were measured 

near or in one east-side lake (Mills Lake) and one west-side lake (Lone Pine). Endosulfans, 

dacthal, and mercury fluxes to snow were higher in ROMO than other western parks. The flux of 

dieldrin to snow was also higher at Mills Lake than elsewhere, possibly reflecting re-emission 

from contaminated soils in Denver where it used to be manufactured (Usenko et al. 2007). 

An MDC for mercury was assessed by Yeardley et al. (1998) for 167 lakes in the northeastern 

U.S. These authors related the concentrations of methylmercury (MeHg) in fish tissue to critical 

loads for piscivorous birds and mammals, as well as to the fish themselves. Critical loads were 

defined from the literature as tissue concentrations that begin to pose a consumption risk. The 

human health critical value was 0.2 μg g-1for MeHg. A critical value of 0.1 μg g-1implies a risk to 

piscivorous mammalian wildlife populations, 0.02 μg g-1 for MeHg implies a risk to piscivorous 

avian populations (Yeardley et al. 1988). Based on the most conservative estimate 0.02 μg g-1

MeHG, fish tissue mercury concentrations in ROMO that range from 0.03 to 1.10 μg g-1

are 

greater than the minimally disturbed condition, the MDC. Mercury concentrations were higher in 

older than in younger fish in both Mills and Lone Pine Lakes, and endosulfans and dacthal were 

fairly high in fish tissue. Mercury concentrations exceeded contaminant health thresholds for 

otter, mink, and kingfishers in both lakes. Methymercury availability will vary between lakes 

depending on the surrounding landscape, so more sampling is necessary in order to evaluate the 

consumption risk from fish to humans and wildlife parkwide, but the measured concentrations 

from Mills and Lone Pine Lakes serve as a warning. Evidence of endocrine and reproductive 

disruption in several park lakes is present. Estrogen-responsive proteins were found in male trout 

from four out of nine lakes, and poorly developed testes and/or intersex male trout were also 

found in five out of nine lakes. Dieldrin in all fish exceeded contaminant health thresholds for 

recreational fishermen in some lakes. 

Historical Reconstructions of Acid Neutralizing Capacity

DayCent-Chem model results since 1900 in Andrews Creek in Loch Vale Watershed on the east 

side of the park suggest ANC has fluctuated with time in response to increases and decreases in 
63

sulfate deposition (Figure 24). The simulations suggest ANC was about 65 μeq L-1for this alpine 

stream in 1900 and declined to its lowest value of less than 20 μeq L-1 corresponding with the 

peak SO4

2- deposition in 1969, and began to recover after that, coincident with the 

implementation of the Clean Air Act of 1970. Peak N deposition amounts occurred in 1994-

1995, coincident with another decline in ANC. Model results suggest there has been a loss of 

approximately 26 μeq L-1 of ANC from Andrews Creek since 1900. This value is similar to that 

proposed by Gibson et al. (1983), who calculated the loss of ANC in Rocky Mountain National 

Park of about 18.5 μeq L-1 based on the difference between estimated ANC from base cation 

concentrations and measured values from a sample for 40 lakes in the park. While one MDC 

value for ANC cannot be set for ROMO, since ANC varies due to many factors, it appears the 

current ANC values are lower than the MDC by approximately 18-26 μeq L-1

. 

Figure 24. Modeled changes in Andrews Creek Acid Neutralizing Capacity (ANC, μeq L-1) over time using 

the DayCent-Chem model and reconstructed wet plus dry N and S deposition values.

Uses and Limitations

Alpine lakes in protected national parks and wilderness areas should represent some of the least 

disturbed environments in the world. Anthropogenic disturbance has three likely sources: 

atmospheric deposition, climate change, or direct human manipulation, such as by introducing or 

removing species. Our evaluation of water quality for ROMO lakes suggests some chemicals 

have exceeded the minimally disturbed condition, the MDC. For some others, we were unable to 

determine whether their concentrations have changed with time; the values reported here can be 

considered a starting point from which to evaluate and interpret future concentrations. All of the 

inorganic values reported here are well below water quality guidelines for drinking water 

standards, and their concentrations are addressed in terms of their possible influence on nonhuman organisms such as algae, zooplankton, and fish. 

Trends in Atmospheric Deposition

Atmospheric deposition is a major driver of environmental change in ROMO, warranting 

continued monitoring and assessment of its constituents. There has been a decrease in wet and 
64

dry deposition of SO4

2- over time, and a corresponding decrease in precipitation acidity. This 

indicates a reduced risk to park biota from acid rain. An increase in Ca2+ deposition at both 

Beaver Meadows and Loch Vale may also have contributed to the decreased annual acidity 

trend. Calcium is an indicator of dust, and evaluation of Ca2+ concentrations over time can be 

indicative of changing climates or land use change. 

Inorganic N, and its components NO3

- and NH4+

, has increased over time at both precipitation 

monitoring sites, and has been strongly related to ecological and water quality changes in 

ROMO. The Colorado Nitrogen Deposition Reduction Plan calls for continued monitoring in 

order to evaluate whether wet N deposition is decreasing according to the glidepath toward 

achieving deposition at or below the critical load of 1.5 kg N ha-1 yr-1 by 2035. 

Potential for acidification from atmospheric deposition

ANC values are lower than the MDC by approximately 18-26 μeq L-1. The DayCent-Chem 

model suggested pH declined from 6.9 in 1900 to 6.6 100 years later (data not shown). From the 

lake survey results, east-side lakes had an average pH of 6.6, while the west-side lakes averaged 

pH 7.0, similar to the modeled results. While confidence in the absolute loss of ANC and pH 

from east-side lakes is lower than if a decline in values over time had been measured, it appears 

there has been a reduction in buffering from acidic deposition over time. Research and policy 

recommendations from the National Acid Precipitation Assessment Program use the following 

guidelines for interpreting levels of ANC and pH (Charles 1991):

• ANC of 50-100 ueq/L, considered acid sensitive but capable of supporting healthy biota

• ANC < 50 ueq/L, sensitive to acidification, potential effects to biota

• ANC < 20 ueq/L, can be episodically acidic, sub-lethal or lethal affects on biota

• ANC < 0 ueq/L, can be chronically acidic, lethal effects on many biota.

A number of species, especially trout and macro-invertebrates, cannot survive, reproduce or 

compete in acidic waters (harmful effects can begin at pH < 6).

ROMO lakes currently have ANC values below 100 μeq L-1

, and east-side lakes have mean 

values of 50 μeq L-1

. ANC values should be monitored in conjunction with atmospheric 

deposition chemistry, because the granitic bedrock that underlies the park, combined with the 

modeled historic loss already of buffering capacity, suggested east-side lakes are close to ANC 

values below which there could be episodic acidification and lethal or sublethal effects to fish or 

invertebrates. 

Potential for eutrophication from atmospheric N deposition

The regional comparison of lake chemistries suggests the reference condition for NO3

-

was ≤0.1 

mg L-1

. Nitrate concentrations were higher than these values in east-side sampled ROMO lakes 

(mean NO3

-

= 0.57; median NO3

-

=0.54), compared with west-side lake concentrations (mean 

NO3

-

=0.03; median NO3

-

=0.00). The spatial comparison is in keeping with nitrogen saturation 

theory proposed by Stoddard in 1994 that suggested no measurable stream or lake NO3

-

should be 

present where atmospheric N deposition was not elevated. Bergström et al. (2005) and 

Bergström and Janssen (2006) also found that lakes receiving extremely low N deposition had 

<0.024 mg L-1 NO3

-

, whereas there was a direct and strong correlation of increasing NO3

-

concentrations in lakes receiving increasing N deposition in Europe and North America. Studies 
65

by Elser et al. (2009a, 2009b), which included lakes in ROMO, found a clear relation between 

atmospheric N deposition and the nitrogen to phosphorus ratio. Increasing N deposition leads to 

increased lake NO3

- and algal assemblages that thrive on high N availability. These phosphorus 

deficient algae are poor food quality for zooplankton, and may eventually alter nutritional 

dynamics up the food chain to fish (Elser et al. 2009a, 2009b). Research in ROMO has already 

established a switch of algal assemblages toward dominance by disturbance species that thrive in 

high N waters (Baron et al. 2000, Baron 2006); research into whether zooplankton populations 

and assemblages have indeed been altered, and research into whether algal diversity has 

decreased as a result if increased N in lakes are needed. 

If the reference condition for NO3

-

is ≤0.1 mg L-1, then select lakes on the east side of the Front 

Range in ROMO, Mount Evans, and Indian Peaks have values that are in excess of the reference, 

as do lake chemistries from several other sites in wilderness areas in Colorado, Utah, and Idaho. 

Particularly for the Front Range lakes, this is consistent with recent studies correlating proximity 

to source areas for N emissions with elevated lake nitrate concentrations (Baron et al. 2000, 

Baron et al. 2004, Burns 2004, Nanus et al. 2008), and with a hindcasting exercise that showed 

increasing wet N deposition corresponded with increased algal productivity and species 

dominance in east-side ROMO lakes (Baron 2006). 

Changes in weathering products with time

Sulfate, chloride, and calcium are chemical species that can weather from bedrock and also have 

an atmospheric deposition source. Conductivity is a good general measure of how dilute or 

concentrated water are. Changes in values of these solutes over time, if correlated with changes 

in measured atmospheric precipitation, can indicate changes in inputs of pollutants such as SOx, 

or dust. Increased concentrations can also indicate glacier melt, and a recent study in Loch Vale, 

as well as other mountain regions, reported increased weathering products from recent warm 

summer temperatures (Baron et al. 2009). For all these compounds, time series, either in current 

monitoring programs or from paleolimnological reconstructions, may be the only way to 

determine if concentrations are changing. Except for increases in SO4

2-

, which leads to 

acidification, effects of increasing concentrations of other salts are not likely to alter lake biota. 

Perhaps the most important reason to monitor these chemicals is as indication of other regional 

or global changes occurring from land use, climate change, or emissions of pollutants. 

Contaminants

Soluble organic chemicals and heavy metals derived from agricultural and industrial practices 

were not present in Rocky Mountain waters prior to their societal applications (Table 21). For

these atmospherically-deposited compounds, any measurable concentration represents a 

deviation away from the historic condition, the HC. The WACAP study provided a comparison 

of contaminant concentrations across select lakes in western national parks, and ROMO has 

lower concentrations of some elements than other park lakes, and higher concentrations of others

(Landers et al. 2007, Usenko et al. 2007). As with nitrate, higher concentrations of mercury and 

dieldrin were found in east-side sites than west-side, reflecting east-side source areas. A 

conclusion of the WACAP study was that total soluble organic chemical concentrations in snow 

were highest in the Rocky Mountain parks of ROMO and Glacier, and Sequoia NP, and these 

parks also had higher concentrations of contaminants in fish tissue and lake sediments. Some of 

these concentrations exceed critical values for wildlife and piscivorous birds. Evidence of 

endocrine and reproductive dysfunction in fish and harmful concentrations of dieldrin are cause 
66

for continued monitoring of contaminants and their toxicological effects. Monitoring, too, will be 

important for providing public notice of health hazards. 

Table 21. Water Quality Thresholds (see text for full explanation)

Measure

Minimally 

Disturbed (MDC) 

or Historic (HC) 

Condition

Current Mean 

ROMO Condition

Data 

Quality Confidence Level of Concern

Lake pH 6.5-7.5 6.6 (east)

7.0 (west)

Good Moderate High

Lake ANC (μeq/L) 80-100 50 (east)

80 (west)

Good Moderate High

Lake ammonium (mg L-1) Below detection 

limits

0.01 (east)

0.01 (west)

Good Moderate High

Lake nitrate (mg L-1) ≤0.1 0.57 (east)

0.06 (west)

Good High High

Lake sulfate (mg L-1) 0.2-109.0 (all lakes) 1.3 (east)

1.5 (west)

Good High High

Lake chloride (mg L-1) 0.03-1.16 (all lakes) 0.08 (east)

0.06 (west)

Good Moderate Low

Lake calcium (mg L-1) 0.1-43.9 1.20 (east)

1.65 (west)

Good Moderate Low

Lake conductivity (μS/cm) 2.2-341.8 9.8 (east)

13.0 (west)

Good High Low

Fish Mercury (μg g-1) <0.01 fish 0.03-0.09 (east)

0.04-1.10 (west)

Good High High

Dieldrin (ng g-1) 0.0 snow; fish >1.0 snow Good High High

DDT (ng g-1) 0.0 fish ≥4.0 fish Good High High

Overall our general estimate is that this is an area of high concern. Our level of confidence for 

this indicator is high amount of agreement and moderate amount of evidence. We rate it this way 

because there is strong literature and data support for cause and effect relationships between lake 

chemistry and atmospheric deposition.
67

Summary: Air and climate – Condition of Alpine Lakes and Atmospheric Deposition

What: Measures atmospheric deposition of nutrients and pollutants in high elevation lakes.

Why: High elevation lakes are bellwethers of ecological change and the extent of human-caused disturbance.

Stressors: Land use change, climate change

Confidence: High degree of concern; moderate evidence, high agreement

Atmospherically-deposited nitrogen has caused east-side ROMO lakes to have higher than background nitrate concentrations and 

lower than expected acid-neutralizing capacity (ANC). East-side concentrations of dieldrin and DDT are also elevated, while westside mercury in fish tissue is at or above concentrations that are harmful for some birds and mammals. West-side nitrate and ANC are 

consistent with reference conditions, and lake pH and chemical concentrations of calcium, chloride, sulfate, ammonium for all ROMO 

lakes are consistent with reference conditions. 

*Additional data sources: Western Airborne Contaminants Study 

(WACAP 2008).

Measure Reference 

Condition

Current condition Confidence Level of 

Concern

Lake pH 6.5-7.5 6.6 (east)

7.0 (west)

Moderate High

Lake ANC 80-100 μeq L-1 50 μeq L-1(east)

80 μeq L-1(west)

Moderate High

Lake 

ammonium

Below detection 

limits

0.01 mg L-1 (east)

0.01 mg L-1 (west)

Moderate High

Lake nitrate ≤0.1 mg L-1 0.57 mg L-1 (east)

0.06 mg L-1 (west)

High High

Lake sulfate 0.2-109.0 mg L-1

(all lakes)

1.3 mg L-1 (east)

1.5 mg L-1 (west)

High High

Lake chloride 0.03-1.16 mg L-1

(all lakes)

0.08 mg L-1 (east)

0.06 mg L-1 (west)

Moderate Low

Lake calcium 0.1-43.9 mg L-1 1.20 mg L-1 (east)

1.65 mg L-1 (west)

Moderate Low

Lake 

conductivity

2.2-341.8 μS cm-1 9.8 μS cm-1 (east)

13.0 μS cm-1 (west)

High Low

Fish Mercury <0.01μg g-1 0.03-0.09 μg g-1 (east)

0.04-1.10 μg g-1 (west)

High High

Dieldrin 0.0 ng g-1 snow >1.0 ng g-1 snow High High

DDT 0.0 ng g-1 fish ≥4.0 ng g-1 fish High High
68

4.2.2 Water: Extent and Connectivity of Wetland and Riparian Areas

Authors: David M. Theobald and John B. Norman, III

Description/Purpose

This indicator measures the extent and connectivity of riparian areas and wetlands along the 

hydrologic network of the park. Fragmentation of habitat in terrestrial systems is widely 

recognized to be one of the primary aspects of change to natural systems. Fragmentation also 

occurs in freshwater systems, and parallels between terrestrial and freshwater systems recently 

have been drawn (Wiens 2002). Here we are particularly concerned about fragmentation and 

connectivity longitudinally (up and down along the stream network), although there can also be 

lateral and vertical fragmentation as well. 

The modification of hydrologic regimes by dams has been studied in particular, and Poff et al. 

(2007) found that regional homogenization can occur through modification of both magnitude 

and timing of both high and low flows that are a critical characteristic of natural river systems. 

Human-made impoundments (reservoirs and ponds) have also been shown to harbor invasive 

aquatic species and serve as “stepping stones” for the spread of exotic organisms (Johnson et al. 

2008). The crossing of streams with roads is another commonly used indicator of likely 

fragmentation or alteration of flow regimes, though we did not include these in our analyses 

because we lacked detailed data about the location and type of bridges and culverts, which is 

needed to conduct a more comprehensive analysis (e.g., USDA 2008).

Within the park, there are a number of natural barriers, such as waterfalls, which affect the 

movement of aquatic organisms, such as the upstream movement of fish. These natural barriers 

have been important in protecting upstream organisms from exotic fish encroaching from below. 

Also, there are a number of introduced human modifications—including a number of ditches and 

dams. These barriers in the park alter hydrologic flows and likely fragment the freshwater 

systems along the streams as well as wetland and riparian communities (Table 22; Woods 2000). 

Table 22. Barrier features that were identified by park resource managers that alter movement and 

connectivity along the freshwater system in the park.

Source HUC 8 basin Features

Natural barriers

Colorado River Granite Falls, Cascade Falls (Tonahutu Creek)

Adams Falls, unnamed Falls (East Inlet)

Big Thompson

Lost Falls (North Fork)

Chasm Falls & unnamed Falls (Fall River)

Fern, Marguerite, and Grace Falls (Fern Lake Canyon)

Alberta, Glacier, and Ribbon Falls (Glacier Creek) 

St. Vrain Columbine, Copeland, and Ouzel Falls and Calypso 

Cascades

Human-made 

barriers

Colorado River Grand & Specimen Ditches (crosses 8 streams)

Eureka Ditch (restored), Big Meadows (restored)

Big Thompson

Lily and Sprague Lakes (dam)

Lawn Lake (restored)

McGraw Ranch, Moraine Park (ditch, restored)

St. Vrain Inlet Ditch to Copeland Lake (dam)

Bluebird, Pear, and Sandbeach Lakes (restored)
69

Water diversions also negatively affect willow seedling establishment in riparian areas of ROMO 

(Woods and Cooper 2005). In addition, wetlands adjacent to alluvial fans are vulnerable to 

upstream water diversions, such as the Grand Ditch that divert water from Lost Creek (Figure 25;

Woods et al. 2006) and other tributaries that often impact fens below. As an example of these 

fragmenting features, Cooper et al. (1998) documented the modification of the hydrologic regime 

from a ditch and the subsequent restoration of Big Meadows fen along the Tonahutu Creek.

Figure 25. Aerial photograph of Woods et al. (2006) Lost Creek study site. Stream-gauging stations are 

depicted by Q1, Q2, and Q3 (triangles), springs S1 and S2, monitoring wells and piezometer nests 1 to 

18 (black circles), piezometers A and B (white circles), and the dam constructed in 1998 to divert water 

into the south channel in 1998 (diamond). The solid white line is the wetland boundary, and the dashed 

lines indicate the north and south channels of Lost Creek. Arrows indicate the direction of stream flow in 

Lost Creek and the Colorado River. The beaver dam in the Colorado River east of the wetland was not 

present during the study.

Approach/Methods

Our overall approach for this indicator was to identify riparian vegetation zones along the 

hydrology network, and then to identify features that are considered barriers to movement 

(connectivity) along the freshwater system. These included both natural and human-made 

barriers. We identified the location of riparian or wetland features by finding patches of 

vegetation from those cover types identified as either riparian or wetland in the ROMO 
70

vegetation dataset (Table 23; Figure 26; Salas et al. 2005). We then removed those patches that 

did not intersect or touch the 1:24K hydrology stream network, so as a result most fens were not 

included in this analysis. Our attention towards those patches that lie along the hydrologic 

network reflects our focus on surface water connectivity; although there are many wetlands that 

are also strongly influenced by groundwater conditions, we excluded these from our analysis. 

