Future of America’s 

Forests and Rangelands

Forest Service 

2010 Resources Planning Act Assessment
Future of America’s 

Forests and Rangelands

Forest Service 2010 

Resources Planning 

Act Assessment

United States 

Department of 

Agriculture

Forest Service

Gen. Tech. Rep.

WO-87

August 2012
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Abstract

The 2010 Resources Planning Act (RPA) Assessment summarizes findings about the status, trends, and projected future of forests, 

rangelands, wildlife and fish, biodiversity, water, outdoor recreation, wilderness, and urban forests, as well as the effects of climate 

change upon these resources. The outlook for U.S. resources is largely influenced by a set of scenarios that have varying assumptions about global population and economic growth, global wood energy consumption, U.S. population and economic growth, 

land use change, and global climate change from 2010 to 2060. Four key themes from the findings are (1) land development will 

continue to threaten the integrity of natural ecosystems, (2) climate change will alter natural ecosystems and affect their ability to 

provide goods and services, (3) competition for goods and services from natural ecosystems will increase, and (4) geographic variation in resource responses to drivers of change will require regional and local strategies to address resource management issues. The 

results from this report will be useful to resource managers and policymakers as they develop strategies to sustain natural resources. 

U.S. Department of Agriculture, Forest Service. 2012. Future of America’s Forest and Rangelands: Forest Service 2010 Resources 

Planning Act Assessment. Gen. Tech. Rep. WO-87. Washington, DC. 198 p.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment i

Contents

List of Tables............................................................................ iii

List of Figures........................................................................... v

Acknowledgments.................................................................... xi

Executive Summary............................................................... xiii

Chapter 1. Introduction ............................................................. 1

Chapter 2. Synthesis of 2010 RPA Assessment Results........... 3

Chapter 3. Resource Highlights of the 2010 RPA 

Assessment............................................................ 11

Chapter 4. Global and U.S. Context ....................................... 19

Chapter 5. Future Scenarios.................................................... 23

Chapter 6. Land Resources..................................................... 29

Chapter 7. Forest Resources ................................................... 47

Chapter 8. Forest Products...................................................... 63

Chapter 9. Urban and Community Forests ............................. 81

Chapter 10. Carbon in Forest Resources and Products........... 87

Chapter 11. Rangeland Resources .......................................... 97

Chapter 12. Water Resources................................................ 109

Chapter 13. Wildlife, Fish, and Aquatic Resources.............. 123

Chapter 14. Outdoor Recreation ........................................... 147

Chapter 15. Future Resource Challenges and 

Opportunities..................................................... 167

Chapter 16. Conclusions....................................................... 175

References............................................................................. 177

Appendix A. Projected Tree Canopy Cover Change ............ 193

Appendix B. List of Acronyms............................................. 194

Appendix C. List of Scientific Names .................................. 195
ii Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment iii

List of Tables

Table 1. Key characteristics of the RPA scenarios..............24

Table 2. Intergovernmental Panel on Climate 

Change scenarios and general circulation 

models used for the 2010 RPA Assessment 

climate projections.................................................24

Table 3. U.S. land and water area by RPA region, 2008.....30

Table 4. Changes in major land cover and uses in the 

conterminous United States, 1982–2007 ...............30

Table 5. Regional and national summary of landscape 

background for the year 2001 within a 38-acre 

neighborhood surrounding a 0.22-acre parcel of 

(a) any land cover, (b) forest land only, 

(c) grassland only, and (d) shrubland only ............36

Table 6. Regional and national summary of selected 

landscape mosaics in landscapes with seminatural background within a 38-acre neighborhood 

surrounding a 0.22-acre parcel of (a) any land 

cover, (b) forest land only, (c) grassland only, 

and (d) shrubland only...........................................37

Table 7. Definition of International Union for the Conservation of Nature (IUCN) categories for the 

Protected Areas Database of the United States 

(PAD-US) ..............................................................42

Table 8. Protected areas from Protected Areas Database 

of the United States (PAD-US) by ownership 

and RPA region......................................................43

Table 9. Acres and percent of Federal land in the 

National Wilderness Preservation System 

(NWPS) by agency and RPA region, 2009 ...........43

Table 10. Definition of International Union for the 

Conservation of Nature (IUCN) categories for 

the Protected Areas Database of the United 

States (PAD-US)....................................................49

Table 11. Protected forest land cover from Protected 

Areas Database of the United States (PADUS) by ownership and RPA region in the 

conterminous United States...................................49

Table 12. Urban area and urban growth within the 

conterminous United States by RPA subregion, 

1990–2000 .............................................................82

Table 13. Estimated carbon storage and gross annual 

sequestration in trees on urban land by RPA 

subregion in the conterminous United States, 

circa 2005...............................................................84

Table 14. Net annual changes in carbon stocks in forest 

and harvested wood pools, 1990–2009..................88

Table 15. Carbon stocks in forest and harvested wood 

pools, 1990–2010...................................................88

Table 16. Projected forest carbon stocks in conterminous 

U.S. forests by RPA scenario, 2010–2060 ............89

Table 17. Net annual changes in carbon stocks in 

conterminous U.S. forests and harvested 

wood product pools, 2010–2060............................91

Table 18. Net annual contribution of harvested wood 

products to U.S. forestry sector carbon 

sinks and emissions, under three accounting 

approaches, by RPA scenario, 2010–2060 ............92

Table 19. Net annual contribution of harvested wood 

products and U.S. forests to carbon sinks and 

emissions, under three accounting approaches, 

by RPA scenario, 2010–2060 ................................93

Table 20. Estimated rangeland area in the conterminous 

United States by RPA region, using Forest 

Inventory and Analysis (FIA) and National 

Resources Inventory (NRI) definitions of 

rangeland for all rangelands, National Forest 

System (NFS) rangelands, and Bureau of Land 

Management (BLM) rangelands............................97

Table 21. Protected area in States with more than 

1 million acres of rangeland ..................................99

Table 22. Estimates of total forage in the conterminous 

United States from rangeland, pastureland, and 

grazeable forest land............................................104
iv Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Table 23. Breakdown of forage appropriated from U.S. 

rangelands to cattle at the county assessment 

level. Analysis represents average forage 

availability (FA) and forage demand (FD), 

2000–2009 ...........................................................106

Table 24. Percentage of breeding bird species with 

increasing, decreasing, and stable trends, 

1997–2008 ...........................................................131

Table 25. Status of the biological condition of lakes larger 

than 10 acres in the conterminous United States, 

by ecoregion, based on planktonic index.............136

Table 26. Great Lakes coastal condition based on five 

quality indices......................................................137

Table 27. Acres of Federal land in the United States by 

agency and RPA region, 2008 .............................148

Table 28. Number and percent of annual recreation 

activity days on privately owned forest by 

activity group and region, 2005–2009.................149

Table 29. Trends in number and percentage of people 

age 16 and older participating in nature-based 

outdoor activities, 1994–1995, 1999–2001, and 

2005–2009 ...........................................................151

Table 30. Percent of recreation participants by RPA 

region in seven recreation activity groups...........155

Table 31. Percent of youth participating in an outdoor 

activity at least once in the preceding week by 

type of outdoor activity and by age group...........155

Table 32. Millions of recreation visits to Federal lands, 

1996–2008 ...........................................................156

Table 33. Millions of visits to State park system areas by 

RPA region, 1992–2008 ......................................156

Table 34. Participants and participation in outdoor 

recreation activities, 2008....................................157

Table 35. List of climate models used in the RPA 

scenario-climate combinations (Climate 1, 

Climate 2, and Climate 3) in the recreation 

participation models.............................................157

Table 36. Developed site projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................158

Table 37. Interpretive site projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................158

Table 38. Nature viewing projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................159

Table 39. Challenge activity projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................159

Table 40. Equestrian activity projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................159

Table 41. Day hiking projected participation and use by 

American adults, 2008–2060, by RPA scenario 

and related climate futures...................................160

Table 42. Primitive area projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................160

Table 43. Motorized off-road projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................160

Table 44. Motorized water projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................161

Table 45. Motorized snow activity projected participation 

and use by American adults, 2008–2060, by 

RPA scenario and related climate futures............161

Table 46. Hunting projected participation and use by 

American adults, 2008–2060, by RPA 

scenario and related climate futures ....................162

Table 47. Fishing projected participation and use by 

American adults, 2008–2060, by RPA 

scenario and related climate futures ....................162

Table 48. Developed skiing projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................162

Table 49. Undeveloped skiing projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................162

Table 50. Swimming projected participation and use by 

American adults, 2008–2060, by RPA scenario 

and related climate futures...................................163

Table 51. Floating activity projected participation and 

use by American adults, 2008–2060, by RPA 

scenario and related climate futures ....................163

Table 52. Changes in total outdoor recreation 

participants, 2008–2060, for all activities 

across all RPA scenarios and climate futures......164

Table 53. Changes in total outdoor recreation days, 

2008–2060, for all activities across all RPA 

scenarios and climate futures...............................165
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment v

List of Figures

Figure 1. Projected cumulative change in the area 

of major non-Federal land uses in the 

conterminous United States, by RPA 

scenario, 2010–2060.............................................4

Figure 2. Past and projected water withdrawal for the 

conterminous United States for nine RPA 

scenario-climate combinations and RPA A1B 

with no climate effects, 1985–2060......................5

Figure 3. Mean annual water yield in the conterminous 

United States by RPA scenario-climate 

combination for four 20-year periods...................5

Figure 4. Mean Terrestrial Climate Stress Index 

(TCSI) based on the average across multiple 

alternative futures .................................................6

Figure 5. Annual U.S. wood fuel feedstock 

consumption, 1970–2010, and projections by 

RPA scenario, 2020–2060 ....................................7

Figure 6. Projected change in U.S. population density 

(people per square mile), 2010–2060, for the 

medium population growth scenario (RPA 

A1B) .....................................................................7

Figure 7. Growth in number of housing units within 

a 30-mile buffer around the outer boundary 

of each national park, national forest, and 

wilderness area, 1940–2000..................................8

Figure 8. Patterns of relative fragmentation of 

rangeland using the Morphological Spatial 

Pattern Analysis (MSPA) index, 2001..................8

Figure 9. Current probability of annual water shortage 

(left) and future vulnerability (probability of 

shortage) in 2060 (right) for RPA A2-

MIROC3.2 ............................................................8

Figure 10. The geographic distribution of species 

associated with aquatic habitats assessed 

to be at risk of extinction at the eight-digit 

hydrologic unit level.............................................9

Figure 11. Historical and projected U.S. population, by 

RPA scenario, 1960–2060 ..................................24

Figure 12. Percent population change at the county level 

in the United States, RPA A1B, 2010–2060 ......25

Figure 13. Historical and projected U.S. Gross Domestic 

Product (GDP), by RPA scenario, 1960–2060 ...25

Figure 14. Projected expansion factors, 2006–2060, in 

the volumes of wood consumed for energy, 

by RPA scenario, including total U.S. wood 

fuel feedstock consumption, U.S. roundwood 

fuelwood consumption, and world roundwood fuelwood consumption..............................26

Figure 15. U.S. temperature and precipitation changes 

from the historical period (1961–1990) to 

the decade surrounding the year 2060 

(2055–2064)........................................................26

Figure 16. RPA Assessment regions and subregions...........29

Figure 17. Predicted risk of wetland habitat loss, 

1992–1997, for all National Resources 

Inventory (NRI) points that were wetland in 

1992 for the Southeastern United States.............31

Figure 18. Spatial correspondence between areas with 

high predicted risk of wetland habitat loss 

and urban areas in Florida, 1992–1997...............32

Figure 19. Projected cumulative change in the area 

of major non-Federal land uses in the 

conterminous United States, by RPA 

scenario, 2010–2060...........................................33

Figure 20. Urban land area increases on non-Federal 

lands in the conterminous United States, by 

RPA region by RPA scenario, 2010–2060 .........33

Figure 21. Change in non-Federal forest area in the 

conterminous United States, by RPA region 

by RPA scenario, 2010–2060 .............................34

Figure 22. The landscape mosaic triangle model.................35

Figure 23. National summary of land cover fragmentation 

for forest, grassland, and shrubland for six 

neighborhood sizes, 2001....................................38
vi Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Figure 24. County-level summaries of edge and interior 

forest, grassland, and shrubland, 2001................39

Figure 25. The area of intact eastern forest types in 

2001 (vertical bars) and the corresponding 

percentage of forest type area that is intact 

(circles) ...............................................................41

Figure 26. Eastern forest ownerships (circa 2001) 

characterized by the percentage of group 

forest land area that is intact (circles) and the 

total area of intact forest (vertical bars)..............41

Figure 27. Relative (a) and absolute (b) housing growth 

rates observed within a 30-mile buffer around 

the outer boundary of each national park, 

national forest, and wilderness area over the 

period 1940–2000 ...............................................45

Figure 28. Timber land, reserved forest, and other forest 

land in the conterminous United States, 2007 ....47

Figure 29. Forest land in the United States by ownership 

category, 2007.....................................................48

Figure 30. Forest land in the conterminous United States 

by ownership category, 2007..............................48

Figure 31. Total acres with outbreak levels of tree 

mortality, 1997–2010..........................................50

Figure 32. Areas with potential risk of greater than 

25 percent tree mortality because of insects 

and diseases ........................................................51

Figure 33. Total area of wildfires in the United States, 

1960–2010 ..........................................................51

Figure 34. Historical and projected forest area in the 

conterminous United States (left) and decadal 

changes in historical and projected forest area 

(right), by RPA scenario, 1952–2060.................54

Figure 35. Area of forest by forest type groups for the 

RPA South Region, RPA A1B-MIROC3.2, 

2010–2060 ..........................................................55

Figure 36. Area of forest by forest type groups for the 

RPA South Region, RPA B2-HadCM3, 

2010–2060 ..........................................................55

Figure 37. Area of forest by forest type groups for the 

RPA North Region, RPA A1B-MIROC3.2, 

2010–2060 ..........................................................56

Figure 38. Area of forest by forest type groups for the 

RPA North Region, RPA B2-HadCM3, 

2010–2060 ..........................................................56

Figure 39. Area of forest by forest type groups for the 

RPA Rocky Mountain Region, RPA A1BMIROC3.2, 2010–2060 ......................................56

Figure 40. Area of forest by forest type groups for 

the RPA Pacific Coast Region, RPA A1BMIROC3.2, 2010–2060 ......................................57

Figure 41. Total historical and projected growing 

stock inventories (top) and decadal changes 

in historical and projected growing stock 

inventories (bottom) for the conterminous 

United States, by RPA scenario, 1960–2060......57

Figure 42. Projected growing stock inventories for the 

conterminous United States, by RPA region 

for the RPA A1B scenario, 2010–2060 ..............58

Figure 43. Projected change in growing stock 

inventories for the conterminous United 

States, by RPA region for the RPA A1B 

scenario, 2010–2060...........................................58

Figure 44. Projected softwood growing stock inventories 

for the conterminous United States, by RPA 

scenario, 2010–2060...........................................59

Figure 45. Projected decadal change in softwood 

growing stock inventories for the 

conterminous United States, by RPA 

scenario, 2010–2060...........................................59

Figure 46. Total projected hardwood growing stock 

inventories (top) and projected decadal 

change in hardwood growing stock (bottom) 

for the conterminous United States, by RPA 

scenario, 2010–2060...........................................59

Figure 47. Absolute difference in percent tree canopy 

cover, 2000–2060, for the RPA A1B scenario ...60

Figure 48. Change in aridity ratio, 2000–2060, for 

RPA A1B-MIROC3.2 (maximum average 

increase in aridity) ..............................................61

Figure 49. Change in aridity ratio, 2000–2060, for 

RPA A2-CSIRO-Mk3.5 (minimum average 

increase in aridity) ..............................................61

Figure 50. Change in aridity ratio, 2000–2060, for 

RPA B2-CGCM2 (median average 

increase in aridity) ..............................................61

Figure 51. Climate and canopy change index for 

RPA A1B-MIROC 3.2 in 2060 (maximum 

average increase in aridity).................................62
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment vii

Figure 52. Climate and canopy change index for RPA 

A2-CSIRO-Mk3.5 in 2060 (minimum 

average increase in aridity).................................62

Figure 53. Climate and canopy change index for RPA 

B2-CGCM2 in 2060 (median average 

increase in aridity) ..............................................62

Figure 54. Industrial roundwood equivalent of U.S. wood 

and paper product consumption, 1965–2010......63

Figure 55. Net import share of consumption in 

roundwood equivalents for wood and paper 

products (percent) and trade-weighted U.S. 

dollar index, 1965–2010 .....................................64

Figure 56. Roundwood equivalent of U.S. forest product 

output, 1965–2010 ..............................................64

Figure 57. Projected average foreign fuelwood (left) and 

industrial roundwood (right) prices by RPA 

scenario, 2020–2060, relative to 2006 average 

prices...................................................................65

Figure 58. Projected foreign fuelwood consumption 

(left) and forest growing stock inventory 

(right), by RPA scenario, 2020–2060, relative 

to 2006 levels......................................................66

Figure 59. Projected global roundwood use for energy 

(left) and for industrial wood products (right), 

by RPA scenario, 2020–2060, relative to 

2006 levels..........................................................66

Figure 60. Global primary energy production for IPCCbased RPA scenarios...........................................67

Figure 61. Annual U.S. wood fuel feedstock 

consumption, 1970–2010, and projections, by 

RPA scenario, 2020–2060 ..................................68

Figure 62. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, 

by feedstock source, RPA A1B scenario, 

2020–2060 ..........................................................68

Figure 63. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, 

by feedstock source, RPA A2 scenario, 

2020–2060 ..........................................................69

Figure 64. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, 

by feedstock source, RPA B2 scenario, 

2020–2060 ..........................................................69

Figure 65. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, 

by feedstock source, RPA HFW scenario, 

2020–2060 ..........................................................69

Figure 66. U.S. timber harvest volumes, 1970–2010, and 

projections, by RPA scenario, 2020–2060 

(excluding harvest residue volumes) ..................69

Figure 67. Projected weighted average delivered 

industrial roundwood prices for United States 

(left) and for all foreign countries worldwide 

(right), by RPA scenario, 2020–2060, relative 

to 2006 prices......................................................70

Figure 68. Projected average U.S. softwood (left) and 

hardwood (right) sawtimber stumpage prices, 

by RPA scenario, 2020–2060, relative to 

2006 prices..........................................................71

Figure 69. Solidwood products used for new residential 

construction in the United States, by product 

category, selected years, 1962–2009 ..................71

Figure 70. Annual U.S. lumber consumption, 1970–2010, 

and projections, by RPA scenario, 2020–2060 ....72

Figure 71. U.S. single-family housing starts, 1960–2010, 

and projections, by RPA scenario, 2020–2060...72

Figure 72. Projected weighted average U.S. lumber 

price trends, by RPA scenario, 2020–2060, 

relative to 2006 price ..........................................72

Figure 73. Annual U.S. lumber production, 1970–2010, 

and projections, by RPA scenario, 2020–2060...73

Figure 74. Annual U.S. lumber net exports, 1970–2010, 

and projections, by RPA scenario, 2020–2060...73

Figure 75. Annual U.S. structural wood panel 

consumption, 1970–2010, and projections, 

by RPA scenario, 2020–2060 .............................73

Figure 76. Annual U.S. structural panel production, 

1970–2010, and projections, by RPA 

scenario, 2020–2060...........................................74

Figure 77. Annual U.S. net exports of structural wood 

panel products, 1970–2010, and projections, 

by RPA scenario, 2020–2060 .............................74

Figure 78. Annual U.S. paper and paperboard 

consumption, 1970–2010, and projections, 

by RPA scenario, 2020–2060 .............................75

Figure 79. Annual U.S. paper and paperboard 

production, 1970–2010, and projections, 

by RPA scenario, 2020–2060 .............................75
viii Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Figure 80. Annual U.S. net exports of paper and 

paperboard, 1970–2010, and projections, by 

RPA scenario, 2020–2060 ..................................75

Figure 81. Annual U.S. softwood timber harvest 

volumes, 1970–2010, and projections, by 

RPA scenario, 2020–2060 ..................................76

Figure 82. Annual U.S. hardwood timber harvest 

volumes, 1970–2010, and projections, by 

RPA scenario, 2020–2060 ..................................76

Figure 83. Projected average U.S. stumpage prices 

of hardwood and softwood sawtimber and 

nonsawtimber, by RPA scenario, 2020–2060, 

relative to 2006 prices.........................................77

Figure 84. Projected wood volume in wood fuel 

feedstock and forest products produced in the 

United States, RPA A1B and HFW scenarios, 

relative to 2006 levels.........................................78

Figure 85. Wood volumes in conventional forest 

products produced in the United States, 2006, 

and projections, RPA A1B (top) and HFW 

(bottom) scenarios, 2020–2060 ..........................79

Figure 86. Urban and community land in Connecticut, 

2000 ....................................................................81

Figure 87. Change in percent urban land by county in 

the conterminous United States, 1990–2000 ......82

Figure 88. Change in percent community land by county 

in the conterminous United States, 1990–2000 ..82

Figure 89. Percent tree cover in urban areas by county 

in the conterminous United States, 2000 ............83

Figure 90. Estimated number of trees that could 

potentially be attacked by Asian longhorned 

beetle (ALB), gypsy moth (GM), southern 

pine beetle (SPB), Dutch elm disease (DED), 

emerald ash borer (EAB), and 1,000 cankers 

disease on Tennessee’s urban tree population....84

Figure 91. Total carbon stored in conterminous U.S. 

forests (left) and changes in carbon stocks by 

decade (right), by RPA scenario, 2010–2060.....89

Figure 92. Total carbon stored per acre of forest in 

the conterminous United States, by RPA 

scenario, 2010–2060...........................................90

Figure 93. Carbon stored in conterminous U.S. forests, 

by RPA region, RPA A1B scenario, 

2010–2060 ..........................................................90

Figure 94. Historical and projected conterminous 

U.S.forest carbon stocks, RPA A1B (left) and 

RPA B2 (right), 1990–2060................................90

Figure 95. Historical and projected annual HWP carbon 

contribution to forest sector sinks and 

emissions using the Production Accounting 

Approach, by RPA scenario, 1990–2060 ...........92

Figure 96. Total carbon flux in U.S. forest sector, 

by decade, by RPA scenario, 2010–2060 ...........93

Figure 97. Projected average annual carbon fluxes for 

conterminous U.S. forest pools and harvested 

wood products, by decade, RPA A1B (top) 

and RPA B2 (bottom), 2010–2060 .....................93

Figure 98. Patterns of relative fragmentation of 

rangeland using the Morphological Spatial 

Pattern Analysis index, 2001 ..............................99

Figure 99. Comparison of rangeland extent using 

National Resources Inventory (NRI) and 

Forest Inventory and Analysis (FIA) 

definitions, 2001 ...............................................100

Figure 100. Areas of disagreement in rangeland extent 

between the National Resources Inventory 

(NRI) and Forest Inventory and Analysis 

(FIA) perspectives, 2001 ..................................100

Figure 101. Mean annual rangeland net primary 

production (NPP), 2000–2009..........................101

Figure 102. Forage availability (FA) and forage demand 

(FD) on rangeland and aboveground 

rangeland biomass appropriated to cattle 

(FA-FD) for 2000 and 2009..............................105

Figure 103. Water resource regions (WRR) (numbered) 

and assessment subregions (ASR) of the 

conterminous United States..............................110

Figure 104. Water networks across the United States at 

the asessment subregion (ASR) level ...............110

Figure 105. Past and projected annual water withdrawals 

in the United States by water use type, 

scenario RPA A1B, no future climate effects, 

1960–2060 ........................................................112

Figure 106. Past and projected annual consumptive water 

use in the United States by water use type, 

scenario RPA A1B, no future climate effects, 

1960–2060 ........................................................114
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment ix

Figure 107. Past and projected annual water use and 

population in the United States, scenario RPA 

A1B, no future climate effects, 1960–2060......114

Figure 108. Past and projected water withdrawals in the 

conterminous United States, by RPA scenario, 

no future climate effects, 1985–2060 ...............115

Figure 109. Percent change in projected water withdrawal, 

by assessment subregion (ASR), RPA A1B 

scenario, no future climate effects, 

2005–2060 ........................................................115

Figure 110. Past and projected water withdrawal for the 

conterminous United States for nine RPA 

scenario-climate combinations and RPA A1B 

with no future climate effects, 1985–2060 .......115

Figure 111. Percent change in projected water withdrawal, 

by assessment subregion (ASR), RPA A1B 

scenario, with climate effects (multimodel 

average), 2005–2060.........................................116

Figure 112. Mean annual water yield in the conterminous 

United States, by RPA scenario-climate 

combination for four 20-year periods...............117

Figure 113. Change from current conditions to 2060 in 

assessment subregion (ASR) mean water yield 

(centimeters per year), based on comparing 

20-year periods centered at 1996 and 2060, 

for a sample of RPA scenario-climate 

combinations: (a) RPA A1B-CGCM3.1; 

(b) RPA A2-CSIRO-Mk3.5; and (c) RPA 

B2-HadCM3 .....................................................117

Figure 114. Assessment subregion (ASR) water storage 

capacity (million acre-feet)...............................117

Figure 115. Current probability of annual water shortage ...118

Figure 116. Vulnerability (probability of shortage) for 

RPA A1B-CGCM3.1, in the year (a) 2020, 

(b) 2040, and (c) 2060 ......................................119

Figure 117. Vulnerability (probability of shortage) for 

the RPA A1B scenario in 2060 using the 

following climate models: (a) CGCM3.1, 

(b) CSIRO-Mk3.5, and (c) MIROC3.2.............119

Figure 118. Vulnerability (probability of shortage) for the 

RPA A2 scenario in 2060 using the following 

climate models: (a) CGCM3.1, (b) CSIROMk3.5, and (c) MIROC3.2 ...............................120

Figure 119. Vulnerability (probability of shortage) for the 

RPA B2 scenario in 2060 using the following 

climate models: (a) CGCM2, (b) CSIROMk2, (c) HadCM3 ............................................120

Figure 120. Population trends in selected big game species 

for the Nation and RPA regions, 1975–2008....124

Figure 121. Long- (1966–2008) and short- (1997–2008) 

term population trends in selected upland game 

birds for the Nation and RPA regions from the 

North American Breeding Bird Survey ............126

Figure 122. Trends in duck populations, 1955–2010, and 

the relation between 2010 population estimates 

for 10 principal duck species and population 

objectives specified in the North American 

Waterfowl Management Plan, measured as 

percent of objective (inset) ...............................127

Figure 123. Trends in goose harvest, 1961–2008, nationally 

and by flyway....................................................128

Figure 124. National and RPA regional trends in total fur 

harvest among 28 species, 1970–2008 .............129

Figure 125. The percent of bird species (by broad lifehistory groupings) with decreasing, increasing, 

and stable trends, 1966–2008, for the Nation 

and by RPA region............................................130

Figure 126. Mean forest bird richness observed on a survey 

route, 2007–2009, using the North American 

Breeding Bird Survey .......................................132

Figure 127. Changes in forest bird richness, by guild, based 

on land use and housing density changes 

projected to 2060, scenario RPA A1B..............133 

Figure 128. Changes in bird richness for (a) species that 

breed in forest habitats and (b) species that 

prefer to breed in interior forest conditions, by 

RPA scenario ....................................................133

Figure 129. Mean Terrestrial Climate Stress Index 

(TCSI) based on the average across alternative 

futures...............................................................134

Figure 130. Current conservation issues associated with 

wildlife resources, including (a) the count 

of terrestrial species considered to be at risk 

of extinction according to NatureServe’s 

conservation ranks, and (b) the proportion 

of the land base that is intensively used by 

humans (agriculture and developed land) 

as an indication of resistance to species 

movement (dispersal resistance), in response 

to climate change ..............................................135

Figure 131. Stream habitat condition as measured by 

macroinvertebrate index of biological 

condition as compared to least-disturbed 

reference sites, nationally and by ecoregion.....137
x Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Figure 132. Commercial landings of (a) all marine 

species and Pacific salmon species in 

U.S. ports and (b) all species from 

U.S. Great Lakes...............................................139

Figure 133. Relative condition of riverine and nearcoastal habitat based on the variables that 

indicate risk to aquatic habitat quality, by 

RPA region .......................................................140

Figure 134. The proportion of species in NatureServe 

conservation status ranks for vertebrates, 

invertebrates, and plants ...................................142

Figure 135. The geographic distribution of species 

assessed to be at risk of extinction 

(conservation ranks G1, G2, and G3) for 

(a) all species at the county level, and (b) 

species associated with aquatic habitats at the 

eight-digit hydrologic unit level, 2010 .............142

Figure 136. The geographic distribution of State-level 

counts of species considered to be extirpated 

from a State for the conterminous 

United States, 2010...........................................143

Figure 137. Number of participants 16 years and older in 

angling, hunting, and nonresidential wildlife 

viewing, 1955–2006 .........................................144

Figure 138. General trends in the number of participants 

and days spent hunting, angling, and viewing 

wildlife (nonresidential), 1996–2006................144

Figure 139. Percent of State park system acres, by type of 

area, 2009..........................................................148

Figure 140. Number of Federal recreation facilities, by 

activity availability (or type of facility) and 

RPA region, 2009 .............................................149

Figure 141. Acres of Federal and State park land per 

county resident, 2008 (top) and 2060 

(bottom), within the 75-mile distance zone 

from the county center, based on the RPA 

A1B population projection ...............................150

Figure 142. Number of participants in four outdoor 

recreation activities in three time periods.........152

Figure 143. Trend in annual activity days for seven 

composites of nature-based outdoor 

recreation activities, 2000–2008 (indexed 

to total activity days in 2000) ...........................153

Figure 144. Trend in annual activity days for viewing and 

photographing nature activities, 2000–2008 

(indexed to total activity days in 2000) ............153

Figure 145. Trend in annual activity days for nonmotorized 

boating activities, 2000–2008 (indexed to 

total activity days in 2000) ...............................153

Figure 146. Trend in annual activity days for motorized 

activities, 2000–2008 (indexed to total 

activity days in 2000)........................................153

Figure 147. Changes in housing density, 1940–2000, 

within 15 and 30 miles of four case study 

areas: the Cleveland National Forest, Mount 

Evans Wilderness Area, Great Smoky 

Mountains National Park, and the HuronManistee National Forest..................................168

Figure 148. Composite of vulnerability (probability of 

shortage), by ASR, projected for 2060 based on 

the nine RPA scenario-climate combinations: 

(a) minimum and (b) maximum........................170
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment xi

Acknowledgments

The Resources Planning Act (RPA) Assessment is the product of a program of research carried out by a team 

of Forest Service scientists. Richard Guldin, Linda Langner, 

and Margaret Connelly manage this research. Thanks to Boris 

Tkacz for contributing the section on forest health. Scientists 

from various research stations have taken lead roles in conducting assessment research:

J. Michael Bowker, Southern Research Station, Outdoor 

Recreation

Thomas C. Brown, Rocky Mountain Research Station, 

Water

H. Ken Cordell, Southern Research Station, Outdoor 

Recreation and Wilderness

Curtis H. Flather, Rocky Mountain Research Station, 

Wildlife, Fish, Aquatics, and Biodiversity

Peter J. Ince, Forest Products Laboratory, U.S. Forest 

Products

Linda A. Joyce, Rocky Mountain Research Station, 

Climate Change

Patrick D. Miles, Northern Research Station, Forest 

Inventory and Analysis RPA Database

David J. Nowak, Northern Research Station, Urban Forests

Jeffrey P. Prestemon, Southern Research Station, 

International Forest Products Trade

Matthew C. Reeves, Rocky Mountain Research Station, 

Rangelands

Kurt H. Riitters, Southern Research Station, Landscape 

Patterns

Kenneth E. Skog, Forest Products Laboratory, Forest 

Products Status and Trends

W. Brad Smith, Washington Office, Forest Inventory and 

Analysis RPA Database

David N. Wear, Southern Research Station, Forests

The lead scientists work with a variety of cooperators internally 

and externally. The lead scientists acknowledge their primary 

contributors to the 2010 RPA Assessment research:

Robert Abt, North Carolina State University

Ashley Askew, University of Georgia

Chuck Bargeron, University of Georgia

John Bergstrom, University of Georgia

Carter J. Betz, Forest Service, Southern Research Station

Joseph Buongiorno, University of Wisconsin-Madison

David Coulson, Forest Service, Rocky Mountain Research 

Station

John Coulston, Forest Service, Southern Research Station

Mark Duda, Responsive Management

Romano A. Foti, Princeton University

Eric Greenfield, Forest Service, Northern Research Station

James L. Howard, Forest Service, Forest Products 

Laboratory

Robert Huggett, North Carolina State University

Richard Knight, Colorado State University

Michael Knowles, Forest Service, Rocky Mountain 

Research Station

Andrew D. Kramp, University of Wisconsin-Madison

Ruhong Li, North Carolina State University

Andrew Loftus, Loftus Consulting

David B. McKeever, Forest Service, Forest Products 

Laboratory

Daniel W. McKenney, Canadian Forest Service

Jason McNees, NatureServe

John Mitchell, Forest Service, Rocky Mountain Research 

Station, retired

Miranda Mockrin, Forest Service, Rocky Mountain 

Research Station

Anna Pidgeon, University of Wisconsin-Madison

David T. Price, Canadian Forest Service

Volker Radeloff, University of Wisconsin-Madison

Jorge Ramirez, Colorado State University

Ronald Raunikar, U.S. Geological Survey
xii Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Ron Regan, Association of Fish and Wildlife Agencies 

Steven Running, University of Montana

John Sauer, U.S. Geological Survey

Lynne Seymour, University of Georgia

Henry Spelter, Forest Service, Forest Products Laboratory, 

retired

David Theobald, Colorado State University

Robert Washington-Allen, Texas A&M University

Bryant White, Association of Fish and Wildlife Agencies 

and Missouri Department of Conservation

Stanley Zarnoch, Forest Service, Southern Research 

Station

Shushuai Zhu, University of Wisconsin-Madison

The RPA Assessment benefited from public and peer review 

comments on the draft document. The following scientific peer 

reviewers generously donated their time and expertise, providing comments that greatly improved the final report:

Darius Adams, Oregon State University

Janaki R. R. Alavalapati, Virginia Polytechnic Institute and 

State University

Steve Brady, Natural Resources Conservation Service

Sergio Capozzi, National Association of Recreation 

Planners

Fred Cubbage, North Carolina State University

David Darr, Forest Service, retired

John A. Dracup, University of California, Berkeley

John Dwyer, Forest Service, retired, and Morton 

Arboretum

Francisco Escobedo, University of Florida

Burnell Fischer, Indiana University

Tom Franklin, Theodore Roosevelt Conservation 

Partnership

Peter Gleick, Pacific Institute

Neil Grigg, Colorado State University

Phillip Guertin, University of Arizona

Robert Hirsch, U.S. Geological Survey

Douglas Inkley, National Wildlife Federation

Ricardo Lopez, Forest Service

Sarah Mincey, Indiana University

Reid Miner, National Council for Air and Stream 

Improvement

Bill Monahan, National Park Service

Roger Moore, North Carolina State University

David Newman, State University of New York

Steve Prisley, Virginia Polytechnic Institute and State 

University

Steve Rose, Electric Power Research Institute

Randall Rosenberger, Oregon State University

V. Alaric Sample, Pinchot Institute for Conservation

Ross L. Self, South Carolina Department of Natural 

Resources

Bruce Stein, National Wildlife Federation

Paul van Deusen, National Council for Air and Stream 

Improvement

Jessica Vogt, Indiana University

Thomas Wilbanks, Oak Ridge National Laboratory

The physical production of the document requires a great deal 

of work. Sonja Beavers was instrumental in cover design and 

made significant contributions to the production of this publication by facilitating editing and layout.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment xiii

Executive Summary

The 2010 Resources Planning Act (RPA) Assessment is the fifth report prepared in response to the mandate in 

the 1974 Forest and Rangeland Renewable Resources Planning 

Act (Public Law 93–378, 88 Stat 475, as amended). This report 

summarizes findings about the status, trends, and projected 

future of forests, rangelands, wildlife and fish, biodiversity, 

water, outdoor recreation, wilderness, and urban forests, and 

the effects of climate change upon these resources. The results 

will be useful to resource managers and policymakers as they 

develop strategies to sustain natural resources. The Forest 

Service, an agency of the U.S. Department of Agriculture, 

will continue to use the results to inform strategic planning 

and forest planning.

The 2010 RPA Assessment outlook for U.S. resources is largely 

influenced by a set of RPA scenarios with varying assumptions 

about global population and economic growth, global wood 

energy consumption, U.S. population and economic growth, 

U.S. land use change, and global climate change from 2010 

to 2060.

Key Themes

Land development will continue to threaten 

the integrity of natural ecosystems.

Urban and developed land area is projected to increase across 

RPA scenarios between 41 and 77 percent by 2060. Although 

urban and developed land area remains a relatively small 

percentage of the U.S. land base, this expansion occurs at the 

expense of forest and rangelands. Forest land area is affected 

the most: forest losses are projected to range from 16 to 34 million acres in the conterminous United States. The South Region 

is expected to have the greatest loss of forest, ranging from 

9 to 21 million acres, roughly 4 to 8 percent of the South’s 

2007 forest land base.

The loss of forest land contributes to reduced growth in total 

forest inventory, reduced forest carbon stocks, and reduced tree 

canopy cover. Forest inventory volumes are expected to peak 

between 2020 and 2030, followed by a decline in volume to 

2060. Only in one RPA scenario is inventory volume in 2060 

less than in 2010, however. Carbon stocks are also projected to 

decrease across all RPA scenarios as a result of declining forest 

land area and changes in carbon stored per acre. The result is 

that forest land becomes an emissions source in future decades, 

the tipping point varying by the particular dynamics of land use 

change and timber harvest levels in each RPA scenario.

Although the loss of acres is important, low-density development may pose a greater threat to the integrity of remaining 

forest and rangelands through the effects of fragmentation. 

The expansion of housing in the wildland-urban interface and 

housing development around public lands fragment natural 

land covers and often lead to additional development. Habitat 

loss and degradation are major causes of species endangerment. At-risk species tend to be prominent in areas with high 

human-population densities, where land use intensification has 

occurred, or where species with restricted ranges are concentrated. Given the projected land use changes, biodiversity in the 

United States is expected to continue to erode.

Climate change will alter natural ecosystems 

and affect their ability to provide goods and 

services.

Changes in temperature and precipitation generally had limited 

effects on the distribution of forest types and forest inventory 

during the RPA projection period, but those effects were more 

noticeable in the Western United States. At least in the immediate 

future, climate change is not posing a risk to having sufficient 

inventory to sustain forest products production. The risk to providing other forest ecosystem services is not known, however, nor 

is the potential effect of increasing occurrences of extreme events.

Rangeland ecosystems typically occur in areas of environmental limitations. The diversity of rangeland ecosystems, 

the multitude of current stressors, and the potential changes 

in climate will result in highly diverse responses to climate 

change across rangeland systems. Effects on forage availability, 

with consequences to ranch enterprises of livestock, game, or 

tourism will require flexible, and possibly novel, management 

to maintain rangeland health and economic viability.

Climate change is projected to have substantial effects on water 

demand and supply. The primary effects of climate change on 

water demand are increases in agricultural irrigation and landscape watering in response to rising plant water needs. Across a 

range of RPA scenario-climate combinations, water withdrawal 
xiv Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

would increase from 2 to 42 percent from 2005 to 2060. The 

result of the combination of increasing water demand and 

declining water yields is an increase in vulnerability of the U.S. 

water supply to shortage, especially in the larger Southwest and 

Great Plains.

Change in terrestrial wildlife habitats will affect both the 

current habitat of wildlife species and their ability to migrate if 

habitats change. The grassland-forest land transition throughout 

the Central United States and the steep elevation gradients in 

the Intermountain West will be most exposed to habitat stress 

caused by a shifting climate regime. A comparison of areas of 

future stress to areas of current stress associated with the distribution of at-risk species and intense land uses indicated that 

the location of high current stressors tends not to overlap well 

with the location of high future stress associated with climate 

change. This lack of overlap potentially complicates the efforts 

of managers to prioritize wildlife conservation actions.

Climate variables only slightly affected outdoor recreation 

participation, with results indicating slight increases or 

decreases in participation rates and days of participation that 

varied across outdoor recreation activities. The exceptions 

were snowmobiling and undeveloped skiing (cross-country 

skiing and snowshoeing), for which climate effects resulted in 

substantial declines. The effects of climate change on the recreation environment are also expected to affect future outdoor 

recreation opportunities.

Competition for goods and services from natural 

ecosystems will increase.

Increasing water demands are likely to increase competition 

between water uses. The water projections indicate that the 

United States is on a pathway to unsustainable levels of 

water use in several regions across a range of RPA scenarios. 

Increased water use efficiencies, water demand reductions, 

increased trading or sale of water rights, and higher pricing for 

water consumed are possible mechanisms that could help to 

bring water supplies into balance with future water demands. 

Future water use levels depend most importantly on uses in the 

agriculture sector because irrigation requirements are highly 

sensitive to changing precipitation and temperature patterns. 

The current outlook indicates that demand pressures will 

increase, continuing or increasing current groundwater mining 

and further depleting streamflows, especially in drier areas of 

the United States. These pressures, in combination with development effects on water quality, raise concerns about the health 

and relative abundance of aquatic species in the future.

Species associated with aquatic habitats have higher proportions of at-risk species than other species groups. The condition 

of aquatic systems varies across the United States. Nationwide, 

more than one-half of monitored lakes were ranked in good 

condition, but only 28 percent of wadeable streams were ranked 

in good condition. Imperiled aquatic species tend to occur in 

areas with high population density, and many of those areas are 

projected to have increased population and development in the 

future. Maintaining or improving water quality and streamflows 

is likely to be challenging, especially in the face of increasing 

development pressure and water demands.

The availability of suitable land may constrain growing recreation 

demand. A stable public land base, a declining private natural 

land base, and increasing numbers of outdoor recreation participants are expected to result in increased conflicts among 

recreationists and declines in the quality and number of perperson recreation opportunities. The ability of recreation resources 

to absorb additional demand varies widely across the United 

States. The limited amount of public land in the East, where 

most forest land is privately owned, will likely be under greater 

stress from additional demand than public lands in the West. 

Pressures are likely to be greatest on public lands near large 

and growing population centers.

In contrast to the water and outdoor recreation situations, 

future demands for livestock forage and forest products 

can be met out to 2060 for most RPA scenarios, despite the 

projected losses in land area devoted to these uses. Currently, 

forage availability exceeds forage demand on 98 percent of all 

rangeland, and little increased grazing pressure on rangeland 

is expected in the near future. Given the current general abundance of forage, effects of climate change on ranch enterprises 

are likely to be localized.

Timber resources are projected to be abundant enough to meet 

demands, especially if we continue to see efficiency gains in 

harvesting and conversion technology. Only the RPA scenario 

with the highest increase in wood biomass use for energy is 

expected to lead to potential competition for land resources 

with other uses, particularly with agriculture. The high harvest 

levels to meet these demands may create conflict with other forest uses. For example, the projected expansion of planted pine 

in the South Region to meet those biomass energy demands 

would displace natural pine, which may be undesirable from a 

biodiversity perspective.

Geographic variation in resource responses to 

drivers of change will require regional and local 

strategies to address resource management 

issues.

Projected population growth rates—and associated urban 

and suburban development patterns—vary across the United 

States. Areas with high population growth rates will see large 

expansions of urban areas, unless local and regional master 

plans are in place to manage the growth effects. Trees in urban 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment xv

areas—the urban forest—deliver a variety of ecosystem services. Retaining and managing trees in newly developed urban 

areas will be increasingly important in the future to continue 

receiving ecosystem services that are critical to urban quality 

of life. Low-density development patterns are more difficult to 

predict but are more likely to occur in rural areas where population continues to grow than in areas where population declines 

are projected.

Development will also affect rangelands, even though the 

proportional loss of rangelands is smaller than for forest land 

area. Rangeland areas with the highest levels of fragmentation 

occur where agricultural land uses are prevalent. Many of these 

areas have projected declines in population, which should stem 

development pressure. Expansion of farming could result in 

the conversion of some rangelands to crops in places suitable 

for increased agricultural production, particularly if crop prices 

remain high. Conversely, several areas—particularly in the 

Southwest—that currently have relatively little fragmentation 

will likely be exposed to development pressure from population 

growth.

The projected changes in vulnerability of the U.S. water supply 

vary geographically. Decreases in water yield (that in turn 

affects water supply) have a greater effect on future vulnerability than the effect of increases in water demand in about 

half of the assessment subregions (ASRs) where vulnerability 

is projected to increase. In some ASRs, the combined effect of 

changes in water yield and demand lead to untenable levels of 

vulnerability, suggesting that adaptation to water shortage there 

will be essential. Currently, the West has more areas of higher 

vulnerability. Future increases in vulnerability may also affect 

some parts of the East, along with becoming more prevalent in 

the West.

Imperiled aquatic species are concentrated in the Eastern and 

Southwestern United States. Where increased risks of water 

shortages are projected, threats to aquatic species are likely to 

increase. Conflicts about water uses, maintenance of instream 

flows, eroding water quality, and prices for water and water 

rights are all likely to increase in the future, exacerbating the 

threats to these species. Water policymakers and water rights 

owners are likely to face more tensions among water uses and 

pressures to change or adapt existing policies to better reflect 

shifting water use values.

Looking Forward

The United States has abundant natural resources. A growing 

population is projected to lead both to increased demands for 

a wide array of goods and ecosystem services from forests and 

rangelands and to shifts in land uses as public values for certain 

goods and services change. Woody biomass production to 

promote domestic energy security is a prime example.

The outlook shown in this report is based on a continuation 

of current natural resource management policies in the face of 

projected changes in demographic and economic conditions 

and social values. The results highlight a number of areas in 

which pressures may emerge on policymakers to change current policies or develop new policy approaches. The negative 

effects on the environment, economy, and society portrayed by 

the scenarios in this RPA Assessment are not foregone conclusions. They can be avoided by timely actions from policymakers and land managers. This RPA Assessment lays the scientific 

foundation for taking action and dealing with the issues before 

their full effect is felt.
xvi Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 1

Chapter 1. Introduction

The 2010 Resources Planning Act (RPA) Assessment is the fifth report prepared in response to the mandate in 

the 1974 Forest and Rangeland Renewable Resources Planning 

Act (Public Law 93-378, 88 Stat 475, as amended). The RPA 

Assessment is intended to provide reliable information on the 

status, trends, and projected future of the Nation’s renewable 

natural resources on forests and rangelands on a 10-year cycle. 

The RPA Assessment includes analyses of forests, rangelands, 

wildlife and fish, biodiversity, water, outdoor recreation, 

wilderness, and urban forests and the effects of climate change 

upon these resources.

The Forest and Rangeland Renewable Resources Planning Act 

recognized the importance of natural resources to people’s 

well-being and quality of life. The American public continues 

to depend on our forests and rangelands to provide a variety 

of ecosystem services. The intent of the RPA Assessment is to 

provide information for policymakers and resource managers 

to help address the challenges of satisfying diverse natural 

resource demands in the future.

The RPA Assessment reports on a body of targeted research 

funded by the Forest Service, an agency of the U.S. Department 

of Agriculture, to address the RPA legislative mandate, both 

providing historical trends and projecting likely futures. The 

RPA Assessment is not a comprehensive synthesis of status, 

trends, and projections of all renewable natural resources. 

Our research focuses on analyzing the influences of multiple 

drivers of change on renewable natural resources during the 

next 50 years. This focus is a particularly important point with 

regard to the potential effects of climate change, as we have 

included climate variables to assess the potential effects on 

future resource trends into our modeling structure, just as we 

incorporate demographic, economic, and other variables in our 

models.

Document Organization

Following this introduction, the key results of the 2010 RPA 

Assessment are presented in two chapters. The first focuses on 

linkages across resource analyses (chapter 2), and the second 

presents highlights by individual resource topic (chapter 3). 

Following these summary chapters, we describe global and U.S. 

trends that affect the renewable resource situation (chapter 4) 

and the future scenarios used as the basis for the 2010 RPA 

Assessment projections (chapter 5).

Chapters 6 through 14 present findings by resource area or 

resource sector. The information presented in these chapters 

begins with historical information that is tracked across RPA 

Assessment reporting cycles. Changes in historical trends are of 

particular interest because future projections are tied to historical trends. Future resource conditions, demand, and supply are 

projected for 50 years (2010 to 2060 in this RPA Assessment 

cycle) for those resources for which sufficient data were available. The projections assume no changes in policies. The 5-year 

RPA Assessment update cycle will evaluate potential effects of 

policy change on resource futures.

This document summarizes the results of analyses that are 

documented in more detail in a series of technical supporting 

documents referenced throughout the following chapters. These 

supporting documents provide more details on data, methods, 

and results.1

Scope of the Analysis

The renewable natural resources included in the RPA Assessment reflect the mandated national focus and the natural 

resources and related economic sectors for which the Forest 

Service also has related management responsibilities, including forests, rangelands, wildlife and fish, outdoor recreation, 

and water. The national focus limited us to analyzing those 

resources for which nationally consistent data are available 

or for which data can be consistently compiled to the national 

level. In many cases, our analyses only cover the conterminous 

United States because of data limitations.

We capitalize on those areas in which the Forest Service has 

research capacity. We also use the expertise of other Federal 

agencies that have responsibilities for national analyses by using their data and incorporating their reports by reference. For 

example, we rely on information from the U.S. Environmental 

Protection Agency about water quality. Similarly, we do not 

1

 RPA Assessment supporting technical documents are available on the Forest Service’s RPA Assessment Web page as they become available: 

http://www.fs.fed.us/research/rpa/.
2 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

analyze renewable energy, with the exception of wood-based 

bioenergy, because the U.S. Department of Energy conducts 

comprehensive analyses of the energy sector.

The 2010 RPA Assessment is the fifth report since the 

RPA legislation was passed in 1974. Rather than attempt to 

synthesize all existing research on the status and trends of 

natural renewable resources, we continue to target our research 

to improve our understanding of the multiple and interacting 

factors that we expect to affect natural resources in the future. 

This focus is a unique contribution that provides important 

information to policymakers and resource managers as they 

develop strategies for sustaining the Nation’s renewable natural 

resources.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 3

Chapter 2. Synthesis of 2010 RPA 

Assessment Results

The 2010 Resources Planning Act (RPA) Assessment summarizes the present condition and future outlook 

for the Nation’s renewable natural resources. We used a set of 

scenarios (hereafter referred to as RPA scenarios) to analyze 

the future effects of human and environmental influences on 

our forests and rangelands. The RPA scenarios differ in their 

assumptions about the rate of population growth, economic 

growth, land use change, biomass energy use, and climate 

change during the next 50 years. The RPA scenarios, described 

in chapter 5, enabled us to test the sensitivity of natural 

resources to these influences to provide better information to 

policymakers and resource managers about potential resource 

futures. In this chapter, we focus on integrative resource management themes that synthesize findings across the resource 

analyses described in chapters 6 through 14, and we identify 

how underlying drivers of change influence future resource 

conditions. In the next chapter, we focus on individual resource 

key findings.

Resource Highlights

Integrative Resource Management Themes

v Land development will continue to threaten the integrity of natural 

ecosystems.

v Climate change will alter natural ecosystems and affect their ability to 

provide goods and services.

v Competition for goods and services from natural ecosystems will increase.

v Geographic variation in resource responses to drivers of change will require 

regional and local strategies to address resource management issues.

Land development will continue to threaten 

the integrity of natural ecosystems.

Land development—including the expansion of housing, commercial enterprises, industrial capacity, and related facilities 

such as roads, mines, and electricity generating plants—already 

strongly influences natural ecosystems, particularly in the Eastern United States, with its higher population density and limited 

amount of Federal land. Whereas urbanization tends to convert 

ecosystems, low-density development in more rural landscapes 

tends to fragment natural ecosystems.

Population and economic growth will fuel the expansion of 

developed and urban land uses, with increases across RPA 

scenarios ranging from 41 to 77 percent. Although urban and 

developed land area remains a relatively small percentage of 

the U.S. land base, this expansion occurs at the expense of 

forest and rangelands. We project a loss of forest and rangeland 

across all RPA scenarios; the only difference across scenarios 

is in the relative scale of the loss (figure 1). These losses have 

a variety of effects on natural resources, both through the 

absolute loss of acres and via changes in the character of the 

remaining resource base. 

Forest land is the land use most affected by urbanization and 

developed uses, with the greatest losses in the South Region, 

where we project losses of up to 21 million acres by 2060, or 

up to 8 percent of the region’s forest land base. Rangeland 

losses are projected to be greatest in the Rocky Mountain 

Region, where up to 4.4 million acres (about 2 percent of total 

rangeland in the region) could be lost.
4 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

The loss of forest land contributes to reduced growth in total 

forest inventory, reduced forest carbon stocks, and reduced 

tree canopy cover. Forest inventory volumes demonstrate the 

same general pattern of change in all RPA scenarios: a peaking 

of volume between 2020 and 2030, followed by a decline in 

volume to 2060. This projected future reverses the historical 

trend of increasing volumes, but only one RPA scenario results 

in a lower inventory volume in 2060 than exists in 2010. This 

lower volume is the combined result of forest land loss and 

high harvest rates. Hardwood inventories are more severely 

affected because forest losses are more often coincident with 

hardwood forest types.

Although forests continue to sequester substantial amounts of 

carbon, carbon stocks are projected to decrease across all RPA 

scenarios, a result of declining forest land area and changes 

in carbon stored per acre. The result is that forests become an 

emissions source in future decades, the tipping point varying by 

the particular dynamics of land use change and timber harvest 

levels in each RPA scenario.

Decreases in tree canopy cover occur because urbanization is 

projected to affect forests more than other land covers. As urban 

growth reduces the area of natural ecosystems, urban forests are 

likely to increase in significance as local residents increasingly 

rely on the crucial ecosystem services these forests can provide.

Rangelands are projected to decline at a slower rate than in 

recent decades. Rangeland ecosystems may be more threatened 

by low-density development and fragmenting agents such as oil 

and gas development.

Land area is crucial to the support of wildlife species. Most 

forest bird communities were projected to support a lower 

variety of species across all RPA scenarios, especially species 

that prefer intact interior habitats. Only species associated 

with human settlements are expected to increase. Habitat loss 

is a major cause of species endangerment. At-risk species 

show some degree of association with high human population 

densities, human population growth, and areas known to support high numbers of species with restricted ranges. Given the 

projected land use changes, we concluded that biodiversity in 

the United States will continue to erode.

Loss of forest and rangelands will reduce outdoor recreation 

opportunities on private lands. In addition to the loss of area, 

land conversion may further fragment surrounding private 

lands. Development around public lands can also reduce access 

to public lands, thereby limiting recreation opportunities on 

public lands.

Land development, in its effects on total area of natural ecosystems and on their pattern and condition, is clearly a crucial 

factor affecting the future of all natural resources considered in 

this assessment. Development is inevitable to meet the needs of 

a growing population, but that same population also demands 

goods and services from natural ecosystems. Balancing those 

needs will remain a challenge into the future.

Climate change will alter natural ecosystems 

and affect their ability to provide goods and 

services.

Native insects and disease, wildfire, and other natural disturbances such as extreme weather events are part of the natural 

landscape. Periods of less-than-normal precipitation and abovenormal temperatures interact with other stresses to increase 

the risk of wildfire and to reduce the resistance of ecosystems 

to insects and pathogens. Climate change will alter these 

natural disturbances and interact with human disturbances. For 

example, climate change may further enhance the spread of 

invasive species.

Climate change is already influencing forest health. High 

levels of tree mortality in the first decade of the 2000s were 

largely the result of bark beetle activity in the West after severe 

regional droughts in combination with susceptible forest stand 

conditions. Climate change is also expected to increase the 

number and size of wildfires, particularly in western forests and 

rangelands. Climate change will affect the ability of natural 

ecosystems and developed areas to sustain trees. Increasing 

aridity is projected for much of the United States, which will 

reduce the ability of some areas to maintain tree cover or 

change species type.

Forest inventory was projected under a range of RPA scenarioclimate combinations. Projected changes in temperature 

and precipitation across a range of RPA scenario-climate 

combinations generally had limited effects on the distribution 

of forest types and inventory across the United States, but those 

effects were more pronounced in the Western United States. 

For example, in the Rocky Mountain Region, both Douglas-fir 

and lodgepole pine decline across all RPA scenarios. For 

Figure 1. Projected cumulative change in the area of major 

non-Federal land uses in the conterminous United States, 

by RPA scenario, 2010–2060.

80,000

60,000

40,000

Change in thousand acres

20,000

Urban Cropland

RPA A1B RPA A2 RPA B2 RPA HFW

Forest

Major land use

Pasture Range

0

– 20,000

– 40,000
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 5

Douglas-fir, declines range from 20 to 38 percent of its 2010 

area; for lodgepole pine, declines range from 6 to 28 percent. 

At least in the immediate future, changes in temperature and 

precipitation are not posing a risk to sufficient inventory to support forest products demand, particularly in the South Region. 

The risk to providing other forest ecosystem services is not 

known, nor is the potential effect of increasing occurrences of 

extreme events.

Rangeland ecosystems typically occur in areas of environmental limitations and, as a consequence, have developed a diverse 

suite of adaptations to the moisture limitations typical of these 

ecosystems. These adaptations, the future changes in precipitation and temperature, and the future increases in atmospheric 

carbon dioxide complicate our ability to determine the potential 

effects of climate change on rangelands. If, as projected, northern latitudes warm while maintaining or increasing precipitation, 

productivity on northern and high-altitude rangelands could be 

enhanced. In the Southwestern United States, where projections 

indicate sharp increases in temperature coupled with decreased 

precipitation, rangeland productivity will likely decrease, with 

only the most drought-tolerant species prevailing. Importantly, 

several studies indicate that climate change, in combination 

with increased fire frequency and intensity, may exacerbate 

current management challenges, such as woody encroachment 

and invasive species. The diversity of rangeland ecosystems, 

the multitude of current stressors, and the potential changes in 

climate will result in highly diverse responses to climate change 

across rangeland systems. Effects on forage availability, with 

consequences to ranch enterprises of livestock, game, or tourism, will require flexible, and possibly novel, management to 

maintain rangeland health and economic viability.

Climate change is projected to have substantial effects on water 

demand and supply. Water demands are increasing, but the 

growth in population and economic activity alone are comparatively minor sources of the increase. The primary effects of 

climate change on water demand are increases in agricultural 

irrigation and landscape watering in response to rising plant 

water needs. In the absence of climate change, total U.S. water 

withdrawal was projected to decrease slightly or increase up to 

8 percent by 2060, depending on the RPA scenario. Across a 

range of RPA scenario-climate combinations, however, water 

withdrawal would increase from 2 to 42 percent from 2005 

to 2060 (figure 2). Similarly, water yields, affected only by 

changes in precipitation and temperature, decline under all RPA 

scenario-climate combinations (figure 3). The combination of 

increasing water demand and declining water yields leads to an 

increase in vulnerability of the water supply to shortage in large 

portions of the United States, especially in the larger Southwest 

and Great Plains.

Terrestrial wildlife habitats, already affected by fragmentation 

and encroachment of urban and developed areas, will be 

Figure 2. Past and projected water withdrawal for the conterminous United States for nine RPA scenario-climate combinations 

and RPA A1B with no climate effects, 1985–2060. Future years 

are multiyear averages.

550

500

450

400

350

300

Billion gallons per day

1985 2005 2020 2040 2060

A1B-no climate effects

A2-CGCM3.1

B2-CGCM2

Historical

A1B-MIROC3.2

A1B-CGCM3.1

A2-CSIRO-Mk3.5

A1B-CSIRO-Mk3.5 B2-CSIRO-Mk2

A2-MIROC3.2

B2-HadCM3

Year

Figure 3. Mean annual water yield in the conterminous United 

States by RPA scenario-climate combination for four 20-year 

periods. “Current” yield is evaluated over the 20-year period, 

1986–2005. Future yield is estimated for three 20-year periods 

centered at 2020, 2040, and 2060. 

28

26

24

22

20

18

16

14

Centimeters per year

Current 2020 2040 2060

A2-CGCM3.1

B2-CGCM2

A1B-MIROC3.2

A1B-CGCM3.1

A2-CSIRO-Mk3.5

B2-CSIRO-Mk2

A1B-CSIRO-Mk3.5

A2-MIROC3.2

B2-HadCM3

Year
6 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

stressed further by changes to terrestrial habitat attributed to 

climate change. We defined an index of habitat stress based on 

the degree of change in an area’s climate regime (temperature 

and precipitation), habitat quality (vegetation productivity), and 

habitat area (distribution of broad vegetation types) between 

the recent history and projected future. Based on this index, 

we found that the grassland-forest land transition throughout 

the Central United States and the steep elevation gradients in 

the Intermountain West will be most exposed to habitat stress 

caused by a shifting climate regime (figure 4). We also compared areas of future stress to areas of current stress associated 

with the distribution of at-risk species and intense land uses, 

and results indicated that the location of high current stressors 

tend not to overlap well with the location of high future stress 

associated with climate change. This lack of overlap potentially 

complicates the efforts of managers to prioritize wildlife 

conservation actions. 

Climate change is expected to affect both individual willingness to participate in outdoor recreation activities and the 

level of participation. Climate variables used in the outdoor 

recreation participation models mostly resulted in a slight 

increase or decrease in the participation rates or average 

days of participation compared with the “no climate change” 

projection. Climate effects resulted in substantial declines in 

snowmobiling and undeveloped skiing, however. We were not 

able to incorporate the effects of climate change on the recreation environment, which is also likely to affect future outdoor 

recreation opportunities.

This analysis of climate change effects on renewable natural 

resources suggests that climate change will alter natural ecosystems in ways that we understand and in ways that will surprise. 

The ability of the forest and rangelands to continue to produce 

ecosystem services will be affected particularly as climate 

change affects human population distribution patterns, which 

in turn will affect patterns of land use change. Consideration 

of these interactive effects will be important for designing flexible resource management strategies, particularly at local and 

regional scales, where the effects will be evident.

Competition for goods and services from natural 

ecosystems will increase.

The United States has abundant natural resources, but an 

increasing population and economic growth combined with 

climate change effects will put continuing pressure on these 

resources. The effects of land use change and climate change 

were summarized in the previous two topics. In this topic, we 

examine areas in which competition for resources is likely to 

require resource managers and planners to address important 

resource tradeoffs.

Increasing water demands are likely to increase competition 

between water uses. The water projections indicate that the 

United States is on a pathway to unsustainable levels of 

water use in several regions across a range of RPA scenarios. 

Increased water use efficiencies, water demand reductions, 

increased trading or sale of water rights, and higher pricing for 

water consumed are possible mechanisms that could help to 

bring water supplies into balance with future water demands. 

Future water use levels depend most importantly on uses in 

the agriculture sector, because irrigation consumes the bulk of 

the U.S. water supply and because irrigation requirements are 

highly sensitive to changing precipitation and temperature patterns. The current outlook indicates that demand pressures will 

increase, continuing or increasing current groundwater mining 

and further depleting streamflows, especially in drier areas of 

the United States. These pressures, in combination with development effects on water quality, raise concerns about the health 

and relative abundance of aquatic species in the future.

Species associated with aquatic habitats have higher proportions of at-risk species than other species groups. The condition 

of aquatic systems varies across the United States. Nationwide, 

more than one-half of monitored lakes were ranked in good 

condition, but only 28 percent of wadable streams were ranked 

in good condition. Imperiled aquatic species tend to occur in 

areas with high population density, and many of those areas are 

projected to have increased population and development in the 

future. Maintaining or improving water quality and streamflows 

will be challenging, especially in the face of increasing development pressure and water demands. In addition to effects on 

imperiled aquatic species, commercial and recreational fisheries 

could be negatively affected.

The availability of suitable land may constrain growing recreation demand. A stable public land base, a declining private 

Figure 4. Mean Terrestrial Climate Stress Index (TCSI) based on 

the average across multiple alternative futures.

Mean of TCSI

Low (< 0.80)

Moderate (1.11–1.72)

High (> 2.10)

Source: Joyce et al. 2008
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 7

Figure 5. Annual U.S. wood fuel feedstock consumption, 

1970–2010, and projections by RPA scenario, 2020–2060.

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Million cubic meters per year

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2 RPA HFW Historical

Year

natural land base, and increasing numbers of outdoor recreation 

participants are expected to result in increased conflicts among 

recreationists and declines in the quality and number of 

per-person recreation opportunities. The ability of recreation 

resources to absorb additional demand varies widely across 

the United States. The limited amount of public land in the 

East, where most forest land is privately owned, will likely put 

greater stress on outdoor recreation opportunities than will be 

experienced in the West. Pressures are likely to be greatest on 

public lands near large and growing population centers.

Despite the loss of land area, we expect the resource base to 

be able to meet future demands for livestock forage and forest 

products under most RPA scenarios. Currently, forage availability exceeds forage demand on 98 percent of all rangeland. 

Projections of slight increases in cattle numbers through 2020 

indicate little increased grazing pressure on rangeland in the 

near future. The ability of rangeland to meet future increases 

in cattle numbers will depend on market developments and 

on future rangeland productivity. Given the current general 

abundance of forage, the effects of climate change on ranch 

enterprises are likely to be localized.

Timber resources are projected to be abundant enough to meet 

demands, especially if we continue to see efficiency gains in 

harvesting and conversion technology. Only the RPA scenario 

with the highest increase in wood biomass use for energy 

(figure 5) is expected to lead to potential competition for land 

resources with other uses, particularly with agriculture. The 

high harvest levels required to meet these demands may create 

conflict with other forest uses. For example, the projected 

expansion of planted pine in the South Region to meet those 

biomass energy demands would displace natural pine, which 

may be undesirable from a biodiversity perspective.

Forest and rangelands are projected to be able to meet commodity demands into the future across most RPA scenarios, 

with the exception of water resources. Water shortages, which 

are already affecting many areas of the country, are expected 

to increase in the future in the absence of policy change. The 

outlook for wildlife, particularly aquatic species, is less assured, as is the outlook for recreation resources. Demands on 

one resource often influence conditions of other resources (such 

as the effect of water demands on instream flow for aquatic 

species). These interactions need to be considered in designing 

resource policy and management strategies.

Geographic variation in resource responses to 

drivers of change will require regional and local 

strategies to address resource management 

issues.

The effects of human and natural forces on natural resources 

vary across geographic regions. Examples from various 

resource analyses are presented here to illustrate that management strategies must be flexible enough to address local and 

regional needs.

Urbanization and development have been identified as future 

causes of both loss of natural environments and degradation 

through fragmentation and other effects. Projected population 

growth rates—and associated urban and suburban development 

patterns—vary across the United States. Figure 6 shows the 

projected change in population density by county between 

2010 and 2060 for the RPA scenario with medium population 

growth, demonstrating the highly variable pattern of population 

growth expected in the future. Those areas with the highest 

increase in population density will drive urbanization, as 

Figure 6. Projected change in U.S. population density (people 

per square mile), 2010–2060, for the medium population growth 

scenario (RPA A1B).

People per 

square mile

< 0

0–50

50–100

100–500

500–750

750 +
8 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

urbanization tends to spread from existing urban areas. In 

urbanizing areas, the importance of urban forests to delivering 

a variety of ecosystem services will increase in the future.

Low-density development patterns are more difficult to predict 

but are likely to occur in rural areas where population continues 

to grow in contrast with areas where population declines are 

projected. Low-density development is often concentrated 

around high-amenity locations, such as public lands. Historical 

patterns of housing development show a clustering around 

public lands, particularly Federal lands. Although the number 

of homes built increased more rapidly in the Eastern United 

States (figure 7), the rate of growth was higher in the Western 

United States. Historical patterns show a significant relationship between rural population growth and the presence of 

natural amenities that are often found on public lands. This 

development pattern creates unique challenges for Federal land 

managers.

Rangelands will also be affected by development, even though 

less area is projected to be lost than for forest land. Rangeland 

areas with the highest levels of fragmentation occur where 

agricultural land uses are prevalent (figure 8). Many of these 

areas have projected declines in population, which should 

stem development pressure. If crop prices remain high, there 

could be pressure to convert some rangelands to crop production in places suitable for increased agricultural production. 

Conversely, several areas—particularly in the Southwest—that 

currently have relatively little fragmentation will likely be 

exposed to development pressure from population growth.

The vulnerability of the U.S. water supply, defined as the 

probability of shortage in the absence of adaptation and further 

groundwater mining, increases under all RPA scenarios because of decreasing water yields and increasing water demands. 

The projected changes have considerable geographic variation, 

however. Figure 9 shows the variation across the conterminous 

Figure 8. Patterns of relative fragmentation of rangeland using 

the Morphological Spatial Pattern Analysis (MSPA) index, 2001. 

Relative fragmentation 

of grass and shrublands

(Shown are counties 

with ≥ 50,000 acres)

High

Low

< 50,000 acres of rangeland

0 210 420 840

Mile

Figure 7. Growth in number of housing units within a 30-mile 

buffer around the outer boundary of each national park, 

national forest, and wilderness area, 1940–2000.

Housing growth

(Number of housing units)

0–100,000

100,000–200,000

200,000–300,000

300,000–400,000

> 400,000

Source: Radeloff et al. 2010 Source: Data adapted from Riitters 2011

Figure 9. Current probability of annual water shortage (left) and future vulnerability (probability of shortage) in 2060 (right) for RPA 

A2-MIROC3.2. 

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Upper end 

of category
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 9

United States for both the current vulnerability of the water 

supply and the most extreme projected vulnerability, assessed 

for 98 large basins covering the conterminous United States.

Current vulnerability is concentrated in the Western United 

States. Projections indicate that increases in vulnerability will 

continue to be concentrated in the West, although they are quite 

variable. Increases also occur in parts of the Eastern United 

States. Vulnerability tends to increase over time as the effects 

of climate change become larger and as these effects have 

implications for other resource areas.