Also note that there is an ongoing inventory of wetlands in the park (D. Cooper, personal 

communication), but these data were not available in time for this study.

Table 23. Land cover types identified as wetland and/or riparian types in the ROMO vegetation map 

(Salas et al. 2005).

Riparian 

Cover 

Class

COMMON_MAP classes Total area 

(ha)

Riparian 

area (ha)

Alpine 

Wetland Herbaceous Wetland Sub-alpine / Alpine – Meadow (100%) 8654 6804

Cottonwood Cottonwood (100%) 31 28

Herbaceous 

Wetland

Herbaceous Wetland Cross Zone – Marsh (2%)

Herbaceous Wetland Cross Zone – Wetland (98%) 2229 2852

Riparian 

Aspen Riparian Aspen (100%) 370 293

Riparian 

Mixed 

Conifer

Riparian Lower Montane Mixed Conifer < 8500 ft (10%)

Riparian Lower Montane Mixed Conifer > 8500 ft (85%)

Blue Spruce (5%) 3939 3592

Shrub 

Riparian

Shrub Riparian Cross Zone < 9600 ft (45%)

Shrub Riparian Cross Zone > 9600 ft (55%) 2906 2607

Shrub 

Upland 

Alpine

Shrub Upland Alpine (100%) 1838 1374
71

Figure 26. Vegetation types identified as wetland and/or riparian in the ROMO vegetation dataset. These 

exclude fens and other groundwater-dominated wetlands.
72

We generated a network representation of the hydrologic network in GIS, using the Functional 

Linkage of Watersheds and Streams (FLoWS) tools (Theobald et al. 2005). We used the 1:24K

blue-line hydrology (updated to include recent modifications to the location of streams and small 

ponds), and processed the GIS data so that stream lines were topologically correct, enabling 

routing of flow along the network. See Technical Appendix 2 for a more detailed description of 

the methods.

We computed two types of measures of freshwater connectivity. First, the patch area was 

computed for each stream reach and then accumulated downstream. Downstream accumulation 

means that the a stream reach is followed to the next reach downstream that it connects with, and 

the various values, such as area of riparian vegetation, is then summed as one works down the 

network. If a barrier was encountered, then the accumulated area was reset to 0. High values 

indicate large patches of wetland/riparian areas that are longitudinally connected. Low values 

indicate small patches and/or disconnected (fragmented) riparian corridors. Second, we 

computed a measure that quantifies aspects of the sub-networks that form between barriers. Subnetworks are the pieces of the full hydrologic network that are created when barriers “cut” them 

into smaller networks that are connected within a sub-network, but isolated from other subnetworks. This measure differs from the downstream accumulation measure in that it 

characterizes connectivity without regard to direction (i.e. upstream or downstream), and so 

might be a better measure for understanding connectivity from a mobile organism perspective 

(e.g., a fish or beaver that can swim up or down stream). For each sub-network, the total stream 

length (km), the total wetland/riparian area (ha), and the average wetland/riparian area (ha per 

stream km) was computed. Natural barriers were assumed to disrupt upstream movement of fish 

in particular, but were assumed to not modify significantly downstream movement of water flow 

or fish. Human barriers were assumed to strongly disrupt (break) both upstream and modify 

downstream flow of water.

Reference Condition

There are two measures associated with this indicator: 1) average connected stream length, and 

2) average area of connected riparian/wetlands. The reference condition for these measures is 

based on “minimally disturbed condition” (MDC); that is, streams without human-made barriers. 

It is established based on interpretation of historic condition—in this case, the absence of humanmade barriers. To compare the values of these measures, we developed a series of scenarios that 

allow us to make inferences. To do this, we examined how the connectivity of the riparian and 

wetland system has likely changed over the time that constitutes our three scenarios: “natural” 

(to approximate natural conditions), 1900-1990s (historic), and current. 

The “natural” connectivity scenario assumes that the natural features that cause barriers to fish 

movement (upstream only) are included, but no other human-made features such as dams or 

ditches interrupt the connectivity of the freshwater system. The “historic” scenario (1900-1999) 

approximates the situation where ditches and diversions were at their maximal extent. This 

hydrological system includes the Grand Ditch. It also includes the Eureka Ditch, which diverted 

water from Tonahutu Creek on the west slope to Spruce Creek on the eastside (this was filled in 

1996). Harbison Ditch diverts water from Tonahutu Creek into Columbine Lake and is still 

operating. A few miscellaneous irrigation/drainage ditches include: Big Meadows (west side) 

and Beaver Meadows (east side), and Moraine Park (eastside); Holzwarth Ranch (diversion gate 
73

next to bridge that crosses the Colorado River); McGraw Ranch Ditch from Cow Creek to small 

pond. Dams that impound water include Lily, Sprague, and Copeland Lakes; Bluebird, 

Sandbeach, Pear, and Lawn Lake have been restored back to “natural” lake levels. The “current” 

connectivity scenario includes all natural barriers, but incorporates the removal of some humanmade barriers, notably the Big Meadows, Eureka, and Upper Beaver Meadows ditches, and as 

outlined in Table 24. 

Table 24. Three scenarios reflecting natural, historic, and current conditions that affect connectivity 

between wetland and riparian areas along stream corridors.

Scenario Hydrologic Features

“Natural” • Natural hydrology in 1:24K NHD

• Natural barriers

Historic

(1900 – 1990)

• All hydrological features in 1:24K NHD

• Natural barriers 

• Ditches: Grand, Eureka, Harbison, Big Meadows (east & west), Holzworth 

Ranch, Cow Creek

• Dams: Lily, Sprague Copeland, Bluebird, Sandbeach, Pear, Lawn

Current

• All hydrological features in 1:24K NHD

• Natural barriers 

• Ditches: Grand, Harbison, Cow Creek

• Dams: Lily, Sprague Copeland

Note that we are using the same vegetation map for all three scenarios, so we are unable to 

include differences between historical and current extent and location of wetlands and riparian 

areas.

Results/Discussion

Under “natural” conditions, the major expanses of connected wetland/riparian vegetation 

included: Kawuneeche Valley (Colorado River) and East Inlet on the west side of the park and 

Forest Canyon (along the Big Thompson River), Fall River, and Cache la Poudre/Hague Creek 

on the eastside (Figure 27). The numerous different watersheds and natural barriers along 

streams generate 94 unique sub-networks of streams (Table 25).

Table 25. Results for the connected hydrologic sub-networks, created by human-barriers fragmenting the 

network (and incorporating natural barriers as well), averaged by 12 digit HUC for the “natural”, historic, 

and current scenarios. The average wetland/riparian density is a measure of the sum of the riparian 

patches that are connected, divided by the stream length (km) that connect each sub-network.

Scenario No. sub-networks Average stream 

length (km)

Average 

wetland/riparian 

area (ha)

Average 

wetland/riparian 

density (ha/km)

“Natural” 94 11.4 (SD=36.6) 146.6 (SD=410.2) 11.7 (SD=17.8)

Historic 123 8.7 (SD=22.3) 112.1 (SD=288.6) 11.5 (SD=19.7)

Current 111 9.6 (SD=26.2) 124.2 (SD=322.6) 11.5 (SD=17.4)
74

Figure 27. Hectares of riparian and wetland systems accumulated downstream, reflecting the “natural” 

scenario.
75

Disruptions in connectivity due to human-made barriers/diversions occurred in the 1900s-1990s, 

from the Grand Ditch as well as low in the Kawuneeche Valley (near Bowen Gulch), Tonahutu 

Creek, and the Poudre/Hague Creek in the north part of the park and along the North St. Vrain 

(Figure 28). This resulted in 123 sub-networks (Table 25), but more importantly caused a 25% 

reduction in both the average stream length of connected sub-networks and the average 

riparian/wetland area that was connected—from 147 to 112 ha.

The current scenario shows some improvement in the connectivity of the freshwater system (

Figure 29 29). More recently (2009), with a number of restoration activities that have taken place 

in the park, additional improvements in the connectivity of the freshwater system have been 

made, particularly as the major disruption along the Kawuneeche Valley was removed, as well as 

on Tonahutu Creek. Both the average stream length (8.7 to 9.6 km) and average wetland/riparian 

area connected (112.1 to 124.2 ha) have increased. 

Figure 30 shows a comparison of the change in connectivity between the “natural” scenario and 

the historic scenario and the change in connectivity between the “natural” scenario and the 

current scenario. This shows that there have been large increases in connectivity as a result of the 

restoration activities reflected in the current scenario. However, high elevation barriers along the 

Grand Ditch remain important in reducing connectivity. Also, the watershed of Tonahutu Creek 

remains fragmented. 

The most extensive riparian/wetland systems historically were along the Kawuneechee Valley 

and the Cache la Poudre River. The Kawuneechee system remains highly modified, with seven 

tributaries having flow modified by ditch diversions.
76

Figure 28. Hectares of riparian and wetland systems accumulated downstream, reflecting the historic

(1900-1990s) scenario. Natural barriers that disrupt downstream flow are shown as black dots; humanmade barriers are shown as red crosses.
77

Figure 29. Area of riparian and wetland systems accumulated downstream, reflecting the current 

scenario. Natural barriers that disrupt downstream flow are shown as black dots, human-made barriers 

are red crosses and barriers that have been removed are hollow crosses.
78

Figure 30. Change in connected stream systems between “natural” scenario and historic scenario, and 

between “natural” scenario and current scenario.

The most extensive riparian/wetland systems historically were along the Kawuneechee Valley 

and the Cache la Poudre River. The Kawuneechee system remains highly modified, with seven 

tributaries having flow modified by ditch diversions. The flow modifications along the Cache la 

Poudre system occur lower in the hydrological system, near the park boundary.

We also provide cumulative distribution frequency graphs of these measures to compare the 

scenarios (Figure 31).

Figure 31. Graphs of cumulative frequency functions for total stream length (left), total riparian/wetland 

area (center), and average riparian/wetland area (right). This shows that roughly 80% of stream segments 

are about 10 km or less.
79

Figure 32 shows the percentage of each watershed that is in riparian/wetland vegetation. 

Figure 32. Percentage of each watershed (HUC-12) that is currently riparian or wetland vegetation.
80

Uses and Limitations

Overall, we believe that this indicator supports a high degree of concern, because of the 

importance of the riparian/wetland areas to the park, the extent and widespread disruption of the 

hydrologic processes in the park, and the adaptation of species to changing climate regimes will 

be impinged. We rank our level of confidence in this indicator as moderate/low. There is 

moderate agreement about the importance of maintaining hydrologic connection to ensure 

natural variability in the hydrograph and its importance for wetland and riparian ecosystems. 

There is a low amount of evidence. Currently there are few data points to make a comprehensive 

conclusion, but there is good field experience and guidance from experts that this is an important 

indicator.
81

Summary: Water - Extent and Connectivity of Wetland and Riparian Areas

What: Measures the extent and connectivity of riparian and wetlands 

along the hydrologic network.

Why: To understand potential impacts on riparian and wetland 

communities as a result of hydrologic fragmentation due to 

human barriers (e.g., dams, ditches)

Stressors: Infrastructure, visitor use, climate change

Confidence: High degree of concern; low evidence, moderate agreement.

Restoration of four dams and four ditches has improved the connectivity of 

riparian and wetland communities somewhat from 1900-1990s conditions, 

but current conditions (shown right) remain substantially fragmented 

compared to “natural” hydrological conditions.

Measure “Natural” Historic 

(1900-1990)

Current

Average connected stream 

length (km)

11.4

SD=36.6

8.7

SD=22.3

9.6

SD=26.2

Average connected 

riparian/wetland area (ha)

146.6

SD=410.2

112.1 

SD=288.6

124.2

SD=322.6

*As mapped in ROMO vegetation map (Salas et al. 2005) and 1:24K hydrology.
82

4.2.3 Biotic Integrity: Extent of Exotic Terrestrial Plants

Authors: David M. Theobald and John B. Norman, III

Description/Purpose

There is a growing concern in the park regarding the loss of habitat and the economic cost of 

controlling invasive exotic species of plants. ROMO estimates that it has about 423 acres (171 

hectares) covered with 29 species of invasive exotic plants (National Park Service 2009). 

There are three datasets that contain information on invasive species for ROMO. One is a 

database that contains “exotic plant locations” that has been developed by maintenance crews for 

weed control and management, and generally has information about exotics in the front country, 

along trails and roads (Figure 33).

Figure 33. Distribution of known locations of exotic species from the Weed Control Database, Rocky 

Mountain National Park.

A second spatial dataset contains data on plant species found in the park that has been compiled 

by the U.S. Geological Survey through an initiative called the National Institute of Invasive 

Species Science (NIISS; Figure 34). This collection effort has provided the basis for a number of 
83

publications about exotic species invasions, including Chong et al. (2001) and Kalkhan et al. 

(2006, 2007). It also provides general patterns of exotics in the park, but because it does not 

cover the entire park (it is limited to a narrow east-west band through the middle of the park) and 

is not based on a probabilistic sample design, it does not provide a rigorous basis to establish 

trends or estimate populations of exotic plant species across the park.

Figure 34. Distribution of known locations of exotic species from the USGS NIISS database.

However, in both of these datasets, there are large expanses of the park (particularly away from 

the central road corridor areas) where no data were collected. Also, because the sample design 

was not a probability-based, random sample design, it is difficult to develop rigorous estimates 

of the extent of exotic plant distribution, since the sampling effort is un-quantified. We provided 

a brief review of these datasets because they are commonly referred to by a variety of park and 

outside scientists. Also, we compared the Weed Control database to the results from the 

estimated extent and distribution of exotics to identify watersheds that might be under-sampled.

Approach/Methods

We used the 1,861 vegetation plots that were collected for the ROMO vegetation map that 

reflects conditions during 2001 (Salas et al. 2005). These locations were based on a GRADSECT 
84

(Gradient-directed transects; Gillison and Brewer 1985) sampling design that targeted natural 

vegetation, and the sampling details are provided in Salas et al. (2005). The plant species’ 

scientific and common names, as well as percent cover per sampling plot (including invasive 

exotics) were catalogued at each plot (852 records). We then reclassified each species type into 

one of two classes—exotic or non-exotic—by using the list of exotic species found in ROMO 

(Rutledge and McLendon 1996; Table 26). We then computed an overall indicator of the 

proportion (or extent) of exotic terrestrial plants, computed as the ratio of the cover of 

individuals by the total plant cover, by summarizing the percent exotic by 8, 10, and 12-digit 

HUCs. We also summarized the ROMO vegetation by the five generalized ecological system 

“zones” generated from simplifying the ROMO vegetation map: alpine tundra, upper montane, 

lower montane, savannah, and riparian ecological systems. See Technical Appendix 3 for more 

details.

Table 26. Known exotic plant species in Rocky Mountain National Park (Rutledge and McLendon 1996).

*Denotes exotics that are known to occur in the park but were not found in the vegetation plots of ROMO 

vegetation. BOLDED species names are designated as invasive species as designated by the Natural 

Resources Conservation Service (USDA 2009).

Scientific Name Common Name Scientific Name Common Name

Agropyroncristatum Crested wheatgrass Lepidiumcampestre* Field Cress

Agropyronintermedium Intermediate wheatgrass Lepidiumdensiflorum* Peppergrass

Agropyronrepens Quackgrass Lepidiumperfoliatum* Clasping pepperweed

Alopecurispratensis Meadow foxtail grass

Linariadalmatica ssp. 

dalmatica Dalmatian toadflax

Alyssum alyssoides Yellow alyssum Linariavulgaris Butter and eggs

Amaranthusretroflexus* Redroot pigweed Loliumperenne Perennial ryegrass

Ambrosia tomentosa* Skeleton Leaf Bursage Lychnis alba* White campion

Arabisglabra Tower Mustard Lythrumsalicaria* Purple loosestrife

Asparagus officinalis* Asparagus Matricariamatricaroides* Pineapple weed

Barbareavulgaris Bitter Wintercress Matricariaperforata* Wild chamomile

Berteroaincana* Hoary alyssum Medicagolupulina* Black medic

Bromusinermis Smooth Brome

Melilotus alba and M. 

officinalis White sweetclover

Bromusjaponicus Japanese brome Menthaspicata Spearmint

Bromustectorum cheatgrass Onobrychisviciaefolia* Sanfion

Camelinamicrocarpa Smallseed false flax Phalarisarundinaceae Reed canary grass
85

Scientific Name Common Name Scientific Name Common Name

Capsella bursa-pastoris*

Common shepherd's 

purse Phleumpratense Timothy

Carduusnutans Musk thistle Plantago major Broadleaf

Carumcarvi* Wild caraway Poaannua* Annual bluegrass

Centaureadiffusa* Tumble knapweed Poabulbosa* Bulbous bluegrass

Centaureamaculosa* Spotted knapweed Poacompressa Canada bluegrass

Cerastiumvulgatum Mouseear chickweed Poapratensis Kentucky bluegrass

Chenopodium album* Lambsquarters Polygonumarenastrum Knotweed

Chenopodiumcapitum Strawberry blite Polygonum convolvulus* Black bindweed

Chenopodiumglaucum* Oak-leafed goosefoot Potentillanorvegica Norway cinquiefoil

Chrysanthememleucanthemem Oxe-eye daisy Psathyrostachysjuncea* Russian wildrye

Cirsiumarvense Canada thistle Rheum rhubarbarum* Rhubarb

Cirsiumvulgare* Bull thistle Rumexacetosella Sheep sorrel

Conium maculatum Poison hemlock Rumexcrispix Curly dock

Convolvulus arvensis* Field bindweed Salsolaiberica* Russian thistle

Conyza Canadensis* Horseweed Sisymbriumaltissimum Jill Hill Mustard

Cynoglossumofficinale* Houndstongue Sisymbriumofficinale* Common hedge mustard

Dactylisglomerata Orchard grass Solanumtriflorum* Cut leafed nightshade

Descurainia Sophia* Flixweed Sonchusarvensis* Meadow sowthistle

Erodiumcicutarium* Redstemfilaree Spergulariarubra* Red sandspurry

Erodiumcicutarium Redstemfilaree Taraxacumofficinale Dandelion

Erysimumcheiranthoides Treacle mustard Thlapsiarvense Field pennycress

Euphorbia esula Leafy spurge Tragopogondubius Western salsify

Festucaovina* Sheep fescue Trifoliumhybridum Aliske clover

Festucapratensis* Meadow fescue Trifoliumpratense Red clover

Gypsophiliapaniculata Babysbreath Trifoliumrepens White clover
86

Scientific Name Common Name Scientific Name Common Name

Hieraciumaurantiacum* Orange hawkweek Triticumaestivum* Wheat

Hypericumperforatum* St. John's wort Verbascum Thapsus Woody mullein

Lactucaserriola* Prickly letttuce Verbena-bracteata* Prostrate vervain

Lappulaoccidentalis var. 

occidentalis Beggar's tick

Reference Condition

To understand the reference condition of this indicator, we used an approach that makes the 

simple assumption that the park was absent of any exotic species in the reference condition – an 

ambient distribution, as defined by Stoddard et al. (2006). Here we make the assumption that, by 

definition, during “natural” conditions there were no exotic species. Although some of these taxa 

in the exotic species list were likely present, they are likely more widespread than before, taking 

advantage of disturbances in the park. However, to our knowledge there is no well-established 

estimate of the distributions and amounts of exotic taxa in the park under “natural” (presettlement) conditions. 