Imperiled aquatic species (figure 10) are concentrated in the 

Eastern United States, but also occur in parts of the Southwest 

that are expected to experience increasing risks of water shortages. Furthermore, the riparian environments in the arid southwest maintain important habitats used by much of the terrestrial 

fauna in this region. Conflicts about water uses, maintenance of 

instream flows, and eroding water quality are likely to increase 

in the future, exacerbating the threats to these species. Areas 

supporting concentrations of at-risk aquatic species occur 

predominantly in forested ecosystems, and the implementation 

of forestry best management practices can help minimize water 

quality effects.

Many of the resource concerns discussed in this chapter are 

common to all regions of the United States. The underlying 

drivers may vary by region or locale, however, so that a “one 

size fits all” management approach will not work in all local 

and regional situations. Because different assumptions about 

driving forces can result in very different economic and 

ecological outcomes, it is important to develop policies and 

management strategies that are flexible enough to be effective 

under a wide range of future conditions.

Figure 10. The geographic distribution of species associated 

with aquatic habitats assessed to be at risk of extinction at the 

eight-digit hydrologic unit level. 

Number 

of species

0–2

3–5

6–10

11–19

20–70

Source: NatureServe 2010
10 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 11

Chapter 3. Resource Highlights of the 2010 

RPA Assessment

This chapter summarizes resource highlights following the organization of the resource-specific chapters 

(chapters 6 through 14). The previous chapter focused on 

identifying common themes and interactions across resources, 

whereas in this chapter, we focus on key findings from the 

individual resource analyses. These findings include key 

historical and current trends and the range of outcomes from 

the Resources Planning Act (RPA) scenarios that were used to 

analyze the effects of human and environmental influences on 

future resource trends.

Land Resources

Urban and other developed land area will 

increase in the future.

We expect continued alteration to natural landscapes in the 

future, as urban and other developed land is projected to expand 

under all RPA scenarios in response to population and economic 

growth. Total urban and developed land area is projected to 

increase between 39 and 69 million acres between 2010 and 

2060, an increase of 41 to 77 percent. Urbanization patterns 

tend to follow population growth patterns, so the greatest amount 

of development during the projection period is expected in the 

South Region, although the rate of growth is projected to be 

highest in the Rocky Mountain Region.

Natural landscape patterns are affected by 

development patterns.

Ecological risks increase as urban and developed land becomes 

more interspersed within natural landscapes. The risk of further 

conversion of natural land covers (forest land, grassland, and 

shrubland) is highest in landscapes dominated by agriculture, 

urban, and other developed land uses. Currently, less than 10 

percent of the area of these natural land covers occurs in human-dominated landscapes, so their exposure to such ecological 

risk is relatively low. The risk varies substantially across the 

Nation, however, and is generally highest in the North Region, 

which has the highest population density.

A higher proportion of natural land cover area is at risk from interspersed development containing 10 to 60 percent agriculture 

and developed land cover, with forest land having the greatest 

area at risk. Excluding Alaska, 23 percent of forest land, 19 

percent of grassland, and 7 percent of shrubland occur in such 

landscapes.

Natural land covers are also at risk from ecological edge effects, 

such as changes in microclimate and introduction of edgeadapted species, as a result of direct adjacency with other land 

cover types. Forest, grassland, and shrubland tend to be the 

dominant types of land cover where they occur, but fragmentation from all causes is so pervasive that the risk of short-range 

(about 100 feet) edge effects threatens 28 percent of all forest, 

30 percent of all shrubland, and 40 percent of all grassland 

area. Forest, grassland, and shrubland all exhibit substantial 

fragmentation and measured fragmentation increases across a 

range of spatial scales. Grassland is the most fragmented and 

shrubland is the least fragmented natural land cover type; forest 

land is fragmented more like shrubland at smaller spatial scales 

and more like grassland at broader spatial scales.

Development pressures around public lands 

threaten ecological integrity.

Development pressures are particularly strong on lands with 

high natural amenity values—lands that often occur in proximity 

to public lands in general and to national forests in particular. 

Housing development pressures on private lands surrounding 

public lands affect the ability of those public lands to sustain 

important ecosystem services and biodiversity. Areas of the 

country where development pressures near public lands have 

been particularly high include peninsular Florida, the southern 

Appalachians, the foothill and front ranges near major metropolitan areas in the Interior West, montane forest habitats in the 

arid Southwest, and southern California—the very same areas 

that also support high concentrations of imperiled species.

Although wilderness areas receive the highest level of protection against development and resource extractions within their 

boundaries, they are not immune to resource effects related to 

development in the surrounding landscape. Some wilderness 

areas are increasingly isolated fragments or remnants of historic 

ecosystems. These threats are more pronounced in wilderness 

areas of the East, but effects also occur in the West. About 

16 million new homes were built within 30 miles of wilderness 

area boundaries between 1940 and 2000. Development near 

wilderness areas has been associated with increasing recreation 
12 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

pressure, which, in turn, has been shown to alter vegetation and population demography of recreationally harvested 

game species.

Forest Resources

Forest area will decline in the future.

The U.S. forest land base has remained relatively stable for 

almost 100 years, despite population growth. The continuing 

need to accommodate a growing population is expected to 

reduce forest area in the future, however, largely as a result of 

urbanization and other land development. Forest land losses 

are projected to range from 16 to 34 million acres in the conterminous United States. The South Region is expected to have 

the greatest loss of forest, ranging from 9 to 21 million acres 

between 2010 and 2060, roughly 4 to 8 percent of the region’s 

2007 forest land base.

Forests face threats to their long-term health 

and sustainability.

Native and exotic pests and pathogens, fire, and other natural 

disturbances, combined with climate change, pose ongoing 

risks to forests. Almost 8 percent, or more than 58 million 

acres, of forest land are at risk to increased activity by forest 

insect pests and pathogens.

Declining forest area, coupled with climate 

change and harvesting, will alter forest-type 

composition in all RPA regions.

The South Region, which is projected to have the largest 

decline in forest area across all RPA scenarios, is also projected 

to lose area in most forest types. The main exception is the 

area of planted pine, which is projected to increase primarily 

at the expense of natural pine. Upland hardwoods decline as 

a result of urbanization pressures in the region. Urbanization 

is also the primary force behind losses of oak-hickory in the 

North Region. The maple-beech-birch forest type in the region 

increases under all RPA scenarios.

Projections of forest types in the West were more sensitive 

to differences in climate projections, often shifting species 

towards different forest-type groups. In the Rocky Mountain 

Region, Douglas-fir and lodgepole pine are projected to decline 

across all RPA scenarios, whereas fir-spruce-hemlock and 

ponderosa pine area increases. In wet west-side forests of the 

Pacific Coast Region, hemlock-Sitka spruce area is projected to 

decline and the area of Douglas-fir to increase. On the east side, 

only ponderosa pine expands.

Forest inventory is projected to peak between 

2020 and 2040, then decline to 2060.

The projected peaking of forest growing stock inventory followed by declines would conclude a long period of inventory 

accumulation on the Nation’s forest lands. Across all RPA 

scenarios, the greatest projected reduction in forest inventory 

occurs in the North Region, a result of expansion of timber 

removals and forest losses from urbanization. Investments 

in plantations partially offset similar declines in the South 

Region. In the highest timber demand RPA scenario, planted 

pine area in the South Region is projected to expand by more 

than 70 percent, to 67 million acres by 2060, whereas just more 

than 20 million acres of agricultural land are planted in highly 

productive agricultural short-rotation woody crops nationwide 

by 2060. Relative to 2010 levels, total forest inventory in 2060 

would range from a loss of 7 percent in the highest timber 

demand RPA scenario to a gain of 2 percent in the lowest 

timber demand RPA scenario. The gains in plantation area and 

timber productivity in the high timber demand RPA scenario 

offset what would otherwise be a more substantial depletion in 

U.S. timber inventory.

Softwood inventories are projected to remain 

relatively stable, whereas hardwood inventories 

show large declines after 2030.

Hardwood forests are more strongly affected by urbanization 

than softwood forests and are also affected by expansion in 

wood energy demands. Therefore, hardwood forest inventories 

are projected to decline across all RPA scenarios. Losses 

in softwood types are offset partially by increasing area of 

intensively managed planted pine in the South Region. This 

offset is particularly important in the RPA scenario that has the 

highest rate of both urbanization and wood energy consumption, because it also has the strongest timber markets providing 

incentives to maintain forest land for production.

Tree canopy cover across all natural landscapes 

will be affected by development and climate 

change.

The conversion of forest to more developed uses generally 

reduces tree canopy cover, whereas development in grassland 

and other nonforest systems tends to increase tree canopy cover 

relative to surrounding ecosystems, if water is not limiting. The 

future land use changes are projected to slightly decrease tree 

canopy cover across the Nation, reflecting the greater effect of 

urbanization on forests compared with other land covers.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 13

Climate change will affect the ability of natural landscapes and 

developed areas to sustain trees. Combining the effects of land 

use change and climate change, the United States is projected to 

lose tree canopy cover nationally, with the greatest losses in the 

Eastern United States and the Pacific Northwest. The results in 

the Intermountain West vary greatly by RPA scenario, reflecting the variations in the mountain and basin landscapes.

Forest Products

The forest products sector was hard hit by the 

recession.

Historically, the volume of roundwood needed to make wood 

and paper products consumed in the United States (including 

product imports) grew at roughly the rate of population growth. 

Per capita consumption has declined with the downturn in 

housing construction since 2005. The lower import share of U.S. 

wood and paper product consumption and an increase in the 

export share of production were positive trade balance effects 

of excess productive capacity and weakening of the U.S. dollar. 

The weaker U.S. dollar and productivity gains by U.S. producers improved the competitiveness of U.S. forest products, and 

the United States recently became a net exporter of wood pulp, 

paper, and paperboard for the first time in many decades.

Timber resources will continue to be adequate 

to meet demands unless there are very large 

increases in wood energy demand.

Real price trends indicate that pulpwood has become relatively 

more abundant in the United States since the late 1990s, a result 

of increasing supplies (continued timber growth and maturation of pulpwood plantations, and other recent investments in 

plantation intensity), a general declining trend in consumption, 

and efficiency gains in timber harvesting and conversion technology. In RPA scenarios that do not have large increases in 

demand for wood energy, the projected supplies of timber are 

adequate to meet demands despite a declining forest land base.

Forest product futures are tied to domestic and 

global wood energy demands.

Assumptions about future expansion in U.S. and global wood 

energy demands directly influence projected U.S. consumption, 

production, and net trade in forest products. U.S. producers of 

forest products can gain competitive advantage in trade in RPA 

scenarios with significant expansion of global wood energy 

consumption if the projected average industrial roundwood 

prices in foreign countries increase more than projected 

average U.S. industrial roundwood prices. The relatively flatto-declining timber price projections of other RPA scenarios 

indicate that, barring really significant and unforeseen structural changes in U.S. forest product demands, timber supplies 

are increasing and that U.S. timber prices are not projected to 

increase significantly.

Urban and Community Forests

Urban forest area will increase.

Urban development will be accompanied by increasing urban 

forest area. Current tree cover on urban lands was estimated at 

35 percent. Tree cover tends to decrease as population density 

increases, so tree cover may decline in some urban areas where 

density and total population increase. Tree canopy cover can 

serve as an indicator of the extent to which trees and forests are 

providing crucial services to local residents. Urban forests are 

likely to become more important in providing these services in 

the future as urban growth reduces natural landscapes.

Urban forests will become increasingly 

important for providing a range of ecosystem 

services to urban populations.

In 2000, almost 80 percent of the U.S. population lived in 

urban areas that covered only 3.1 percent of the U.S. land area. 

As urbanization increases, so will the value of urban forests 

and surrounding rural forests in providing ecosystem services 

required by urban residents. Trees in urban areas provide many 

benefits and values to society, including improving air and 

water quality and providing aesthetic benefits. Urban trees also 

store about 700 million tons of carbon. Urban forests in the 

Northeast, Southeast, and South Central RPA subregions store 

and sequester the most carbon.

Carbon in Forest Resources and 

Products

Carbon stored in forests is projected to peak 

between 2020 and 2040, then decline.

The projected declines in forest area and forest biomass result 

in an overall decline in the amount of carbon stored in forests 

by 2060. Forest carbon stocks in 2060 would be reduced by 

between 0.8 and 2.5 billion metric tons from 2010 levels. 

Roughly one-half of the carbon losses are projected to occur in 

the North Region.
14 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Carbon storage projections are highly sensitive 

to forest land area projections.

Forest land is projected to decline in all of the RPA scenarios. 

The large decrease in forest land area by the later decades, 

particularly in the final decade between 2050 and 2060, results 

in large amounts of carbon being transferred out of the forest 

and into other land uses, usually developed uses. A complete 

accounting of carbon across the entire land base would tell a 

more complete story and more appropriately emphasize the 

effect of land use change.

Carbon stored in harvested wood products 

depends on future timber markets.

The projected annual additions to carbon stored in harvested 

wood products (HWP) dropped dramatically with the recession. 

Annual additions to carbon stored in HWPs would be higher 

than historical levels under RPA scenarios in which U.S. forest 

product production increases to greater-than-historical levels. 

RPA scenarios with a lower level of U.S. production and higher 

levels of product imports, however, result in declines in annual 

additions to carbon storage that never recover to historical 

levels during the projection period.

Rangeland Resources

Rangelands are projected to continue their slow 

decline in area.

The historically slow decline in rangeland area is expected to 

continue under all RPA scenarios, with losses ranging from 

about 6 to 9 million acres between 2010 and 2060. This loss 

that is projected to occur during 50 years is roughly the same 

as the loss of about 9 million acres that occurred in the 25 

years between 1982 and 2007. Therefore, the rate of projected 

decline is less than the recent historical rate of loss. Rangelands 

will continue to face pressure from expansion of urban and developed land, creating additional fragmentation on rangelands. 

Currently, Nevada and Arizona have the least fragmented 

rangelands, and the most fragmented rangelands occur in proximity to areas with high agricultural usage, including Nebraska, 

Oklahoma, and Texas.

Rangeland productivity is stable.

Rangeland productivity between 2000 and 2009 was unchanged 

to slightly increasing. Variability in net primary productivity 

was highest in very dry regions, most likely in response to 

interannual variation in precipitation.

Rangeland health is difficult to consistently 

evaluate.

Roughly 80 percent of non-Federal rangeland in the conterminous United States was judged to be in relatively healthy 

condition in the latest National Resources Inventory and 

exhibited no significant soil, hydrologic, or biotic integrity 

problems. Several estimates indicate that woody encroachment 

and invasive species are becoming increasing problems that 

may be exacerbated by climate change, however. Nonnative 

species were present on roughly 50 percent of non-Federal 

rangeland and represented more than 50 percent of the total 

plant cover on 5 percent of non-Federal rangeland in 2007. As a 

whole, approximately 10 percent of U.S. rangeland is currently 

occupied by invasive juniper species, and mesquite species are 

a dominant woody plant on more than 94 million acres of what 

has been considered semiarid southwestern grasslands.

Rangeland forage supply is sufficient to meet 

demand.

Domestic livestock numbers have generally declined during 

the last decade, although the U.S. Department of Agriculture 

projected that cattle numbers are expected to increase slightly 

through 2020. For most rangeland, forage availability exceeded 

demand for domestic livestock. Only 2 percent of total rangeland was estimated to have forage demand in excess of forage 

availability. Texas and California had the largest number of 

acres where demand exceeded availability.

Water Resources

Climate change will increase future water 

demands.

In the absence of future climate change, total water withdrawals were projected to remain close to current levels despite 

large increases in population, because of improving efficiencies 

in water use. Climate change, however, is projected to increase 

water demand substantially. For example, one RPA scenario 

projects water withdrawals will increase between 12 and 41 

percent by 2060, as opposed to 3 percent without accounting 

for future climate change. About three-fourths of the increase is 

attributable to agricultural irrigation increases.

Projected percentage changes in consumptive water use are 

larger than those for withdrawal because of improving efficiency in how water withdrawals are being used. In comparison 

with the increases in withdrawal listed above, consumptive 

use is projected to increase between 26 and 86 percent with 

climate change, as opposed to 10 percent without accounting 

for climate change.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 15

Projected water withdrawal varies considerably 

across regions.

Assuming medium population growth without future climate 

effects, water withdrawals were projected to drop in 42 of the 

98 assessment subregions (ASRs), increase by less than 25 percent in 38 ASRs, and increase by more than 25 percent in the 

remaining 18 ASRs. The ASRs where withdrawals are projected 

to drop are rather evenly divided between the East and West, as 

are the ASRs expecting increases greater than 25 percent.

Including climate effects in the same RPA scenario results in 

wide variation in future water withdrawal across ASRs. Projected withdrawals drop in 12 ASRs but increase by less than 

25 percent in 34 ASRs, by 25 to 50 percent in 37 ASRs, and by 

more than 50 percent in 15 ASRs.

Future water use depends most importantly on 

the agricultural sector.

Irrigation accounts for the bulk of consumptive water use, and 

irrigation requirements are highly sensitive to climate change. 

Further, future irrigated area depends on changes in water 

markets, agricultural markets, and policies that are very difficult 

to project.

U.S. water yield is projected to decrease.

For the conterminous United States, water yield is projected to 

decrease throughout the 21st century. For example, average annual yields are projected to decline between 16 and 22 percent 

by 2060 under the medium population growth RPA scenario 

across three future climates. Decreases are projected for most, 

but not all ASRs. In general, the magnitude of the decrease is 

larger in humid areas. In those areas with increases in average 

annual yield, the increase is very small in absolute terms.

The vulnerability of the U.S. water supply will 

increase.

Vulnerability is defined as the probability that supply is less 

than demand at the ASR scale. The water supply system of 

much of the United States west of the Mississippi River is 

vulnerable under current hydroclimatic and socioeconomic conditions. Only a few ASRs show vulnerability values exceeding 

a 0.05 probability, however. Increases in vulnerability are projected to occur mainly in arid and semiarid areas of the United 

States, where the current conditions are already precarious (e.g., 

California, the Southwest, and the central and southern Great 

Plains). At the ASR scale, most of the Eastern United States 

is currently characterized by water abundance. Some RPA 

scenario-climate combinations project moderate shortages in 

the East. Localized areas within an ASR (such as the Atlanta 

area), however, may experience substantial vulnerability that 

is not captured at the ASR scale of analysis. Vulnerability 

tends to increase over time as the population expands and the 

effects of climate change become larger. The rate of increase 

in vulnerability is greatest in the last decade of the projection 

period, when climate effects become most pronounced.

Increases in vulnerability depend both on changes 

in water yield and on growth in water demand.

Although climate change will increase water demand, 

continued improvements in water use efficiency will mitigate 

that effect. As a result, in about one-half of the ASRs where 

vulnerability is projected to increase, decreases in water yield 

(that in turn affect water supply) have a greater effect on future 

vulnerability than the effect of increases in water demand. In 

some ASRs, the combined effect of changes in water yield and 

demand lead to untenable levels of vulnerability, suggesting 

that adaptation to water shortage there will be essential.

Wildlife, Fish, and Aquatic Resources

Wildlife populations and harvests have mixed 

trends.

The recent historical trends in wildlife resources vary. Big 

game and waterfowl have shown a general pattern of increasing 

population or harvest trends. Although such gains have generally been regarded as a favorable resource situation, evidence 

suggests that populations of some species may be exceeding the 

ability of habitat capacity to sustainably support them.

Many small upland and webless migratory game species have 

shown notable declines in population or harvest. Many of these 

species are associated with grassland, farmland, and early 

successional forest habitats and point to the need for active 

management to increase these habitat types on the landscape. 

These species have shown little sign of recovery from declines 

noted in past RPA Assessments.

Fur harvests can change for a number of reasons that are independent of population levels, including changes in pelt prices, 

the number of trappers and their effort in pursuing furbearers, 

and changes in the accessibility of land for trapping. The 

notable declines in fur harvest since the last assessment appear 

to be driven by substantial declines in pelt prices. Wildlife 

damage complaints associated with furbearers are likely to 

become a more prominent management issue in the absence of 

any economic incentives to increase harvests.

Birds have long been thought to be good indicators of landscape 

change. For the 426 species with sufficient data to estimate 

nationwide trends, 45 percent had stable abundance since the 
16 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

mid-1960s. A higher percentage of species had declining trends 

(31 percent) than increasing trends (24 percent) during this 

period. Species groups that nest on or near the ground or in 

grassland habitats had relatively high proportions of species 

with declining trends. RPA regions already characterized by 

prominent human effects (the North and South Regions) tended 

to have higher proportions of species with declining abundance.

Projected land use changes are expected to 

reduce the variety of forest bird species.

As human populations grow, native habitats are lost to agriculture, road construction, or urbanization. The RPA land use 

projections indicate that intensive land uses and housing development are expected to increase in forested landscapes. In response to these land use changes, most forest bird communities 

are expected to support a lower variety of species, particularly 

among those forest species that prefer intact interior habitats 

(that is, low fragmentation) or nest on or near the ground. The 

only group of birds expected to show an increase in species 

richness are those associated with human settlements. These 

patterns of bird richness response were similar across all RPA 

scenarios.

Climate change will differentially stress 

terrestrial wildlife habitats across the country.

Those areas most exposed to habitat stress attributable to 

climate change occur along the grassland-forest land transition 

throughout the central portion of the United States and the 

steep elevation gradients in the Intermountain West. Areas less 

sensitive to climate-induced habitat stress include the southern 

portions of the Great Plains and the Middle Atlantic States. A 

comparison of areas of likely future stress to areas of current 

stress associated with the distribution of imperiled species 

indicate that the locations of high current stressors tend not to 

overlap with the locations of high future stress associated with 

climate change, potentially complicating the efforts of managers to prioritize wildlife conservation actions.

Freshwater habitat conditions vary widely 

across the United States.

Nationwide, the U.S. Environmental Protection Agency ranked 

more than one-half of monitored lakes in 2007 to be in good 

condition, but the percentage varied from a high of 91 percent 

in the Upper Midwest to a low of 1 percent in the northern 

plains ecoregion. An assessment of small streams in 2004–2005 

indicated that 42 percent of stream lengths sampled nationally 

were determined to be in poor condition. The southern Appalachians, southern plains, and northern plains ecoregions have 

50 percent or more of their sampled stream lengths in poor 

condition. The biological condition of small streams was best 

in the western mountains ecoregion, where only 25 percent of 

sampled stream lengths were determined to be in poor condition.

Some commercially and recreationally 

important fish populations are in decline.

Data availability continues to limit comprehensive evaluations 

of freshwater fish population trends. Of the 253 marine fish 

stocks assessed in 2009, 23 percent were deemed to be overfished 

(fish populations are less than the level needed to replenish 

themselves) and 15 percent subject to overfishing (fishing mortality is too high to sustain the current fishing levels). Pacific 

salmon have declined throughout much of their range, although 

stocks native to Alaska have fared better than those in the Pacific 

Northwest. Of the 52 distinct populations of salmon and steelhead in the Pacific Northwest, 28 are currently listed as threatened or endangered under the Endangered Species Act of 1973 

(ESA). Excessive siltation from land use changes, water removals, 

and obstructions that prevent fish from reaching spawning 

habitats have all contributed to Pacific salmon declines.

Biodiversity in the United States continues to 

erode.

Since the 2000 RPA Assessment, there has been a net gain 

of 278 species formally listed as threatened or endangered 

under the ESA. The greatest gains were among plants (152 

species) and fish (31 species). Just more than one-fourth of all 

vertebrates and one-third of vascular plants are of conservation 

concern—defined as species determined to be possibly extinct 

or at risk of extinction. Vertebrates with a relatively high 

percentage of species-of-conservation concern include amphibians (41 percent), freshwater fishes (37 percent), and reptiles 

(21 percent). Among invertebrates, mollusks (58 percent) and 

crustaceans (53 percent) have the greatest percentages of species that are of conservation concern. Species associated with 

aquatic habitats have higher proportions of at-risk species than 

other species groups.

Concentrations of at-risk species vary 

geographically.

At-risk species tend to be prominent in areas with high human 

populations or where land use intensification has occurred (for 

example, peninsular Florida, the Florida panhandle, coastal 

California) or in areas known to support high numbers of species with restricted ranges (the arid Southwest). At-risk species 

that are associated with aquatic habitats are concentrated in 

watersheds in the southern Appalachians and the southeastern 

coastal plain.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 17

The number of species that have been extirpated from each State 

show areas of concentration among the heavily populated Middle 

Atlantic States. This pattern gives an indication of how much 

historical biodiversity has been altered by human settlement.

Outdoor Recreation

Outdoor recreation resources are expected to 

decline on a per-person basis.

The outlook for recreation resources is generally of declining 

opportunities per person. The public land base is not expected 

to expand significantly. Therefore, an increasing population 

will result in decreasing per-person opportunities for recreation 

across most of the United States. Although many other factors 

are involved in recreation supply, recreation resources are likely 

to become less available as more people compete to use them. 

A major challenge for natural resource managers and planners 

will be to ensure that recreation opportunities remain viable 

and grow along with the population. This goal would more than 

likely be accomplished through management and site attribute 

inputs and plans, rather than through any major expansions or 

additions to the land and water base for recreation.

Outdoor recreation participation continues to 

grow, but activity choices are changing.

The number of outdoor recreation participants increased about 

7 percent between 2000 and 2009 for 50 nature-based outdoor 

recreation activities, and the number of activity days increased 

30 percent. Activities oriented toward viewing and photographing nature were among the fastest growing activities. Offhighway vehicle driving increased 34 percent and physically 

challenging activities, such as kayaking and snowboarding, 

also had relatively large increases, although a small percentage 

of the population engages in these activities. Several activities 

have declining participation rates, including traditional activities such as hunting and fishing.

The five outdoor recreation activities projected to have the fastest growth in participation rate across the RPA scenarios are developed skiing, challenge activities (e.g., caving and mountain 

climbing), equestrian activities, motorized water activities, and 

day hiking. The activities with the largest projected declines are 

motorized off-road activities (indicating a reversal from recent 

increases), motorized snow activities, hunting, fishing, and 

floating activities. The largest growth in number of participants 

is projected to occur for activities associated with visiting 

developed sites and nature viewing, where more than 100 million participants could be added by 2060. For most activities, 

the effect of the climate on future recreation participation was 

a slight increase or decrease compared with the “no climate 

change” projection. Climate effects dramatically lowered 

participation in snowmobiling and undeveloped skiing.

Outdoor recreation choices are strongly 

influenced by socioeconomic characteristics.

Non-Hispanic Whites continue to dominate participation in all 

outdoor recreation activity groups, with the exception of Native 

Americans, who are as likely to participate in backcountry 

activities. Males are more likely than females to participate in 

all activity groups except visiting recreation or historic sites 

and viewing and photographing nature. People that were young 

to middle-aged and had college educations and higher incomes 

also tended to participate more in recreation activities. The 

demographic groups consistently less likely to participate in 

outdoor recreation activities were African-Americans, people 

65 or older, and people with less education and lower income. 

Females, Hispanics, and Asians were less likely to participate 

in some activities, but the pattern varied across activities.

Future outdoor recreation participation will 

reflect the preferences of a changing U.S. 

population.

The growing diversity of the American population, coupled 

with the relatively low participation rates of most groups except 

non-Hispanic Whites, suggest shifting recreation preferences 

can be expected. Similarly, the aging population may require 

different types of recreation opportunities. Recreation activities 

that have been dominated by rural residents are also likely to 

decline as the American population becomes increasingly urban. The outdoor recreation projections reflect these changing 

preferences. For example, several activities that rural residents 

are more likely to participate in, such as hunting, snowmobiling, and off-road driving, are projected to decline. Day hiking 

has one of the fastest projected increases in participation rate, 

reflecting high participation by Hispanics.

Growing recreation demand may be constrained 

by recreation resource availability.

A stable public land base, a declining private natural land 

base, and increasing outdoor recreation participants will result 

in declines in per-person recreation opportunities. The ability 

of recreation resources to absorb additional demand varies 

widely across the United States, but will likely stress recreation 

opportunities in the Eastern United States to a greater extent 

than the West. Pressure is likely to be greatest on local, State, 

and Federal facilities that are in close proximity to population 

centers.
18 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

The projected changes in participation in some recreation 

activities reflect the declining land base. Generally, land and 

water availability positively influence activity participation. 

Therefore, declines in the per capita area of forest and rangeland and of Federal land resulted in participation declines in 

spatially extensive activities like equestrian, hunting, motorized 

off-road driving, visiting primitive areas, and nature viewing. 

Similarly, participation in water-based activities like swimming, motorized boating, and nonmotorized boating were all 

positively correlated with the per capita availability of water 

area.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 19

Chapter 4. Global and U.S. Context

The Millennium Ecosystem Assessment (MEA 2005) undertook a comprehensive review of the world’s 

ecosystems, emphasizing the ability of these systems to provide 

goods and services that are crucial to humans. Ecosystem change 

and conversion to meet human needs for food, water, fiber, 

and fuel substantially changed the extent and quality of natural 

ecosystems globally between 1950 and 2000. These changes 

included conversions of natural ecosystems to agricultural uses, 

loss and degradation of coral reefs, increased water impoundments, increased water use, and increased number of species at 

risk of extinction. Between 1960 and 2000, the global economy 

grew sixfold and the global population doubled (MEA 2005).

Population and income are both key drivers in the projection 

of resource demands that affect the future condition of forests 

and rangelands. In this chapter, we provide a brief overview of 

recent global and national population and economic trends.

Population Growth

Global population grew from 6.1 billion in 2000 to 6.8 billion 

in 2009 and is expected to reach 9.6 billion by 2060 (United 

Nations 2009). The percentage of the global population living 

in urban areas surpassed the 50-percent mark in 2009, and 

future population growth in urban areas is expected to more 

than double between 2009 and 2050. Urban populations will 

expand at rates greater than overall population growth, resulting 

in a decline in the number of rural inhabitants. Urbanization is 

expected to continue rising in both the more developed and less 

developed regions, so that by 2050, urban dwellers are expected 

to account for 86 percent of population in the more developed 

regions and 66 percent of the population in less developed 

regions (United Nations 2010).

Unlike many developed countries where population is declining, the U.S. population continues to increase. The 2010 Census 

indicated that the U.S. population increased almost 10 percent 

between 2000 and 2010, when it reached almost 309 million. 

Regional population growth was faster in the South and West 

than in the Midwest and Northeast. Overall, the South and West 

accounted for about 84 percent of the U.S. population increase. 

The States with the highest numeric increases were, in descending order, Texas, California, Florida, Georgia, North Carolina, 

and Arizona. These six States accounted for 54 percent of the 

overall increase in the last decade.

Almost 84 percent of the U.S. population in 2010 lived in a 

metropolitan statistical area, and population in these areas 

grew at a faster rate than the overall U.S. rate (USCB 2011). 

Although the 2010 Census data on urban areas were not yet 

available, the growth in population in metropolitan statistical 

areas likely will be mirrored by growth in urban areas.

Although the South and West had the largest increases in 

population, the U.S. population is still concentrated on the two 

coasts. Although only one State, Michigan, lost population in 

the last decade, depopulation occurred in a number of counties, continuing decades of population loss in areas such as 

Appalachian counties in eastern Kentucky and West Virginia, 

many Great Plains counties, and a group of counties around the 

Mississippi delta. Many counties along the Great Lakes and the 

northern U.S. border either lost population or grew at very low 

rates (USCB 2011).

Economic Outlook

The global economy has gone through considerable change 

during the last several decades. The 1970s saw oil price shocks; 

the 1980s were a time of general deflation of commodity prices. 

During the 1990s, the industrial structure of many developed 

economies, including the United States, shifted in emphasis to 

service sectors as growth in their manufacturing sectors declined. 

The decade of 2000–10 brought considerably more change—

particularly in the last years of the decade, when a global 

recession had major effects on the global and U.S. economy, 

especially in the real estate and housing construction sectors. 

At the end of the decade, high oil prices and high commodity 

prices were also influencing the pace of economic recovery, 

with persistent high unemployment, although some commodity 

prices began declining in 2011.

Global gross domestic product (GDP) increased 57 percent 

between 2000 and 2010, from $36.3 to $56.9 trillion (2005 

U.S. dollars at market prices) (World Bank 2012). GDP growth 

in less developed regions has been at higher levels than in the 

developed world. The global economy moved toward recovery 

in 2010 with positive economic growth in most regions and 

rebounding world trade. Emerging-market economies are increasingly important to world economic growth and to the U.S. 

economy (Council of Economic Advisors 2011). In the latter 

part of 2011, however, global economic activity weakened. 
20 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Global GDP forecasts have been lowered, with global growth 

now forecast to increase 4 percent through 2012, and growth in 

the advanced economies projected at only 1.5 percent in 2011 

and 2 percent in 2012. Even these low levels of growth depend 

on the United States and European countries adopting policies 

to address deficit reduction and the crisis in the Eurozone. 

Growth is expected to continue to be higher in emerging and 

developing economies than in developed economies (International Monetary Fund 2011).

The U.S. economy is still recovering from the recession that 

began at the end of 2007, which was both the longest and worst 

since the Great Depression. U.S. GDP now exceeds its prerecession peak in real dollar terms (USDC Bureau of Economic 

Analysis 2012), and the private sector has added jobs. Although 

the U.S. economy shows signs of recovery, the continued weakness in the U.S. housing market, budget constraints faced by 

State and local governments, the need to reduce the Federal 

deficit, concerns about the fiscal situation in Europe, high oil 

and commodity prices, and political unrest in various parts of 

the world continue to complicate economic recovery in the United 

States and other regions (Council of Economic Advisors 2011).

Forests and Rangelands

The world has abundant forest and rangeland resources. The 

Food and Agriculture Organization (FAO 2010) estimated 

global forest area to be about 8.8 billion acres, covering 31 

percent of the total global land area. The five most forest-rich 

countries, in descending order, are the Russian Federation, 

Brazil, Canada, the United States, and China. These countries 

account for more than one-half (53 percent) of the total global 

forest area. U.S. forest land accounts for about 7 percent of the 

world’s forest area.

The rate of global deforestation shows signs of decreasing, but 

it is still high. The largest net losses are in South America and 

Africa. Large-scale planting of trees is significantly reducing 

the net loss of forest area globally, through a combination 

of afforestation and natural expansion of forest. The area of 

planted forest is increasing and now accounts for 7 percent of 

total global forest area (FAO 2010).

The lack of a common definition for rangeland hinders global 

estimates. White et al. (2000) estimated that grassland ecosystems (which are not synonymous with rangeland) cover some 

40 percent of the Earth’s land area (excluding Greenland and 

Antarctica). They are found in every region of the world, but 

the largest areas are found in sub-Saharan Africa and Asia. The 

five countries with the largest grassland areas, in descending 

order, are Australia, the Russian Federation, China, the United 

States, and Canada.

The United Nations recently released a report (FAO 2011) 

indicating that 70 percent more food needs to be grown by 

2050 to support the growing world population. This growing 

demand will continue to put pressure on forest and rangelands 

both domestically and globally.

Conclusions

The increasingly globalized economy has had major effects on 

U.S. renewable resources. Reduction of trade barriers between 

countries, a key factor in increasing economic globalization, 

has increased the flow of goods, services, and capital internationally. The result has been developing countries increasing 

their share of world trade and increasing the importance of 

international markets in meeting U.S. domestic demands 

(Martin 2001). These effects have been most noticeable in 

the forest sector, where changes during the last few decades 

include restructuring in the pulp and paper sector and the loss 

of a significant portion of domestic furniture production to 

overseas producers.

Currently, U.S. forest products producers have increased 

exports relative to imports because of a variety of factors. 

Similarly, high agricultural commodity prices are providing 

strong export markets for U.S. farmers and causing increases 

in farmland values. Rising farmland values relative to other 

rural land values could increase the potential for the expansion 

of cropland, which would reverse a decades-long trend in the 

United States. At the same time, domestic policies to encourage 

biofuels production create incentives to convert land to energy 

crops, thereby competing with food crops and timber production. Concerns about water shortage, often linked to a changing 

climate, will affect the feasibility of future production, because 

irrigation is the largest consumptive use of water in the United 

States. Increased opportunities for travel have raised interest in 

U.S. forest and rangelands as destinations for increasing numbers of international tourists (e.g., to sites such as Yellowstone 

National Park that highlight the diversity of U.S. ecosystems). 

International travel and trade are also conduits for the introduction of invasive species, however, which often threaten the 

sustainability of these ecosystems.

Although the United States has significant forest and rangeland 

resources, pressures from competing demands that have higher 

economic returns (e.g., some types of agricultural production 

and urban development) will result in the loss of natural ecosystems. As incomes rise, the demand for ecosystem services 

and protection of natural ecosystems also tends to increase, 

creating potential conflicts between commodity production and 

environmental protection.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 21

In the following chapters, we describe historical trends in resource conditions and use, then use a set of scenarios to project 

alternative futures. Those futures are strongly influenced by 

population and economic assumptions. Global and U.S. populations are projected to continue increasing in the future. The 

outlook for economic growth is more uncertain, particularly in 

the short term, but the longer term trend is expected to be positive. To a large extent, the Resources Planning Act Assessment 

outlook for U.S. resources is influenced by scenarios with varying assumptions about global economic growth, global wood 

energy consumption, forest products trade, domestic population 

and economic growth, and global climate change. Our analyses 

indicate the importance of these factors in assessing the alternative resource futures and likely challenges for future renewable 

resource management.
22 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 23

Chapter 5. Future Scenarios

The Resources Planning Act (RPA) Assessment addresses a wide range of economic and ecological 

phenomena. Individually, the economic, social, and biological 

systems are quite complex; integrating effects across these 

systems increases complexity. Because there are uncertainties 

about future political, economic, social, and environmental 

change, we used scenarios to explore a range of possible futures 

for U.S. renewable natural resources. The scenarios used in the 

2010 RPA Assessment are described in this chapter.

RPA Scenarios

Scenarios define alternative futures and provide a framework 

for evaluating a plausible range of future resource outcomes. 

We selected a set of comprehensive global scenarios that were 

used in the Intergovernmental Panel on Climate Change (IPCC) 

Third Assessment Report (TAR) and Fourth Assessment Report 

(AR4) to provide global context and quantitative linkages between U.S. and global trends. The range of scenarios considered 

in the IPCC Assessments provided a broad spectrum of potential futures from which we selected a subset that are relevant to 

evaluating potential U.S. future resource conditions and trends.

We developed three RPA scenarios that describe alternative 

national- and county-level futures that are linked to IPCC assumptions and projections of global population growth, economic 

growth, bioenergy use, and climate (IPCC 2007a). For continuity, 

we retained the scenario designations used in the IPCC TAR 

and AR4, with the addition of “RPA” to remind readers that 

these scenarios are tied to IPCC assumptions described in the 

Special Report on Emission Scenarios (SRES) (Nakicenovic 

and Swart 2000), but that some adjustments were made that 

are briefly described in this chapter. The RPA scenarios are, 

therefore, designated as RPA A1B, RPA A2, and RPA B2.2

We developed a fourth scenario that uses the same economic 

and population assumptions as RPA A1B, but is not tied to 

IPCC assumptions about future bioenergy use. The fourth RPA 

scenario is called “historical fuelwood” (HFW). The RPA HFW 

scenario has all of the same global economic growth assumptions as the RPA A1B scenario, but it projects much less future 

expansion in U.S. and global wood energy consumption. Detailed 

information about the selection of IPCC scenarios and adjustments that were made to define RPA A1B, RPA A2, and RPA 

B2 are found in USDA Forest Service (2012).

In addition to the scenario-specific assumptions described in 

this chapter, a variety of additional detailed assumptions were 

needed for specific resource or sector analyses. For example, 

housing starts is an important variable in determining the 

outlook for some forest products. Resource- or sector-specific 

assumptions are described in their respective resource chapters 

and in the referenced supporting documents.

Table 1 describes characteristics of the four RPA scenarios. 

The global assumptions are identical to IPCC assumptions, 

except for the biomass energy assumption for RPA HFW. The 

U.S. population and gross domestic product (GDP) projections 

were updated as described in the following sections, but the 

rates of change over time are almost identical to IPCC rates of 

change for the United States. What is noteworthy is that projected GDP growth is considerably lower for the United States 

and other developed countries than the global growth rate. This 

difference in projected growth rates reflects the assumption that 

economic growth rates in the developing world will continue 

to outpace rates in the developed world, continuing the global 

trends described in chapter 4.

Six integrated assessment modeling groups used the IPCC 

SRES scenarios to estimate greenhouse gas (GHG) emissions. 

The results from the modeling groups became the basis of 

multiple climate projections associated with each scenario. The 

climate projections vary across scenarios in response to the 

associated levels of GHG emissions, but they also vary within 

a scenario because general circulation models (GCMs) differ 

in their approaches to modeling climate dynamics. Therefore, 

we selected climate projections from three GCMs for each of 

the three RPA scenarios to capture a range of future climates. 

For the RPA HFW scenario, the climate projections associated 

with RPA A1B were used. Table 2 lists the IPCC scenarios and 

associated GCM projections that were used to develop climate 

projections for the RPA scenarios.

2

 As described in the IPCC SRES (Nakicenovic and Swart 2000), modeling groups developed 40 future scenarios with varying levels of greenhouse gas 

emissions. The three RPA scenarios are based on the marker scenarios for A1B, A2, and B2.
24 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Figure 11. Historical and projected U.S. population, by RPA 

scenario, 1960–2060.a

600

500

400

300

100

200

0

Million people

19601965

19701975

1980

19851990

1995

2000

200520102015

2020

2025

2030

20352040

2045

2050

2055

2060

Historical RPA A1B RPA A2 RPA B2

Year

a

 The population projection for RPA HFW is identical to that for RPA A1B.

We updated the IPCC projections of U.S. population and GDP, 

which were based on data from the early 1990s. Population and 

economic growth are significant drivers of change in resource 

demand and production. It was, therefore, important to have 

more current information to initialize the projections of U.S. 

population and economic growth. We disaggregated these 

updated estimates to obtain county-level income and population 

data (USDA Forest Service 2012). The county-level projections 

should not be taken as statistically reliable projections of possible economic or demographic futures for specific counties. 

Rather, the overall spatial pattern of change in response to 

alternative RPA scenarios is more important in our analyses, 

displaying the heterogeneity that would not be evident if we 

only made projections at the regional or national level.

U.S. Population Projections

The U.S. population projections for the IPCC A1B scenario 

were based on the 1990 Census. We updated those projections 

to align with the 2004 Census population series for 2000–50 

(USCB 2004), with an extrapolation to 2060, and used this 

updated projection for the RPA A1B scenario. The population 

projections for RPA A2 and RPA B2 were updated to begin 

at the same starting point in year 2000 as RPA A1B, then to 

follow a projection path that maintained the same proportional 

relationship between RPA scenarios as in the original IPCC 

projections. Figure 11 illustrates the population projections for 

the RPA scenarios (RPA HFW has the same projection as RPA 

A1B) relative to historical population trends in the United States.

Table 1. Key characteristics of the RPA scenarios.a

Characteristic Scenario RPA A1B Scenario RPA A2 Scenario RPA B2 Scenario RPA HFW

IPCC general global description Globalization, economic 

convergence

Regionalism, 

less trade

Slow change, 

localized solutions

Globalization, 

economic convergence

IPCC global real GDP growth 

(2010–2060)

High (6.2X) Low (3.2X) Medium (3.5X) High (6.2X)

IPCC global population growth 

(2010–2060) 

Medium (1.3X) High (1.7X) Medium (1.4X) Medium (1.3X)

IPCC global expansion 

of primary biomass 

energy production

High Medium Medium Fuelwood demand 

follows historical trends 

in all countriesb

U.S. GDP growth 

(2006–2060)

Medium (3.3X) Low (2.6X) Low (2.2X) Medium (3.3X)

U.S. population growth

(2006–2060)

Medium (1.5X) High (1.7X) Low (1.3X) Medium (1.5X)

a

 Numbers in parentheses are the factors of change in the projection period. For example, U.S. GDP increases by a factor of 3.3 times between 2010 and 2060 for scenario RPA A1B. 

b

 Not based on IPCC assumptions. 

GDP = gross domestic product. IPCC = Intergovernmental Panel on Climate Change.

Table 2. Intergovernmental Panel on Climate Change scenarios 

and general circulation models used for the 2010 RPA Assessment 

climate projections.a

Scenario GCM Model vintage

A1B

CGCM3.1 (T47)

MIROC3.2 (medres) AR4

CSIRO-Mk3.5

A2

CGCM3.1 (T47)

MIROC3.2 (medres) AR4

CSIRO-Mk3.5

B2

CGCM2 

CSIRO-Mk2 TAR

UKMO-HadCM3

a

 AR4 climate projections were downloaded from the Web portal for the World Climate Research 

Program Coupled Model Intercomparison Project phase 3 and TAR climate projections were downloaded from the IPCC Data Distribution Centre. See Joyce et al. (in prep.) for details on the climate 

data and the downscaling procedures used. 

600

500

400

300

100

200

0
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 25

County-level population projections were developed for the 

RPA scenarios (Zarnoch et al. 2010). Figure 12 shows the 

percent change in county-level population from 2010 to 2060 

for the RPA A1B scenario. The spatial and temporal patterns 

are similar for the other RPA scenarios, with greater population 

change in RPA A2 and less population change in RPA B2.

U.S. Economic Projections

Macroeconomic trends (e.g., trends in GDP, disposable 

personal income (DPI), and labor productivity) influence the 

supply of and demand for renewable resources. The original 

IPCC data were based on economic data from the early 1990s, 

so we updated the GDP projections to start with the official 

U.S. GDP value for 2006 for all RPA scenarios (USDC Bureau 

of Economic Analysis 2008a).

We applied GDP growth rates provided by a commissioned 

report (Torgerson 2007) to develop an adjusted projection of 

GDP for the RPA A1B scenario. We revised the RPA A2 and 

RPA B2 GDP projections to maintain the same proportional 

relationship between the RPA scenarios as defined by the original IPCC GDP projections. Figure 13 shows the differences 

among the scenario projections for updated GDP in comparison 

to historical U.S. GDP.

Projections of personal income (PI) and DPI were also 

developed. The official U.S. 2006 statistics for PI and DPI 

were used to start the updated projection for the RPA A1B 

scenario (USDC Bureau of Economic Analysis 2008b). We 

calculated the RPA A2 and RPA B2 projections for PI and 

DPI to maintain the same proportional relationship across RPA 

scenarios that were used in calculating the trajectories for GDP. 

The national DPI and PI projections were also disaggregated to 

the county level (USDA Forest Service 2012).

The RPA scenarios were completed before the recent global 

economic downturn. We chose 2006 as the base year for the 

U.S. economic variables because they were the most recent 

data available when the RPA scenarios were constructed. 

Long-term projections are not intended to predict temporary 

ups and downs, meaning that recessions are not explicitly part 

of our projected 50-year trends. The range of RPA scenarios 

included in this assessment have varying rates of economic 

growth, however, both for the United States and globally, 

which provide a robust set of projections across the range of 

potential futures. In fact, the long-term U.S. GDP growth rates 

of the RPA A1B scenario are consistent with the historical 

GDP growth rate trend from 1950 to 2010 (through the recent 

recession), whereas the RPA A2 and RPA B2 scenarios have 

somewhat lower growth rates.

U.S. and Global Bioenergy Projections

We linked assumptions about the role of biomass in global 

energy projections to the IPCC global emissions scenarios, 

as we did with the population and income projections. In this 

case, we accounted for relevant regional land use projections 

and regional biomass energy projections provided by IPCC 

scenarios and their supporting database (Nakicenovic and 

Swart 2000). The IPCC scenarios all project that global energy 

production from oil will peak in the decade 2020–30, resulting 

in varying levels of expansion in alternative energy sources 

including bioenergy. For a detailed explanation of the RPA 

Assessment bioenergy assumptions, see Ince et al. (2011).

In all three IPCC-based RPA scenarios, expansion of biomass 

energy plantation area projected for global macroregions used 

in the IPCC Assessments (Nakicenovic and Swart 2000) was 

directly correlated with projected regional expansion in primary 

Figure 12. Percent population change at the county level in the 

United States, RPA A1B, 2010–2060.

Percent 

change

< 0

0–25

26–50

51–75

76–100

> 100

Figure 13. Historical and projected U.S. Gross Domestic 

Product (GDP), by RPA scenario, 1960–2060.a

50,000

45,000

40,000

35,000

25,000

30,000

10,000

15,000

5,000

20,000

0

Billion 2006 dollars

19601965

19701975

1980

19851990

1995

2000

200520102015

2020

2025

2030

20352040

2045

2050

2055

2060

Historical RPA A1B RPA A2 RPA B2

Year

a

 The GDP projection for RPA HFW is identical to that for RPA A1B.
26 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

biomass energy production. Comparing among IPCC scenarios, 

IPCC A1B had the largest global expansion in the area of biomass energy plantations and total biomass energy production, 

whereas expansions of biomass energy plantation area and 

biomass energy production were both smaller in the IPCC A2 

and IPCC B2 scenarios. Projected expansion of biomass energy 

plantations occurred primarily on nonforest lands (agricultural 

and other lands) and primarily in Africa, Asia, and Latin America. 

Biomass energy plantations are not sufficient to supply the expanded bioenergy consumption of these IPCC scenarios, however, leading to significant projected expansion in fuelwood 

consumption from forests in the United States and globally.

The U.S. projections of expansion in wood energy consumption 

are prodigious in the IPCC-based RPA scenarios, but are by 

far the highest in the RPA A1B scenario, followed by the RPA 

A2 scenario, and lowest in the RPA B2 scenario (figure 14). In 

the RPA A1B scenario, for example, U.S. wood fuel feedstock 

consumption climbs to levels that dwarf U.S. consumption of 

wood for all other end uses (about five times higher by 2060 

than all other wood uses), whereas in the RPA B2 scenario, 

U.S. wood fuel feedstock consumption climbs to a level just 

slightly higher than all other commercial uses. Although world 

roundwood fuelwood expansion factors are much lower than 

wood energy expansion factors for the United States (figure 

14), worldwide fuelwood consumption is currently far higher 

than that of the United States, and projected worldwide consumption of fuelwood remains far higher than U.S. consumption throughout the projection period.

Because this expansion of wood energy consumption is higher 

for the United States than projections in the 2010 Annual 

Energy Outlook (USDOE EIA 2010), we developed the RPA 

HFW scenario, wherein projected U.S. and global fuelwood 

demand trends follow historical trends instead of the IPCC bioenergy projections. RPA HFW could be regarded as a scenario 

in which policies and technologies do not emerge that would 

enhance the role of wood in energy production, or in which 

alternative energy resources such as natural gas become more 

plentiful than projected by IPCC, reducing future expansion 

of wood energy. In comparison to the increases seen for RPA 

A1B, RPA A2, and RPA B2, RPA HFW results in wood energy 

expansion factors that are considerably lower (figure 14).

U.S. Climate Projections

The IPCC climate projections were first downscaled to the approximately 10-kilometer scale, then aggregated to the county 

scale. Three climate variables were downscaled for the RPA 

scenarios: monthly mean daily maximum temperature, monthly 

mean daily minimum temperature, and monthly precipitation. 

We also estimated mean daily potential evapotranspiration 

using the downscaled temperature values. Detailed documentation of the development of the RPA scenario-based climate 

projections and downscaling process can be found in Joyce et 

al. (in prep.).

At the scale of the conterminous United States, the RPA A1B 

scenario mean annual temperature and total annual precipitation show the greatest warming and the driest climate of all 

RPA scenarios at 2060 (figure 15). The RPA A2 scenario 

becomes the wettest, although the precipitation changes at the 

scale of the United States are small at 2060. The RPA B2 scenario projects the least warming of these three RPA scenarios. 

The individual RPA scenario-climate combinations highlight 

the variation within each scenario of the individual climate 

model projections. For example, within the RPA A2 scenario, 

Figure 14. Projected expansion factors, 2006–2060, in the 

volumes of wood consumed for energy, by RPA scenario, 

including total U.S. wood fuel feedstock consumption, U.S. 

roundwood fuelwood consumption, and world roundwood 

fuelwood consumption.

Figure 15. U.S. temperature and precipitation changes from 

the historical period (1961–1990) to the decade surrounding the 

year 2060 (2055–2064).a

100

80

60

20

0

– 20

– 40

– 60

– 80

– 100

– 120

40

Precipitation changes (mm) by 2060

2 2.5 3 3.5 4 4.5

Temperature changes (degrees C) by 2060

a

 The Scenario-GCM pairs are defined in table 2.

C = Celsius. mm = millimeters.

B2 A1B

A2

A2-CGCM3.1

B2-CGCM2

A1B-MIROC3.2

A1B-CGCM3.1

A2-CSIRO-Mk3.5

B2-CSIRO-Mk2

A1B-CSIRO-Mk3.5

A2-MIROC3.2

Scenario-GCM Scenario mean

B2-HadCM3

35.0

15.7

4.5

21.9

9.4

2.2

6.0

3.7

2.6

1.2

1.6

1.1

U.S. roundwood 

used for fuel (fuelwood, logging residues, and pulpwood)

Total U.S. wood fuel 

feedstock, including 

mill and logging 

residues

World roundwood 

fuelwood (excluding 

residues)

RPA A1B

RPA A2

RPA B2

RPA HFW

Expansion factor

0 X 10 X 20 X 30 X
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 27

the CSIRO-Mk3.5 model projects the least warming and the 

MIROC3.2 model projects the greatest warming. Although 

all areas of the United States show increases in temperature, 

the rate of change varies. Regional differences in precipitation 

projections vary greatly (Joyce et al., in prep.).

The IPCC projection period for these models was 100 years. 

By 2100, the RPA A2 scenario shows the greatest surface 

warming, in contrast to the results at 2060, the end of the 

RPA projection period, in which RPA A1B shows the greatest 

surface warming. By 2100, the GHG emissions associated with 

RPA A2 are greater than the emissions associated with both 

RPA A1B and B2.

Conclusions

The socioeconomic, bioenergy, and climate projections 

described in this chapter were used in projections of future 

resource uses and conditions. Although none of the resource 

and sector analyses use all of the scenario variables, all of the 

analyses use a subset of these variables. As a result, the RPA 

scenarios and their underlying assumptions provide a common 

framework for comparing results across resource analyses.
28 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 29

Pacific

Northwest

Pacific

Coast

Pacific

Southwest

Intermountain

Great

Plains

North

Central

North

South Central

South

Northeast

Southeast

Rocky Mountain

Chapter 6. Land Resources

Resource Highlights

The United States has extensive land and water resources. We begin this chapter with a brief overview of the land 

and water area of the United States, then focus on the U.S. land 

base. The United States does not have a consistent wall-to-wall, 

ground-based inventory of land use across all ownerships, but a 

wall-to-wall inventory of land cover from remotely sensed data 

does exist. Therefore, we present information about land use 

and land cover from both ground-based and remotely sensed 

sources.

In the first part of this chapter, we used data from the National 

Resources Inventory (NRI) to describe historical trends in 

major land uses in the conterminous United States (USDA 

NRCS 2009). The NRI data were also a key input to projecting 

major land uses to 2060. These land use projections were used 

in many of the resources analyses discussed in subsequent 

chapters.

Second, we used remotely sensed data from the National Land 

Cover Database (NLCD) to portray forest, grassland, and 

shrubland land cover3

 across all ownerships in all 50 States. We 

also used the NLCD to analyze landscape patterns that affect 

the ability of the resource base to provide goods and services 

from the Nation’s forests and rangelands. We present results 

for the entire United States and for the four Resources Planning 

Act (RPA) Assessment regions (figure 16). 

The final section provides an overview of protected areas in the 

United States. Protected areas on private and public ownerships 

are important resources in sustaining ecosystem services from 

forests and rangelands. Subsequent chapters will describe the 

extent of protected areas that contain forest and rangelands.

v Urban and other developed land area will increase in the future.

v Natural landscape patterns are affected by development patterns.

v Development pressures around public lands threaten ecological integrity.

3

 The NLCD does not define rangeland as a land cover class; therefore, the grassland and shrubland NLCD cover classes were used in these analyses.

Land Use and Cover Trends

The U.S. land area is about 2.3 billion acres (table 3), with 

about 1.9 billion acres in the conterminous United States. An 

additional 169 million acres is covered by water. More than 60 

percent of U.S. land is privately owned. The Federal Government owns nearly 28 percent, more than one-third of which is 

in Alaska. State and local governments own about 9 percent, 

Figure 16. RPA Assessment regions and subregions.
30 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

and more than 2 percent is held in trust by the Bureau of Indian 

Affairs. Overall ownership patterns have not changed in the last 

decade (Lubowski et al. 2006). 

Land Use and Cover Trends, 1982–2007

The NRI provides data on land use and land cover on nonFederal land in the conterminous United States. The public 

land base tends to remain relatively stable over time; therefore, 

much of the effect of human-driven land use change occurs on 

the non-Federal land base. The changes in land use and land 

cover between 1982 and 2007 are shown in table 4, which 

presents the data in a matrix that shows both net changes in 

land use over time and the source of the area change (USDA 

NRCS 2011). Cropland, pastureland, and rangeland all declined 

during the 25-year period, but forest land increased slightly. 

The major increase occurred in the area of developed land. 

Cropland had the largest decline in acres, with total acres 

declining about 15 percent during the 25-year period. The two 

largest changes in cropland occurred from the enrollment of 33 

million acres of cropland in the Conservation Reserve Program 

(CRP) (included in other land cover/uses in table 4) and the 

conversion of 30 million acres to pastureland. Conversion to 

developed uses accounted for 11 million acres of change, or 

about 12 percent of the total decrease in cropland.

Although total acres of forest land remained relatively stable, 

there were a number of conversions to and from forest land 

from other land use categories. For example, forest land gained 

almost 18 million acres from pastureland, but lost about 17 million acres to developed land. In fact, forest land was the largest 

contributor (source of conversion) for developed land, followed 

by pastureland. Developed lands include urban, built-up areas 

and land developed for rural transportation. Conversions to 

urban or developed uses are usually permanent.

Wetlands are also inventoried in the NRI, but not as a separate 

land use or cover. In 2007, there were about 111 million acres 

of non-Federal palustrine and estuarine wetlands. Almost 60 

percent of those wetlands (66 million acres) occurred on forest 

land (USDA NRCS 2009). Wetlands are generally very valuable 

habitat for wildlife and provide a variety of other services, such 

as flood control, aquifer recharge, and carbon sequestration 

(Mitsch and Gosselink 2007; Scodari 1997). The remaining 

wetlands are all the more important nationally, because less 

than 50 percent of the wetlands present in the conterminous 

United States in the 1700s remain (Dahl 1990, 2006). Wetlands 

continue to face conversion threats despite a series of laws and 

regulation aimed at wetland protection (see sidebar, Predicting 

the Fate of Wetland Habitats).

Table 3. U.S. land and water area by RPA region, 2008.

Table 4. Changes in major land cover and uses in the conterminous United States, 1982—2007.a

Type of resource

North South Rocky Mountain Pacific Coast United States

thousand acres

2007 land cover/uses (million acres)

1982 total

 Cropland Pastureland Rangeland Forest

land

Developed

land

Other land 

cover/uses

Land area 412,621 532,887 741,872 572,987 2,260,367

Total water area 57,649 30,868 7,572 72,955 169,044

Inland water 12,692 18,641 7,572 16,396 55,301

Coastal water 3,676 4,125 0 19,836 27,637

Great Lakes 38,373 0 0 0 38,373

Territorial water 2,907 8,100 0 36,722 47,729

Total land and water area 470,269 563,753 749,443 645,941 2,429,406

Cropland 326 30 7 9 11 37 420

Pastureland 19 78 5 18 7 4 131

Rangeland 7 3 392 3 5 8 418

Forest land 2 5 2 372 17 5 403

Developed land — — — — 70 1 71

Other land cover/usesb 3 3 4 4 1 481 495

2007 total 357 119 409 406 111 536 1,938

Source: U.S. Census Bureau 2008

a

 To read this table: The number at the intersection of rows and columns with the same land cover/use represents acres that were in the same land cover/use category in both 1982 and 2007. The 

numbers to the left or right of this number represent acres lost to another land use during the period. The numbers above or below this number represent acres gained from another land use during the 

period. Comparing the “1982 total” column to the “2007 total” row represents the new acres gained or lost over the 25-year period. 

b

 Other land cover/uses include land under contract in the Conservation Reserve Program of the U.S. Department of Agriculture, Natural Resources Conservation Service (NRCS); other rural land; water 

areas; and Federal land areas.

Source: USDA NRCS 2011 1982 land cover/uses (million acres)
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 31

Predicting the Fate of Wetland Habitats

A number of Federal programs and statutes have been directed at 

wetland protection. Much of the reduction in agricultural conversion of wetlands has been attributed to the Wetland Conservation 

Provisions of the Food Security Act of 1985, commonly called 

“Swampbuster,” which withheld U.S. Department of Agriculture 

(USDA) farm program benefits from producers who converted 

wetlands to grow commodity crops (Williams 2005). A diverse set 

of statutes (e.g., Clean Water Act, Federal Aid to Wildlife Restoration Act, North American Wetlands Conservation Act), Executive Orders (e.g., Protection of Wetlands, Conservation of Aquatic 

Systems for Recreational Fisheries), and administrative policies 

(e.g., No-net-loss) also affect wetland conservation policy, making 

for a complicated system of protection and jurisdictional authority 

(Mitsch and Gosselink 2007).

Despite this collection of laws and regulations, wetland conversion continues to occur as a consequence of permitting systems, 

exemptions, mitigation, and enforcement problems (Hansen 

2006). Between 1992 and 1997, just less than 505,000 acres of 

palustrine and estuarine wetlands were lost in the United States; 

75 percent of these losses were attributed to either development 

(49 percent) or agriculture (26 percent) (USDA NRCS 2000). The 

greatest loss of wetlands during this period occurred in the South 

Region (59 percent of the national losses), and 78 percent of the 

losses in this region were due to development (58 percent) or 

agriculture (20 percent).

We developed a model to predict the probability of wetland loss 

based on local characteristics of the wetland itself (e.g., type 

of wetland, incidence of periodic cultivation) and the wetland’s 

landscape context (surrounding land uses and land covers). We 

focused our analysis of wetland conversion risk on the South 

Region, because the region is home to nearly one-half of the 

wetlands in the conterminous United States and had the majority 

of wetlands converted during the 1990s. We used National Resources Inventory (NRI) data (Nusser and Goebel 1997) to identify 

inventory points that were classified as wetland habitat in 1992 

and that, by 1997, either remained wetland or were converted to 

a nonwetland status. Both local and landscape-level processes 

are thought to influence wetland fate (retained or converted) 

(Daniels and Cumming 2008). For this reason, we defined two 

sets of predictors: local predictors derived directly from the NRI 

point inventory, and landscape predictors derived from the 1992 

National Land Cover Database (NLCD) (Vogelmann et al. 2001) 

that characterized land use and land cover in the vicinity of wetland points. The modeling approach related wetland fate to local 

features of the wetland and characteristics of the surrounding 

landscape (Gutzwiller and Flather 2011).

Findings

Overall prediction accuracy across the five test data sets was 75 

percent, with nearly 80 percent accuracy on predicted wetland 

loss. Important predictors of wetland conversion were land use 

surrounding the wetland, wetland ownership, and proximity to 

developed land (including roads) and other wetlands. Predicted 

risks of wetland habitat loss (figure 17) were generally greater 

for highlands (Appalachian region and western parts of the study 

area) than they were for lowlands (coastal plains, piedmont, 

and Mississippi basin). Highlands are likely to be better drained 

than lowlands, and therefore, wetlands situated in highlands 

may be less extensive and more isolated. Indeed, based on the 

NLCD, wetlands in highlands were smaller and farther apart than 

were wetlands in lowlands. Compared to altering a wetland in a 

generally wet landscape (lowlands), there may be fewer financial 

costs associated with converting a wetland in better drained 

landscapes. Although the coastal plain and piedmont region was 

characterized by generally lower probabilities of wetland conversion relative to the highlands, there are notable areas of high risk 

interspersed throughout the South Region. Higher predicted risks 

of wetland habitat loss occurred in and near large urban areas in 

the region, as illustrated by specific sites in Florida (figure 18).

Figure 17. Predicted risk of wetland habitat loss, 1992—

1997, for all National Resources Inventory (NRI) points 

that were wetland in 1992 for the Southeastern United 

States.a

 

a

 Gray polygons are Federal lands that were not sampled by the NRI.

Source: Gutzwiller and Flather 2011

Federal lands

0 150 300 450 600 750 Miles

0.00–0.07

0.07–0.13

0.13–0.17

0.17–0.23

0.23–0.30

0.30–0.38

0.38–0.48

0.48–0.62

0.62–0.79

> 0.79

Predicted 

conversion risk
32 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Implications

Conservation resources are scarce, so it is essential to focus 

on geographic areas where the risks for further wetland habitat 

loss are greatest. Our model and associated predictive map of 

conversion risk can be used to help prioritize wetland areas for 

conservation. Wetland habitats with high conservation value, high 

conversion risk, and low prediction errors would receive the highest priority for acquisition or other forms of long-term protection.

The risk of wetland habitat conversion can also be used in 

broad-scale evaluations of wetland habitat connectivity. Species 

dependent on wetland habitats, including many plants, amphibians, birds, and mammals, may benefit from landscapes in which 

wetland connectivity is high. A set of wetland sites may be 

functionally connected by being within the dispersal distance of a 

species. Functional connectivity may erode over time as wetlands 

with a high risk of conversion get transformed into some other 

land use or land cover, however. Network analysis methods 

(e.g., Minor and Urban 2007) can be used to identify wetland 

habitats that are essential for maintaining connectivity while 

also accounting for differential conversion risk. In this context, 

the quality of wetland sites for maintaining connectivity would 

be inversely related to their conversion risk. Such analyses would 

provide conservationists with fundamental information needed to 

rank the value of individual wetlands or wetland complexes for 

maintaining wetland habitat linkages.

The model can be used to aid planning decisions concerning 

projected urban development. In our study region, and especially 

in Florida, urbanization and housing development increased at a 

greater rate than they did in any other area in the country from 

the early 1980s into the late 1990s, a pattern that is expected to 

continue well into the 21st century (Wear 2011). Planners can 

use our model in conjunction with land-development forecasts to 

anticipate where wetland conversion pressures may be increasing (or decreasing) and use this information to guide development 

designs that may lessen wetland conversion pressures and 

ultimately reduce wetland loss.

Finally, our model can be used to assess the effectiveness of 

wetland conservation programs. The Wetland Reserve Program 

is an example of an important land-retirement program that 

authorizes USDA to purchase conservation easements to restore 

and protect wetlands (Williams 2005). But are the wetlands that 

landowners voluntarily enroll in this program those that face the 

greatest conversion risk? Characteristics of enrolled wetlands, 

including their location, can be used to estimate our model 

predictor variables, enabling prediction of wetland conversion risk 

for enrolled wetland habitats. If the land-retirement program was 

actually targeting at-risk wetlands, the average risk for enrolled 

wetland habitats would be expected to be higher than the average risk for unenrolled wetland habitats.

a

 Gray polygons are Federal lands that were not sampled by the National Resources Inventory.

Source: Gutzwiller and Flather 2011

Figure 18. Spatial correspondence between areas with high predicted risk of wetland habitat loss and urban areas in Florida, 

1992–1997.a

Federal land

Developed land

0 50 100 200 300 Miles

0.00–0.07

0.07–0.13

0.13–0.17

0.17–0.23

0.23–0.30

0.30–0.38

0.38–0.48

0.48–0.62

0.62–0.79

> 0.79

Predicted conversion risk
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 33

Future Land Use: Projections to 2060

Land use change is a major driver of resource change. We 

projected land use change for all counties in the conterminous 

United States for five major land use classes: pastureland, 

cropland, forest land, rangeland, and urban and developed uses. 

Details of the methods and results can be found in Wear (2011). 

All land use change was assumed to occur on non-Federal 

land within these categories; all other uses are held constant 

during the projection period, including Federal land, water 

area, enrolled CRP lands, and utility corridors. The land use 

projections do not assume any significant change in land use 

policy or regulations (i.e., projections are policy-neutral, based 

on historical land use relationships driven by future population 

and economic growth assumptions).

The land use projections are not linked to projections of land 

use change in Intergovernmental Panel on Climate Change 

(IPCC) scenarios because the IPCC data were not available at 

the national (individual country) level. The U.S. population and 

income variables that are drivers in the land use model are from 

the IPCC-based RPA scenarios, however. We were unable 

to incorporate climate effects into the land use change model 

because of the lack of county-level data on potential changes in 

productivity and/or associated returns to rural land uses across 

the United States. Depending on the changes in projected temperature and precipitation, it is possible that agriculture and forestry 

production possibilities may change in some RPA regions.

The land use model had two major components. The first used 

county-level population changes and personal income (PI) to 

simulate future urbanization, because urban uses were assumed 

to be the dominant land type in all land use conversions (in 

other words, land is converted to urban, but urban is not converted to other land uses). The second component allocated the 

remaining rural land among competing uses based on economic 

returns to the various rural land uses. The econometric models 

were fit to NRI land use change data from 1987 and 1997 to 

ensure the projected land use changes were generally consistent 

with observed urbanization intensities and rural land use 

changes. We held the real rents of both agricultural and forest 

land uses constant for the RPA A1B, A2, and B2 scenarios—in 

effect assuming that the relative returns to these uses remain 

constant through the projection period. For the RPA historical 

fuelwood (HFW) scenario, we accounted for a change in 

returns to forest use relative to agricultural uses.

The changes in major land uses during the 50-year projection 

period for the RPA A1B, A2, B2, and HFW scenarios are 

shown in figure 19. In all RPA scenarios, increased urban and 

developed use is the dominant force in land use change, and all 

other land uses are projected to lose area.

The highest rate of urbanization occurs in scenarios RPA 

A1B and RPA HFW (which share the same population and 

income assumptions), indicating that the strong growth in PI 

in combination with moderate population growth created more 

development pressure than population growth alone (figure 20). 