Results/Discussion

Roughly 1.7% of the plots (weighted by percent cover) inside ROMO park boundaries were 

composed of exotic species (n=1,279), whereas outside of the park roughly 4.6% of the plots 

were composed of exotic species (n=571). Comparing the west and east sides of the continental 

divide, roughly 1.8% (n=483) and 2.8% (n=1,367) of the plots were composed of exotic plants, 

respectively. The lower montane and riparian ecological systems had the highest percentages of 

exotics – 5.2% and 4.6%, respectively (Table 27). The majority of plots with exotics occurred 

below 9,500’ elevation, but 4.1% of plots between 9,500-10,500’ had at least 1% exotics (1.7% 

of plots above 10,500’).

Table 27. Proportion of plots comprised by exotic plants, summarized by ecological system class (sorted 

by elevation).

Ecosystem Area (ha) %ROMO n % Exotic Standard Error

(+/- %)

Alpine 35,883 33 438 0.04 0.015

Upper 

Montane 56,492 52 597 2.4 0.036

Lower 

Montane 4,680 5 204 5.2 0.051

Riparian 7,482 7 432 4.6 0.048

Savanna 3,070 3 179 1.5 0.028

Most of the exotics are located near the boundaries of the park, particularly in the heavily 

developed portion of the park on the eastern boundary with Estes Park (Figure 35 and Figure 36). 

However, exotics are found distributed throughout the ecological systems (though very few were 

found in alpine tundra).
87

Figure 35. Percentage of exotic vegetation for each of the 1,861 vegetation plots in and near Rocky 

Mountain National Park (data from 2005).
88

Figure 36. Percentage of exotic vegetation summarizing the 1,861 vegetation plots in and near Rocky 

Mountain National Park by 12-digit Hydrologic Unit Code (data from Salas 2005).

Watersheds outside of the park—and in particular on the east side near the Town of Estes Park—

have higher levels of exotic plant species. Most of the front-country areas that have exotic plants 

are also mapped in the Weed Database, but there are a number of additional watersheds that 

might be important to monitor, particularly the North and East Inlet and the South Fork of the 

Cache la Poudre watersheds. Because of the central position within the park of the Big 

Thompson River West watershed, it is a priority watershed for restoration.
89

Pathways for exotic species

A particular management issue of concern was the relationship between visitor and exotic 

species patterns in the park. In particular, we examined how visitor use, accessibility, and type of 

use (through differentiation of trails such as horse trails and pedestrian use) are related to 

distributions of cheatgrass, the most abundant exotic plant species in the park. Cheatgrass is one 

of the most abundant exotic species in the park. We examined the relationship between where 

cheatgrass has been found as a function of accessibility. The strongest database to draw inference 

from is the ROMO Vegetation Map (Salas et al. 2004), which clearly shows that cheatgrass

occurs in more accessible areas—roughly 80% of plots are within 1-hour travel time (Figure 37, 

Figure 38, Figure 39). The average time to plots that contain cheatgrass was 0.86 hours

(SD=0.432), while the travel time to plots without cheatgrass was a mean of 3.24 hours 

(SD=9.1). Note that we have not conducted an analysis to infer a causal relationship. That is, 

relatively low-elevation, riparian areas are more accessible and so there may be some correlation 

with ecological systems and not accessibility per se.

Figure 37. The relationship between cheatgrass and accessibility using the ROMO vegetation plots.
90

Figure 38. ROMO vegetation plots that have cheatgrass shown with accessibility map behind.
91

Figure 39. Data from two additional datasets provide further support that cheatgrass occurs in more 

accessible areas. Left: The relationship between cheatgrass and accessibility using data from NIISS. 

Right: accessibility compared to plot locations from the ROMO Weed Control Database.

Uses and Limitations

Overall, we believe that this indicator supports a high degree of concern, because of the extent 

and widespread disruption of the ecological processes in the park due to exotic species. We rank 

our level of confidence in this indicator as moderate/moderate. There is moderate agreement

about the damaging effects of exotic species. There is a moderate degree of evidence—the 

ROMO vegetation plots provide a reasonable set of data on which to draw rigorous conclusions, 

though complementing this dataset with trend data is needed.
92

Summary: Biotic Integrity – Extent of Exotic Terrestrial Plants

What: Measures the proportion and abundance of nonnative, exotic plant species weighted by percent 

cover in each plot of the ROMO vegetation database 

(Salas et al. 2005).

Why: Exotic species can displace native species, impact 

wildlife habitat by reducing forage and altering 

natural processes such as fire.

Stressors: Visitor use, land use change, climate change

Confidence: High degree of concern; moderate evidence, 

moderate agreement 

The distribution of known locations of exotic plants (especially 

cheatgrass) is strongly correlated with elevation (most less than 

9,500’) accessibility, with roughly 80% of known locations in 

highly accessible areas (<1 hour). East-side watersheds have higher 

rates of invasive species (2.8% vs. 1.8%), and lower montane and 

riparian ecological systems are particularly affected. 

Measure Current condition

Proportion of exotic species* 1.7% ROMO

4.6% outside ROMO

Proportion by ecological system*

Alpine

Upper montane

Lower montane

Riparian

Savannah

0.1% (0.015 SE) 

2.4% (0.036 SE)

5.2% (0.051 SE)

4.6% (0.048 SE)

1.5% (0.028 SE)

*Total of 1,861 plots: 1,279 within ROMO boundary, 571 outside of park.
93

4.2.4 Biotic Integrity: Extent and Connectivity of Fish Distributions

Authors: David M. Theobald and John B. Norman, III

Description/Purpose

This indicator provides information about the extent and proportion of streams in ROMO that 

have non-native versus native fish. Barriers can isolate populations, potentially leading to local 

extinction (Gilpin and Soule 1986; Hildebrand and Kershner 2000), yet barriers can also isolate 

native populations from real or potential threats from non-native invaders. These potential tradeoffs are particularly acute in western river systems with salmonid species (Fausch et al. 2009).

There are seven native and four exotic fish species that inhabit the aquatic system of Rocky 

Mountain National Park. Due to cold water temperatures and barriers to fish migration, it is 

probable that many of the waters within the park were originally fishless. As with most waters 

within Colorado, the stocking of native and non-native fish species to establish and maintain 

harvestable populations of trout probably started in the late 1800s and continued until the 1970s.

The objective of this analysis was to provide information about where native fish are isolated 

from non-native fish and where they co-exist based on three barrier scenarios: “natural”, historic, 

and current. (These scenarios are the same as those described in the wetland/riparian area 

connectivity indicator section above—section 3.2.) In ROMO, the original distribution of fish

was not documented until 1923, but stocking of native and non-native trout likely occurred in the 

late 1800s and continued until 1968. Note that at least 63 lakes were stocked with Eastern brook 

(n=3,492,582), Rainbow (n=750,267) and Colorado cutthroat (n=10,864) trout from ~1920-1962 

(Figure 40). This provides a broad context to understand the current distribution of species, both 

exotic and native. 

Approach/Methods

This analysis relies on the Functional Linkages of Watersheds (FLoWS; Theobald et al. 2006) 

tools to represent the hydrologic network that native and non-native fish travel up and down, 

including the restrictions to their movements based on documented barriers to movement (e.g., 

waterfalls). This analysis uses data from the USGS National Hydrography Dataset (1:24K) to 

represent streams and lakes. These relationships assume that movement throughout the stream 

network is unrestricted. To better inform the FLoWS with movement restrictions in ROMO, a 

stream barriers dataset (that was also used for the wetland/riparian area connectivity indicator)

was used to alter relationships so that fish can move downstream through a barrier but not 

upstream (Figure 41).
94

Figure 40. Total number of fish stocked in ROMO lakes from ~1920-1962.
95

Figure 41. The known hydrologic barriers that were in the park as of 1987. There are 94 natural and 47

human barriers known within ROMO (mapped on 1:24K stream reaches, sampled and compiled by U.S.

Fish & Wildlife Service, Chris Kennedy; updated March 2010). A number of ditches and dams (e.g., 

Sandbeach, Bluebird, and Pear lakes) were removed in the 1990s.
96

We used a dataset from ROMO that combined records of observed fish species in lakes and 

streams in and around the park that had been compiled over 20 years (U.S. Fish & Wildlife 

Service, Chris Kennedy).We grouped the trout species identified in the fish sample dataset into 

native and non-native categories. Native species include: Colorado River cutthroat and green 

back cutthroat trout; non-native species include: Brook trout, brown trout, cutthroat trout,

rainbow trout, and Yellowstone cutthroat trout. 

Figure 42 provides a map of known locations where these electro-fishing surveys were

conducted (or not). We placed the native and non-native fish observations on the hydrologic 

network (joined to the FLoWS stream reaches) using the fish observation datasets supplied by 

ROMO, so that the individual stream reach in which the observation occurred was attributed. 

This resulted in a static spatial representation of native and non-native fish distributions within 

ROMO, however, does not account for possible movement of fish between stream reaches where 

the observations were made, nor for any potential overlap. To account for the movement of fish, 

we used the native and non-native designations at stream locations and then routed them up and 

down stream via the FLoWS network and barriers dataset (Table 28). We also defined stream 

reaches that were “secure” as those sections of reaches that had either “native only” or were 

fishless above a known barrier.

Table 28. Features that affect connectivity of fish scenarios. Natural 1900- 1990s Current

Feature Barrier

X X X National Hydrography Dataset 1:24K flow lines No restrictions

X X X Naturally occurring lakes No restrictions

X X X Natural barriers Up stream

X X Ditch/diversion barriers Down / Up stream

X X Dams Down / Up stream

X X Other human modified features Down / Up stream
97

Figure 42. Sections of stream reaches that are likely accessible to fish (i.e. no barrier present between 

the reach location and the fish sampling location) and that have been surveyed using electro-fishing by 

U.S. Fish & Wildlife Service personnel (by U.S. Fish & Wildlife Service, Chris Kennedy).
98

Reference Condition

The reference condition we used to examine this indicator was to interpret the historical 

conditions using datasets recorded at earlier times (Interpreting Historical Condition; Stoddard 

et al. 2006). 

Results/Discussion

We examined three scenarios to examine how barriers altered the distribution of native and nonnative fish: pre-establishment of ROMO or “natural” (Figure 43); 1900s-1990s or “historic”

(Figure 44), and current (Figure 45). We found that fish are present in the majority of streams 

(~ 90% or 923 km) for all three scenarios (Table 29). These stream reaches where fish are 

present also account for about 85% of hydrologically connected wetlands in ROMO. The 

“secure” zones (above known barriers) are highly fragmented and located high in watersheds 

(stream orders 1 and 2) with stream reaches where only native fish occur. These secure zones 

account for 77% of the native-only stream kilometers (currently) and 94% of hydrologically 

connected wetlands across all scenarios. The native streams experienced the smallest amount of 

change for the three scenarios with no change between the natural and current scenarios and 

minimal change for the mid-century scenario (~ 2% difference). The fishless streams exhibited 

the largest amount of change between scenarios with on average 24% difference from the natural 

scenario, but this is expected since is occupies the smallest component of the network and is 

restricted to first order streams. 

By mid-century the creation of additional (human) barriers fragmented the “natural” hydrologic 

network, resulting in an expansion of streams available for non-native and fishless reaches, and a 

contraction of available native stream reaches (Table 29). Although there have been a few 

barriers removed from mid-century to current (e.g., Eureka Ditch), the current scenario shows 

little change in fish distributions from the mid-century scenario due to the location of removed 

barriers. Comparing the current configuration to the “natural” scenario (no human-modified 

barriers), currently there are more stream kilometers that do not contain native fish or do not 

contain any fish currently.

Table 29. Total stream length for scenarios and fish distributions.

Total Stream Length (km)

Natural Historic Current

Native 74.5 72.1 75.5

Non-native 183.4 230.1 215.1

Both native 

and nonnative

794.3 738.3 756.7

Fishless 22.1 33.8 27.8
99

Figure 43. Distribution of native and non-native fish based on the “natural” scenario. There are non-native 

fish reaches in this map because the “natural” scenario reflects the removal of human-modified barriers, 

not removal of non-native fish. That is, given current native and non-native fish distributions, this scenario 

reflects how naturally occurring barriers fragment fish habitat.
100

Figure 44. Distribution of native and non-native fish habitat based on the historic (1900-1990s) scenario.
101

Figure 45. Distribution of native and non-native fish habitat based on the current scenario, as of current 

conditions in 2010. There are slight differences between this scenario and the mid-century scenario (e.g., 

non-fish reach in the Kawuneeche Valley). However, these scenarios are similar because the humancaused barriers that have been removed in the current scenario are located on minor streams or streams 

high in the watershed.
102

Figure 46 summarizes, by HUC12, the percent of the different fish populations by length of 

stream.

Figure 46. Summary of current native fish distribution, in total km of stream per HUC12.

Uses and Limitations

Overall, we believe that this indicator supports a high degree of concern, because of the 

importance of the fish to the park and the extent and widespread disruption of the hydrologic 

processes in the park. We rank our level of confidence in this indicator as moderate/low. There is 

a moderate level of agreement from experts that having refugia for native fish species is 

important to the long-term viability of endangered trout species. There is a low level of evidence

about the distribution data, particularly because the distribution is based on an ad hoc sample 

design and there is uncertainty around the genetics of the Greenback cutthroat trout populations.
103

Summary: Biotic Integrity – Extent and Connectivity of Fish Distributions

What: Measures the distribution of native and non-native 

fish, by incorporating observation data at streams 

and lakes with hydrologic connectivity (and barriers 

to up-stream movement).

Why: Non-native species displace native species.

Stressors: Riparian vegetation, visitor use, climate change

Confidence: High degree of concern; low evidence, moderate 

agreement.

Currently the proportion of streams that are “secure” (native only or 

fishless above a barrier) is 10% and there are 54 known human 

barriers, and native-only streams have increased slightly from 7.0% 

to 7.3% in the past 50 years. 

Measure Reference 

Condition

Current 

condition

Proportion of streams with native-only fish 97.3% 7.3%

Proportion of streams with native fish 100% 75.8%

Proportion of streams* in ROMO above a 

barrier

100% 54%

Mean length of native-only fish streams 

(km)

228.5 76.1

*Total length of streams in ROMO is 1,074.2 km (at 1:24K).
104

4.2.5 Biotic Integrity: Extent of Suitable Beaver Habitat

Authors: Richard Scherer, Ian Leinwand, Barry R. Noon, and David M. Theobald

Description/Purpose

A key indicator for ROMO is the extent of suitable beaver habitat. In North America, the beaver 

(Castor canadensis) has near iconic status as the quintessential ecosystem engineer (Baker and 

Hill 2003)—a species that changes the physical structure of the environment and in the process 

creates niches to be exploited by otherwise absent species. As a result of their dam building, 

canal construction, and foraging activities, beaver have profound effects on ecosystem structure 

and function. Their alteration of hydrological regimes, and the many subsequent effects resulting 

from these hydrology changes, has been well documented (Naiman et al. 1988, Baker and Hill 

2003). In a nutshell, beaver-induced changes to the structure and function of the aquatic 

environment have many beneficial effects on plant and animal diversity (Baker and Hill 2003). 

The absence, or low abundance, of beaver from a landscape can serve as a powerful indicator of 

declines in biological diversity. Thus, the distribution of current and potentially suitable beaver 

habitat was a logical choice as an important indicator of biological integrity of ROMO. Though 

beaver occur in lentic and riverine (and disconnected wetlands) systems, the focus of our 

modeling work for ROMO was on streams and rivers. 

Within ROMO beaver abundance and distribution may be at a historic low because of declines in 

tall (> 3m) willow (Salix sp.; Peinetti et al. 2002, Baker 2003). In fact, declines in beaver and 

willow populations are strongly correlated in environments within ROMO that are heavily 

browsed by elk (Cervuselaphus). There is an apparent mutualism between beaver foraging on 

willow for food and building materials, and the establishment and survival of willow (Baker 

2003). Beaver preferentially select tall, unbrowsed patches of willow and avoid short, hedge 

willow patches created by heavy elk browsing. The correlation between elk browsing, decline in 

the stature and vigor of willow, and declines in beaver populations has led Baker (2003:177) to 

conclude “...when beaver cut willow, and intense elk browsing suppresses growth, the 

interaction of beaver and elk herbivory will create a feedback mechanism that is negative for 

beaver and willow but positive, or negative, for elk depending on local conditions.” In addition 

to elk, moose are increasingly significant browsers on willows. 

While demographic and distribution data on beaver have been periodically collected in ROMO

over the last 70 years, collection of these data was based on ad hoc sampling plans, thus 

inference to the entire park is problematic. However, these data can be used to examine trends in 

beaver populations in particular areas of ROMO. Data were collected in 1939-1940, 1964, 1980, 

1994, and 1999-2000. Surveys conducted prior to 1999 are less spatially explicit and only 

provide data on beaver activity and abundance that can be attributed at the drainage scale. 

Surveys conducted by Mitchell et al. in 1999 and 2000 provide a more spatially explicit 

description of the areas surveyed as well as coordinates for active and historic beaver signs such 

as lodges, dams, and food caches. From the early survey descriptions and the results reported by 

Mitchell et al. (2000) we mapped current and historic beaver activity observed in 1999 and 2000 

(Figure 47 and Figure 48).
105

Figure 47. Historic distribution of beaver based on park survey data from 1940 to 2000. Location of 

lodges and dams digitized from Mitchell 1999 to 2000 data. Only portions of the drainages displayed as 

having signs of beaver are suitable beaver habitat. No spatial data were available for surveys conducted 

prior to 1999; therefore we assume that historic beaver activity mainly occurred in areas where active and 

abandoned dams and lodges were located. That is, we assume that dams and lodges are useful 

surrogates for beaver activity.
106

Figure 48. Areas surveyed for beaver sign in 1999 to 2000. Data digitized from ROMO beaver survey 

reports by Mitchell et al. (2000).

Approach/Methods

We developed a GIS of potential beaver habitat using information from the published literature 

and then used these data to develop a habitat suitability model. Though the published literature 

suggests many biotic and abiotic variables influence landscape use by beaver, spatial data were 
107

not available for many of these variables (e.g., width of stream channel; Beier and Barrett 1987, 

Barnes and Mallik 1997). Our final model identified potentially suitable beaver habitat in ROMO

primarily using three spatial datasets: data from a digital elevation model (DEM), a stream 

gradient dataset and a vegetation dataset. Our map provides an estimate of the suitability of 

locations in the park (as pixels in the GIS data) to beaver based on the degree to which a location

is accessible to beaver (defined by streams of 0-2% gradient), and the suitability of vegetation.

Below we provide an overview of the data and methods used to produce and validate the final 

map; see Technical Appendix 4 for more detailed methods.

We developed stream gradient using a 10 m DEM. Our model used cost distances to delineate 

the area around streams that was accessible to beaver. The concept of cost distance arose out of 

the observation that a species’ behavior, perceptual abilities, and physiology will make 

movement relatively more costly (or less probable) across some locations or cells. For example, 

wood frogs (Ranasylvatica) are known to avoid movement through areas with dry soils and low 

canopy cover (Vasconcelos and Calhoun 2004). Therefore, we would expect wood frogs to avoid 

movement through these areas, and if they choose to move across them, we would expect the 

cost (in terms of physiological condition and/or survival) to be high relative to pixels that 

represent areas that are wetter and have high canopy cover. 

Conceptually, we thought of source pixels as those pixels beaver would most likely select for 

dam-building. In many parts of their range, beaver create favorable aquatic conditions by 

damming streams (Baker and Hill 2003). We assumed pixels that represented streams where 

beaver would most likely build dams would serve as the centers of home ranges for individual 

beaver colonies. We identified the initial set of source cells by reclassifying the stream gradient 

dataset to select those pixels with the most suitable gradients for beaver dams. Insights into a 

suitable stream gradient were based on a literature review of studies from mountainous terrain in 

the western U.S. Of the 53 beaver dams detected by Beier and Barrett (1987) in the Truckee 

River Basin in California and Nevada, 48 (91%) were found at locations where the stream 

gradient was 0 – 2%. Of the 45 sections of stream with beaver dams sampled by Slough and 

Sadelier (1977) in British Columbia, 36 (80%) had gradients < 2%. Therefore, we reclassified 

stream pixels as source cells if their gradient was < 2%. All other stream pixels were not

considered sources cells (i.e. they were reclassified as ‘NoData’). 

We removed from consideration short (<50 m) stretches of stream as well (roughly 10%). Beaver 

are characterized as central-place foragers, which means they cut vegetation from one location 

and bring it back to a central location (e.g., dam, food cache or lodge; Baker and Hill 2003). As 

they exhaust the supply of suitable vegetation around a central place, they must establish a new 

central location. Therefore, we assumed short stretches of stream would not be able to support 

beaver over long periods of time, as they would not be able to move up and down the stream in 

response to the dwindling supply of suitable vegetation. Therefore, we eliminated all source cells 

that were connected to fewer than six other source cells. We suspect stretches of stream much 

greater than 50 m are required for beaver populations to persist over long periods of time. 

However, the literature did not provide clear guidance on a more appropriate length, and we 

chose to error toward inclusion. 
108

We designated the resistance or cost of movement to be an integration of the willingness of the 

species to cross a particular landscape type, the physiological cost of movement across the 

landscape type, and expected reductions in survival associated with movement across the 

landscape type (Compton et al. 2007). We assigned costs to pixels based on the gradient of the 

pixel. We assumed beaver would be less willing, or find it more costly, to move through pixels 

with steeper slopes. For a given slope, we also assumed the cost of movement for beaver within a 

stream channel was much lower than the cost of movement over land. Therefore, we assigned 

different costs to pixels based on the slope of the pixel and whether the pixel represented a 

stream or terrestrial area (Table 30). Cost was assumed constant across vegetation types.

Table 30. Cost weights assigned to stream and terrestrial pixels of different gradients.

Gradient Stream Terrestrial

0 – 2% Source pixels 10

> 2% – 10% 1 30

> 10% – 20% 3 50

> 20% 100 150

We then found all locations within 1,000 cost-meters of source pixels. This maximum distance 

assumed beavers could access terrestrial areas up to 100 m from a source pixel, if the intervening 

terrestrial landscape had a gradient from 0% to less than 2%. Since each pixel is approximately 9 

m on a side and the relative cost of movement across pixels that represent terrestrial areas with a 

slope from 0% to 2% is 10 (Table 30), a beaver would use approximately 90 cost-meters to move 

across a single pixel. Therefore, it could travel across a maximum of 11 of these pixels or 99 m. 

In terrestrial areas of higher slope, the maximum geographic distance of accessible terrestrial 

areas was approximately 30 m. This geographic distance is consistent with the maximum 

movement distances for beaver away from water reported by Baker and Hill (2003).

To identify pixels that were more likely to contain vegetation and other cover types used by 

beaver, we used the ROMO vegetation dataset (Salas et al. 2005). Nearly all of the cover types in 

the map were characterized by a list of associated plant species. We used this list to identify 

classes that were likely to contain vegetation used by beaver. For the vegetated cover types, we 

considered classes characterized as having deciduous shrubs and trees of a variety of species 

such as willow, alder, aspen, and cottonwood (Table 31); Baker and Hill 2003, DeStefano et al. 

2006). Beaver appear to prefer relatively tall (≥ 3m) shrubs (Baker 2003). However, we had 

limited data with which to infer shrub height from the vegetation dataset, so our map will likely 

overestimate the area of vegetation useful to beaver. Beavers are also known to use lakes, 

reservoirs and other water bodies (Baker and Hill 2003) with access to suitable vegetation. 

Therefore, we also considered pixels classified as streams, lakes or reservoirs as suitable for 

beaver. Ultimately, we identified 10 of the 46 classes as likely to contain vegetation used by 

beaver and 3 of the 46 classes as suitable types of water bodies (Table 31). Although herbaceous 

wetlands may facilitate dispersal, they were considered unsuitable habitat because they generally 

do not support the size of woody plants needed for dams, lodges, and forage. All other classes 

were considered unlikely to contain vegetation used by beaver or to represent unsuitable habitat. 
109

Table 31. The original vegetation classes of Salas et al. (2005) and our reclassification with respect to 

each class’s utility and suitability to beaver.

CLASS LIKELY/SUITABLE 

BEAVER HABITAT CLASS UNLIKELY/UNSUITABLE BEAVER HABITAT

13 Shrub Upland – Alpine 1 Herbaceous Upland – Alpine

15 Riparian Aspen 2 Herbaceous Upland - Alpine Fellfield

18 Upland Aspen 4 Herbaceous Upland – Montane

32 Cottonwood 5 Herbaceous Wetland – Marsh

51 Streams – Rivers 6 Herbaceous Wetland – Wetland

52 Natural Lakes – Ponds 7 Herbaceous Wetland - Alpine Meadow

53 Reservoir - Stock Tanks 9 Alpine - Ice Field – Glacier

120 Shrub - Riparian - Cross Zone > 9600 ft 10 Rock (Alpine - Upper Subalpine)

121 Shrub - Riparian - Cross Zone < 9600 ft 11 Rock (Foothill - Lower Subalpine)

161 Mixed conifer w/ aspen (Ponderosa Pine) 14 Shrub Upland - Lower Montane

162 Mixed conifer w/ aspen (Lodgepole Pine) 20 Montane Douglas Fir

163 Mixed conifer with aspen (Douglas-fir) 22 Subalpine mixed conifer

164 Mixed conifer with aspen (Spruce - Fir) 23 Lodgepole - high elevation > 9500 ft.

24 Lodgepole - low elevation < 9500 ft.

26 Lodgepole pine – Rock

33 Juniper Woodland

34 Ponderosa Pine – Graminoid

35 Ponderosa Pine – Rockland

36 Ponderosa Pine – Shrubland

38 Limber Pine

39 Ribbon Forests

41 Disturbance – Dead and Down

43 Blue Spruce

46 Talus

47 Outwash

48 Exposed Soil - Man made

49 Cliff Face - Bare Soil

141 Shrub Upland - Big Sagebrush

142 Shrub Upland – Bitterbrush

190 Upper Montane, Mixed Conifer – Riparian

191 Upper Montane, Mixed Conifer – Riparian

400 Krummholz

999 Un-vegetated Surfaces

We assumed that pixels would have relatively high suitability scores if they contained a suitable 

cover type and were accessible to beaver from sections of stream with low gradient. Pixels that 

had suitable vegetation cover but were outside the 1,000 cost-meter boundary were not 

considered suitable. Pixels that had suitable vegetation cover and had a cost distance <1,000 

cost-meters were considered suitable.

The preceding steps resulted in a pixel-based map of habitat suitability for beaver across the park 

(Figure 49). Pixels in the map are approximately 80 m2

. However, daily and seasonal movements 

and dispersal occur over much broader spatial scales for beaver (Wheatley et al. 1997, DeStefano 
110

et al. 2006), which suggests they would integrate information across multiple pixels in our map. 

Therefore, we aggregated pixels into patches of suitable beaver habitat. We delineated patches 

by considering a pixel part of a larger patch if it had a beaver habitat suitability score greater than 

0 and was directly adjacent (i.e., shared an edge or corner [an 8-cell neighborhood]) to another 

pixel with a suitability score greater than 0. In some areas, our model identified lakes, ponds and 

reservoirs as suitable beaver habitat despite the fact that no suitable vegetation was nearby (e.g., 

alpine lakes). We considered these to be misclassification errors. These errors were caused by the 

fact that some lakes and ponds were represented as streams in the original stream gradient data 

set, and we included lakes, ponds, and reservoirs as useful cover types for beaver. To correct 

these errors, we eliminated patches when the majority of pixels in the patch were classified as 

lakes, ponds and reservoirs and no pixels of suitable vegetation types were nearby. We used 

aerial photography of the park to confirm that the deletions were appropriate. Our logic was that 

a pixel classified as a lake, pond or reservoir would only be used by beaver if it were adjacent to 

pixels with suitable vegetation.

To validate our suitable habitat map, we used data on current and past beaver presence. Mitchell 

et al. (2000) and Mitchell and Ducharme (unpublished data) conducted surveys for beaver in 

four drainages and nine creeks in Rocky Mountain National Park in 1999 and 2000. These 

reports include lists of locations of current and past beaver presence (e.g., dams, lodges, and food 

caches). Current locations of beaver activity were distinguished from locations of past beaver 

activity by the presence of fresh sign (e.g., active beaver trails, fresh cuttings, and mud; Mitchell 

et al. 2007). The reports yielded evidence of beaver presence at 180 locations in the park. If our 

final beaver habitat suitability map was useful, we would expect the locations of current beaver 

presence to be within pixels of high suitability.
111

Figure 49. Areas of beaver habitat suitability in Rocky Mountain National Park. Areas in boxes represent 

the largest and highest scoring areas of potential beaver habitat within the park. A) The confluence of the 

Colorado River with Squeak Creek, Phantom Creak, Beaver Creek, and Timber Creek from north to 

south; B) The Colorado River and Baker Creek; C) The confluence of the Colorado River with Onahu 

Creek; D) The Cache la Poudre River meeting Chapin Creek; E) Fall River with Roaring River in the 

northwest and Hidden Valley Creek to the south; F) The Big Thompson River to the north of Cub Creek 

and Mill Creek.
112

Our final beaver habitat suitability map would not have predicted the locations of 66 of the 180 

locations of current or past beaver presence (from Mitchell et al. 2007). These omission errors 

may be attributable to a too conservative definition of suitable habitat. Forty (22% of all the 

locations) were greater than 1000 cost-meters from the nearest source cell (i.e., inaccessible to 

beaver according to our model). However, the fact that over 80% of these locations were close to 

patches (within 50 m; Figure 50) suggests our map provides a reasonable representation of 

current and potential suitable beaver habitat in the park. It also indicates that our assumptions 

resulted in a fairly conservative estimate of beaver space use. Twenty-six of the 180 locations 

(14%) were in pixels that were accessible to beaver but not likely to be useful or suitable (beaver 

habitat suitability score = 0). These may be a consequence of inadequacies in the map or any of a 

variety of mapping errors (e.g., errors in the vegetation data set, errors in the locations of beaver 

presence, or projection errors). The majority of locations of beaver presence (114 of 180 

locations or 63%), however, were in pixels with suitability values that ranged from greater than 0 

to 1, even though pixels with these suitability values were rare according to our map (< 3% of 

the pixels). Seventy-eight locations of beaver presence (43%) were in pixels with high suitability 

values (suitability values > 0.8), but only 0.2% of the pixels in our model had the suitability 

values >0.8.

Figure 50. The distribution of geographic distances of locations of current and past beaver presence from 

the nearest patch. Most locations (114) of beaver presence are within patches (0 m from the nearest 

patch). However, some locations were greater than 300 m from a patch.

Reference Condition

Establishing the reference condition for beaver abundance or density in the park requires historic 

data from a comprehensive survey of all watersheds or from surveys based on a well-designed 

sampling plan. Though much less desirable, reference condition could also be established using 

data on beaver abundance or density from other locations that are similar to ROMO. Based on 

the benchmark typology of Stoddard et al. (2006), Interpreting Historical Condition may not be 

available. Instead, the reference distribution used for beaver habitat mapping is based, for the 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

20

40

60

80

100

120

0

102030405060708090

100200300

More

Frequency of Locations

of Beaver Presence

Distance from Beaver Patch (Meters) 

Frequency
113

most part, on Best Professional Judgment, informed by published empirical habitat studies and 

incomplete historic survey data from ROMO. The key data limitation was the structural 

condition of the vegetation. Vegetation structure, as well as composition, is essential for reliable 

habitat mapping of most wildlife species.

Results/Discussion

To qualitatively assess trends in the beaver population over the past 70 years, we summarized 

and mapped the data from the surveys in 1939-1940 (Packard 1947) and 1999-2000 (Mitchell et 

al. (2000) and Mitchell and Ducharme (unpublished data); Figure 51). Estimated population was 

based on the number of active lodges and dens observed. For each active site we assumed a 

colony of six beavers occupies the site (Packard 1947).

Figure 51. Comparison of beaver densities from surveys in 1939-1940 (left) and 1999-2000 (right) in 

several drainages in ROMO. Estimates of beaver density from surveys in 1939-1940 are generally much 

larger than estimates from surveys in 1999-2000. Data attributed to 1:24K stream reaches.

These data suggest declines in beaver populations over that time period, and beaver populations 

appear to have disappeared from several drainages. The methods used to collect survey data 

varied between the surveys, but in general, they collected the same kinds of data. Therefore, we 

suspect the declines in beaver abundance suggested by these surveys are real and not an artifact 

of different field methods. Based on these surveys and an assessment of willow populations in 

Moraine Park, Baker et al. (2005) noted declines in beaver populations and a corresponding 

decline in tall willow over the same time period: 
114

Beaver were once abundant in the study area but declined dramatically after 1940; for example, population 

estimates in Moraine Park were 315 in 1939–1940, 102 in 1964, 12 in 1980, and six in 1999. In a 

comparison of 1937/1946 and 1996 aerial photographs, Peinetti et al. (2002) found that tall willow (> 3 m) 

cover declined by 54% in Moraine Park and 65% in Horseshoe Park. Short willow plants (< 1.5 m) have 

dominated the study area for several decades, probably a result of a change in individual plant stature rather 

than in willow species composition. Thus, beaver and willow populations have both declined in heavily 

browsed environments within [ROMO].

Mitchell et al. (2000) estimated browsing impact on willows during the 1999 to 2000 beaver 

survey (Figure 52). Packard (1947) estimated that 600 beaver lived in the Kawuneeche Valley 

around 1940. Today, beaver are rare in the valley (Rick Scherer, personal observation).

Estimates of beaver abundance and density from similar habitat in other locations also tend to be 

much higher than current densities in ROMO. Baker and Hill (2003) summarized densities 

reported in the literature and found there to be a wide range in the density of beaver colonies, 

from near zero to at least 4.6/km

2 

(reviews by Hill 1982; Novak 1987). Observers in different 

regions have attempted to estimate the maximum density or saturation point in local populations. 

Saturation has been reported to vary from 0.4 colony/km of stream in northern Alberta to 1.2 

colonies/km of stream in New York and Utah (reviews by Hill 1982; Novak 1987). In the 

headwaters of four Alabama watersheds, saturation approached 1.9 colonies/km of stream (Hill 

1976). Based on available survey data and other published research, it appears the abundance of 

beaver in ROMO is far below historic levels. 
115

Figure 52. Map showing drainages surveyed in 1999 to 2000 classified based on impact of browsing on 

willow. Drainages in dashed light grey were not surveyed. Browsing data comes from the 1999 to 2000 

beaver surveys conducted by Mitchell et al. (2000).
116

We delineated 536 patches for a total area of suitable beaver habitat of 1,040 ha (about 1%; 

Table 32). We considered a patch to be active if it contained at least one of the locations of 

current beaver activity from the surveys of Mitchell et al. (2007) and Mitchell and Ducharme 

(unpublished data). A patch was considered no longer active if it was surveyed and only 

locations of past beaver activity were observed. Most patches (409) were in drainages or along 

creeks that were not recently surveyed. 

Table 32. Summary statistics for the patches of suitable beaver habitat in Rocky Mountain National Park. 

Std. Dev. Area and Std. Dev. Suitability Score are the standard deviations of patch area and average

patch suitability score, respectively.

Summary Statistics All Patches Active Past Not 

Surveyed

No 

Activity

Number of Patches 536 39 87 409 1

Area (ha) 1040 497 297 244 2

Average Area (ha) 1.94 12.7 3.42 0.6 N/A

Std. Dev. Area(ha) 7.8 21.53 9.6 1.61 N/A

Maximum Area (ha) 93.6 93.6 67.9 18.2 N/A

Avg. Suitability Score 0.43 0.62 0.6 0.37 0.61

Std. Dev. Suitability Score 0.26 0.18 0.24 0.24 N/A

A general principle of ecology is that larger patches of suitable habitat and habitat of higher 

quality should support larger populations of a species (Hanski 1999). Our data provide further 

support for the validity of this principle. Higher proportions of large patches (≥ 10 ha) and 

patches of high quality (high average beaver habitat suitability score across pixels in a patch) 

contain evidence of current or past beaver activity (Figure 53). One possible reason for the 

higher proportion of large patches with evidence of current or past beaver activity is that they 

cover a larger percentage of the study area, and therefore, it is more likely that beaver locations 

fall within their boundaries.

According to our model and the method we used to delineate patches, most patches of suitable 

beaver habitat in the park are small (≤ 1 ha; Figure 54) relative to estimates of home range size 

for beaver. Wheatley (1997) reported home range sizes as large as 18 ha and the results of a 

modeling project suggest a colony of beaver comprised of 6 individuals requires a minimum of 4 

ha for long-term persistence (Baker et al. 2005). Thus, much of the potentially suitable habitat in 

the park may be in patches that are too small to support persistent beaver populations. Of course, 

the abundance of small patches of suitable beaver habitat may be an artifact of the method we 

used to delineate patches. A limitation of the method is that individual suitable pixels were 

considered part of a larger patch only if they shared an edge or corner with another suitable pixel. 

Individual pixels that were suitable (had a suitability score greater than 0) but did not share an 

edge or corner with a neighboring suitable pixel were identified as individual patches. In some 

cases, patches comprised of a single pixel were near, but not connected to, other suitable patches. 

While our model considers these pixels to be individual patches, beaver may perceive collections 

of unconnected, suitable pixels that are near one another as part of a single, larger patch. In fact, 

our data support this idea. Evidence of recent beaver activity in patches as small as 0.01 ha (one 
117

pixel) was observed during the surveys in 1999 and 2000. It is highly unlikely that suitable patch 

sizes were smaller than our data resolution.

Figure 53. Distribution of size (top) and quality (bottom) for the 536 patches in the study area. The 

number of patches in each size or beaver habitat suitability class that contains evidence of beaver activity 

is shown by the portions of each bar shaded in red (current beaver activity) and blue (past beaver 

activity). The beaver habitat suitability scores in the bottom figure are averages across all pixels that 

make up each patch. Small proportions of the patches in the smaller size classes and of lower quality 

contain evidence of current or past beaver activity, while larger proportions of patches in the larger size 

classes and of higher quality contain evidence of current or past beaver activity.

In general, however, evidence of current beaver activity was found in larger patches with higher 

average suitability scores (red squares in Figure 54). Examination of the distribution of patch 

sizes and average habitat suitability scores for patches with evidence of active beaver colonies 

suggests a minimum patch size of 0.10 ha and a minimum average suitability score of 0.4 for 

beaver to occupy a patch. 
118

Figure 54. Minimum patch size of 0.01 ha and a minimum average suitability score of 0.4 for locations of 

current beaver activity.