Scenario RPA B2 has the lowest rate of urbanization. Urban 

and developed area increases by 69 million acres between 2010 

and 2060 for RPA A1B, almost doubling the amount of urban 

area during the projection period.

The regional pattern of urbanization follows projected patterns 

of regional population growth (figure 20). Urban growth is 

projected to be highest in the South Region, which also has the 

highest projected population growth, gaining about 33 million 

acres. The North Region has the second greatest gain in urban 

areas (22 million acres), followed by the Rocky Mountain 

(8 million acres) and Pacific Coast (6 million acres) Regions. 

Although gains in urban acres tend to reflect current concentrations of urban areas, which are more concentrated in the 

Eastern United States, the rate of urban growth is highest in the 

Rocky Mountain Region, followed by the South, Pacific Coast, 

and North Regions.

During the projection period, forest land declines by approximately 34 and 31 million acres in RPA A1B and HFW, 

respectively, whereas RPA B2 projects a loss of 16 million 

Source: Wear 2011

Figure 19. Projected cumulative change in the area of major 

non-Federal land uses in the conterminous United States, by 

RPA scenario, 2010–2060.

Figure 20. Urban land area increases on non-Federal lands in 

the conterminous United States, by RPA region by RPA scenario, 

2010–2060.

Source: Wear 2011

80,000 35,000

60,000 30,000

40,000 25,000

20,000 20,000

0

15,000

– 20,000

10,000

– 40,000

5,000

0

Change in thousand acres

Change in thousand acres

Urban Cropland Forest Pasture Range South North Rocky Mountain Pacific Coast

Major land use RPA region

RPA A1B RPA A2 RPA B2 RPA HFW RPA A1B RPA A2 RPA B2 RPA HFW
34 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

acres (figure 21). The South Region is projected to experience 

the largest decline in forest area by 2060, losing about 21 

million acres in scenario RPA HFW. The large losses in the 

region reflect both an abundant forest resource and the highest 

projected population growth and urbanization. The difference 

in forest area loss for the South Region between the RPA HFW 

and A1B scenarios reflects the responsiveness of rural land uses 

in that region to relative returns to forest and crop uses (HFW 

has identical population and income projections as A1B, but 

forest returns are reduced relative to crop returns). The North 

Region has the second largest loss of forest land in RPA A1B 

(almost 10 million acres), followed by smaller losses in the 

Rocky Mountain and Pacific Coast Regions. Although losses of 

forest land are smaller in scenarios RPA A2 and B2, the pattern 

of forest land loss among RPA scenarios is similar among 

regions, with the exception of the Pacific Coast Region. In the 

Pacific Coast Region, the RPA A2 scenario has higher forest 

loss than RPA A1B, but the difference is quite small.

Cropland has the next greatest loss of acres, and those losses 

are concentrated in the Eastern United States, where most cropland is found. The losses are nearly equally split between the 

North and South Regions. Rangeland losses are concentrated 

in the Rocky Mountain Region, which has about one-half of 

the total rangeland losses. The remainder of rangeland losses is 

split between the South (primarily in Texas) and Pacific Coast 

(mostly southern California) Regions.

land uses are projected to decline and only urban and developed 

uses increase. Forest land is the most heavily affected by these 

development pressures, especially in the Eastern United States. 

These net changes mask dynamic changes within and across 

land use categories, however. As shown in the historical trends 

from 1982 to 2007, land frequently transfers between major 

land use classes (e.g., cropland is converted to forest land and 

vice-versa). These types of changes are strongly influenced by 

policy, ranging from national agricultural policy to local zoning 

policies. For example, the CRP removed millions of acres from 

cropland in the last two to three decades.

The effects of land use change on forest and rangeland resources are discussed in more detail in subsequent chapters. We now 

turn our attention to examining how the spatial arrangement of 

land cover types may affect the ability of forests and rangelands 

to continue to provide an array of goods and services.

Land Cover Patterns in the United 

States

Many environmental processes are affected by, or depend 

upon, the spatial arrangement of natural resources within 

landscapes. Therefore, analyses of landscape patterns are 

needed to complement knowledge of the absolute areas of 

different land uses and land covers. Land cover pattern is one 

aspect of landscape pattern that can be evaluated by using the 

2001 NLCD (Homer et al. 2007). A recent assessment of forest 

sustainability (USDA Forest Service 2011) provides conceptual 

models and examples showing why and how the results presented in this section can contribute to more effective natural 

resource management.

The NLCD provides land cover data for all of the United 

States, including Alaska, Hawaii, and Puerto Rico. The NLCD 

supports consistent and relatively high-resolution evaluation 

of several key patterns, including landscape context and fragmentation, as reported in this section. Although the NLCD is 

ideal for evaluating land cover patterns, it does not portray the 

human use of the land like the NRI and Forest Inventory and 

Analysis (FIA) systems do. For example, grassland cover may 

include a recreational use facility and land used for grazing. On 

the other hand, because the NLCD data cover the entire country 

wall to wall, they permit a spatial analysis of land cover fragmentation and juxtaposition that cannot be obtained from samplebased land use inventory systems like NRI and FIA. To avoid 

potential confusion of area statistics derived from different 

inventory systems, we present the statistics about landscape 

context and fragmentation as percentages of total NLCD areas 

rather than as absolute areas.

Source: Wear 2011

Figure 21. Change in non-Federal forest area in the conterminous United States, by RPA region by RPA scenario, 2010–2060.

0

– 5,000

– 10,000

– 15,000

– 20,000

– 25,000

Change in thousand acres

South North Rocky Mountain Pacific Coast

RPA region

RPA A1B RPA A2 RPA B2 RPA HFW

Conclusions

Land use change is a dynamic process. Population and economic growth both tend to motivate conversion of other land covers 

to developed uses. The RPA land use projections to 2060 

indicate the dominance of this influence, because most major 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 35

Landscape Context of Forest, Grassland, and 

Shrubland

Effective resource management takes into account the context 

within which natural resources occur. A context may be 

described in many ways, for example, by land ownership, by 

roadless designation, by dominant vegetation, or by topography. A description of landscape context in terms of nearby human activities such as farming and home construction is needed 

to inform conservation planning, because those activities 

introduce environmental risks while limiting land management 

options (Heinz Center 2008; Margules and Pressey 2000; Stein 

et al. 2009). There is ample evidence of widespread risks from 

human activities. For example, in 16 of the 31 Eastern States, 

the wildland-urban interface now encompasses more than 25 

percent of total land area (Radeloff et al. 2005). Approximately 

60 percent of the conterminous United States forest land is 

within 1,970 feet of either agricultural or developed land cover, 

and approximately one-third of the eastern forest exists within 

neighborhoods that also contain at least 10 percent agricultural 

land cover (Riitters 2011).

This section considers the anthropogenic landscape context 

of natural vegetation—forest, grassland, and shrubland—as 

defined by its co-occurrence with agricultural and/or developed 

land cover within a landscape. The “landscape mosaic” model 

(Riitters et al. 2009) was applied to the 2001 NLCD national 

land cover map (Homer et al. 2007). The model identified the 

landscape mosaic of each 0.22-acre land parcel on the land 

cover map according to the amounts of agriculture and developed land cover in the surrounding neighborhood. Subsets of 

forest, grassland, and shrubland parcels, defined by the original 

land cover map, were extracted to provide resource-specific 

statistics.

The landscape mosaic classification model (figure 22) uses 

the “landscape mosaic triangle” to classify a parcel of land 

according to the proportions of three generalized land cover 

types—agriculture, developed, and natural—in its surrounding 

neighborhood. “Natural” land cover includes water, forest, 

grassland, wetland, and shrubland. The acronyms in figure 22 

refer to the landscape mosaic as explained in the caption of 

figure 22. Landscape background, a simplified version of the 

landscape mosaic model as indicated by the shading in figure 22, 

is called agricultural, seminatural, developed, or mixed depending on which types of land cover dominate the neighborhood.

Smith et al. (2009) reported landscape mosaics in the 38-acre 

neighborhoods containing forest and grassland. That analysis 

was updated for this report to add Alaska, Hawaii, and Puerto 

Rico, and to include shrubland with forest and grassland. For 

comparisons of the effects of neighborhood size, five additional 

neighborhood sizes from 11 acres to 185 square miles are 

reported elsewhere (Riitters 2011).

About three-fourths of the total area of the United States 

exists in a neighborhood characterized as having a seminatural 

background, with regional percentages ranging from 46 percent 

to nearly 100 percent (table 5a). More than 90 percent of forest 

(table 5b), grassland (table 5c), and shrubland (table 5d) appear 

in a seminatural background. Although the developed and 

agricultural backgrounds apply to 1.9 percent and 17.8 percent, 

respectively, of all land (table 5a), much smaller percentages 

of forest, grassland, and shrubland appear in developed and 

agricultural backgrounds. Anthropogenic environmental risks 

may be very high in predominantly agricultural or developed 

landscapes, but the overall percentages of forest, grassland, 

and shrubland exposed to that risk are relatively small. On the 

other hand, those same small percentages indicate that the risk 

of direct loss of natural vegetation is of much concern in those 

types of landscapes (Riitters et al. 2009).

The forest, grassland, and shrubland area in seminatural backgrounds is described in more detail in terms of its landscape 

mosaic in table 6. Overall, approximately two-thirds of all 

forest and grassland, and 90 percent of shrubland, are found 

in neighborhoods that contain only natural land cover types 

(mosaic class NN). There is substantial variation among RPA 

regions, and Alaska exhibits almost exclusively the mosaic 

Figure 22. The landscape mosaic triangle model.

The axes of the landscape mosaic classification triangle show the 

proportions of natural (forest, grassland, shrubland, water, and 

wetland), agriculture (cultivated crops and pastures), and developed 

(urban and infrastructure) land cover types in the neighborhood. 

The shading and legend indicate the landscape background, and the 

acronyms within the figure indicate the landscape mosaic. In a mosaic 

acronym, the letters ‘N’ and ‘n’ refer to natural land cover, ‘A’ and ‘a’ 

refer to agriculture land cover, and ‘D’ and ‘d’ refer to developed land 

cover. A letter is upper case if that land cover occupies more than 60 

percent of a neighborhood and lower case if it occupies from 10 to 60 

percent of a neighborhood. A letter does not appear if that land cover 

occupies less than 10 percent of a neighborhood. The three corners 

of the triangle, indicated by double upper case letters, correspond to 

neighborhoods that contain only that one land cover type.

Landscape background

Seminatural

Agricultural

Developed

Mixed

Natural

Developed

Agricultural

DD AA

NN
36 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

class NN because of the low amounts of agricultural and developed land there. Agriculture and developed land are most common and widespread in the North and South Regions, where 

typically 10 to 20 percent of forest, grassland, and shrubland 

is contained in seminatural background neighborhoods that 

contain at least some, but less than 10 percent of developed and 

agriculture land cover (mosaic class N). In those two regions, 

an additional 20 to 30 percent (the sum of mosaic classes 

Nd (natural-developed), Na (natural-agricultural), and Nad 

(natural-agricultural-developed) in table 6 of forest, grassland, 

and shrubland is typically contained in seminatural background 

neighborhoods with more than 10 percent agriculture or developed land. In comparison, agricultural and developed lands 

in the Rocky Mountain and Pacific Coast Regions tend to be 

concentrated closer to grassland and farther away from forest 

and shrubland. As a result, 10 to 15 percent of grassland is 

contained in seminatural background neighborhoods with more 

than 10 percent agriculture or developed land, roughly twice 

the percentages for forest and shrubland.

These results generally indicate that 90 percent of all shrubland 

is not exposed to anthropogenic risks associated with proximity 

to substantial (more than 10 percent) developed or agriculture 

land cover. In contrast, excluding Alaska, approximately onehalf of all forest and grassland area is exposed to those risks. 

There is likely a very high risk of degradation of grassland and 

forest condition in seminatural landscapes containing 10 to 40 

percent developed land cover, and such landscapes are also 

very likely to shift to developed landscape backgrounds over 

time as a result of urban sprawl (Riitters et al. 2009).

Fragmentation of Forest, Grassland, and 

Shrubland

Conversion of natural resources to other land uses isolates the 

remnant vegetation and exposes it to further degradation from 

edge effects encompassing a wide range of negative biotic 

and abiotic influences (Forman and Alexander 1998; Harper 

et al. 2005; Laurance 2008; Murcia 1995; Ries et al. 2004). 

Table 5. Regional and national summary of landscape background for the year 2001 within a 38-acre neighborhood surrounding a 0.22-acre 

parcel of (a) any land cover, (b) forest land only, (c) grassland only, and (d) shrubland only. Each row shows the percentages of the total 

area in a region classified as each of four types of landscape background.a

RPA Regionb

 Seminatural Agricultural Developed Mixed

percent

(a) Any land cover

Alaska 99.9 0.0 0.0 0.0

North 45.9 38.8 4.0 11.4

Pacific Coast 84.2 10.4 2.7 2.7

Rocky Mountain 79.6 16.1 0.5 3.7

South 67.1 18.7 3.0 11.3

All regions 74.1 17.8 1.9 6.3

(b) Forest land only

Alaska 100.0 0.0 0.0 0.0

North 87.4 3.4 0.4 8.9

Pacific Coast 98.9 0.1 0.1 0.8

Rocky Mountain 98.0 0.8 0.0 1.2

South 90.9 1.7 0.4 7.1

All regions 93.4 1.6 0.2 4.8

(c) Grassland only

Alaska 100.0 0.0 0.0 0.0

North 63.2 15.5 0.7 20.7

Pacific Coast 95.2 0.9 0.6 3.3

Rocky Mountain 94.0 1.9 0.0 4.0

South 88.2 2.7 0.3 8.9

All regions 92.7 2.2 0.1 5.0

(d) Shrubland only

Alaska 100.0 0.0 0.0 0.0

North 83.4 5.7 0.3 10.6

Pacific Coast 98.5 0.4 0.1 1.1

Rocky Mountain 99.3 0.1 0.0 0.5

South 94.0 1.5 0.1 4.3

All regions 98.5 0.4 0.0 1.1

a

 The data in this table differ slightly from an earlier RPA compilation (Table 2d.1 in Smith et al., 2009). The differences are attributable to exclusion of the separate dataset for roads and to the inclusion of 

Alaska, Hawaii, and Puerto Rico. 

b

 Alaska is not included in the Pacific Coast Region total.

Source: 2001 National Land Cover Database
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 37

Table 6. Regional and national summary of selected landscape mosaics in landscapes with seminatural background within a 38-acre 

neighborhood surrounding a 0.22-acre parcel of (a) any land cover, (b) forest land only, (c) grassland only, and (d) shrubland only. Each 

row shows the percentages of the total area of the indicated land cover type in a region in each of the five landscape mosaic types.a

Except for rounding errors, the row sums equal the corresponding table entry in the “seminatural” column in table 5.

RPA regionb All Natural (NN) Natural (N) Naturaldeveloped (Nd)

Naturalagricultural (Na)Natural-agriculturaldeveloped (Nad)

percent

(a) Any land cover

Alaska 99.3 0.3 0.2 0.0 0.0

North 22.5 10.0 4.4 7.8 1.2

Pacific Coast 67.2 9.0 6.3 1.5 0.2

Rocky Mountain 68.6 5.4 2.1 3.4 0.3

South 35.2 14.8 5.7 9.8 1.5

All regions 57.0 8.0 3.5 5.0 0.7

(b) Forest land only

Alaska 98.8 0.7 0.4 0.0 0.0

North 44.9 19.9 7.3 13.5 1.8

Pacific Coast 78.7 12.2 7.1 0.8 0.1

Rocky Mountain 90.9 4.0 1.2 1.8 0.1

South 42.7 24.8 7.4 14.0 1.8

All regions 64.0 14.9 5.2 8.2 1.1

(c) Grassland only

Alaska 99.7 0.2 0.1 0.0 0.0

North 15.8 16.8 10.1 17.1 3.4

Pacific Coast 64.5 16.2 10.5 3.5 0.6

Rocky Mountain 72.4 10.8 2.8 7.5 0.5

South 45.4 20.1 7.9 13.0 1.8

All regions 67.7 12.1 4.2 7.9 0.8

(d) Shrubland only

Alaska 99.7 0.2 0.1 0.0 0.0

North 48.5 14.7 6.2 12.2 1.8

Pacific Coast 84.8 7.6 4.3 1.6 0.2

Rocky Mountain 92.1 4.0 2.0 1.1 0.1

South 68.8 12.4 4.7 7.2 1.0

All regions 89.5 4.7 2.2 1.8 0.2

a

 The data in this table differ slightly from an earlier compilation (Table 2d.2 in Smith et al., 2009). The differences are attributable to exclusion of the separate dataset for roads and to the inclusion of 

Alaska, Hawaii, and Puerto Rico.

b

 Alaska is not included in the Pacific Coast Region total.

Source: 2001 National Land Cover Database

At the local scale, actual edge effects naturally depend on 

circumstances such as the particular land use or the intensity of 

land use in the vicinity of edge. At the national scale, broader 

indicators of land cover patterns are appropriate to gauge 

the risks of edge effects (Heinz Center 2008). These broader 

indicators do not distinguish natural from anthropogenic 

fragmentation, which is important because disturbance and 

recovery are natural fragmenting processes, and natural mosaics 

of forest, grassland, and/or shrubland are characteristic of some 

areas. Although land use change is the dominant driver of current fragmentation (USDA Forest Service 2011), multitemporal 

data are needed to interpret these broader indicators with 

respect to anthropogenic fragmentation. To address a variety of 

questions about fragmentation in a nationally consistent way, 

the overarching problem is to characterize the type, extent, 

and location of natural resource spatial patterns. Land cover 

pattern indicators can inform policy regarding the needs for 

mitigation of fragmentation or the resulting ecological effects. 

Maps showing where the land cover is fragmented can suggest 

geographic regions where policies could be implemented.

Procedures described by Riitters (2011) were applied to the 

2001 NLCD land cover map (Homer et al. 2007) to identify 

and map fragmentation of forest, grassland, and shrubland at 

the national scale. A pattern metric known as “area density” 

was used to describe each 0.22 acre parcel on the land cover 

map by the proportion (P) of a surrounding neighborhood that 

was a specified land cover type. Three specific land cover types 

were evaluated by looking separately at forest, grassland, and 

shrubland density. The analyses for each land cover type were 

repeated using six neighborhood sizes: 11 acres, 38 acres, 162 

acres, 1,460 acres, 13,100 acres, and 118,000 acres (shown as 

triangles on the x-axis of figure 23). The maps of area density 

were then intersected with the original land cover map to 

extract values of forest area density for the forest parcels, 
38 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

grassland area density for the grassland parcels, and shrubland 

area density for the shrubland parcels. The extracted values 

were then expressed as the percentages of total area of each 

land cover type (forest, grassland, or shrubland) that met the 

criteria for intact (P = 1.0), interior (P ≥ 0.9), and dominant 

(P ≥ 0.6) land cover (figure 23).

A finer scale analysis, using pattern metrics derived from 

mathematical morphology (Soille and Vogt 2009), indicated 

that a high percentage of each land cover type was within 98 

feet of the nearest edge: 40 percent of grassland, 30 percent of 

shrubland, and 28 percent of forest (Riitters 2011). Percentages 

for larger edge widths may be estimated from the area density 

measurements because of the correspondence between neighborhood size and minimum distance to edge in an intact neighborhood (Riitters and Wickham 2003). From the complement 

of the intact percentages (solid lines in figure 23), it may be 

inferred that, (1) the percentage of edge increases rapidly with 

edge width for all three land cover types, (2) the percentage 

remains smallest for shrubland with increasing edge width, and 

(3) the percentage of forest edge approximates that of grassland 

for edge widths larger than approximately 1,970 feet (i.e., the 

edge width corresponding to an intact 162-acre neighborhood). 

Essentially, all grassland and forest resides in an edge condition 

for edge widths larger than 5,710 feet (an intact 1,460-acre 

neighborhood), and all shrubland is edge for edge widths larger 

than 3.2 miles (an intact 13,100-acre neighborhood).

Comparisons of the interior class (dashed lines in figure 23) 

indicate that grassland is the most fragmented and shrubland 

is the least fragmented of the three land cover types for all 

neighborhood sizes. Comparisons of land cover dominance 

(dotted lines in figure 23) indicate that grassland tends to be 

less dominant where it occurs than either forest or shrubland 

are where they occur, and that forest and shrubland dominance 

is similar for all neighborhood sizes. Less than one-half of all 

forest, grassland, or shrubland exists within an intact 11-acre 

neighborhood, and less than 10 percent of each land cover type 

exists within an intact 1,460-acre neighborhood (solid lines in 

figure 23). A sidebar examines in more detail the intact forest 

in 11-acre neighborhoods in the Eastern United States (see 

sidebar, Focus on Intact Eastern Forest).

In summary, all three land cover types tend to be dominant 

where they occur, yet fragmentation is so pervasive that the 

potential risk of short-range (98 feet) edge effects threatens 28 

percent of all forest, 30 percent of all shrubland, and 40 percent 

of all grassland. Shrubland is the least fragmented land cover 

type, grassland is the most fragmented, and forest is fragmented 

like shrubland in smaller neighborhoods or for lower fragmentation thresholds and like grassland in larger neighborhoods or 

for higher fragmentation thresholds.

This comparative assessment of forest, grassland, and shrubland fragmentation may be used to suggest opportunities to 

improve the benefits that society draws from natural resources. 

One objective could be to increase the amount of relatively 

intact land cover. Recognizing that the management decision 

is ultimately where to convert land cover, the knowledge of 

current patterns can indicate where the additions will most 

efficiently increase intactness. As an example, the percentages 

of edge (98 feet) and interior-plus-intact land cover (162-acre 

neighborhoods) are summarized by county in figure 24. Note 

that the percentages shown in figure 24 are based on extant 

land cover area, not total county area. For example, counties 

in the Great Plains Subregion show a high percentage of forest 

edge, even though the total forest area in those counties is 

relatively small. Thus, figure 24 portrays the patterns of the 

existing land cover in whatever amounts those land covers 

actually occur. Restoring and promoting the intact condition 

will be more efficient in counties where the proportions of 

both edge and interior-plus-intact are relatively high already. 

For forest, that includes counties across most of the Eastern 

United States except in public lands along the Appalachian 

Mountains. Similarly, grassland restoration in western Kansas 

would increase the overall amount of interior of this geographically limited resource, and southern Colorado is an example 

of comparatively high edge and interior values for shrubland. 

But county-level information is only a partial guide, and the 

national maps of patterns could be used to identify and rank 

potential sites within counties, because patterns are mapped at 

the parcel level. For example, to reduce the occurrence of internal edge (perforations), the maps could be inspected to focus 

attention on filling holes in otherwise intact forest. To improve 

overall connectivity across landscapes, the maps can indicate 

where to expand or connect existing clusters of intact forest.

Source: 2001 National Land Cover Database 

Figure 23. National summary of land cover fragmentation for forest, 

grassland, and shrubland for six neighborhood sizes, 2001.

100%

80%

60%

40%

20%

Percent of forest, grassland, or shrubland

0%

10 100 1,000 10,000 100,000

Intact forest

Intact grassland

Intact shrubland

Neighborhood size

Interior forest

Interior grassland

Interior shrubland

Dominant forest

Dominant grassland

Dominant shrubland

Landscape area (acres)
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 39

Source: 2001 National Land Cover Database 

Figure 24. County-level summaries of edge and interior forest, grassland, and shrubland, 2001. 

Each county is shaded according to the percentage of the indicated land cover type in that county, which was labeled as (a) forest edge, (b) grassland 

edge, (c) shrubland edge, (d) forest interior, (e) grassland interior, and (f) shrubland interior. Edge includes area within 98 feet of a different land cover 

type, and interior includes both intact and interior area density classes as measured in 162-acre neighborhoods. Note that the percentages are based on 

extant land cover area of the indicated type, not total county area. Counties lacking a given land cover type are shown as missing data. The inset maps 

show Alaska, Hawaii, and Puerto Rico; map scale varies among maps.

0–10

11–20

21–30

31–4041–50

51–60

61–70

71–80

81–90

91–100

(a) Forest edge

(b) Grassland edge

(c) Shrubland edge

(d) Forest interior

(e) Grassland interior

(f) Shrubland interior

Percent of forest, grassland, or shrubland area
40 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Conclusions

Most of the total area of forest, grassland, and shrubland occurs 

in landscapes dominated by seminatural land cover, yet fragmentation is so pervasive that only a small percentage of that 

area is free of the ecological risks that are posed by proximity 

to human land uses. Overall, grassland is not only rarer but 

also more fragmented than either forest or shrubland. There is 

substantial regional variation in the degree of fragmentation 

and the type of ecological effects that might be expected. Fragmentation poses less risk in Alaska than in other areas simply 

because the overall intensity of human land use is relatively 

lower there. Anthropogenic land uses pervade the conterminous 

United States, and as a result the associated effects from 

fragmentation are likely to be widespread. Although trend 

analyses have been hampered by a lack of comparable national 

data, the land cover patterns observed in 2001 do not suggest 

that conditions have improved since the last RPA Assessment. 

If the historical patterns of land uses continue, then future 

fragmentation will pose higher risks to seminatural land cover 

that occurs on privately owned land or that is close to existing 

intensive land uses.

Focus on Intact Eastern Forest

The forest fragmentation measurements reported elsewhere in 

this assessment do not account for potential differences among 

forest types or ownerships. Those differences may be important 

when translating assessment findings to land management policy 

and action. The national analysis of forest fragmentation was 

refined for eastern forests in the 31 States east of the Mississippi 

River by incorporating field plot observations of forest types and 

ownerships from the Forest Inventory and Analysis Program (USDA 

Forest Service 2010). We focused on intact forest in these results. 

Intact forest was defined as an 11-acre neighborhood containing 

only forest land cover; all nonforest land cover was treated as a 

fragmenting agent (Riitters et al. 2012). Overall, 161 million acres 

of eastern forest land—equivalent to 44 percent of the total FIA 

forest land area—qualified as intact forest land cover.

Which Forest Types Are Intact?

Fragmentation varies naturally among forest types because of 

the biophysical differences where those types occur and because 

human land uses tend to fragment some forest types more than 

others. That makes it more difficult to quantify and manage the 

benefits of intact forest on forest-dependent goods and services 

that are tied to specific forest types. For example, an intact black 

spruce forest offers habitat for a different set of species than 

the ones found in an intact longleaf pine forest, and the quality 

of intact forest habitat depends on which species are found in a 

given type of forest. Information about the current extent of intact 

forest can also inform land management policy by identifying forest 

types of special concern for conservation or remediation, for example the ones that do not have a high proportion of intact forest.

The forest-type analysis considered the 75 eastern forest types 

that occupy at least 173,000 acres each. Exotic forest types 

such as paulownia and eucalyptus were thus excluded, but that 

criterion also eliminated five native forest types that are relatively 

uncommon—spruce-pine, mangrove, table mountain pine, Fraser 

fir, and Atlantic white-cedar. Using the FIA statistical estimators, 

the total area of intact forest was calculated for each type. Those 

estimates were also expressed as the percentage of total forest 

land area of each forest type that was intact forest land cover.

The percentage of intact forest varied from 13 to 78 percent of 

individual forest-type area, with the median forest type having 

38 percent of its area as intact (figure 25). As expected, lower 

percentages were obtained for some naturally fragmented forest 

types such as bur oak, cottonwood, and willow, and higher 

percentages were obtained for some forest types that tend to be 

inaccessible because of steep slopes or protected status (e.g., 

chestnut oak) or hydric soils (e.g., northern white-cedar, black 

spruce, and pond pine). Considering the forest types that are not 

naturally fragmented and that are usually found in accessible 

locations, typically less than one-half of the total area of those 

forest types qualified as intact forest.

Policy concerns are more likely to be driven by estimates of total 

area of intact forest instead of percentages of intact forest. It 

should be noted that the intact area associated with a forest 

type is the product of its intact percentage and total forest-type 

area. Because total forest-type area varies substantially among 

forest types (Smith et al. 2009), some forest types exhibit a large 

absolute area of intact forest even if the percentage of intact 

area is low. More than one-third of the total intact forest area in 

the East is associated with the three forest types—white oak-red 

oak-hickory, sugar maple-beech-yellow birch, and loblolly pine—

that together comprise approximately one-third of total forest 

land area, even though the percentage of intact forest is less than 

50 percent for two of them (figure 25). The exception is the sugar 

maple-beech-yellow birch forest type that contributed 13 percent 

of total intact forest land area, because it was the third most 

common forest type and exhibited the second highest percentage 

(65 percent) of intact forest. In comparison, the eight other forest 

types exhibiting more than 60 percent intact forest—chestnut 

oak, chestnut oak-black oak-scarlet oak, northern white-cedar, 

black spruce, pond pine, eastern hemlock, bald cypress-water 

tupelo, and red spruce—contributed a total of 11 percent of the 

total area of intact forest land because those types are relatively 

less common.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 41

0%

20%

40%

60%

80%

100%

0.0

0.2

0.4

0.6

0.8

1.0

Intact forest in 11-acre neighborhood

Intact forest in 11-acre neighborhood

Cottonwood/willow Scotch pine

Gray birch

Pin cherry

Black walnut

Cottonwood Other exotic hardwoods

Bur oak

Black locust

White spruce

Silver maple/American elmPalms

Willow

Sand pine

Longleaf pine/oak

Eastern white pine/E hemlock Balsam poplar Pitch pine

Black cherry Jack pine

Eastern redcedar

Pond pine

Baldcypress/pondcypress

River birch/sycamore

Swamp chestnut oak/cherrybark

Slash pine/hardwood

Sassafras/persimmon Scarlet oak

Red spruce/balsam fir

Eastern redcedar/hardwood

Overcup oak/water hickory

Sycamore/pecan/American elm

Red spruce

Virginia pine/S red oak

Virginia pine

Eastern hemlock

Lowland red maple 

Other pine/hardwood Red pine

Longleaf pine

Tamarack

Other hardwoods

Elm/ash/black locust Yellow-poplar

Shortleaf pine/oak

E white pine/N R oak/white ash Red maple/oak Baldcypress/water tupelo

Shortleaf pine

Eastern white pine Bl ash/American elm/R maple

Cherry/white ash/yellow-poplar

Sweetgum/yellow-poplar Balsam fir Post oak/blakjack oak

Sugarberry/hackberry/elm/Gr.

Paper birch

Black spruce

Northern white-cedar Upland red maple

Slash pine Northern red oak

White oak

Loblolly pine/hardwood Yellow-poplar/Wh oak/N red oak Chestnut oak

Sweetgum/Nuttall oak/willow oak Sweetbay/swamp tupelo/R.

Hard maple/basswood Aspen

Chestnut oak/Bl oak/scarlet oak Mixed upland hardwoods Loblolly pine

Sugar maple/beech/Ye birch

Million acres Million acres

Percent of forest type area Percent of forest type area

0%

20%

40%

60%

80%

100%

0

5

10

15

20

25

Wh oak/red oak/hickory

Who Owns the Intact Forest?

Fragmentation varies among ownerships primarily because of 

differences in the land uses that occur on different ownerships. 

The summary of intact forest by ownership (figure 26) considered 

three ownership classes defined by the FIA inventory—private, 

State and local government, and Federal Government (USDA 

Forest Service 2010). The percentage of forest land that was 

intact forest was lower on private land than on public land, but 

nearly three-fourths of total intact forest area in the 31 Eastern 

States was on private land because 80 percent of all forest in the 

East is privately owned.

Sources: 2001 National Land Cover Database; USDA Forest Service, Forest Inventory and Analysis

Figure 25. The area of intact eastern forest types in 2001(vertical bars) and the corresponding percentage of forest type area that is intact 

(circles). Forest types are sorted by intact area; note the scale change between the two charts. Forest types are as defined in USDA Forest 

Service (2010); some types are abbreviated using E (eastern), N (northern), S (southern), Bl (black), Gr (green), R (red), Wh (white), or Ye 

(yellow). 

Sources: 2001 National Land Cover Database; USDA Forest Service, Forest Inventory 

and Analysis

Figure 26. Eastern forest ownerships (circa 2001) characterized 

by the percentage of group forest land area that is intact (circles) 

and the total area of intact forest (vertical bars). Forest ownership 

groups are as defined in USDA Forest Service (2010). 

150 100%

80%

60%

40%

20%

0%

120

90

60

30

0

Million acres

Percent of ownership area

Federal

Government

State and local

government

Private

Intact forest in 11-acre neighborhood
42 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Protected Areas in the United States

Forests and rangelands of the United States are in protected 

status for a variety of purposes, but primarily to preserve 

functioning natural ecosystems, provide refuges for species, 

and maintain ecological processes (Anderson et al. 2010). The 

Federal Government holds almost 30 percent of the country’s 

total land area. The National Park Service (79 million acres), 

National Wildlife Refuge System (145 million acres), National 

Forest System (NFS, 193 million acres), and public lands 

administered by the Bureau of Land Management (BLM, 

250 million acres) all include forests and rangelands that are 

managed according to their varying legal mandates. State and 

local governments also manage forests and rangelands through 

State parks, State forests, and other holdings. Government 

and nongovernmental organizations protect natural values on 

private lands through ownership, conservation easements and 

other programs such as the CRP.

Although the Nation lacks a comprehensive inventory of the 

protected status of all public and private lands, significant progress has been made in the past decade by a collaborative effort 

involving Federal and State agencies and private conservation 

organizations. The resulting Protected Areas Database of the 

United States (PAD-US) includes detailed maps of the known, 

proposed, and protected areas for all 50 States, and the status 

of each protected area according to guidelines developed by the 

International Union for the Conservation of Nature (IUCN). 

Definitions of the IUCN categories are found in table 7. This 

section summarizes the Nation’s protected areas by owner type 

and IUCN designation as shown in the PAD-US (Conservation 

Biology Institute 2010).

Public ownership generally offers protection from conversion 

to more developed uses. Public lands are not immune to natural 

resource threats that are traceable to land use changes in 

proximity to their boundaries, however (see sidebar, Housing 

Growth Near Public Lands). Because these boundary lands 

have high amenity values, they are subject to increasing development pressures from housing. The natural resource threats 

from this development are many and include increased recreational pressure, increased poaching of resources, increased 

fire incidence, increased pollution, and additional barriers to 

wildlife dispersal.

Not all public lands meet the IUCN criteria. The PAD-US 

includes an approximate area of 333 million acres of land 

formally classified as protected using the IUCN designations. 

About 30 percent of these lands are strictly set aside to protect 

biodiversity or are formally designated wilderness areas. More 

than 99 percent of the designated area is either Federal (274 

million acres) or State (57 million acres) land (table 8). The 

remaining 511 million acres (21 percent of total U.S. area) held 

in Federal and State ownership is in the “unassigned” category. 

Overall, the protected lands in the East are not as extensive as 

in the West, reflecting the distribution of Federal ownership. Of 

the total area meeting IUCN protected area criteria, approximately 51 percent is found in Alaska, 21 percent in the Rocky 

Mountain Region, 11 percent in the Pacific Coast Region, 10 

percent in the North Region, and 7 percent in the South Region.

National Wilderness Preservation System

The National Wilderness Preservation System (NWPS) 

encompasses about 109 million acres, managed by four Federal 

agencies (table 9). A wilderness area is a special congressional 

designation that is intended to protect the wild character inherent in the land, as outlined in the Wilderness Act of 1964. Most 

of the land in the NWPS is in the Western United States, with 

52 percent of the total area in Alaska.

Although the Wilderness Act did not provide a mandate to 

achieve full representation of all ecosystems, it is interesting to 

examine to what extent ecosystems types are protected within 

the NWPS. Representation of ecosystem types within the NWPS

is irregular. In terms of percentage of NWPS among Bailey’s 

Ecoregion Divisions, the greatest portions are Tundra Regime 

and Subarctic Mountains in Alaska, Marine Regime Mountains 

in Washington, Oregon, and southeast Alaska, and Temperate 

Table 7. Definition of International Union for the Conservation of Nature (IUCN) categories for the Protected Areas Database of the United 

States (PAD-US).

IUCN category Definition

Ia Strict Nature Reserve: protected area managed mainly for science.

Ib Strict Nature Reserve: protected area managed mainly for wilderness protection.

II National Park: protected area managed mainly for ecosystem protection and recreation.

III Natural Monument: protected area managed mainly for conservation of specific natural features.

IV Habitat/Species Management Area: protected area managed mainly for conservation through management intervention.

V Protected Landscape/Seascape: protected area managed mainly for landscape/seascape conservation and recreation.

VI Managed Resource Protected Area: protected area managed mainly for the sustainable use of natural ecosystems.

Unassigned In the PAD-US database, this category includes public and private land that was evaluated by the cooperators and was 

determined to not have an applicable IUCN code. It includes, for example, a large share of National Forest System land and 

almost all Native American land.

Source: International Union for the Conservation of Nature 1994
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 43

Table 9. Acres and percent of Federal land in the National Wilderness Preservation System (NWPS) by agency and RPA region, 2009.

Federal agency

North South Rocky Mountain Pacific Coast United States

Acres

(thousands) % Acres(thousands) % Acres(thousands) % Acres(thousands) % Acres(thousands) %

Bureau of Land Management 0 0.0 0 0.0 4,606 52.8 4,120 47.2 8,726 8.0

Fish & Wildlife Service 64 0.0 470 2.3 1,465 7.1 18,703 90.3 20,702 18.9

Forest Service 1,428 3.9 755 2.1 18,208 50.4 15,769 43.6 36,160 33.0

National Park Service 179 0.4 1,488 3.4 1,343 3.1 40,883 93.1 43,891 40.1

U.S. total 1,671 1.5 2,712 2.5 25,621 23.4 79,474 72.6 109,479 100.0

Table 8. Protected areas from Protected Areas Database of the United States (PAD-US) by ownership and RPA region.a

RPA

region Owner Designated IUCN category (thousand acres) (excludes unassigned area) Rowtotal Ia Ib II III IV V VI

Alaska Federal 151 53,764 6,840 4,284 67,462 28,899 58 161,458

State 0 198 1,949 0 3,546 1,126 2,164 8,984

Other 0 0 0 0 0 0 0 0

Region total 151 53,962 8,789 4,284 71,008 30,025 2,222 170,441

North Federal 8 1,630 366 32 1,248 3,344 656 7,283

State 0 434 936 0 5,635 4,351 11,689 23,045

Other 0 0 0 0 3 2,345 0 2,349

Region total 8 2,064 1,302 32 6,886 10,040 12,345 32,677

Pacific 

Coastb

Federal 248 18,116 3,061 437 1,340 8,785 16 32,004

State 0 0 722 0 330 3,639 1,080 5,770

Other 0 0 0 0 0 23 0 23

Region total 248 18,116 3,783 437 1,670 12,447 1,095 37,797

Rocky

Mountain

Federal 645 24,670 6,020 6,037 7,577 15,453 582 60,983

State 0 0 238 1 2,749 6,005 15 9,007

Other 0 0 94 0 11 319 0 424

Region total 645 24,670 6,352 6,038 10,337 21,776 597 70,414

South Federal 11 1,060 3,518 95 3,239 3,598 670 12,191

State 0 10 564 0 6,812 1,403 971 9,759

Other 0 0 0 0 1 54 34 89

Region total 11 1,069 4,083 95 10,052 5,054 1,675 22,039

National Federal 1,064 99,239 19,805 10,885 80,866 60,078 1,982 273,919

State 0 642 4,409 1 19,071 16,524 15,917 56,565

Other 0 0 95 0 15 2,741 34 2,885

National total 1,064 99,881 24,309 10,886 99,952 79,343 17,934 333,369

a

 Entries may not sum to row or column totals because of rounding.

b

 Alaska is not included in the regional total because it is listed separately. 

IUCN = International Union for the Conservation of Nature.

Source: Conservation Biology Institute 2010

Steppe Mountains, mostly in Montana, Idaho, Wyoming, and 

Utah. Also represented is the Tropical/Subtropical Desert Division of the Southwest. In terms of the percentage of Bailey’s 

Divisions designated as Wilderness, Alaskan Tundra and Subarctic Divisions, Marine Mountains, Temperate Steppe Mountains,

and Tropical Desert are among the highest (Cordell in press).

Designation as a wilderness area is not sufficient to maintain 

its inherent characteristics. Numerous external and internal 

conditions and influences threaten wilderness resource values. 

Wilderness areas in many States are increasingly isolated 

fragments or remnants of historic ecosystems. Wilderness areas 

sometimes become ecological islands as surrounding landscapes

become more developed with higher population density. This 

concern is most pronounced in the wildernesses of the East, but 

also occurs in the West as natural landscapes are increasingly 

affected by human development and use (Dawson and Hendee, 

in press).

Wilderness areas are particularly affected by exurban and rural 

sprawl (Cordell et al. 2005). The pressures of human development and private land ownership within the protected landscape 

create challenging issues for managers of public lands. Development within and around protected lands can affect both the 

ecology and management of these ecosystems by increasing 

forest, range, and wildlife habitat fragmentation, reducing air 

and water quality, and decreasing recreation opportunities and 

access. Continued increase in low-density residential development poses a threat to wilderness areas. Development near 

wilderness areas has also been associated with increasing 
44 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

recreation pressure, which, in turn, has been shown to alter vegetation and population demography of recreationally harvested 

game species (Braun et al. 1993).

Procedures used to evaluate development risks around national 

forests (Stein et al. 2005) were adapted and applied to 600 

wilderness areas of greater than 640 acres to assess which 

areas are most threatened by development. Of the top 10 most 

threatened wilderness areas, 5 are in the Pacific Coast Region: 

Juniper Dunes Wilderness in Washington (BLM-managed), 

Table Rock (BLM) and Mountain Lakes (Forest Service) 

Wilderness Areas in Oregon, and the Ishi Wilderness Area in 

California (Forest Service). The remaining five were scattered 

widely across the United States: Hells Canyon Wilderness 

Area in Arizona (BLM), Swanquarter Wilderness Area in 

North Carolina (U.S. Fish and Wildlife Service [FWS]), Mingo 

Wilderness Area in Missouri (FWS), Kisatchie Hills Wilderness Area in Louisiana (Forest Service), and Soldier Creek 

Wilderness Area in Nebraska (Forest Service).

One of the complicated problems presented by climate change 

is that the current system of protected areas may not be equally 

effective under changing climatic conditions. Conservation 

is naturally aimed at protecting current conditions in specific 

places, but those conditions may disappear, or migrate, to other 

locations as a result of a changing environment. Unfortunately, 

the available climate projections at the county level are too 

coarse to enable projecting the protected status of the potential 

new conditions.

The data from the PAD-US provide a broad overview of the 

status of protected lands in the United States. We conclude this 

section with a brief discussion of trends in land conserved by 

private land trusts and State and local governments.

Private Land Trusts

Land trusts are one venue for protection of private lands. The 

2005 National Land Trust Census Report (Land Trust Alliance 

2006) provided information on national trends in private land 

conservation. In 2005, total acreage conserved through private 

means was 37 million acres, a 54-percent increase from 2000. 

This acreage is managed by local, State, and national land conservation groups. Local and State land trusts doubled to almost 

12 million acres in 2005. Many of these acres are protected 

under conservation easements—accounting for a little more 

than 6 million of the total acres in 2005.

The primary focus of land trust efforts was protecting natural 

areas and wildlife habitat, followed by open space and water 

resources, particularly wetlands. The Western United States 

was the fastest growing region in both numbers of acres 

conserved and in the number of land trusts, especially for 

protection of rangeland.

State and Local Lands

State governments protected almost 9 million acres between 

1998 and 2005, with 61 percent of those acres purchased in 

fee-simple title, and the remainder protected through conservation easements (Trust for Public Lands 2011b). States that both 

spent the most and conserved the most acres over that time 

period were California, Florida, New Jersey, New York, and 

North Carolina.

Support for State and local conservation often comes through 

ballot initiatives. Despite challenging economic times, voters 

have continued to support public funding of land conservation. 

Between 1990 and 2010, local governments were the most 

successful at passing ballot measures for land conservation, 

although successful State measures resulted in the most 

funding. The North Region had the highest rate of success at 

passing ballot measures and generated the most funding for 

land conservation (du Moulin and Alford, in press).

County-level ballot measures specifically targeted at forest 

land generated more than $1 billion between 1990 and 2010, 

whereas almost $5 billion was approved for conservation of 

farmland. Spending in 2009 and 2010 was dramatically less 

than previous-year levels, however, reflecting budget issues 

faced by many county and municipal governments. Nonetheless, a number of conservation initiatives were successfully 

passed in 2010 in 23 States, indicating continuing support for 

land conservation.

Conclusions

The Nation lacks a comprehensive inventory of the protected 

status of natural resources. The available data provide a first 

approximation according to IUCN definitions of protected area. 

Of the approximately 333 million acres classified as protected 

by IUCN definitions, approximately 110 million acres are 

strictly set aside to protect either biodiversity or wilderness 

settings. Primarily reflecting the extent of public ownership in 

different regions, about one-half of the total protected area is 

located in Alaska, one-third is in the West, and the remainder is 

in the East. Although many protected areas effectively preserve 

rare or endangered species and habitats, it is an open question 

whether the overall amount and location of protected area is 

appropriate for protecting all biodiversity values nationally. 

Furthermore, given the possibility of species and habitat migration with climate change, it is also not clear whether the current 

design of protected areas will serve to sustain biodiversity over 

the long term. To address these uncertainties, natural resource 

managers must consider biodiversity effects of land use and 

climate change on all land irrespective of its ownership and 

protected status.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 45

Open lands are under increasing pressure from housing and road 

development to support a growing human population (Hawbaker 

et al. 2006; Radeloff et al. 2005; Stein et al. 2005; Stein et al. 

2007). These development pressures are particularly evident on 

lands with high natural amenity values (Huston 2005; Radeloff et 

al. 2005)—lands that often occur in proximity to public lands in 

general and to national forests in particular (Radeloff et al. 2010).

Expanding human populations and the associated land use 

changes are the primary factors driving changes in biological 

diversity (Vitousek 1997). As private lands bear the growing 

burden of human-associated ecosystem stresses, public lands 

are becoming increasingly important for the conservation of 

biological resources (Flather et al. 2009; Robles et al. 2008). Land 

use activities on surrounding private lands affect the ability of 

public lands to sustain important ecosystem services, however. 

We reviewed the recent historical trends in housing development 

in and near national parks, national forests, and wilderness areas 

throughout the conterminous United States. In particular, we 

identified regions experiencing the most exurban growth near and 

within the boundaries of public lands to assess where housing 

development poses the most risk to conservation values.

Findings

The spatial distribution of housing and the boundaries of national 

parks, national forests, and wilderness areas were linked to 

estimate the number and density of homes that occurred within 

approximately 30 miles of the boundary of public lands (Radeloff 

et al. 2005, 2010). From 1940 to 2000, a total of 28 million 

homes were constructed within 30 miles of the protected areas 

examined, with the majority of those homes (25.8 million) built 

near national forests. Although wilderness areas receive the 

highest level of protection against development and resource 

extractions within their boundaries, they are not immune to 

development in the surrounding landscape—a total of 16.1 

million new homes were built within 30 miles of wilderness area 

boundaries between 1940 and 2000. National parks saw the 

lowest gain in new housing unit construction (1.5 million) within 

30 miles during the 60-year period.

Private in-holdings are important, particularly for national forests, 

because there can be substantial areas within the administrative 

boundary of a national forest that remain in private ownership. 

Between 1940 and 2000, just more than 940,000 new homes 

were constructed on these private in-holdings—more than 

tripling housing density within this prized real estate.

The pattern of housing growth in the vicinity of protected lands 

varied geographically. The relative rate of housing growth by the 

year 2000 was the greatest in the West, because initial housing 

density was relatively low (figure 27a). Eastern protected areas 

show relatively lower relative growth rates, but have experienced 

the greatest absolute gain in new home construction (figure 27b). 

Several areas of the country had both relative and absolute high 

housing growth, including peninsular Florida; the southern Appalachians; the foothill and front ranges near major metropolitan 

areas in Colorado, Utah, and Washington; montane habitats in 

the arid Southwest; and southern California (figure 27)—the 

very same geographic areas that also support particularly high 

concentrations of imperiled species.

Housing Growth Near Public Lands

Source: Radeloff et al. 2010 

Figure 27. Relative (a) and absolute (b) housing growth rates observed within a 30-mile buffer around the outer boundary of each national 

park, national forest, and wilderness area over the period 1940–2000.

(a) (b)

63–100

100–200

200–300

0–100,000

100,000–200,000

200,000–300,000

300,000–400,000

> 400,000

300–400

> 400

Housing 

growth rate (%) Housing growth

(No. of housing units)
46 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Implications

Establishing protected areas is an important conservation strategy that has long been thought to offer sanctuary from human 

activities (Flather et al. 2009). Growing human populations are 

extending the human footprint and are projected to have broad 

global effects on biodiversity conservation (Sala et al. 2000), 

however. Because public lands attract development, the potential 

ecological consequences of housing growth could be substantial. 

Such considerations will be particularly important in the coming 

decades as housing projections indicate that a total of 17 million 

new housing units may be built within 30 miles of these public 

lands by 2030 (Radeloff et al. 2010) if individual preferences for 

locating close to natural amenities continue into the future.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 47

Chapter 7. Forest Resources

v Forest area will decline in the future.

v Forests face threats to their long-term health and sustainability.

v Declining forest area, coupled with climate change and harvesting, will alter 

forest-type composition in all RPA regions.

v Forest inventory is projected to peak between 2020 and 2040, then decline to 2060.

v Softwood inventories are projected to remain relatively stable, whereas 

hardwood inventories show large declines after 2030.

v Tree canopy cover across all natural landscapes will be affected by development 

and climate change.

Forest Extent and Ownership

The United States has about 751 million acres of forest land, 

with 623 million acres in the conterminous United States.4 Forest land in the United States is widely but unevenly distributed 

(figure 28). Areas vary from sparse scrub forest of the arid Interior West to highly productive forests along the Pacific Coast 

and in the South, and from pure hardwood forests to multispecies mixtures and coniferous forest. More detailed information 

about forest resources can be found in Smith et al. (2009).

Almost two-thirds (514 million acres) of the Nation’s forests 

are classified as timber lands.5

 An additional 75 million acres of 

forest are reserved for nontimber uses under the management of 

public agencies. The remaining 162 million acres do not qualify 

as timber land, but are important for watershed protection, 

4

 Measurements in areas not previously inventoried by the Forest Inventory and Analysis program will add about 50 million acres to this total. Those statistics were not available for the 2010 RPA Assessment, and all 2010 analyses are based on the 751 million total acres. Future reports will adjust historical 

areas for compatible forest trend analysis.

5

 Forests capable of producing 20 cubic feet per acre of industrial wood annually and not legally reserved from timber harvest.

Figure 28. Timber land, reserved forest, and other forest land 

in the conterminous United States, 2007.

Forest land use

Timber land

Reserved forest

Other forest land

No data

500 miles

Albers Equal-Area Conic Projection.

Source: USDA Forest Service, Forest Inventory and Analysis

Resource Highlights

The United States has extensive forest resources that provide a variety of benefits to the American public. 

This chapter provides an overview of the extent and ownership 

of U.S. forests, the proportion of forests that are in protected 

status, and the threats to forest health. Projections of the future 

composition of U.S. forests under the Resources Planning Act 

(RPA) scenarios are presented, as are projected effects on future 

tree canopy cover. 
48 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

wildlife habitat, grazing, and recreation. About 87 percent 

of these acres are found in the Interior West (figure 28) and 

interior Alaska.

Wood production is primarily from timber lands, of which 

72 percent are in the East. Growing stock volumes on timber 

land increased in almost all RPA regions between 1953 and 

2007, and volume per acre increased in all regions. Growth 

has exceeded harvest since the 1950s, so timber volume on 

timber land has increased by about 50 percent since that time. 

Two-thirds of that increase was on private lands and one-third 

on public lands, and the largest increases occurred in the North 

and South Regions (Smith et al. 2009).

Most of the Nation’s forests are naturally regenerated. Planted 

forests are found primarily in the South Region, which 

produces the majority of timber. Nationally, 63 million acres 

(8 percent of forest area) is planted; in the South Region, 20 

percent is planted. Virtually all planted forest land is classified 

as timber land. Nearly all planted stands are established with 

native species, although not always the species that previously 

dominated in the area (e.g., loblolly pine has largely replaced 

longleaf pine in planted stands in the Southeast). The age 

structure of planted stands is markedly skewed towards the 

youngest age classes. Planted stands are supplying an increasing proportion of the Nation’s timber supplies. In the South 

Region, planted forests accounted for 43 percent of softwood 

removals in 2007 (Smith et al. 2009).

Net annual growth has been steadily increasing, with only the 

Rocky Mountain Region showing a decline in net growth since 

1996. The annual rate of growth since 1996 has been about 3.5 

times the increase in mortality during the same period. Growth 

rates are highest in the South Region. Historically, millions 

of intensively managed and highly productive forest industry 

timber land acres have been the primary reason for the higher 

average productivity on private timber land. The future productivity of those lands is unknown as a consequence of ownership 

changes (Smith et al. 2009).

More than one-half of the Nation’s forest land is in private 

ownership. In 2007, 11.3 million private forest owners owned 

56 percent of U.S. forest land. These owners include private 

individuals, Native Americans, or corporate entities (figure 29). 

Although more than 60 percent of private forest owners own 

between 1 and 9 acres of forest land, most of the private forest 

land acreage is in holdings of at least 200 acres. More than 20 

percent of private forest land is in holdings of at least 10,000 

acres, owned primarily by corporations (Butler 2008). About 

44 percent of forests are in public ownership, with the largest 

portion (147 million acres) administered by the Forest Service. 

The proportion of public ownership has remained stable for at 

least the past 50 years.

Ownership patterns vary greatly by East and West (figure 30). 

In the East, more than 75 percent of forest land is privately 

owned, whereas in the West, about one-third is privately 

owned. One of the largest changes in private ownership in the 

last two decades has been the divestiture of tens of millions of 

acres of forest industry lands to other types of private ownerships—lands sold primarily by integrated forest corporations 

to timber investment management organizations and real estate 

investment trusts. Within the category of family forest owners, 

the age of current owners and survey results indicate that turnover in privately held forest lands may have significant effects 

on future forest resource management, particularly in the East 

(Butler 2008).

Figure 30. Forest land in the conterminous United States by 

ownership category, 2007.a

Forest—Public

Forest—Private

Nonforest

National forest

Other public

Public

Private

Lakes and rivers

500 miles

Albers Equal-Area Conic Projection.

100% corporate

0% corporate

No data

a

 Corporate land includes land held by timber investment management companies and real 

estate investment trusts. 

Source: USDA Forest Service, Forest Inventory and Analysis

Figure 29. Forest land in the United States by ownership 

category, 2007.

National Forest System 

20%

Bureau of Land 

Management 

6%

Other Federal 

7%

State 

9%

County and municipal 

2%

Private 

corporate 

18%

Private 

noncorporate 

38%

Source: Butler 2009
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 49

Forest Protected Area

Protected areas of forest can be considered from two perspectives. The 75 million acres of reserved forests mentioned 

previously are publicly owned forests that are reserved for 

nontimber purposes. Reserved forest land has tripled since 1953 

and now accounts for 10 percent of all forest land in the United 

States. This reserved area includes State and Federal parks and 

wildernesses, but does not include conservation easements, 

areas protected by nongovernmental organizations, many 

wildlife management areas, and most urban and community 

parks and reserves. The majority of reserved lands are in the 

Western United States, where most Federal public lands are 

found (Smith et al. 2009).

A second perspective is offered by estimating the amount of 

forest land in protected areas as defined by the Protected Areas 

Database of the United States (PAD-US) described in chapter 6.

Because PAD-US does not identify the specific land use or land 

cover that is contained in the protected areas, the PAD-US map 

was combined with a 2001 forest land cover map for the conterminous United States to estimate the area of forest land cover 

within the protected areas (Ruefenacht et al. 2008; USDA Forest 

Service 2004). Table 10 displays definitions of the categories 

of International Union for the Conservation of Nature (IUCN) 

protection. Table 11 shows the estimates of forest land within 

protected areas. Because these statistics refer to forest land 

cover area, they are not directly comparable to the forest land 

Table 10. Definition of International Union for the Conservation of 

Nature (IUCN) categories for the Protected Areas Database of the 

United States (PAD-US).

IUCN 

category Definition

Ia Strict Nature Reserve: protected area managed mainly 

for science

Ib Strict Nature Reserve: protected area managed mainly 

for wilderness protection

II National Park: protected area managed mainly for 

ecosystem protection and recreation

III Natural Monument: protected area managed mainly for 

conservation of specific natural features

IV Habitat/Species Management Area: protected area 

managed mainly for conservation through management 

intervention

V Protected Landscape/Seascape: protected area 

managed mainly for landscape/seascape conservation 

and recreation

VI Managed Resource Protected Area: protected area 

managed mainly for the sustainable use of natural 

ecosystems

Unassigned In the PAD-US database, this category includes public 

and private land that was evaluated by the cooperators 

and was determined to not have an applicable IUCN 

code. It includes, for example, a large share of National 

Forest System land and almost all Native American land.

Source: International Union for the Conservation of Nature 1994

Table 11. Protected forest land cover from Protected Areas Database of the United States (PAD-US) by ownership and RPA region in the 

conterminous United States.a

RPA region Owner Designated IUCN category (thousand acres) (excludes unassigned area)

Ia Ib II III IV V VI Row total

North Federal 7 1,366 198 25 467 3,128 476 5,666

State 0 429 630 0 3,902 3,559 10,934 19,454

Other 0 0 0 0 2 2,265 0 2,267

Region total 7 1,795 828 25 4,371 8,952 11,410 27,387

Pacific Coastb Federal 193 9,105 1,893 257 125 1,521 16 13,111

State 0 0 75 0 71 2,457 493 3,096

Other 0 0 0 0 0 8 0 8

Region total 193 9,105 1,968 257 196 3,987 509 16,215

Rocky Federal 378 15,799 3,394 2,309 901 4,506 103 27,391

Mountain State 0 0 62 0 768 857 10 1,697

Other 0 0 6 0 1 51 0 58

Region total 378 15,799 3,462 2,309 1,670 5,414 114 29,146

South Federal 11 961 1,184 73 1,996 3,086 635 7,945

State 0 10 384 0 4,938 618 937 6,888

Other 0 0 0 0 0 22 27 48

Region total 11 971 1,568 73 6,934 3,725 1,599 14,881

Nationalb Federal 589 27,232 6,668 2,665 3,489 12,241 1,230 54,113

State 0 438 1,151 0 9,679 7,491 12,375 31,135

Other 0 0 6 0 3 2,346 27 2,382

National total 590 27,670 7,826 2,665 13,170 22,078 13,632 87,629

a

 Protected areas were defined by The Conservation Biology Institute (2010) and forest land cover was defined by USDA Forest Service (2004).

b

 Excludes Alaska and Hawaii.

IUCN = International Union for the Conservation of Nature.
50 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

area estimates shown elsewhere in this assessment. The land 

cover map identified a total of 655 million acres of forest land 

cover in the conterminous United States. Of that area, 88 million 

acres occurred in a designated IUCN category (table 11) that 

represents approximately 13 percent of total forest land cover 

area. Of total protected forest land cover area, 36 million acres 

of forest land cover (41 percent) are in IUCN categories Ia, Ib, 

and II, which provide the strictest level of protection.

Approximately 97 percent of the forest land cover area designated as protected by PAD-US in the conterminous United 

States is either Federal (54 million acres) or State (31 million 

acres) land. Overall, the protected forest land cover in the 

East (42 million acres) approximates the total in the West (45 

million acres). Approximately 33 percent is found in the Rocky 

Mountain Region, 31 percent in the North Region, 19 percent 

in the Pacific Coast Region, and 17 percent in the South Region 

(table 11). In comparison to PAD-US statistics for all protected 

land in chapter 6, these regional forest statistics reflect the 

higher percentage of forest in the East. Although total protected 

areas are more extensive in the West, they contain larger shares 

of nonforest land cover.

Forest Health

The forests of the United States continue to face a variety of 

threats to their long-term health and sustainability (USDA Forest Service 2009a). Native and exotic pests have killed trees on 

millions of acres of U.S. forests with significant environmental 

and social effects. Similarly, wildfires have severely affected 

forests and the waters and wildlife that depend upon them. Severe droughts and other climatic changes also lead to additional 

stress on forest ecosystems. In this section, we provide a broad 

overview of major trends and issues related to forest health. 

More detailed information can be found on the Forest Service’s 

Forest Health Monitoring (FHM) Web site (http://fhm.fs.fed.us/).

Forest Insects and Pathogens

Forest insects and pathogens are crucial components of forest 

ecosystems that can periodically reach outbreak levels when 

susceptible forest conditions are combined with weather stress. 

Periods of less-than-normal precipitation and above-normal 

temperatures can stress trees and reduce their resistance to 

insects and pathogens. Analysis of trend data for the last decade 

from the FHM Program indicated an upward trend in tree mortality since 2000, with the highest levels reached in 2003 and 

2009. This increase was largely because of bark beetle activity 

in the West after severe regional droughts in combination with 

susceptible forest stand conditions (figure 31).

Most of the increase in mortality from 2002 through 2004 

resulted from a large outbreak of Ips beetles in pines in the 

Southwestern States of Arizona, Colorado, New Mexico, and 

Utah. Pinyon and ponderosa pine trees were stressed by severe 

drought conditions in this area from 2000 to 2003. Mountain 

pine beetle and other native conifer bark beetles have killed 

an increasing number of trees throughout the West. From 

2000 through 2009, the Intermountain West experienced bark 

beetle-caused mortality in an estimated 22 million acres across 

all ownerships, with 18 million acres on national forests. These 

outbreaks are most widespread in dense, aging lodgepole 

pine forests that dominate the mountains of Colorado, Idaho, 

Montana, Utah, and Wyoming.

This level of mortality is unprecedented in its environmental 

and social effects. An estimated 100,000 beetle-killed trees fall 

per day in Colorado and Wyoming, escalating concerns about 

public safety and effects on public utilities and roads. Wildlife 

habitat and watershed conditions have also deteriorated in areas 

of high mortality because of the impaired ability of forests in 

high-elevation watersheds to provide shade and shelter that help 

to maintain the winter snowpack and prevent quick runoff during the spring melt and summer storms. Other species besides 

lodgepole pine are also being affected. Whitebark pine and 

other high-elevation pines are being killed by beetle outbreaks 

occurring on sites previously thought to be too cold for large 

epidemics. This mortality may, in part, be because of warmerthan-normal winter temperatures at these high elevations. These 

species are also being attacked by the invasive white pine blister 

rust, further threatening their survival (see sidebars, Whitebark 

Pine Decline and Alaska Yellow-Cedar Decline).

In the eastern forests, native pest insects are currently at low 

levels. For example, populations of the southern pine beetle 

are at historically low levels with very few spots reported 

throughout the South. This insect is, however, killing pitch 

pines in increasing numbers in New Jersey. Nonnative invasive 

species present some of the greatest threats to eastern forests. 

The emerald ash borer was first reported in the United States on 

ash trees in the Detroit, MI, area in 2002. Transport of infested 

ash trees and wood accelerates the spread of this insect. This 

Figure 31. Total acres with outbreak levels of tree mortality, 

1997–2010.

14

12

10

8

6

4

2

0

Acres (millions)

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Year

Source: USDA Forest Service, Forest Health Monitoring Program
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 51

insect has since been widely distributed throughout the North 

Central United States, killing more than 50 million ash trees 

in 15 U.S. States and Canada. In southeastern Michigan and 

northwestern Ohio, the mortality rates for ash trees are nearly 

100 percent. The Asian longhorned beetle has also been found 

infesting maples and other hardwoods in new locations. More 

than 20,000 infested trees have been found and removed near 

Worchester, MA, since this infestation was found in 2008.

A national risk assessment identified areas where more than 25 

percent of the trees of greater than 1-inch diameter are expected 

to die within 15 years because of insects and pathogens (Krist 

et al. 2007) (figure 32). More than 58 million acres of forest 

lands are at risk to increased activity by forest insects and 

pathogens, including bark beetles of western conifers, oak 

decline, southern pine beetle, root diseases, and gypsy moth. 

This national risk assessment is being used to develop broad 

prevention strategies for the major forest insects and pathogens 

threatening the forests of the United States.

Wildfire

Wildfire is a major disturbance in many forests of the United 

States. The annual amount of area burned varies depending on 

weather conditions, fuel loading, and forest stand conditions. 

Much of the recent increase in area burned is because of increased fuel loads and recent droughts and warm temperatures, 

especially in the Western United States. The total area burned 

in 2006 was the largest fire-affected acreage during the period 

1960–2010 (figure 33).

There is growing scientific evidence that climate change will 

increase the number and size of wildfires, both globally and 

in North America. The effects of climate change on wildfire 

occurrence, extent, and severity will vary in different regions of 

the country. Much of the recent increase in fire in the Western 

United States can be correlated with increasing temperatures, 

changes in precipitation patterns, and longer fire seasons since 

the mid-1980s. No single event, however, can be linked specifically to climate change. Climate change and changing wildfire 

patterns will cause changes in the distribution of individual 

plant species and in the dynamics of forest and rangeland 

ecosystems. Even where rainfall remains the same or increases, 

warming temperatures can greatly increase plants’ need for 

water and increase drought stress and fire hazard. Overall, more 

fire is expected in the western forests and rangelands. Complex 

spatial land use patterns and active prescribed fire programs 

Figure 33. Total area of wildfires in the United States, 

1960–2010.

12

10

8

6

4

2

0

Acres (millions)

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

Year

Source: National Interagency Fire Center 2011

Figure 32. Areas with potential risk of greater than 25 percent 

tree mortality because of insects and diseases.

Legend

Area at risk (thousand acres)

Percent of forested land in risk

Risk* by Ownership Class

Acres at risk: Approximately 58 million

* The expectation that 25% or more of the standing live volumne 

of trees greater than 1” in diameter will die over the next 15 years

Risk of mortality

Water

National Forest System 25,026

Other Federal 5,861

National Forest System 14.16

Other Federal 4.34

State, county, and private 27,107

State, county, and private 5.59

Source: USDA Forest Service, Forest Health Monitoring Program

200 0 200 400

Miles

complicate predictions for the East. Forest management techniques, such as prescribed burning or thinning dense forests, 

can make forests more resilient to wildfire and decrease fire 

emissions (McKenzie et al. 2011).
52 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Whitebark Pine Decline

years. High-elevation whitebark pines are being killed on sites 

previously thought to be too cold for serious beetle outbreaks 

(Logan et al. 2010). These changes in beetle activity have been 

linked to warmer winter temperatures that have led to quicker 

development and higher survival rates for over-wintering insects. 

A warmer and moister weather pattern may also favor white pine 

blister rust by producing frequent “wave years” of conditions 

that promote massive numbers of infections. Whitebark pine is a 

keystone species throughout the high mountain ranges of western North America. It is often the only tree species capable of 

surviving in harsh subalpine areas and is crucial in stabilizing soil 

and moisture and creating habitats that support a wide diversity 

of plants and animals. For example, the nuts of whitebark pine 

provide a critical food source for grizzly bears as they prepare for 

winter hibernation. Range-wide restoration strategies for whitebark pine are needed to reverse the current trends. The Forest 

Service is collaborating with partners, including the National 

Park Service, University of Colorado, and University of Montana, 

to develop and implement restoration and gene conservation 

strategies for whitebark pine and other threatened tree species 

(Schwandt 2006). 

Whitebark pine in Crater Lake National Park

Photo: John Schwandt, Forest Service

Whitebark pine mortality in Yellowstone National Park

Photo: Ken Gibson, Forest Service, retired

Whitebark pines are declining throughout their range because of 

a complex of stresses (Tomback et al. 2001). Extensive dieback 

of older trees and mortality of seedlings is being caused by white 

pine blister rust, an invasive pathogen. Large outbreaks of the native mountain pine beetle have affected extensive areas in recent 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 53

Alaska Yellow-Cedar Decline

Alaska yellow-cedar stands have been declining in southeast 

Alaska during the last century. More than 500,000 acres of dead 

and dying trees have been mapped by aerial surveys. Analysis of 

the data reveals that this widespread tree mortality is concentrated at lower elevations and on wet soil types. The problem 

began about 100 years ago at the end of the Little Ice Age. Tree 

death appears to result from root freezing, predisposed by low 

snow accumulations since the 1900s. Shallow roots in anaerobic 

soil and a unique vulnerability to cold injury in early spring are 

associated with the decline (Hennon et al. 2006; Schadberg et 

al. 2008). Yellow-cedar has extremely valuable wood; thus, the 

problem has a considerable economic impact. This tree species 

also has ecological and cultural importance; Native people have 

long used its wood and bark. Knowledge of the cause of yellowcedar decline and associated site risk factors is leading to a 

conservation strategy for this valuable tree species in the context 

of a warming climate with reduced snow.

Standing dead Alaska yellow cedar in Southeast Alaska

Photo: Paul Hennon, Forest Service

Dead Alaska yellow cedar

Photo: Paul Hennon, Forest Service

Forests Resources in the Future

Forests develop in response to their physical environment, biological dynamics, and decisions regarding their uses. Acting on 

different time frames, these forest dynamics ultimately determine the ecosystem services that flow from the Nation’s forests. 

This section describes projections of forest resource conditions 

for the conterminous United States. The results are based 

on the United States Forest Assessment System (USFAS), a 

modeling system designed to project alternative futures for U.S. 

forests (Wear 2010). USFAS is a forward-looking adjunct to 

the ongoing inventories of forest conditions conducted by the 

Forest Inventory and Analysis (FIA) Program. The FIA system 

provides nationwide monitoring through repeated inventories 

that measure forest conditions at a high level of detail, and the 

USFAS addresses how biological, physical, and human factors 

could alter these forest inventories in the future.

We generated projections of detailed forest inventories along 

with the land use projections described in chapter 6 and forest 

product markets described in chapter 8. We modeled the effects 

of changing climate, market-driven timber harvesting, and land 

use change along with changes driven by successional transitions in forest conditions. This section summarizes projections 

and implications for the future of forests in the four RPA 

regions (figure 16).

Population and income projections and changes in rural land 

rents at the county level determine changes in land use (Wear 
54 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

2011); changes in various climate metrics drive forest-type 

transitions; and forest market projections, driven by each RPA 

scenario’s global resource conditions and projected economic 

conditions, determine harvest levels. More details on the 

regional results and modeling approach can be found in Wear 

et al. (in press).6 For each RPA region, forest projections were 

generated for each of the RPA scenario-climate combinations 

(see table 2). Each of these nine alternatives contains a timber 

harvest model future based on various models of harvest 

choice. To link the forest projections to timber market projections from the U.S. Forest Products Module (USFPM) for the 

economic/bioenergy futures attached to the four RPA scenarios, 

we evaluated additional alternatives with either timber prices 

or harvest levels adjusted to match harvest projections for the 

RPA region. Comparisons of projections across the multiple 

climate realizations are described in the RPA supporting document on forest forecasts (Wear et al., in press).

Future Forest Area

To account for changes in the area frame of the FIA survey, we 

used the RPA land use projections described in Wear (2011). 

Projected changes in forest area were used to rescale the 

projected forest inventory to reflect these land use dynamics. 

Non-Federal FIA forest area within a county is assumed to 

change in direct proportion to the area change projected by 

the land use model, and Federal forest area is held constant. 

The RPA land use projections described changes in forest 

area based on National Resources Inventory (NRI) definitions, 

whereas this section translates those changes to the FIA basis 

and a 2010 starting point.

Projected changes in forest land to 2060 indicate a reversal 

of the net accumulation of forest land in the United States 

between the 1980s and 2010 (figure 34). Historical land use 

changes during this period derive from the intersection of two 

countervailing dynamics: an expansion in developed uses of 

land and a more-than-compensatory transition of marginal 

agricultural land to forest cover. The latter derived from 

market-driven shifts in the returns to forests and agricultural 

production but was also encouraged by various conservation 

programs, including the Conservation Reserve Program. This 

accumulation offset observed declines in forests between the 

1960s and 1980s, likewise driven by the comparative economics of agriculture and forest uses. Projections show urbanization 

continuing to dominate future land use, without a compensatory 

movement of land from agriculture to forest uses.

Effects of Future Climate

Climate projections provide another key input to defining 

future forest conditions. Various climate variables derived from 

these projections influence the forest inventory projections, 

usually expressed as long-run averages in these models. Forest 

dynamics models incorporate climate inputs in different ways 

for the Eastern and Western United States. In the East, paired 

inventories enable modeling of forest-type transitions on observed climate and inventory data. In the West, forest types are 

modeled using forest-type classification approaches comparable 

to tree species migration models (e.g., Iverson et al. 2008; Rehfeldt et al. 2006). Climate data are expressed as averages for 

the life of the forest in the East and as fixed-length averages (20 

years) for forests in the West. These long-run averages change 

6

 Some of these forecasts are examined at a finer scale of detail in two regional assessments: the Southern Forest Futures Project (Wear and Greis [in 

press]) and the Northern Forest Futures Project.

Figure 34. Historical and projected forest area in the conterminous United States (left) and decadal changes in historical and projected

forest area (right), by RPA scenario, 1952–2060.

700

600

500

400

300

200

100

1960

0

Acres (millions)

RPA A1B RPA A2 RPA B2 RPA HFW Historical

Year

1970

1980

1990

2000

2010

2020

2030

2040

2050

2060

15

10

5

0

– 5

– 10

– 15

1960–1970

– 20

– 25

Changes in acres (millions)

Years

1970–1980

1980–1990

1990–2000

2000–2010

2010–2020

2020–2030

2030–2040

2040–2050

2050–2060

RPA A1B RPA A2 RPA B2 RPA HFW Historical
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 55

slowly, reflecting the long-term persistence of forest vegetation. 

Forest responses to climate changes in the East are especially 

muted. In the West, models enable climate thresholds to trigger 

forest-type switching sooner, so long-term trends (e.g., 50 

years) are more informative than shorter run trends (e.g., 20 

years). Climate also affects productivity through the association 

of projected climates with forest plots in comparable climates 

from the historical record.

Overall, because of the use of long-term averaging and the slow 

development of significant changes in these and other climate 

variables, climate effects are largely muted until toward the end 

of our 50-year projection period. The strongest climate signal is 

detected in projections of forest types for the western regions.

Future Forest Types

Forest-type composition is projected to change in response 

to spatially explicit land use changes, forest harvests and 

other disturbances, natural succession, and other management 

choices. Projections of the areas of various forest types are 

consistent with the projections of total forest area (described in 

figure 34), climate projections, and timber market projections 

associated with the four RPA scenarios.

In the South Region, we projected changes in five forest 

management types: planted pine, natural pine, oak-pine, upland 

hardwood, and lowland hardwood. Figure 35 presents the 

results for the RPA A1B-MIROC3.2 scenario, which is one of 

two RPA scenarios with more than 20 million acres of forest 

loss (the other is RPA historical fuelwood [HFW]). The RPA 

B2-HadCM3 scenario had the smallest total area change over 

time (figure 36). The upland and lowland hardwood types are 

projected to comprise between 51 and 53 percent of all forests 

in 2060, a decline from about 54 percent in 2010. The greatest changes, however, are found among the softwood types 

(planted pine, natural pine, and oak-pine). These forest dynamics are heavily influenced by the interaction of land use changes 

and management for forest products, which in turn is driven by 

timber market conditions and by the rate of forest planting.

The area of planted pine forest in the South Region, currently at 

about 39 million acres, or 19 percent, of forest area is projected 

to increase by 2060 (figure 35). Planted area ranges from 47 

to 67 million acres in 2060, depending on future land use and 

market projections. The 67 million acres of planted pine projected for RPA A1B result from that scenario’s high bioenergy 

demands and exceeds the projections from previous RPA Assessments. The forecast of 47 million acres for RPA B2 is more 

in keeping with earlier RPA analyses. Projected losses in the 

area of naturally regenerated pine forest types mirror the gains 

in planted pine forests. At more than 80 million acres in 2010, 

upland hardwoods are the predominant forest type in the South 

Region, more than double the area of the next largest forest 

type. Upland hardwoods are projected to decline for all RPA 

scenarios, and variations in projections are associated more 

with rates of urbanization than with timber market futures. The 

area of lowland hardwoods shows a smaller amount and rate of 

decline.

Hardwoods dominate forest types in the North Region, accounting for about 83 percent of forest area. Among hardwood 

forest types, maple-beech-birch and oak-hickory groups are the 

largest, accounting for 26 and 36 percent of total forest area in 

2010, respectively (figure 37). Among softwood types, sprucefir (9 percent) and white-red-jack pine (5 percent) account 

for the largest shares of total forest area, but these areas are 

concentrated in northern parts of the region. For all projections, 

Figure 35. Area of forest by forest type groups for the RPA 

South Region, RPA A1B-MIROC3.2, 2010–2060.

250

200

150

100

50

Lowland hardwood

Upland hardwood

Mixed hardwood

Natural pine

Planted pine

2010

2020

2030

2040

2050

2060

0

Acres (millions)

Year

Figure 36. Area of forest by forest type groups for the RPA 

South Region, RPA B2-HadCM3, 2010–2060.

250

200

150

100

50

Lowland hardwood

Upland hardwood

Mixed hardwood

Natural pine

Planted pine

0

Acres (millions)

Year 2010

2020

2030

2040

2050

2060
56 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

there is little change in the overall shares represented by hardwoods and softwoods in the North Region. Shifts among forest 

types within the hardwood and softwood groups result from the 

various RPA scenarios, however.

The area of oak-hickory, the largest forest type in the North 

Region, is projected to decline between 3 and 6 million acres 

(5 to 10 percent) between 2010 and 2060, with the greatest 

losses for the A1B-MIROC3.2 scenario (figure 37) and the least 

losses for the B2-HadCM3 scenario (figure 38). Rates of urbanization have the greatest influence on projections of change for 

oak-hickory forests across RPA scenarios. In contrast, the area 

of maple-beech-birch increases between 2010 and 2060 for all 

RPA scenarios, with increases ranging between 1 and 3 million 

acres (3 to 7 percent), and projections of these forest types vary 

in response to differences in area harvested. Among the hardwood types, elm-ash-cottonwood forests decline by the greatest 

percentage across RPA scenarios, falling by 13 to 20 percent 

(1 to 2 million acres) between 2010 and 2060. Aspen-birch 

projections are the most variable across hardwood types, and 

all RPA scenarios lead to losses in aspen-birch area: minimum 

losses are 1.4 million acres (7.7 percent) and maximum losses 

are 3.0 million acres (16.9 percent). For aspen-birch, change 

patterns indicate less forest loss as harvesting increases.

The two dominant softwood types in the North Region show 

very different patterns of change across the RPA scenarios. 

Projected spruce-fir area decreases range from 8 to 17 percent 

of area and indicate an inverse relationship between area and 

harvesting. In contrast, projected area of white-red-jack pine is 

the most variable of all forest types, ranging from a 10-percent 

gain to a 16-percent loss, depending on the RPA scenario. 

Note that, unlike in the South Region, there is little change in 

the area of intensively managed (planted) forests projected for 

the North Region. This result reflects the lack of substantial 

historical precedent for such management in the North Region, 

but the large demands for biomass in RPA A1B suggest that 

structural changes, including adoption of new forest management regimes, might be anticipated.

The Rocky Mountain Region contains a diverse array of forest 

types, nearly all composed of a mix of conifer species (figure 39). 

The forest-type group comprising the greatest area in the region 

is the pinyon-juniper group, with about 50 million acres, or 

35 percent, of the area classified as forest. The next largest are 

spruce-fir-hemlock, with 23 million acres (15 percent), and the 

Douglas-fir group, with 18 million acres (13 percent). Lodgepole pine and ponderosa pine comprise 12 and 14 million acres 

(8 and 9 percent), respectively. Aspen-birch, at about 8 million 

acres, represents about 5 percent of forest area and several 

other forest-type groups combine to make up the remainder of 

about 15 percent.

Figure 37. Area of forest by forest type groups for the RPA 

North Region, RPA A1B-MIROC3.2, 2010–2060.

200

180

160

140

120

100

80

60

20

40

0

Other

White-red-jack pine

Spruce-fir

Oak-hickory

Mixed pine

Maple-beech-birch

Elm-ash-cottonwood

Aspen-birch

Acres (millions)

Year 2010

2020

2030

2040

2050

2060

Figure 38. Area of forest by forest type groups for the RPA 

North Region, RPA B2-HadCM3, 2010–2060.

200

180

160

140

120

100

80

60

20

40

0

Other

White-red-jack pine

Spruce-fir

Oak-hickory

Mixed pine

Maple-beech-birch

Elm-ash-cottonwood

Aspen-birch

Acres (millions)

Year 2010

2020

2030

2040

2050

2060

Figure 39. Area of forest by forest type groups for the RPA 

Rocky Mountain Region, RPA A1B-MIROC3.2, 2010–2060.

160

140

120

100

80

60

20

40

0

Other

Ponderosa pine

Pinyon-juniper

Lodgepole pine

Fir-spruce-hemlock

Douglas-fir

Aspen-birch

Acres (millions)

Year 2010

2020

2030

2040

2050

2060
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 57

In the West, where inventory data are relatively scarce compared 

with inventory data in the East, future forest types are projected 

based strictly on climate projections and not on observed forest 

transitions between inventories. Compared with the East, the 

projection models for forest types in the West are more sensitive 

to differences in climate projections. This sensitivity enables 

more immediate effects of climate changes on future foresttype groups.

In the Rocky Mountain Region, projections capture multiple 

and offsetting forest dynamics across the varied conditions of 

the region but clearly show several strong trends in forest types 

(figure 39). Two forest types, Douglas-fir and lodgepole pine, 

are projected to decline across all RPA scenarios. For Douglasfir, declines range from 20 to 38 percent of its 2010 area; for 

lodgepole pine, declines range from 6 to 28 percent. In contrast, 

the areas of fir-spruce-hemlock and ponderosa pine increase, 

ranging from 4 to 17 percent and 8 to 46 percent, respectively. 

Other forest types, including aspen-birch and pinyon-juniper, 

have relatively constant forest areas between 2010 and 2060.

The Pacific Coast Region contains a diverse complement of 

forest types in two distinct ecological zones. The west side of 

the Cascade Mountain Range contains wet and highly productive sites, and Douglas-fir and hemlock-Sitka spruce types 

dominate. The east side of the Cascade Range is much drier and 

much less productive, with forest types similar to the Rocky 

Mountain Region. Overall forest-type dynamics are dominated 

by changes on the west side forests. Hemlock-Sitka spruce area 

is projected to decline across all RPA scenarios between 2010 

and 2060, whereas the area of Douglas-fir forests is projected 

to increase. Area of alder-maple also falls somewhat during 

this period. Among east-side forest types, area of fir-sprucehemlock and lodgepole pine are projected to decline, whereas 

ponderosa pine area expands (figure 40).

Future Forest Inventory

Shifts in climate affecting productivity, changing markets affecting growing stock removals, and land use change affecting 

the area of forests interact to determine the amount of inventory 

contained in the Nation’s forests. The results shown in this 

section are based on the CGCM3.1 general circulation model 

(GCM) climate projections for RPA scenarios A1B, A2, and 

HFW and on the CGCM2 GCM for RPA scenario B2. Figure 41 

shows projections of the growing stock inventory of U.S. forest 

land from 2010 to 2060 across the four RPA scenarios. All four 

demonstrate the same general pattern of change: a peaking of 

volume between 2020 and 2030 followed by a decline in volume to 2060. The magnitude of change differs, however, with 

only RPA A1B projecting less volume in 2060 than observed 

in 2010. Scenario RPA B2 shows only a slight reduction from 

its peak of about 1 trillion cubic feet and RPA A2 shows just 

a slightly higher volume in 2060, but with a strong downward 

trajectory. The RPA HFW (based on A1B economic assumptions) yields growing stock levels similar to RPA B2. The 

downward trajectory for RPA A1B reflects the very high levels 

of removals for this scenario in 2050–2060 coupled with large 

forest losses due to urbanization.

Figure 40. Area of forest by forest type groups for the RPA 

Pacific Coast Region, RPA A1B-MIROC3.2, 2010–2060.

90

80

70

60

50

40

20

10

0

30

2010

Other

Western oak

Ponderosa pine

Pinyon-juniper

Other softwood

Lodgepole pine

Hemlock-Sitka spruce

Fir-spruce-hemlock

Douglas-fir

California mixed 

conifer

Alder

2020

2030

2040

2050

2060

Acres (millions)

Year

Figure 41. Total historical and projected growing stock inventories (top) and decadal changes in historical and projected 

growing stock inventories (bottom) for the conterminous United 

States, by RPA scenario, 1960–2060.

1,200

100

1,000

80

800

60

600

40

400

20

200

0

– 40

– 20

0

– 60

Billion cubic feet Change in billion cubic feet

1960

1970

1960–1970

1970–1980

1980–1990

1990–2000

2000–2010

2010–2020

2020–2030

2030–2040

2040–2050

2050–2060

1980

1990

2000

2010

2020

2030

2040

2050

2060

RPA A1B

RPA A1B

RPA A2

RPA A2

RPA B2

RPA B2

RPA HFW

RPA HFW

Historical

Historical

Year

Years

RPA A1B RPA A2 RPA B2 RPA HFW Historical

RPA A1B RPA A2 RPA B2 RPA HFW Historical
58 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

This peaking of inventory followed by declines would conclude 

a long period of inventory accumulation on the Nation’s forest 

lands (figure 42). Between 1950 and 2010, growing stock 

inventories accumulated rapidly, as abandoned and cut-over 

forest lands were restocked, especially in the Eastern United 

States. Many of these stands are reaching their capacity in 

terms of biomass accumulation while projected forest area 

losses trim inventories.

Under all RPA scenarios, the greatest reduction in forest inventory occurs in the North Region. Under scenario RPA A1B, 

for example, large expansions in timber removals for biomass 

energy coupled with forest losses driven by urbanization lead 

to substantial declines of growing stock volumes in the region 

(figures 42 and 43). Very similar dynamics occur in the South 

Region, as well, but do not result in the same degree of inventory drawdown. The difference relates to a strong demonstrated 

forest investment response by forest owners in the South Region, who are projected to expand forest plantations in response 

to the strong timber markets observed for RPA A1B. The RPA 

A1B scenario also contains an expansion of productivity of 

planted pine forests in anticipation of genetic improvements 

and intensified management. This type of response has not been 

demonstrated and is not a part of the projections for the North 

Region. The tenability of this assumption (no expansion in intensive management in the North Region) is higher for the RPA 

B2 and RPA HFW scenarios, wherein output growth is modest, 

but is much less certain for the RPA A2 and, especially, RPA 

A1B scenarios, wherein output growth is greater.

The pattern of volume changes differs between softwoods and 

hardwoods in the United States. Figures 44 and 45 show that 

projected softwood inventories would likely remain relatively 

stable during the next 50 years, because of an expected investment response to higher wood energy demand in the form of 

timber plantations in the South Region and a relatively stable 

softwood inventory in the Rocky Mountain Region. The RPA 

A2, B2, and HFW scenarios show increases in softwood 

volume between 2010 and 2060, whereas RPA A1B, with 

the highest level of timber removals, indicates some declines 

in softwood inventory by 2060. In contrast, projections of 

hardwood growing stock inventories show strong declines in 

volume beginning in 2030 and progressing through 2060, as 

expected investment response to higher wood energy demand is 

negligible in the case of hardwoods and forest losses from land 

use changes are more often coincident with hardwood forest 

types (figure 46).

Figure 42. Projected growing stock inventories for the conterminous United States, by RPA region for the RPA A1B scenario, 

2010–2060. 

350

300

250

200

150

100

50

0

Billion cubic feet

2010 2020 2030 2040 2050 2060

RPA A1B South North Rocky Mountain Pacific Coast

Year

Figure 43. Projected change in growing stock inventories for 

the conterminous United States, by RPA region for the RPA A1B 

scenario, 2010–2060. 

20

15

10

5

0

– 5

– 10

– 15

– 20

– 25

– 30

Change in billion cubic feet

2010–2020

2020–2030

2030–2040

2040–2050

2050–2060

RPA A1B

Years

Conclusions

Forest resources in the United States have been remarkably 

resilient during the past 100 years, with the area of forests 

remaining relatively stable. The stability of the total forest 

area masks substantial change in the character of forest lands, 

however. As described in the previous chapter, forests are 

subject to considerable fragmentation, reducing their ability to 

provide some types of ecosystem goods and services. Ownership changes on forest land have also created uncertainty about 

future management and retention of forest lands, because lands 

once owned by the forest industry have been sold to firms and 

individuals whose primary focus is not active forest management. Forests continue to face a variety of threats to their 

South North Rocky Mountain Pacific Coast
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 59

long-term health and sustainability. The interaction of climate 

change, wildfire, and insect and disease activity may exacerbate 

forest health issues by increasing the incidence and severity of 

wildfires, extending the range of both native and exotic pests 

and pathogens.

We are projecting losses in forest land in the future across all 

RPA scenarios, reversing the trend of forest land stability. The 

projections are dominated by conversions of forest land and 

other land uses to urban and developed uses. The effects of land 

use change, climate, and harvest pressure on the area of forest 

types and inventory volumes interact and the net results vary 

by region. Climate effects generally had limited effect on forest 

types and inventory during the projection period, but those 

effects were more noticeable in the Western United States.

Forest area losses are most pronounced under the RPA A1B 

and HFW scenarios, in which population- and income-driven 

Figure 44. Projected softwood growing stock inventories for 

the conterminous United States, by RPA scenario, 2010–2060.

600

500

400

300

200

100

0

Billion cubic feet

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Year

Figure 45. Projected decadal change in softwood growing stock 

inventories for the conterminous United States, by RPA scenario, 

2010–2060. 

Figure 46. Total projected hardwood growing stock inventories 

(top) and projected decadal change in hardwood growing stock 

(bottom) for the conterminous United States, by RPA scenario, 

2010–2060. 

60

500

50

400

450

40

350

30

300

20

250

10

200

0

150

– 10

100

– 20

50

70

60

50

40

30

20

10

0

– 10

– 20

– 30

– 40

– 30

0

Change in billion cubic feet

Billion cubic feet Change in billion cubic feet

Years

Years

1960–1970

1960–1970

1970–1980

1970–1980

1980–1990

1980–1990

1990–2000

1990–2000

2000–2010

2000–2010

2010–2020

2010–2020

2020–2030

2020–2030

2030–2040

2030–2040

2040–2050

2040–2050

2050–2060

2050–2060

urbanization consumes rural lands at the highest rate. The 

acceleration of forest loss for HFW relative to A1B reflects the 

role that strong wood products markets can have on retaining or 

even expanding forest land in parts of the United States. Even 

under these scenarios, with the largest losses in area and inventory, nowhere in the 50-year projections do these values fall 

below values observed since the 1980s for the United States. 

Although they represent trend reversals, these projections do 

not represent unprecedented future conditions.

Tree Canopy Cover in the Future

The previous sections in this chapter focused on forest resources on forest lands. Trees, however, are widely distributed 

outside of forest land across the United States, and those trees 

serve a variety of important functions. Whereas chapter 9 

specifically addresses urban forests, this section examines tree 

1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Year

RPA A1B RPA A2 RPA B2 RPA HFW Historical

RPA A1B RPA A2 RPA B2 RPA HFW Historical

RPA A1B RPA A2 RPA B2 RPA HFW Historical

RPA A1B RPA A2 RPA B2 RPA HFW Historical
60 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

canopy cover7 across the conterminous United States and therefore includes tree cover across all land covers. (See sidebar, 

National Tree Cover Estimates.)

Land use change and climate change will influence future tree 

cover. We projected future tree canopy cover change first using 

only the county-scale RPA land use change projections, then 

using both the land use projections and climate projections. For 

the combined analysis, we developed an aridity ratio from the 

climate projections for each of the nine RPA scenario-climate 

combinations (table 2) and provided an index of the combined 

risk to both tree canopy cover loss and increased aridity based 

on projected changes in climate and canopy projections.

7

 Tree canopy cover and tree cover are identical, both referring to the amount or percentage of land area that, when viewed from above, is covered by tree 

canopies.

8

 The RPA HFW scenario was not evaluated because the land use projections for RPA HFW were not available until after this analysis was completed.

National Tree Cover Estimates

Forest Service research scientists (e.g., Nowak and Greenfield 

2008) have produced State estimates of urban area and population statistics for each of the lower 48 States and estimates of 

tree cover based on 2001 National Land Cover Database (NLCD) 

tree cover maps. State-level data can be accessed from the 

Northern Research Station’s urban forest site (http://www.nrs.

fs.fed.us/data/urban). An accuracy assessment of all 65 NLCD 

mapping zones indicates that NLCD tree cover maps underestimate tree cover nationally by an absolute amount of 9.7 percent, 

however, compared with photo-interpreted estimates (Nowak 

and Greenfield 2010). Differences in photo-interpreted and NLCD 

tree cover estimates vary by mapping zone and NLCD land cover 

class. These estimated differences were used to adjust the NLCD 

tree cover map to produce estimates with each NLCD land covermapping zone combination that matched the photo-interpretation 

estimate. This adjusted cover map was then used to estimate 

projected changes in tree cover associated with land use and 

climate projections from the RPA scenarios.

Tree Canopy Change Under Varying Land Use 

Projections

We developed tree canopy cover projections to 2060 for 

RPA scenarios A1B, A2, and B28 by applying the land use 

projections to adjusted 2001 National Land Cover Database 

(NLCD) tree canopy values of land cover classes (Nowak and 

Greenfield 2010) and projected to 2060. The NLCD classes 

of developed land (classes 21 through 24) were associated 

with the RPA land use “urban” category. The NLCD forested 

land cover classes (classes 41 through 43) were related with 

the “forest” category. The NLCD classes for agriculture (81 

and 82) were associated with a summed agriculture category 

derived from the “cropland” and “pastureland” categories. The 

methods and results are discussed in more detail in Greenfield 

and Nowak (in prep.).

The three RPA scenario results exhibited similar patterns of 

future changes in tree canopy cover across the United States. 

The RPA A1B scenario (figure 47), which has the most urban 

sprawl outside the more urbanized counties, had the largest 

change, a 1.6-percent decrease in canopy cover in the conterminous United States between 2000 and 2060 (relative change, 

-4.7 percent), whereas the RPA B2 scenario, which had the 

most urban growth within the already urbanized counties, had 

the smallest change, a 1.1-percent decrease. Rhode Island had 

the greatest tree canopy cover decrease in all RPA scenarios, 

ranging from 10 percent (RPA A2) to 5.5 percent (RPA B2); 

South Dakota had the largest increase, ranging from 0.2 percent 

(RPA A2 and B2) to 0.3 percent (RPA A1B).

Difference of percent canopy for RPA A1B 2000–2060

– 44.2 to – 20.1%

– 20 to – 5%

– 4.9 to 0%

+ 0.1 to + 5%

+ 5.1 to + 9.2%

Figure 47. Absolute difference in percent tree canopy cover, 

2000–2060, for the RPA A1B scenario.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 61

Even though tree canopy cover changes within a State or 

nationwide are relatively small (mostly less than 10 percent), 

changes at the county level can be much higher and more variable (see appendix A). The county with the greatest projected 

tree canopy cover loss is Dare County, NC, losing 44.2 percent 

in all three RPA scenarios. The counties projected to gain the 

most tree canopy cover are Val Verde County, TX (gaining 

15 percent in RPA A2), and Lincoln County, WA (gaining 9.2 

percent in RPA A1B and 8.1 percent in RPA B2). The increase 

in tree cover in these counties was mostly because of projected 

increases in developed land in Lincoln County and agricultural 

land in Val Verde County.

Because of expanding urbanization at the expense of agricultural and forested land uses, the tree canopy cover decreased 

in the more forested counties of the Pacific Northwest and east 

of the Mississippi, whereas the canopy cover increased in the 

agricultural, grassland, and desert counties of the Midwest 

and West. Tree canopy cover tends to be greater in the urban 

areas of grassland, desert, and agricultural counties than in 

rural areas, whereas tree canopy cover tends to be less in urban 

areas compared with rural areas in forested counties (Nowak 

and Greenfield, 2012a). Areas with projected increases of tree 

canopy cover (i.e., desert, grassland, and agricultural areas) are 

likely because of active human management associated with 

urbanization (e.g., tree planting). This projection of increased 

canopy cover is often dependent on sufficient water to sustain 

tree populations, and these increases may not occur if necessary 

resources, such as water for irrigation, become scarce.

Changes in Tree Canopy Cover and Changes in 

Climate

We summarized climate data from each of the GCMs (Coulson 

et al. 2010a, 2010b) into annual values from decadal averages, 

in this case, 2055 through 2064. The summarized precipitation 

and potential evapotranspiration values were used to calculate 

an annual aridity ratio, defined as precipitation divided by 

potential evapotranspiration. We used this ratio to determine 

which areas of the country would likely get drier because of 

projected climate change. The ratio was then multiplied by -1 

so that positive numbers indicate increased aridity.

In all nine climate projections, the climate for the majority of 

the conterminous United States is projected to be more arid in 

2060 as compared with 2000 conditions (1995 through 2004). 

Overall, the RPA A1B-MIROC3.2 results exhibited the maximum average increase (0.138) in the aridity ratio (figure 48) 

by 2060, whereas the RPA A2-CSIRO-Mk3.5 scenario had the 

minimum average increase (0.038) in aridity (figure 49). The 

scenario with median change (0.077) in average aridity index 

values was RPA B2-CGCM2 (figure 50). The most pronounced 

increases in aridity tend to be in the Pacific Northwest and east 

of the Mississippi River.

Difference in aridity ratio 2000–2060 for RPA A1B-MIROC3.2 

+ 0.58 to + 0.25

+ 0.24 to + 0.10

+ 0.09 to + 0.05

+ 0.04 to 0.00

Figure 48. Change in aridity ratio, 2000–2060, for RPA A1BMIROC3.2 (maximum average increase in aridity).

Difference in aridity ratio 2000–2060 for RPA A2-CSIRO-Mk3.5

+ 0.67 to + 0.25

+ 0.24 to + 0.10

+ 0.09 to + 0.05

+ 0.04 to 0.00

– 0.01 to – 0.05

– 0.06 to – 0.07

Figure 49. Change in aridity ratio, 2000–2060, for RPA A2-

CSIRO-Mk3.5 (minimum average increase in aridity).

Difference in aridity ratio 2000–2060 for RPA B2-CGCM2

+ 0.49 to + 0.25

+ 0.24 to + 0.10

+ 0.09 to + 0.05

+ 0.04 to 0.00

– 0.01 to – 0.03

Figure 50. Change in aridity ratio, 2000–2060, for RPA B2-

CGCM2 (median average increase in aridity).
62 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

We used the difference in tree canopy cover and difference in 

aridity values from 2000 to 2060 to develop a climate and tree 

canopy cover change index. This index was developed to determine areas that have relatively high amounts of environmental 

and/or climatic changes that are considered to be negative (i.e., 

tree loss and/or increased aridity). Cover and aridity change 

values were standardized based on the greatest change observed 

in the model (i.e., all values were divided by the maximum 

absolute value of change; this value was always negative and 

Climate and canopy change index 2000–2060 for RPA A1B-MIROC3.2 

– 1.00 to – 0.25

– 0.24 to – 0.10

– 0.09 to – 0.05

–0.04 to 0.00

+ 0.01 to + 0.05

+ 0.06 to + 0.09

Figure 51. Climate and canopy change index for RPA A1B-MIROC 

3.2 in 2060 (maximum average increase in aridity). Negative 

index indicates greatest combined loss of tree canopy cover and

increased aridity.

Climate and canopy change index 2000–2060 for RPA A2-CSIRO-Mk3.5

– 1.00 to – 0.25

– 0.24 to – 0.10

– 0.09 to – 0.05

– 0.04 to 0.00

+ 0.01 to + 0.05

+ 0.06 to + 0.28

Figure 52. Climate and canopy change index for RPA A2-CSIROMk3.5 in 2060 (minimum average increase in aridity). Negative 

index indicates greatest combined loss of tree canopy cover and 

increased aridity.

Climate and canopy change index 2000–2060 for RPA B2-CGCM2

– 1.00 to – 0.25

– 0.24 to – 0.10

– 0.09 to – 0.05

– 0.04 to 0.00

+ 0.01 to + 0.05

+ 0.06 to + 0.28

Figure 53. Climate and canopy change index for RPA B2-CGCM2 

in 2060 (median average increase in aridity). Negative index 

indicates greatest combined loss of tree canopy cover and 

increased aridity.

thus the index ranges from -1 to some positive value that is less 

than 1). Counties with values approaching -1 indicate areas 

with the greatest changes in terms of the combination of both 

getting more arid and losing tree canopy cover. Counties with 

positive values indicate areas with the greatest changes in terms 

of decreasing aridity and/or increased tree canopy cover.

According to the index results, most counties will lose tree cover 

and become more arid, but some counties will gain tree cover 

and/or become less arid. With a few exceptions, the majority 

of areas with increased aridity and tree cover loss are in the 

Pacific Northwest and various counties in the East (figures 51 

through 53). Various parts of the Great Plains and Intermountain 

Subregions and the desert Southwest are projected to become 

less arid and/or have increased tree canopy cover.

Conclusions

Future development and climate change will have significant 

and varying effects on tree cover in the United States, with 

most areas losing tree cover and becoming more arid by 2060. 

The increases in canopy cover are often because of projected 

changes from sparsely treed land uses to developed land uses, 

which tend to bring in trees and increase tree cover. Thus, the 

tree and forest landscapes of the United States are likely to 

undergo significant changes in the coming century. Managers 

and planners need to understand these changes to help sustain 

healthy and functioning landscapes to meet the needs of changing society.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 63

Chapter 8. Forest Products

Forest products provide benefits to consumers, jobs in the forest industry, and timber revenues to forest owners. 

Timber harvest revenues and related forest management also 

help sustain forest ecosystem conditions and forest ecosystem 

services discussed elsewhere in this report. This chapter 

describes recent trends in U.S. forest product consumption, 

production, and net trade, and also discusses economic projections of production, consumption, net trade, timber harvest 

levels, and timber prices, as influenced by global scenarios 

regarding future economic growth, population, and biomass 

energy demand.

Resource Highlights

v The forest products sector was hard hit by the recession.

v Timber resources will continue to be adequate to meet demands unless there

are very large increases in wood energy demand.

v Forest product futures are tied to domestic and global wood energy demands.

The volume of roundwood needed to make the wood and paper 

products consumed in the United States (including product 

imports) was growing at roughly the rate of population growth 

until recently (Skog et al. 2012). The roundwood equivalent 

of wood and paper product consumption per capita was fairly 

stable in the United States from the mid-1960s to 2005, but 

consumption has declined since the 2005 downturn in new 

housing construction (a major end use for wood products) and 

the subsequent economic recession (figure 54).

The net import share of U.S. wood and paper product consumption peaked at more than 20 percent in the past decade, but 

declined with the contraction in housing construction and 

weakening of the U.S. dollar in recent years (figure 55). The 

United States has been a net exporter of hardwood lumber and 

paperboard for years, and recently became a net exporter of 

wood pulp and total paper and paperboard. Although still a 

net importer of other major products such as softwood lumber, 

imports of those products have declined.

The estimated roundwood equivalent of annual forest product 

production (excluding roundwood imports) provides an estimate of annual timber harvest (Howard in prep.). U.S. timber 

harvest increased from 1965 to the late 1980s, peaked in 1988, 

and has declined since then (figure 56). U.S. timber harvest 

declined by just more than one-third (34 percent) from 1988 to 

2010, based on roundwood equivalent estimates, with most of 

that decline occurring since 2005. In 2009, 89 percent of the 

timber harvest was industrial roundwood used for lumber, panels, paper, other industrial products, or export, and 11 percent 

was roundwood fuelwood. More than 40 percent of total wood 

harvest is used ultimately for energy, however, because large 

volumes of industrial wood and bark-mill residues and pulp 

byproducts are used for energy.

The future market outlook for forest products was projected 

from 2020 to 2060 for four Resources Planning Act (RPA) 

scenarios using the U.S. Forest Products Module/Global Forest 

Figure 54. Industrial roundwood equivalent of U.S. wood and 

paper product consumption, 1965–2010.

2.5

2.0

1.5

1.0

0.5

0.0

Cubic meters per capita

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Hardwood industrial roundwood Softwood industrial roundwood

Year

Source: Howard in prep.
64 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Products Model (USFPM/GFPM) market modeling system 

(Ince et al. 2011). Key elements in all RPA scenarios are the 

projected future levels of global wood energy consumption, 

which for three of the RPA scenarios were based on interpretation of Intergovernmental Panel on Climate Change (IPCC) 

global biomass energy projections. The varying future levels of 

wood energy consumption have significant effects on projected 

wood raw material markets, global forest product trade, and 

U.S. forest product production. The assumed fourfold expansion of U.S. wood energy consumption by 2060 in the lowest 

of the three IPCC scenarios (B2) is roughly consistent with the 

projected doubling of U.S. biomass energy production by 2030 

in the 2010 Annual Energy Outlook reference case (USDOE 

EIA 2010), whereas wood energy consumption is higher in 

the other IPCC-based scenarios and generally much higher 

than historical levels of wood energy consumption. Therefore, 

projections were also made for the RPA historical fuelwood 

(HFW) scenario.9

 The HFW scenario has all of the same global 

economic growth assumptions as the RPA A1B scenario, but 

it projects much less future expansion in U.S. and global wood 

energy consumption, as determined by historical fuelwood consumption relationships to gross domestic product (GDP) rather 

than the IPCC biomass energy projections. This scenario could 

be regarded as a scenario in which policies or technologies do 

not emerge that enhance the role of wood in the production of 

energy, or in which alternative energy resources such as natural 

gas become more plentiful than projected by IPCC, reducing 

future expansion of wood energy.

Global Outlook

The GFPM provided the global framework and context for the 

RPA analysis of U.S. forest product and timber market trends. 

This section highlights the use of the GFPM and some specific 

global modeling results for the RPA scenarios.

Within the USFPM/GFPM framework, the GFPM was used to 

project global market trends for forest products, fuelwood, and 

industrial roundwood in all foreign countries for each of the 

RPA scenarios. The results indicated that, on average, foreign 

fuelwood prices would increase under all RPA scenarios, leading to increased global competition for industrial roundwood 

depending on the level of projected global fuelwood consumption. The market effect is to increase the average global industrial roundwood price, particularly in RPA scenarios with high 

global fuelwood consumption, and in that case the projected 

global average fuelwood price converges with the average 

industrial roundwood price as early as 2030.

Figure 57 shows the USFPM/GFPM projections of average 

foreign real prices of fuelwood and industrial roundwood for 

the RPA scenarios. The average foreign industrial roundwood 

price is projected by 2060 to be higher than the 2006 average in 

both the RPA A1B and B2 scenarios, which feature the highest 

levels of global fuelwood consumption. The average foreign 

industrial roundwood price remains below the 2006 price average throughout the projection period in the RPA A2 and HFW 

scenarios, however, mainly because those scenarios feature less 

projected expansion in global fuelwood consumption and less 

competition for industrial roundwood from wood energy demands.10

Figure 55. Net import share of consumption in roundwood 

equivalents for wood and paper products (percent) and tradeweighted U.S. dollar index, 1965–2010. 

Figure 56. Roundwood equivalent of U.S. forest product output, 

1965–2010. 

0.30% 140

130

120

110

100

90

80

0.25%

0.20%

0.15%

0.10%

0.05%

0.00%

600

500

400

300

200

100

0

U.S. dollar index

Percent

Cubic meters (millions)

Net imports as a share of consumption

Real trade weighted U.S. dollar exchange rate

Roundwood and chips net export

Fuelwood

Other industrial products

Plywood and veneer

Pulpwood-based products

Lumber

Year

Year

Sources: Howard in prep.; Federal Reserve Source: Howard in prep.

1965

1965

1970

1970

1975

1975

1980

1980

1985

1985

1990

1990

1995

1995

2000

2000

2005

2005

2010

2010

9

 The HFW scenario is similar to the “A1B Low Fuelwood” scenario described in Ince et al. (2011), except the HFW uses the smaller U.S. timber supply 

shifts of the RPA A2 scenario.

10 Price projections presented here are not identical to those reported by Buongiorno et al. (2012) because the analysis by Buongiorno et al. used the 

GFPM without USFPM and used different assumptions regarding global roundwood price elasticities, different projections of global fuelwood demands for 

the IPCC-based scenarios, and different specifications of demands for U.S. forest products.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 65

Global expansion in fuelwood consumption leads to a projected 

declining trend in total forest growing stock inventory of 

foreign countries by 2060 in the RPA A1B scenario, although 

forest inventory is projected to continue increasing in the other 

RPA scenarios with less expansion of fuelwood consumption. 

Figure 58 shows projections of annual foreign fuelwood consumption and forest growing stock inventory. In the RPA A1B 

scenario, wood use for energy leads to a leveling out of the total 

forest inventory of foreign countries by around 2050, followed 

by a declining trend to 2060. The projected forest inventory 

trends are also influenced to a lesser extent by projected trends 

in industrial roundwood consumption, which is highest in the 

RPA A1B and HFW scenarios (with highest global GDP growth).

Under the RPA A1B scenario, projected global consumption of 

roundwood (industrial roundwood and fuelwood) reaches 9.1 

billion cubic meters annually by 2060, which is greater than 

the annual growth potential of world forests under the forest 

growth assumptions of the GFPM, hence, forest growing stock 

inventory is projected to decline by then. Total projected global 

roundwood consumption by 2060 is lower in the other RPA 

scenarios—6.2 billion cubic meters per year in the RPA B2 

scenario, 5.2 billion in the RPA A2 scenario, and 3.9 billion in 

the RPA HFW scenario (not much higher than the 3.5 billion 

consumed globally in 2006). In all four RPA scenarios, most 

projected growth in roundwood consumption is attributable to 

wood energy, with global roundwood consumption in industrial 

wood products remaining generally in the range of 1.2 to 1.8 

billion cubic meters per year throughout the projection period 

across all RPA scenarios.

Figure 59 illustrates the corresponding projections of total 

global roundwood use for energy and for industrial wood 

products across the four RPA scenarios. Global demand for 

industrial wood products is highest in the RPA A1B and HFW 

scenarios because those scenarios share the highest global GDP 

growth among RPA scenarios. Hence those RPA scenarios 

feature the highest consumption of roundwood for industrial 

wood products, but they are at opposite extremes in terms of 

projected global roundwood use for energy. In the RPA A1B 

scenario, global fuelwood demands are driven by IPCC biomass energy projections, whereas fuelwood demands are driven 

by more modest historical relationships to GDP growth in the 

RPA HFW scenario. The RPA A2 and B2 scenarios have lower 

projected roundwood use in industrial wood products because 

of lower forest product demand with lower GDP growth. 

Whereas the RPA B2 scenario has the lowest GDP growth and 

lowest consumption of roundwood in industrial wood products, 

the RPA A2 scenario has less global use of roundwood for 

energy than RPA B2. These variations in wood use and global 

markets are important in the RPA analysis because they influence the forest product production and trade outlook for U.S. 

producers, as explained subsequently in more detail.

Finally, growth in consumption of roundwood for industrial 

forest products is projected to be relatively modest in spite 

of worldwide population and income growth. This modest 

growth is partly because the GFPM (and USFPM) assume 

material productivity gains in forest products (higher future 

product recovery per unit of roundwood input and increased 

paper recycling, for example). It is also partly a reflection 

Figure 57. Projected average foreign fuelwood (left) and industrial roundwood (right) prices by RPA scenario, 2020–2060, relative to 

2006 average prices.

250 250

200 200

150 150

100 100

50 50

0 0 Dollars per cubic meter (2006 dollars)

Dollars per cubic meter (2006 dollars)

RPA A1B RPA A2 RPA B2 RPA HFW 2006 base price

Year Year

Average foreign fuelwood price Average foreign industrial roundwood price

2000 2010 2020 2030 2040 2050 2060 2000 2010 2020 2030 2040 2050 2060
66 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

of limited projected growth in worldwide average per capita 

forest product consumption in the decades ahead (apart from 

the expansive wood energy consumption that varies by RPA 

scenario).

The modest projected increase in global forest product 

consumption results in only small changes in the real prices 

of forest products such as wood-based panels and pulp and 

paper products, although the large variation in roundwood 

price across RPA scenarios causes wider variation in projected 

lumber prices. The fairly moderate global trends mask crosscountry variations: all RPA scenarios project the consumption 

of all wood products to grow more rapidly in Asia, in large part 

because of the fast economic growth of China and India. As 

a result, Asia is projected to be a large importer of industrial 

roundwood, and exports are largely supplied by South America 

(Brazil) and Europe (the Russian Federation). Asia is also 

projected to be the largest and fastest growing importer of paper 

and paperboard, and exports of paper and paperboard are supplied 

primarily from Europe, but also from North America.

Figure 58. Projected foreign fuelwood consumption (left) and forest growing stock inventory (right), by RPA scenario, 2020–2060, 

relative to 2006 levels.

Figure 59. Projected global roundwood use for energy (left) and for industrial wood products (right), by RPA scenario, 2020–2060, 

relative to 2006 levels.

8,000

8,000

550,000

525,000

500,000

475,000

450,000

425,000

400,000

375,000

2,500

2,000

1,500

1,000

500

0

7,000

7,000

6,000

6,000

5,000

5,000

4,000

4,000

3,000

3,000

2,000

2,000

1,000

1,000

0

0

Cubic meter (millions) Cubic meter (millions)Cubic meter (millions)

Cubic meter (millions)

RPA A1B RPA A2 RPA B2 RPA A1B RPA A2 RPA B2

RPA HFW 2006 Consumption RPA HFW 2006 Inventory

Year

Year

Year

Year

Foreign fuelwood consumption

Global roundwood use for energy

Foreign forest growing stock inventory

Global roundwood use for industrial wood products

2000

2000

2000

2000

2010

2010

2010

2010

2020

2020

2020

2020

2030

2030

2030

2030

2040

2040

2040

2040

2050

2050

2050

2050

2060

2060

2060

2060

RPA A1B RPA A2 RPA B2 RPA HFW 2006 consumption
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 67

Wood Energy Trends

Wood energy includes roundwood fuelwood harvest and wood 

and bark fuel residues generated in the production of forest 

products. Other wood byproducts are also used for energy, such 

as combustible black liquor generated at kraft pulp mills. As 

such, energy is a leading use of wood in the United States, and 

the largest use of wood globally. In addition, there is competition for the cleaner wood residues (wood chips or particles) as 

raw material in wood pulp or wood panel production (in particleboard or fiberboard). Excluding black liquor (a byproduct 

of pulpwood), roundwood fuelwood harvest provided roughly 

40 percent of wood fuel feedstock in the United States in 2006, 

whereas wood and bark mill residues accounted for about 60 

percent (Smith et al. 2009).

Residential fuelwood is used mainly for heating. Residential 

wood energy use is driven primarily by the price of alternative 

heating fuels. The wood pellet industry has expanded in recent 

years, with wider use of more efficient wood pellet furnaces, 

but pellets still account for a relatively small fraction of total 

wood energy, less than 10 percent or so (Spelter and Toth 

2009). Wood pellet consumption in the United States has been 

driven primarily by use in residential heating. Most pellets 

are made from wood residues, but the use of roundwood has 

been increasing as sawmill production has declined (yielding 

less wood residue). Both domestic and foreign pellet demand 

have increased. Wood pellet exports to Europe have increased 

sharply since 2006 (Chudy 2011).

Industrial wood energy consumption is primarily driven by 

the need for steam energy and power generation in pulp and 

paper production, with a secondary driver being heat energy 

needs at lumber mills for kiln drying. In the United States, most 

industrial wood energy is consumed at pulp and paper mills and 

other wood product mills. Industrial use of wood and bark for 

energy at pulp and lumber mills is facilitated by ready access to 

fuel residues for energy.

Use of wood for energy at electric power plants, including 

cofiring with coal, has also expanded at some locations, but 

use of wood depends on relative prices of fuel feedstocks and 

incentives or policies that support power production from 

biomass such as State renewable portfolio standards.

Technology for economical conversion of wood biomass to 

liquid biofuels has yet to be demonstrated, but production 

pathways are being explored, including conversion of wood 

cellulose and hemicelluloses to sugars, fermentation to ethanol 

or other fuels, and thermal conversion pathways such as gasification and conversion of syngas to alkane fuels via the FischerTropsch process. Based on current prices, production of wood 

pulp still offers much higher revenue and value added for 

pulpwood than does production of wood-based biofuels such 

as cellulosic ethanol. Higher future market prices for ethanol 

or other biofuels or improved production cost efficiency could 

increase the competitiveness of biofuel production from wood. 

Also, joint production of wood pulp and biofuels at integrated 

forest product biorefineries is a potentially more lucrative opportunity that is being explored by the forest industry.

Wood Energy Projections

Wood energy assumptions for three of the RPA scenarios 

were based on global energy projections developed previously 

for IPCC (Nakicenovic and Swart 2000). The IPCC outlook 

reflects projected continued expansion in overall global energy 

consumption, with peaking of global petroleum (oil) production and consequent expansion in alternative forms of energy 

production, including biomass energy (figure 60). In all three 

Figure 60. Global primary energy production for IPCC-based RPA scenarios.

1,600 1,600 1,600

1,400 1,400 1,400

1,200 1,200 1,200

1,000 1,000 1,000

800 800 800

600 600 600

400 400 400

200 200 200

0 0 0

ExajoulesExajoulesExajoules

RPA A1B RPA A2 RPA B2

Other renewables Biomass Nuclear Gas Oil Coal

Year Year Year

1990 2000 2010 2020 2030 2040 2050 2060 1990 2000 2010 2020 2030 2040 2050 2060 1990 2000 2010 2020 2030 2040 2050 2060

IPCC = Intergovernmental Panel on Climate Change.

Source: Nakicenovic and Swart 2000
68 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

IPCC-based scenarios, global production of energy from oil 

peaks during the decade from 2020 to 2030, followed by 

expansion in other categories of energy production, primarily 

natural gas but also biomass, other renewable energy, and 

nuclear energy (and, in the RPA A2 scenario, coal energy). 

Biomass energy accounts for an expanding fraction of global 

energy production in all three scenarios, but the magnitude 

of expansion varies by scenario. In all three IPCC-based scenarios, nonforest biomass (from nonforest energy plantations 

and cropland residues) was estimated to account for much more 

than one-half of the projected biomass energy production by 

2060, but global wood energy production also expands significantly, particularly in the high-growth RPA A1B scenario.

Projected rates of expansion for wood energy consumption in 

the United States are generally higher than the global rate of 

expansion in the IPCC-based scenarios. The distribution among 

countries of projected wood energy consumption was linked in 

the USFPM/GFPM model to the projected future distribution 

of GDP among countries. In all RPA scenarios, the United 

States retains a relatively large share of global GDP. Projected 

fuelwood consumption in foreign countries still remains much 

higher in aggregate than U.S. wood energy consumption in all 

RPA scenarios, however, because current U.S. fuelwood consumption is only a small share (less than 3 percent) of global 

fuelwood consumption.

Figure 61 shows historical U.S. wood fuel feedstock consumption and future consumption levels for all four RPA scenarios. 

The range of projected expansion in U.S. wood fuel feedstock 

consumption from 2006 to 2060 is from less than 2-fold in the 

RPA HFW scenario to about 4-fold in the RPA B2 scenario, 

9-fold in the RPA A2 scenario, and 16-fold in the RPA A1B 

scenario. In three of the RPA scenarios (A1B, A2, and B2), 

U.S. and global wood energy demands are driven by IPCC 

projections of biomass energy production (after deducting 

estimated biomass energy from nonforest energy plantations 

and cropland residues), whereas wood energy demands in the 

RPA HFW scenario are driven by historical econometric relationships of fuelwood demand to GDP growth in each country 

(using the RPA A1B assumptions regarding population and 

economic growth, along with more modest RPA A2 assumptions regarding expansion in U.S. timber supply).

In recent decades, most U.S. wood fuel feedstock production 

has consisted of conventional fuelwood harvest and fuel 

residues (mill residue byproducts used as fuel, such as bark and 

other wood waste). More costly alternate sources of wood fuel 

feedstock (harvest residues and pulpwood) become important, 

however, in RPA scenarios with large increases in wood energy 

consumption, such as the RPA A1B scenario. Figure 62 shows 

projected U.S. wood fuel feedstock production by source 

for the RPA A1B scenario, which has the largest increase in 

U.S. wood fuel feedstock consumption among RPA scenarios 

(nearly 16-fold by 2060). The projections are based on relative 

feedstock costs, as determined by the USFPM/GFPM economic 

model. At high consumption levels, the more costly alternate 

sources of wood energy become dominant, including harvest 

residues, hardwood and softwood pulpwood, and mill fiber 

residues that would conventionally be used to make wood pulp, 

oriented strand board (OSB), and particleboard.

Compared with the RPA A1B scenario, the RPA A2 scenario 

features somewhat less expansion in U.S. wood fuel feedstock 

consumption (ninefold by 2060), and thus lower production 

of wood fuel feedstocks, as shown in figure 63. There is still 

a large projected expansion of pulpwood use in the RPA A2 

scenario, however. In both the RPA A1B and A2 scenarios, the 

expansion of softwood pulpwood use for energy is facilitated 

by projected expansion of pine plantation area in the South 

Region coupled with declining regional wood pulp production.

Figure 61. Annual U.S. wood fuel feedstock consumption, 

1970–2010, and projections, by RPA scenario, 2020–2060.

2,000

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011

Figure 62. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, by feedstock 

source, RPA A1B scenario, 2020–2060. 

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Harvest residue

Mill fiber residue

Hardwood pulpwood

Sortwood pulpwood

Fuel residue

Fuelwood harvest

Historical production

Year

Source: Ince et al. 2011

RPA A1B
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 69

Projections of wood fuel feedstock production for the RPA B2 

and RPA HFW scenarios are generally much lower, as shown 

in figures 64 and 65, where expansion of wood fuel feedstock 

consists mainly of harvest residues. According to the USFPM/

GFPM economic model, higher levels of wood energy consumption would result in the use of more costly industrial roundwood 

feedstocks, such as pulpwood roundwood, in the RPA A1B and 

A2 scenarios, but not in the RPA B2 or HFW scenarios.

Timber Harvest and Price Trends

Figure 66 shows the historical trend in annual U.S. timber harvest 

volume from 1970 to 2010 (based on roundwood equivalent 

estimates) and market equilibrium projections of annual U.S. 

timber harvest volumes from 2020 to 2060 for the four RPA scenarios. Projections of U.S. timber harvest generally depart from 

historical timber trends of recent decades (figure 66), particularly 

Figure 65. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, by feedstock 

source, RPA HFW scenario, 2020–2060.

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Harvest residue

Mill fiber residue

Hardwood pulpwood

Sortwood pulpwood

Fuel residue

Fuelwood harvest

Historical production

Year

Source: Ince et al. 2011

Figure 64. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, by feedstock 

source, RPA B2 scenario, 2020–2060.

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Harvest residue

Mill fiber residue

Hardwood pulpwood

Sortwood pulpwood

Fuel residue

Fuelwood harvest

Historical production

Year

Figure 63. Annual U.S. wood fuel feedstock production, 

1970–2010, and projection of production, by feedstock 

source, RPA A2 scenario, 2020–2060.

1,800

1,600

1,400

1,200

1,000

800

600

400

200

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

Harvest residue

Mill fiber residue

Hardwood pulpwood

Sortwood pulpwood

Fuel residue

Fuelwood harvest

Historical production

Year

Source: Ince et al. 2011

in the RPA scenarios that feature significant expansion in 

wood energy consumption and shifts in forest product trade. 

Total U.S. timber harvest has declined since the late 1980s, but 

projections for the RPA scenarios show a range of increasing 

timber harvest levels. The largest increase occurs in the RPA 

A1B scenario, followed by RPA A2 and B2, reflecting primarily the magnitudes of biomass energy output projected by IPCC 

for those scenarios. Even in the RPA B2 scenario, U.S. timber 

harvest is projected to reach levels much greater than the peak 

harvests of the 1980s. Only in the RPA HFW scenario that 

follows historical relationships for wood energy consumption 

does U.S. timber harvest remain within the range of historical 

levels during the projection period. Projected expansion in U.S. 

and global timber harvest levels might also be problematic 

under the current regulatory environment. With a tripling of 

U.S. timber harvest and elevated timber prices (as in the RPA 

A1B scenario by 2060), there would be more intensified timber 

Figure 66. U.S. timber harvest volumes, 1970–2010, and 

projections, by RPA scenario, 2020–2060 (excluding harvest 

residue volumes). 

1,400

1,200

1,000

800

600

400

200

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011

RPA HFW

RPA B2 RPA A2
70 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

management, and thus a projected expansion of more than 70 

percent in the area of pine plantations in the South Region in 

the RPA A1B scenario, but only around 20 percent in the RPA 

B2 scenario.

Wood energy demands are the main long-run driver of projected timber harvests in the RPA scenarios, and thus timber 

harvests are highest for RPA A1B and much lower for the RPA 

B2 and HFW scenarios. The difference in projected timber 

harvest between the RPA A1B and HFW scenarios is mainly a 

result of the wood energy demand assumptions, with the RPA 

A1B wood energy demands based on high global biomass energy projections from IPCC, and the RPA HFW demands based 

on much more conservative historical relationships of wood 

energy demand to GDP growth in each country. Projected U.S. 

timber harvest remains within the range of its recent historical 

levels in the RPA HFW scenario, but increases to higher levels 

in the other three scenarios.

Because of different levels of timber demand, the projected 

prices for delivered industrial roundwood vary widely across 

the RPA scenarios (figure 67). The future wood energy 

consumption levels of the IPCC scenarios were implemented 

in the USFPM/GFPM model by imposing fixed trajectories of 

minimum wood fuel feedstock consumption, as determined by 

IPCC biomass energy projections (after deducting estimated 

biomass supply from nonforest biomass plantations and 

cropland residues). With such inelastic shifts in wood energy 

demands ranging from a 4-fold expansion in the RPA B2 scenario, to 9-fold in RPA A2, to 16-fold in RPA A1B, the RPA 

scenarios resulted in a wide spectrum of projected equilibrium 

real prices for industrial roundwood, with the highest projected 

roundwood prices in the RPA A1B scenario. On the other 

hand, average U.S. real prices for industrial roundwood were 

projected to decline in the RPA B2 scenario and the RPA HFW 

scenario, in which U.S. and global wood energy demands were 

based on historical relationships to GDP growth. Declining 

U.S. roundwood price projections in the RPA B2 and HFW 

scenarios (figure 67) show that projected growth in U.S. timber 

supply is more than adequate to meet projected U.S. timber 

demands in those scenarios, in which logging residues supply 

most of the projected U.S. expansion in wood energy and 

there is only modest demand pressure on industrial roundwood 

markets. At the other extreme, high wood energy demands in 

the RPA A1B scenario result in heavy use of pulpwood for 

energy, and real U.S. industrial roundwood prices are projected 

to increase more than threefold.

Projected industrial roundwood prices in foreign countries 

increase much more on average than in the United States in 

the RPA A1B and B2 scenarios (figure 67), mainly because of 

high global wood energy consumption in those RPA scenarios. 

Thus, even though the RPA A1B scenario results in very high 

U.S. industrial roundwood prices, it also results in a reduction 

in the size of projected U.S. trade deficits for wood products 

because of even higher average foreign roundwood prices. 

By contrast, the U.S. trade deficit in wood products continues 

unabated into the future under the RPA A2 scenario, in which 

there is the least expansion in global fuelwood consumption. 

In general, the projections show that U.S. and global fuelwood 

demands and corresponding industrial roundwood price projections could imply some improvement in the U.S. trade position 

compared with historical experience. These results, however, 

reflect assumptions about U.S. and global wood energy demand 

trends that are unique to the RPA scenarios, and there are other 

Figure 67. Projected weighted average delivered industrial roundwood prices for United States (left) and for all foreign countries 

worldwide (right), by RPA scenario, 2020–2060, relative to 2006 prices.

250 250

200 200

150 150

100 100

50 50

0 0 Dollars per cubic meter (2006 dollars)

Dollars per cubic meter (2006 dollars)

Year Year

U.S. average industrial roundwood price Foreign average industrial roundwood price

2000 2010 2020 2030 2040 2050 2060 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2 RPA HFW 2006 base price
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 71

factors and trends that could influence future timber prices. Increased wood energy consumption will not necessarily result in 

trade gains for U.S. producers of forest products, but it could 

happen under certain assumptions (e.g., in RPA scenarios for 

which expansion of global demands for wood energy cause foreign 

roundwood prices to increase more than in the United States).

Differing levels of timber demand and industrial roundwood 

prices also result in differing projections of real timber stumpage prices. For example, average U.S. sawtimber stumpage 

prices remain relatively flat to declining in the RPA B2 and 

HFW scenarios, in which there is modest expansion in wood 

energy consumption, and in the RPA A2 scenario, there is 

only a slight increase in stumpage prices. By contrast, there 

is a threefold-to-fourfold increase in average U.S. sawtimber 

stumpage prices in the RPA A1B scenario (figure 68).

Figure 68. Projected average U.S. softwood (left) and hardwood (right) sawtimber stumpage prices, by RPA scenario, 2020–2060, 

relative to 2006 prices.

250 250

200 200

150 150

100 100

50 50

0 0 Dollars per cubic meter (2006 dollars)

Dollars per cubic meter (2006 dollars)

Year Year

U.S. softwood sawtimber weighted average price U.S. hardwood sawtimber weighted average price

2000 2010 2020 2030 2040 2050 2060 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2 RPA HFW 2006 base price

Solidwood Products Trends

Solidwood products include primarily lumber and wood panel 

products. They are used extensively to meet needs in construction, 

manufacturing, and shipping segments of the U.S. economy. 

U.S. solidwood product consumption is closely linked to construction activity because of the use of lumber and wood panels 

in housing and commercial construction and the related use 

of wood in furnishing, flooring, and cabinets. Consequently, 

overall U.S. solidwood product consumption levels have tended 

to follow new housing starts, a leading indicator of construction 

activity, and consumption notably declined with the decline in 

housing starts from 2005 to 2009. Also, between 1965 and 2008, 

the lumber volume share of solidwood product consumption 

Figure 69. Solidwood products used for new residential construction in the United States, by product category, selected 

years, 1962–2009. 

60

50

40

30

20

10

0

Cubic meters (millions)

1962 1970 1976 1986 1998 2005 2009

Lumber Structural panels Nonstructural panels

Source: McKeever 2011

declined from 83 to 70 percent, whereas structural panels increased from 9 to 17 percent, and nonstructural panels increased 

from 8 to 13 percent. These trends reflect the expanding use of 

composite and engineered wood products in the U.S. market.

Solidwood product consumption in new residential construction was sharply reduced by the recent decline in housing 

construction. According to the U.S. Census Bureau, total new 

privately owned housing units started (housing starts) dropped 

from 2.07 million in 2005 to fewer than 1 million in 2008, 

0.554 million in 2009, and 0.587 million in 2010. U.S. housing 

starts had previously not been less than 1 million since at least 

the 1950s. By 2009, this housing downturn had resulted in 

solidwood product consumption in new residential construction 

sinking to levels not seen for at least 50 years (figure 69). New 

housing construction dropped out of its traditional place as the 
72 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

largest single end use for solidwood products, but 60 percent of 

solidwood products were still consumed in general construction 

because of continued demand in residential repair and remodeling and in commercial construction uses. Thus, construction 

in general remains the principal market for lumber and wood 

panel products, although demand has shifted recently from new 

housing construction to residential repair and remodeling.

In addition to construction, a number of manufacturing industries, such as flooring, furniture, and cabinetry, make products 

entirely from wood. Many of these industries, however, have 

relocated to other countries in recent years, a situation that has 

particularly affected hardwood furniture and fixtures manufacturing. U.S. production and consumption of hardwood lumber 

and sawlogs have declined as a result of off-shoring of manufacturing, declines in housing demand, and the recent recession.

Solidwood Products Projections

RPA projections of U.S. lumber consumption (figure 70) are 

driven by projected future housing starts and real GDP growth 

(highest for RPA A1B). Housing construction is projected on 

the basis of demographic analysis to recover to trend levels by 

2020 (as shown by RPA projections of single-family housing 

starts in figure 71), and projected housing starts are greatest for 

the RPA A2 scenario, which projects the greatest population 

growth. The RPA B2 scenario has the lowest projection of U.S. 

lumber consumption because it has the fewest projected U.S. 

housing starts and the lowest assumed rate of growth in U.S. 

GDP. Projected housing starts and GDP growth in the RPA HFW 

scenario are the same as in RPA A1B, but the economic analysis 

shows that lumber consumption would be higher in the RPA 

HFW scenario because of less competition for industrial roundwood from wood energy and lower lumber prices (figure 72).

U.S. lumber prices, as projected by the USFPM/GFPM model 

(figure 72), are influenced by projected sawtimber price trends 

(figure 68), and thus projected lumber prices of the RPA A1B 

scenario are about $100 per cubic meter higher by 2060 than 

for the other RPA scenarios. Projected lumber prices are also 

influenced by future efficiency gains in lumber production that 

are programmed into the USFPM/GFPM model and by projected values of harvest residues and mill residues that are sold 

for energy. Thus, the weighted average real price of lumber in 

the United States (in constant 2006 dollars) was projected to 

gradually decline in all of the RPA scenarios until after 2040, 

when expansion of wood energy demands cause notable price 

increases in timber and consequently lumber prices, particularly 

in the RPA A1B and A2 scenarios. U.S. lumber demands are 

fairly inelastic with respect to change in price, however, and 

thus lumber consumption is not projected to decline in either 

the RPA A1B or A2 scenarios (figure 70).

Figure 70. Annual U.S. lumber consumption, 1970–2010, and 

projections, by RPA scenario, 2020–2060.

Figure 71. U.S. single-family housing starts, 1960–2010, and 

projections, by RPA scenario, 2020–2060.

Figure 72. Projected weighted average U.S. lumber price 

trends, by RPA scenario, 2020–2060, relative to 2006 price.

300

2.50

250

300

250

2.00

200

200

1.50

150

150

1.00

100

100

0.50

50

50

0

0.00

0

Cubic meters per year (millions) Housing starts per year (millions) Dollars per cubic meter (2006 dollars)

1970

1960 1970

1980

1980

1990

1990

2000

2000

2000

2010

2010

2010

2020

2020

2020

2030

2030

2030

2040

2040

2040

2050

2050

2050

2060

2060

2060

RPA A1B

RPA A1B

2006 Base price RPA A1B

RPA A2

RPA A2

RPA A2

RPA B2

RPA B2

RPA B2

RPA HFW Historical

Historical

Historical

Year

Year

Year

Source: Ince et al. 2011

Source: Ince et al. 2011

Source: Ince et al. 2011
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 73

RPA projections of U.S. lumber production (figure 73) are 

determined by projected lumber consumption plus projected 

net exports (figure 74). The RPA A1B scenario has the greatest 

projected U.S. lumber production because it has a high level of 

projected U.S. lumber consumption and the greatest U.S. net 

exports of lumber. U.S. lumber producers slightly improve their 

trade position in the RPA A1B scenario (although the United 

States still remains a net importer) because of high levels of 

global fuelwood demand and higher foreign industrial roundwood prices (figure 67). By contrast, the RPA A2 and HFW 

scenarios have the least projected global fuelwood demands and 

lowest projected foreign industrial roundwood prices (figure 

67). Thus, U.S. lumber producers do not substantially improve 

their trade positions in the RPA A2 or HFW scenarios, and 

lumber imports are high, resulting in the lowest projected levels 

of U.S. lumber production in RPA A2 and HFW scenarios, 

despite high levels of housing starts and high levels of lumber 

consumption in those scenarios. Clearly the effects of wood 

energy demand on global roundwood prices and lumber trade 

have an overriding influence on projected domestic lumber 

production, according to the global economic analysis of the 

USFPM/GFPM model. Also, as expected, lumber production 

and net trade projections of the RPA HFW scenario are most 

closely in line with the long-term historical trends before the 

recent housing downturn.

The next leading category of U.S. solidwood products is structural wood panels, including chiefly OSB and softwood plywood, 

both of which are used primarily in housing and other construction applications. The historical trends and projections for U.S. 

structural wood panel consumption follow a very similar pattern to those of lumber, because they are driven largely by the 

same factors that affect housing and other construction (figure 

75). Among structural panel products, the consumption share 

of OSB had risen to about 60 percent of the total U.S. market 

since it was first introduced in the late 1970s to 2010 (Adair 

2010). It is projected to occupy 80 percent of the market by 

2060, as softwood plywood is increasingly confined to specialty 

markets. OSB has gained market share largely because of lower 

costs, because OSB is made from pulpwood roundwood, whereas 

plywood is made from typically more expensive veneer logs or 

sawlogs.

Because OSB is made primarily from pulpwood roundwood, 

and Southern pine pulpwood is the leading timber raw material 

used for OSB production in the United States, the pattern of 

projected U.S. structural wood panel production (figure 76) is 

rather different from the lumber production pattern. Specifically, in the RPA HFW scenario, there is virtually no projected 

use of pulpwood for energy (figure 65) but there are still fairly 

abundant and expanding supplies of pulpwood, particularly 

Southern pine pulpwood. Thus, there is a large projected 

increase in U.S. OSB production that contributes to a large 

projected expansion in U.S. structural panel production in the 

RPA HFW scenario (figure 76). 

The projected U.S. net exports of structural panels (including 

mainly OSB) are also highest in the RPA HFW scenario 

Figure 73. Annual U.S. lumber production, 1970–2010, and 

projections, by RPA scenario, 2020–2060.

250

200

150

100

50

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011

Figure 74. Annual U.S. lumber net exports, 1970–2010, and 

projections, by RPA scenario, 2020–2060.

0

– 50

– 100

– 150

– 200

– 250

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011

Figure 75. Annual U.S. structural wood panel consumption, 

1970–2010, and projections, by RPA scenario, 2020–2060.

120

100

80

60

40

20

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011
74 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

(figure 77), because of relatively abundant U.S. pulpwood supplies in that scenario with little competition from wood energy. 

By contrast, in the RPA A1B and A2 scenarios, there is strong 

competition for pulpwood from wood energy that severely 

limits expansion of U.S. OSB production in those scenarios 

(figure 76), especially in the latter decades when pulpwood use 

for energy really expands in the RPA A1B and A2 scenarios 

(figures 62 and 63). Also, in the RPA A2 scenario, there is 

a reversion to the long-run historical trend of declining net 

exports, because U.S. producers gain no significant advantages 

in roundwood prices relative to foreign producers and because 

high housing demands draw in higher structural wood panel 

imports. Therefore, projected net exports of structural panels in 

the RPA A2 scenario are more in line with the declining longterm historical trend (before 2005) and much lower than in the 

other RPA scenarios (figure 77).

The results show that since structural panels have become 

largely dependent on pulpwood as raw material (via the advent 

of OSB technology since the 1970s), the U.S. structural wood 

panel industry has become vulnerable to potential disruptive 

effects from large-scale expansion in wood energy use (as in 

the RPA A1B or A2 scenarios). Without large-scale expansion 

in wood energy consumption, and assuming a recovery in U.S. 

housing construction, the U.S. structural panel industry (and 

particularly the OSB industry) is expected to experience significant growth in output (as in the RPA HFW scenario), however.