Potential beaver habitat is not distributed evenly within the park. Our model identified more 

potential beaver habitat of higher quality in the western half of the park compared to the east. 

The Colorado River as it flows through the Kawuneeche Valley has the greatest area of potential 

beaver habitat within the park, estimated at 189 hectares. Two other drainages in the 

Kawuneeche Valley, Onahu Creek (98 hectares) and Baker Creek (84 hectares) had the second 

and third highest amounts of potential beaver habitat within the park. On the east side of the park 

Fall River and the Big Thompson River had an estimated 62 and 56 hectares of potential habitat 

respectively. 

Scenarios for Restoration

Given that the current distribution and abundance of beaver in ROMO is far below historic levels

(Figure 51), it is important to prioritize areas for habitat restoration or reintroduction of beaver. 

We have addressed the question of prioritization in two ways: first, by summing the area of 

potentially suitable habitat within all HUC-12 drainages in the park; and second, by computing 

the proportion of a HUC-12 drainage that is potentially suitable beaver habitat. We combined 

these two ways of evaluating potential for suitable habitat into a map we referred to as 

restoration potential (Figure 55). The HUC-12 drainages of highest restoration potential are 

Colorado River-North, Big Thompson River-West, Onahu Creek, Cache la Poudre-South, Fall 

River, and Glacier Creek.
119

Figure 55. Beaver restoration potential by HUC12. Restoration potential was based on the total area of 

potentially suitable beaver habitat multiplied by the change in estimated beaver population between 1940 

and 1999.

Uses and Limitations

Although the data on beaver locations suggest the beaver habitat suitability map provides a 

reasonable approximation of the amount and location of beaver habitat in the park, there are a 

number of caveats that should accompany our research results.

We primarily used data on stream gradient, the slope of the terrestrial environment and the 

distribution of suitable vegetation and other land cover types to develop the map. Clearly, the set 
120

of environmental elements that influence the suitability of an area for beaver exceeds those used 

to build our habitat model. The results from field studies in other parts of the geographic range of 

beaver suggest beaver presence is correlated with stream width and depth, the amount of bare 

ground and other elements of the landscape (Beier and Barrett 1987; Barnes and Mallik 1997). 

These components were not incorporated into our map because data were not available or were 

at a spatial resolution that we felt was inappropriate for this project. For example, the map of 

Salas et al. (2005) contains data on the locations of areas of bare ground in the park. However, 

Salas et al. (2005) used aerial photo interpretation to classify vegetation and land cover in the 

park. Consequently, they could only delineate relatively large, contiguous areas of bare ground, 

so their data were too coarse to be useful in our mapping process.

Despite apparently high levels of interest in beaver by natural resource managers and the general 

public, there was a surprising lack of information on movement behavior. Radio telemetry is 

often used to collect movement data on animal species, and problems associated with mounting 

radio telemeters on beaver appear to be an important cause of this lack of information (Baker 

2005). In particular, we lacked information with which to develop more than coarse, general 

estimates of the relative costs of movement through various habitat types. That is, we were only 

able to assign costs to pixels based on a coarse assessment of the slope of the pixel and whether 

the pixel represented a stream or the terrestrial environment. If information becomes available 

from future studies, it could be incorporated into the mapping process and may improve the 

utility of the map. 

Also, the vegetation map provided little information on willow height, and willow height appears 

to be an important criterion when it comes to determining beaver use. We used the vegetation 

map without consideration of the actual height of shrubs. Beaver need taller shrubs for dam and 

lodge construction.

Beaver use other habitat types besides streams, so our source cells may have been too restrictive.

Due to the limited availability of relevant spatial data and movement behavior in beaver, the final 

beaver habitat suitability map should be considered primarily a map of potential beaver habitat. 

This model is a first-run approximation and is likely to include both errors of omission and 

commission with regard to identifying suitable beaver habitat. A follow-up study to inventory 

beaver sites would be valuable.

At this point it appears that the amount of suitable habitat in the park may be limited by the area 

of mature willow stands within riparian areas (Baker et al. 2005). In order for willow stands to 

develop sufficient girth for dam and lodge construction, elk browsing intensity will need to 

decrease (Baker 2003, Baker et al. 2005). Thus, at this time the size of the elk population may be 

indirectly limiting the size of the beaver population through herbivory. Our mapping exercise 

was unable to discriminate mature willow stands from those heavily browsed by elk. As a result 

of the extent of elk browsing in willow stands, we have mapped potential habitat rather than 

currently suitable habitat.

Overall, we believe that this indicator supports a high degree of concern, because of the key role 

beaver have in riparian ecosystems of the park and the extent and widespread disruption of the 
121

hydrologic processes in the park. We rank our level of confidence in this indicator as 

moderate/moderate. We rated this as moderate agreement, because some uncertainty remains 

among experts as to the direct role beaver play in riparian condition. Because of the complicated 

linkages of riparian condition with other factors such as elk population levels, trapping of beaver, 

and lack of top predator (wolf), the additive effects of beaver to riparian ecosystems is difficult 

to estimate. We rated this indicator as moderate level of evidence, because this indicator is 

mapped on consistent landscape data, validated by aerial photography for current (and some 

historical distributions), and relatively consistent with historical surveys of beaver and expert 

opinion. 
122

Summary: Biotic Integrity - Extent of Suitable Beaver Habitat

What: Measures the distribution and proportion of potentially suitable beaver habitat and compares it to historical surveys.

Why: Important to understand the historical role beaver had in riparian vegetation and is critical to understand elk/willow 

dynamics.

Stressors: Over-abundance of elk, Grand Ditch, climate change

Confidence: High degree of concern; moderate evidence, moderate 

agreement

The current distribution of beaver is far below historic levels (bottom map). 

Highest areas for restoration potential (right map) include Colorado River 

(north); Big Thompson River (west), Onahu Creek, Cache la Poudre 

(south), Fall River, and Glacier Creek watersheds.
123

4.2.6 Landscapes: Extent and Proportion of Major Ecological Systems

Authors: Barry R. Noon, David M. Theobald, and John B. Norman, III

Description/Purpose

Here we report on an initial attempt to characterize the landscape of the park in terms of a small 

set of coarse-filter landscape measurements. These measurements are based on a characterization 

of the area and spatial arrangement of the dominant vegetation communities of the park at the 

scale of 12 digit-Hydrologic Unit Codes (Seaber et al. 1987), and in some cases summarized by 

major watersheds (HUC 8s)—the Big Thompson, Cache la Poudre, Saint Vrain, and Colorado 

Rivers. This partition of the park into its HUC-12 watersheds allowed us to compare and contrast 

the metrics developed in this chapter across watersheds to detect similarities and differences at a 

resolution meaningful to broad-scale ecological processes. There are numerous characterizations

that use landscape metrics, such as: Jones et al. (1997, 2001), and Riitters et al. (2002).

Approach/Methods

For this assessment, we used the 2007 LANDFIRE land cover data developed by the 

Departments of Agriculture and Interior and the Nature Conservancy. We used LANDFIRE 

because it provides a much finer classification of ecological systems over the National Land 

Cover Dataset, and is consistent across the full Southern Rockies, whereas the Southwest 

Regional GAP dataset does not include the northern portion of the Southern Rockies (Wyoming). 

The LANDFIRE (LF) data have a resolution of 30m and are derived from Landsat Thematic 

Mapper data and other ancillary data sources. The original LF data recognized 36 land cover 

categories. To simplify the analysis we reclassified the LF data into 7 categories reflecting major 

ecological systems found in the park: riparian/wetland, aspen forest and woodland, lodgepole 

pine forest, lower-, mid-, and upper-montane forests, alpine tundra, and snow and ice (Table 33).

We aggregated these 0.09 ha (0.22 ac, 30 m cells) up to 0.81 ha (90m) to filter out finer-scale 

variation in the data, as well as reduce the computational requirements. Prior to estimating the 

metrics, the LF coverage was partitioned by the HUC-12 boundaries to generate patch 

boundaries along watershed boundaries. We did this so that our patch-based metrics reflected 

watershed boundaries.
124

Table 33. Listing of the eight major ecological systems from LANDFIRE existing vegetation types, for 

which we measured the extent and proportion.

Ecological 

systems Classes Dominant types

Alpine tundra 2006, 2070, 2143, 2144 Alpine/montane sparsely vegetated; alpine dwarf shrubland; alpine 

fell-field; dry turf

Upper elevation 

forest

2055, 2056, 2057 Dry-mesic spruce-fir forest and woodland; wet-mesic spruce-fir forest 

and woodland; subalpine montane limber-bristlecone pine woodland; 

Mid elevation 

forest

2011, 2051, 2052, 2061, 

2066

Aspen forest and woodland; dry-mesic mixed conifer forest and 

woodland; mesicmontane mixed conifer forest and woodland; aspenmixed conifer forest;

Lower elevation 

forest

2016, 2049, 2054, 2059, 

2117, 2119

Pinyon-juniper woodland; limber pine-juniper woodland; ponderosa 

pine woodland; ponderosa pine savanna; juniper woodland and 

savanna

Aspen forest 2011, 2061 Aspen forest and woodland; aspen-mixed conifer forest and 

woodland

Lodgepole pine 

forest

2050 Lodgepole pine forest

Riparian/wetland 2159, 2160, 2162, 2164 Montane riparian systems; upper montane riparian systems

Savannah/steppe 2062, 2064, 2066, 2072, 

2080, 2081, 2086, 2093, 

2106, 2107, 2115, 2125, 

2126, 2127, 2135, 2139

Mountain Mahogany; Mixed / low/ big Sagebrush; Saltbrush; Mixed 

Salt Desert Scrub; Lower Montane-Foothill shrubland; Colorado 

Plateau Sand Shrubland; Montane-Foothill Deciduous Shrubland; 

Gamble Oak-mixed Montane Shrubland; Juniper Savanna;

Ponderosa pine Savanna; Big Sagebrush Steppe; Montane 

Sagebrush Steppe; Semi-Desert Shrub-Steppe and Grassland; 

Lower Montane-Foothill-Valley Grassland

We conducted two sets of analyses. The first involved envisioning the landscape as a categorical 

patch mosaic and used FRAGSTATS 3.3 (McGarigal et al. 2002) with an 8-neighbor rule to 

identify and determine the boundaries between land cover patches. An 8-neighbor rule 

eliminated many small patches that would have been formed with a 4-neighbor rule and also 

ignored any fragmentation caused by local and secondary roads (most of the highway and 

interstates are 1-2 pixels wide). Although there are potential fragmentation effects of the 

landscape structure by roads, we decided that in general local and secondary roads do not 

strongly fragment patches for most ecological processes we were interested in (e.g., wildfire, 

mammal movement, etc.). FRAGSTATS computes landscape metrics at three scales: individual 

patches (patch-level), across patches within a patch type (class-level), and across all patch types 

(landscape-level). A very large number of metrics can be computed with FRAGSTATS but many 

of these are redundant (e.g., Li and Reynolds 1994, Hargis et al. 1997) and many are difficult to 

interpret ecologically (Baker 2000). We chose a small set of metrics from among those 

recommended by Botequilha-Leitao et al. (2006) for conservation planning, and metrics used to 

compare real with simulated landscapes (e.g., Gardner and Urban 2007). Following patch 

identification by land cover type and watershed, we computed the following metrics (Table 34):

- percent of the watershed in a given land cover groups;

- number of patches > 1ha;

- weighted mean patch size (across all patches of a cover type); and

- largest patch index.
125

Table 34. Landscape metrics computed for the Big Thompson, Cache la Poudre, Saint Vrain, and 

Colorado River watersheds, Rocky Mountain National Park.

For the most part, the metrics we computed focus on patch size and functions of the patch size 

distribution. We believe this focus is justified because one of the most clear and lasting 

indicators of disturbance at the landscape scale is the proportion of the vegetation mosaic. In 

addition, by substituting space for time we believe there is value among watershed comparisons 

to provide an initial estimate of the range of natural variability (Landres et al. 1999) in the 

estimated metrics. Note that as a result of the decisions we made about grain size and number of 

land cover categories, our focus is not on contemporary human-caused fragmentation. Rather, in 

this report our emphasis is on the major patterns of landscape heterogeneity that have arisen from 

natural topographic and edaphic variability, past land-use history, and past disturbance events.

A second analysis was done to generate a cumulative distribution function (CDF) that portrayed 

the distribution of the area of patches of different ecological systems. We then produced a CDF 

for HUC-12 watersheds overlapping ROMO versus other watersheds outside the park but within 

the Southern Rocky Mountain ecoregion (SRE).

Reference Condition

Our approach for the reference condition is to use an Ambient Distribution, comparing ROMO to 

its broader context of the southern Rockies Ecoregion. One challenge with this approach, 

however, is the relatively few number of watersheds within ROMO, which might result in 

somewhat rapid jumps in cumulative distribution function values. Note that we tried using 

LANDFIRE’s biophysical setting dataset as a benchmark to compare back to existing vegetation 

types (current), but there was little observed difference.

Landscape Metric Equation Units Interpretation

Percent of landscape (P) Percentage

Proportion of the landscape 

occupied by cover type i

Number of patches (NP) Number

Number of patches in the 

landscape of cover type i

Patch density (PD)

(10,000)(100)

Number of

patches/100 ha

Number of patches of land 

cover type i per 100 ha

Area weighted mean patch 

size (Sav)

Hectares

Area-weighted mean patch 

size for land cover type i

Largest patch index (LPI) Percentage

Largest patch of land cover 

type i divided by total 

landscape area
126

Results/Discussion

We provide summaries of results by both the four main watersheds (8-digit HUCs) and HUC-12s 

constrained to be within ROMO so that they are consistent with other indicators. The HUC-8s 

varied widely in area within the boundaries of ROMO: Big Thompson 42,091 ha; Cache la 

Poudre 14,569 ha; Saint Vrain 15,212 ha; and the Colorado River 48,162 ha. The watersheds 

were similar in terms of percent area by land cover type with all watersheds dominated by upper 

montane forest (Table 35; range 31.8 - 62.7%). The next most dominant land cover type was 

alpine tundra (range 14.3 – 20.2%). Less than five percent of any watershed was in wetland or 

riparian vegetation. Overall, the distributions of patch proportions by cover type were similar 

across watersheds.

The total number of patches varied widely across watershed ranging from 1999 patches ≥ 1ha in 

the Big Thompson to 481 in the Cache la Poudre. Correcting for watershed area, patch density (#

patches/100 ha) was less variable ranging from 11.5 to 6.1 in alpine tundra to 11.7 to 8.1 in upper 

montane forest. The distribution of the number of patches by cover type was most equitable in 

the Big Thompson watershed but dominated by snow/ice, alpine tundra, and upper montane 

forest in the other watersheds. By patch number and composition, lower montane forest and 

aspen forest/woodland were the most rare cover types.

Table 35. Summary of class-level landscape metrics by watershed for the areas within ROMO. P-percent 

of watershed in cover type; NP – number of patches >1 ha; PD – patch density (number/100 ha); Sav –

weighted mean patch size; LPI – largest patch index.

Watershed Land Type P(%) NP (>1ha) PD Sav (ha) LPI (%)

Big 

Thompson

Snow/ice 4.4 242 2.6 39.7 (7.9) 0.5

Alpine tundra 14.3 246 6.1 472.8 (33.2) 3.1

Upper montane forest 31.8 343 10.2 1768.2 (74.1) 8.5

Mid-montane forest 5.2 187 3.2 46.8 (8.5) 0.4

Lower montane forest 3.7 153 3.4 52.0 (7.4) 0.5

Lodgepole pine forest 18.0 250 6.8 697.5 (42.9) 4.5

Aspen forest/woodland 13.5 311 9.3 457.4 (25.7) 3.2

Riparian/wetland 3.5 267 2.6 10.8 (3.6) 0.1

Cache la 

Poudre

Snow/ice 5.9 99 3.6 37.5 (7.6) 0.9

Alpine tundra 20.2 143 11.5 149.2 (16.1) 3.2

Upper montane forest 62.7 115 8.1 6747.2 (228.8) 53.8

Mid-montane forest < 1 0 0 --- ---

Lower montane forest < 1 0 0 --- ---

Lodgepole pine forest 3.3 44 4.1 25.9 (4.5) 0.5

Aspen forest/woodland 0.1 1 0.3 1.5 (0.6) 0

Riparian/wetland 4.6 79 1.4 36.5 (10.3) 0.8

Colorado Snow/ice 7.0 262 3.2 83.5 (13.3) 0.7

Alpine tundra 11.7 394 10.3 114.3 (11.3) 1.4

Upper montane forest 43.6 313 8.9 5579.0 (16.5) 22.5

Mid-montane forest 1.0 47 1.3 23.1 (4.2) 0.2

Lower montane forest 0.1 4 0.4 0.9 (0.4) 0.1

Lodgepole pine forest 23.1 196 5.0 2113.5 (99.0) 9.7

Aspen forest/woodland 2.2 129 3.9 16.8 (3.0) 0.2
127

Watershed Land Type P(%) NP (>1ha) PD Sav (ha) LPI (%)

Riparian/wetland 5.8 266 2.2 43.3 (10.4) 0.4

Saint Vrain Snow/ice 7.2 109 3.8 35.9 (8.0) 0.9

Alpine tundra 17.7 71 8.2 268.3 (24.0) 5.0

Upper montane forest 39.9 120 11.7 1318.1 (67.0) 16.7

Mid-montane forest 1.3 22 1.2 17.5 (4.3) 0.3

Lower montane forest 0.5 8 0.5 8.6 (2.7) 0.1

Lodgepole pine forest 14.6 71 5.4 241.6 (25.3) 4.4

Aspen forest/woodland 11.2 66 5.0 292.5 (25.6) 3.9

Riparian/wetland 3.9 81 1.9 10.6 (4.2) 0.3

Weighted-mean patch size (Sav) and largest patch index (LPI) are good measures of the degree to 

which a cover type is characterized by large to very large patches. With the notable exception of 

upper montane forest in the Cache la Poudre watershed, there is very little dominance by large 

patches for any of the cover types (Figure 56).

Figure 56. Weighted mean patch size (log10 transformed) by land cover type, by watershed, Rocky 

Mountain National Park. Error bars are +/- 20% of the mean.
128

Cumulative distribution functions (CDFs) allow one to easily infer the proportion of patches 

below (above) a given patch size (Figure 57 and Figure 58). For example, about 60% of aspen 

patches in the park are < 100 ha. Comparison of CDFs by cover type across watersheds shows 

that the watersheds are mostly similar in their patch size distributions. The Colorado River 

watershed is somewhat distinct from the others in having fewer small and more large patches for 

all cover types. Comparing the CDF between the park and SRE (Figure 57 and Figure 58), we 

found that the park and the southern Rocky Mountain ecosystems were similar in their patch size 

cumulative distributions. There was some indication that there were more large patches of high 

elevation forests, especially lodgepole pine and riparian vegetation within the park. Based on 

these analyses, and an assumption that patch sizes are a product of mostly natural disturbance 

events, we conclude that ROMO does not deviate substantially from the broader southern Rocky 

Mountain ecoregion to which it belongs.
129

Figure 57. Cumulative distribution functions comparing patches found inside the park vs. outside (but in 

the southern Rockies Ecoregion): Alpine/Tundra, High Elevation Forest, Mid-elevation Forest, and Low 

Elevation Forest.
130

Figure 58. Cumulative distribution functions comparing patches found inside the park vs. outside (but in 

the southern Rockies Ecoregion): Aspen, Lodgepole Pine, Riparian/Wetland, and Savannah.
131

We also compared the weighted mean patch size (WMPS) values between SRE and ROMO 

(Table 36). WMPS weights large patches more heavily than small patches. The result is that the 

WMPS estimates are skewed in favor of large patches which may have greater ecological 

importance than small patches. In general, WMPS are smaller in ROMO than in the SRE, except 

for upper- and mid-elevation forests. It is difficult to provide a clear causal explanation for these 

differences, which may be a consequence of differing disturbance histories.

Table 36. Weighted mean patch size values between SRE and ROMO.

Ecological system

Weighted mean patch size (ha)

SRE ROMO

Alpine tundra

Upper elevation forest

Mid-elevation forest

Low-elevation forest

Aspen

Lodgepole

Riparian/wetland

Savannah

654

1,868

2,873

3,140

1,311

2,114

44

3,146

180

2,600

1,205

443

262

1,293

48

15

Creation of patch maps from classified imagery is a function of pixel size, the number of land 

cover categories recognized, and the neighborhood rule used to define patch boundaries. Our 

results must be viewed in the context of the decisions we made on resolution, reclassification and 

the 8-neighbor patch geometry. 

Extensive heterogeneity or patchiness appears to be the rule for the dominant cover types in 

ROMO. There are two main sources of heterogeneity that led to vegetation patchiness—these are 

environmental heterogeneity and disturbance history. Environmental heterogeneity in ROMO is 

the result of changes in climatic variables along steep and extensive elevational gradients, 

sharply contrasting slope and aspect positions, variable soil and substrate conditions, and 

variable moisture conditions at local (differing aspects) and regional (east and west of the 

Continental Divide) scales. These physical attributes of the park are differentially aligned with 

the fundamental niches of the dominant plant species resulting in fine scale heterogeneity and 

patchiness (see discussion in Knight and Reiners, 2000). 

Variable disturbance regimes add to environmental heterogeneity and contribute to vegetation 

patchiness. The main disturbance factor in the forests of ROMO is probably fire (Veblen et al. 

1994) and it differentially affects various plant communities. For example, the continuous 

distribution of fuels in upper montane (spruce/fir) and lodgepole forests predispose these forests 

to stand-replacing or crown fires. In contrast, most fires in lower montane forests (dominated by 

ponderosa pine) are surface fires carried mainly by grass fuels (Veblen 2000). Disturbance by 

fire also varies in its return interval and this in turn varies by forest type. Lower montane forests 

experience light understory fires every 5-40 years (Veblen 2000). In contrast, upper montane 

forests experience stand-replacing fires about every 100-500 years (Veblen 2000). Patch size 

varies as a consequence of what components of the stand are removed by fire (e.g., crown or 

understory and crown) and the return interval. Thus, we would expect upper montane forests and 

lodgepole pine forests to be characterized by large, even-aged patches. On the other hand, lower 
132

montane forest should be characterized by numerous small patches. In general, this pattern was 

apparent in the LANDFIRE data set.

Other disturbance factors such as insect outbreaks (e.g., mountain pine beetle, spruce beetle, and 

western spruce budworm) and windthrow also contribute to vegetation patchiness. Currently, 

lodgepole pine forests (and to a lesser degree ponderosa pine forests) on the Colorado River 

watershed are experiencing a significant mountain pine beetle infestation (Sibold et al. 2007), 

particularly around Grand Lake on the west side of the Park. Based on its extent and the pattern 

of mortality, this infestation should result in the production of large, continuous patches of 

lodgepole pine in the future. In general, if a disturbance event is both spatially extensive and 

results in the mortality of canopy dominant vegetation, large patches should be produced. On the 

other hand, small, frequent disturbances that only infrequently lead to mortality of dominant 

vegetation will result in many small patches. It is a tenable hypothesis that the patterns we 

observed in the patch-size distributions reflected these differential disturbance processes.

By substituting space for time, the comparison of landscape metrics among watersheds provides 

an initial estimate of the range of natural variability in the environment. For the park, this range 

of variability appears to be very narrow since watersheds were most notable for their similarity 

in land cover composition and patch-size distributions and not their differences. It is highly 

likely that these watersheds would begin to show more extensive differences if lower elevation 

sites with greater human disturbance were included in an analysis.

Our analyses did not account for fragmentation arising from roads and developments, except for 

major highways and dense urban areas that are mapped in the LANDFIRE dataset. However, in 

ROMO these influences are relatively minor and restricted to the community of Estes Park and 

Trail Ridge Road. Subsequent development of human infrastructures will inescapably lead to 

greater fragmentation, particularly to lower montane plant communities. We believe that most of 

the variability in patch size distributions in ROMO is not the consequence of human-induced 

fragmentation. Rather, we believe that the observed patchiness of the landscape is due to past 

natural disturbances and topographic variability. Variation in elevation, slope, aspect, and 

edaphic conditions probably contribute extensively to vegetation heterogeneity in ROMO.

Uses and Limitations

Overall, we believe that this indicator supports a low degree of concern. Our level of confidence

in this indicator is moderate/low. There is moderate agreement among experts that the extent and 

proportion of ecological systems is a valuable way to characterize a landscape, but there is less 

agreement on how this is interpreted in terms of ecological health. 
13

3

Summary: Landscapes - Extent and pattern of major ecological systems

What: Landscape metrics that characterize the extent and pattern of 

major terrestrial ecological systems using LANDFIRE 

existing vegetation types.

Why: These provide the landscape context of major ecological 

systems.

Stressors: Visitor use, land use change, climate change, exotics

Confidence: Low degree of concern; low evidence, moderate agreement

Generally the extent and pattern of patches within the park are similar to 

those found within the broader Southern Rockies Ecoregion (SRE), although 

there was some indication that the park contains more large patches of upper 

elevation forest, but smaller patches of alpine tundra, mid- and lowelevation forest. 

Measure Vegetation Type

Reference 

Condition 

(SRE)

Current 

Condition 

(ROMO)

Weighted 

mean patch 

size (ha)

Alpine tundra 

Upper-elevation forest

Mid-elevation forest

Low-elevation forest

Aspen

Lodgepole

Riparian/wetland

Savannah

654 

1,868 

2,873 

3,140 

1,311 

2,114 

44

3,146

 180

 2,600 

 1,205 

 443 

 262 

 1,293 

 48

15

1

Patches are defined by 12-digit HUC boundaries and contiguous (8 neighbor)
134

4.2.7 Landscapes: Connectivity of Natural Landscapes

Author: David M. Theobald

Description/Purpose

An important research challenge for ecology and conservation science is to better understand 

how land use changes and other human modifications affect the flow of ecological processes and 

movement of species across a landscape (Taylor et al. 1993). Landscapes have been and continue 

to be modified by humans, particularly intense human land uses beyond the urban fringe that 

occupy 10-20% of the U.S. (Theobald 2001). These lands form the landscape context for most 

parks and protected areas. Natural resource managers in particular have a clear and immediate 

need to examine landscape connectivity issues. For example, the challenges presented by 

changing landscapes surrounding protected lands have been identified as a top priority issue by 

public land managers (GAO 1994; Bosworth 2004; Fancy et al. 2009). These landscape changes 

around and within a park provide key information especially to better understand how a park is 

connected into a broader network of protected areas (e.g., Rosenberg et al. 1997; Quinby et al. 

2000; Goetz et al. 2008), what adjacent areas are most likely to be used for broader ecological 

flows into and out of a protected area (Hansen and DeFries 2007), and what the likely 

consequences of ongoing land use change and transportation fragmentation might be (WGA 

2008). The effects of land use have increasingly been a concern to ecologists (Dale et al. 2000, 

Brown et al. 2005, Theobald et al. 2005, Hilty et al. 2006), particularly the effects on 

connectivity for species of conservation concern in the western U.S. (Chetkiewicz et al. 2006, 

Beier 2007).

In addition to general purpose metrics, specific composite indices have been developed to 

characterize the effects of human activities, such as maps of “wildness” or “remoteness” (Lesslie 

et al. 1988; Kliskey 1998; Aplet et al. 2000) and the human footprint (Hannah et al. 1995; 

Sanderson et al. 2002; Leu et al. 2008; Woolmer et al. 2008). Typically, these efforts combine 

mapped attributes on human population density, land cover, roads, and utility infrastructure. 

Although these maps have found broad application, the interpretation of the index values is 

challenging because the ranked scores of several factors, which are created by converting raw 

values (e.g., people/ha) through arbitrarily defined classes, are combined to generate a composite 

index. These indices are difficult to interpret because they have no physical interpretation and 

because the classed values typically violate the additivity axiom (Schultz 2000). 

The natural landscapes indicator described here builds on work that evaluated temporal changes 

in forest fragmentation (Wickham et al. 2008; Riitters et al. 2009) and measures “naturalness” of 

a landscape -- or conversely, the degree of human modification. “Natural” landscapes are 

characterized by a high proportion of natural land cover types (i.e. forest, grassland, wetlands), 

as opposed to human-dominated types (i.e. urban/built-up, agricultural, roads). This approach 

incorporates data on land cover modifications including presence of roads, human activities 

associated with developed land use and road use (traffic), and how the broader landscape context 

modifies local conditions. As such, it does not model the movement of a particular species, but 

rather provides a general indication of the permeability of a landscape. This indicator assumes 

that, in general, animals avoid human-modified lands – which is a reasonable assumption 

particularly for species that were identified by the park managers to be of interest for broader, 

landscape connectivity (i.e. Canada lynx, mountain lion, and gray wolf).
135

Approach/Methods

The Natural Landscapes (NL) metric estimates the proportion of natural cover I at a location 

(e.g., a raster cell), which can be loosely interpreted as the probability p that it is natural (or 

probability that it serves as habitat). The likelihood that a given cell pc will be influenced by one 

of its neighboring cells i is the product of the proportions (pcpi):

Ij = pi pc

i

n

∑ n

where pc and pi are the proportions of a land cover class in the center and neighboring cell (8 

neighbors), at resolution or level j with j=1...k. This follows Jaeger’s (2002) interpretation of meff

approximating the joint probability of use between two adjacent patches. Note that the center cell 

c is included in the neighborhood of i to n cells, so n=9. 

To generate an integrated, multi-scale metric, Ij is integrated across neighborhoods using a 

hierarchical, multi-level method where I is computed at each scale j, then aggregated using a 3x3 

window to compute the mean value at a coarser resolution (Ij+1), and so on until j=k. Here the 

resolution at j=1 is 270 mI was computed using a logarithmic progression of neighborhood sizes 

(after Riitters et al. 2002) so k=6: 7.3, 65.6, 590, 5,314, 47,829, and 430,467 ha. Once Ij is 

computed for each neighborhood, the simple arithmetic mean is calculated across all scales at the 

finest resolution (j=1, 270 m), which is called the Natural Landscapes (NL) metric:

NL = Ij

j=1

k

∑ k

This provides a computationally efficient way to incorporate both local and broad-scale structure 

into a comprehensive metric whose values rise monotonically from 0.0 to 1.0 with decreasing 

human modification. Because NL integrates the proportion of naturalness at different scales, it 

characterizes both compositional and structural aspects of the landscape and measures structural 

connectivity at a broad, landscape level. Note that the effective weighting at each level j (fine to 

coarse) is 29%, 24%, 18%, 14%, 10%, and 5% because the neighborhoods are cumulative rather 

than incremental.

There are four sources of data that were used to generate the NL metric for ROMO: 1) land 

cover; 2) housing density; 3) roads; and 4) highway traffic volume. The selection of these 

surrogate variables is supported by Woolmer et al.’s (2008) finding that the three most important 

variables of impact were land use/cover, human settlement, and roads. 

For land cover, I used data from the Multi-Resolution Land Characteristics Consortium’s 

National Land Cover Dataset Retrofit Land Cover Change Product (NLCDr; Fry et al. 2009; 

www.mrlc.org). This product was developed using a consistent processing method specifically 

designed to capture land cover changes between 1992 and 2001. The major NLCDr cover types 

were assigned a value of 1 for each 30 m pixel of “natural” cover types and a value of 0 to 

“human-modified” types (Table 37). Note that in the NLCDr most major roads (interstates, 

highways) are classified as urban/built-up as an artifact of the classification process. These 
136

artifacts are particularly problematic in rural areas, so the NLCDr data were reprocessed to 

replace cells classified as urban/built-up with adjacent natural land cover types. Note that most 

secondary and local roads in rural areas tend not to be classified as urban/built-up in the NLCDr 

data.

Table 37. Assignment of natural/human-dominated values for each National Land Cover Dataset (retrofit) 

class. The land cover types water (rivers, lakes, and reservoirs, oceans) and barren (which can be both 

natural rock/talus in alpine tundra areas) – and were coded as “no data” to exclude them from the 

analysis, because there is ambiguity between reservoirs and lakes, and tundra and mining.

Class Anderson Level I 

Code

Natural/

Human-dominated

Water 1 No data

Urban/built-up 2 0

Barren 3 No data

Forest 4 1

Grassland/shrubland 5 1

Ag (cropland) 6 0

Wetlands/riparian 7 1

Snow/ice 8 1

Land cover modifications associated with low-density residential housing (<1 unit/ha) were not 

captured in the urban/built-up class of NLCDr (Theobald 2001; Irwin and Bockstael 2007) but 

have important and widespread effects on habitat and ecological processes (Theobald et al. 1997; 

Hansen et al. 2005; Merenlender et al. 2009). Consequently, I incorporated data on housing 

density by finding the minimum value of the proportion of natural land cover (I1) and amount of 

human modification of habitat caused by residential development. Commonly, the extent of 

human modification is estimated as a 100 m radius buffer around each housing unit (Theobald et 

al. 1997; Gonzalez-Abraham et al. 2007), but here I used empirical estimates of the “footprint” 

of land cover modifications that was visible from aerial photography around each housing unit 

(Leinwand 2009): 4.65, 2.65, and 0.33 ha per unit for rural (<1 unit per 16 ha), exurban (1 unit 

per 1 to 16 ha), and suburban/urban (>1 unit per 1 ha) densities. To calculate NL for 1992, I 

adjusted the NLCDr values by the human modification associated with housing density for 1990. 

Similarly, I adjusted the 2001 NLCDr using housing density for 2000. To approximate future 

conditions, I used housing density that was forecasted for 2030 (see Theobald 2005 for detailed 

methods) with NLCDr for 2001. Because predicted land cover data in 2030 do not exist, land 

cover types remained static from 2001 to 2030.

I incorporated further likely reductions in “naturalness” due to the presence of highways, 

secondary, and local roads. This was estimated by measuring the footprint (road and adjacent 

disturbed area) visible from aerial photography to estimate the proportion of a 30 m cell that was 

affected by different road types from the Streetmap 2006 database (ESRI 2007): interstates and 

state highways 100%; secondary roads 50%; local roads 30%; 4WD roads 10%. Because 

incorporating the effects of road use, not just presence, is important (Forman & Alexander 1998; 

Chruszcz et al. 2003; Alexander et al. 2005), I included further reductions of naturalness from 

likely disturbance near highways from traffic (Jaeger et al. 2005). I used data on highway traffic 

volume measured by the annual average daily traffic (AADT; USDOT 2007). I then applied a 
137

quadratic kernel density filter to generate a “smoothed” traffic volume raster, s, reflecting the 

assumption that the impact declines with distance out to 1 km away from a road (based on 

Forman & Alexander 1998). The proportion of habitat loss from highway use r was computed as 

a non-linear function of the smoothed AADT values:

For example, the impact r for a cell on a highway with AADT ≥10,000 is 1.0, while an AADT of 

5,000 is 0.71 and AADT of 100 is 0.1. For a cell that is 1 km or more from a highway, regardless 

of its use level, r = 0.0. Although the form of distance-decay and level of impact is arbitrary, it is 

based on general findings from road ecology on the distance decay effects from vegetation 

modifications, additions of materials and chemicals (e.g., salt), changes to hydrology, generation 

of noise and light, and habitat collisions (Forman et al. 2003, pg. 308). I also incorporated habitat 

loss due to the presence of active railways, t, by assuming the impact was 0.5 on 270 m cells that 

intersect railways and declined to 0.0 at a distance of 1 km (because we do not have data on rail 

use, here we are estimating only the effect of railway presence). The incremental models do not 

assume a cumulative effect; rather I make the conservative assumption that the degree of human 

modification is the maximum effect of any one dataset.

I examined the landscape dynamics of the ROMO by calculating NL for recent (1992), current 

(2001), and near-term (2030) conditions, and I summarized NL scores by the ROMO boundary 

and the Southern Rockies Ecoregion (Bailey 1995). Note that we did not summarize this 

indicator by watershed here, because this metric characterizes patterns that are much broader 

than the watersheds (HUC12) that were used to summarize patterns within the park.

Reference Condition

The approach for the reference condition for this indicator was to use the Best Professional 

Judgment method to identify the weight values for the human-modified metric. From these 

metric values computed both within the park and for the broader Southern Rockies Ecosystem 

(SRE), the Ambient Distribution computed for the SRE was computed to characterize how the 

park compares to its broader context. 

Results/Discussion

As expected, the NL metric values indicate that ROMO is a highly natural setting, with a mean 

value of 0.9577 in 1992 and 0.9555 for 2001 (Figure 59). Within the park, some fine-scale 

variation can be seen, such as roads (Trail Ridge Road), but in surrounding areas where much 

more human-modification has occurred there are more patterns visible, such as adjacent 

highways (e.g., US-34 to Estes Park). The mean NL value for ROMO has declined slightly in the 

past from 0.9577 to 0.9555 from 1992 to 2001 (Figure 60), and will likely decline further to 

0.9471 by 2030 (Figure 61). This corresponds (roughly) to a loss to the park of 237 ha by 2001 

and an additional 903 ha due to forecasted housing growth outside the park by 2030. 

Park and ecoregional scores can be compared to understand the immediate ecological context

(Figure 62): e.g., Rocky Mountain’s score is about 0.026-0.030 above its ecoregion and declining 

over time: but the decline in ecoregional NL value (0.0075) is roughly 3 times the decline for the 

park (0.0022) from 1992 to 2001. 
138

Compared with other national parks, ROMO is set within a fairly natural context, compared to 

Delaware Water Gap (0.6431) and Yosemite (0.9520), but Yellowstone is slightly better 

(0.9749). Landscape change surrounding a park is readily quantified over time with NL in 

anticipation of residential growth in nearby areas.
139

Figure 59. Natural landscapes (NL) metric values in 1992 for Rocky Mountain National Park, Colorado (in center of image, boundary shown in 

black). The mean NL value is 0.9577 – with high values in blue, low values (highly-modified) in yellow/orange, and water/snow as white (NO 

DATA).
140

Figure 60. Natural landscapes (NL) metric values in 2001 for Rocky Mountain National Park, Colorado. The mean NL value is 0.9555 – more 

natural landscapes are shown in blue, while highly-modified landscapes (low values) are shown in orange/red.
141

Figure 61. Natural landscapes (NL) metric values in 2030 for Rocky Mountain National Park, Colorado. The mean NL value is 0.9471 – which 

includes only changes due to forecasted housing density outside of the park. Natural landscapes are shown in blue, while highly-modified 

landscapes (low values) are shown in orange/red.
142

Figure 62. Natural landscapes (NL) values in 2001 for the Southern Rocky Mountain Ecoregion

(boundary shown in thick grey line). Natural landscapes are shown in blue, while highly-modified 

landscapes (low values) are shown in orange/red.
143

Uses and Limitations

The results of this indicator suggest a high degree of concern, because the NL values for much of 

the ROMO area are among the top 10% within the Southern Rockies Ecoregion. Moreover, 

residential growth at the southwestern boundary of ROMO (with Granby, Frasier River corridor, 

etc.), more extensive land use associated with the Colorado State Forest, and widespread beetle 

outbreak, all combine to justify a high degree of concern. Overall our level of confidence in this 

indicator is moderate/low. There is moderate agreement among scientists that broad-scale, 

landscape connectivity among large patches of relatively intact habitat is critical for long-term 

ecological processes. However, we have relatively low amount of evidence to parameterize 

landscape connectivity models—particularly in the response of species and processes to likely 

fragmentation effects of roads and urban areas.