Pulp and Paper Trends

U.S. paper and paperboard consumption increased historically 

in line with U.S. GDP growth until 1999, when consumption peaked and subsequently declined. The decline reflects 

structural changes in paper and paperboard markets. The largest 

U.S. demands for paper and paperboard are for packaging and 

printing applications, which are linked to industrial production 

and advertising expenditures. Growth in U.S. industrial production since 2000 has fallen to a much lower level than in the 

second half of the 20th century, however, offset by a structural 

shift in manufacturing of consumer goods to overseas locations. 

Shifts in advertising expenditures from print media to electronic media have also negatively affected demands for graphic 

paper products (newsprint and other printing paper grades).

Declining demands have led to industry downsizing and 

consolidation, which have helped to improve the productivity 

and competitiveness of U.S. producers. A weaker dollar 

exchange value in recent years also helped improve the global 

cost competitiveness of U.S. producers. Thus, for the first time 

in decades, the United States became a net exporter of total 

paper and paperboard in 2009 and 2010, but U.S. paper and 

paperboard consumption and production have nevertheless 

gradually declined since 1999.

Declining trends in paper and paperboard production since 

the late 1990s, along with earlier increases in paper recycling, 

have had major effects on trends in U.S. wood pulp production. 

For most of the 20th century, U.S. wood pulp production 

was increasing, but production peaked in the mid-1990s. The 

utilization rate of recovered fiber in U.S. paper and paperboard 

(tons of recycled fiber used per ton of product output) has 

remained fairly level since the late 1990s, thus, more recent 

changes in U.S. wood pulp production were determined primarily by changes in U.S. paper and paperboard production and 

shifts in net trade. U.S. wood pulp production has been slightly 

declining since 2001, whereas there has been an increase in 

U.S. net exports of wood pulp. Pulpwood consumption at 

U.S. pulp mills has also been slightly declining during the last 

decade, and pulpwood use at OSB mills declined more rapidly 

with the drop in housing construction since 2005.

Figure 76. Annual U.S. structural panel production, 1970–2010, 

and projections, by RPA scenario, 2020–2060.

100

90

80

70

60

50

40

30

20

10

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011

Figure 77. Annual U.S. net exports of structural wood panel 

products, 1970–2010, and projections, by RPA scenario, 

2020–2060.

10

0

– 10

– 20

– 30

– 40

– 50

– 60

– 70

– 80

– 90

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 75

Real price trends indicate that pulpwood has become relatively 

more abundant in the United States since the late 1990s, a result 

of increasing supplies (continued timber growth and maturation of pulpwood plantations), a general declining trend in 

consumption, and efficiency gains in harvesting and conversion 

technology. Supply gains, however, have been partly offset by 

some recent shifts in nonwood costs of production, particularly 

increases in the price of diesel fuel used in timber harvesting. Nevertheless, between 1998 and 2008, the real price of 

delivered pulpwood in the United States dropped by 42 percent, 

according to the Bureau of Labor Statistics producer price 

index for pulpwood adjusted for inflation. Consolidation in the 

paper industry helped to avoid a similar collapse in real paper 

and paperboard prices.

Pulp and Paper Projections

Historical data and projections for total U.S. paper and paperboard consumption are shown in figure 78. Projections diverge 

modestly by RPA scenario, mainly because of varied GDP 

growth. GDP is the primary driver of paper and paperboard 

demands in the USFPM/GFPM model, so projected consumption is greatest in the RPA A1B and HFW scenarios, which feature the greatest U.S. GDP growth, and projected consumption 

is less in the RPA A2 and B2 scenarios that feature less GDP 

growth. Divergence in consumption, however, is narrowed 

by inelasticity of paper and paperboard demands with respect 

to price, and thus there is not much variation in projected 

consumption among the RPA scenarios, all showing a similar 

gradual decline in total consumption over time. U.S. consumption is projected to decline primarily in the newsprint and 

printing and writing paper grades, with consumption remaining 

fairly stable in packaging paper and paperboard grades and 

increasing for tissue and sanitary paper products.

There are, however, distinct differences in projected U.S. paper 

and paperboard production and net exports among the RPA 

scenarios (figures 79 and 80). According to the USFPM/GFPM 

model, the greatest projected U.S. production and net exports 

of paper and paperboard occur in the RPA HFW scenario, 

because there is virtually no competition for pulpwood from 

wood energy demands in that scenario. The RPA HFW is the 

only scenario in which U.S. paper and paperboard production is 

projected to expand in the long run, which is primarily a result 

of projected expansion in U.S. net exports of paper and paperboard. U.S. paper and paperboard production and net exports 

are projected to decline over the long run in the other three 

RPA scenarios. In terms of foreign trade, however, U.S. producers fare distinctly better in the RPA A1B and B2 scenarios, 

because high global fuelwood consumption causes relatively 

high foreign roundwood prices in those scenarios (figure 67). 

U.S. producers do not gain advantages over foreign producers 

Figure 78. Annual U.S. paper and paperboard consumption, 

1970–2010, and projections, by RPA scenario, 2020–2060.

100

90

80

70

60

50

40

30

20

10

0

Metric tons per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011

Figure 79. Annual U.S. paper and paperboard production, 

1970–2010, and projections, by RPA scenario, 2020–2060.

120

100

80

60

40

20

0

Metric tons per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Figure 80. Annual U.S. net exports of paper and paperboard, 

1970–2010, and projections, by RPA scenario, 2020–2060.

35

30

25

20

15

10

5

0

– 5

– 10

– 15

Metric tons per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011
76 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

in the RPA A2 scenario, in terms of roundwood costs. In that 

scenario, there is also fairly strong domestic competition for 

pulpwood from wood energy, so projected U.S. paper and 

paperboard production and net exports remain relatively low.

The RPA analysis indicates that projected U.S. consumption, 

production, and net trade in forest products are directly 

influenced by assumptions about future expansion in U.S. 

and global wood energy demands. Across RPA scenarios, 

projected effects of expansion in U.S. and global wood energy 

consumption are to dampen expected growth in forest product 

consumption (because of price effects on demands) and to 

provide, in some cases, enhanced global competitiveness and a 

movement toward lower trade deficits and, in some instances, 

trade surpluses of selected forest products. There is also some 

vulnerability to high wood energy demands for U.S. forest 

product producers, however, particularly those who depend 

on pulpwood as raw material, such as the OSB industry and 

producers of pulp, paper, and paperboard. Production and net 

exports of those products are depressed in RPA scenarios with 

competing demands from wood energy for pulpwood. With 

less competition for pulpwood from wood energy, however, 

there are prospects for U.S. expansion in output and net exports 

of pulpwood-based products (OSB, paper, and paperboard 

products), as in an RPA scenario with high economic growth 

but limited expansion in wood energy consumption, such as the 

RPA HFW scenario.

Timber Dynamics and Sustainability

Both hardwood and softwood timber harvest are projected to 

increase with higher levels of U.S. wood energy consumption 

(figures 81 and 82). In the RPA HFW scenario, however, 

with only a doubling in U.S. wood fuel feedstock consumption by 2060, the projected softwood and hardwood timber 

harvest trends are relatively flat and remain within the range 

of historical harvest levels. In the RPA B2 scenario, with 

fourfold expansion in U.S. wood fuel feedstock consumption, 

the projected U.S. softwood timber harvest begins to increase 

toward the end of the projection period, and hardwood timber 

harvest nearly doubles during the projection period. Hardwood 

has some advantages as a wood fuel feedstock because of 

higher density, and thus higher energy content per unit volume, 

although in the United States, softwoods have been more 

commonly grown in productive industrial timber plantations. 

With much larger increases in wood energy consumption in the 

RPA A1B scenario, softwood timber harvest more than doubles 

(with expansion in the area of pine plantations in the South 

Region), and hardwood timber harvest roughly triples (with 

expansion in the area of short-rotation woody crops such as 

hybrid poplars on agricultural land). In the RPA A1B scenario, 

most of the projected increase in hardwood and softwood 

timber harvest is attributable to expanded use of hardwood and 

softwood roundwood as fuel feedstock, and much of the expansion would derive from plantations (both forest plantations and 

woody crop plantations on agricultural land).

Among RPA regions, the South has accounted for the largest 

share of domestic timber harvest in recent decades, producing 

57 percent of U.S. timber harvest in 2006, for example (Smith 

et al. 2009). Unlike the U.S. trend, timber harvest in the South 

Region did not peak in the late 1980s, but in the mid-1990s, 

and then declined. In the RPA projections, the South Region 

continues to be the largest timber-producing region in the 

United States, accounting for at least one-half of total timber 

harvest throughout the projection period in all RPA scenarios. 

Just as the South Region retains the lead among RPA regions 

in overall timber production, it also has the largest share of 

projected wood fuel feedstock production in all RPA scenarios.

Figure 83 shows projected average U.S. stumpage prices of 

hardwood and softwood sawtimber and nonsawtimber for the 

RPA scenarios (weighted averages of all RPA regions). The 

Figure 81. Annual U.S. softwood timber harvest volumes, 

1970–2010, and projections, by RPA scenario, 2020–2060.

1,000

900

800

700

600

500

400

300

200

100

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011

Figure 82. Annual U.S. hardwood timber harvest volumes, 

1970–2010, and projections, by RPA scenario, 2020–2060.

1,000

900

800

700

600

500

400

300

200

100

0

Cubic meters per year (millions)

1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

RPA HFW Historical

Year

Source: Ince et al. 2011
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 77

Figure 83. Projected average U.S. stumpage prices of hardwood and softwood sawtimber and nonsawtimber, by RPA scenario, 

2020–2060, relative to 2006 prices.

250

250

250

250

200

200

200

200

150

150

150

150

100

100

100

100

50

50

50

50

0

0

0

0

Dollars per cubic meter (2006 dollars) Dollars per cubic meter (2006 dollars)Dollars per cubic meter (2006 dollars) Dollars per cubic meter (2006 dollars)

Year

Year

Year

Year

U.S. hardwood sawtimber weighted average

U.S. softwood sawtimber weighted average

U.S. hardwood nonsawtimber weighted average

U.S. softwood nonsawtimber weighted average

2000

2000

2000

2000

2010

2010

2010

2010

2020

2020

2020

2020

2030

2030

2030

2030

2040

2040

2040

2040

2050

2050

2050

2050

2060

2060

2060

2060

RPA A1B RPA A2 RPA B2 RPA HFW 2006 base price Source: Ince et al. 2011

projected real price trends for timber stumpage are relatively 

flat to declining in the RPA B2 and HFW scenarios. This flat 

growth indicates that projected timber supplies are expanding 

sufficiently to accommodate at least the level of timber harvest 

projected in the RPA B2 and HFW scenarios. In all RPA scenarios, the overall U.S. consumption of industrial roundwood 

for forest products is projected to gradually decline (more or 

less in line with historical U.S. industrial timber harvest trends). 

These results support a key finding that real U.S. timber prices 

are not projected to increase significantly over the long run 

without substantial increases in wood energy consumption. 

Sawtimber stumpage prices are projected to increase only in 

the RPA A1B and A2 scenarios, with large expansions in U.S. 

wood energy consumption.

High levels of timber demand result in price increases for both 

sawtimber and nonsawtimber stumpage and, in the RPA A1B 

scenario, the nonsawtimber price eventually climbs higher 

than the sawtimber price by about 2040 and beyond. This 

outcome is because nonsawtimber has larger harvest residue 

and fuelwood components and higher average bark content 

than sawtimber, and with very high and inelastic fuel feedstock 

demands, the projected price of nonsawtimber can exceed the 

price of sawtimber. On the other hand, nonsawtimber prices 

are projected to increase much more modestly in the RPA A2 

scenario, whereas nonsawtimber price projections are relatively 

flat to declining in the RPA B2 and HFW scenarios (figure 83).

In general, real U.S. timber stumpage prices are not projected 

to increase without substantial increases in wood energy 
78 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

consumption (e.g., at least equal to or greater than the fourfold 

expansion of the RPA B2 scenario). If U.S. and global wood 

energy consumption follow historical relationships to GDP 

growth (as in the RPA HFW scenario), U.S. timber stumpage 

prices are projected to gradually decline in real terms during 

the projection period, despite assumed recovery in housing 

construction and relatively robust GDP growth. In essence, 

substantial increases in real U.S. timber prices are not expected 

to be sustained over the long run in the absence of significant 

and unforeseen structural changes in U.S. forest product 

demands that would substantially increase timber demand.

Projected increases in timber demand and timber prices under 

scenarios such as RPA A1B would result in projected structural 

changes in U.S. wood supply, including expansion in the 

area of pine plantations in the South Region and in supply of 

agricultural short-rotation woody crops (SRWC). Higher prices 

for woody biomass make plantation forestry and agricultural 

SRWC more economically feasible in latter decades of the 

projection period. In the RPA A1B scenario, planted pine area 

in the South Region is projected to expand by more than 70 

percent, from 39 million acres currently to 67 million acres by 

2060, and just more than 20 million acres of agricultural land 

are projected to be planted in agricultural SRWC nationwide 

by 2060. These structural changes in timber supply help to 

offset effects of expanded timber harvest on forest inventory, 

although timber inventory volume is nevertheless projected to 

gradually decline toward the end of the projection period in 

the RPA A1B scenario. The cumulative U.S. timber harvest in 

the RPA A1B scenario from 2010 to 2060 exceeds the timber 

harvest in the RPA HFW scenario by 66 percent, or 16.4 billion 

cubic meters (578 billion cubic feet). Despite this large difference in timber harvest, the projected U.S. timber growing stock 

inventory in the RPA A1B scenario is only about 90 billion 

cubic feet (about 9 percent) less by 2060 than in the RPA HFW 

scenario. If not for the gains in plantation area and timber productivity, the large cumulative timber harvests of the RPA A1B 

scenario would certainly lead to more substantial depletion in 

U.S. timber inventory. The lower projected timber demand and 

lower projected timber prices in the RPA B2 and HFW scenarios provide little incentive for any appreciable expansion of 

agricultural SRWC supply or expansion of pine plantation area 

in the South Region. Improvements in plantation productivity 

and yield could potentially expand the future role of plantation 

forestry or SRWC in any case.

In addition to structural change in timber supply, the RPA 

analysis indicates that relative volumes of wood consumed 

as wood products and wood energy could vary substantially 

depending on the future wood energy projections. This variation is illustrated by comparison of projected wood volumes 

in wood fuel feedstock and conventional forest products 

produced in the United States for the RPA A1B and HFW 

scenarios, which make the same assumptions regarding future 

GDP and population growth but have very different projected 

expansion of U.S. wood energy consumption. Figure 84 shows 

comparative wood volumes in forest products and wood fuel 

feedstock for the RPA A1B and HFW scenarios. The wood 

volumes in forest products include the volume of solidwood 

products produced plus pulpwood and fiber residue volumes 

consumed in wood pulp, whereas the volumes in wood fuel 

feedstock include all roundwood and residues used to produce 

wood energy. The RPA A1B scenario has much higher wood 

fuel feedstock production in the United States (and also much 

higher global wood energy consumption) based on IPCC 

biomass energy projections, whereas RPA HFW wood energy 

demands are much lower, based on historical relationships of 

fuelwood consumption to GDP growth. The two scenarios have 

similar total volumes of wood use in forest products but different projected volumes of wood use by forest product category.

Two additional charts (figure 85) compare volumes of wood 

in conventional forest products produced in the United States 

by major product category for the RPA A1B and HFW 

scenarios. In the RPA A1B scenario, there is a more significant 

expansion of U.S. lumber output, boosted by relatively lower 

roundwood input costs compared with other countries and by 

strong demands for mill residues with very high global wood 

energy consumption. U.S. OSB (structural panels) production, 

Figure 84. Projected wood volume in wood fuel feedstock and 

forest products produced in the United States, RPA A1B and HFW 

scenarios, relative to 2006 levels.

2,000

1,500

1,000

500

0

Cubic meters per year (millions)

2000 2010 2020 2030 2040 2050 2060

RPA A1B forest products RPA HFW fuel feedstock

RPA A1B fuel feedstock

RPA HFW forest products 2006 fuel feedstock

2006 forest products

Year
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 79

however, is crimped in the RPA A1B scenario by competing 

domestic demands for pulpwood from wood energy. There is 

less expansion in U.S. lumber production in the RPA HFW 

scenario, because there is no gain in relative roundwood costs 

for U.S. versus foreign lumber producers. There is expansion 

of wood use in OSB in this scenario, however, and less of a 

decline in wood use in pulp production because of more abundant pulpwood supply and little competition for pulpwood from 

wood energy. In both scenarios, wood use for pulp production 

declines in the long run because of limited growth in U.S. paper 

and paperboard production and projected future gains in paper 

recycling.

Figure 85. Wood volumes in conventional forest products produced in the United States, 2006, and projections, RPA A1B (top) 

and HFW (bottom) scenarios, 2020–2060.

250

250

300

300

350

350

200

200

150

150

100

100

50

50

0

0

Cubic meters per year (millions) Cubic meters per year (millions)

Year

Year

2000

2000

2010

2010

2020

2020

Other industrial roundwood Wood used for pulp Nonstructural panels Structural panels Lumber

2030

2030

2040

2040

2050

2050

2060

2060

RPA HFW

RPA A1B
80 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Lastly, the RPA analysis used exogenous projections of global 

wood energy demands and did not analyze trends in other 

energy prices, but some comparative results can be computed 

for wood fuel pellets that are used for space heating in relation 

to the energy-equivalent price of heating oil. With the 16-fold 

expansion of U.S. wood energy consumption by 2060 in the 

RPA A1B scenario, the real price of wood fuel feedstock is 

projected to reach more than $150 per cubic meter, or several 

hundred dollars per dry ton (in constant 2006 dollars), which 

is about six times higher than current wood fuel feedstock 

prices. At that feedstock price, assuming nonwood production 

costs remain the same, the corresponding real price of wood 

fuel pellets in 2060 would be about $410 per ton, or about 

two-to-three times current wood fuel pellet prices that range 

from about $140 to $200 per ton (in 2006 dollars). At $410 per 

ton, the price of wood fuel pellets would be equivalent in gross 

energy value to a heating oil price of about $3.60 per gallon, 

or not much higher than recent price levels for heating oil (in 

2006 dollars). Wood fuel pellets currently sell at prices that 

are only about one-half of the price of heating oil in terms of 

gross energy value, however, and whether wood fuel pellets 

could sell for two-to-three times as much by 2060 is plausible if 

global oil production peaks within the next decade, as assumed 

in the IPCC A1B scenario.

In general, the RPA findings indicate that higher levels of 

wood energy demand would be associated with higher prices 

for wood fuel feedstock and wood-based biofuels, because of 

timber supply price responses to higher demand levels. Price 

projections suggest that the economic feasibility of using wood 

biofuels for energy might come into question if wood fuel 

feedstock consumption were to expand by 15-fold or more 

by 2060 (as in the RPA A1B scenario). At less than 10-fold 

expansion (as in the other RPA scenarios), wood-based biofuels 

would probably remain economical alternatives to conventional 

energy sources, especially if supplies of conventional energy 

sources such as oil are limited and energy prices increase in 

the future. Of course, further expansion in supplies of other 

substitute energy sources, such as other renewables, natural 

gas, or nuclear energy, could limit future demand or prices for 

wood energy.

Conclusions

Historically, the volume of industrial roundwood needed 

to make wood and paper products consumed in the United 

States (including product imports) grew at roughly the rate of 

population growth until recently, when per capita consumption 

declined with the downturn in housing construction. Net 

imports and timber prices have varied as influenced by cyclical 

trends such as housing construction, economic recessions, and 

the U.S. dollar exchange rate. Annual U.S. timber harvest volumes have gradually declined since the 1980s as both industrial 

roundwood and fuelwood harvest declined, but harvest could 

increase in the future with expansion in wood energy consumption, particularly if global oil production peaks within the next 

decade or two, leading to expansion in consumption of biomass 

energy.

The range of alternative levels of wood energy consumption 

across the four RPA scenarios resulted in projected U.S. timber 

harvest levels and market trends that diverged in varying degrees from recent historical trends, with high levels of projected 

timber harvest and timber prices in the RPA scenario with the 

highest projected wood energy consumption.

The relatively flat-to-declining timber price projections in RPA 

scenarios with modest or historical wood energy demand trends 

indicate that timber supplies are increasing and that U.S. timber 

prices are not projected to increase significantly over the long 

run without substantial increases in wood energy consumption 

or other new timber demands. Without future growth in timber 

harvest revenues, there will likely be limited growth in financial 

resources to support forest management and help sustain forest 

ecosystem conditions and forest ecosystem services. Thus, 

enhancing the future market value of wood resources remains a 

challenge for future forest managers, forest product researchers, 

and biomass energy developers.

The future expansion rates for U.S. and global wood energy 

demands strongly influenced U.S. forest product net exports 

and related domestic production in the RPA analyses. The 

United States could gain competitive advantages in production 

of some paper and paperboard products while shrinking the 

trade deficits in other product categories if global roundwood 

prices exceed U.S. roundwood prices, even in an RPA scenario 

in which both the U.S. and global roundwood prices are 

increasing. On the other hand, U.S. output of pulpwood-based 

products, such as OSB or wood pulp, could be negatively 

affected if expanding wood energy demands result in expanded 

domestic consumption of pulpwood for energy. These analyses 

did not evaluate potentially offsetting developments such as 

changes in greenhouse gas emission policies (related to use of 

biomass for energy), other possible changes in environmental 

regulations, or the possible development of nonwood-based 

substitutes for forest products that might be driven by higher 

wood prices.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 81

Chapter 9. Urban and Community Forests

Urban and community forests are an essential component of America’s “green infrastructure,” and their 

benefits extend well beyond the cities and towns where they are 

located. These forests include all publicly and privately owned 

trees within an urban or community area, including individual 

trees along streets and in backyards, and stands of remnant 

forest (Nowak et al. 2001). Trees play particularly important 

roles in the functioning of cities and towns and significantly 

affect ecosystem services and human health in these areas. 

Trees improve air and water quality, reduce energy use if 

appropriately placed around buildings, cool air temperatures, 

reduce ultraviolet radiation, and provide many other environmental, economic, and social benefits (e.g., Kuo and Sullivan 

2001; Nowak and Dwyer 2007; Ulrich 1986; Westphal 2003; 

Wolf 2003). Costs associated with trees can be economic (e.g., 

planting and maintenance), social (e.g., obstructed views, litter, 

and storm debris), and environmental (e.g., pollen and volatile 

organic compound emissions) (Nowak and Dwyer 2007). In 

addition, there can be transaction costs associated with the 

necessary institutional arrangements (setting, communicating, 

and adapting policy) that aid urban and community forest 

management (Hardy and Koontz 2010; Ostrom 1990).

Resource Highlights

v Urban forest area will increase.

v Urban forests will become increasingly important for providing a range of 

ecosystem services to urban populations.

Urban and Community Land

Urban land is included in the more broadly defined “developed 

land” category in both the National Resources Inventory (NRI) 

data and the land use projections. For the purposes of the urban 

forest assessment, however, we rely on the U.S. Census Bureau 

data and definitions, which focus on densely populated areas. 

Both urban and community areas, which overlap, are included 

in the urban forest assessment. The census defines urban as all 

territory, population, and housing units located within urbanized areas or urban clusters, which are based on population 

density (USCB 2007).11 Community lands are places that have 

geopolitical boundaries (e.g., cities, towns, or unincorporated 

named places) that may include all, some, or no urban land 

within their boundaries. As seen in figure 86, there is urban 

land found outside of community boundaries and areas within 

communities that are not urban.

11 Urbanized area and urban cluster boundaries encompass densely settled territories, which are described by one of the following: (1) one or more block 

groups or census blocks with a population density of at least 1,000 people per square mile, (2) surrounding block groups and census blocks with a population density of 500 people per square mile, and (3) less densely settled blocks that form enclaves or indentations, or are used to connect discontinuous 

areas. More specifically, urbanized areas consist of territory of 50,000 or more people. Urban clusters, a concept new to the 2000 Census, consist of territory with at least 2,500 people but fewer than 50,000 people.

Community boundaries

Urban land

Figure 86. Urban and community land in Connecticut, 2000.

Source: U.S. Census Bureau 2007
82 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Urban land reveals the more heavily populated areas (population density-based definition) and community land indicates 

both urban and rural communities that are recognized by their 

geopolitical boundaries (political definition). Both definitions 

provide information related to human settlements and the forest 

resources within those settlements. The combined category 

of “urban or community” was created to understand forest 

attributes accumulated by the union of these two definitions.

Urban land in the conterminous United States increased from 

2.5 percent of total land area in 1990 to 3.1 percent in 2000, 

yet this small percentage of land contained 79 percent of the 

population, or more than 220 million people, in 2000 (USCB 

2001). In 2000, the State with the greatest percentage of urban 

population was California (94 percent) and the State with the 

least percentage was Vermont (38 percent). Nevada had the 

greatest percentage urban population growth (1990 to 2000) 

at 72.3 percent, and Maine had the greatest percentage urban 

population decrease at 6.4 percent.

The Southeast and Northeast Subregions exhibited the greatest 

increase in percentage of urban land between 1990 and 2000 

(table 12). The most urbanized areas of the United States are 

the Northeast (9.7 percent) and Southeast (7.5 percent). Between 1990 and 2000, most of the urban expansion across the 

United States occurred in forest (33.4 percent of the expansion) 

or agricultural (32.7 percent) land (Nowak et al. 2005).

Community land comprised 4.5 percent of the lower 48 States. 

New Jersey had the greatest proportion of both urban land (38 

percent) and community land (27 percent), Wyoming had the 

least urban land (0.2 percent), and Idaho had the least community land (0.7 percent). Between 1990 and 2000, urban area 

increased 17 percent and community land increased 23 percent 

(figures 87 and 88).

Table 12. Urban area and urban growth within the conterminous United States by RPA subregion, 1990–2000.

RPA subregiona

Percent urban Increase in

percent urban

Percent increase in

urban area Urban growth (km2

) 

2000 1990–2000

Northeast 9.7 1.5 18.8 8,120

Southeast 7.5 1.8 33.0 11,450

Pacific Southwest 5.0 0.7 17.0 2,984

North Central 4.2 0.7 19.0 7,905

South Central 2.8 0.5 23.2 8,412

Pacific Northwest 1.9 0.4 24.2 1,598

Intermountain 0.7 0.2 33.2 3,727

Great Plains 0.5 0.1 17.7 637

U.S. Total 3.1 0.6 23.0 44,833

a

 Alaska and Hawaii data are not included in their respective RPA subregions. Ohio is included in the North Central Region in this table instead of the Northeast Region. 

Source: Nowak et al. 2005

Difference of percent urban land 1990–2000

– 29.3 to 0%

+ 0.1 to + 5%

+ 5.1 to + 10%

+ 10.1 to + 63.3%

No urban land

Difference of percent community land 1990–2000

– 20 to 0%

+ 0.1 to + 5%

+ 5.1 to + 10%

+ 10.1 to + 78.4%

No community land

Figure 87. Change in percent urban land by county in the 

conterminous United States, 1990–2000.

Figure 88. Change in percent community land by county in the 

conterminous United States, 1990–2000.

Source: U.S. Census Bureau 2001
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 83

Urban and Community Tree Cover

Tree cover12 in urban areas (circa 2005) is estimated at 35 

percent, tree cover in communities at 34 percent, and tree 

cover in urban or community lands at 35 percent (Nowak and 

Greenfield, 2012a). In urban or community areas, percentage 

tree cover is highest in Connecticut (67 percent) and lowest in 

Nevada (10 percent). States with the highest proportion of their 

total tree cover occurring in urban and community areas are 

New Jersey (39 percent), Connecticut (36 percent), and Massachusetts (36 percent) (Nowak and Greenfield, 2012a).

Urban tree cover tends to be highest in urban areas in forested 

ecoregions, followed by grasslands and deserts. The percentage 

of tree cover in urban areas tends to decrease as population 

density increases and can vary within a city based on the 

distribution of land use types and ecoregion (e.g., vacant land 

in forested regions often supports tree cover, but vacant land 

in deserts is often devoid of trees) (Nowak et al. 1996, 2001). 

Understanding the factors most associated with urban tree cover 

can improve projections of future tree cover and inform policies 

to improve urban canopy cover.

Urban tree cover is on a slight decline nationally (circa 2002 to 

2009) with a loss rate equal to about 0.03 percent of urban land 

per year. In more major cities, the loss rate is higher, with 17 of 20 

cities analyzed exhibiting statistically significant declines in tree 

cover (circa 2004 to 2009), with an average annual loss in tree 

cover of 0.27 percent per year (Nowak and Greenfield 2012b). 

Tree canopy cover can serve as an indicator of the extent to which 

trees and forests are providing crucial ecosystem services to local 

residents. The percentage of tree cover in urban areas is typically 

greater in the Eastern United States (figure 89), and tree canopy 

cover per person is typically greatest in the Southeast and the New 

England States.

Urban Forest Ecosystem Services

Urban trees and forests provide several ecosystem services in 

the form of provisioning services such as food, water, timber, 

and fiber; regulating services that affect climate, floods, disease, 

wastes, and water quality; cultural services that provide recreational, aesthetic, and spiritual benefits; and supporting services 

such as soil formation, photosynthesis, and nutrient cycling 

(MEA 2005).

Enhancing urban forest cover generally increases these benefits, 

but can also potentially increase the costs and risks, such 

as fire risk, energy costs, and water use (especially if native 

vegetation is converted). In some cases, particularly in the 

arid West, water availability could limit increases in canopy 

cover. In other areas, impervious surfaces, mowing practices, 

public investments, water scarcity, or seed sources could limit 

increases in canopy. In addition, tree management and urban 

development policies can hinder or enhance the expansion of 

urban forest cover based on the limitations or incentives they 

establish, affecting individual and collective decisionmaking.

Trees in urban areas provide many benefits and values to society based on their current structure and functions. Urban trees 

are estimated to remove 783,000 tons of pollutants (ozone, 

particulate matter, nitrogen dioxide, sulfur dioxide, and carbon 

monoxide) annually (Nowak et al. 2006). These estimates are 

based on mid-1990s-to-early-2000s data and are currently being 

updated with more recent data. Recent carbon estimates based 

on 2000 Census data and circa 2005 urban tree cover data reveal 

that U.S. urban trees store about 704 million tons of carbon with 

a gross carbon sequestration rate of 28 million tons per year. 

Urban forests in the Northeast, Southeast, and South Central 

Subregions store and sequester the most carbon (table 13).

Urban Forest Health

Urban forests face a myriad of management challenges from a 

wide range of human-caused and natural disturbances. Urban 

forests are severely affected by numerous insects and diseases, 

many of them invasive species. Some, such as gypsy moth, 

emerald ash borer, Dutch elm disease, and Asian longhorned 

beetle, have caused significant tree mortality (USDA National 

Agricultural Library 2011). Endemic pests such as mountain 

pine beetle also cause severe damage to urban forests. Invasive 

plants can modify, and in some cases degrade, urban forests 

Percent urban tree 

canopy 2000

0 to 10%

10.1 to 25%

25.1 to 79.5%

No urban land

Figure 89. Percent tree cover in urban areas by county in the 

conterminous United States, 2000.

Source: 2001 National Land Cover Database

12 The amount or percentage of land area that, when viewed from above, is covered by tree canopies.
84 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

a

 Alaska and Hawaii data are not included in their respective RPA subregions. 

Source: Nowak et al. in prep.

by removing native plants and altering ecosystem structures. 

In addition, natural events such as ice storms and severe wind 

can damage urban forests and significantly alter urban forest 

structure and health.

Monitoring of urban forests will be vital to quantifying changing urban forest health and assessing risks that threaten future 

forest health. Various State assessments of urban forests reveal 

the magnitude and potential risks and health issues related to 

urban forests (Cumming et al. 2008). For example, in the urban 

forests of the State of Tennessee, the most common damages 

noted on trees were trunk bark inclusion (8.7 percent), vine in 

crowns (7.9 percent), dead or dying crowns (3.2 percent), and 

canker or decay (2.9 percent) (Nowak et al. 2011). Species with 

the highest percentage of crown dieback were black walnut 

(16.3 percent), sassafras (7.8 percent), and shagbark hickory 

(7.1 percent). Based on Tennessee’s urban forest composition, 

some of the greatest potential risks to this resource could come 

from Asian longhorned beetle, gypsy moth, and southern pine 

beetle (figure 90).

There are a myriad of additional risks to urban and community 

forests. Wildlife can cause substantial damage to trees, particularly in the wildland-urban interface. High population growth 

and development in these areas tend to increase fire risk and 

human-caused fire ignitions. Human development can alter forest lands and fragment existing forest stands. In both urban areas and at the wildland-urban interface, air pollution can reduce 

tree growth and weaken natural resistance to other threats such 

as pests and drought. In addition, climate change is expected 

to produce warmer air temperatures and change precipitation 

patterns, and result in more extreme weather events (IPCC 

2007b) that can alter urban forest structure and health. These 

potential natural and human-caused threats will directly affect 

urban forests, as well as interact with other threats, potentially 

creating cumulative and emergent risks yet to be understood.

Urban forests are dynamic systems, heavily influenced by 

human activities. Urban land area is projected to continue to 

increase in response to growing populations. Population growth 

and associated urban expansion will have a considerable effect 

on the wildland-urban interface because urban expansion is 

expected to occur as extensions to existing urban centers. Issues 

such as timber harvesting, fire protection, ecological functions, 

recreation uses, scenic views, wildlife, invasive species, and 

forest fragmentation become more contentious as urbanization 

increases (Nowak et al. 2010).

Conclusions

Urban forests are a significant resource nationally and are 

likely to increase in significance in the coming decades. Urban 

growth in the United States is going to have an increasingly 

important effect on forest management, environmental quality, 

and human well-being. As urbanization increases, so will the 

value of urban forests and surrounding rural forests in providing ecosystem services required by urban residents. Significant 

amounts of U.S. forest land are projected to be transformed 

by urbanization, particularly in the Northeastern and Southern 

United States.

Table 13. Estimated carbon storage and gross annual sequestration in trees on urban land by RPA subregion in the conterminous United 

States, circa 2005. 

RPA subregiona Carbon storage Gross sequestration

tons tons/acre tons/year tons/acre/year

Northeast 228,490,000 15.1 7,600,000 0.50

Southeast 164,600,000 14.6 7,920,000 0.70

South Central 131,540,000 12.0 5,930,000 0.54

North Central 93,680,000 9.9 3,020,000 0.32

Pacific Southwest 34,630,000 6.8 1,750,000 0.35

Pacific Northwest 24,150,000 12.1 790,000 0.39

Intermountain 18,560,000 5.0 620,000 0.17

Great Plains 8,320,000 8.0 290,000 0.28

 U.S. Total 703,980,000 12.0 27,930,000 0.48

Figure 90. Estimated number of trees that could potentially be 

attacked by Asian longhorned beetle (ALB), gypsy moth (GM), 

southern pine beetle (SPB), Dutch elm disease (DED), emerald 

ash borer (EAB), and 1,000 cankers disease on Tennessee’s urban 

tree population.

80

70

60

50

40

30

20

10

0

Number of trees (millions)

ALB

Estimated tree population at risk

GM SPB DED EAB 1,000 

cankers

 Source: Nowak et al. 2011
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 85

In addition, urban growth is projected to increase canopy cover 

in parts of the United States, particularly in grassland areas. 

Climate changes are also projected to have varying effects on 

the local climate that will affect future forest health and composition. Various parts of the country (e.g., various counties in 

the Pacific Northwest and Eastern United States) are projected 

to lose tree cover to development and become more arid in 

the future. Future regional resource planning and management 

activities need to understand, adapt to, and direct the changing 

landscape to sustain forest health and productivity, as well 

human health and well-being in landscapes that are urbanizing 

and projected to experience changes in climate. An increased 

understanding of the biophysical, social, and institutional factors affecting urban and community forestry by decisionmakers—from households and neighborhoods to nongovernmental 

organizations and public agencies—would enhance planning.
86 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 87

v Carbon stored in forests is projected to peak between 2020 and 2040, then

decline.

v Carbon storage projections are highly sensitive to forest land area projections.

v Carbon stored in harvested wood products depends on future timber markets.

Chapter 10. Carbon in Forest Resources 

and Products

Forests and forest products retain carbon as part of the Earth’s larger carbon cycle through the land, water, and 

atmosphere. Forests in the United States were in approximate 

carbon balance with the atmosphere from 1600 to 1800. Utilization and land clearing caused a large pulse of forest carbon 

emissions during the 19th century, followed by regrowth and 

net forest carbon sequestration in the 20th century. Recent 

data and general knowledge of the behavior of forests after 

disturbance suggest that the rate of forest carbon sequestration 

is gradually declining (Birdsey et al. 2006). Significant regional 

differences in past and projected carbon storage reflect longterm changes in land use and wood harvesting.

Forest carbon stocks and stock changes (flux) are measured in 

five pools: aboveground biomass, belowground biomass, dead 

wood, litter, and soil organic carbon. In addition, changes in 

two harvested wood pools are estimated: harvested wood products (HWP) in end uses and HWP in solid-waste disposal sites 

(SWDS). The United States reports changes in forest and HWP 

carbon stocks and total carbon levels annually for the U.S. Environmental Protection Agency (EPA) inventory of greenhouse 

gas emissions and sinks. The methods for estimating forest and 

HWP carbon stock changes are described in detail elsewhere 

(Heath et al. 2011; Skog 2008; U.S. EPA 2011c).

Resource Highlights

Historical Forest Carbon Stocks and 

Flows

Carbon stocks for U.S. forests are estimated using forest survey 

data from the Forest Service’s Forest Inventory and Analysis 

(FIA) data. The official U.S. reports include carbon on all forest 

land in the conterminous United States and coastal Alaska. Net 

annual change in forest carbon stocks (flux) include effects of 

growth, mortality, harvesting, and other disturbances, and any 

changes in total forest land area. Annual estimates of changes 

in HWP pools are based on additions to and removals from 

products held in end uses (such as housing and furniture) and 

products disposed of in landfills. Carbon stored in urban forests 

is reported in the “settlements” land use category in the official 

U.S. reports (Heath et al. 2011).

The most recent reporting year for carbon stocks in the United 

States is 2010, whereas the most recent year reported for flux 

is 2009 (U.S. EPA 2011c). During 2009, 235 million metric 

tons of carbon were added to carbon held in forests and in HWP 

(table 14). The annual additions to HWP carbon stocks declined 

notably between 2005 and 2009 as a result of the economic 

recession and associated decreases in timber harvest and wood 

product production. Total carbon stocks have increased slightly 

during the historical period, with most of the carbon residing in 

aboveground biomass and soil organic carbon (table 15).

Future Carbon Stocks and Flows

Forest Carbon

Forests represent a vast reservoir of stored carbon in the United 

States. Depending on forest management, land use management, 

and other changes to forest inventories, forests can either sequester or emit atmospheric carbon. Analysis of historical FIA 

records shows a net gain in forest carbon stocks between 1990 

and 2008 of about 2.8 billion metric tons (7 percent), mainly 

because of an accumulation of biomass in the Nation’s forests 

(Heath et al. 2011). Forests sequestered carbon over this period.
88 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Forest carbon stocks are a function of area of forest land and 

carbon per acre. Changes in forest carbon can result from 

changes in forest land area and changes in carbon per acre. 

Changes in forest land area transfer a large amount of carbon 

from one land use to another, rather than release carbon to or 

store carbon from the atmosphere.

We used the inventory projections presented in chapter 7 as 

the basis for estimates of future forest carbon pools for the 

conterminous United States (we do not project forest carbon for 

coastal Alaska). The forest carbon projections are based on a 

newly revised carbon accounting framework for the FIA inventory, therefore these projections of forest carbon stocks and 

fluxes are not directly comparable to the historical estimates in 

tables 14 and 15.

Forest area and forest inventory are projected to decline between 

2010 and 2060 in all Resources Planning Act (RPA) scenarios 

(chapter 7). The result is an overall decline in the amount of 

carbon stored in forests by 2060 (table 16 and figure 91).13

Forest area is strongly correlated with future soil carbon, and 

standing biomass is strongly correlated with vegetative carbon 

pools. Carbon losses are a result of a transfer of forest land to 

other land uses (especially developed uses), a transfer of carbon 

in biomass to storage in wood products, and losses to the atmosphere. Following the pattern of inventory projections, forest 

carbon is projected to peak sometime between 2020 and 2040 

at between 41.4 and 41.7 billion metric tons and then to decline 

(figure 91). Forest carbon stocks in 2060 would be reduced by 

between 0.8 (RPA B2) and 2.5 (RPA A1B) billion metric tons 

from 2010 levels.

Figure 92 shows the forest carbon projections on a per-forestacre basis and demonstrates the interplay of forest area changes 

and forest management in determining carbon outcomes, 

although the variation by RPA scenario is not large. The RPA 

historical fuelwood (HFW) scenario has the highest amount of 

carbon per acre. In this scenario, the combination of the lowest 

harvest levels and the highest loss of forest area results in fewer 

Table 14. Net annual changes in carbon stocks in forest and harvested wood pools, 1990–2009.

Table 15. Carbon stocks in forest and harvested wood pools, 1990–2010.

Carbon pool

1990 2000 2005 2009

million metric tons of carbon per year

Carbon pool

1990 2000 2005 2010

million metric tons of carbon

Forest—Total (149.8) (72.4) (219.9) (220.6)

Live, aboveground (98.2) (78.3) (122.1) (122.1)

Live, belowground (19.3) (15.7) (24.1) (24.1)

Dead wood (8.6) (3.5) (8.4) (9.1)

Litter (8.8) 7.5 (11.4) (11.4)

Soil organic carbon (14.9) 17.6 (53.8) (53.8)

Harvested wood products—Total (35.9) (30.8) (28.7) (14.8)

Products in use (17.7) (12.8) (12.4) 1.9

Products in SWDS (18.3) (18) (16.3) (16.7)

Total annual change (flux) (185.7) (103.2) (248.6) (235.4)

Forest—Total 42,783 44,108 44,886 45,988

Live, aboveground 15,072 16,024 16,536 17,147

Live, belowground 2,995 3,183 3,285 3,405

Dead wood 2,960 3,031 3,060 3,105

Litter 4,791 4,845 4,862 4,919

Soil organic carbon 16,965 17,025 17,143 17,412

Harvested wood products—Total 1,859 2,187 2,325 2,449

Products in use 1,231 1,382 1,436 1,474

Products in SWDS 628 805 890 974

Total carbon stock 44,643 46,296 47,211 48,437

SWDS = solid waste disposal sites.

Source: U.S. Environmental Protection Agency 2011c

SWDS = solid waste disposal sites.

Source: U.S. Environmental Protection Agency 2011c

13 The results shown in this chapter are based on the CGCM3.1 GCM for scenarios RPA A1B, A2, and HFW, and on CGCM2 GCM for RPA B2, as discussed in chapter 7.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 89

Table 16. Projected forest carbon stocks in conterminous U.S. forests by RPA scenario, 2010–2060.

Scenario

2010 2020 2030 2040 2050 2060

million metric tons of carbon

RPA A2

RPA B2

RPA HFW

RPA A1B

Forest—Total 41,325 41,500 41,097 40,459 39,696 38,851

 Live, aboveground 13,910 14,241 14,080 13,722 13,304 12,822

 Live, belowground 2,867 2,938 2,903 2,829 2,748 2,651

 Dead wood 2,673 2,690 2,670 2,647 2,598 2,554

 Litter 4,896 4,901 4,895 4,866 4,804 4,727

 Soil organic carbon 16,978 16,730 16,549 16,394 16,242 16,097

Forest—Total 41,325 41,384 41,256 40,875 40,304 39,532

 Live, aboveground 13,910 14,180 14,234 14,082 13,847 13,455

 Live, belowground 2,867 2,922 2,933 2,903 2,856 2,776

 Dead wood 2,673 2,692 2,682 2,665 2,637 2,600

 Litter 4,896 4,913 4,919 4,901 4,852 4,790

 Soil organic carbon 16,978 16,676 16,488 16,325 16,112 15,911

Forest—Total 41,325 41,460 41,679 41,445 40,880 40,473

 Live, aboveground 13,910 14,233 14,486 14,386 14,102 13,911

 Live, belowground 2,867 2,935 2,987 2,966 2,905 2,868

 Dead wood 2,673 2,693 2,710 2,706 2,670 2,653

 Litter 4,896 4,906 4,929 4,927 4,890 4,865

 Soil organic carbon 16,978 16,694 16,568 16,460 16,313 16,177

Forest—Total 41,325 41,470 41,163 40,864 40,505 40,004

 Live, aboveground 13,910 14,269 14,235 14,135 14,053 13,868

 Live, belowground 2,867 2,943 2,933 2,912 2,898 2,861

 Dead wood 2,673 2,691 2,678 2,669 2,648 2,618

 Litter 4,896 4,900 4,897 4,904 4,877 4,833

 Soil organic carbon 16,978 16,668 16,420 16,243 16,029 15,825

Figure 91. Total carbon stored in conterminous U.S. forests (left) and changes in carbon stocks by decade (right), by RPA scenario, 

2010–2060.

45,000

35,000

25,000

15,000

5,000

40,000

30,000

20,000

10,000

0

400

200

0

– 200

– 400

– 600

– 800

– 1,000

Million metric tons

Change in million metric tons

Year

Years

2010–2020

2020–2030

2030–2040

2040–2050

2050–2060

2010

2020

2030

2040

2050

2060

RPA A1B RPA A2 RPA B2 RPA HFW
90 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

but older forest acres. RPA B2 has a comparable total carbon 

stock but on a larger forest area (i.e., it results in more but 

younger forest acres).

Projected carbon losses are greatest in absolute and percentage 

terms for the North Region (roughly one-half of the total), 

where inventory declines most strongly, especially for the RPA 

A1B scenario (figure 93). The Pacific Coast Region also loses 

carbon across the RPA scenarios, but the Rocky Mountain 

Region gains a slight amount of carbon. The South Region’s 

forest carbon pool is projected to remain relatively constant or 

decline only slightly, depending on the RPA scenario. Although 

these projections indicate a change from sequestration to 

carbon emissions for forest pools, they are not a full accounting 

of related carbon dynamics (i.e., they do not account for the 

ongoing contribution of wood products to carbon sequestration 

[see next section] and the replacement of fossil fuels by wood 

energy). Although these projections do not explicitly track 

emissions from forest fires, projected stocks do account for the 

net effects of fire consistent with fire histories observed during 

the past 10 to 20 years.

Historical and projected forest carbon stock by forest carbon 

pools are shown in table 17 in the four RPA scenarios. The 

accumulation of forest carbon stocks by pool over time is 

shown in figure 94 for the RPA A1B and RPA B2 scenarios, 

demonstrating the peaking of carbon stocks in the early years 

of the projection period, followed by a slow decline.

Figure 92. Total carbon stored per acre of forest in the conterminous United States, by RPA scenario, 2010–2060.

68.0

67.5

67.0

66.5

66.0

65.5

65.0

64.5

64.0

63.5

63.0

Metric tons per acre

2010 2020 2030 2040 2050 2060

Year

Figure 93. Carbon stored in conterminous U.S. forests, by RPA 

region, RPA A1B scenario, 2010–2060.

45,000

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0

Million metric tons

2010 2020 2030 2040 2050 2060

Pacific Coast Rocky Mountain North South

Year RPA A1B

RPA A1B RPA B2

Figure 94. Historical and projected conterminous U.S. forest carbon stocks, RPA A1B (left) and RPA B2 (right), 1990–2060.

45,000 45,000

35,000 35,000

25,000 25,000

15,000 15,000

5,000 5,000

40,000 40,000

30,000 30,000

20,000 20,000

10,000 10,000

0 0

Million metric tons

Million metric tons

Year Year

2010 2020 2030 2040 2050 2060 2010 2020 2030 2040 2050 2060

Soil organic carbon Litter Dead wood Live, belowground Live, aboveground

RPA A1B RPA A2 RPA B2 RPA HFW
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 91

Forest—Total (15) 31 30 36 50

 Live, aboveground (36) 3 10 8 19

 Live, belowground (8) 1 2 1 4

 Dead wood (2) 1 1 2 3

 Litter 0 0 (1) 3 4

 Soil organic carbon 31 25 18 21 20

Harvested wood products—part of total (33) (36) (37) (37) (33)

Total (48) (5) (7) (1) 17

Scenario

2010–2020 2020–2030 2030–2040 2040–2050 2050–2060

change in million metric tons of carbon

RPA A2

RPA B2

RPA HFW

RPA A1B

Forest—Total (18) 40 64 76 85

 Live, aboveground (33) 16 36 42 48

 Live, belowground (7) 3 7 8 10

 Dead wood (2) 2 2 5 4

 Litter (1) 1 3 6 8

 Soil organic carbon 25 18 16 15 15

Harvested wood products—part of total (35) (38) (45) (53) (52)

Total (53) 2 19 23 33

Forest—Total (6) 13 38 57 77

 Live, aboveground (27) (5) 15 24 39

 Live, belowground (6) (1) 3 5 8

 Dead wood (2) 1 2 3 4

 Litter (2) (1) 2 5 6

 Soil organic carbon 30 19 16 21 20

Harvested wood products—part of total (30) (29) (28) (28) (23)

Total (36) (16) 10 29 54

Forest—Total (14) (22) 23 56 41

 Live, aboveground (32) (25) 10 28 19

 Live, belowground (7) (5) 2 6 4

 Dead wood (2) (2) 0 4 2

 Litter (1) (2) 0 4 2

 Soil organic carbon 28 13 11 15 14

Harvested wood products—part of total (33) (33) (33) (34) (36)

Total (47) (55) (10) 22 5

Table 17. Net annual changes in carbon stocks in conterminous U.S. forests and harvested wood product pools, 2010–2060. Negative 

numbers indicate net additions to stocks.

Harvested Wood Products

We estimated the net annual contribution of HWP to the forestry 

sector carbon sinks and emissions for each RPA scenario using 

methods provided by the Intergovernmental Panel on Climate 

Change (IPCC) 2006 guidelines for sinks and emissions reporting (IPCC 2006). Historical estimates are those provided in 

the EPA greenhouse gas sinks and emissions report (U.S. EPA 

2011c) and prepared using the WOODCARB II model (Skog 

2008). Estimates of HWP contribution were made using three 

IPCC accounting approaches (table 18). The United States reports HWP contribution (U.S. EPA 2011c) using the Production 

Accounting Approach, which tracks carbon from wood leaving 

harvest sites in the United States and includes estimated carbon 

stored in products exported to other countries. The projections 

are made using the assumption that primary wood products 

produced after 2009 are used in end uses and have the same 

decay and disposal patterns as for the late-2000s period.

The annual HWP carbon contribution under the Production 

Accounting Approach (net addition in 2009) is driven primarily 

by historical and current levels of solidwood product production from U.S. harvest. Annual HWP carbon contribution has 

decreased in recent years because of a decrease in U.S. timber 

harvest associated with the economic recession (table 14). 

Harvest levels are projected to have recovered by 2020, and 

the HWP contribution is projected to be at or above historical 

levels (tables 17 and 18). The HWP contribution is greatest 

for the RPA A1B and B2 scenarios, because they show the 

highest levels of U.S. production of forest products from U.S. 

harvested timber—particularly solidwood products, which 

have a longer use life and longer life in landfills. The HWP 
92 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

contribution is least for the RPA A2 scenario, because it shows 

the lowest level of product production from U.S. harvested 

timber (figure 95).

The annual HWP carbon contribution can also be estimated 

using the Stock-Change and Atmospheric-Flow Approaches, 

which, respectively, track annual additions to HWP carbon 

stocks held in the United States and net flux of carbon between 

HWP held in the United States and the atmosphere. For all 

RPA scenarios, HWP contributions under the Stock-Change 

and Atmospheric-Flow Approaches are generally higher and 

lower, respectively, than under the Production Approach (table 

18). The stock-change estimate is higher because the United 

States remains a net importer of solidwood products, and net 

imports increase the stock-change estimate, but not the production estimate. The stock-change estimate is high for RPA A1B 

because of high U.S. production and high for RPA A2 because 

of high net imports—additions of 60 and 66 million metric tons 

of carbon per year from 2050 through 2060 (table 18).

The contribution under the Atmospheric-Flow Approach is 

determined by the degree to which annual carbon emissions 

from HWP in the United States are lower than annual harvest 

from U.S. forests. The contribution is high (emissions are 

less than harvest) with high net exports and emissions from 

U.S. products occurring in other countries. This contribution 

by HWP is the least for RPA A2, in which the United States 

has the highest net imports—a loss of 9 million metric tons of 

carbon per year from 2050 through 2060—and highest for RPA 

A1B, in which exports are higher (table 18).

Forest Sector Carbon Flux

Projections of net annual carbon flux for the forest sector 

include combined contributions from forest carbon and HWP 

(Production Approach). The RPA A2, B2, A1B, and HWF 

scenarios have net annual increases in carbon stocks for the 

first two decades (table 17 and figure 96). The RPA A1B scenario, with the highest harvest rates, has increases only through 

2010 to 2020. The RPA HFW scenario, with the lowest harvest 

rates, has net increases for the longest period—through 2040 

to 2050. The RPA B2 scenario has increases through 2030 to 

2040. Figure 97 shows the average annual carbon fluxes for the 

individual pools for the RPA A1B scenario and the RPA B2 

scenario, providing a comparison between the RPA scenarios 

with the greatest (RPA A1B) and least (RPA B2) changes during the projection period.

Overall change in forest carbon during the projection period 

can be explained by changes in forest area and carbon per acre 

Approach

2010–2020 2020–2030 2030–2040 2040–2050 2050–2060

Change in million metric tons of carbon

Production

RPA A1B (35) (38) (45) (53) (52)

RPA A2 (30) (29) (28) (28) (23)

RPA B2 (33) (33) (33) (34) (36)

RPA HFW (33) (36) (37) (37) (33)

Stock Change

RPA A1B (47) (49) (53) (58) (60)

RPA A2 (46) (48) (52) (58) (66)

RPA B2 (47) (44) (43) (43) (47)

RPA HFW (47) (49) (52) (57) (61)

Atmospheric Flow

RPA A1B (36) (39) (45) (51) (46)

RPA A2 (28) (20) (11) (4) 9

RPA B2 (34) (30) (27) (28) (32)

RPA HFW (35) (38) (39) (40) (37)

Table 18. Net annual contribution of harvested wood products to U.S. forestry sector carbon sinks and emissions, under three accounting

approaches, by RPA scenario, 2010–2060. Negative numbers indicate net additions to stocks or net removals from the atmosphere.

Figure 95. Historical and projected annual HWP carbon contribution to forest sector sinks and emissions using the Production 

Accounting Approach, by RPA scenario, 1990–2060. Negative indicates addition to HWP carbon stocks.

0.0

– 10.0

– 20.0

– 30.0

– 40.0

– 50.0

– 60.0

Million metric tons per year

1990 2000 2010 2020 2030 2040 2050 2060

RPA A1B

RPA A2 RPA B2

Historical RPA HFW

Year
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 93

Figure 96. Total carbon flux in U.S. forest sector, by decade, 

by RPA scenario, 2010–2060.

80

60

40

20

0

– 20

– 40

– 60

– 80

Million metric tons per year

2010–2020

2020–2030

2030–2040

2040–2050

2050–2060

Years

Figure 97. Projected average annual carbon fluxes for conterminous U.S. forest pools and harvested wood products, by decade, 

RPA A1B (top) and RPA B2 (bottom), 2010–2060.

60

40

40

30

– 30

20

20

10

– 10

0

0

– 20

– 20

– 40

– 40

– 50

– 60

– 60

Change in million metric tons Change in million metric tons

2010–2020

2010–2020

2020–2030

2020–2030

2030–2040

2030–2040

2040–2050

2040–2050

2050–2060

2050–2060

Live, aboveground Dead wood Soil organic carbon

Live, belowground Harvested wood products

Total

Litter

Years

Years

in each RPA scenario. The contribution of these two changes 

varies by RPA scenario (figure 92 and table 16). For scenario 

RPA A1B, with the highest harvest rates, about one-half of the 

decline is because of decrease in forest area and one-half is 

because of decline in carbon per acre. For RPA A2, almost all 

the decline is because of decrease in forest area, with a small 

portion because of decrease in carbon per acre. For scenarios 

RPA B2 and HFW, with the lowest harvest rates, the decline is 

entirely because of decrease in forest area, whereas carbon per 

acre actually increases. The forest carbon decreases are largest 

for RPA scenarios A1B and A2, in which both forest area and 

carbon-per-acre decline.

Overall cumulative carbon stock increases during the projection 

period, including both forest carbon and HWP carbon (Production Approach), are largest for scenarios RPA HFW and RPA 

Table 19. Net annual contribution of harvested wood products and U.S. forests to carbon sinks and emissions, under three accounting 

approaches, by RPA scenario, 2010–2060. Negative numbers indicate net additions to stocks or net removals from the atmosphere.

Approach

2010–2020 2020–2030 2030–2040 2040–2050 2050–2060

Change in million metric tons of carbon

Production

RPA A1B (53) 2 19 23 33

RPA A2 (36) (16) 10 29 54

RPA B2 (47) (55) (10) 22 5

RPA HFW (48) (5) (7) (1) 17

Stock Change

RPA A1B (65) (9) 11 18 25

RPA A2 (52) (35) (14) (1) 11

RPA B2 (61) (66) (20) 13 (6)

RPA HFW (62) (18) (22) (21) (11)

Atmospheric Flow

RPA A1B (54) 1 19 25 39

RPA A2 (34) (7) 27 53 86

RPA B2 (48) (52) (4) 28 9

RPA HFW (50) (7) (9) (4) 13

RPA A1B RPA A2 RPA B2 RPA HFW
94 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

B2, in which harvest is lowest. For RPA B2, later decades 

have larger decreases than RPA A1B because of higher harvest 

(tables 17 and 19). Note that these cumulative carbon stock 

increases do not account for changes in fossil carbon emissions 

that occur with varying levels of wood energy use.

Overall cumulative carbon flux changes during the projection 

period using the Stock-Change and Atmospheric-Flow Approaches for HWP estimates are highest (most carbon emission 

offset) for RPA B2, very closely followed by RPA HFW (table 

19). The estimated cumulative offsets are highest for all RPA 

scenarios under the Stock-Change Approach.

Wood Substitution Effects on Carbon Stocks 

and Flows

Estimates of annual changes in forest and forest products 

carbon stocks are used to monitor carbon fluxes over time. 

Changes in policy can affect forest investment and management 

and wood products production and use, which in turn affect 

carbon fluxes. Areas of particular interest are the potential 

change in carbon flux over time in response to (1) changes in 

use of wood as a substitute for materials that emit more carbon 

in their manufacturing, and (2) changes in wood use for energy.

The 2010 RPA Assessment did not evaluate specific policies or cases in which solidwood product use increases to 

displace nonwood products, particularly steel and concrete, 

in construction. We have information, however, that suggests 

the magnitude of the possible effect of such increases if they 

were to occur. A recent meta-analysis of studies of wood-based 

building systems determined that the average displacement factor value was 2:1, meaning that for each ton of carbon in wood 

products substituted for nonwood products, there is an average 

greenhouse gas emission reduction of approximately 2 tons 

of carbon (Sathre and O’Connor 2008, 2010). This reduction 

corresponds to a 3.9-ton carbon dioxide equivalent emission 

reduction per ton of oven-dry wood used, or a roughly 1.9-ton 

carbon dioxide equivalent (or 0.52 tons carbon) emission 

reduction per cubic meter of wood product.

The difference in net carbon emissions between producing 

wood products and nonwood products for construction is 

largely because of two factors. First, wood is assembled 

using energy from the sun via photosynthesis, whereas other 

materials are assembled using humanmade energy largely from 

fossil fuels. Second, wood mill residues are used for energy. 

The emissions from burning mill residue for energy are offset 

during some time period by the absorption of carbon in forests. 

One can view this absorption as occurring after harvest when 

the forest regrows.

When a policy causes an increase in wood products production 

and use, and an associated decrease in nonwood products 

production and use and emissions, three changes occur in 

carbon flux between the wood product-increase case and the 

no-increase case. First, there is a reduction in manufacturing 

emissions. Second, there is a decrease in forest stock from 

harvest to make the wood product. Third, the harvest of wood 

for the product can increase (with sustainable management) 

the long-term carbon accumulation rate of the forest relative 

to what it would have been without the harvest. This increase 

is a longer term carbon benefit from producing and using the 

solidwood product. This third factor would only be additional if 

it is not included into calculating the first factor—the emission 

offset factor. The third factor has not been used in most studies 

that estimate the first factor.

The 2010 RPA Assessment also did not examine different policies that might affect the use of biomass for energy, although 

the four RPA scenarios varied widely in assumptions about the 

future use of woody biomass for energy. We will be examining 

wood energy options and potential effects on forest resources 

and product markets in the assessment update cycle.

Conclusions

U.S. forests sequester substantial amounts of carbon. During 

the last few decades, carbon stocks on forest land and in harvest 

wood products have increased. Carbon storage on forest land is 

highly dependent on forest area and carbon per acre of forest. 

The RPA scenarios all project decreasing forest land, although 

at different scales. Carbon stocks are projected to decrease 

across all RPA scenarios, primarily or entirely because of 

declining forest land area, depending on the RPA scenario. 

Harvest levels that change the age class distribution and carbon 

per acre on remaining forest land also influence stock change.

Differences in projected carbon storage in HWP (Production 

Approach) reflect varying levels of forest harvest and resulting 

solidwood product production and use. The RPA A1B scenario, 

which projects the highest harvest, stores the most carbon during the projection period. The contribution of HWP to carbon 

flux is vital to keeping the combined forest and HWP pools as a 

carbon sink for an additional decade in three of four scenarios. 

For the RPA HFW scenario, carbon additions to HWP keep the 

forest sector as a carbon sink for an additional three decades.

Overall cumulative carbon stock increases during the projection 

period, including both forest and HWP carbon (Production 

Approach), are largest for scenarios RPA HFW and RPA B2, 

in which harvest is lowest. In identifying RPA scenarios with 

the largest carbon increases (emission offsets), however, we 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 95

have not included the effect of varying levels of wood energy 

use in changing fossil fuel emissions. An evaluation comparing 

RPA scenarios that differ only in the level of wood energy use 

may show greater carbon offsets from the higher wood energy 

case—over a given time period—to the extent that forest 

carbon and HWP carbon are expanded because of the increased 

wood energy use. Policies that result in substitution of wood 

products for nonwood products in construction could decrease 

overall carbon emissions, although the RPA scenarios were not 

set up to evaluate the effect of such policies.

The forest carbon change projections in this chapter are 

determined by how forest area and forest growth are modified 

in response to changing harvest for timber products and wood 

energy. Sources of uncertainty include natural disturbance; 

forest landowner response in the form of harvest, land conversion, or management change; and forces to convert land to 

nonforest. HWP carbon change projections are determined 

primarily by how solidwood products production changes 

in response to changing U.S. and foreign demand for timber 

products and wood energy. Sources of uncertainty include the 

relative increase in foreign versus U.S. product prices, which 

will determine net trade and factors that determine the lifetime 

of solidwood products in use and disposal after use.
96 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 97

Chapter 11. Rangeland Resources

Resource Highlights

v Rangelands are projected to continue their slow decline in area.

v Rangeland productivity is stable.

v Rangeland health is difficult to consistently evaluate.

v Rangeland forage supply is sufficient to meet demand.

Extent of U.S. Rangelands

Rangelands are areas where the natural vegetation consists 

principally of grasses, forbs, grasslike plants, and shrubs 

that are suitable for browsing or grazing. Rangelands are 

distinguished from grazing lands, with grazing land identified 

as any vegetated land that is grazed or has the potential to be 

grazed (SRM 1998), including rangeland, pastureland, grazed 

forest land, native and naturalized pasture, hayland, and grazed 

cropland. Consistent quantification of the extent of the Nation’s 

land resources is crucial to providing a baseline against which 

repeated measures of land health, carbon sequestration, and 

forage availability can be compared in the future. Unlike forest 

lands, a consistent measure of rangelands across all ownerships 

does not currently exist because different definitions are applied 

in different inventory processes.

Because of these different definitions, current estimates of U.S. 

rangeland area vary widely. To better understand the differences in U.S. rangeland estimates, we compared the extent of 

U.S. rangeland by applying two different rangeland definitions 

to spatially explicit vegetation data that cover rangeland on all 

ownerships (Reeves and Mitchell 2011): the rangeland definitions used by the Forest Service in the Forest Inventory and 

Analysis (FIA) program and the definition used by the Natural 

Resources Conservation Service (NRCS) in the National 

Resources Inventory (NRI) (see sidebar, Defining Rangeland 

Extent in the United States).

Using this geospatial modeling process, the FIA and NRI definitions yielded estimates of 511 and 662 million acres of rangelands in the conterminous United States, respectively (table 20). 

These estimates do not match the rangeland figures in table 4 

Table 20. Estimated rangeland area in the conterminous United States by RPA region, using Forest Inventory and Analysis (FIA) and 

National Resources Inventory (NRI) definitions of rangeland for all rangelands, National Forest System (NFS) rangelands, and Bureau of 

Land Management (BLM) rangelands. 

RPA region

All range

FIA

All range

NRI

NFS range

FIA

NFS range

NRI

BLM range

FIA

BLM range

NRI

million acres

North 3.6 15.2 < 0.1 0.6 NA NA

South 61.4 121.6 0.7 1.6 < 0.1 < 0.1

Rocky Mountain 386.6 436.4 24.0 34.8 108.6 115.8

Pacific Coast 59.2 89.1 4.0 13.5 21.7 23.6

U.S. Total 510.8 662.3 28.7 50.5 130.3 139.4

NA = not applicable.

Source: Reeves and Mitchell 2011

Rangelands are found in many ecoregions encompassing a diverse suite of vegetation. This chapter begins with 

a description of the extent of U.S. rangelands across various 

ownerships and of the area of rangelands in protected status. 

Recent trends in rangeland productivity and health are reviewed, 

as is the role of rangelands in livestock and forage production. 

The final section reviews current scientific knowledge about 

the potential effects of climate change on rangelands and their 

ability to continue to deliver goods and services into the future. 

Additional details on data sources and methods can be found in 

Reeves and Mitchell (2012).
98 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

that are based on the 2007 NRI because both the FIA and NRI 

definitions were applied to all ownerships, whereas the NRI 

estimates include only non-Federal lands. Assuming that Alaska 

contains roughly 173 million acres of rangeland (USDA Forest 

Service 1989) reveals a national total of 681 million acres (FIA 

perspective) or 835 million acres (NRI perspective). The biggest discrepancies between these estimates arise from their different treatments of oak, pinyon-juniper, and mesquite woodlands. Another area of discrepancy occurs in the Southeastern 

United States, particularly in forested flatwoods and longleaf 

pine sites that were historically lightly treed savannahs.

Non-Federal Rangeland

Unlike total rangeland extent, NRI data can be used directly as 

a reliable estimator of non-Federal rangeland extent over time. 

Most rangeland in the United States is privately owned and lies 

west of the 95th meridian. According to the 2007 NRI, the current area of non-Federal rangeland in the conterminous United 

States is 409 million acres. There was a net loss of 8.8 million 

acres of non-Federal rangeland between 1982 and 2007 (USDA 

NRCS 2009), about 2 percent of the current non-Federal rangeland base. The Rocky Mountain Region contains the greatest 

amount of non-Federal rangeland and exhibited the greatest 

loss of all Resources Planning Act (RPA) regions since 1982. 

Florida exhibited the highest State loss of non-Federal rangeland in the country, 1.75 million acres between 1982 and 2007.

Conservation Reserve Program Lands

Although Conservation Reserve Program (CRP) lands are never 

considered rangeland in the NRI because the cover is not considered “permanent,” CRP lands planted to range vegetation 

may provide similar ecological functions and act to decrease 

fragmentation in landscapes dominated by rangeland vegetation. 

Currently, there are about 34 million acres of lands enrolled 

in the CRP. These lands have provided benefits from reduced 

erosion and wildlife viewing and hunting (Sullivan et al. 2004). 

In addition, CRP lands generally improve ecological condition, 

and the recent emphasis on biological carbon sequestration from 

rangelands emphasizes the potential of CRP lands hosting rangeland vegetation to sequester a significant quantity of atmospheric 

carbon dioxide (Jordan et al. 2007). Participation in the CRP 

has possibly led to some unintended negative consequences, 

however (Baker and Higgins 2009; Noss et al. 1995). For 

example, millions of acres enrolled in the CRP are seeded with 

nonnative species, such as crested wheatgrass and intermediate 

wheatgrass.

Federal Rangelands

The Forest Service and Bureau of Land Management (BLM) 

manage a significant area of rangeland. Using the Reeves and 

Mitchell (2011) approach described previously, National Forest 

System (NFS) lands had about 29 million acres (about 15 percent of the total NFS land base) based on the FIA definition and 

almost 48 million acres (about 25 percent of the total NFS land 

base) using the NRI definition (table 20). As with all rangeland 

estimates, the difference reflects the disparate treatment of species common in pinyon-juniper, mesquite, and other woodland 

environments and the different canopy cover thresholds between 

definitions. Of the 175 million acres of land administered by 

the BLM in the conterminous United States, about 131 million 

acres were estimated to be rangeland from the FIA definition 

and about 139 million acres from the NRI definition, which 

means that roughly 75 percent of BLM lands within the conterminous United States are rangeland.

Rangelands in Protected Status

Rangeland accounts for about 195 million acres (45 percent) 

of the protected areas in the Protected Areas Database of the 

United States (PAD-US) (table 21). Approximately 82 percent 

of the protected rangelands are found in just five States: Arizona, California, Idaho, Nevada, and Oregon. Table 21 lists the 

protected area in States containing more than 1 million acres 

of rangeland. Rangeland area estimates are provided for both 

the FIA and NRI definitions, and for the percent of rangeland 

protected based on the NRI definition.