Advantages of the NL metric in evaluating the effect of humans on natural landscapes include: a 

simple metric that has a direct physical interpretation related to proportion of natural habitat lost 

at a location; includes broader, landscape-scale patterns to differentiate the spatial context; and 

represents landscapes as a gradient rather than binary or categorical objects (e.g., patch). Because 

it does not rely on patch definition nor on pre-established critical scales, NL avoids a persistent 

problem of the arbitrariness of defining a patch. Although the specification of neighborhoods 

used in computing NL was arbitrary, there is broad support in the literature (e.g., Forman et al. 

2003; Hansen et al. 2005) for a distance-decay effect from localized land cover changes. 

Expanding or contracting the size of a neighborhood along the logarithmic progression results in 

relative weighting changes less than 4.7%. There are some slight artifacts in the maps (vertical 

and horizontal lines) that are caused by aggregating hierarchically to coarser resolution (rather 

than using moving windows), but the advantage to use aggregation is that overall proportions are 

maintained precisely with each larger neighborhood, whereas there can be some smoothing 

effects using moving windows. 
144

Summary: Landscapes - Connectivity of natural landscapes

What: Measures the natural landscape context of ROMO 

within the Southern Rockies Ecoregion (SRE).

Why: Movement of animals and ecological processes connect 

the park to adjacent landscapes beyond the park 

boundary. 

Stressors: Land use change, climate change

Confidence: High degree of concern; moderate evidence, moderate 

agreement

Rocky Mountain National Park maintains natural landscapes that are 

placed within a key location to maintain connections to adjacent 

natural areas, especially to the north and west.

Time period

Natural Landscapes 

metric

Reference (SRE)

Natural Landscapes 

metric

ROMO

1992

2001

2030

0.931678

0.924163

0.916705

0.957785

0.955563

0.947110

* Data sources: NLCD retrofit product, SERGoM (Theobald 2005); 

National Transportation Atlas
145

5. Natural Resource Condition Summaries

Our goal in this section is to summarize findings about the natural resource condition of ROMO. 

Our approach here is explicitly not to provide a cumulative score that integrates across all 

indicators for each watershed or management unit, nor a single, overall park condition score. We 

do not provide an overall score both because there is a paucity of data and knowledge to make 

such statements for nearly all indicators, and because of challenges to combining indicators that 

respond in non-linear ways (Schultz 2001). Moreover, the advisory team provided guidance that 

the best use of this report and datasets is to provide some general indication of concern, and to 

provide summaries of the indicators by watershed. The ideal use of the findings of this report and

the spatial datasets provided with the assessment is to inform and support park managers and 

scientists in developing recommendations about overall conditions in the park. We provided a 

general discussion and summarize each indicator by HUC-12 watershed (Table 38) as well as the 

raw and normalized data in a well-documented summary GIS dataset that can be used by park 

staff to explore further. Our intent is to help provide a decision support system that facilitates 

park managers and scientists to explore the spatial pattern of high and low scores that reflect 

different incorporation of and weighting of different indicators. We also discussed our indicators 

within the context of management zones.

Generally, lakes in east-side watersheds exhibit higher than background nitrate concentrations, 

lower than expected acid-neutralizing capacity, and elevated concentrations of dieldrin and DDT, 

while lakes in west-side watersheds exhibit harmful concentrations of mercury. We did not 

summarize the indicators of atmospheric deposition by watershed because they reflect a broader 

scale pattern.

Roughly half of the riparian and wetland areas are in the Fall River, East Inlet, Hague Creek, 

Colorado River West, Big Thompson River West, Cache la Poudre South, Colorado River North, 

and North Inlet watersheds (Figure 63; ordered highest to lowest). Of these watersheds, the Fall 

River, East Inlet, Hague Creek Big Thompson River West, Cache la Poudre South, and Colorado 

River West currently have higher than average levels of freshwater connectivity (ordered highest 

to lowest). Compared to other areas within the broader Southern Rockies Ecoregion, ROMO has 

slightly larger patches of riparian and wetlands areas.
146

Figure 63. Watershed summary of riparian/wetland condition, calculated as the percent of connected 

area currently, normalized by “natural” conditions. Darker green watersheds have higher condition of 

riparian/wetland communities.

Watersheds that have higher than one standard deviation above average of exotic terrestrial plant 

species include the West Fork of the Little Thompson, the Little Thompson River, North Fork of 

the Big Thompson River (East), Colorado River West, and the Big Thompson River West 

(ordered highest to lowest). Of these, a key watershed to focus on would be the Big Thompson 

River West because of its high proportion of exotics and central location within the park. 

Watersheds with the lowest proportion of exotic species include Pennock Creek, Buchanan 

Creek, South Fork Michigan River, Comanche, East Inlet, La Poudre Pass Creek, Hague Creek, 

North Inlet, Cache la Poudre, and Michigan Rivers (ordered lowest to higher). The southern and 
147

northern watersheds have relatively low percentages of exotic species, though the southern 

watersheds (East and North Inlets) should be monitored closely because of their adjacency with 

areas of high exotic species (Colorado River West, Big Thompson River West). The key 

watersheds in terms of beaver restoration potential include the Big Thompson River West, 

Colorado River North, and Onahu Creek watersheds. Historically these had relatively high 

densities of beaver.

The patch sizes of upper-elevation forests and riparian/wetland communities, particularly in the 

north and south-central watersheds (Figure 64 and Figure 65), are significantly larger than those 

on average in the Southern Rockies.

Figure 64. The ratio of patch size in ROMO to the weighted mean patch size for the Southern Rockies 

Ecoregion (the reference distribution), averaged within each HUC12 watershed. Areas in red show 

watershed condition lower than the reference distribution, while blue shows higher than reference 

condition.
148

Figure 65. The ratio of patch size in ROMO to the weighted mean patch size for the Southern Rockies 

Ecoregion (the reference distribution), averaged within each HUC12 watershed. Areas in red show 

watershed condition lower than the reference distribution, while blue shows higher than reference 

condition.

About 7% of the park is accessible to visitors within one-hours travel time (one-way), but about 

50% of the park is accessible to day use (up to four hours one-way) and 80% within eight hours. 

In terms of potential effects related to visitor use, Aspen Brook, Black Canyon Creek, Fall River, 

Cabin Creek, and the Cache la Poudre South, Onahu Creek, Glacier Creek, and Colorado River 

North watersheds are relatively accessible (ordered most to least accessible Table 38). 

Note that we also provide the summary pages from each individual indicator below.
149

Table 38. A summary of indicator condition by watershed (HUC-12). Note that three condition classes 

(high, moderate, low) provided below are to be used for general interpretation only; they are arbitrarily 

assigned to the values calculated by relating the raw indicator value to its reference distribution. Summary 

values: P = pristine (>95% rip/wet and <2% exotic species); R = restoration opportunity (>10% suitable 

beaver habitat); I = issue (either <2.5 hours travel time or >20% affected by beetle).

Catchment Name

(*mostly outside ROMO)

Rip/Wet 

% Nat

% 

Exotic 

Plants

% Beaver 

Habitat

Upper-elev 

Forest 

Ratio

Travel 

Time (hrs)

% Affected 

by Beetle 

2008

Summary

EAST SIDE

Aspen Brook 100.0 4.50 0.15 0.15 1.59 22.62 I

*Big Thompson River East 93.5 3.68 1.29 0.04 2.04 3.57

Big Thompson River West 100.0 4.62 12.01 0.62 3.67 24.17 R, I

Black Canyon Creek 100.0 1.14 0.90 0.18 2.12 10.46 P, I

Cabin Creek 100.0 3.44 0.09 0.34 2.46 33.84 I

*Cache la Poudre North 35.4 0.31 1.87 2.18 6.26 12.91

Cache la Poudre South 55.7 0.18 10.55 2.00 2.53 46.60 R, I

Comanche 100.0 0.00 0.00 1.44 8.30 0.00 P

Cow Creek 48.5 3.13 0.52 0.52 3.60 8.57

Fall River 97.8 3.64 7.22 0.57 2.39 25.74 I

*Fish Creek 100.0 3.29 0.61 0.12 2.14 4.99

Glacier Creek 76.7 3.10 3.30 0.95 2.98 15.46

Hague Creek 89.4 0.15 4.88 0.91 5.90 25.01 I

La Poudre Pass Creek 57.7 0.11 2.99 2.06 5.54 30.55 I

*Little Thompson River 100.0 8.41 0.00 0.02 2.84 5.15

Michigan River 11.0 0.28 0.00 0.99 7.44 0.00

Middle Saint Vrain Creek 100.0 1.67 0.00 0.52 4.28 11.49 P

*Miller Fork 100.0 2.60 0.00 0.00 3.71 4.95

North Fork Big Thompson (East) 25.9 7.98 0.00 0.00 2.58 0.93 I

North Fork Big Thompson (West) 52.7 0.83 0.38 0.44 3.93 6.04

North Saint Vrain Creek (East) 67.8 0.74 6.93 0.89 4.13 24.93

*North Saint Vrain Creek (West) 62.6 0.95 0.00 0.00 3.20 2.48

*Pennock Creek 100.0 0.00 0.00 0.69 5.49 4.90

*Rock Creek 100.0 2.75 0.00 0.44 2.84 41.71

South Fork Cache la Poudre 100.0 1.25 1.65 0.68 7.30 11.65 P

*South Fork Michigan River 100.0 0.00 0.00 1.36 6.38 0.00

*West Fork Little Thompson River 100.0 9.00 0.00 0.00 2.30 0.57

WEST SIDE

*Bowen 50.4 4.15 7.81 0.62 2.96 10.36

Buchanan Creek 100.0 0.00 0.00 2.78 9.94 0.00 P

Colorado River North 56.3 0.92 12.37 2.68 3.00 23.19 R, I

*Colorado River West 44.3 5.36 0.30 1.21 8.40 10.58

East Inlet 100.0 0.07 2.70 1.76 5.74 12.75 P

North Inlet 91.2 0.16 3.64 1.36 4.51 18.00

Onahu Creek 51.6 3.28 17.83 0.56 2.64 19.03 R

CONDITION/ISSUE (absence)

High 90.0 2.00 5.00 1.10 5.00 10.00

Moderate 80.0 5.00 2.00 0.90 2.50 25.00

Low 50.0 10.00 0.00 0.00 0.00 50.00
150

Table 39. Summary of raw indicator values by management zones outlined in the ROMO Backcountry 

Wilderness Management Plan (BWMP).

Indicator Sub-indicator

measure

Management zones

RNAs Alpine 

Tundra

Cross 

Country Corridors Front 

Country

Not 

covered 

in BWMP

Air & 

climate

Condition of alpine 

lakes

Deposition 

closer to 

pristine

Deposition 

impacted; 

climate at 

risk

- - - -

Atmospheric deposition

Elevated 

on east 

side

Elevated on 

east side - -

Elevated 

on east 

side

-

Water Wetland/riparian areas 7.5% 3% 2% 5.5% 2.5% 0.1%

Biotic 

integrity

Exotic terrestrial

(average % exotic by 

zone)

0.15% 0.04% 6.2% 3.0% 22.5 35.6%

Non-native fish (% of 

streams) 22% 0.08% 13% 16.6% 13.4% 4%

Potentially suitable 

beaver habitat (% of 

zone in habitat)

3% 14% 2% 14% 14% 6%

Landscapes

Ecological systems

(% of zone by system)

Alpine 

Lower Montane

Open Water

Riparian/Wetland

Savanna

Upper Montane

39.0

2.8

1.5

9.1

0.8

46.8

-

42.0

1.3

0.06

6.3

0.001

50.3

19.5

3.7

1.3

14.8

1.7

59.0

1.3

22.0

1.2

9.5

14.4

51.6

3.1

10.8

1.1

22.6

11.2

51.2
151

Discussion

Each indicator described in this report represents only one method for evaluating the rich and 

diverse resources in Rocky Mountain National Park. To conclude with a simple and single 

assessment of the condition of the park as a whole is a complex and complicated task, and one 

that may have very little meaning either to managers or other stakeholders. The approach 

presented here provides a diversity of indicators—based on the best data and science available—

that, when used together, can provide an overall sense of what is occurring in different locations 

and by different measures (Table 40). Through exploring the relevant datasets from each 

indicator and overlaying them with each other, patterns may be observed, additional questions 

will arise, and new strategies for management may evolve.

Table 40. Main findings of the watershed condition assessment.

Level 1 Indicator Reference 

distribution Concern Summary

Air & 

Climate

Condition of 

alpine lakes and 

atmospheric 

deposition

Interpretation of 

historical condition 

and ambient 

distributions to 

identify Minimally 

Disturbed Condition

High degree of 

concern; 

moderate 

evidence, 

high agreement

East-side lakes have higher than 

background nitrate concentrations 

and lower than expected acidneutralizing capacity (ANC) and

concentrations of dieldrin & DDT are 

also elevated, while west-side 

mercury in fish tissue is at or above 

concentrations that are harmful for 

some birds and mammals. 

West-side nitrate and ANC are 

consistent with reference conditions, 

and lake pH and chemical 

concentrations of calcium, chloride, 

sulfate, ammonium for all ROMO 

lakes are consistent with reference 

conditions

Water

Extent and 

connectivity of 

wetland and 

riparian areas

Interpretation of 

historical condition to 

identify Minimally 

Disturbed Condition

High degree of 

concern; 

low evidence, 

moderate 

agreement.

Restoration of four dams and four 

ditches has improved the 

connectivity of riparian and wetland 

communities somewhat from 1900-

1990s conditions, but current 

conditions (shown right) remain 

substantially fragmented compared to 

“natural” hydrological conditions.

Biotic 

Integrity

Extent of exotic 

terrestrial plants

Interpretation of 

historical condition to 

identify Minimally 

Disturbed Condition

High degree of 

concern; 

moderate

evidence, 

moderate 

agreement.

The distribution of known locations 

of exotic plants (especially 

cheatgrass) is strongly correlated 

with accessibility, with roughly 80% 

of known locations in highly 

accessible areas (<1 hour). East-side 

watersheds have higher rates of 

invasive species (2.8% vs. 1.8%), 

and lower montane and riparian 

ecological systems are particularly 

affected.
152

Level 1 Indicator Reference 

distribution Concern Summary

Left blank on purpose

Extent of fish 

distributions

Interpretation of 

historical condition to 

identify Minimally 

Disturbed Condition

High degree of 

concern; 

low evidence, 

moderate 

agreement.

Currently the proportion of streams 

that are “secure” (native only or 

fishless above a barrier) is 10% and 

there are 54 known human barriers, 

and native-only streams have 

increased slightly from 7.0% to 7.3% 

in the past 50 years. 

Extent of 

suitable beaver 

habitat

Best professional 

judgment to identify 

Minimally Disturbed 

Condition

High degree of 

concern; 

moderate

evidence, 

moderate 

agreement.

The current distribution of beaver is 

far below historic levels. Highest 

areas for restoration potential (right 

map) include Colorado River (north); 

Big Thompson River (west), Onahu 

Creek, Cache la Poudre (south), Fall 

River, and Glacier Creek watersheds.

Landscapes

Extent and 

pattern of major 

ecological 

systems

Best professional 

judgment to identify 

Ambient Distribution

Low degree of 

concern; 

low evidence, 

moderate 

agreement.

Generally the extent and pattern of 

ecological systems within the park 

are similar to those found within the 

broader Southern Rockies Ecoregion, 

although there was some indication 

that there were more large patches of 

high montane forest and 

riparian/wetland

Natural 

landscapes and 

connectivity

Best professional 

judgment and 

Ambient Distribution

High degree of 

concern; 

moderate

evidence, 

moderate 

agreement.

How are ranges in habitat and Rocky 

Mountain National Park maintains 

natural landscapes that are placed 

within a key location to maintain 

connections to adjacent natural areas, 

especially to the north and west.

Data and Knowledge Gaps

Major data gaps in this assessment include data on hydrologic variability–both surface and 

ground water flows–as there are only three stream gages in the park with a decent historical 

record. Fine-grained vegetation data outside of the park that is comparable to the ROMO 

vegetation would also be quite useful to better understand the extent and pattern of major 

ecosystem types (rather than relying on relatively coarse 30 m satellite imagery-based data, i.e.,

LANDFIRE). There also were suggestions to better integrate this analysis with the Facility 

Management (FMSS) database.
153

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169

Technical Appendix 1: Stream Connectivity Methods

This document is a brief description of procedures involved in classifying 1:24K streams in 

Rocky Mountain National Park (ROMO) with native and non-native interactions. The objective 

of this analysis is to provide information where native fish are isolated from non-native and 

where they co-exist. This analysis relies on the Functional Linkages of Watersheds (FLoWS) 

data structure to route native and non-native fish up and down stream restricting movement 

based on documented movement barriers (e.g., waterfalls). The starting point of this analysis 

began by generating a FLoWS landscape network, which builds reach to reach relationships 

based on the topology of the NHD 1:24K stream polylines. These relationships assume that 

movement throughout the stream network is unrestricted. To better inform the FLoWS with 

movement restrictions in ROMO, a stream barriers dataset (Figure 41) was used to alter 

relationships so that fish can move downstream through a barrier but not upstream. With the 

network developed, native and non-native fish observations were joined to the FLoWS network 

edges (stream reaches) using the fish observation datasets supplied by ROMO (Figure 42). This 

resulted in an a-spatial representation of native and non-native fish distributions within ROMO, 

but does not account for movement between observations (gaps in observation data) and 

potential overlap. This was address by taking the native and non-native designations and routing 

them up and down stream via the FLoWS edges and barriers dataset. The outline below describes 

this analysis in detail:

I. FLoWS network development

1) Networking NHD 1:24K

a. Generate a Landscape network using the enhanced NHD 1:24K stream reaches 

using the FLoWS tool Polyline to Landscape Network. 

b. Check and clean up network topology issues

a. Check for network errors using the FLoWS tool Check Network Topology, which 

identifies stream reaches with connectivity errors. The stream reaches with topology 

errors are fixed resulting in a topologically correct network and relationships.

b. Generate main channels through braided stream channels using the FLoWS tool 

Clean Braided Channels, which builds a new relationships with braided stream 

channel relationships removed.

c. Build stream barriers into network relationships

i. Attribute stream reaches with barrier information.

ii. The landscape network edges were attributed with barrier information 

using the FLoWS tool Snap Points to Landscape Network Edges, which 

finds the nearest edge to each barrier and attributes the barrier point with 

the edge_rid and the point’s distance ratio from the end of an edge. 

iii. Alter the relationships table to reflect barrier conditions within the full 

network. 

1. Edges to edge relationships (via the relationships table) are removed 

based on edges that have barriers. This “breaks” the network where 

barriers exist, preventing movement upstream.
170

II. Process ROMO fish observation datasets

1) The ROMO streams and lakes datasets are a version of NHD 1:24K stream and water 

bodies that have been attributed with observed fish spp. (species_1, species_2, 

species_3, and species_4). The four fish spp. attributes for the streams and lakes 

datasets where grouped into native and non-native classes based on the species codes 

from the four original attributes, resulting in a 0 if no fish were observed (native or nonnative) and a 1 if a fish was observed (native or non-native). The native fish attribute 

entailed Colorado River Cutthroat (species code CRC) and the Green Back Cutthroat 

(Species code CBC). The non-native fish attribute entails all other fish documented 

within the datasets, which include:

a. Brook Trout (BKT)

b. Brown Trout (BNT)

c. Cutthroat Trout (CUT)

d. Rainbow Trout (RBT)

e. Yellowstone Cutthroat Trout (YCT)

III. Network attribution 

1) The stream and lake fish observation datasets where merged into two rasters 

representing native and non-native fish occurrences for water bodies and stream reaches 

(romo_nat and romo_nonat). These two rasters were evaluated using Zonal Statistics as 

Table using the edges_rid as the zone raster and the romo_nat and romo_nonat rasters 

as the value rasters. This resulted in two tables (romo_nat.dbf and romo_nonat.dbf) 

which were joined to the FLoWS edges feature class (joining on RID) and romo_native 

and romo_non_native fields calculated to 0 or 1.

IV. Network analysis/fish distribution modeling

1) Fish movement analysis

a. The first step of the network analysis is to accumulate the ROMO native and nonnative fields downstream with the FLoWS tool Accumulate Values Downstream.

This analysis generates a new field that contains the total number of reaches 

upstream that have native or non-native fish populations.

b. To capture the upstream and downstream movement potential from observation 

locations, the two downstream accumulation fields were accumulated upstream 

using the FLoWS tool Accumulate Values Upstream, which captures fish movement 

upstream stopping at barriers.

c. To assess the potential spatial distribution of native and non-native fish within the 

network, two fields were added to the FLoWS edges feature class that capture the 

global influence of native and non-native fish distributions (romo_nat and 

romo_nonat). These fields were calculated with a 0 (absent) or a 1 (present) by 

assessing if the sum of the downstream and upstream accumulations were > 0.

d. The global native and non-native attributes were broken into four classes based on 

the relationships between romo_nat and romo_nonat using the flowing rules:

a. If romo_nat > 0 and romo_nonat = 0 (calculate to ‘Native Only’)

b. If romo_nat > 0 and romo_nonat > 0 (calculate to ‘Native / Non-native’)

c. If romo_nat = 0 and romo_nonat > 0 (calculate to ‘Non-native Only’)

d. If romo_nat = 0 and romo_nonat = 0 (calculate to ‘No Fish recorded’)
171

Technical Appendix 2. Life-zone Class Delineation Methods

I. Methods

a. Generate life-zone classes lookup table for ROMO vegetation shapefile 

(romoveg.shp) supplied by ROMO. 

i. The VegReclass attribute (romoveg.shp) was summarized in ArcGIS 9.2 Analysis 

Tools > Statistics > Summary Statistics, generating a new table 

(vegreclass_summary.dbf), which provides all unique vegetation classes (n = 28) 

and the backbone of the lookup table.

ii. Develop six life-zone classes and one class non-life zone class that will be merged 

with adjacent life-zone classes.

1. Six life-zone classes

a. Alpine (GRIDCODE = 1)

b. Savanna (GRIDCODE = 2)

c. Lower Montane (GRIDCODE = 3)

d. Open Water (GRIDCODE = 4)

e. Riparian/Wetland (GRIDCODE = 5)

f. Upper Montane (GRIDCODE = 6)

2. Non-life zone

a. Barren/human modified (GRIDCODE = 0)

iii. Assign each of the 28 unique VegReclass values in the vegreclass_summary.dbf 

one of the seven life-zone classes

b. Develop ROMO life-zone shapefile

i. Add GRIDCODE field to romoveg.shp which will hold a numeric value associated 

with the life-zone class

ii. Join vegreclass_summary.dbf to romoveg.shp joining on Vegreclass field from both 

tables.

iii. Calculate GRIDCODE equal to life-zone gridcode

iv. Convert romoveg.shp to a raster data structure with raster cell values equal to the 

GRIDCODE field with a cell size (10 meters) and extent set to ROMO Digital 

Elevation Model (DEM) to match the other rasters involved in the NRCA project.

v. Assign non-life-zones (GRIDCODE 0) with adjacent life-zone classes with the 

Spatial Analyst Tools > Distance > Euclidean Allocation (ecu_lif_zn)

vi. Convert ecu_lif_zn to a feature class (romo_life_zones.shp) generalizing polygon 

boundaries instead of retaining gridded boundaries.

173

Technical Appendix 3: Beaver Suitable Habitat Modeling
174

ArcCatalog Operations

ProjectRaster_management C:\Beaver\romo_dem_gcs 

C:\Beaver_3\romo_dem_NAD 

PROJCS['NAD_1983_UTM_Zone_13N',GEOGCS['GCS_North_American_1983',DATUM[

'D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],

PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION[

'Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['

False_Northing',0.0],PARAMETER['Central_Meridian',-

105.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin'

,0.0],UNIT['Meter',1.0]];IsHighPrecision NEAREST 9.372539 # # 

"PROJCS['NAD_1983_UTM_Zone_13N',GEOGCS['GCS_North_American_1983',DATUM

['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]]

,PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION

['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER[

'False_Northing',0.0],PARAMETER['Central_Meridian',-

105.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin'

,0.0],UNIT['Meter',1.0]];-5120900 -9998100 

450445547.391054;#;#;0.001;#;#;IsHighPrecision"

ProjectRaster_management C:\Beaver\strm_grad_per 

C:\Beaver_3\strm_grad_NAD 

"PROJCS['NAD_1983_UTM_Zone_13N',GEOGCS['GCS_North_American_1983',DATUM

['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]]

,PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION

['Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER[

'False_Northing',0.0],PARAMETER['Central_Meridian',-

105.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin'

,0.0],UNIT['Meter',1.0]];-5120900 -9998100 

450445547.391054;#;#;0.001;#;#;IsHighPrecision" NEAREST 8.97133 # # 

"PROJCS['NAD_1983_Albers',GEOGCS['GCS_North_American_1983',DATUM['D_No

rth_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIME

M['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Albe

rs'],PARAMETER['False_Easting',0.0],PARAMETER['False_Northing',0.0],PA

RAMETER['Central_Meridian',-

96.0],PARAMETER['Standard_Parallel_1',29.5],PARAMETER['Standard_Parall

el_2',45.5],PARAMETER['Latitude_Of_Origin',23.0],UNIT['Meter',1.0]];-

16901100 -6972200 266467840.990852;#;#;0.001;#;#;IsHighPrecision"

Project_management C:\Beaver\rmnp_veg.shp C:\Beaver_3\rmnp_veg_NAD 

PROJCS['NAD_1983_UTM_Zone_13N',GEOGCS['GCS_North_American_1983',DATUM[

'D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],

PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION[

'Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['

False_Northing',0.0],PARAMETER['Central_Meridian',-

105.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin'

,0.0],UNIT['Meter',1.0]] # 

PROJCS['NAD_1983_Albers',GEOGCS['GCS_North_American_1983',DATUM['D_Nor

th_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM

['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Alber
175

s'],PARAMETER['False_Easting',0.0],PARAMETER['False_Northing',0.0],PAR

AMETER['Central_Meridian',-

96.0],PARAMETER['Standard_Parallel_1',29.5],PARAMETER['Standard_Parall

el_2',45.5],PARAMETER['Latitude_Of_Origin',23.0],UNIT['Meter',1.0]]

ArcMap operations

Project_management rmnp_bdry_albers C:\Beaver_3\rmnp_bdry_nad 

PROJCS['NAD_1983_UTM_Zone_13N',GEOGCS['GCS_North_American_1983',DATUM[

'D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],

PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION[

'Transverse_Mercator'],PARAMETER['False_Easting',500000.0],PARAMETER['

False_Northing',0.0],PARAMETER['Central_Meridian',-

105.0],PARAMETER['Scale_Factor',0.9996],PARAMETER['Latitude_Of_Origin'

,0.0],UNIT['Meter',1.0]] # 

PROJCS['NAD_1983_Albers',GEOGCS['GCS_North_American_1983',DATUM['D_Nor

th_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM

['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Alber

s'],PARAMETER['False_Easting',0.0],PARAMETER['False_Northing',0.0],PAR

AMETER['Central_Meridian',-

96.0],PARAMETER['Standard_Parallel_1',29.5],PARAMETER['Standard_Parall

el_2',45.5],PARAMETER['Latitude_Of_Origin',23.0],UNIT['Meter',1.0]]

Buffer_analysis rmnp_bdry_nad C:\Beaver_3\rmnp_bdry_buff_1K.shp '1000 

Meters' FULL ROUND NONE #

Executing (Buffer_3): Buffer rmnp_bdry_nad 

C:\Beaver_3\rmnp_bdry_buff_1K.shp "1000 Meters" FULL ROUND NONE #

ExtractByMask_sa strm_grad_nad rmnp_bdry_buff_1K 

C:\Beaver_3\romo_strm_grd

Executing (ExtractByMask_4): ExtractByMask strm_grad_nad 

rmnp_bdry_buff_1K C:\Beaver_3\romo_strm_grd

Resample_management romo_dem_nad C:\Beaver_3\romo_dem_9m 

C:\Beaver_3\romo_strm_grd NEAREST

Executing (Resample_6): Resample romo_dem_nad C:\Beaver_3\romo_dem_9m 

C:\Beaver_3\romo_strm_grd NEAREST

ExtractByMask_sa romo_dem_9m rmnp_bdry_buff_1K C:\Beaver_3\romo_dem

Executing (ExtractByMask_7): ExtractByMask romo_dem_9m 

rmnp_bdry_buff_1K C:\Beaver_3\romo_dem

The grid cells of romo_strm_grd and romo_dem were not aligned. They 

were off by approximately 3 m. Using the Georeferencing tool, 
176

we aligned romo_dem with romo_strm_grd. We named the aligned DEM 

romo_dem1

Reclassify_sa romo_strm_grd Value '0 2 1;2 68.139595031738281 NODATA' 

C:\Beaver_3\rmnp_strm_0-2 DATA

Executing (Reclassify_8): Reclassify romo_strm_grd Value "0 2 1;2 

68.139595031738281 NODATA" C:\Beaver_3\rmnp_strm_0-2 DATA

RegionGroup_sa rmnp_strm_0-2 C:\Beaver_3\seeds_temp EIGHT WITHIN 

ADD_LINK #

Executing (RegionGroup_9): RegionGroup rmnp_strm_0-2 

C:\Beaver_3\seeds_temp EIGHT WITHIN ADD_LINK #

Lookup_sa seeds_temp COUNT C:\Beaver_3\rmnp_strm_ct

Executing (Lookup_10): Lookup seeds_temp COUNT 

C:\Beaver_3\rmnp_strm_ct

Reclassify_sa rmnp_strm_ct VALUE '1 5 NODATA;5 1181 1' 

C:\Beaver_3\strm_seeds DATA

Executing (Reclassify_11): Reclassify rmnp_strm_ct VALUE "1 5 NODATA;5 

1181 1" C:\Beaver_3\strm_seeds DATA

Slope_sa romo_dem1.img C:\Beaver_3\rmnp_slope_9m PERCENT_RISE 1

Executing (Slope_12): Slope romo_dem1.img C:\Beaver_3\rmnp_slope_9m 

PERCENT_RISE 1

Reclassify_sa rmnp_slope_9m Value '0 2 10;2 10 30;10 20 50;20 

1492.6871337890625 150' C:\Beaver_3\slope_wt DATA

Executing (Reclassify_13): Reclassify rmnp_slope_9m Value "0 2 10;2 10 

30;10 20 50;20 1492.6871337890625 150" C:\Beaver_3\slope_wt DATA

Reclassify_sa romo_strm_grd Value '0 2 0;2 10 1;10 20 3;20 

68.139595031738281 100' C:\Beaver_3\strm_wt DATA

Executing (Reclassify_14): Reclassify romo_strm_grd Value "0 2 0;2 10 

1;10 20 3;20 68.139595031738281 100" C:\Beaver_3\strm_wt DATA

SingleOutputMapAlgebra_sa 'con (isnull (strm_wt), slope_wt, strm_wt)' 

C:\Beaver_3\strm_slop_wt #
177

Executing (SingleOutputMapAlgebra_15): SingleOutputMapAlgebra "con 

(isnull (strm_wt), slope_wt, strm_wt)" C:\Beaver_3\strm_slop_wt #

CostDistance_sa strm_seeds strm_slop_wt C:\Beaver_3\beaver_cd 1000 #

Executing (CostDistance_16): CostDistance strm_seeds strm_slop_wt 

C:\Beaver_3\beaver_cd 1000 #

PolygonToRaster_conversion rmnp_veg_NAD Beaver 

C:\Beaver_3\romo_veg_beav CELL_CENTER NONE C:\Beaver_3\beaver_cd

Executing (PolygonToRaster_17): PolygonToRaster rmnp_veg_NAD Beaver 

C:\Beaver_3\romo_veg_beav CELL_CENTER NONE C:\Beaver_3\beaver_cd

SingleOutputMapAlgebra_sa 'romo_veg_beav * (1 - (beaver_cd / 1000))' 

C:\Beaver_3\veg_cost #

Executing (SingleOutputMapAlgebra_18): SingleOutputMapAlgebra 

"romo_veg_beav * (1 - (beaver_cd / 1000))" C:\Beaver_3\veg_cost #

Patch statistics by drainage in ROMO. 

Total Area = the total area of potentially suitable beaver habitat in the drainage

# patches = the number of patches of potentially suitable beaver habitat in the drainage

Ave. Area = average area of the patches in the drainage

Std. Area = the standard deviation of the areas of patches in the drainage

Ave. Score = the average beaver habitat suitability score of patches in the drainage

Std. Score = the standard deviation of average beaver habitat suitability scores of patches in the 

drainage

Status 1999-2000 = Active, if evidence of current beaver activity was observed in any patch in 

the drainage during surveys in 1999-2000. If evidence of past beaver activity was 

observed in any patch in a drainage, it is classified as Past Activity, and if no surveys 

were conducted in any of the patches in a drainage, it is classified as No Survey.
178

Drainage Name

Total Area 

(HA)

# 

Patches

Ave. 

Area 

(HA)

Std. Area 

(HA)

Ave 

Score

Std. 

Score

Status 

1999-2000

Colorado River 188.92 47 4.02 10.29 0.60 0.31 Active

Onahu Creek 97.74 7 13.96 35.12 0.36 0.22 Active

Baker Creek 83.73 1 83.73 0.00 0.67 0.00 Active

Fall River 61.67 14 4.40 11.08 0.30 0.25 Active

Big Thompson 

River 56.05 20 2.80 8.56 0.54 0.27 Active

Hague Creek 43.90 8 5.49 8.17 0.50 0.21 Active

Cub Creek 41.94 12 3.50 8.34 0.51 0.18 Active

North Saint 

Vrain Creek 24.60 6 4.10 9.69 0.44 0.18 Active

North Inlet 23.47 11 2.13 6.11 0.46 0.25 Active

Mill Creek 13.15 5 2.63 5.61 0.27 0.30 Active

Boulder Brook 8.68 6 1.45 2.90 0.47 0.19 Active

Black Canyon 

Creek 8.27 1 8.27 0.00 0.72 0.00 Active

Cow Creek 3.77 4 0.94 0.97 0.58 0.18 Active

Beaver Creek 0.18 3 0.06 0.08 0.21 0.12 Active

Cache la Poudre 

River 98.21 14 7.01 18.29 0.50 0.23

Past 

Activity

Willow Creek 33.85 5 6.77 12.02 0.52 0.08

Past 

Activity

East Inlet 18.05 20 0.90 2.05 0.58 0.26

Past 

Activity

Beaver Brook 12.71 16 0.79 1.37 0.38 0.24

Past 

Activity

Glacier Creek 11.36 20 0.57 0.75 0.52 0.25

Past 

Activity

Hidden Valley 

Creek 9.29 10 0.93 1.61 0.47 0.22

Past 

Activity

Hunters Creek 5.76 7 0.82 1.02 0.42 0.32

Past 

Activity

Sandbeach Creek 5.45 5 1.09 1.96 0.31 0.22

Past 

Activity

North Fork Big 

Thompson River 4.35 16 0.27 0.44 0.36 0.20

Past 

Activity

Wind River 1.58 5 0.32 0.36 0.30 0.19

Past 

Activity

Roaring Fork 0.20 1 0.20 0.00 0.35 0.00

Past 

Activity

Cony Creek 5.13 5 1.03 1.08 0.54 0.24

No 

Activity 
179

Drainage Name

Total Area 

(HA)

# 

Patches

Ave. 

Area 

(HA)

Std. Area 

(HA)

Ave 

Score

Std. 

Score

Status 

1999-2000

Ouzel Creek 2.18 1 2.18 0.00 0.61 0.00

No 

Activity 

Chapin Creek 17.27 2 8.64 10.77 0.59 0.02 No Survey

Cascade Creek 16.41 13 1.26 2.47 0.36 0.23 No Survey

Mummy Pass 

Creek 12.38 3 4.13 3.08 0.53 0.14

No Survey

Tonahutu Creek 9.79 30 0.33 0.57 0.35 0.24 No Survey

Fish Creek 6.76 5 1.35 2.62 0.49 0.23 No Survey

South Fork 

Cache la Poudre 

River 6.66 10 0.67 0.96 0.46 0.23

No Survey

Sawmill Creek 2.72 2 1.36 1.86 0.31 0.40 No Survey

Little Columbine 

Creek 2.57 2 1.28 1.44 0.57 0.06

No Survey

Roaring River 1.57 5 0.31 0.56 0.11 0.09 No Survey

Bighorn Creek 1.41 4 0.35 0.24 0.41 0.23 No Survey

Columbine 

Creek 1.15 2 0.58 0.21 0.41 0.13

No Survey

West Creek 0.89 3 0.30 0.50 0.31 0.30 No Survey

Ptarmigan Creek 0.81 3 0.27 0.28 0.53 0.03 No Survey

Shelf Creek 0.80 3 0.27 0.24 0.45 0.27 No Survey

Phantom Creek 0.80 3 0.27 0.19 0.40 0.11 No Survey

Tyndall Creek 0.35 3 0.12 0.13 0.12 0.09 No Survey

Ranger Creek 0.32 1 0.32 0.00 0.47 0.00 No Survey

Chiquita Creek 0.21 1 0.21 0.00 0.25 0.00 No Survey

Squeak Creek 0.15 2 0.08 0.10 0.25 0.03 No Survey

Big Dutch Creek 0.08 1 0.08 0.00 0.17 0.00 No Survey

Echo Creek 0.06 1 0.06 0.00 0.14 0.00 No Survey

Timber Creek 0.04 1 0.04 0.00 0.17 0.00 No Survey

Icy Brook 0.02 1 0.02 0.00 0.01 0.00 No Survey

UNKNOWN*** 92.71 165 0.56 1.19 0.39 0.25

The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific and 

other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and affiliated 

Island Communities.

NPS 121/105044, July 2010
National Park Service

U.S. Department of the Interior

Natural Resource Program Center

1201 Oakridge Drive, Suite 150

Fort Collins, CO 80525

www.nature.nps.gov

EXPERIENCE YOUR AMERICA TM