Outlook for U.S. Rangeland

Mitchell (2000) identified consolidation, subdivision, and 

urbanization of rangeland as important factors in determining 

the future of rangeland. The extent of Federal rangeland is not 

expected to change substantially in the future. Between 1982 

and 2007, non-Federal rangeland was lost primarily to cropland 

and developed land uses (table 4). Rangeland is projected 

to continue its slow decline under all RPA scenarios (Wear 

2011). The largest loss of rangeland is projected to occur under 

scenario RPA A1B, almost 9 million acres (2 percent) of nonFederal rangeland between 2010 and 2060. The largest percentage decline (6 percent) occurs in the Pacific Coast Region, and 

the greatest acreage loss (4.5 million acres) occurs in the Rocky 

Mountain Region.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 99

Building from the spatial patterns for grasslands and shrublands 

described in chapter 6, an index of rangeland fragmentation 

was developed to provide a relative value indicating the ratio of 

rangeland vegetation edge to the area of urban and agricultural 

landscapes. Higher values indicate areas of rangeland vegetation that are relatively more fragmented (figure 98). Fragmentation is detrimental to natural landscapes because of factors 

such as loss of goods and services, decreased gene pools, and 

barriers to species depending on rangelands for all or part of 

their life cycle. Arizona and Nevada are the States with the least 

fragmented rangeland areas, whereas the most fragmented areas 

correspond to areas with high agricultural usage. This method 

does not account for some types of development effects, such 

as the effects of oil and gas development on rangeland.

Table 21. Protected area in States with more than 1 million acres of rangeland.

State

Total

protected area

Protected

rangeland area

(FIA definition)

Protected

rangeland area

(NRI definition)

Percent of protected area 

that is rangeland

(NRI definition)

thousand acres

FIA = Forest Inventory and Analysis. NRI = National Resources Inventory.

Source: Conservation Biology Institute 2010

Nevada 97,519 64,361 74,098 76

California 61,381 26,333 36,700 60

Oregon 39,082 16,630 20,848 53

Idaho 34,021 12,656 15,477 45

Arizona 19,728 7,988 13,018 66

Utah 15,656 3,742 5,353 34

New Mexico 12,472 2,692 4,796 38

Wyoming 13,770 3,085 3,582 26

Colorado 17,657 2,587 3,441 19

Montana 21,592 2,562 3,301 15

South Dakota 3,229 1,965 2,361 73

Florida 12,141 461 2,206 18

Texas 5,525 1,607 1,981 36

North Dakota 2,614 1,643 1,978 76

Washington 12,312 1,206 1,498 12

Relative fragmentation 

of grass and shrublands

(shown are counties 

with > 50,000 acres)

High

Low

< 50,000 acres of rangeland

0 210 420 840

Miles

Figure 98. Patterns of relative fragmentation of rangeland 

using the Morphological Spatial Pattern Analysis index, 2001.

Source: Adapted from Riitters 2011

Defining Rangeland Extent in the United States

A consistent set of spatially explicit data with appropriate 

precision to enable mapping of rangeland area, based upon any 

definition, has been lacking. Definitions of rangeland often include 

land cover, land use, and potential vegetation or administrative 

characteristics, with each approach having unique problems 

(Lund 2007). Not only are different concepts applied to identify 

rangeland, different tree canopy thresholds are used to determine 

whether a stand is classified as forest or rangeland. Woodlands 

are also treated differently, sometimes classified as forests and 

sometimes as rangeland.

Reeves and Mitchell (2011) accounted for all rangeland in the 

conterminous United States by applying two different definitions 

of rangeland from land management agencies to spatially explicit 

data describing vegetation composition, structure, and historical 

makeup. Specifically, rangeland extent was characterized using 

rangeland definitions from the Forest Service’s Forest Inventory 

and Analysis (FIA) Program and from the Natural Resources 

Conservation Service’s National Resources Inventory (NRI) (figure 

99). Areas of disagreement generally reflect different tree canopy 
100 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Rangeland Productivity

In its most basic form, rangeland productivity can be described 

as the rate of change in vegetative biomass accumulation 

(aboveground and belowground) expressed on an area basis. 

Vegetation productivity is often measured as net primary productivity (NPP) and because primary production provides the 

NRI-LANDFIRE Model

95th meridian

FIA-LANDFIRE Model

95th meridian

0

420

210 630

105 840Miles

Afforested rangeland

Agriculture or not rangeland

Forest or woodland

Open water

Forest or woodland

Open water

Other

Rangeland

Other

Rangeland

Transitional rangeland

Agriculture or not rangeland

No disagreement

Western Great Plains Mesquite Woodland and Shrubland (Prosopis)

Apacherian-Chihuahuan Mesquite Upland Scrub (Prosopis)

Edwards Plateau Limestone Shrubland (Juniperus)

Tamaulipan Mesquite Upland Scrub (Prosopis)

North-Central Interior Dry-Mesic Oak Forest and Woodland (Quercus)

Northern and Central California Dry-Mesic Chaparral 

(Ceanothus/Adenostoma)

Mogollon Chaparral (Quercus)

California Mesic Chaparral (Quercus/Adenostoma)

Edwards Plateau Limestone Savanna and Woodland (Juniperus)

Individual vegetation types represent less than 100,000 hectares of 

disagreement

Northern Rocky Mountain Ponderosa (Pinus/Pseudotsuga)

Pine Woodland and Savanna (FIA does not consider chapparal to be 

rangeland in California)

Areas of disagreement where the NRI-LANDFIRE model results in rangeland 

classification but the FIA-LANDFIRE model does not. The vegetation classes 

(terrestrial ecological systems) are the top 10 vegetation types most responsible for disagreement. The most common genera associated with each 

terrestrial ecological system are shown in parentheses.

0 210

420

630

105 840

Miles

Figure 99. Comparison of rangeland extent using National 

Resources Inventory (NRI) and Forest Inventory and Analysis 

(FIA) definitions, 2001.

Figure 100. Areas of disagreement in rangeland extent 

between the National Resources Inventory (NRI) and Forest 

Inventory and Analysis (FIA) perspectives, 2001.

Source: Reeves and Mitchell 2011

cover thresholds and treatment of woodland species (such as 

juniper, oak, and mesquite species) between the FIA and NRI 

rangeland definitions (figure 100).

The spatially explicit vegetation data, supplied by the LANDFIRE 

project, included existing vegetation type, existing vegetation 

height, existing vegetation cover and biophysical settings (Reeves 

et al. 2009; Rollins 2009; Zhu et al. 2006). The vegetation classification used by LANDFIRE to describe current and pre-Euro-American 

vegetation was Ecological Systems (Comer et al. 2003; Comer and 

Schulz 2007). Three classes of rangeland were identified: rangeland, 

afforested rangeland, and transitory rangeland (Spreitzer 1985). 

Afforested rangeland represents areas that were historically 

dominated by herbs and shrubs but currently support a tree 

canopy cover exceeding the amount allowed by rangeland definitions. The NRI definition identified 47 million acres of afforested 

rangeland, but a comparable analysis was not possible using 

the FIA definition. These afforested areas would be considered 

forest using the FIA definition. Transitory rangeland represents 

areas that were historically forested, but currently are temporarily 

dominated by shrub and/or herbaceous vegetation after logging, 

severe fires, or other disturbances.

The total rangeland area quantified using the FIA and NRI 

perspectives from Reeves and Mitchell (2011) in the conterminous 

United States is 511 and 662 million acres, respectively (table 20).

foundation for all herbivory, it is a crucial component to monitor on rangeland. Although rangeland productivity ultimately 

controls grazing capacity, data are not available for a nationally 

consistent assessment. To fill this gap, a combination of ecosystem modeling and remote sensing was used to quantify spatial 

and temporal trends in productivity for rangeland vegetation 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 101

across large areas. The trend in NPP was estimated annually 

from 2000 through 2009 using the Moderate Resolution 

Imaging Spectroradiometer (MODIS) vegetation product suite 

(Running et al. 2004). This approach estimates landscape-level 

NPP, but does not provide a framework for quantifying the 

proportion of aboveground production suitable for grazing. The 

analysis excludes all areas in the conterminous United States 

exhibiting tree canopy cover greater than 10 percent.

The most productive systems (using National Vegetation Classification Standard Groups and Macrogroups) occur in coastal 

California (generally chaparral types), Florida peninsula and 

scrub vegetation, Eastern North American grassland meadow 

and shrubland, Western North American warm temperate forest, and Great Plains tallgrass prairie and shrubland. Figure 101 

depicts the spatial patterns of rangeland productivity across the 

conterminous United States. From 2000 to 2009, U.S. rangeland averaged approximately 0.218 kilograms of carbon per 

square meter per year (about 1,960 pounds per acre). This value 

includes aboveground and belowground vegetative structures 

and includes areas with shrubs.14

U.S. rangeland with the highest variability in NPP occurred 

in more xeric regions, such as the Southwestern United States 

and the southern Great Plains, presumably in response to 

interannual variability in precipitation (Reeves et al. 2006; 

Zhao and Running 2010). The vegetation types exhibiting 

the greatest variation were Great Plains shortgrass prairie and 

shrubland, North American warm desert scrub and grassland, 

and Western North American warm temperate scrub woodland 

and shrubland. Rangeland vegetation exhibiting the highest average productivity tends to have the lowest variability between 

years, reflecting greater stability (less interannual variation) in 

precipitation.

Overall, from 2000 to 2009, U.S rangelands exhibited a weakly 

positive, albeit insignificant, trend in productivity. The Rocky 

Mountain Region, however, exhibited a stronger increasing 

trend from 2000 to 2009 than other RPA regions, but the cause 

of the increase is not known.

Rangeland Health

The concept of rangeland health has evolved from comparing 

current vegetation composition to a climax plant community 

to judging the degree to which the integrity of soil and the ecological processes of rangeland ecosystems are sustained (Joyce 

et al. 2000; National Research Council 1994). Rangeland health 

is currently characterized using a variety of both qualitative and 

quantitative indicators (Herrick et al. 2010) describing multiple 

facets of ecosystem integrity such as erosion, percentage bare 

ground, species composition, and annual production (Pellant et 

al. 2005). Because no rangeland health monitoring protocol is 

being consistently used by land management agencies across 

all rangeland, we report on two separate analyses of BLM 

lands and non-Federal rangeland, the two largest nationwide 

rangeland ownership categories.

BLM Lands

The BLM has been monitoring the ecological status of rangeland since 1978, using systems to classify BLM lands into four 

ecological status categories that are based on the percentage 

similarity of the current vegetation to the Potential Natural 

Community (PNC) (Habich 2001). More recently, the BLM 

created the Standards for Rangeland Health (USDI BLM 2001) 

that provide standards for ecological processes, water quality, 

habitat of protected species, and watershed function. Between 

2004 and 2009, the condition of all BLM lands was quite stable, a 

conclusion also reached in the last RPA Rangeland Assessment 

(Mitchell 2000). Roughly 75 percent of the lands administered 

by the BLM are in the mid and late seral stages. Approximately 

89 percent of all BLM lands are classified as either meeting 

all land health standards, making significant progress toward 

meeting the standard, or not meeting all standards or making 

significant progress toward meeting the standards but appropriate action has been taken (USDI BLM 2001).

14 The values reported here cannot be reliably compared with herbaceous productivity from most previous studies, because productivity is often only 

reported for aboveground structures of herbaceous species.

0 210 420 840Miles

2.40 (~ 30,000 pounds/acre)

0.001 (~ 18 pounds/acre)

Not rangeland or NPP undetectable

Mean rangeland net 

primary production (NPP) 

(2000–2009) kg Cm– 2 yr– 1

Figure 101. Mean annual rangeland net primary production 

(NPP), 2000–2009. NPP values represent both aboveground and 

belowground production.a

a

 Only patches comprised of rangeland occupying ≥ 198 contiguous acres are shown, thus 

eliminating about 90 percent of the rangeland areas patches of the Eastern United States.

Source: Based on Moderate Resolution Imaging Spectroradiometer Collection 4.5 data
102 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Non-Federal Rangeland

The NRI recently incorporated a rangeland health protocol 

fashioned after Pellant et al. (2005). Herrick et al. (2010) summarized the results for three attributes based on data collected 

between 2003 and 2006: soil and site stability, hydrologic function, and biotic integrity. Biotic integrity exhibited the largest 

departure from reference conditions, followed by hydrological 

function and site stability. In general, the northern Great Plains 

appeared more intact in all three attributes than the Southwestern 

United States (Herrick et al. 2010). Biotic integrity appeared to 

be most affected by the presence of nonnative species, although 

invasive native species also contributed to decreased biotic 

integrity, especially mesquite and juniper species. Nonnative 

species were present on roughly 50 percent of non-Federal 

rangeland and represented more than 50 percent of the total 

plant cover on 5 percent of non-Federal rangeland. Overall, 

roughly 80 percent of the non-Federal rangeland in the conterminous 48 States is in relatively healthy condition and exhibits 

no significant soil, hydrologic, or biotic integrity problems.

Woody Encroachment on Non-Federal Rangeland

Woody encroachment is the establishment, development, and 

spread of tree or shrub species onto rangeland sites that are 

postulated to have hosted less dense cover by woody species in 

the past. The densification and encroachment of woody species 

can induce significant ecological change by transforming grasslands into savannas and savannas into shrublands or woodlands 

(Hughes et al. 2006), which can alter fire regimes (Ansley 

and Rasmussen 2005; Chambers et al. 2005; Miller and Rose 

1999), nutrient cycling (Rau et al. 2010; Strand et al. 2008), 

carbon sequestration, biodiversity, and forage yield (Miller et 

al. 2005). In arid regions, increases in the abundance of shrubs 

at the expense of grasses are a type of desertification often 

accompanied by accelerated rates of wind and water erosion. 

Likewise in semiarid and subhumid areas, encroachment of 

shrubs and trees into grasslands and savannas may promote 

primary production but potentially reduce streamflow, groundwater recharge, livestock production, and biological diversity 

(Archer et al. 2001). Shrub encroachment can also increase 

soil organic matter, which has the ultimate effect of increasing 

sequestered carbon. The increased carbon accumulation, however, is dependent on many factors, such as temperature and 

rainfall, with relatively wetter sites receiving a greater amount 

of sequestered carbon (Knapp et al. 2001).

Three key genera—juniper, mesquite, and pine—are the 

primary concerns for woody encroachment. As a whole, 

approximately 10 percent of U.S. rangeland is currently 

occupied by invasive juniper species. In general, pinyon and 

juniper woodlands occupy approximately 74 million acres in 

the Western United States (Miller et al. 2005), mostly between 

2,000 and 6,000 feet in elevation (Gedney et al. 1999). The 

increased abundance of western juniper is also reducing aspen 

in some stands (Mitchell 2000) and decreasing the streamflow 

for watersheds because of increased transpirational demand. 

Finally, densification of western juniper is also linked to 

reduced understory biomass and diversity of wildlife and plant 

species (Wall et al. 2001).

Without disturbance or management, most invaded landscapes 

will become closed woodlands, resulting in the loss of understory plant species, decline of sagebrush communities, loss of 

habitat, decline in herbaceous production, decline of landscape 

heterogeneity, and greater costs for restoration (Miller et al. 

2008). Soil erosion resulting from juniper encroachment is 

a major concern, with grassland communities in the Great 

Plains being especially vulnerable (Ansley and Rasmussen 

2005). Eastern redcedar and Ashe juniper now occupy more 

than 6 million acres of rangeland and forest land in Oklahoma 

(approximately 15 percent of the land area), influencing almost 

30 percent of the estimated 21.6 million acres in native plant 

communities (Bidwell et al. 1995).

Eastern redcedar tends to invade more northerly rangeland, 

especially former tallgrass prairie. The species occurs in nearly 

every State east of the Rocky Mountains, but appears invasive 

toward the western edge of its range. Eastern redcedar is the 

most widely distributed conifer east of the Mississippi River 

and pioneers aggressively into abandoned fields and grasslands 

(Schmidt and Leatherberry 1995). Relative to other juniper species, invasions by Eastern redcedar are particularly problematic 

because they threaten tallgrass prairie, one of the most endangered ecosystems in North America (Briggs et al. 2005).

Whereas altered fire regimes and overgrazing are often cited as 

inducing invasions by juniper species, overgrazing in general 

is postulated to drive encroachment by mesquite species, but 

debate still remains as to the exact causes (Kupfer and Miller 

2005). Ecological systems dominated by mesquite occupy 

a large region of the southwestern area of the conterminous 

United States, with significant coverage in Arizona, New 

Mexico, and Texas. Mesquites occupy semiarid and arid landscapes, creating the potential for an increasing area of arid land 

or desertification. In these landscapes, mesquite can exploit 

the additional soil moisture that infiltrates under intermittent 

streambeds and in local areas where water accumulates during 

runoff (Schlesinger et al. 1990).

Domestically, mesquite species are the dominant woody plant 

on more than 94 million acres of what has been considered 

semiarid southwestern grasslands (Van Auken 2000). Such a 

large distribution and, in some cases, high stem densities create 

similar ecological consequences as juniper species. In addition 

to altering nutrient cycles, mesquite invasions greatly reduce 

herbaceous forage and thus create an economic burden for 

working ranches.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 103

Invasive Plants Abundance and Distribution

The spread of exotic or nonindigenous plants throughout U.S. 

rangelands has had harmful effects on overall rangeland health 

and presents management obstacles (Mitchell 2000). Generally, 

invasive species result in a loss of income for stock growers 

and landowners from reduced forage, productivity, increased 

control costs, and reduced land value. Today, an estimated 

3,310 nonnative species occur within the conterminous United 

States, and 126 million acres are infested by 16 prominent 

invasive plant species (Duncan et al. 2004). Nonnative species 

have been shown to degrade natural ecosystem integrity and are 

now estimated to be present on 50 percent of U.S. rangeland 

(Herrick et al. 2010). Some of the most problematic species that 

commonly invade rangeland include cheatgrass, leafy spurge, 

Dalmatian Toadflax, red brome, knapweeds, and starthistles 

(DiTomaso et al. 2010).

Cheatgrass is considered one of the most abundant invasive 

plant species in North America. It is most prominent in the 

Great Basin and throughout the Western United States, has 

steadily increased in the last decades, and is expected to 

continue expanding. It outcompetes native species, changes 

fire regimes, and alters forage supply. Dalmatian Toadflax is 

most prominent in the Northwestern United States and southern 

California. It is estimated to infect almost 400,000 acres in the 

Western United States (Duncan et al. 2004) and contains a toxic 

substance that causes a health risk to grazing animals.

Knapweeds are found mostly in the Western United States, 

particularly in the Southwest and Intermountain regions, and 

are estimated to infest 5 million acres (Wilson and Randall 

2005). Infested areas are often associated with increased runoff 

and sediment yield and loss of topsoil. Leafy spurge occurs 

predominantly in the Northwestern United States, especially in 

eastern Montana and Wyoming. It has invaded approximately 

3.7 million acres in Western States and almost 1 million acres 

in the East (Duncan et al. 2004). Red brome occurs in the 

Southwestern United States, where it alters fire regimes and 

threatens the native species.

Livestock and Forage Production

Rangeland is an important forage source for livestock grazing, 

both on public and private rangeland. Grazed forage includes 

any vegetated land that is grazed or has the potential to be 

grazed, so it encompasses rangeland, pasture, grazed woodland, 

and grazed cropland.

Livestock Numbers

The United States has a large cattle industry, although U.S. 

cattle production peaked in 1982 at more than 104 million 

animals (USDA NASS 2009). Cattle numbers fell between 

1997 and 2007, but losses were distributed asymmetrically. The 

North Region lost nearly 26 percent but the Pacific Coast Region gained 10 percent. The South Region has the most cattle, 

primarily because of Texas and Oklahoma, which together 

contain 56 percent of the region’s cattle population. In the 

short run, the cattle inventory is projected to expand slightly, 

from less than 92 million in 2012 to almost 97 million by 2020 

(USDA 2011). Sheep numbers have declined by approximately 

26 percent since 1997, the continuation of a decades-long 

decline. A combination of low lamb consumption, decreased 

dependence on wool, increased competition from imports, and 

disease and predator losses raising domestic production costs 

have greatly decreased U.S. sheep populations (Jones 2004).

Horses have increased approximately 33 percent, and goats 

have increased 96 percent from 1997 to 2007. The trend in 

domestic goat production follows a global trend of increases. 

Goats are gaining popularity because they efficiently convert 

feed and are valuable as vegetation management tools. Bison 

numbers on private lands are estimated at about 198,000 

animals (USDA NASS 2009). Bison numbers are greatest on 

the northern Great Plains, particularly North Dakota, which accounted for 15,000 bison on private lands. In addition to these 

commercial livestock groups, the 2007 Census of Agriculture 

also revealed that there are about 270,000 deer, 68,000 elk, 

245,000 alpacas and llamas, and 284,000 mules, burros, and 

donkeys. The majority of these animals are raised on private 

land in managed pasture settings and do not significantly affect 

the forage availability on rangelands.

Demand for red meat strongly influences livestock production. 

Per capita consumption of beef declined from about 1985 

through the mid-1990s, but stabilized until the early years of 

the 2000s. Total per capita meat consumption fell between 

2004 and 2009. Per capita red meat consumption is projected to 

continue to decline through 2012, then increase slightly through 

2020 (USDA 2011).

Forage Supply

Demand for grazeable forage is derived from red meat demand 

(not including grazing by wild ungulates). Joyce (1989) concluded that expanded contributions from private land could meet 

the increased demand for grazeable forage from cattle and sheep. 

For the 2000 RPA Rangeland Assessment, Van Tassell et al. 

(2001) tested various scenarios to conclude that changes in supply remained tied to land use changes, and that technology was 

not expected to significantly change the forage supply per unit 

in most regions. Given the projected declines in rangeland area, 

declines in forage supply from that source were also expected.

We used remote sensing to estimate total forage supply. Data 

from the MODIS NPP Collection 4.5 were converted to 
104 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

aboveground biomass, using a series of assumptions regarding 

root-to-shoot ratios, and amount of carbon in biomass. Average 

annual forage supply was estimated to be between 1.9 and 2.6 

trillion pounds of grazeable pasture, rangeland forage, and 

forage beneath tree canopies (table 22). This estimate includes 

forage from rangelands, forested lands, and pasture lands. A 

per-acre average pasture forage figure of 4,000 pounds was 

applied to the estimated pasture area, and an average estimate 

of 215 pounds per acre of forage beneath forested canopies was 

applied to forest land. At these levels, every 1 million acres of 

pasture corresponds to 5.1 million animal unit months (AUMs), 

or approximately 427,000 cattle per year, assuming they are 

grazed or cropped continuously.

Whereas most pasture land in the Southern United States is 

used on an annual basis, only a small fraction of grazeable 

forest land is used (Joyce 1989) and therefore might represent a 

relatively untapped reservoir of forage. Although the estimates 

presented here are rough, the total forage supply should support 

approximately 204 to 280 million animal units per year, or 2.5

to 3.3 billion AUMs at full utilization.

Livestock Grazing on Federal Lands

The Forest Service and BLM administer the largest holdings of 

Federal grazeable lands. During the 1980s and 1990s, permitted 

livestock use fluctuated within 10 percent of 10 million AUMs. 

During the last decade, however, permitted livestock grazing 

use on BLM lands decreased by 12 percent and was at a 

decadal low in 2004 (USDI BLM 2001–2010). In comparison, 

permitted use on NFS lands fluctuated within 19 percent of 7.6 

million AUMs between 2000 and 2008 (USDA Forest Service 

2000–2008).

Permitted livestock use is the sum of all animals permitted to 

graze, whereas authorized or actual use is the actual amount 

of use granted to the permit holder. Authorized or actual use

can differ substantially from permitted use for a number of 

reasons, including drought-induced forage shortage, restrictions 

because of timber harvest or revegetation, or even ranch-level 

economics. Quantifying the nonuse of permitted AUMs on 

NFS lands was discussed in both Joyce (1989) and Mitchell 

(2000). Compared with the 1980s and 1990s, the most recent 

decadal data suggested a slight increase in the proportion of 

nonuse. Between 1977 and 1994, the average nonuse by sheep 

and cattle were roughly 14 and 20 percent, respectively, but 

during the period between 2000 and 2008, those numbers 

both rose to approximately 28 percent, suggesting a slightly 

increasing trend in nonuse. Many areas in the Southwest and 

Intermountain regions were in drought between 2000 and 2008, 

causing a reduction in stocking rates that may partially explain 

the nonuse trend in those areas.

Livestock Appropriation of Forage

Forage is used by a variety of domestic and wild ungulates. Information regarding numbers and spatial distribution of wildlife 

and less abundant livestock are scant and difficult to interpret, 

however. As a result, we focused on forage demand for sheep, 

goats, and cattle, based on data from the National Agricultural 

Statistics Service (NASS), and evaluated the estimated amount 

of NPP allocated to these livestock by comparing forage availability and forage demand on rangeland.

Forage availability was estimated using remote sensing (Running et al. 2004) as described in the section on forage supply. 

NASS data were used to estimate livestock numbers from 2000 

to 2009 at both the State and county levels. Cattle numbers 

were converted to forage demand from rangeland by subtracting the number of cattle fed in feedlots from the total number 

of cattle as determined by census in each county. Because 

feedlot data are not available for goats and sheep, they were 

assumed to use rangeland forage exclusively, and data are only 

consistently available at the State level.

Forage availability showed a slightly increasing trend from 

2000 to 2009. The spatial pattern of forage availability showed 

an increasing gradient from west to east, with lower production 

values in the Southwestern United States and the Great Basin 

(figure 102). Forage demand follows a similar pattern as forage 

availability, increasing from east to west and from north to 

south, with the majority of counties in Arizona, Colorado, New 

Mexico, Nevada, Utah, and west Texas having a forage demand 

between 0 to 9 pounds per acre (assuming a typical 6-month 

grazing period) (figure 102). In most States with significant 

rangeland area, forage demand declined from 2000 to 2009, 

resulting in slightly lower forage demand than in 2000.

Table 22. Estimates of total forage in the conterminous United States from rangeland, pastureland, and grazeable forest land.a

Root:Shoot ratio 

assumption

Forage from 

rangelands

Forage from 

pasturelands

Forage from forest 

lands

Annual total forage 

estimate AUM 

 capacity aboveground biomass (billion pounds)

70:30 1,379 1,103 142 2,624 3,363,707,435

50:50 985 788 142 1,915 2,454,626,153

a

 Estimates do not account for agriculturally derived feedstuffs such as wheat, barley, and sorghum. In addition, estimates do not account for forage present in areas dominated by transitional rangeland. 

See Reeves and Mitchell 2011 for additional details. 

AUM = animal unit month.

Source: Based on Moderate Resolution Imaging Spectroradiometer Collection 4.5 data
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 105

Appropriation 

(%) of aboveground 

biomass to cattle (FD/FA *100)

2000 and 2009

0 to 33

33 to 66

66 to 100

≥ 100

Data incomplete or not evaluated 0 420 840

Miles

210

10

Data incomplete or not evaluated

≥ 16,000

Annual average 

forage availability 

(Pounds per acre)

1

Data incomplete or not evaluated

≥ 20,000

Annual average 

forage demand 

(Pounds per acre)

2000 forage demand

2000 forage availability

2000 NPP appropriated to cattle (FD/FA)

2009 forage demand

2009 forage availability

2009 NPP appropriated to cattle (FD/FA)

Figure 102. Forage availability (FA) and forage demand (FD) on rangeland and aboveground rangeland biomass appropriated to cattle 

(FA-FD) for 2000 and 2009. Values ≥100 indicate areas where estimated rangeland forage is not sufficient to meet the forage demand, 

which is estimated at the county level using the U.S. Census of Agriculture. Missing forage demand estimates result from no data found 

in some States from the Census of Agriculture at the time the data were acquired. Only FD for cattle is shown here because data for 

sheep and goats are only available at the State level.

Source: Forage availability was estimated from the Moderate Resolution Imaging Spectroradiometer net primary production (NPP), Collection 4.5 data
106 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Table 23 shows the comparison of forage availability (FA) and 

forage demand (FD) on rangeland using county-level data. The 

results of the comparison were classified as being “hotspots” 

if FD exceeded FA or as “cool spots” if FD was less than FA. 

From 2000 to 2009, approximately 11.2 million acres of rangeland appear to have unsustainable FD (hotspots), whereas 590 

million acres show a positive trend (cool spots). New Mexico 

showed the fewest hotspots and Texas the most.

This national-scale analysis examining the relationship between 

FD and FA indicates that most regions with significant rangeland area harbor sustainable numbers of livestock and suitable 

quantities of forage. The analysis, however, does not consider 

local rangeland conditions or the direct effect of feedlots or 

pastures on forage availability or forage demand. Nevertheless, 

feedlots and pastures reduce forage demand from rangelands, 

to the extent that feedlots and pastures supplant grazing on 

rangeland.

Climate Change and Rangelands

Climate change will affect U.S. rangelands because changes 

in temperature and precipitation affect vegetation growth and 

distribution. Changes in these climate components will be 

distributed asymmetrically and, therefore, expected effects on 

rangeland vegetation are difficult to characterize as a result of 

uncertainty, regional variability, poorly understood vegetation 

dynamics, and complicated interactions and feedbacks. Research 

is being conducted to examine the effects of modeled future 

climates on rangeland vegetation. Although no projections 

of the effects of climate on rangelands were possible for this 

assessment, available research enables us to suggest some possible future implications of climate change for U.S. rangelands.

Precipitation and temperature have been reliable predictors of 

the extent and distribution of plant groups (e.g., cool-season 

C3 and warm-season C4 species) across the landscape (Epstein 

et al. 1997; Knapp et al. 2001; Paruelo and Lauenroth 1996). 

Changes in these drivers have clear and well-understood implications for vegetation. Rising carbon dioxide levels may complicate these relationships in the future, however. For instance, 

warmer and drier conditions should favor C4 grasses (Knapp et 

al. 2001; Winslow et al. 2003) so that short and tallgrass prairies may stand to benefit, but rising carbon dioxide should favor 

C3 species (Morgan et al. 2004, 2007; Polley et al. 2003, 2006; 

Reich et al. 2001). Further complicating these relationships 

are changing temperature and precipitation regimes. Increased 

variation, intensity, and changes in the timing of precipitation 

can also influence species composition and productivity of U.S. 

rangelands. For example, as springtime temperatures increase 

in the Great Basin, the extent and magnitude of cheatgrass 

infestations may increase.

Most models predict that northern latitudes will warm while 

maintaining or increasing precipitation. This combination of 

factors should enhance productivity on northern and high-altitude rangelands through increased growing seasons for some 

time. If temperatures continue to rise, however, as suggested in 

all of the RPA climate projections, gains in production related 

to longer growing seasons and increased precipitation may be 

offset by decreased moisture availability at some time in the 

Table 23. Breakdown of forage appropriated from U.S. rangelands to cattle at the county assessment level. Analysis represents average 

forage availability (FA) and forage demand (FD), 2000–2009.

State

Area affected through forage appropriated to cattle

Hotspots

(FD > FA)

Cool spots

(FD < FA)

Hotspots

(FD > FA)

Cool spots

(FD < FA)

percent thousand acres

Arizona 1.90 98.10 1,008 52,004

California 6.97 93.03 2,488 33,204

Colorado 1.41 98.59 423 29,536

Idaho 5.67 94.33 1,246 20,715

Kansas 7.22 92.78 954 12,252

Montana 0.49 99.51 239 48,105

Nebraska 1.29 98.71 342 26,238

Nevada 0.01 99.99 6 57,309

New Mexico 0.00 100.00 1 59,617

North Dakota 0.26 99.74 35 13,526

Oklahoma 6.36 93.64 848 12,482

Oregon 0.02 99.98 6 24,823

South Dakota 2.36 97.64 626 25,884

Texas 2.81 97.19 2,651 91,716

Utah 0.49 99.51 143 29,016

Washington 1.19 98.81 96 7,962

Wyoming 0.24 99.76 112 45,659

Total 11,224 590,048
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 107

future. Despite this possibility, recent research suggests that 

increased temperatures, when coupled with increased carbon 

dioxide, actually improve plant water relations because of 

decreased transpirational demand (Morgan et al. 2011). The 

situation is just the opposite in the Southwestern United States, 

where projections indicate increases in temperature coupled 

with decreased precipitation. If this situation unfolds as climate 

projections suggest, rangeland productivity should decrease and 

only the most drought-tolerant species, such as desert shrubs 

and succulents, will prevail. Predicting the future states of 

species assemblages and plant functional groups is probably 

more difficult than evaluating the effects of changing climates 

on productivity, however.

Understanding what changing climates mean for future 

management strategies is difficult. We can note a few possible 

effects that are likely to influence management decisions in 

the future, however. First, although increased carbon dioxide 

generally increases rangeland productivity, it can decrease 

leaf nitrogen content, which decreases protein content and 

therefore nutritional value. This effect implies stocking rates 

and grazing systems will need to be adjusted accordingly so 

that animal performance and rangeland health are not adversely 

affected. Changing species composition may also affect forage 

quality because higher carbon dioxide seems to favor C3 over 

C4 plants, and C3 plants often have higher forage digestibility 

(Wilson and Brown 1983). Recent experimental results, 

however, confound this generality, finding that relatively less 

desirable genera belonging to the C3 photosynthetic pathway 

strongly increased production under increased carbon dioxide 

scenarios on a shortgrass steppe (Morgan et al. 2004, 2007). 

Management strategies aimed at adapting to changing species 

composition could include increased use of alternative livestock, such as goats, that readily use species that are generally 

unpalatable for cattle. In addition, Federal land managers 

and landowners may need to consider a more diverse suite of 

rangeland goods and services that could thrive under a more 

drought-prone environment. Second, warmer temperatures 

will likely result in increased fire frequency and intensity, 

creating more favorable conditions for invasive species such as 

cheatgrass, which would likely decrease overall forage quality 

and biodiversity. Thus, management schemes must be flexible 

and sensitive to changes in species composition resulting from 

climate change. Changes are likely to manifest in unexpected 

ways, and effects may be revealed subtly, suggesting that rigorous and comprehensive monitoring strategies could be needed.

Conclusions

Rangelands occupy around 600 million acres in the conterminous United States. Differences in rangeland area estimates 

reflect inconsistent treatment of woodland species and canopy 

cover thresholds between Federal agency inventory definitions. 

The majority of rangelands are privately owned, the area of 

which has remained relatively constant since 2000. The area 

of rangeland is expected to slowly decline between 2 and 

6 percent by 2060.

The stable rangeland base produces an impressively steady flow 

of goods and services. Livestock numbers have been relatively 

constant: cattle have averaged around 96 million animals per 

year, whereas goats and horses have increased substantially. 

The trend in goat production is likely to continue as ranches 

become smaller and more diversified. Livestock numbers are 

quite sustainable given the current relationship between forage 

demand and forage production on rangelands. Rangeland productivity has remained relatively constant since 2000, although 

the Rocky Mountain Region exhibited a slight but significant 

increasing trend from 2000 to 2009. In fact, our findings suggest that from a national perspective, U.S. rangelands have the 

potential to support a good deal more grazing from both wild 

and domestic herbivores. Indeed, only a small proportion of 

rangelands are chronically overstocked.

The stable rangeland productivity and overall reasonable 

stocking rates contribute to the overall healthy status of U.S. 

rangelands. Only 20 percent of privately owned rangelands 

exhibit notable departure from reference conditions. Exotic 

and native invasive species contribute most significantly to 

decreased health of private rangelands, and shrub encroachment continues to decrease rangeland health, particularly in the 

Great Basin and Southwestern United States. Nonnative species 

are now present on at least 50 percent of private rangelands. 

Invasive species arguably represent the biggest threat to 

rangeland sustainability. The situation is less clear on Federal 

lands because of a lack of rangeland health information on NFS 

lands. The BLM rangeland health evaluation protocols reveal, 

however, that most BLM lands exhibit reasonably healthy characteristics. Our ability to sufficiently inventory and monitor the 

health of all Federal lands is hampered by a lack of effective 

and consistent sampling schemes; a situation noted in previous 

RPA Rangeland Assessments.
108 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 109

Chapter 12. Water Resources

In this chapter, we focus on the vulnerability of U.S. freshwater supplies considering all lands, not just forest and 

rangelands. We do not assess the condition of those lands or 

report on how much of our water supply originates on lands of 

different land covers or ownerships, because earlier Resources 

Planning Act (RPA) Assessment work addressed these topics. 

Regarding the source of water supply, we found that forests 

are the source of more than one-half of the U.S. water supply 

and of fully two-thirds of the water supply in the West and the 

South, and that national forests and grasslands alone are the 

source of one-half of the water supply in the Western States 

(Brown et al. 2008). Because forests are also generally the 

source of the highest quality runoff (Brown and Binkley 1994), 

it is not an exaggeration to say that forests play an extremely 

important role in the provision of water in the United States. 

Regarding the current condition of watersheds with National 

Forest System lands, we found, among other things, that the 

watersheds in the Interior West are generally at lower risk of 

impairment than those along the West Coast, which in turn 

are generally at lower risk than those in the East (Brown and 

Froemke 2010; Brown and Froemke 2012).

Resource Highlights

v Climate change will increase future water demands.

v Projected water withdrawal varies considerably across regions.

v Future water use depends most importantly on the agricultural sector.

v U.S. water yield is projected to decrease.

v The vulnerability of the U.S. water supply will increase. 

v Increases in vulnerability depend both on changes in water yield and on 

growth in water demand.

Off-stream freshwater use in the United States increased more 

than 10-fold during the 20th century in response to tremendous 

population and economic growth. Although aggregate water 

withdrawal in the United States has leveled off in recent years 

and water use efficiency has been improving, future population 

and income growth may place additional demands on raw 

water supplies. As withdrawals increase, more water is often 

consumed, leaving less water in lakes, streams, and reservoirs. 

In addition, climate change is increasing hydrologic uncertainty 

and may reduce available supplies and increase demands. Taken together, these forces are making careful water management 

ever more important and call for a broad-scale understanding of 

the vulnerability of our water supply to shortage.

In assessing vulnerability, we are not attempting to show how 

water allocation will actually change in response to population 

growth and climate change. Rather, we aim to show where and 

to what extent water shortages would occur if populations grew 

and the climate changed as projected, but water management 

infrastructure and allocation procedures did not change and 

past trends in water use rates continued into the future. In other 

words, we are assessing the vulnerability of water supplies to 

shortage and showing where and when adaptation to changing 

circumstances is likely to be most essential.

Many different aspects of water resources could have been covered in this RPA Assessment, including changes in water quality, flooding, dwindling groundwater supplies, and instream 

flow issues. Our focus on shortages of renewable water supply 

should not be taken as an indication that other water-related 

challenges are less important.
110 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Assessing Vulnerability to Water 

Supply Shortage

Vulnerability has been much discussed recently (e.g., Fowler 

et al. 2003; Füssel 2007; Gleick 1990; Vörösmarty et al. 2000). 

Some definitions emphasize not only the likelihood of problems, 

but also the ability to cope with those problems (Schneider et al. 

2007; Wilby and Miller 2009). Given the broad geographic and 

temporal scope of this assessment, a limited definition of vulnerability was adopted, one that focuses on the consequences 

of projected trends if adaptation (e.g., additional conservation 

measures, water trading, and reservoir storage capacity) were 

not forthcoming. We estimate the vulnerability of renewable 

freshwater supply to shortage in the conterminous United 

States from now to 2060 in light of projected socioeconomic 

and climate changes.

Vulnerability is defined here as the probability of shortage, 

equal to the probability that the quantity of water demanded 

exceeds the available supply. “Current” vulnerability is evaluated 

during the 20-year period from 1986 to 2005. Future vulnerability is estimated for three 20-year periods centered at 2020, 

2040, and 2060. For a detailed report on the water supply and 

demand projections and the assessment of future vulnerability 

of freshwater supplies to shortage, see Foti et al. (in press).

Vulnerability is estimated for the 98 assessment subregions 

(ASRs) of the conterminous United States. The ASRs and 

the water resource regions (WRRs) to which they belong are 

shown in figure 103. The ASRs are nearly identical to those 

defined by the U.S. Water Resources Council (1978) for its 

second national water assessment. Most of the ASRs are part 

of linked networks. Two or more ASRs are part of the same 

network when a sequence of water links, either natural (because 

of upstream-to-downstream flow) or artificial (via water diversions), connects them. The ASR-based water supply system 

for the United States consists of three multi-ASR networks 

and 15 single-ASR systems (figure 104). The biggest of the 

three multi-ASR networks includes 69 ASRs in the Central 

and Western United States. The other two multi-ASR networks 

include, respectively, 10 ASRs in the Northeast and 4 ASRs 

in the Southeast. Of the 15 single-ASR systems, 8 drain to the 

ocean, 5 drain into Canada, and 2 are closed basins.

A hydrologic network model (Labadie et al. 1984) was used to 

simulate water management in each water network. The model 

performs year-by-year linear optimizations of water allocation 

in a network consisting of a system of nodes connected by 

links. Each link is subject to capacity constraints and is assigned a priority that reflects the operating rules of the system. 

Each node is a point of water storage, reservoir evaporation, 

and/or water diversion. The simulations provide annual values 

of water flows in any link, storage levels and reservoir evaporation in each ASR, and water assigned to each demand, all of 

which depend on both climate and the set of priorities.

Ideally, the priorities would represent all of the detailed agreements about water storage and allocation that exist across the 

country. Lacking information on many of those agreements, we 

implemented a simple set of priorities in the following order: 

(1) instream flow requirements, (2) trans-ASR diversions, 

(3) consumptive water uses, and (4) reservoir storage. These 

priorities recognize the importance of guaranteeing a minimal 

amount of water for environmental and ecosystem needs before 

water is diverted for other uses and enable transbasin diversions to occur before within-basin diversions. For multi-ASR 

networks, water demands belonging to the same category were 

assigned the same priority regardless of their position in the 

Figure 104. Water networks across the United States at the 

asessment subregion (ASR) level.

Figure 103. Water resource regions (WRR) (numbered) and 

assessment subregions (ASR) of the conterminous United States.

1 = New England. 2 = Mid-Atlantic. 3 = South Atlantic-Gulf. 4 = Great Lakes. 5 = Ohio. 

6 = Tennessee. 7 = Upper Mississippi. 8 = Lower Mississippi. 9 = Souris-Red-Rainy. 

10 = Missouri. 11 = Arkansas-White-Red. 12 = Texas-Gulf. 13 = Rio Grande. 

14 = Upper Colorado. 15 = Lower Colorado. 16 = Great Basin. 17 = Pacific Northwest. 

18 = California.

Natural links 

Artificial links (trans-ASR diversions)

Mexico commitment 

Isolated red dots indicate unconnected ASRs
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 111

network. Because reservoir storage was assigned the lowest 

priority level, water is stored in a given year only after all the 

demands reachable by a reservoir are satisfied. Water stored at 

the end of 1 year, minus an evaporation loss, is available for use 

the following year.

Modeling water allocation at the ASR scale makes the aggregate water supply in the ASR available to meet the aggregate 

water demand in the ASR. It is as if, within an ASR, the water 

were ideally located to satisfy as much of the total demand 

as possible, whereas, in fact, it may not be. A more accurate 

assessment of vulnerabilities could be obtained if the modeling 

were accomplished at a smaller spatial scale.

To capture in a rough sense the uncertainty about the estimates 

of vulnerability, water yields and water demands were 

estimated for each of the RPA scenario-climate combinations 

discussed previously (table 2), enabling nine separate estimates 

of vulnerability for each ASR.

Trends in Water Use: Past and 

Projected

Estimates of water withdrawal across the United States at a 

fairly fine scale are available at 5-year intervals from the U.S. 

Geological Survey (USGS) for the period 1985 through 2005 

(Hutson et al. 2004; Kenny et al. 2009; Solley et al. 1988, 1993, 

1998). Additional USGS water withdrawal data at a larger scale 

are available for the period 1960 to 1980. These data, along 

with data on water use drivers and rates of withdrawal per unit 

of driver, were used to simulate past and current conditions and 

as a source of information to project future levels of desired 

water withdrawal (from surface and groundwater combined) by 

ASR. Consumptive use proportions (the portion of withdrawal 

that does not return to the stream) from the USGS for years 

1985, 1990, and 1995 were then used as the basis for converting estimates of withdrawal to estimates of consumptive use. 

The resulting estimates of desired consumptive water use, also 

called demand in this section, were produced for five water 

use sectors—domestic and public, industrial and commercial, 

freshwater thermoelectric, agricultural irrigation, and livestock 

and aquaculture—which were aggregated to a single estimate of 

demand for modeling vulnerability.

Withdrawal was estimated as number of demand units (e.g., a 

person for domestic use or an irrigated acre for agricultural use) 

times the withdrawal rate (withdrawal per demand unit), plus 

the future withdrawal attributable to climate or other factors 

that are largely unrelated to past levels of water use. Future 

levels of withdrawal rates were estimated by extending past 

trends to show where future water use will go if future supplies 

are no more constraining to withdrawals than in the recent past. 

This extension of past trends, of course, provides an unrealistic 

estimate of actual future water use for some locations, but suits 

our objective of showing where adaptation will be needed as 

population and climatic conditions change. At a large spatial 

scale, water withdrawal rates in most cases have changed gradually, rather than abruptly, presenting an orderly trend. Extrapolation is an accepted approach for projecting future trends when 

the past trend has been orderly, and in the absence of detailed 

knowledge of the underlying mechanisms affecting change or 

adequate data to model those mechanisms (Wilmoth 1998).

For comparison purposes, future water use was first projected 

with no future climate effects, using the population and income 

assumptions of the RPA A1B, A2, and B2 scenarios.15 Climate 

effects on water use for the nine RPA scenario-climate combinations were then incorporated. The following six subsections 

describe past and projected withdrawals and consumptive 

use for the five water use sectors assuming no future climate 

change, with the projections corresponding to population and 

income estimates for the RPA A1B scenario. Those projections 

are then compared with those of the RPA A2 and B2 scenarios, 

followed by the introduction of effects of climate change on 

water use. Results are summarized here for the United States as 

a whole and sometimes also for eastern and western divisions 

of the United States, where the eastern division consists of 

WRRs 1 through 9 and the western division consists of WRRs 

10 through 18 (see figure 103).

Domestic and Public Withdrawals

From 1960 to 2005, total domestic and public withdrawals 

in the United States steadily increased, from 16 to 35 billion 

gallons per day (bgd) (figure 105). The increase in withdrawals 

reflects the steady growth in population, which rose from 177 

to 294 million during that period, and masks an important 

change in the domestic and public per capita withdrawal rate. 

Although U.S. per capita domestic and public withdrawals 

steadily increased from 1960 to 1990, from 90 to 122 gallons 

per day, since 1990 the nationwide withdrawal rate has leveled 

off, fluctuating between 118 and 122 gallons per day. The increasing per capita water use from 1960 to 1990 is attributable 

to a variety of factors, including a decrease in average household size (a certain minimum level of water use per household 

is largely unrelated to household size), the conversion of older 

or rural households to complete plumbing, and an increase in 

use of water-using appliances. These changes are consistent 

with the increasing real incomes and decreasing real domestic 

water prices that were experienced in many areas of the United 

States during the 1960-to-1990 period (Schefter 1990).

The leveling off of the per capita domestic and public withdrawal rate may be the result of conservation programs, the 

15 The RPA HFW scenario does not have any assumptions that vary from the RPA A1B scenerio for the purpose of the water analysis.
112 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

expansion of water metering to previously unmetered taps, rising water rates, and the use of more efficient plumbing fixtures 

in newer homes and renovations, plus the completion of the 

conversion to modern plumbing and tapering off of the drop 

in household size (Brown 2000). Although the recent trends in 

the withdrawal rate do not provide a clear indication of future 

changes, the most recent change, from 2000 to 2005, was a 

decrease in the rates of both the eastern and western divisions.

Assuming a small but consistent decrease in per capita domestic and public withdrawals from 118 gallons per day in 2005 to 

109 in 2060, and a steady increase in total population, from 294 

to 444 million per the RPA A1B scenario, results in a projected 

gradual increase in total domestic and public withdrawals from 

35 to 48 bgd (figure 105).

Industrial and Commercial Withdrawals

Industrial and commercial withdrawals in the United States 

steadily increased from 1960 to 1980, remained at about 36 bgd 

from 1985 to 2000, then dropped to 31 bgd in 2005 (figure 105). 

Because of the great variety of outputs of the industrial and 

commercial sector, the withdrawal rate is measured per dollar 

of total annual personal income (in year 2006 dollars). The 

rate declined from 11 gallons per day per $1,000 in 1960 to 

about 3 gallons in 2005. The drop in withdrawal rate is largely 

attributable to changes in the type and quantity of industrial 

and commercial outputs, such as a shift from water-intensive 

manufacturing and other heavy industrial activity to serviceoriented businesses, and to enhanced efficiency of water use. 

Efficiency improved in response to environmental pollution 

legislation, which regulated discharges and thereby encouraged 

reductions in withdrawals, and technological advances facilitating recycling (David 1990). The most recent data show that the 

rate of decrease in water withdrawal per dollar of income has 

slackened somewhat.

The reasons for past declines in the industrial and commercial 

withdrawal rate—loss of heavy manufacturing plants and 

ever-present environmental concerns—are likely to continue to 

play a role, suggesting that recent past trends in the withdrawal 

rate are a good indication of future changes. Assuming a future 

drop in the industrial and commercial withdrawal rate from 3.0 

gallons per $1,000 per day in 2005 to 1.3 in 2060, and a steady 

increase in total annual income from $11 to $36 trillion, results 

in a projected increase in total industrial and commercial withdrawals from 31 bgd in 2005 to 46 bgd in 2060 (figure 105).

Electric Energy Withdrawals

Freshwater use in the electric energy sector depends largely 

on how much electricity is produced at thermoelectric plants. 

About 90 percent of the electric energy produced in the United 

States is generated at thermoelectric power plants (USDOE 

EIA 2009), which require large amounts of water, mostly to 

cool and condense the steam used to drive the turbines. From 

1960 to 2005, there was relatively little growth in production at 

hydroelectric and other renewable plants, such that production 

at thermoelectric plants grew at an impressive rate in response 

to population growth and the increasing per capita electricity 

use rate. Largely in response to this increasing production of 

electricity, freshwater withdrawals at U.S. thermoelectric plants 

rose rapidly from 1960 to 1980 and somewhat more slowly 

from 1985 to 2005, reaching 143 bgd (figure 105).

This near-complete reliance on thermoelectric power to 

accommodate expanding demand is now changing. Although 

aggregate production at hydroelectric plants is projected to 

remain roughly at its current level into the future, as the modest 

additions to capacity serve only to replace losses, production at 

other renewable plants (e.g., wind and solar), which use very 

little water, has begun to rise and is expected to continue to rise 

until at least 2035 (USDOE EIA 2010).

The average water withdrawal rate at freshwater thermoelectric 

plants dropped consistently from 29 gallons per kilowatt hour 

(kWh) in 1985 to 20 in 2005, as once-through plants—those 

that use water only once before returning it to the stream (at a 

higher temperature)—were retired or converted to recycling 

plants and as new recycling plants were added to the grid. Although withdrawal rates differ markedly between the East and 

West—in 2005, the rate was 24 gallons per kWh in the East but 

only 11 in the West, where recycling is more common—rates 

in all regions have been consistently dropping. The reasons for 

past declines in withdrawal rate are likely to continue to play a 

role, suggesting that recent past trends are a good indication of 

future changes.

Total annual electric energy production at thermoelectric plants 

is projected to grow from 2.5 trillion gigawatt hours in 2005 to 

3.6 trillion in 2060, as the population increases but renewable 

Figure 105. Past and projected annual water withdrawals in 

the United States by water use type, scenario RPA A1B, no future 

climate effects, 1960–2060.

160

140

120

100

80

60

40

20

0

Billion gallons per day

1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

2060

IR TF IC DP LA

Year

DP = domestic and public. IC = industrial and commercial. IR = agricultural irrigation. 

LA = livestock and aquaculture. TF = freshwater thermoelectric.

RPA A1B
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 113

energy sources provide a growing share of total production. 

Countering this growth in production at freshwater thermoelectric plants is the change in withdrawal rate, which for the 

United States as a whole is projected to drop from 20 gallons 

per kWh in 2005 to 12 in 2060. Combining these projections 

yields a projected total withdrawal at freshwater thermoelectric 

plants that drops from 143 bgd in 2005 to 118 bgd in 2035, then 

rises to 121 bgd by 2060 (figure 105).

Irrigation Withdrawals

Total irrigation withdrawals rose rapidly from 1960 to 1980, 

were stable at about 136 bgd from 1985 to 2000, then dropped 

to 128 bgd in 2005 (figure 105). This trend reflects most 

importantly the trend in irrigated acreage, which grew rapidly 

from 1960 to 1980 and, since 1980, has fluctuated between 58 

and 62 million acres. These national totals, however, obscure 

an important regional difference. Irrigated acres in the arid and 

semiarid western division, where the vast majority of irrigation 

occurs, grew steadily from 1960 to 1980, declined steadily from 

1980 to 1995, and in 2005 returned to the 1995 level of about 

46 million acres. The drop occurred as farmers sold some land 

or water to cities, industries, and rural domestic users, and as 

pumping costs, crop prices, and government incentive programs 

caused marginal lands to be removed from irrigation. Irrigated 

acreage in the Eastern States grew continuously from 1960 to 

2005, to 15 million acres, as farmers moved to rely more on 

irrigation water to supplement precipitation during dry times 

(Moore et al. 1990).

Since 1985, the irrigation withdrawal rate in the East has fluctuated between 1.28 and 1.41 feet per acre, and was 1.33 feet 

per acre in 2005, whereas in the West the rate fell consistently 

from 2.95 feet per acre in 1985 to 2.70 feet per acre in 2005. 

The much lower rate in the East is attributable to the higher 

precipitation levels in the East and to the prevalence of more 

efficient (sprinkler, drip) irrigation methods. The drop in the 

West reflects the gradual switch from flood to more efficient 

irrigation methods.

Irrigated acreage in the West is projected to continue the 

downward trend begun in the early 1980s, dropping from 46 

million acres in 2005 to 42 million acres in 2060. In the East, 

irrigated acreage is projected to continue to increase, although 

at a decreasing rate, from 15 million acres in 2005 to 20 million 

acres in 2060. Total irrigated acreage is projected to peak in 

2040 at 63 million acres and drop to 62 million acres in 2060. 

In the West, the withdrawal rate is projected to continue falling, 

reaching 2.4 feet in 2060, whereas eastern rates are projected to 

drop only slightly, reaching 1.3 feet in 2060. Combining these 

trends yields a drop in annual western irrigation withdrawal 

from 110 bgd in 2005 to 91 bgd in 2060, and a rise in eastern 

withdrawals from 18 bgd in 2005 to 23 bgd in 2060, for a total 

change from 128 bgd in 2005 to 114 bgd in 2060 (figure 105).

Livestock and Aquaculture Withdrawals

U.S. livestock and aquaculture withdrawals increased gradually 

from 1960 to 1995, then rose more steeply as the aquaculture 

sector expanded, reaching 10 bgd in 2005 (figure 105). Livestock 

withdrawal per capita has been dropping since at least 1990, 

largely because of changing consumer tastes (Haley 2001). 

In the West, daily per capita withdrawals dropped more than 

35 percent between 1990 and 2005, reaching 12.5 gallons in 

2005, whereas in the East the rate dropped 10 percent during 

the same time period, reaching 4.3 gallons in 2005. By 2060, 

the withdrawal rates are projected to decline to 8.0 gallons per 

capita per day in the West and 3.9 in the East.

Aquaculture withdrawal per capita per day consistently rose 

from 1990 to 2005, from 9.9 to 42.7 gallons in the West and 

from 8.6 to 19.0 gallons in the East. The rate is higher in the 

West because of the prevalence of coldwater species such as 

trout, which benefit from a high dissolved oxygen content and 

are typically farmed using quick once-through withdrawals. 

Farming of warmwater species, which generally employs more 

slowly replenished ponds, is more common in the East, especially in the South. The withdrawal rates are projected to reach 

72 gallons/capita/day in the West and 39 in the East in 2060. 

Total livestock and aquaculture withdrawals are projected to 

increase from 10 bgd in 2005 to 26 bgd in 2060 (figure 105).

Consumptive Water Use

A portion of most water withdrawals returns to the stream and 

becomes available for additional uses downstream. The quantity that does not return to the stream, called the consumptive 

use, is the appropriate quantity to compare with available supplies to assess the vulnerability of water supplies to shortages. 

Consumptive use was computed as a proportion of withdrawals 

based largely on consumptive use rates estimated from USGS 

data, as mentioned previously. Minor increases in these proportions are expected in the thermoelectric and irrigation sectors 

as producers gradually shift to more efficient technologies, and 

decreases are expected in the livestock and aquaculture sector 

as aquaculture grows as a percentage of total livestock and 

aquaculture withdrawal. Consumptive use rates vary widely by 

water use sector and by region of the country within a sector. 

The rates tend to be highest in the irrigation and livestock sectors and lowest in the thermoelectric and aquaculture sectors.

Irrigation was estimated to account for 81 percent of total 

consumptive use in 2005 (figure 106). As irrigation withdrawal 

lowers and some other withdrawals increase (figure 105), the 

portion of total consumptive use attributable to irrigation is 

projected to decrease, to 73 percent in 2060. The domestic and 

public sector was estimated to account for 8 percent of total 

consumptive use in 2005, with the other sectors each accounting for less than 5 percent of the total.
114 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Other Water Uses

In the effort to decrease our reliance on petroleum, many 

changes in liquid fuel production are expected in the coming 

years, most notably a rapid growth of biofuel production. 

Because processing of liquid fuels from biomass and other 

nontraditional sources is a relatively new industry, future 

water use in this sector is not represented in industrial water 

use projections that are based on past water use, and thus were 

computed separately.

In production of alternative liquid fuels, water is used for fuel 

processing and in irrigating some crops used to produce ethanol. Estimates of water use in processing were based on Energy 

Information Administration projections of future production of 

corn-based and cellulosic ethanol, biodiesel, and coal-to-liquid 

fuel needed to meet the renewable fuel standard (RFS) goals of 

the Energy Independence and Security Act of 2007 (USDOE 

EIA 2010). Estimates of additional irrigation attributable 

to ethanol projections were tied to estimates of the effect of 

ethanol on agricultural acreage (Malcolm et al. 2009). Meeting 

the RFS goals is estimated to increase total U.S. consumptive 

water use by 1.3 percent above what would otherwise occur in 

2005. This percentage increase diminishes to about 1 percent 

by 2025 as less water-intensive crops are substituted for corn in 

ethanol production. Irrigation is projected to account for about 

90 percent of the additional consumptive use that is needed in 

2010 for production of liquid fuels, a percentage that drops to 

about 75 by 2060.

Other energy-related water uses involve drilling for oil and gas 

in shale deposits. The United States has vast oil shale reserves, 

but U.S. production of oil shale is in its infancy. Extraction of 

natural gas using newly employed hydraulic fracturing technology is developing as a major new energy source, however. 

Exploitation of these deposits could use significant quantities of 

water, but because of the great uncertainty about future production levels and water needs, we did not attempt to include 

exploitation of shale deposits as a projected water use.

Projected Total Water Use Assuming No Future 

Climate Effects

Based on past trends, and in the absence of future climate 

change, water withdrawal rates were projected to decrease 

in all sectors but livestock and aquaculture. Changes in most 

drivers of water use—population, per capita income, per capita 

electricity consumption—are expected to increase pressure on 

water supplies; the projected decrease in irrigated acreage in 

the West, however, is an exception to this general trend. Combining these factors, in the absence of future climate change, 

aggregate U.S. withdrawal is projected to increase by only 

3 percent from 2005 to 2060 despite a 51-percent increase in 

population under the RPA A1B scenario, whereas consumptive 

use increases by 10 percent (figure 107).

As would be expected given the relative levels of population 

among the three RPA scenarios, the projected withdrawals 

and consumptive use of the RPA A1B scenario fall in between 

the levels of the RPA A2 (higher population) and RPA B2 

(lower population) scenarios. With no future climate effects, 

withdrawals actually decline for many years with the RPA B2 

scenario, although they begin to increase slightly after 2050. 

Withdrawals are slightly greater under RPA A2 than RPA A1B, 

reflecting greater population growth under RPA A2, but also 

higher projected income levels under RPA A1B (figure 108).

Projected changes in water withdrawal vary widely among 

the ASRs. From 2005 to 2060, for the RPA A1B scenario 

(figure 109), withdrawals are projected to drop in 42 of the 

98 ASRs, increase by less than 25 percent in 38 ASRs, and 

increase by more than 25 percent in the remaining 18 ASRs. 

The ASRs where withdrawals are projected to drop are rather 

evenly divided between the East and West, as are the ASRs 

expecting increases above 25 percent.

Figure 106. Past and projected annual consumptive water use 

in the United States by water use type, scenario RPA A1B, no 

future climate effects, 1960–2060. 

90

80

70

60

50

40

30

20

0

Billion gallons per day

1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

2060

IR DP TF IC LA

Year

DP = domestic and public. IC = industrial and commercial. IR = agricultural 

irrigation. LA = livestock and aquaculture. TF = freshwater thermoelectric.

10

RPA A1B

Figure 107. Past and projected annual water use and population in the United States, scenario RPA A1B, no future climate 

effects, 1960–2060.

400

350

300

250

200

150

Withdrawal

Population

Consumptive use 100

50

Billion gallons per day

1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

2060

Year

0

RPA A1B

Million people

400

450

500

350

300

250

200

150

100

50

0
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 115

Projected Water Use Under a Changing Climate

We now add in the future climate change effects to compute 

projected future water use for the nine RPA scenario-climate 

combinations. The effects of climate change on water withdrawals were estimated for irrigation use based on changes in 

precipitation and potential evapotranspiration, for domestic 

and public use based on changes in precipitation and potential 

evapotranspiration, and for thermoelectric use based on 

temperature changes. Whereas temperature is projected to 

increase everywhere (although more in some areas than others), 

precipitation is projected to increase in some areas and decrease 

in others. The precipitation projections of the different general 

circulation models (GCMs) differ considerably, yielding a 

range of resulting changes in water withdrawal. The primary 

climate change effect is that of potential evapotranspiration 

changes on plant water demand, most importantly in irrigated 

agriculture and secondarily in domestic and public landscape 

maintenance. In the thermoelectric sector, the primary effect is 

expected to be temperature increases on space cooling, which 

almost always relies on electricity.

Climate change is projected to increase water use substantially. 

For example, under the RPA A1B scenario, and averaging 

results from the three associated GCMs, U.S. withdrawals 

are projected to increase from 2005 to 2060 by 26 percent as 

compared with only 3 percent without future climate change. 

Of the 23-percent difference, 76 percent is due to increases 

in agricultural irrigation, 10 percent to increases in landscape 

irrigation, and 14 percent to increases in withdrawals at thermoelectric plants to handle the increase in space cooling demand. 

There is great variation across the RPA scenario-climate 

combinations in both projected withdrawals (figure 110) and 

consumptive use. Projections for 2060 vary from 354 bgd with 

the RPA B2-CSIRO-Mk3.5 future to 493 bgd with the RPA 

A2-MIROC3.2 future. Given the 2005 withdrawal level of 347 

bgd, these projections for 2060 represent increases of 2 and 

42 percent, respectively. The MIROC3.2 model projects the 

highest temperatures and lowest precipitation levels of the four 

GCMs for 2060.

Similar to the results under the assumption of no future climate 

change (figure 109), there is wide spatial variation in projections of future water withdrawals under a changing climate 

(figure 111). From 2005 to 2060, based on a GCM multimodel 

average, withdrawals under the RPA A1B scenario are projected 

to drop in 11 ASRs and increase by less than 25 percent in 

Figure 108. Past and projected water withdrawals in the conterminous United States, by RPA scenario, no future climate 

effects, 1985–2060.

400

390

380

370

360

350

RPA A2 RPA A1B RPA B2 Historical

340

330

320

310

Billion gallons per day

1985

1990

1995

2000

2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

2055

2060

Year

300

Figure 109. Percent change in projected water withdrawal, 

by assessment subregion (ASR), RPA A1B scenario, no future 

climate effects, 2005–2060.

Upper end 

of category

– 10

0

10

25

50

150

Figure 110. Past and projected water withdrawal for the conterminous United States for nine RPA scenario-climate combinations and RPA A1B with no future climate effects, 1985–2060. 

Future years are multiyear averages. 

550

500

400

350

300

450

Billion gallons per day

1985 2005 2020 2040 2060

Year

A2-CGCM3.1

B2-CGCM2

A1B-MIROC3.2

A1B-CGCM3.1

Historical

A1B-no climate effects

A2-CSIRO-Mk3.5

A1B-CSIRO-Mk3.5 B2-CSIRO-Mk2

A2-MIROC3.2

B2-HadCM3
116 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

37 ASRs, by from 25 to 50 percent in 35 ASRs, and by more 

than 50 percent in the remaining 15 ASRs. The ASRs where 

withdrawals are projected to drop are mostly in the East, but 

ASRs where withdrawals are projected to increase by more 

than 50 percent are scattered across the country.

Projected increases in consumptive use remain much less than 

50 percent throughout much of the West regardless of RPA 

scenario or climate projection, whereas projected increases in 

the East often reach well above 50 percent, especially for scenario RPA A2. This regional difference reflects principally the 

projected changes in irrigated acres in these two broad regions 

of the United States, with decreases in the West and increases 

in the East.

These projections, and the GCM models on which the projected 

effects of climate change rely, are educated guesses. The wide 

ranges highlight the uncertainty about the effects of increases 

in greenhouse gases on temperature and precipitation across 

the United States. Although we cannot be sure that the ranges 

reported here span the full extent of the future possibilities, it is 

notable that with all nine RPA scenario-climate combinations 

the long-term effects of climate change are always to increase 

aggregate water demands. Further, the principal effect is that 

of increasing temperature on vegetative water demand (for 

agricultural irrigation and landscape maintenance), not that of 

increasing temperature on electricity demand or of changing 

precipitation. Increasing precipitation in some locations ameliorates the effect of temperature increases, but precipitation 

increases, where they occur, are insufficient to balance out the 

temperature effect.

Aside from the projections of climate variables, perhaps the most 

crucial assumption made for projecting future water demand 

is that about future irrigated area, because irrigation accounts 

for the bulk of consumptive use and because irrigation requirements are more sensitive than the other water use categories 

to climate changes. Although recent trends in irrigated area 

provide some basis for extrapolation, unexpected changes in 

world markets for agricultural products could easily alter the 

trajectory.

Future Water Supply

The water supply of an ASR is its water yield as modified (either 

amplified or diminished) by water redistribution (via natural 

flow and artificial diversions) and storage, as explained previously. Water yield, the sum of surface and subsurface runoff, 

was estimated as precipitation minus evapotranspiration using 

Eagleson’s (1978) annual water balance model. The water yield 

model was implemented on a 5x5-kilometer grid for the United 

States and calibrated using three different streamflow datasets 

of measured or reconstructed natural flows.

In light of the lack of comprehensive information on the direct 

effects of elevated atmospheric carbon dioxide on plant water 

use across the various ecological conditions (e.g., mature forest, 

young forest of various species, agricultural crops) (Tubiello 

et al. 2007), these estimates assume no regional-scale direct 

effect of increasing carbon dioxide on plant water use per unit 

area. The major cause of the decrease in future water yield is 

the general increase in potential evapotranspiration that all 

GCMs project. Further, the water yield estimates do not reflect 

the effect of changing vegetation as the climate and land uses 

change over time.

Using annual temperature, precipitation, and potential evapotranspiration estimates from downscaled global climate model 

output, the water yield model was used to estimate future yield, 

and yield estimates were then aggregated to the ASR scale. 

For the United States as a whole, water yield is projected to 

decrease throughout the 21st century (figure 112). Considerable 

uncertainty surrounds the overall level of decrease, however. 

Projections differ by RPA scenario and by climate projection 

for a given scenario. Using the results from the CGCM3.1 

and CGCM2 GCM models, for example, average annual yield 

decreases of 16, 22, and 17 percent are projected by 2060 under 

the RPA A1B, A2, and B2 scenarios, respectively. Taking 

the RPA A1B scenario as an example, average annual yield 

decreases of 22, 16, and 18 percent are projected by 2060 with 

the CGCM3.1, CSIRO-Mk3.5, and MIROC3.2 models. The 

variation in projected yield is primarily the result of differences 

among the models in estimates of temperature and precipitation.

Decreases in yield are projected for most but not all ASRs, as 

indicated in figure 113, which shows changes in yields for three 

RPA scenario-climate combinations. In general, the magnitude 

of the decrease is larger in humid areas (the Eastern United 

States and along the northwestern coast). Increases are projected for a few arid basins, most often in the Southwest (figure 

113). The unexpected increases in average annual yield occur, 

Figure 111. Percent change in projected water withdrawal, 

by assessment subregion (ASR), RPA A1B scenario, with climate 

effects (multimodel average), 2005–2060.

Upper end 

of category

– 10

0

10

25

50

150
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 117

Figure 112. Mean annual water yield in the conterminous United States, by RPA scenario-climate combination for four 20-year 

periods. “Current” yield is evaluated over the 20-year period, 

1986–2005. Future yield is estimated for three 20-year periods 

centered at 2020, 2040, and 2060. 

28

26

22

20

18

16

14

24

Centimeters per year

Current 2020 2040 2060

Year

A2-CGCM3.1

B2-CGCM2

A1B-MIROC3.2

A1B-CGCM3.1

A2-CSIRO-Mk3.5

B2-CSIRO-Mk2

A1B-CSIRO-Mk3.5

A2-MIROC3.2

B2-HadCM3

Figure 113. Change from current conditions to 2060 in assessment subregion (ASR) mean water yield (centimeters per year), 

based on comparing 20-year periods centered at 1996 and 2060, 

for a sample of RPA scenario-climate combinations: (a) RPA A1BCGCM3.1; (b) RPA A2-CSIRO-Mk3.5; and (c) RPA B2-HadCM3.

Upper end 

of category

(a)

(b)

(c)

– 40

– 35

– 30

– 25

– 20

– 15

– 10

– 5

– 1

0

1

5

10

despite increasing potential evapotranspiration and sometimes 

decreasing precipitation, because of increases in the variance

of projected precipitation and potential evapotranspiration; 

an increasing variance produces higher flows in wet times, 

whereas flows during dry times can only drop to zero. Increases 

in average yield are more likely in arid climates because of 

their highly skewed distributions of precipitation and water 

yield. Note that the increases in average yield are very small in 

absolute terms.

As mentioned, supply depends not only on water yield but also 

on storage capacity, transbasin diversions, and instream flow 

requirements. Reservoir storage capacity for each ASR was 

determined by aggregating the normal storage capacities of 

natural and humanmade impoundments for the 1,196 reservoirs 

with a normal surface area of at least 5 square kilometers based 

on the U.S. Army Corps of Engineers National Inventory of 

Dams (USACE 2009). Storage capacities of the ASRs range to 

more than 40 million acre-feet for an ASR along the Missouri 

River (figure 114). Thirteen ASRs have at least 10 million 

acre-feet of storage. Reservoir evaporation was estimated from 

storage-to-surface area relationships and estimates of potential 

evaporation.

Information on trans-ASR diversions—water diverted from 

one ASR to another, usually as a result of legal agreements 

between jurisdictions—is scattered and difficult to gather. We 

relied on summaries by the USGS (Mooty and Jeffcoat 1986; 

Petsch 1985), supplemented by more recent information when 

available (California Department of Water Resources 1998; 

Colorado Water Conservation Board 1998, 2010; Litke and 

Appel 1989).

Figure 114. Assessment subregion (ASR) water storage capacity 

(million acre-feet).

Upper end 

of category

0

2.5

5

10

25

50
118 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Instream flow requirements are meant to ensure adequate 

supply for downstream users, including ecosystems, recreation, 

and hydropower. Determination of instream flow requirements 

involves a complicated mix of socioeconomic, biological, 

and environmental factors, which is not practical at the ASR 

scale. Because instream flow requirements cannot be ignored, 

we adopt the general guideline of Tennant (1976) and set the 

instream flow requirement of each ASR for both current and 

future conditions at 10 percent of average historical streamflow, 

computed from data for the period 1953 through 1985.

Vulnerability of U.S. Water Supply

Vulnerability, the probability that supply is less than demand, 

was computed for each of the 98 ASRs in the United States 

for current conditions and for future conditions of each of 

the 9 RPA scenario-climate combinations. Each estimate of 

vulnerability was based on 20 years of simulation, which were 

used to estimate distributions of vulnerability and other key 

variables (Foti et al., in press). Supply of an ASR in a given 

year was computed as water yield within the ASR plus inflow 

from upstream and net transbasin diversion into the ASR minus 

releases to downstream ASRs, with movements of water into 

and out of an ASR determined by the network model given the 

priorities imposed and storage capacities available.

Current Vulnerability

The climate of the period 1986 through 2005 was taken as the 

current climate. The water supply systems of four-fifths of 

the western ASRs and about one-third of the eastern ASRs are 

vulnerable under current hydroclimatic and socioeconomic 

conditions, although in most ASRs the probability of shortage 

is less than 0.1 (figure 115). The most vulnerable ASRs tend 

to rely heavily on groundwater mining, a nonrenewable source 

of water that was not included in water supply as estimated 

for this analysis. This constraint should not detract from the 

principal focus of the RPA Assessment, which is the change 

in vulnerability from the current situation to the future. Some 

localized, within-ASR areas that are known to have faced 

shortages in the past are not revealed as areas of shortage at the 

ASR scale. This situation is most likely for areas located in the 

upper reaches of an ASR, which places them upstream of the 

bulk of the available water supply in the ASR, as in the case of 

Atlanta (Feldman 2009).

Future Vulnerability

Traces of future water yield for the period 2006 through 2060 

and beyond were obtained by applying the water balance model 

using the climatic estimates of the nine RPA scenario-climate 

combinations. Each simulation used a distinct sequence of 

water demands that also reflect the climatic projections. The 

physical structure of the water network (links and nodes configuration), operating rules, storage capacities, and transbasin 

diversions were left unchanged for all simulations.

Vulnerability was assessed for years 2020, 2040, and 2060, 

each estimate representing 20-year periods centered at those 

years. Increases in vulnerability are projected to occur mainly 

in arid and semiarid areas of the United States where the 

current conditions are already precarious (figures 116 to 119). 

Most of the Eastern United States, on the other hand, is currently characterized by water abundance, and no eastern ASRs 

exhibit a probability of shortage greater than 0.1.

Vulnerability tends to increase over time as the effects of 

climate change become larger (figure 116). For a given RPA 

scenario, projected levels of vulnerability differ considerably 

across the climate models used (figures 117, 118, and 119). 

Compared with the RPA A1B scenario (figure 117), vulnerability 

is generally greater with the RPA A2 scenario (figure 118) and 

generally less with the RPA B2 scenario (figure 119). These 

differences are expected, given the higher population and 

temperatures in the RPA A2 scenario and lower levels of those 

variables in the RPA B2 scenario, especially later in the century. Notably, in all cases, the increases in vulnerability largely 

occur in the southwestern part of the country (California, the 

Southwest, the Great Basin, and the central and southern Great 

Plains).

The increasing vulnerability, evident by comparing figure 115 

with figures 116 through 119, results mainly from the decreasing water supply and increasing water demand caused by the 

changing climate. Increase in population and economic activity 

alone are comparatively minor sources of the increasing vulnerability. Although decreasing precipitation (where it occurs) and 

Figure 115. Current probability of annual water shortage.

Upper end 

of category

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 119

These results assume no modifications to the physical structure 

of U.S. water networks. In addition, instream flow requirements 

and trans-ASR diversions were set constant, thereby ignoring 

possible future changes in surface water redistribution. Indeed, 

it is the purpose of this analysis to point to those locations 

where adaptation (e.g., larger transbasin diversion capacity or 

within-basin water transfers and enhanced water conservation) 

will be most needed. The simulations project a persistent 

decline in reservoir storage for many of the ASRs of the larger 

Southwest, with storage reaching zero and never returning to 

capacity in 10 of those ASRs, most notably in the ASRs along 

the Colorado River that contain Lakes Powell and Mead. This 

projected decline indicates that water scarcity there occurs 

primarily because of supply-demand imbalance rather than 

insufficient storage capacity, suggesting that increasing storage 

capacity there is probably not a successful adaptation strategy. 

Figure 116. Vulnerability (probability of shortage) for RPA A1BCGCM3.1, in the year (a) 2020, (b) 2040, and (c) 2060.Figure 117. Vulnerability (probability of shortage) for the 

RPA A1B scenario in 2060 using the following climate models: 

(a) CGCM3.1, (b) CSIRO-Mk3.5, and (c) MIROC3.2.

(a) (a)

(b) (b)

(c) (c)

Upper end 

of category

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Upper end 

of category

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

increasing potential evapotranspiration both lead to decreases 

in water supply, the major effect comes from increases in 

evapotranspiration.

Furthermore, we find that in roughly half of the ASRs, future 

increases in the vulnerability of the water supply to shortage 

will depend more on decreases in water yield than on growth in 

water demand; in the remaining ASRs, the reverse is true. Total 

water use in the United States has leveled off in recent years, as 

irrigated area in the West has diminished and the efficiency of 

water withdrawals in nearly all sectors has improved. Although 

climate change will increase water demand, future water use 

efficiency improvements will mitigate that effect so that overall 

increases in desired water use in many ASRs are expected to 

be modest in comparison with the climate-induced decreases in 

water yield and thus in water supply.
120 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

The simulations do show that some other ASRs of the larger 

Southwest might benefit from additional storage capacity, 

however. In addition, note that these results apply to aggregate 

ASR storage, and thus do not preclude the possibility of useful 

additions to storage in selected upstream locations.

As figures 117 to 119 make clear, there is much uncertainty 

about the precise levels of vulnerability projected for the ASRs 

of the United States. The utility of this assessment is not in its 

exact estimates of vulnerability but rather in the general pattern 

of changes in vulnerability that emerges—indicating that ASRs 

of the larger Southwest are likely to face substantial adaptation 

challenges—and in the finding that, except in a few ASRs or 

in selected, generally upland, locations, major additions to 

reservoir storage capacity would probably not be helpful.

Conclusions

Estimates of future conditions are inherently uncertain. This 

uncertainty is highlighted by the variation in projected vulnerability among the nine RPA scenario-climate combinations. 

Additional uncertainty arises because the water yield, water 

use, and downscaling models used with all of those combinations rely on numerous assumptions and judgment calls. That 

said, this RPA Assessment represents a concerted effort to 

realistically project future water demand and supply.

Assuming a stable climate, aggregate water withdrawal in the 

United States is projected to rise by 2060 by only 3 percent 

under the RPA A1B scenario. This low level of increase is projected to occur because expected future improvements in the 

Figure 118. Vulnerability (probability of shortage) for the 

RPA A2 scenario in 2060 using the following climate models: 

(a) CGCM3.1, (b) CSIRO-Mk3.5, and (c) MIROC3.2.

Figure 119. Vulnerability (probability of shortage) for the 

RPA B2 scenario in 2060 using the following climate models: 

(a) CGCM2, (b) CSIRO-Mk2, (c) HadCM3.

(a) (a)

(b) (b)

(c) (c)

Upper end 

of category

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Upper end 

of category

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 121

efficiency of water use largely balance out the effects of population and income growth. Correspondingly, consumptive use 

is projected to increase by 10 percent. Climate change has the 

potential to greatly increase water demands, however, especially 

in the agricultural and domestic sectors because plant water 

demands increase as the ambient temperature rises, all else equal. 

Again assuming the RPA A1B scenario, aggregate water withdrawal is projected to increase by from 12 to 41 percent depending on which climate model is used. Corresponding increases in 

aggregate consumptive use with the RPA A1B scenario range 

from 26 to 86 percent. Projections are higher for the RPA A2 

scenario and lower for the RPA B2 scenario. With all RPA 

scenarios and climate projections, decreases in water demand 

are projected for some ASRs, especially in the eastern portion 

of the country, but most ASRs would face increased demands.

Aggregate water yield is projected to decrease with all RPA 

scenario-climate combinations. For example, with the RPA 

A1B scenario water yield is projected by 2060 to decrease 

by from 16 to 22 percent depending on which GCM is used. 

Greater decreases are projected for the RPA A2 scenario. Decreases are projected for nearly all ASRs, and any increases 

are very small in absolute terms.

When assessed at the ASR scale, the larger Southwest—including 

parts of California, the southern Rocky Mountain States, and 

the central and southern Great Plains—is projected to face 

significant water shortages. Most scenario-climate combinations 

show the probability of shortage in any one year reaching above 

the 0.5 level in several basins. The highest vulnerability levels 

occur with the RPA A2 scenario. The CSIRO model yields the 

most widespread positive vulnerability, but the MIROC model 

tends to yield the most ASRs with vulnerability levels above 0.5.

These projections of vulnerability are of course not a prediction 

of future conditions. Clearly they are based on unsustainable 

levels of water use. Rather, the projections show the portions 

of the country that, based on current evidence, are likely to face 

the challenge of bringing water demand more in balance with 

water supply. Achieving such a balance would certainly include 

lowering water demand but may also include some efforts to 

increase supply. Note also that the projections are for renewable water resources (they do not allow for water mining) and 

assume that a minimum level of instream flow is maintained. 

The projected levels of vulnerability suggest that drier areas of 

the United States will continue to experience pressures to mine 

groundwater and deplete streamflow.
122 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 123

Chapter 13. Wildlife, Fish, and Aquatic 

Resources

The ecosystems occurring in the United States support a rich diversity of terrestrial and aquatic species (Ricketts 

et al. 1999). During the last one-half century, scientists and 

managers have learned much about how this diversity contributes 

to the well-being of humans (MEA 2005). The societal benefits 

attributed to wildlife and fish resources are many and include 

the provisioning of food, recreational opportunities, spiritual 

enlightenment, intellectual stimulation, and the maintenance of 

important ecosystem functions (Daily 1997). Increasing human 

populations, land use conversion, and intensive use of natural 

resources may compromise the ability of ecosystems to provide 

these services (Balmford and Bond 2005). For this reason, it is 

important to document changes in wildlife and fish resources 

as a gauge against which to judge whether notable shifts in 

resource status might prompt shifts in resource management 

policies. To that end, we report on recent historical trends in 

populations, harvests, and users of wildlife, fish, and aquatic 

resources; future projections of selected wildlife resources in 

response to land use and climate change; and recent trends and 

patterns of geographic concentration among species considered 

to be imperiled. We focus on species that would likely be affected by forest and rangeland management, but in many cases 

the data we have pertain to aggregate nationwide trends for 

species with diverse habitat affinities.

Resource Highlights

v Wildlife populations and harvests have mixed trends.

v Projected land use changes are expected to reduce the variety of forest bird

species.

v Climate change will differentially stress terrestrial wildlife habitats across the

country.

v Freshwater habitat conditions vary widely across the United States.

v Some commercially and recreationally important fish populations are in decline.

v Biodiversity in the United States continues to erode.

v Concentrations of at-risk species vary geographically.

Wildlife Resources: Status and 

Trends

This section describes the status and trends of wildlife species 

that are commonly harvested for recreation, subsistence, 

or commercial use and trends in breeding birds. Because 

management of resident wildlife is the responsibility of the 

States, population and harvest data on big game, small game, 

and furbearers were largely compiled from cooperating State 

wildlife agencies as coordinated through the Association of 

Fish and Wildlife Agencies. Management of migratory species 

largely rests with the Federal Government, and much of the 

population and harvest data for migratory game birds were 

obtained from U.S. Fish and Wildlife Service reports. Breeding 

bird trends were based on the North American Breeding Bird 

Survey (BBS), an annual survey that provides trends in relative 

abundance of more than 400 bird species nationwide (Robbins 

et al. 1986). Details on data sources and methods are reviewed 

in Flather et al. (in press a).

Big Game

Big game species were important in stimulating public concern 

for wildlife conservation (Organ et al. 2010). Big game 
124 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

includes large mammal species and wild turkey hunted for 

sport or subsistence. State data on big game populations and 

harvest were sufficient to document trends in black bear, whitetailed deer, mule deer, elk, pronghorn, and wild turkey, all of 

which are species affected in some way by forest or rangeland 

management activities.

Trends in big game populations have shown a general pattern 

of increase since the mid-1970s (figure 120). The most substantial increases nationally were observed among wild turkey, 

which grew at an annual rate of 4.8 percent. American black 

bear has also shown robust population growth at a 3.5-percent 

annual rate. White-tailed deer, pronghorn, and elk showed 

National Regional

National Regional

American

black

bear 

(n = 19)

White-tailed

deer

(n = 29)

Mule deer

(n = 9)

Elk

(n = 11)

0

50

100

150

200

250

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

0

20

40

60

80

100

120

140

1975 1980 1985 1990 1995 2000 2005 2008

National North South Rocky Mountain Pacific Coast

0

5,000

10,000

15,000

20,000

25,000

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1975 1980 1985 1990 1995 2000 2005 2008

Population (thouands)

0

500

1,000

1,500

2,000

2,500

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

0

200

400

600

800

1,000

1,200

1,400

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

0

100

200

300

400

500

600

700

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

0

100

200

300

400

500

600

1975 1980 1985 1990 1995 2000 2005 2008

Population (thouasnads)

700

800

900

usands)

700

800

900

usands) Population (thousands)

Year

Year

Year

Year

Year

Year

Year

Year

Figure 120. Population trends in selected big game species for the Nation and RPA regions, 1975–2008. The number of States providing 

population estimates is given by “n =”. Species and regions lacking a graph indicate that no State within that region provided population data.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 125

more modest, but still strong, positive annual growth near 2.5 

percent. Although white-tailed deer numbers have increased in 

the long term, there is some evidence in the North and South 

Regions that such population increases may not be sustainable, 

because populations have remained relatively constant since 

2000. Unlike the other big game species reviewed, mule deer 

population estimates have generally declined. Since 1980, 

mule deer populations have declined annually by 2 percent. 

The causes of this decline are not fully understood owing to a 

complex set of interacting factors, including weather, urban and 

residential development, oil and gas development, habitat loss 

and degradation, predation, and competitive interactions with 

other big game species (Hurley et al. 2011; Mule Deer Working 

Group 2004; Unsworth et al. 1999).

Harvests generally tracked population trends among big game 

species in States where both harvest and population estimates 

were available. Growth in wild turkey harvests (4.8 percent) 

and elk harvest (2.4 percent) was nearly identical to population 

growth. Harvest growth among pronghorn lagged population 

growth rates by nearly 2 percent. Much of the habitat occupied 

by pronghorn occurs either on private lands or where private landowners control access to public habitat. Therefore, 

inadequate hunter access may account for the failure of harvest 

to keep pace with population growth (O’Gara and Morrison 

2004). Harvest growth rates have also lagged population growth 

for black bear—a pattern that may be related to declining hunter participation (Mockrin et al., in press). Harvest growth rates 

among white-tailed and mule deer exceeded population growth 

rates by about 1 percent. In the case of white-tailed deer, this 

situation may reflect intentional liberalization of the harvest in 

an attempt to control what have become overabundant populations throughout much of their range (Côté et al. 2004).

Small Game

Small game species are small-bodied resident mammals and 

birds that can be native or desired nonnative species that were 

intentionally introduced to provide hunting opportunities. Few 

States are able to estimate small game populations over time, so 

we relied on the BBS to provide estimates of abundance trends 

among resident upland game bird species, most of which are 

affiliated with forest, shrubland, or grassland habitats.

Small game populations are highly variable, and several species 

show cyclical patterns that make it difficult to detect population 

National Regional

Elk

(n = 11)

Pronghorn

(n = 14)

Wild turkey

(n = 22)

National North South Rocky Mountain Pacific Coast

National North South Rocky Mountain Pacific Coast

0

100

200

300

1975 1980 1985 1990 1995 2000 2005 2008

Population (th

0

100

200

300

1975 1980 1985 1990 1995 2000 2005 2008

Population 

0

100

200

300

400

500

600

700

800

900

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

0

100

200

300

400

500

600

700

800

900

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands) 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

0

500

1,000

1,500

2,000

2,500

3,000

1975 1980 1985 1990 1995 2000 2005 2008

Population (thousands)

Year

Year

Year

Year

Year

Year

Figure 120 (continued). Population trends in selected big game species for the Nation and RPA regions,1975–2008. The number of 

States providing population estimates is given by “n =”. Species and regions lacking a graph indicate that no State within that region 

provided population data. 

Source: State wildlife agency data request coordinated by the Association of Fish and Wildlife Agencies; data on file with Michael S. Knowles, Rocky Mountain Research Station, Fort Collins, CO
126 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

trends. For example, of the 88 long- (1966 to 2008) and short-term 

(1997 to 2008) population trend estimates depicted in figure 121, 

more than one-half (57 percent) showed no evidence of a trend. 

Northern bobwhite has shown the most substantial and geographically consistent population declines among all species. 

Annual population declines have averaged 3.8 percent in the 

long term and 4.2 percent in the short term—declines that have 

been attributed to urban development, intensive agriculture, 

and habitat fragmentation (Williams et al. 2004). California 

quail was the only species of upland game bird that showed 

evidence of both long- (1.0 percent per year) and short-term 

(2.4 percent per year) population increases at the national level. 

Ring-necked pheasant have shown mixed population trends, 

with long-term declines in the North (2.0 percent per year) and 

Pacific Coast (2.3 percent per year) Regions, but increases in 

the South Region (2.1 percent per year).

The general pattern of small game harvests is one of declining 

trends at both the national and regional levels (Flather et al., 

in press a). The substantial decline in the number of hunters 

pursuing small game (Mockrin et al., in press) has undoubtedly 

played a role in these harvest declines. The greatest harvest 

decline was observed among hares with an average annual 

decline of 7.2 percent since 1975. Quail harvests dropped by 

more than 20 million birds from 1975 to 2008, an average 

annual loss of 5.2 percent. Cottontail harvests declined by a 

similar magnitude, averaging reductions of 4.5 percent annually 

since 1975. More modest declines were observed for squirrel 

(2.9 percent per year), forest grouse (2.9 percent per year), and 

prairie grouse (2.7 percent per year) harvests. The only species 

deviating from this pattern of broad decline was pheasant in the 

South and Rocky Mountain Regions, where harvest estimates 

indicate a general increase in the number of birds bagged since 

the mid-1980s.

National North South

Rocky 

Mountain

Pacific 

Coast National North South

Rocky 

Mountain

Pacific 

Coast

Native

Northern bobwhite

Mountain quail

Scaled quail

California quail

Gambel's quail

Blue grouse

Ruffed grouse

Greater prairie-chicken

Sharp-tailed grouse

Sage grouse

Nonnative

Gray partridge

Chukar

Ring-necked pheasant

Species

Long term

1966–2008 

Short term

1997–2008

Figure 121. Long- (1966–2008) and short- (1997–2008) term population trends in selected upland game birds for the Nation and RPA 

regions from the North American Breeding Bird Survey. Bolded arrows indicate the direction of significant (P≤0.05) trends; minus (–) and 

plus (+) indicate a trend that was not determined to be significantly different from stable. Missing value entries (•) occur when there 

was an insufficient sample (≤14 routes) to estimate a trend. 

Source: J.R. Sauer, personal communication
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 127

Migratory Game Birds

Migratory game birds collectively refer to waterfowl (ducks, 

geese, and swans) and the so-called “webless” migratory species that include mourning dove and woodcock. This species 

group has a rigorous management history that is traceable to 

a series of international agreements signed at the turn of the 

20th century to protect and conserve this important biological 

resource. This focused management has led to the development 

of what many consider to be the leading monitoring system for 

continentally distributed species (Nichols et al. 1995). Many of 

the species in this group will occupy habitat or aquatic ecosystems that are within or in proximity to forests and rangelands.

Waterfowl

Breeding duck population estimates in 2010 were 21 percent 

higher than the long-term (1955 to 2009) average (figure 122). 

After reaching record lows in 1990 (25 million birds), duck 

populations increased nearly 63 percent, to 41 million birds by 

2010. Breeding population trends among the 10 most common 

duck species have been variable. Seven of the 10 most common 

species have 2010 breeding populations that exceed their 

long-term means, with green-winged teal, shoveler, gadwall, 

and redhead exceeding those averages by more than 60 percent. 

Mallard, the most abundant duck (8.4 million), exceeded its 

long-term average by 12 percent. Three duck species remain 

less abundant than their long-term average breeding population: 

scaup (16 percent), northern pintail (13 percent), and American Population (millions)

Year

44

40

36

32

28

24

20

16

12

8

4

0

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Mallard

Pintail

Gadwall

Wigeon

Green-winged teal

Blue-winged teal

Shoveler

Redhead

Canvasback

Scaup

Percent of Population Objective225

200

175

150

125

100

75

50

25

0

Figure 122. Trends in duck populations, 1955–2010, and the relation between 2010 population estimates for 10 principal duck species 

and population objectives specified in the North American Waterfowl Management Plan, measured as percent of objective (inset).

Sources: U.S. Department of the Interior, U.S. Fish and Wildlife Service 2010; Environment Canada Canadian Wildlife Service, and Secretario de Desarrollo Social Mexico, 1994.

widgeon (7 percent). These same three species also remain 

less abundant than the population objectives specified in the 

North American Waterfowl Management Plan (1994) (figure 

122 [inset]). Given that harvests of ducks are established 

adaptively, with population monitoring data feeding harvest 

regulation decisions (Nichols et al. 2007), it is not surprising 

that harvest trends mirror breeding population trends (Flather et 

al., in press a).

Populations of geese and swans (including Canada geese, brant, 

snow geese, Ross’ geese, emperor geese, white-fronted geese, 

and tundra swans) are monitored by surveying 30 separate 

population segments. A total of 11 populations showed at least 

marginal evidence of increases during the 2001-to-2010 period, 

and 16 populations showed no evidence of a trend. Therefore, 

90 percent of goose and swan populations were determined to 

be stable or increasing since 2000. Two populations (Atlantic 

Flyway resident populations of Canada geese and Dusky 

Canada geese) showed at least marginal evidence of population 

declines. The general increasing trend in goose populations is 

reflected in goose harvest trends. Since the early 1990s, there 

has been a steady and substantial increase in the goose harvests 

nationally and across all flyways (figure 123). The one flyway 

showing deviations from this pattern is the Central Flyway—

after reaching peak harvests in 2000, the number of geese taken 

by hunters has declined by nearly 34 percent. Swan harvest 

estimates, like their populations, have been variable, with little 

evidence of a trend (Flather et al., in press a).
128 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Webless Migratory

Woodcock populations continue to show a long-term pattern of 

population decline at an annual rate of nearly 1 percent compared with the 1968-to-2010 period. Regional population trends 

in the eastern and central management areas16 generally mirror 

the national counts, suggesting that the causes for the declines 

are widespread. The population trend estimates are consistent 

with BBS trends—the latter showing an average annual decline 

of 2.5 percent from 1966 to 2008. Recent harvest estimates for 

woodcock have also declined by more than 50 percent since 

1999 in both management areas.

Although they are adapted to urban and rural landscapes, 

mourning doves declined in abundance across all three management areas since the mid-1960s. Unlike woodcock, regional 

call-counts indicated variable population declines, with the 

greatest decline occurring in the western management area (1.3 

percent per year) and the least in the eastern management area 

(0.3 percent per year). Cumulative declines in call counts were 

45, 22, and 12 percent for the western, central, and eastern 

management areas, respectively. The population trend estimates 

are consistent with BBS trends, the latter showing an average 

annual decline of 0.4 percent from 1966 to 2008. Harvests have 

declined since 1999, with the greatest drop occurring in the 

central management area (34 percent). Harvests in the western 

management area have actually increased slightly (5.6 percent) 

since 1999.

Furbearers

Furbearers are a group of mammals, many of which are forest 

dwellers that have traditionally been harvested for the commercial value of their fur (Organ et al. 2001). The nocturnal and 

secretive nature of many furbearers makes it difficult to evaluate the population status of most of these species, so we relied 

on harvest statistics as the only quantitative measure of status 

and trends for this species group. We did not expect harvest 

trends to closely track population trends because variation in 

furbearer harvests is a complex interaction between population 

size, trapping effort, pelt prices, and the susceptibility of species to harvest (DeVink et al. 2011) and because the former are 

strongly influenced by pelt prices (Flather et al. 1999).

Pacific

Central

Mississippi Atlantic

National

Harvest (thousands)4,000

3,200

2,400

1,600

800

0

1960

1970

1980

1990

2000

2010

Harvest (thousands)1,600

1,400

1,200

1,000

800

600

400

200

Harvest (thousands)

0

Harvest (thousands)1,600

1,400

1,200

1,000

800

600

400

200

Harvest (thousands)

0

Harvest (thousands)1,600

1,400

1,200

1,000

800

600

400

200

Harvest (thousands)

0

Harvest (thousands)1,600

1,400

1,200

1,000

800

600

400

200

0

1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

1960

1970

1980

1990

2000

2010

Figure 123. Trends in goose harvest, 1961–2008, nationally and by flyway.

Sources: P. Padding, personal communication; R. Raftovich, personal communication

16 Management areas for webless migratory species (eastern, central, and western) are defined by the U.S. Fish and Wildlife Service.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 129

National trends in fur harvests show three distinct periods: a 

period of rapidly increasing harvest during the 1970s, a period 

of rapidly declining harvest during the 1980s, and a relatively 

stable harvest level since 1990 (figure 124). After reaching a 

peak of 20 million pelts in 1979, harvests declined to 2.7 million pelts in 1990. The previous Resources Planning Act (RPA) 

Assessment documented the strong influence of pelt prices on 

harvest (Flather et al. 1999), with peak prices during the late 

1970s and mid-1980s associated with peaks in harvest (figure 

124). Prices during the 1990s were about 60 percent less than 

peak levels.

Fur harvest trends vary regionally. The North Region has always dominated fur harvests and, since 1995, nearly 70 percent 

of all pelts came from the region (figure 124). Fur harvests in 

the Pacific Coast Region have always contributed the least to 

the national total, accounting for about 1 percent of the total 

harvest during the same period.

Although prices that trappers receive for their pelts are a strong 

determinant of harvest, other factors likely have also played a 

role in fur harvest trends. The number of people choosing to 

trap has declined in recent years (Organ et al. 2001). Furthermore, furbearer management remains controversial and there 

have been efforts by some segments of society to prohibit trapping (Andelt et al. 1999; Conover 2001). Recent research has 

shown that negative opinions about trapping are on the decline 

(Duda et al. 2010), however, and this shift in opinion may be 

traced back to the increasing incidence of wildlife damage 

to personal property and concern for human health (Conover 

2001; Organ et al. 2001; Southwick et al. 2005). In the absence 

of economic incentives (increasing pelt prices), the public will 

bear an increasing proportion of the costs associated with the 

control of furbearer populations.

National

North

South

Pacific Coast

Rocky Mountain

0

2

4

6

8

10

12

Harvest (millions)

fa

0

1

2

3

4

5

6

Harvest (millions)

0

2

4

6

8

10

12

Harvest (millions)

0

2

4

6

8

10

12

Harvest (millions)

1970

1980

1990

2000

2010

0

3

6

9

12

15

18

21

Harvest (millions)

1970

1980

1990

2000

2010

1970

1980

1990

2000

2010

1970

1980

1990

2000

2010

1970

1980

1990

2000

2010

Figure 124. National and RPA regional trends in total fur harvest among 28 species, 1970–2008.

Source: B. White, personal communication
130 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Breeding Birds

Birds have long been thought to be good indicators of landscape 

change, because changes in habitat affect the abundance and 

diversity of bird species that occupy a particular region (Flather 

and Sauer 1996; Pidgeon et al. 2007). We used the BBS to 

evaluate the status and trends among commonly occurring 

species throughout the United States, grouping bird species by 

life-history characteristics to provide a more detailed accounting 

of how birds have responded to changes in their environments. 

We examined three broad bird groups, defined by nest type 

and location, migration status, and breeding habitat type. The 

number of species with increasing, decreasing, and stable trends 

was estimated for each of the life-history groups and was based 

on a hierarchical modeling approach (Sauer and Link 2002).

We documented long-term (1966 through 2008) abundance 

trends to establish the broad geographic pattern of species 

with increasing, decreasing, and stable trends. Second, we 

documented abundance trends since 1996 to assess the degree 

to which these trends have changed from the previous RPA 

Assessment (Flather et al. 1999).

Long-Term Abundance Trends (1966 through 2008)

For the 426 species of birds with sufficient data to estimate 

nationwide trends, 45 percent had stable abundance during the 

42-year period. A higher percentage of species had declining 

trends (31 percent) than increasing trends (24 percent). The 

North Region had the greatest percentage of species with 

declining trends (32 percent), followed by the Pacific Coast (30 

percent), South (25 percent), and Rocky Mountain (19 percent) 

Regions (figure 125). The majority of species in the Rocky 

Mountain and Pacific Coast Regions had stable abundance 

trends (57 and 52 percent, respectively).

Among the bird groups examined (figure 125), species that 

breed in and around human settlement (62 percent), nest on 

or near the ground (49 percent), or nest in grassland habitats 

0% 20% 40% 60% 80% 100%

0% 20% 40% 60% 80% 100%

0% 20% 40% 60% 80% 100%

0% 20% 40% 60% 80% 100%

0% 20% 40% 60% 80% 100%

All Birds

Cavity

Open Cup

Ground

Midstory

Neotropical

Short Distance

Perm. Resident

Woodland

Shrubland

Grassland

Wetland

All Birds

Cavity

Open Cup

Ground

Midstory

Neotropical

Short Distance

Perm. Resident

Woodland

Shrubland

Grassland

Wetland

All Birds

Cavity

Open Cup

Ground

Midstory

Neotropical Short Distance

Perm. Resident

Woodland

Shrubland

Grassland

Wetland

All Birds

Cavity

Open Cup

Ground

Midstory

Neotropical

Short Distance

Perm. Resident

Woodland

Shrubland

Grassland

Wetland

All Birds

Cavity

Open Cup

Ground

Midstory

Neotropical

Short Distance

Perm. Resident

Woodland

Shrubland

Grassland Decreasing Wetland

Stable

Increasing

National

North

South

Pacific Coast

Rocky Mountain

Figure 125. The percent of bird species (by broad life-history groupings) with decreasing, increasing, and stable trends, 1966–2008, for 

the Nation and by RPA region. Species were counted as increasing or decreasing if the trend was different from 0 at P ≤ 0.05.

Source: J.R. Sauer, personal communication
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 131

(44 percent) had the greatest proportions of declining species. 

Given that urban land has been increasing (Wear 2011), the 

high number of species with declining abundance that breed in 

and around human settlement is surprising and may be a result 

of relocation of BBS routes away from urbanizing areas, where 

traffic noise makes it more difficult to detect species (USDI 

USGS 2007). Unlike the findings from the previous RPA Assessment (Flather et al. 1999), we did find different responses 

across migratory strategies. Nearly 40 percent of neotropical 

and short-distance migrants showed significant declining 

abundances compared with only 24 percent of those species 

that are permanent residents. Regional patterns of abundance 

trends among bird groups did indicate that regions already characterized by prominent human effects tended to have higher 

proportions of declining species. Weighted mean percentages 

across bird groups indicated that the North Region had the 

highest percentage declining species on average (40 percent); 

followed closely by the Pacific Coast (39 percent) Region. The 

South Region had an average decline among bird groups of 

34 percent. The Rocky Mountain Region had the lowest mean 

percentage of declining species (22 percent) and was the only 

region where the mean percentage of increasing species (27 

percent) exceeded the percentage of declining species.

Abundance Trends Since the Last RPA Assessment 

(1997 through 2008)

The last RPA Assessment (Flather et al. 1999) reported on bird 

abundance trends through 1996. Estimation of trends from 1997 

to 2008 gave us an opportunity to see if recent trends were 

consistent with long-term trends. Because the trend is being 

estimated over a shorter time period, detection of significant 

trends is statistically more difficult. For this reason, it is not 

surprising that all bird groups showed a much greater percentage of species with stable abundance trends than were observed 

during the longer term (table 24). Compared with the long-term 

trends, there was a higher percentage of species with increasing 

trends (28 percent) relative to decreasing trends (18 percent). 

Among the 12 bird groups, there were 6 cases in which the 

percentage of species with increasing trends exceeded the 

percentage with decreasing trends in the short term—compared 

with only 2 cases in the longer term. These results suggest that 

in the shorter term, bird abundance trends have been dominated 

by species with stable-to-increasing trends since 1997. It is 

noteworthy that species associated with grassland habitats, and 

those that nest on or near the ground showed abundance trends 

that were consistent with the long-term patterns, however, 

which is strong evidence that these species groups have continued to decline in the near term (table 24).

Conclusions

The recent historical trends in wildlife resources show varied 

responses depending on the species considered—a fact that 

by itself suggests variations in resource conditions by region 

or habitat type. This variation in response is no doubt caused 

by a complex interaction involving land use changes that can 

convert or create habitats for different sets of species, shifts 

in the intensity with which humans manage lands that can differentially affect species habitat, shifts in public demands and 

preferences for goods and services provided by wildlife, and 

interactions among wildlife species themselves.

A general pattern of increasing population or harvest trends 

was observed among big game and waterfowl species. When 

considered in a historical context, the observed trends in 

these two groups of species are often considered wildlife 

management success stories (Organ et al. 2010). Population 

gains are not immune to negative resource consequences, 

however. Habitats have limited capacity to sustainably support 

Table 24. Percentage of breeding bird species with increasing, decreasing, and stable trends, 1997–2008.

Species group Total species Increasing Decreasing Stable

percent

All species 426 27.9 18.1 54.0

Nest type/location

Cavity 60 36.7 8.3 55.0

Open cup 178 24.7 27.5 47.8

Ground/low 110 19.1 30.9 50.0

Midstory/canopy 121 32.2 19.0 48.8

Migration status

Neotropical 137 33.6 19.7 46.7

Short distance 101 26.7 29.7 43.6

Permanent resident 88 23.9 12.5 63.6

Breeding habitat

Woodland 130 34.6 16.2 49.2

Shrubland 84 20.2 21.4 58.3

Grassland 27 25.9 29.6 44.4

Wetland/open water 84 23.8 8.3 67.9

Source: J.R. Sauer, personal communication
132 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

individuals, and there is growing evidence that some species 

may be exceeding those limits. White-tailed deer and several 

species of geese are commonly referred to as “overabundant,” 

and these population excesses are being blamed for widespread 

habitat degradation (Ankney 1996; Côté et al. 2004). Because 

there remains a strong interest in maintaining harvestable surpluses of these species, efforts to reduce populations are often 

met with public resistance. Therefore, the long-term population 

increases observed among these species represent an emerging 

unfavorable and controversial resource management issue that 

is deserving of closer scientific and management scrutiny from 

the ecological, biological, and social perspectives (Levy 2006; 

Menu et al. 2002).

For many small game species, webless migratory game 

birds, and birds that generally choose to breed on or near the 

ground, or in grassland and shrubland habitats, population 

and harvest trends (for game species) have shown notable 

declines. Particularly prominent declines were observed for 

northern bobwhite. A common attribute shared by many of 

these species is their association with grassland, farmland, and 

early successional habitat. These species show very little sign 

of recovery from the declines noted in the 1989 and 2000 RPA 

Assessments (Flather and Hoekstra 1989; Flather et al. 1999). 

Although there is local evidence that small game species can 

respond favorably to geographically extensive land use policies 

that provide suitable habitat (Brennan and Kuvlesky 2005), 

these local benefits have not yet translated into population and 

harvest benefits at regional and national scales. Furthermore, 

early successional forest habitats that were once maintained by 

natural disturbance regimes now require active management 

to perpetuate their occurrence across the landscape in more 

densely populated regions (Litvaitis 2003). For these reasons, 

the trends in these species remain an important management 

issue of concern.

Furbearers are a special case, because the data we reviewed 

focus solely on harvest. Fur harvests can change for a number 

of reasons that are independent of population levels, including 

changes in pelt prices, the number of trappers and their effort in 

pursuing furbearers, and changes in the accessibility of land for 

trapping. The notable declines in fur harvest since the last assessment are in large part driven by substantial declines in pelt 

prices. Wildlife damage complaints associated with furbearers 

are likely to become a more prominent management issue in 

the absence of any economic incentives to increase harvests.

Wildlife Resources in the Future

Land Use Change Effects on Forest Bird Species 

Richness

As human populations grow, more and more of a landscape’s 

native habitats are lost to agriculture, road construction, or 

urbanization. One of the more general signs that such land 

conversions may be stressing ecosystems is a reduction in the 

variety of organisms inhabiting a given place (Rapport et al. 

1985). We combined BBS data on bird richness in ecoregions 

supporting forest vegetation, land use cover data from the National Land Cover Database (NLCD) (Vogelmann et al. 2001), 

and housing data from the U.S. Census Bureau (USCB 2001) to 

relate the current pattern of forest bird richness to land use and 

housing variables (Pidgeon et al. 2007). The results were used 

to assess bird diversity response to projected changes in land 

use and housing under three RPA scenarios.17 These scenarios 

are useful for determining the sensitivity of bird communities 

to alternative futures.

Forest bird richness tends to be highest in areas characterized 

by high topographic relief (figure 126). Notable concentrations 

of high forest bird richness occur along the Appalachian 

Mountains, major mountain ranges of the Pacific coast, northern Rocky Mountains, and the Ozark and Ouachita highlands 

of Arkansas and Missouri. The mixed deciduous-coniferous 

forests of the Great Lakes also support high numbers of forest 

breeding birds because of increased habitat diversity associated 

with this boreal-hardwood transition.

Bird groups are defined by broad life-history characteristics 

and include forest birds (species that regularly breed in forest 

ecosystems), neotropical migrants (forest birds that winter 

south of the United States-Mexico border), ground nesting 

17 The RPA HFW scenario was not analyzed because the land use projections for RPA HFW were not available until after this analysis was completed.

Figure 126. Mean forest bird richness observed on a survey 

route, 2007–2009, using the North American Breeding Bird 

Survey.

Source: Sauer et al. 2009

Number 

of species

1–6

6–16

16–22

22–28

28–64
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 133

The RPA land use change projections (Wear 2011) and separate 

housing projections (Radeloff et al. 2010) indicate that intensive land uses and housing development are expected to have 

a greater footprint on forested landscapes. In response to these 

changes in land use and housing, most forest bird communities 

are expected to support a lower variety of species, particularly 

among those forest species that prefer intact interior habitats 

(8 percent decline) or nest on or near the ground (5 percent 

decline) (figure 127). Synanthropes are the one species group 

that show moderate increases (about 3 percent)—a pattern expected given their tolerance for the land uses associated with 

human settlements.

Forest bird communities were relatively insensitive to the 

alternative futures as specified by land use changes in the RPA 

scenarios examined (figure 128a). The least effect on forest bird 

richness was observed under scenario RPA B2—a future with 

lower population growth and intermediate economic growth. 

The greatest decline in forest richness was observed under 

scenario RPA A1B—a future characterized by the greatest loss 

of forest land. Forest birds that prefer to nest in interior habitats 

away from forest edges showed greater sensitivity among RPA 

scenarios (figure 128b). Again, the least effect was associated 

with scenario RPA B2 and the greatest effect was observed 

with scenario RPA A1B. The difference between these two 

responses spanned 2 percentage points. Deviation in predicted 

forest bird richness among RPA scenarios should be interpreted 

Year

Relative change

1.04

1.02

1.00

0.98

0.96

0.94

0.92

2000 2010 2020 2030 2040 2050 2060

Synanthropes

Forest birds

Neotropical migrants

Ground nesting

Interior nesting

Guild

Figure 127. Changes in forest bird richness, by guild, based 

on land use and housing density changes projected to 2060, 

scenario RPA A1B.

Sources: Pidgeon et al. 2007; Wear 2011

(a) (b)

Year Year

Relative change

Relative change

1.04

1.02

1.00

0.98

0.96

0.94

0.92

2000 2010 2020 2030 2040 2050 2060

1.04

1.02

1.00

0.98

0.96

0.94

0.92

2000 2010 2020 2030 2040 2050 2060

RPA A1B RPA A2 RPA B2

Figure 128. Changes in bird richness for (a) species that breed in forest habitats and (b) species that prefer to breed in interior forest 

conditions, by RPA scenario.

Sources: Pidgeon et al. 2007; Wear 2011

(forest birds that build their nest on or near the ground), interior 

nesting (forest birds that prefer to nest away from the edge of 

forest habitats), and synanthropes (forest birds that tolerate and 

thrive in habitats associated with human settlement).
134 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

with caution, however, because such differences did not become evident for several decades into the future (about 2040). 

On the other hand, these results may be conservative, because 

they take into account only gross change in broad land cover 

and use categories (for example, forest, agriculture, and urban) 

and housing density. They do not take into account the direct 

effects of climate change, nor the effects of projected shifts in 

forest management, such as the expansion of forest plantation 

area or declines in forest inventory projected in the RPA A1B 

scenario as compared with other RPA scenarios (because of 

higher biomass energy demands).

Habitat and Climate Change

A great deal of uncertainty remains about how climate change 

will affect biological systems (Parmesan and Yohe 2003). The 

complex feedbacks between climate, land use, land cover, and 

biodiversity (Hansen et al. 2001) make it difficult to predict 

how wildlife may respond to some future climate. Wildlife will 

be affected by habitat changes in response to climate-driven 

shifts in land use and natural disturbances (Dale 1997; Hansen 

et al. 2001). Given that land use and cover are recognized as 

the most important drivers of biodiversity change (Sala and 

Jackson 2006), habitat alterations serve as leading indicators 

of biodiversity response to climate change (Ibáñez et al. 2006; 

Inkley et al. 2004). In this analysis, we assumed that projected 

shifts in habitat under alternative future climates provide information for evaluating potential wildlife resource responses 

and biodiversity risks attributable to climate change across a 

systematic grid.

We ranked each grid cell’s habitat stress from climate change 

across the conterminous United States (Joyce et al. 2008) based 

on its (1) historical baseline climate, (2) future climate from 

global circulation models, and (3) climate-induced changes in 

productivity and distribution of broad vegetation types, as projected by a dynamic vegetation model (Bachelet et al. 2001).18

This information was combined into a single index of habitat 

stress that quantified the degree of change between the recent 

history and the projected climate and vegetation. We defined 

the Terrestrial Climate Stress Index (TCSI) as the sum of three 

separate terms that reflect changes in the climate regime (shifts 

in temperature and precipitation), habitat quality (change in 

productivity), and habitat area (distribution shifts in broad 

vegetation types). We estimated a mean TCSI score for each 

grid cell across a set of different scenarios, climate models, and 

assumptions about the effect of carbon dioxide on plant growth. 

The mean TCSI thus represents the average across a suite of 

alternative futures.

The terrestrial areas most sensitive to climate change in the 

conterminous United States were associated with transitions 

between major biomes and areas of high topographic relief. 

The areas most exposed to habitat stress occurred along the 

grassland-forest land transition throughout the central portion 

of the country and the steep elevation gradients in the Intermountain West (figure 129). The areas least sensitive to climate 

induced habitat stress were located in the southern portions of 

the Great Plains, the Middle Atlantic States from North Carolina 

to southern Pennsylvania, and the eastern coast of Florida. The 

States with the highest TCSI scores tended to be located inland 

and include Iowa and Missouri, whereas coastal States like 

Delaware and Maryland tended to have relatively low average 

TCSI scores. Interestingly, the variability in TCSI scores among 

alternative futures was generally low in high-stress areas and 

high in low-stress areas. This pattern of scenario uncertainty 

(Joyce et al. 2011) indicated that there was relatively greater 

agreement among alternative futures in identifying those regions 

of high stress and less agreement in identifying those regions of 

relatively low stress (Joyce et al. 2008).

Additional uncertainty in the climate stress rankings can be 

traced to factors not included in our definition of stress. The 

TCSI captured the projected shifts in natural vegetation in 

response to climate change, but did not incorporate land use 

as a factor affecting the area of habitat available to species. 

Moreover, we have likely underestimated the effects of climate 

change on terrestrial wildlife habitats in coastal areas, because 

18 The climate projections used to estimate terrestrial climate stress were based on IPCC scenarios A2 and B2, described in Chapter 5. The GCMs used 

here differed from those used in other RPA analyses in the following ways: the GCMs associated with the A2 scenario were earlier model versions of the 

same GCMs used in RPA A2 (the B2 models were the same); the downscaling methods were similar, but the geographic resolution was restricted to 

one-half-degree units of latitude and longitude. See Joyce et al. (2008) and Vulnerability and Impacts of North American Forests to Climate: Ecosystem 

Responses and Adaptation (2011).

Figure 129. Mean Terrestrial Climate Stress Index (TCSI) based 

on the average across alternative futures.

Mean of TCSI

Low (< 0.80)

Moderate 

 (1.11–1.72)

High (> 2.10) Source: Joyce et al. 2008
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 135

we did not account for effects attributable to sea level rise. 

The 2010 State of the Birds report (North American Bird 

Conservation Initiative, U.S. Committee 2010) indicated that 

the potential effects of sea level rise on wildlife resources may 

be considerable, based on findings that showed substantially 

higher proportions of vulnerable species associated with island 

and coastal habitats when compared with relatively low proportion of vulnerable species associated with forest, grassland, or 

arid land habitats.

In addition to quantifying future climate stress with TCSI 

(figure 129), we also quantified two current conservation issues 

that could affect how State wildlife agencies plan and manage 

wildlife resources in the face of climate change. These current 

conservation issues include the prevalence of at-risk species 

and the dispersal resistance that current land use activities may 

have on the ability of species to move across the landscape in 

response to climate change. The prevalence of at-risk species 

was estimated as the count of terrestrial vertebrate species considered to be at risk of extinction according to NatureServe’s 

Conservation Ranks (see Imperiled Species section). Dispersal 

resistance was estimated as the proportion of the terrestrial land 

base under intensive human use (agriculture and developed 

land) from the NLCD (Homer et al. 2007). Generally, the locations where current conservation issues were most pronounced 

(figure 130) tended not to overlap with the location of high 

future stress associated with climate change (figure 129), potentially complicating the efforts of managers to prioritize wildlife 

conservation actions (Joyce et al. 2008).

The mean TCSI was used to identify areas of relatively high 

and low habitat stress across the conterminous United States. 

Such information can provide managers and planners with 

information on the potential for climate-induced stress to 

wildlife habitats within regions and States. Spatially explicit 

information on habitat stress attributed to climate change 

can be integrated with the location of current conservation 

issues (e.g., areas supporting high numbers of at-risk species; 

concentrations of intensive land uses that could affect wildlife 

movements) to evaluate the coincidence of future climate 

change threats with important wildlife conservation issues. 

Furthermore, using several alternative futures enabled us to 

assess the implications of scenario uncertainty on our conclusions. Agreement was more consistent in areas of high stress, 

implying greater confidence, than it was for areas of low stress.

Although the analysis reviewed here provides a repeatable 

process by which climate stress can be evaluated, its use needs 

to acknowledge the limitations associated with its focus on 

terrestrial habitats. Extension of this analysis to include aquatic 

systems and issues associated with sea level rise, as is being 

considered by other vulnerability assessment efforts both 

within and outside of the Forest Service, would broaden its 

applicability (Glick et al. 2011; Tomosy et al. 2011).

Fish and Aquatic Resources

Aquatic ecosystems provide a variety of ecosystem services and 

economic benefits to society. These services and benefits range 

from products as fundamental as safe drinking water to healthy 

and abundant fish populations that provide food for consumers 

and sustain leisure opportunities for recreational anglers.

The ecological condition of these resources is driven by many 

factors. Aquatic systems are the recipients of the byproducts 

from activities in the surrounding landscape and are also 

affected by inputs that can originate from well outside the 

local ecosystem. Nonpoint pollution delivery is affected by 

land cover, forest and rangeland management, and agricultural 

Figure 130. Current conservation issues associated with wildlife resources, including (a) the count of terrestrial species considered to 

be at risk of extinction according to NatureServe’s conservation ranks, and (b) the proportion of the land base that is intensively used 

by humans (agriculture and developed land) as an indication of resistance to species movement (dispersal resistance), in response to 

climate change. Both indices have been normalized to vary between 0 (least concern) and 1 (most concern).

Source: Joyce et al. 2008

Index Index

Low (0.00)

(a) (b)

Low (< 0.01)

Moderate (0.10–0.30) Moderate

 (0.06–0.42)

High (> 0.51) High (> 0.77)
136 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

activities within watersheds (Allan et al. 1997), and freshwater 

ecosystems are particularly affected by the introduction of 

exotic species originating from intercontinental exchanges 

(Rahel 2002). Furthermore, aquatic systems are often sinks 

for elements from atmospheric deposition. As such, the biotic 

communities in these systems are heavily influenced by forces 

external to their immediate surroundings (Kennen et al. 2005).

This section focuses on the general condition of aquatic habitats, species, and usage trends as reflected in existing surveys 

and data collection efforts from multiple sources. Details on 

data sources and methods are reviewed in Loftus and Flather 

(2012).

Freshwater Habitats

There are more than 3.5 million miles of freshwater rivers and 

streams and 40 million acres of lakes and reservoirs (excluding 

the Great Lakes) in the United States (U.S. EPA 2006, 2009). 

These aquatic systems play a vital role in the economic, social, 

and ecological framework of the country. Freshwater lakes and 

reservoirs provide 70 percent of the Nation’s drinking water, 

hydropower for industry, irrigation for agriculture, and transportation corridors for shipping, among other uses (U.S. EPA 

2011a). Ecologically, freshwater resources provide vital habitat 

that supports freshwater and estuarine fisheries, aquatic species 

of conservation concern, and other aquatic resources, which in 

turn support commercial and recreational fishing activities.

Inland lakes

In 2007, the U.S. Environmental Protection Agency (EPA) 

completed the National Lakes Assessment evaluating the condition of freshwater lakes, ponds, and reservoirs of greater than 

10 acres, excluding the Great Lakes, in the conterminous United States (table 25). Overall, 22 percent of the lakes were rated 

in “poor” condition, 56 percent were rated in “good” condition, 

and 21 percent were rated “fair.” In general, natural lakes were 

in better condition than manmade lakes. The upper Midwest 

had by far the greatest percentage of lakes rated in good biological condition, whereas the northern plains ecoregion had the 

greatest number of lakes rated in poor biological condition. A 

comparison of these results to comparable studies in the 1970s 

revealed that 75 percent of the 800 lakes sampled in the 1970s 

showed either improvements or no change in phosphorus levels 

in the 2007 study (U.S. EPA 2009). There is some evidence that 

reservoirs may facilitate the spread of exotic aquatic organisms 

throughout a landscape (Havel et al. 2005).

Wadeable Streams

The EPA conducted an assessment of the biological attributes 

of 1,392 “wadeable” stream locations in the United States in 

2004 and 2005. Wadeable streams constitute 90 percent of 

the stream and river miles in the conterminous United States. 

Overall, 42 percent of wadeable streams were rated in poor 

condition and 28 percent were rated as being in good condition; 

25 percent were fair. Streams in the western mountains ecoregion, where only 25 percent of stream lengths sampled were 

determined to be in poor condition, were in the best biological 

condition. The southern Appalachians, southern plains, and 

northern plains ecoregions had 50 percent or more of their 

stream lengths sampled in poor condition (figure 131).

Great Lakes

The Great Lakes is the largest system of surface freshwater 

on Earth. The five Great Lakes and connecting waters contain 

approximately 20 percent of the Earth’s freshwater and 90 percent of the surface freshwater in the United States (U.S. EPA 

2011b). There is no single assessment of water quality or habitat 

condition that can characterize the state of the lakes. We were 

able to use the compilation of assessments from extensive collaboration between jurisdictions to summarize conditions in the 

Great Lakes, however.

Human settlement within the watersheds of the lakes ranges 

from relatively sparse areas of the northern regions of Lake 

Table 25. Status of the biological condition of lakes larger than 10 acres in the conterminous United Statesa, by ecoregion, based on 

planktonic index.

Ecoregion Number of lakes

Good condition Fair condition Poor condition

percent

Northern Appalachian 5,226 55 30 15

Southern Appalachian 4,690 42 27 31

Coastal Plains 7,009 47 25 27

Upper Midwest 15,562 91 5 4

Temperate Plains 6,327 24 40 35

Southern Plains 3,148 34 36 29

Northern Plains 2,660 1 6 90

Western Mountains 4,122 58 31 11

Xeric West/Southwest 802 35 14 49

Nationwide 56 21 22

a

 Excludes the Great Lakes.

Source: U.S. Environmental Protection Agency 2009
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 137

Superior to some of the most densely populated urban centers, 

including Chicago, Detroit, Buffalo, Cleveland, and other areas 

of the southern parts of the region. To varying degrees, all areas 

face major stresses of toxic and nutrient pollution, invasive 

species, and habitat degradation (U.S. EPA 2011b). Pollution 

sources include sedimentation and agricultural pollutants, 

industrial discharges, runoff and wastewater discharges from 

urban areas, and pollutants from atmospheric deposition.

Overall, the Great Lakes coastal condition is rated as fair to 

poor in the lakes and connecting waterways (U.S. EPA 2008) 

(table 26), although some indicators are improving. Invasive 

nonnative species (e.g., zebra mussel, quagga mussel, and 

round gobies) continue to be an important management challenge in all five lakes. In the lower Great Lakes (Lakes Erie 

and Ontario and connecting waterways), the establishment of 

invasive zebra and quagga mussels has eliminated more than 

99 percent of the native freshwater mussel population. Levels 

of contaminants in colonial waterbirds and fish have been 

improving across all lakes, although organic compounds (e.g., 

Polychlorinated Biphenyls) and mercury continue to persist in 

the food web. The status of biotic communities varies from lake 

to lake, with Lake Superior having a more positive status than 

the other lakes. Lake Superior is the only lake where natural 

reproduction of lake trout has been maintained. The proportion 

of forest land in the landscapes improves water quality, and 

there is a gradient of increasing forest cover from the lower to 

more northern Great Lakes. The proportion of land converted 

to agriculture is higher in the landscapes surrounding Lake Ontario and Lake Erie, where nuisance algal blooms and invasive 

mussels have affected recreation, utility operations, and water 

quality management. The effects of climate change on the 

physical and biotic condition of the Great Lakes are a growing 

concern (U.S. EPA and Environment Canada 2009).

Water Quality

Sections 305(b) and 303(d) of the Clean Water Act require 

States to report attainment of water quality goals for their State 

water bodies. According to the most recent data available 

Ecoregion

Northern Appalachians

Southern Appalachians

Coastal Plains

Upper Midwest

Temperate Plains

Southern Plains

Northern Plains

Western Mountains

Xeric West/Southwest

Nationwide

Macroinvertebrate Index

Good

Poor Fair

Not

assessed

Figure 131. Stream habitat condition as measured by macroinvertebrate index of biological condition as compared to least-disturbed 

reference sites, nationally and by ecoregion.

Source: U.S. Environmental Protection Agency 2006

Table 26. Great Lakes coastal condition based on five quality 

indices.

Index Rating

Water quality (eutrophic condition, water clarity, dissolved 

oxygen levels, and phosphorus concentrations)

Fair

Fish tissue contaminants (concentrations of PCBs, 

mercury, chlordane, dioxin, and toxaphene)

Fair

Sediment quality (toxic contamination) Poor

Coastal habitat (amphibian and wetland-dependent bird 

abundance and diversity, extent of coastal wetlands)

Poor

Benthic community Poor

PCB = Polychlorinated Biphenyls.

Source: U.S. Environmental Protection Agency 2008
138 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

(U.S. EPA 2011d), just less than 50 percent of river and stream 

miles assessed were designated as “good,” nearly 50 percent as 

“impaired,” and 1 percent as “threatened.” For lakes, reservoirs, 

and ponds, nearly 34 percent of the acres assessed were rated 

as good, 66 percent as impaired, and less than 1 percent as 

threatened.

At the national level, the most widespread probable sources 

contributing to these impairments include agricultural activities 

and atmospheric deposition. The combined effects of activities 

associated with urbanization (e.g., urban runoff, municipal 

discharge, industrial activities, and construction) are also significant causative factors behind the water quality impairments. 

Although all of these activities affect aquatic ecosystem health, 

urbanization is known to dramatically affect the condition of 

fish communities, primarily resulting from the effects related to 

increasing impervious surface and road building in these areas.

Fish Population and Harvest Trends

Responsibility for managing freshwater and near-shore marine 

fisheries generally resides with States and, in some cases, 

Native American tribal governments (exceptions exist for 

Federal trust species). Individual States conduct most analyses 

of the status and trends of freshwater fisheries. This system of 

management works well for State fisheries management, but 

does not lend itself to conducting regional or national analyses 

of the status and trends of freshwater aquatic populations (Mac 

et al. 1998). As a result, data for a national assessment are quite 

limited. Little has changed since the findings of previous RPA 

Assessments (Flather et al. 1999; Loftus and Flather 2000). 

Information is still substantially lacking, particularly in the 

freshwater environment, to conduct a comprehensive status and 

trends analysis. For this reason, we summarized the population 

and harvest status of commercial stocks and highlight recent 

efforts to standardize across-State agency data to assess the 

status and trends of important species.

Commercial Populations

A series of interstate fisheries commissions covering U.S. 

coastal waters has improved coordination among the States 

for management and stock assessment of species that migrate 

through multiple jurisdictions. Fisheries occurring predominately in the Exclusive Economic Zone are managed by the 

Federal Government acting through a series of regional management councils and, in some cases, international agreement. 

Data collection has tended to focus on those species of high 

economic or social importance (USDC NMFS 2009a, 2009b).

Twenty-three percent of 253 marine fish stocks were deemed 

to be overfished and 15 percent subject to overfishing (USDC 

NMFS 2009b). These results are similar to those found in 2008, 

when 23 percent of assessed fish stocks were overfished and 16 

percent of the stocks were subject to overfishing. Four stocks of 

fish have been rebuilt.

Pacific salmon have declined throughout much of their range, 

although stocks native to Alaska tributaries are much more 

robust than those in the Pacific Northwest (Loftus and Flather 

2000, 2012; Piccolo et al. 2009). Factors contributing to this 

decline are generally related to the human-induced changes 

to salmon spawning and rearing habitat (Buck and Upton 

2010; Loftus and Flather 2000). Among the factors presenting 

challenges to successful reproduction of Pacific salmon are 

excessive siltation caused by landscape changes (grazing in 

the riparian areas and watershed, other agricultural activities 

removing vegetation, urbanization, and impervious surfaces in 

the watershed, etc.); water removals for irrigation, consumption, and industrial uses; obstructions preventing salmon from 

reaching spawning habitats (hydroelectric facilities, road 

culverts, etc.); and direct physical changes to their spawning 

and rearing habitats (Buck and Upton 2010).

Of the 52 distinct populations of salmon and steelhead in the 

Pacific Northwest, 28 are currently listed under the Endangered 

Species Act (ESA) (Buck and Upton 2010). All but two of 

these species are considered “stable or increasing.” Trends in 

abundance alone may not indicate the true potential for recovery, however. Risk factors such as low levels of abundance, 

lack of access to historical spawning habitats, extirpation of 

component populations, and the lack of spatial connectivity 

among extant component populations are significant factors 

in determining recovery status (USDC NMFS 2010). Of 32 

Northwest Pacific salmon populations assessed, 8 populations 

were classified in the most severe category (“danger of extinction”) and 19 were classified as “likely to become endangered 

in the foreseeable future.” None were classified as “not likely to 

become endangered” (Good et al. 2005).

Commercial Harvest

Landings data alone are not indicative of population change 

because management restrictions (e.g., harvest controls) and 

changes in fishing effort will influence landings. The landings 

data are an indication of human use of aquatic resources. The 

total harvest (edible and industrial) of commercial species by 

U.S. fishermen at U.S. ports in 2008 was 8.3 billion pounds, 

valued at $4.4 billion. This harvest value is a decrease of 11 

percent from the previous year, continuing a trend of the past 

3 years (figure 132a), but it was an increase of 5 percent in 

ex-vessel value compared with the previous year. Finfish accounted for 87 percent of these landings but only 51 percent of 

the value.

Trends in commercial harvest landed in U.S. Great Lakes ports 

continued their decline (figure 132b). There is now evidence 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 139

that nonnative fishes like Asian carps can have ecosystem-scale 

effects on the condition of native fish species (Irons et al. 2007). 

Given that more than 180 nonnative species have already been 

detected in the Great Lakes (including sea lamprey, zebra mussel, round goby, spiny water flea, and Eurasian watermilfoil), 

there are mounting concerns that colonization of the Great 

Lakes by Asian carp species will further erode the integrity of 

the native fish communities and diminish populations of species 

that are important to recreational and commercial fisheries 

(International Joint Commission 2011; Rasmussen et al. 2011).

Conclusions

Since the first RPA Assessment in 1975, the data needed to 

conduct a thorough evaluation of fisheries and aquatic resources 

have slowly improved. Comprehensive water quality data are 

now readily available, a nationwide assessment of the condition 

of freshwater streams and lakes has been conducted, socioeconomic data for extractive use are widely available, and regional 

information-sharing efforts are underway to facilitate the compilation and exchange of fisheries information.

Despite these improvements, data remain lacking for a comprehensive assessment of the status and trends of most aquatic 

biota, including the status of fish species that are an important 

commercial, cultural, and recreational component of society. 

Positive strides have been made to this end, most noticeably 

where jurisdictions share management authority over a common 

stock. On a limited basis, States have developed cooperative 

arrangements for the assessment and management of freshwater 

stocks such as Lake Erie yellow perch and walleye. The inability to assess the status and trends of a single species or species 

complex nationwide hinders the evaluation of large-scale 

factors that may be affecting aquatic populations across larger 

landscapes, however. These data limitation in turn affect the 

ability to conduct large-scale program planning efforts required 

by the Forest Service and other agencies. Programs are underway to improve data for larger scale assessments. Increasingly, 

States are standardizing the way in which they collect, store, 

and report data (Loftus 2006). In addition, multiple Federal 

agencies, including the Forest Service, collect data throughout 

the United States that could be applied to the assessment of 

status and trends of aquatic species.

One such cooperative program for sharing fisheries information 

is the Multi-State Aquatic Resources Information System 

(MARIS). Initiated in the 1990s, MARIS has made incremental 

progress. Assessments of the utility of MARIS (Loftus and 

Flather 2000; Nate and Loftus 2012) have indicated its potential for assessing states and trends of fisheries populations both 

within individual waterbodies and across broader regions. A 

component of the National Fish Habitat Action Plan (NFHAP) 

(see sidebar, National Fish Habitat Assessment) involves a 

national infrastructure to exchange data from disparate sources 

for multiple purposes, including the development of future national fish habitat assessments that incorporate comprehensive 

fisheries data. If this system is fully implemented as planned, 

it will provide a strong basis for future RPA Assessments of 

fisheries and aquatic resources.

12,000

10,000

8,000

6,000

4,000

2,000

0

All species (million pounds)

(a) (b)

Year

Pacific salmon species only (million pounds)

90

50

40

30

20

10

0

All species U.S. Great Lakes landings

(million pounds)

80

70

60

NOAA fisheries data USGS data

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Year

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

All species Pacific salmon

1,200

1,000

800

600

400

200

0

Figure 132. Commercial landings of (a) all marine species and Pacific salmon species in U.S. ports and (b) all species from U.S. 

Great Lakes.a

a

 Great Lakes data from l962–1970 are unavailable.

Sources: U.S. Department of Commerce, National Oceanic and Atmospheric Administration 2010; U.S. Department of the Interior, U.S. Geological Survey 2010
140 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

National Fish Habitat Assessment19

Introduction

The States (represented by the Association of Fish and Wildlife 

Agencies), the U.S. Department of the Interior, and the U.S. Department of Commerce formally adopted the National Fish Habitat 

Action Plan (NFHAP) in early 2006. The Forest Service participates 

in NFHAP through several bodies of the NFHAP; including the 

National Fish Habitat Board, the Federal Agency Caucus, and 

various working groups and national fish habitat partnerships.

The focus of the NFHAP “is to protect, restore, and enhance 

the Nation’s fish and aquatic communities through partnerships 

that foster fish habitat conservation and improve the quality of 

life for the American people” (National Fish Habitat Action Plan 

2006). An initial task of the NFHAP was the development of a 

condition analysis of all fish habitats within the United States. 

This assessment has relied on existing national-level (and in 

some cases large regional-level) datasets of known stressors to 

aquatic habitats.

Methodology

The inland NFHAP assessment focused on streams and rivers 

of the conterminous United States using the 1-to-100,000-level 

National Hydrography Dataset Plus as the spatial framework. 

To predict the condition of fish habitats, landscape disturbance 

was analyzed for every river through a consistent process. This 

process assumed that landscape-scale patterns and human activities reflected for the variables measured correspond to patterns in 

local-scale stressors. Variables used to assess habitat conditions 

included characteristics of the catchment (elevation, slope, 

catchment area, soil permeability, and mean annual temperature 

and precipitation) and anthropogenic stressors (human population 

density, urban land, agricultural land, road density, fertilizer application, grazing pressure, pollution discharge sites, toxic release 

sites, national superfund sites, minimum density, and ground- and 

surface-water withdrawals). Streams were scored according to 

their condition as indicated by these variables in each location.

A variant of this methodology was required for Alaska and Hawaii. 

For this reason, making direct comparisons with the lower 48 

States is difficult. For estuaries, river discharge, pollutant levels, 

eutrophication, and urban, agricultural, and estuarine wetland 

land cover were used to measure habitat stress.

Results

In the conterminous United States, 27 percent of stream miles 

are at high or very high risk of current habitat degradation, and 

44 percent are at low or very low risk. Areas with urban development, livestock grazing, agriculture, and point-source pollution 

had higher risk of degradation, as did areas with high numbers of 

mines and dams. Rural areas without these disturbance factors 

(for example, New England, the upper Midwest, and the Intermountain West) were at lower risk of degradation based on the 

variables analyzed (figure 133).

There is a general pattern among RPA regions of lower habitat 

risk as one moves east to west (figure 133). The two eastern 

regions were characterized by relatively high proportions of 

watershed in the high- or very high-risk categories. The South 

Region had the greatest percentage of watersheds in the very 

high-risk category (12 percent), whereas the North Region had 

the greatest proportion of watersheds in the high-risk category 

(39 percent). In both of these RPA regions, urban development 

and agriculture were important factors in explaining the location 

of high-risk watersheds along the northeast corridor, Florida Gulf 

coast, southern Mississippi valley, and east central Texas.

Watersheds composing the two western RPA regions show a 

general pattern of much lower habitat risk with scattered hotspots 

of high to very high risk (figure 133). Only 14 percent of watersheds in the Rocky Mountain Region and less than 9 percent of 

watersheds in the Pacific Coast Region were determined to have 

high or very high habitat risks. The greatest concentration of 

watersheds that were ranked as at least high risk occur along the 

eastern boundary of the Rocky Mountain Region, where agricultural 

development is a prominent human disturbance on the landscape. 

Grazing was a factor in the high-risk areas in northern Montana, 

whereas intensive row crop agriculture contributed to high-risk 

aquatic habitat in eastern Washington, southeastern Idaho, and the 

Central Valley in California. The effects of urban development on 

aquatic habitat quality can be seen in high-risk areas around Denver, 

CO, Salt Lake City, UT, and Fairbanks and Anchorage, AK.

Risk level

Very high

High

Moderate

Low

Very low

Not scored/unavailable at this scale

North

Pacific

Coast

Rocky

Mountain

South

14.4%

8.7%

44.3%

31.4%

Figure 133. Relative condition of riverine and near-coastal 

habitat based on the variables that indicate risk to aquatic 

habitat quality, by RPA region.

Source: National Fish Habitat 

Board 2010

19 Based on the National Fish Habitat Board (2010).
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 141

Conclusions

The habitat conditions outlined in this report reflect the limited 

range of variables analyzed that represent or are correlated with 

particular habitat stressors. Some factors that are very important 

in specific geographic regions may not be included. Factors 

known to affect the condition of aquatic habitats that lacked 

spatially extensive and consistent collection methods were not 

included in the condition index. For example, although large dams 

and road crossings (that are in the analysis) could be used as 

surrogate indicators for fish passage obstructions, actual smaller 

scale obstructions (e.g., culverts or, conversely, fishways around 

dams) are not reflected in the analysis. Neither are some of the 

significant factors leading to species endangerment, such as 

invasive species (Jelks et al. 2008). Because large-scale stressors 

to aquatic systems, such as urbanization, agriculture, and road 

building, are represented in the analysis, however, the analysis 

has utility for evaluating major threats to aquatic systems at the 

geographic scale (1 to 100,000) analyzed.

Whereas the national-level compilation of scores at broader 

spatial units is useful for policymakers and planners in assessing 

national budgetary and program needs, the finer scale watershed 

analyses will be most useful to on-the-ground efforts to protect, 

restore, and enhance habitats for healthy fish populations. These 

analyses represent the initial attempt to characterize habitat 

factors affecting fish populations and will be refined as additional 

national-scale data become available. The data download and 

mapping capability are made available to fish habitat partnerships and others through an interactive web tool that will enable 

these partnerships to visualize specific habitat factors influencing 

the local and catchment watersheds (http://www.nbii.gov/far/

nfhap/). This delivery mechanism is expected to facilitate application of the assessment to the implementation of restoration 

projects and likely result in an information exchange that will 

help to refine data elements for use in future assessments.

Imperiled Species

Conservation science is concerned with anticipating how 

natural or human-induced disturbance to ecosystems affects 

the pattern of commonness and rarity of the biota inhabiting 

that system. The rarer a species becomes, the greater its risk of 

extinction. Those species whose populations have reached some 

threshold risk of extinction are what we are calling “imperiled” 

species. Identifying those species that are imperiled and identifying those areas where those species are concentrated has long 

been used to judge the status of biodiversity (Flather and Sieg 

2007; Gaston and Fuller 2008).

We continue this approach here and review the trends and geographic patterns of imperiled species, including (1) species formally listed as threatened or endangered under the ESA, and (2) 

species considered to be imperiled based on global conservation 

status ranks. We highlight changes in the counts and geographic 

concentrations of species under these two classifications that 

have occurred in the last decade to assess whether biodiversity 

is improving or becoming more impoverished. Finally, we 

tallied the number of species that have been extirpated from 

each State, highlighting areas where conservation efforts were 

insufficient to maintain species composition.

Imperilment Status

As of October 27, 2010, there were 1,368 total species formally 

listed as threatened or endangered within the United States, a 

net gain of 278 species since the 2000 RPA Assessment. The 

largest increases occurred among plants (152), fish (31), insects 

(27), mollusks (20), mammals (20), and amphibians (8). Since 

March 1, 2006, the listing rate has nearly doubled (about 23 

species per year) over the listing rate observed for the earlier 

one-half of the decade (Flather et al. 2008)—a listing rate that 

could easily be sustained for more than a decade depending on 

the degree of political support for species listings and how rapidly final determinations are made on species that are currently 

proposed (23) or candidates20 (253) for listing.

A set of conservation status ranks developed by NatureServe 

(2011) provided a broader biological assessment of imperilment and revealed that slightly more than one-fourth of all 

vertebrates and one-third of vascular plants are of conservation 

concern (figure 134).21 Vertebrate species of conservation concern are prominent among amphibians (41 percent), freshwater 

fishes (37 percent), and reptiles (21 percent); birds ranked the 

lowest, with 14 percent of species assessed to be of conservation concern. Among invertebrates, mollusks (58 percent) and 

crustaceans (53 percent) have the greatest percentage of taxa 

that are of conservation concern. Taxonomic groups associated 

with aquatic habitats have higher proportions of imperiled 

species than other taxonomic groups, and there is evidence 

that this proportion has been increasing. Jelks et al. (2008) 

found that the number of freshwater fish taxa considered to be 

imperiled or extinct in the United States increased 179 percent 

between 1979 and 2008 and nearly doubled in the last 10 years 

20 Candidates are taxa for which the U.S. Fish and Wildlife Service has sufficient information on file to support proposals to list the species as threatened or 

endangered, but for which preparation and publication of a listing proposal is precluded by other listing activities.

21 The data on global conservation status is based on NatureServe’s Central databases (NatureServe 2010), as queried for counts of species in each 

conservation rank (http://www.natureserve.org/explorer/ranking.htm). Species of conservation concern are defined as those to be presumed extinct (GX), 

possibly extinct (GH), critically imperiled (G1), imperiled (G2), or vulnerable (G3) to extinction.
142 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

of that period. Among those species that are associated with 

forest habitats, the percentage of imperiled species also appears 

to have grown in the short term (Flather et al. in press b).

22 At-risk species include those determined to be critically imperiled (G1), imperiled (G2), and vulnerable (G3) to extinction.

Figure 135. The geographic distribution of species assessed to be at risk of extinction (conservation ranks G1, G2, and G3) for (a) all 

species at the county level, and (b) species associated with aquatic habitats at the eight-digit hydrologic unit level, 2010. Legend 

categories reflect approximately the 0–40th percentile, 40–60th percentile, 60–80th percentile, 80–95th percentile, and >95th 

percentile.

Source: NatureServe 2010

Number of 

species

Number of 

species

0–6

7–10

11–20

21–65

66–385

0–2

3–5

6–10

11–19

20–70

(a) (b)

0

0.2

0.4

0.6

0.8

1.0

Vertebrates

Amphibians

Freshwater fish

Reptiles

Mammals

Birds

Invertebrates*

Mollusks

Crustaceans

Insects*

Vascular plants

GU - Unrankable

GNR - Not yet ranked

G5 - Secure

G4 - Apparently secure

G3 - Vulnerable

G2 - Imperiled

Nonvascular plants*

G-rank

G1 - Critically imperiled

GH - Possibly extinct

GX - Presumed extinct

At-risk

Figure 134. The proportion of species in NatureServe conservation status ranks for vertebrates, invertebrates, and plants. 

*Taxonomic groups with uncertain proportions because many species are awaiting 

conservation assessments. 

Source: NatureServe 2011

The geographic distribution of ESA-listed species has been 

shown to vary geographically (Flather et al. 1994, 1998), 

with prominent concentrations of threatened and endangered 

species occurring in the southern Appalachians, peninsular 

Florida, coastal areas, and the arid Southwest. This pattern has 

remained largely unchanged for the last 15 years. Counties 

that have moved into the highest class of endangered species 

counts based on thresholds used in the 2000 RPA Assessment 

are often in close proximity to counties that remained in the 

highest endangerment class for both the 2000 and 2010 RPA 

Assessments. Exceptions to this pattern include emerging 

concentrations in the Midwest among scattered counties from 

east central Missouri through northern Indiana; counties along 

portions of the Atlantic and Gulf coasts; eastern portions of 

the Edwards Plateau in Texas; the basin and range region of 

southern New Mexico; and the Colorado Plateau region of Utah 

(Flather et al. in press b).

The geographic concentration of at-risk species22 based on 

NatureServe’s criteria show decidedly more contiguous 

concentrations in peninsular Florida, the Florida Panhandle, 

coastal California and Oregon, and the Southwestern United 

States (figure 135a). These more contiguous concentrations of 

at-risk species show some degree of association with human 

population density, human population growth, and areas known 

to support high numbers of species with restricted ranges (Stein 

et al. 2000). Furthermore, the riparian environments in the arid 

southwest maintain important habitats used by much of the 

terrestrial fauna in this region (Levick et al. 2008). If we focus 

on those taxonomic groups that are associated with aquatic 

habitats (figure 135b), then the southern Appalachians and 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 143

scattered watersheds in the Central United States and southeastern coastal plain emerge as prominent areas of concentration 

(Loftus and Flather 2012).

The geographic pattern of extirpated species23 deviates significantly from the current distribution of listed or at-risk species 

(figure 136). Extirpated species are concentrated among Middle 

Atlantic States—a pattern that indicates how much historic 

biodiversity has been altered under human settlement. Given 

the higher proportion of public lands, the lower density of 

human populations, and their larger size,24 it is not surprising 

that Western States have tended to lose fewer species than their 

eastern counterparts.

Conclusions

In a world with limited resources to direct toward biodiversity 

conservation, focusing on the subset of species that are thought 

to have the highest extinction risk is a common priority setting 

strategy (Flather et al. 2011). Targeting species that are vulnerable to extinction has become especially important because 

recent estimates of global extinction rates are two to three orders of magnitude greater than the so-called natural background 

level (Levin and Levin 2002). Because important ecosystem 

functions can be degraded with the loss of species, there is 

concern that the goods and services humans derive from 

ecological systems will become diminished as more species are 

lost or threatened with extinction.

The trends reviewed here indicate that biodiversity in the 

United States has continued to erode since 2000. Moreover, 

patterns of extirpation serve to highlight those regions where 

conservation efforts have failed to maintain the integrity of the 

expected species composition. These national patterns parallel 

what has been observed globally (Mooney and Mace 2009; 

Stokstad 2010), that despite a growing trend in conservation 

investment (Rands et al. 2010; Shifley in press) species imperilment has continued to increase.

Wildlife and Fish Recreation

The American public derives substantial recreational value 

from the Nation’s wildlife and fish resources. Moreover, participation in recreational activities focused on wildlife and fish is 

associated with considerable contributions to local economies: 

hunters and anglers spent $76.6 billion and wildlife viewers 

spent $45.7 billion on equipment and trip-related expenditures 

in 2006 (USDI FWS and USCB 2006). The participation trends 

reviewed here are derived from the National Survey of Fishing, 

Hunting, and Wildlife-Associated Recreation. Participation in 

these activities are reviewed here rather than in the Outdoor 

Recreation chapter to facilitate the comparison between wildlife 

and fish population trends and participation in those recreational 

activities that depend on those populations. Details on data 

sources and methods are reviewed in Mockrin et al. (in press).

Hunting

The total number of hunters grew from 1955 through the 

1970s, slowly declined through 1996, and then declined more 

markedly during the past 10 years (figure 137). After reaching 

a peak of 14.3 million participants in 1975, the number of 

people that hunted wildlife declined during the next three 

decades to 12.5 million participants, a decline of 12.4 percent 

since 1975. The hunting participation rate declined from 7.5 

percent of the U.S. population to 5.5 percent between 1996 and 

2006, resulting in 1.5 million fewer hunters than reported in 

the last RPA Assessment (Flather et al. 1999). The number of 

days spent hunting followed a similar pattern to the number of 

participants, increasing through the mid-1970s but declining 

since then (Mockrin et al. in press).

The pattern of participation among types of hunting activity 

varies (figure 137). Currently, big game hunting is the most 

popular, with 10.7 million participants in 2006. The number of 

Number

of species

6–51

52–77

78–123

124–152

153–319

166

166

166

235

235

259

280

319

Vertebrates Invertebrates

Plants

Figure 136. The geographic distribution of State-level counts 

of species considered to be extirpated from a State for the 

conterminous United States, 2010. Legend categories reflect 

approximately the 0–30th percentile, 30–50th percentile, 50–

70th percentile, 70–85th percentile, and >85th percentile. Pie 

charts represent the proportion of extirpated species that were

plant, vertebrate, and invertebrate species.

23 Extirpation from a State is based on the State-level conservation ranks in NatureServe and document species that historically occurred within a State, 

but are now presumed (SX) or possibly (SH) extinct within that State. State extirpations often occur at the periphery of a species geographic range.

24 All other things being equal, small States are likely to have more extirpations because the range and abundance of species will be smaller than in large 

States.
144 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

hunters seeking big game showed sustained growth through the 

mid-1990s, but declined 5.4 percent from 1996 to 2006. Even 

with these recent declines, the number of big game hunters in 

2006 was nearly three times greater than the number of big 

game hunters in 1955. Small game hunting was the most popular form of hunting up to 1975. Since the mid-1970s, however, 

there has been a sustained and substantial decline in small 

game hunters, with fewer than 4.8 million participants pursuing 

small game by 2006 (a 58-percent decline). The activity with 

the fewest hunters is waterfowl hunting. Like small game, the 

number of waterfowl hunters declined after the mid-1970s. By 

2006, the total number of waterfowl hunting participants was 

less than the number in 1955.

Regionally, the number of hunting participants declined in all 

RPA regions between 1996 and 2006, with significant declines 

(33 percent) observed in the Pacific Coast Region (figure 138). 

Regional trends in the number of participants since 1996 varied 

by hunting activity, but tended to follow national trends. The 

number of big game hunters increased slightly in the South 

Region, declined in the North and Rocky Mountain Regions, 

and declined significantly in the Pacific Coast Region. Small 

game hunter numbers declined significantly (ranging from 26 

to 52 percent) in all regions except the Rocky Mountain where 

declines were not statistically significant. Negative trends in the 

number of hunters pursuing migratory game birds were indicated in all regions, with significant and notable declines in the 

North (26 percent) and Pacific Coast (47 percent) Regions. The 

number of days devoted to each form of hunting showed trends 

that were similar to participation trends with the exception 

of the number of days spent pursuing big game in the North 

Region, which showed a positive but nonsignificant increase 

despite declining participants (figure 138).

Fishing

In 2006, a total of 30 million individuals (13 percent of the U.S. 

population age 16 years and older) participated in recreational 

fishing and spent 517 million days on the water. The most 

popular species caught and the days spent pursuing them were 

black bass (161 million days), panfish (102 million days), 

catfish and bullheads (98 million days), crappie (91 million 

days), and trout (70 million days). From 1955 through 1991, 

the number of anglers grew steadily. Since 1991, the number 

of anglers has decreased by 16 percent, although the number of 

days spent fishing increased by 1 percent (figure 137). Despite 

these substantial declines, fishing is more popular than hunting, 

with nearly 2.5 anglers for every hunter.

North South

Rocky 

Mountain

Pacific 

Coast North South

Rocky 

Mountain

Pacific 

Coast

Hunters

Big game

Small game

Migratory bird

Anglers

Wildlife viewers

Recreation 

Type

Participants Days

Figure 138. General trends in the number of participants and days spent hunting, angling, and viewing wildlife (nonresidential), 

1996–2006. Bolded arrows indicate the direction of significant trends; minus (–) and plus (+) signs indicate a trend that was not 

determined to be significantly different from stable.

Sources: Loftus and Flather 2012; Mockrin et al. in press; U.S. Department of the Interior, U.S. Fish and Wildlife Service; U.S. Census Bureau 2006

0

5

10

15

20

25

30

35

40

1955 1960 1965 1970 1975 1980 1985 1991 1996 2001 2006

Participants (millions)

Angling

Viewing

Hunting

Big game

Small game

Waterfowl

Year

Figure 137. Number of participants 16 years and older in angling, hunting, and nonresidential wildlife viewing, 1955–2006.

Sources: Loftus and Flather 2012; Mockrin et al. in press; U.S. Department of the Interior, 

U.S. Fish and Wildlife Service; U.S. Census Bureau 2006
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 145

Regionally, there is little variance in the participation trends 

since the 2000 RPA Assessment (figure 138). All regions have 

undergone significant declines in the number of anglers since 

1996, with the steepest declines occurring between 2001 and 

2006. The number of days that anglers devote to fishing also 

declined significantly between 1996 and 2006 across all RPA 

regions except the Rocky Mountain. During the period of years 

reviewed in Loftus and Flather (2012), the number of days 

spent angling actually peaked in 1996 in the North, South, 

and Pacific Coast Regions; days devoted to angling peaked in 

2001 for the Rocky Mountain Region. Since reaching those 

peaks, the number of days spent angling has declined in all 

regions except the South, where number of days was unchanged 

between the 2001 and 2006 surveys.

Wildlife Viewing

Surveys of participants in wildlife viewing began in 1980. The 

number of nonresidential wildlife viewers—individuals who 

watched wildlife more than 1 mile from home—declined by 8.1 

percent between 1980 and 2006. The number of days devoted 

to nonresidential wildlife viewing has shown some variation 

from survey to survey, without a clear direction in trend. Days 

initially rose by a statistically significant 19 percent from 1996 

to 2001, then declined slightly (5 percent) by 2006.

Nonresidential wildlife viewing shows a U-shaped trajectory in 

participants for each region, initially declining after 1991, then 

increasing from 2001 to 2006. The Rocky Mountain and Pacific 

Coast Regions have roughly the same number of participants. 

Between the two regions, however, the Rocky Mountain 

Region showed the smallest initial decline in number of 

wildlife viewers and the most positive overall trend in number 

of participants. Since the last RPA Assessment, the number 

of participants has declined significantly in the North Region, 

trended negative in the South Region, and trended positive in 

the Rocky Mountain and Pacific Coast Regions (figure 138). 

The total number of days spent viewing wildlife was stable or 

increasing for each region from 1991 to 2006. The number of 

days devoted to nonresidential wildlife watching has deviated 

somewhat from the participation trends, trending positive in the 

North, South, and Pacific Coast Regions and trending negative 

in the Rocky Mountain Region (figure 138).

Role of Public and Private Lands in Wildlife 

Recreation

More than one-half of all hunters reported hunting solely on 

private land. Between 1991 and 2006, there was a decrease 

in hunters using both public and private land (from a high of 

30 percent to 24 percent in 2006), with these hunters shifting 

to rely only on private land. Public lands hosted 54 million 

days of hunting (25 percent of all hunting days), whereas 164 

million days, or 75 percent of all hunting days, took place on 

private land (USDI FWS and USCB 2006). In contrast to hunting, most nonresidential wildlife viewers pursue viewing on 

public land. From 1991 through 2006, an average of 51 percent 

of all wildlife viewers used public land exclusively, with 30 

percent dividing their time between public and private land and 

11 percent relying exclusively on private land.

Public lands, including National Forest System (NFS) lands, 

are nationally important for recreation. About 205 million 

Americans live within 100 miles of a NFS boundary. In 2008, 

the Forest Service’s visitor monitoring program reported an 

annual 14.4 million visits to NFS lands for the primary purpose 

of hunting, with an additional 2.3 million visits primarily for 

wildlife viewing.

Conclusions

The economic and ecological effects of changing participation 

in wildlife and fish recreation are substantial, and understanding these changes is essential if resource managers are to adjust 

their management goals. The United States has a long history 

of wildlife and fish recreation, but these recreation patterns are 

currently shifting. Only 5.5 percent of Americans over the age 

of 16 currently hunt wildlife and 10 percent view wildlife away 

from home. Fishing is the most popular activity, with about 

13 percent of the population participating in this activity. In 

the last couple of decades, the number of participants in these 

activities has shown a general pattern of decline.

As wildlife recreation participation changes, the rationale and 

funding for wildlife management is also changing. Wildlife 

conservationists are now becoming concerned that the core 

principles guiding wildlife management in the United States 

could be in jeopardy (Mahoney and Cobb 2010). Clearly, the 

trends reported here do provide evidence that such concerns 

are justified. The number of participants in recreational hunting 

is not only declining in absolute terms, but also the collective 

voice of those who actively seek wildlife and fish for harvest 

or observation is declining even more substantially relative 

to a growing American population. Such trends suggest that 

relying on license fees and excise taxes on hunting and fishing 

equipment as the primary funding mechanism supporting 

wildlife conservation in the United States will be insufficient 

to maintain a science-based management program in the future 

(Regan 2010).
146 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 147

Chapter 14. Outdoor Recreation

Outdoor recreation plays a significant role in American lives. In this chapter, we begin by describing the 

outdoor recreation resources available in the United States, then 

describe the status and trends in outdoor recreation participation, regional variation in recreation use, and differences in 

participation by demographic groups. Tracking these trends is 

important because both the public and private sector make large 

investments and have management responsibilities as providers 

of recreation opportunities. The final section presents projections of outdoor recreation participation to the year 2060.

Resource Highlights

v Outdoor recreation resources are expected to decline on a per-person basis.

v Outdoor recreation participation continues to grow, but activity choices are 

changing.

v Outdoor recreation choices are strongly influenced by socioeconomic 

characteristics.

v Future outdoor recreation participation will reflect the preferences of a 

changing U.S. population. 

v Growing recreation demand may be constrained by recreation resource 

availability.

Outdoor Recreation Resources

The United States has extensive land and water resources 

(table 3). Public lands held in trust by local, State, and Federal 

Governments are crucial resources for nature-based outdoor 

recreation. Although recreation also occurs on private land 

ownerships, data are less available to describe and evaluate the 

role of private lands. Therefore, we focus on public lands and 

their management in assessing outdoor recreation resources.

Local Government Lands

Local governments own a small percentage of total public 

lands, but these holdings are highly important because they are 

almost entirely in close proximity to people. With 83 percent 

of Americans living in metropolitan areas, the location of local 

parks and recreation areas may be more important than the size 

or number of facilities.

Many of these facilities are used for sports, but they also 

include green spaces and areas for activities such as hiking 

and bird watching. Across the United States, 12 percent of 

local governments provide park and recreation services, with 

the largest providers being counties, followed by municipal 

governments.25 The South Region has the highest proportion 

of local governments providing parks and recreation services, 

and the Rocky Mountain Region has the lowest. The North 

Region has the highest number of parks and recreation units. 

Given its smaller population base, the Rocky Mountain Region 

has the highest number of park and recreation units per capita, 

followed by the North, South, and Pacific Coast Regions.

25 This proportion is relatively low because nearly one-half of the Nation’s 76,425 local government units are Special Districts, only 2 percent of which 

provide park and recreation services. The percentage is much higher for county (41 percent) and municipal (29 percent) governments.
148 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Urban parklands are an important resource in areas of high 

population density. According to 2011 City Park Facts (Trust 

for Public Land, 2011a), the 100 most populous cities operate 

more than 1.5 million acres of parks and recreation areas within 

their city limits. Parkland area per resident varies widely. For 

example, even though New York City has one of the largest 

park areas for a high-density city, there are still only 4.5 acres 

per thousand residents, as compared with a less densely populated city such as Phoenix, AZ, with a similar park area, but 

28 acres per thousand residents. Visitor use of these parks is 

extremely high: Central Park in New York City is estimated to 

have the highest visitation, at 35 million visitors per year.

State Lands

States manage a variety of lands that can provide recreation 

opportunities, including State parks, State forests, State wildlife 

areas, and other designations. State lands tend to occupy a 

niche between the heavily natural land-dependent Federal 

lands and the much more facility- and development-oriented 

local lands and parks. A key feature of State resources is their 

proximity to populated areas, especially in the Eastern United 

States, where State lands play a much more significant role in 

providing outdoor recreation opportunities than in the West, 

where Federal land dominates. Still, because of the lower 

population in the West, there are more State park system acres 

per capita in the West than in the East.

State park systems account for almost 14 million acres, and 

include State parks, recreation areas, historic sites, and other 

special categories (figure 139). Total acreage in State park 

systems increased 6 percent between 2002 and 2009. The North 

and Pacific Coast Regions have the largest area (about 5.2 million acres each), the South Region has about 2.2 million acres, 

and the Rocky Mountain Region about 1.4 million acres.

About 25 million acres of U.S. forest land are managed by 

State forestry agencies. The largest proportion (64 percent) is 

in the North Region, followed by the Pacific Coast Region (20 

percent). State wildlife and fish agencies own about 19 million 

acres, and manage an additional 12 million acres through easements and leases (Wildlife Management Institute 1997). These 

lands are often available for recreation purposes, especially 

fishing, hunting, and wildlife watching.

Federal Lands

Federal lands cover about 640 million acres in the United 

States, about 28 percent of the total land area. Nearly all 

Federal land is open and available to the public for recreation. 

More than 92 percent of Federal land is located in the West, 

with about 36 percent of all Federal land in Alaska. The Forest 

Service and Bureau of Land Management manage the majority 

of Federal land. Table 27 shows the acres of land managed by 

the main U.S. land management agencies and the distribution 

by Resources Planning Act (RPA) region. Because the Pacific 

Coast Region includes large Federal holdings in Alaska, table 

27 shows acreage for Alaska separately.

Congressional designations offer additional direction to the 

management of Federal lands that often affect the recreation 

opportunities available on those lands. Wilderness areas 

Figure 139. Percent of State park system acres, by type of area, 

2009.a

Other areas 19%

Natural areas 8%

Total acres = 13,973,344

Historic sites 1%

Recreation areas 9%

State parks 63%

a

 Natural areas include environmental education sites and areas classified as scientific sites. 

Other areas include forests, fish, and wildlife management areas, and other miscellaneous 

State park system sites.

Source: USDA Forest Service 2009b

Table 27. Acres of Federal landa in the United States by agency and RPA region, 2008.

Federal agency North South Rocky 

Mountain

Pacific

Coastb Alaska United States

Percent of 

U.S. total

thousand acres

Forest Service 12,240 13,320 99,419 45,764 21,970 192,713 30

National Park Service 1,349 5,195 11,080 10,087 51,114 78,825 12

U.S. Fish & Wildlife Service 1,711 4,357 9,893 1708 76,836 94,504 15

Bureau of Reclamation 0 197 5,470 854 0 6,522 1

Bureau of Land Management 4 44 142,962 31,843 78,513 253,367 40

Tennessee Valley Authority 0 248 0 0 0 248 <.1

U.S. Army Corps of Engineers 2,557 7,104 3,540 526 19 13,746 2

All Federal agencies 17,862 30,466 272,364 90,782 228,452 639,926 100.0

a

 Acres reported by the Bureau of Reclamation and U.S. Army Corps of Engineers include water area. Data from the Tennessee Valley Authority include developed recreation lands only. 

b

 Pacific Coast acreage in this column does not include Alaska.

Source: Cordell et al. in press
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 149

represent the most pristine and protected of Federal lands, 

with more than 109 million acres (see chapter 6 for additional 

information about the National Wilderness Preservation System). Other congressionally designated acres provide unique 

recreation resources: National Recreation Areas (NRAs), 

National Wild and Scenic Rivers (NWSRs), and National Recreation Trails (NRTs). NRAs are intended to serve primarily as 

a recreation resource and be accessible to population centers. 

There are 41 NRAs covering 7.4 million acres. The NWSR 

designation requires qualifying rivers to have outstanding 

scenic, wild, and/or recreation values. A little more than 12,500 

miles were designated as of June 2009, with the Pacific Coast 

Region containing more than one-half of the designated areas. 

The NRT system is unique in that it can be managed by any 

government agency at any level of government. In 2009, there 

were slightly more than 20,000 miles in the system, with the 

East accounting for almost 70 percent of the total trail miles.

Federal Recreation Facilities

Federal lands provide numerous types of recreation facilities to 

accommodate the wide range of U.S. recreation demands. The 

Recreation Information Database (2009) records information 

for more than 9,000 defined recreation facilities on Federal 

lands. Nearly 96 percent of those facilities provide campgrounds; hiking and fishing opportunities are a distant second 

and third, respectively, with about 34 percent of facilities 

providing such opportunities. As expected, the large majority 

of facilities are located in the West, where most Federal land 

occurs, although the proportion of facilities in the West (73 

percent) is less than the proportion of Federal land in the West 

(92 percent). Although the Rocky Mountain Region dominates 

in almost all categories in number of facilities, interpretive 

programs and visitor centers are particularly highly represented 

in the North Region, and boating facilities are well represented 

in the South Region, reflecting regional preferences and opportunities. Figure 140 shows the number of facilities available 

for specific recreation activities by the four RPA regions. The 

Rocky Mountain Region has more than 10 times the number 

of available Federal facilities per capita than both the North 

and South Regions, and nearly twice as many as the Pacific 

Coast Region. The combination of a much smaller population 

base and large concentrations of Federal land, much of which 

is managed for recreation use, help explain this significant 

advantage in the Rocky Mountain Region.

Private Lands and Enterprises

Although outdoor recreation is often associated with public 

lands and facilities, private lands also play an important role 

in outdoor recreation. Survey results indicate that total days of 

recreation on private forest land is a small number in relation to 

those on public forest land, especially in the West, but there is 

still a significant amount of use of private lands, particularly for 

certain recreation activities (table 28). The dramatic differences 

Figure 140. Number of Federal recreation facilities, by activity 

availability (or type of facility) and RPA region, 2009.

Camping

Hiking

Fishing

Picnicking

Recreational 

vehicles

Biking

Horseback 

riding

Hunting

Boating

Wildlife 

viewing

1,000

Rocky Mountain Pacific Coast South North

Number of facilities

0 2,000 3,000 4,000

Table 28. Number and percent of annual recreation activity days on privately owned forest by activity group and region, 2005–2009.a

Activity group

East West United States

Annual days Annual Days Total private 

annual days

Percent of

all days

All annual 

days

millions % millions % millions % millions

Visiting recreation and historic sites 834 28 262 9 1,096 37 2,960

Viewing and photographing nature 12,175 34 3,332 9 15,507 43 35,865

Backcountry activities 580 19 237 8 817 26 3,119

Motorized activities 488 43 91 8 579 51 1,126

Hunting 242 47 38 8 280 55 512

Cross-country skiing 11 30 4 10 14 40 36

a

 Days and percentages may not sum across exactly to national totals because of rounding. Cross-country skiing was the only winter activity with sufficient annual days data in forested settings. All annual 

days are the sum of days that occur on private and public lands. 

Source: National Survey on Recreation and the Environment 2005–2009, Version 3b, n = 5,374
150 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

between East and West reflect the larger population in the 

Eastern United States and the relative balance of public land. 

Activities such as hunting and motorized activities have traditionally been common on private lands in the East, whereas in 

the West there are abundant opportunities on public lands for 

these activities.

Many private landowners recreate on their own land and allow 

friends and relatives to recreate there as well. Family forest 

owners have indicated that recreation is one of many reasons 

for owning land (Butler 2008). Only 15 percent of family 

forest land is open to the general public for recreation, but that 

amounts to almost 40 million acres of forest land (Butler 2008). 

Recreation also occurs on agricultural land. There has been a 

growing trend for farms to include agritourism attractions as 

part of their operations, which often include allowing recreation 

activities such as hunting, fishing, and horseback riding (Brown 

and Reeder 2007). One study estimated that American farms 

produced recreational experiences for about 62 million people 

in 2001 (Barry and Hellerstein 2004).

Private lands also provide access to public lands that have 

recreation opportunities. Studies such as Radeloff et al. (2005, 

2010) demonstrated that historic housing growth in the United 

States in close proximity to public lands has occurred at higher 

rates than overall housing growth and that these trends are 

likely to continue. This type of development can also be seen 

in close proximity to State parks, which tend to be distributed 

more evenly with population than Federal lands. Both primary 

and secondary home development provide major means of 

access to public lands. The number of secondary (or seasonal) 

homes in the United States increased from slightly more than 

2 million in 1970 to about 3.6 million in 2000 (USCB 2010).

Private commercial businesses are significant providers and 

facilitators of outdoor recreation opportunities on both public 

and private lands. For example, in 2007, there were 4,413 

privately operated recreational vehicle parks and campgrounds 

in the United States. More than 69 percent of these facilities 

are in the Eastern United States, with the North Region having 

the greatest number. Other types of private facilities include 

recreational and vacation camps, marinas, nature parks, and 

skiing facilities.

The private sector also plays an important role in providing 

concession services on public lands. Concessions on National 

Forest System lands, U.S. Army Corps of Engineer reservoirs, 

and the national parks include ski areas, lodges, campgrounds, 

camp stores, and outfitting and guide services, among others. 

Private-sector concessions have played a significant role in 

State parks for decades, operating similar types of services.

Recreation Resource Availability: Present 

and Future

The availability of recreation resources in relation to people 

can be shown geographically by mapping the distribution of 

population against recreation resource distribution. Because 

Federal lands and State lands are assumed to be relatively fixed 

over time, we can compare recreation resources against the 

U.S. population projected to 2060. If the resource base remains 

fixed, per capita availability can only decline over time. 

The ability of recreation resources to meet future recreation 

demands also depends on future recreation participation rates 

and the distribution of recreation participants in relation to 

recreation resources. We are not able to map the geographic 

distribution of participants at the same geographic scale (county 

level) as total population, however.

We evaluated recreation resources within 75 miles of the 

center of a county as a measure of opportunities for recreation 

activities for residents of each respective county that can be 

accessed within a day. The dominant influence of the western 

Federal lands is quite evident, as almost all of the counties with 

the highest per-person availability are west of the Mississippi 

River (figure 141). Population growth and redistribution of the 

Acres of Federal 

and State park 

land per person

0 < 0.01-0.06 0.07–1.35 1.36–13.82 > 13.82

Figure 141. Acres of Federal and State park land per county 

resident, 2008 (top) and 2060 (bottom), within the 75-mile 

distance zone from the county center, based on the RPA A1B 

population projection. 

Sources: USDA Forest Service 2008; U.S. Department of the Interior, National Park Service 2009; 

U.S. Department of the Interior, Bureau of Land Management 2009; USDA Forest Service 2009b
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 151

population change the proportions of per-person acres available 

in 2060. Although the number of counties with the highest 

category of acres available shrinks by 2060, those areas are 

still concentrated in the West. Assuming constant Federal and 

State land area, counties that are projected to gain population 

will see declining recreation resource availability per resident, 

whereas counties projected to lose population will experience 

an increase in recreation resource availability per resident. 

Similar evaluations are available for future availability of water 

resources, snow, mountains, and other categories of recreation 

resources (Cordell et al., in press).

Outdoor Recreation Participation

Participation Trends

The number of U.S. participants26 in 50 nature-based outdoor 

recreation activities (table 29) increased 7.1 percent between 

2000 and 2009, and the number of activity days increased 40 

percent (Cordell 2012).27 Outdoor recreation participation grew 

dramatically through the 1960s and 1980s. Traditional activities, such as fishing, maintained popularity.28 Activities such 

Table 29. Trends in number and percentage of people age 16 and older participating in nature-based outdoor activities, 1994–1995, 

1999–2001, and 2005–2009.a

Activity

1994–1995

total

participantsb

1999–2001

total

participantsc

millions

2005–2009

total

participantsd

2005–2009

percent of 

population

Percent change 

1999–2001 to 

2005–2009

View natural scenery NA 127.1 149.8 63.7 17.9

Visit outdoor nature center/zoo 110.9 121.0 133.3 56.6 10.2

Sightsee 117.5 109.0 123.9 52.7 13.7

View wildflowers/trees NA 93.8 121.3 51.6 29.4

View wildlife besides birds and fish 62.8 94.2 118.1 50.2 25.4

Visit a beach 128.8 84.4 102.0 43.3 20.7

Swim in lakes, streams, etc. 87.4 85.5 97.5 41.5 14.0

View or photograph birds 54.3 68.5 84.1 35.7 22.8

Day hike 53.5 69.1 79.7 33.9 15.4

Visit a wilderness NA 67.2 79.1 33.6 17.7

Gather mushrooms/berries NA 60.0 77.2 32.8 28.6

View salt/freshwater fish 27.6 52.3 63.5 27.0 21.4

Visit waterside besides beach NA 53.2 56.5 24.0 6.3

Developed camp 46.5 55.3 56.0 23.8 1.1

Warmwater fish 49.3 47.6 55.7 23.7 17.1

Motorboat 59.5 50.7 55.0 23.4 8.6

Visit archaeological sites 36.1 44.0 48.8 20.8 11.1

Off-highway vehicle drive 35.9 36.0 48.4 20.6 34.5

Take Boat tours or excursions NA 40.8 46.1 19.6 13.1

Bicycle on mountain/hybrid bike NA 44.0 42.7 18.1 – 3.0

Primitive camp 31.4 33.1 34.2 14.5 3.2

Coldwater fish 25.1 28.4 30.9 13.1 8.7

Saltwater fish 22.9 21.4 25.1 10.7 17.2

Backpack 17.0 21.5 23.2 9.9 7.9

Canoe 17.9 19.3 22.8 9.7 18.2

Waterski 22.7 16.0 21.3 9.0 33.1

Use personal watercraft 12.0 19.1 21.1 9.0 10.9

Horseback ride on trails 15.1 15.8 16.1 6.8 1.6

Downhill ski 22.8 17.4 15.9 6.8 – 8.5

Snorkele 16.2 13.6 15.2 6.5 11.8

Kayak 3.4 7.0 14.2 6.0 103.8

Mountain climb 9.0 13.2 12.4 5.3 – 5.9

Snowboard 6.1 9.1 12.2 5.2 33.7

Snowmobile 9.6 11.3 10.7 4.5 – 5.5

Anadromous fish 11.0 8.6 10.7 4.5 24.1

Sail 12.1 10.4 10.4 4.4 – 0.4

Cave 9.5 8.8 10.4 4.4 18.4

26 A participant is any individual 16 years of age or older who engaged in one or more recreation activities during the 12 months before the survey interview 

date.

27 The National Survey on Recreation and the Environment (NSRE) is the primary source of data for participation trends. It is a general population telephone 

survey of people 16 years of age and older that asks about outdoor recreation participation and other topics related to conservation and natural resources.

28 Hunting and fishing numbers from the NSRE vary from the numbers reported by the U.S. Fish and Wildlife Service (FWS) survey presented in Chapter 13. 

Hunting and fishing, as referenced for the NSRE respondents, includes any amount of participation, whether or not it was the primary activity of choice for 

an outing, whereas the FWS survey focuses on primary participants. Surveys are also conducted in different years, complicating comparison of trends.
152 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Table 29 (continued). Trends in number and percentage of people age 16 and older participating in nature-based outdoor activities, 

1994–1995, 1999–2001, and 2005–2009.a

Activity

1994–1995

total

participantsb

1999–2001

total

participantsc

millions

2005–2009

total

participantsd

2005–2009

percent of 

population

Percent change 

1999–2001 to 

2005–2009

Rock climb 7.5 9.0 9.8 4.2 9.5

Row 10.7 8.6 9.4 4.0 8.9

Orienteer 4.8 3.7 6.2 2.6 67.8

Cross-country ski 8.8 7.8 6.1 2.6 – 21.7

Migratory bird hunt 5.7 4.9 4.9 2.1 – 1.1

Ice fish 4.8 5.7 4.8 2.1 – 15.5

Surf 2.9 3.2 4.7 2.0 46.3

Snowshoe NA 4.5 4.1 1.7 – 9.4

Scuba dive NA 3.8 3.6 1.5 – 5.6

Windsurf 2.8 1.5 1.4 0.6 – 10.1

as camping, canoeing, kayaking, and bicycling grew rapidly, 

influenced partly by improving equipment technology. New 

activities appeared and there were few declines in participation.

Table 29 presents trends in participation in nature-based outdoor 

recreation activities in three time periods between 1994 and 

2009. The most popular outdoor activity was viewing natural 

scenery. Activities oriented toward viewing and photographing 

nature have been among the fastest growing activities, both 

in terms of number of participants and days of participation. 

Off-highway vehicle driving realized a 34-percent increase in 

participants. Several physically challenging activities, such as 

kayaking, snowboarding, and surfing also had relatively large 

increases.

Although there were increases in the number of participants 

for the majority of activities during the last decade, there were 

declines in several activities. Most of the traditional winter 

recreation activities, with the exception of snowboarding, 

experienced decreasing participation rates and days of activity. 

Activities with decreasing participation rates also exhibited 

declines in the total number of activity days. In addition, several 

activities that had increased numbers of participants experienced 

a drop in total days of activity, indicating that the average 

number of days per participant declined. Examples included 

day hiking and horseback riding on trails (Cordell 2012).

Figure 142 illustrates how rapidly outdoor activities can shift 

in popularity. Kayaking and snowboarding showed strong increases in participation between the mid-1990s and mid-2000s, 

whereas cross-country skiing and, more recently, snowmobiling 

have been in decline.

We grouped nature-based recreation activities into seven 

composites that either occur in similar recreation settings or 

have a similar focus:

• Visiting recreation and historic sites—family gatherings, 

picnicking, visiting beaches, visiting historic or prehistoric 

sites, and camping.

• Viewing and photographing nature—viewing and photographing birds, natural scenery, other wildlife, wildflowers, 

trees, etc.

• Backcountry activities—backpacking, day hiking, horseback 

riding on trails, mountain climbing, and visiting a wilderness 

or primitive area.

a

 Numbers are annual estimates based on pooled National Survey on Recreation and the Environment data from the three time periods.

b

 1994–1995 participants based on 201.26 million people age 16+ (Woods & Poole Economics 2007). 

C

 1999–2001 participants based on 214.02 million people age 16+ (2000 Census).

d

 2005–2009 participants based on 235.30 million people age 16+ (2008 Census estimate).

e

 Snorkeling in 1994–1995 included scuba diving. 

NA = Data not collected for this activity in 1994–1995. NSRE = National Survey on Recreation and the Environment.

Sources: NSRE 1994–1995 (n = 17,217); NSRE 1999–2001 (n = 52,607); and NSRE 2005–2009 (n = 24,073) 

Figure 142. Number of participants in four outdoor recreation 

activities in three time periods.a

16

14

12

10

8

6

4

2

0

Number of participants (millions)

Kayaking Snowboarding Snowmobiling Cross-country 

skiing

1994–1995 1999–2001 2005–2009

a

 Numbers are annual estimates based on pooled National Survey on Recreation and the 

Environment (NSRE) data from the three time periods.

Source: NSRE
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 153

• Motorized activities— motorboating, off-highway vehicle 

driving, snowmobiling, using personal watercraft, and 

waterskiing.

• Hunting and fishing—anadromous fishing (salt to freshwater 

migratory fish, e.g., salmon), coldwater fishing, warmwater 

fishing, saltwater fishing, big game hunting, small game 

hunting, and migratory bird hunting.

• Nonmotorized boating—canoeing, kayaking, rafting, rowing, and sailing.

• Snow skiing and snowboarding—cross-country skiing, 

downhill skiing, and snowboarding.

Figure 143 summarizes the trends across the seven composite 

groups of activities using a 3-year moving average of the 

total annual number of activity days, indexed to the year 

2000. The indexed values represent the percentage change 

since 2000. Motorized activities showed growth up to about 

2005. This activity group, along with hunting and fishing, 

visiting recreation and historic sites, backcountry activities, 

and nonmotorized boating, ended up toward the end of this 

decade at about the same level of days of participation as in 

2000. Visiting recreation and historic sites and nonmotorized 

boating showed moderate growth of between 10 to 20 percent 

in total activity days. Various forms of skiing declined during 

this decade. Viewing and photographing nature activities grew 

considerably, however.

Trends in the composites of activities can mask considerable 

differences among individual activities. Viewing and photographing nature showed the greatest growth, but there was 

variation among activities within this category. Although all 

five activities showed growth by the middle years of this decade, there was slower growth for viewing and photographing 

birds and for visiting nature centers. As a group of activities, 

the ones shown in figure 144 showed consistent growth patterns, likely indicating increasing interest in nature.

In contrast, nonmotorized boating had varying trends. Canoeing, rowing, and sailing maintained about the same level of 

total days of activity as in 2000. Kayaking and rafting showed 

moderate growth up through the middle years, but by 2006, 

rafting had dropped to less than its 2000 level before rebounding somewhat in 2008. Kayaking grew steadily throughout the 

decade, with only a slight dip in 2006 (figure 145).

A final example is the trends in motorized activity participation (figure 146). Until the middle years of this decade, only 

snowmobiling was declining. That downward trend continued 

Figure 143. Trend in annual activity days for seven composites 

of nature-based outdoor recreation activities, 2000–2008 

(indexed to total activity days in 2000).

Figure 145. Trend in annual activity days for nonmotorized 

boating activities, 2000–2008 (indexed to total activity days in 

2000).

Figure 144. Trend in annual activity days for viewing and 

photographing nature activities, 2000–2008 (indexed to total 

activity days in 2000).

Figure 146. Trend in annual activity days for motorized 

activities, 2000–2008 (indexed to total activity days in 2000).

1.0

1.0

1.0

1.0

0.5

0.5

0.5

0.5

0.0

0.0

0.0

0.0

– 0.5

– 0.5

– 0.5

– 0.5

– 1.0

Index Index

Index Index

2000

2000

2000

2000

2001

2001

2001

2001

2002

2002

2002

2002

2003

2003

2003

2003

2004

2004

2004

2004

Year

Year

Year

Year

2005

2005

2005

2005

2006

2006

2006

2006

2007

2007

2007

2007

2008

2008

2008

2008

Visiting recreation and historic sites

Backcountry activities

Hunting and fishing

Snow skiing and boarding

Canoeing

Rowing

Kayaking

Sailing

Rafting

Motorboating

Snowmobiling

Waterskiing

Off-highway vehicle driving

Using personal watercraft

Viewing/photographing nature

Motorized activities

Nonmotor boating

Viewing/photographing birds

Viewing/photographing other wildlife

Visiting nature centers, etc.

Viewing/photographing natural scenery

Viewing/photographing wildflowers, 

trees, etc.
154 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

through 2006 but rebounded slightly through 2008. Offhighway vehicle driving grew steadily until 2005 before falling 

back to its 2000 level by 2008. Only snowmobiling had a lower 

level of participation in 2008 than it had in 2000.

Demographics and Recreation Trends

Demographic characteristics play an important role in individual 

choices of outdoor recreation activities. Significant changes are 

expected in recreation participation as the U.S. population becomes older and more ethnically and racially diverse. Gender, 

race, age, education, income, and rural or urban residency all 

contribute to explaining differences in recreation participation.

Across the seven recreation activity groups, there were several 

common findings. Non-Hispanic Whites tended to dominate 

participation in all groups. The exception is that Native Americans were as likely to participate in backcountry activities. 

Males were more likely than females to participate in all groups 

except visiting recreation or historic sites and viewing and 

photographing nature. People who are young to middle aged 

and had college educations and higher incomes also tended to 

be more likely to participate in most activity groups. Place of 

residence was influential in hunting activities and motorized 

and nonmotorized activities (rural residents were more likely 

to participate) and in backcountry activities and snow skiing 

(urban residents were more likely to participate).

The demographic groups consistently less likely to participate 

were African-Americans, people 65 or older, and people with 

less education and lower incomes. Females, Hispanics, and 

Asians were less likely to participate in some activities, but the 

pattern varied across activities. Understanding the constraints 

on participation would improve the ability of recreation providers to deliver recreation opportunities.

Green et al. (2012) focused on the role of social factors such as 

the lack of time, money, transportation, facilities, or information, 

and crowding at sites, poorly maintained facilities, and pollution 

as constraints felt by potential recreationists. People over age 

65 tended to participate less because they felt constrained for 

health reasons, but they felt less constrained in terms of time 

and money. Constraints between genders varied considerably. 

Men felt more constrained than women only because of limited 

time, whereas women felt more constrained than men by many 

factors, including family obligations, money, transportation 

issues, and safety concerns. Immigrants felt most constrained 

by language barriers. People with lower incomes felt constrained 

by a number of factors, including not only money, but also 

health issues, inadequate transportation, and outdoor pests. 

Urban dwellers were more likely to feel constrained by time, 

inadequate transportation, crowded sites, and safety problems 

than were rural dwellers.

African-Americans, Asians, and Hispanics all generally felt 

more constrained than non-Hispanic Whites. Asians felt less 

deterred than Whites by crowded sites, and Hispanics felt 

more constrained about language issues (Green et al. 2012). 

In-depth field research (Chavez 2012) on Latino recreationists, 

conducted mostly in southern California, indicated that most 

people preferred to receive information about recreation areas 

by word of mouth, particularly from family and friends. For 

Latinos, onsite information, such as bulletin boards, signage, 

and brochures, was most beneficial when it was site-specific, 

for example, the best times to visit the area to avoid crowds, 

safety-related information, and specific camping rules. Some of 

the constraints most strongly experienced by Latinos included 

“feeling uncomfortable in the outdoors,” finding travel and 

recreation in natural areas “too much trouble,” and being 

discriminated against while traveling to or when recreating 

in natural areas. Study respondents also indicated a desire for 

more Latino employees at the recreation areas.

Given the growing diversity of the American population, the 

relatively low participation rates of all groups except nonHispanic Whites are a concern for overall future recreation participation. Similarly, the aging population may require different 

types of recreation opportunities. Recreation activities that have 

been dominated by rural residents are also likely to decline, as 

the American population becomes increasingly more urban.

Recreation Participation by Region

Differences in participation in outdoor recreation activities 

among regions reflect regional population, recreation resource 

availability, and recreation preferences. Table 30 presents the 

percentage of outdoor recreation participants for the seven 

composite recreation activity groups by the four RPA regions. 

The North Region, with the highest proportion of the U.S. 

population, tends also to have the highest percentage of total 

recreation participants, but not necessarily the highest percentage of the regional population participating.

Youth Recreation

The National Kids Survey (NKS) was started in 2007 to provide 

information on outdoor activity of youth age 6 to 19. From 

data collected between 2007 and 2009, the NKS indicated 

that youth spent significant time outdoors, with the dominant 

activity being “just playing or hanging out outdoors,” followed 

by biking, jogging, walking, skateboarding, etc. (table 31). 

Whereas participation rates tend to increase in the middle age 

groups, there tended to be a decline in the 16–19 age group, 

with a few exceptions. Youth in the 6–15 age group spent more 

time outdoors than those in the 16–19 age group. Hispanic 

youth spent more time outdoors than other ethnic groups. Girls 

generally spent less time outdoors than boys.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 155

Table 30. Percent of recreation participants by RPA region in seven recreation activity groups.a

Activity group RPA region All

participants

U.S.

population

Regional

population 

participating

Visiting recreation and historic sites North 39.8 38.6 78.8

South 31.9 33.6 74.5

Rocky Mountain 9.4 9.4 79.9

Pacific Coast 18.9 18.4 80.4

Viewing and photographing nature North 38.6 38.6 74.8

South 32.7 33.6 71.6

Rocky Mountain 9.8 9.4 77.0

Pacific Coast 18.9 18.4 76.7

Backcountry activities North 38.2 38.6 40.2

South 27.6 33.6 34.4

Rocky Mountain 12.4 9.4 54.7

Pacific Coast 21.8 18.4 49.6

Motorized activities North 37.9 38.6 34.4

South 32.9 33.6 35.4

Rocky Mountain 10.8 9.4 41.0

Pacific Coast 18.4 18.4 36.1

Hunting and fishing North 37.0 38.6 32.6

South 37.3 33.6 37.8

Rocky Mountain 10.5 9.4 37.7

Pacific Coast 15.2 18.4 28.0

Nonmotor boating North 42.1 38.6 22.7

South 28.7 33.6 17.7

Rocky Mountain 9.4 9.4 20.6

Pacific Coast 19.9 18.4 22.4

Snow skiing and boarding North 46.8 38.6 13.4

South 15.4 33.6 5.2

Rocky Mountain 12.5 9.4 15.0

Pacific Coast 25.3 18.4 15.6

a

 Percentages sum to 100 within each activity group in the first two columns. 

Source: National Survey on Recreation and the Environment (NSRE) 2005–2009, Versions 1-4. N = 24,073. Interview dates: 1/05 to 4/09

Table 31. Percent of youth participating in an outdoor activity at least once in the preceding week by type of outdoor activity and by age 

group. 

Outdoor activity Age 6–9 Age 10–12 Age 13–15 Age 16–19

Just play outdoors or hang out 86.5 94.9 80.9 68.1 

Bike, jog, walk, skate board, etc. 84.8 87.2 67.4 78.4 

Listen to music, watch movies, or use electronic device outdoors 39.4 46.8 74.8 64.5 

Play or practice team sports 47.2 49.6 61.0 47.3

Read, study while sitting outdoors 42.2 52.4 39.1 50.4 

Play other sports, e.g., tennis, golf 43.9 50.3 29.2 30.1 

Attend camps, field trips, outdoor classes 36.4 37.9 37.1 28.8 

Bird watch, wildlife view, etc. 38.8 39.2 36.9 25.3

Swim, dive, snorkel, etc. 41.4 43.6 24.9 18.6

Hike, camp, fish, etc. 39.5 23.9 32.3 26.6 

Ride motorcycles, ATVs, other off-road vehicles 19.4 15.7 23.1 21.1 

Snow ski, snowboard, cross-country ski 3.8 9.3 13.3 11.3

Boat, jet ski, water ski, etc. 7.0 8.1 9.1 10.7 

Row, kayak, canoe, surf, etc. 8.3 9.5 6.8 8.8

Other outdoor activities 9.5 10.9 10.8 8.1

ATV = all-terrain vehicle. 

Source: National Survey on Recreation and the Environment (NSRE) National Kids Survey, N = 1,201. Interview dates: 9/15/07 to 4/27/09
156 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

The National Survey of Fishing, Hunting, and WildlifeAssociated Recreation also looked at youth trends in hunting. 

Whereas participation in hunting among those age 16 and older 

has declined during the last two decades, the number of girls 

6 to 15 years old who hunt nearly doubled between 1991 and 

2006, and the number of boys of that age who hunted stayed 

about level. Despite the increase in young female hunters, they 

still numbered less than one-fourth of the more than 1.2 million 

young male hunters in 2006 (Aiken and Harris 2012).

Results from the 2009 Outdoor Foundation recreation survey 

(The Outdoor Foundation 2009) indicated that youth participation in outdoor activities is high at early ages. Male participation rates tend to decline between ages 16 and 25, then begin 

to rise again. Female participation rates also begin to decline 

at age 16, but begin to increase in the 21 to 25 age group, 

although female participation rates are lower than male rates in 

both youth and adulthood.

Over the past 3 years of the Outdoor Foundation’s annual 

participation survey, youth participation (6 to 17 years) 

declined more than 11 percent between 2006 and 2007 and 

declined 6 percent between 2007 and 2008. The decline was 

most pronounced among the youngest age group, those aged 

6 to 12, with a sharper decline among girls. There is now a 

significant gap between boys’ and girls’ participation that did 

not exist when the annual surveys began. Youth are most often 

introduced to outdoor recreation by their family and friends, 

with parents being the top influence by a large margin.

Role of Public Lands

Federal lands provide crucial recreation opportunities, particularly in the Western United States. Table 32 reports recreation 

visits to lands managed by the five primary Federal providers 

of recreation opportunities. Whereas the National Park Service, U.S. Fish and Wildlife Service, Bureau of Land Management, and Forest Service provide a wide range of recreation 

opportunities, the U.S. Army Corps of Engineers primarily 

provides opportunities associated with water recreation on its 

reservoir systems.

State park systems are a major provider of recreation opportunities, often located in closer proximity to population centers 

than Federal lands. State park visits from 1992 to 2008 are 

shown in table 33 for the Nation and for the four RPA regions.

Recreation Participation in the Future

We examined adult recreation participation for 17 recreation 

activity composites, which we organized into seven activity 

groups (table 34). We modeled per capita participation and 

average annual days per participant. We calculated total participants and total annual days of participation by multiplying 

Table 32. Millions of recreation visits to Federal lands, 1996–2008.

Year

National

Park 

Service

U.S.

Fish & 

Wildlife 

Service

Bureau 

of Land 

Management

Forest

Service

U.S. Army 

Corps of 

Engineers

1996 266 30 57 NA 372

1997 275 30 61 NA 378

1998 287 32 61 NA 381

1999 287 35 55 NA 379

2000 286 37 54 NA NA

2001 280 39 52 214 NA

2002 277 38 53 NA 358

2003 266 40 53 NA 349

2004 277 40 54 205 359

2005 274 38 56 196 362

2006 273 38 55 180 371

2007 276 40 58 179 363

2008 275 41 57 176 357

2009 286 43 57 174 370

NA = data not available. 

Table 33. Millions of visits to State park system areas by RPA 

region, 1992–2008.a

Year North South Rocky 

Mountain

Pacific 

Coast

U.S.

Total

1992 312.2 162.9 49.0 179.6 703.8

1993 325.6 164.1 52.6 182.5 724.8

1994 329.3 167.7 54.3 174.3 725.5

1995 351.3 169.0 58.9 173.1 752.3

1996 358.5 152.3 58.8 176.0 745.6

1997 355.5 147.6 57.2 223.1 783.4

1998 354.7 153.1 59.9 193.1 760.8

1999 375.0 152.9 56.4 182.6 766.8

2000 370.6 151.5 58.9 205.6 786.6

2001 367.9 149.0 59.0 190.2 766.0

2002 367.7 145.0 60.9 184.6 758.2

2003 351.6 143.5 61.1 178.8 735.0

2004 340.2 135.6 62.2 180.8 718.8

2005 342.6 130.7 62.9 175.2 711.5

2006 373.7 131.3 61.7 173.5 740.2

2007 371.4 135.6 57.1 168.9 732.8

2008 370.6 134.5 63.7 179.2 748.0

2009 357.0 133.5 64.3 172.2 727.1

a

 The time period covered by each report is the previous 12-month period of July 1 to June 30. 

For example, the 2009 report covers July 1, 2008, through June 30, 2009. In a few cases, some 

States did not report visitation statistics for certain years. Previous year statistics were used in 

place of missing data. States and years include Idaho in 2007 and 2006 (used 2005 data), Hawaii 

in 2006 (used 2005 data), New Hampshire in 2005 and 2006 (both used 2004 data), Illinois in 

2004 (used 2003 data), and Rhode Island in 2004 (used 2003 data).

Source: National Association of State Park Directors 2009

the RPA scenario population projections by the participation 

rate and average days per participant. Projections were done 

for each of the three RPA scenarios without climate variables. 

Then, we ran the models with climate variables for each of the 

nine RPA scenario-climate combinations (table 2) (Bowker and 

Askew 2012; Bowker et al. 2012).

Key differences in the model variables drive the future trends 

in recreation participation. Population growth often is the most 

important driver, and therefore the RPA A2 scenario, with 

the largest projected population growth, often has the greatest 
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 157

changes, whereas the RPA B2 scenario has the smallest. Income 

growth also has differential effects on participation. In activities 

that require more capital or income for effective participation, 

such as developed skiing, challenge activities, equestrian 

activities, hunting, and motorized activities, the combination of 

population growth and higher income growth in scenario RPA 

A1B resulted in larger participation changes than RPA A2.

The effects of population growth were often offset by more 

indirect effects. A growing population combined with a stable 

public land base and declining private natural land base resulted 

in a decline in per capita recreation opportunities during the 

projection period. These declines tend to have negative effects 

on recreation participation. Increasing population density tends 

to have a negative effect on recreation participation as a result 

of crowding. In most cases, population growth is sufficient to 

result in overall growth in total participants and total days of 

participation, even when participation rates and/or average days 

of participation are projected to decline.

Generally, land and water availability positively influence 

activity participation. Therefore, declines in the per capita area 

of forest and rangeland and Federal land induced participation 

declines in spatially extensive activities such as equestrian, 

hunting, motorized off-road driving, visiting primitive areas, 

and viewing and photographing nature. Similarly, participation 

in water-based activities such as swimming, motorized boating, 

and nonmotorized boating were all positively correlated with 

the per capita availability of water area.

Climate variables were added to the projection models to test 

whether participation and participation intensity were sensitive 

to climate effects. Temperature, precipitation, and evapotranspiration variables were tested, with a single climate variable 

introduced into each recreation activity model. More details 

about the use of climate variables in the participation models 

can be found in Bowker et al. (2012).

Adding climate variables to the projection models did not 

greatly change future participation except for a few activities. 

Generally, the effect of the climate variables was a slight increase or decrease in the metrics compared with the “no climate 

change” projection. For snowmobiling and undeveloped skiing, 

the effect of the climate variables was substantial decreases 

in the number of participants and annual participation days. 

Table 35 lists the general circulation models (GCMs) used as 

Table 34. Participants and participation in outdoor recreation activities, 2008.a

Activity group and activity Participants

millions

Participation rate

percent

Visiting developed sites

Developed site use-family gatherings, picnicking, developed camping 192.7 81.9

Visiting interpretive sites—nature centers, zoos, historic sites, prehistoric sites 157.4 66.9

Viewing and photographing nature

Birding 81.4 34.6

Viewing—viewing, photography, study, or nature gathering related to fauna, flora, or natural settings 189.4 80.5

Backcountry activities

Challenge activities—caving, mountain biking, mountain climbing, rock climbing 25.1 10.7

Equestrian 16.4 7.0

Hiking-day hiking 78.3 33.3

Visiting primitive areas—backpacking, primitive camping, wilderness 90.1 38.3

Motorized activities

Motorized off-road use 47.9 20.4

Motorized snow use 9.4 4.0

Motorized water use 62.0 26.3

Hunting and fishing

Hunting—small game, big game, migratory bird, other 27.9 11.9

Fishing—anadromous, coldwater, saltwater, warmwater 72.7 30.9

Nonmotorized winter activities

Downhill skiing-downhill skiing, snowboarding 23.7 10.1

Winter activities—cross-country skiing, snowshoeing 7.8 3.3

Nonmotorized water activities

Swimming—swimming, snorkeling, surfing, diving, visiting beaches or watersides 143.2 60.9

Floating—canoeing, kayaking, rafting 39.8 16.9

a

 Activities are individual or activity composites derived from the National Survey on Recreation and the Environment (NSRE). Participants are determined by the product of the average weighted frequency 

of participation by activity for NSRE data from 2005–2009 and the adult (> 16) population in the United States during 2008 (235.4 million).

Source: NSRE 2005–2009, Versions 1 to 4 (January 2005 to April 2009), N = 24,073 

Table 35. List of climate models used in the RPA scenario-climate 

combinations (Climate 1, Climate 2, and Climate 3) in the recreation participation models. 

RPA scenario Climate 1 Climate 2 Climate 3

A1B CGCM3.1 CSIRO-Mk3.5 MIROC3.2

A2 CGCM3.1 CSIRO-Mk3.5 MIROC3.2

B2 CGCM2 CSIRO-Mk2 HadCM3
158 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

the basis for the climate projections for the results listed as 

“Climate 1,” “Climate 2,” and “Climate 3” in tables 36 through 

51. The results of participation models are shown in a series of 

tables (tables 36 to 51) that describe the results with no climate 

change (No CC) (i.e., historical climate trends are assumed to 

continue) for each of the three RPA scenarios and also with 

results for the nine RPA scenario-climate combinations.

Visiting Developed Sites

The activities associated with developed site use include venues 

popular with all age groups. Per capita participation is currently 

high and is projected to remain relatively constant across all 

the RPA scenarios. RPA A1B showed the greatest change from 

2008, with a 3-percent increase (table 36). Days per participant 

are projected to decline slightly. Incorporating climate variables 

resulted in consistently lower results, but the effect was quite 

small across all RPA scenario-climate combinations.

Visiting interpretive sites is also popular across all ages and 

occurs primarily in developed settings. The projections indicate 

participation rates could increase 4 to 9 percent by 2060 across 

the RPA scenarios (table 37). For this activity, climate effects 

resulted in little difference in participation rates but consistently 

projected higher numbers of days per participant. The greater 

participation rate growth in this activity group compared with 

developed site use has several possible causes: developed site 

use is negatively correlated with age, which is expected to rise 

by 2060, and positively correlated with available Federal land 

per capita. Those variables are less important in interpretive 

site participation.

Viewing and Photographing Nature

This category includes birding and nature viewing, which adds 

viewing wildlife and nature, gathering, and nature study. Adult 

participation in birding averaged 35 percent in 2008. Nearly 81 

percent of adults participated in the more broadly defined nature 

viewing during the same period (table 38). The participation rate 

for nature viewing was projected to increase by up to 4 percent 

to 2060, whereas the participation rate for birding could vary 

from a 4-percent decrease to an 8-percent increase. RPA A1B 

yielded the highest participation-rate growth, primarily because 

of higher incomes, which correlate positively with the viewing 

activities. The days per participant declined across all RPA scenarios, resulting in one of the largest relative declines in days 

per participants across all activities. Adding climate variables 

to the model had little effect on the results.

Backcountry Activities

Backcountry activities are pursued in undeveloped but accessible lands. Challenge activities are often associated with 

young and affluent adults. The participation rate is projected 

to increase under all of the RPA scenarios, by 4 to 20 percent, 

with the largest increase projected for RPA A1B (table 39). 

Table 36. Developed site projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.819 0.840 3 2 1 1

RPA A2 0.819 0.829 1 0 0 (1)

RPA B2 0.819 0.830 1 0 0 0

days per participant percent increase (decrease) from 2008

RPA A1B 11.67 11.49 (2) (3) (4) (4)

RPA A2 11.67 11.48 (2) (3) (3) (4)

RPA B2 11.67 11.52 (1) (3) (3) (2)

 CC = climate change.

Table 37. Interpretive site projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures. 

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.669 0.728 9 9 8 7

RPA A2 0.669 0.705 5 4 5 4

RPA B2 0.669 0.706 6 5 5 5

days per participant percent increase (decrease) from 2008

RPA A1B 7.81 8.40 8 9 10 13

RPA A2 7.81 8.12 4 6 5 8

RPA B2 7.81 8.11 4 6 6 6

 CC = climate change.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 159

The higher participation growth is driven by higher projected 

income relative to RPA A2 and B2. Days per participant are 

almost unchanged across RPA scenarios. The effects of climate 

on the projections were generally positive or were no different 

than the results with no climate effects.

Participation in equestrian or trail riding per capita is projected 

to increase between 2 and 19 percent by 2060 across RPA 

scenarios (table 40). The increase is particularly high for RPA 

A1B, which has the highest income growth. The number of 

days per participant changes very little across RPA scenarios, 

suggesting that higher income participants have more competing uses for their time. Incorporating climate change into the 

models consistently increased participation rates compared with 

the model with no climate change, showing considerably larger 

differences than for most activity groups. Whereas climate 

change had a positive effect on participation rates, it had a 

negative effect on days per participant, which is opposite of the 

trend projected without climate effects. The effect of climate 

change varies considerably across the nine outcomes and some 

of the effects are quite large, with 15- to 20-percent decreases 

in days per participant.

Hiking is the most popular single backcountry activity, with 

33-percent adult participation in 2008. By 2060, the participation rate is projected to increase between 3 and 10 percent 

across RPA scenarios, with the largest growth in RPA A1B 

(table 41). Hiking is the only activity for which Hispanic 

ethnicity resulted in a higher participation rate than other 

ethnic groups. Days spent hiking are projected to increase 

slightly more than participation. Models that incorporated 

climate change consistently projected smaller increases in 

Table 38. Nature viewing projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.805 0.810 4 3 3 2

RPA A2 0.805 0.810 1 0 1 0

RPA B2 0.805 0.815 1 1 1 1

days per participant percent increase (decrease) from 2008

RPA A1B 169.6 150.5 (11) (12) (13) (14)

RPA A2 169.6 154.8 (9) (10) (10) (10)

RPA B2 169.6 155.3 (8) (10) (10) (9)

 CC = climate change.

Table 39. Challenge activity projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.107 0.126 18 15 20 20

RPA A2 0.107 0.114 7 5 4 9

RPA B2 0.107 0.115 7 8 7 6

days per participant percent increase (decrease) from 2008

RPA A1B 4.77 4.71 (1) 0 1 1

RPA A2 4.77 4.69 (2) 0 0 1

RPA B2 4.77 4.73 (1) 1 0 1

 CC = climate change.

Table 40. Equestrian activity projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.07 0.083 19 22 26 34

RPA A2 0.07 0.071 2 10 5 12

RPA B2 0.07 0.072 4 9 8 9

days per participant percent increase (decrease) from 2008

RPA A1B 16.3 16.8 3 (4) (9) (20)

RPA A2 16.3 16.8 3 (10) (4) (15)

RPA B2 16.3 16.8 3 (6) (6) (8)

 CC = climate change.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3
160 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

participation rates than the models without climate change, 

but had almost no effect on days per participant.

The final backcountry activity is visiting primitive areas. The 

participation rate is projected to decline between 1 and 9 percent across RPA scenarios (table 42). Increased population 

density and declines in wilderness, forest, and rangeland acres 

per capita appeared to influence the participation rate decline. 

Activity days per participant were projected to decline slightly 

less than participation rates. Climate effects consistently led to 

larger declines than the results with no climate change.

Motorized Activities

We considered three categories of motorized activities: off-road 

driving, motorized water use, and motorized snow use. Participation in off-road driving is projected to stay about the same 

under RPA A1B, as opposed to declining 7 and 18 percent in 

RPA scenarios B2 and A2, respectively (table 43). The larger 

decline in RPA A2 can be attributed to lower projected income 

growth than in RPA A1B and a greater projected decline in 

private forest land and rangeland than in RPA B2. Annual days 

per participant are also projected to decline, ranging from 3 to 

7 percent across all alternatives.

Motorized water use has the highest participation rate among 

motorized activities. Under the RPA A1B scenario, the participation rate is expected to increase between 5 and 15 percent, 

whereas rates are expected to decline under RPA A2 and B2 

(table 44). Income growth under RPA A1B is the biggest factor 

affecting this difference. The projection models with climate 

variables consistently projected smaller increases or larger 

declines than the models with no climate variables.

Table 41. Day hiking projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.333 0.365 10 8 5 4

RPA A2 0.333 0.360 8 4 5 3

RPA B2 0.333 0.357 7 5 5 4

days per participant percent increase (decrease) from 2008

RPA A1B 22.9 24.2 6 7 6 6

RPA A2 22.9 24.2 6 6 6 6

RPA B2 22.9 24.3 6 7 6 6

Table 42. Primitive area projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.383 0.381 0 (1) (2) (5)

RPA A2 0.383 0.363 (5) (8) (6) (9)

RPA B2 0.383 0.365 (5) (6) (6) (6)

days per participant percent increase (decrease) from 2008

RPA A1B 13.2 13.1 (1) (3) (5) (5)

RPA A2 13.2 13.0 (1) (5) (4) (5)

RPA B2 13.2 13.1 (1) (3) (3) (4)

 CC = climate change.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

Table 43. Motorized off-road projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.204 0.203 0 (1) 1 1

RPA A2 0.204 0.169 (18) (18) (18) (16)

RPA B2 0.204 0.189 (8) (7) (7) (8)

days per participant percent increase (decrease) from 2008

RPA A1B 21.6 20.2 (6) (6) (3) (3)

RPA A2 21.6 20.2 (7) (5) (4) (4)

RPA B2 21.6 20.3 (6) (5) (5) (5)

 CC = climate change.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 161

Motorized snow use (snowmobiling) has one of the largest 

projected declines in participation rates across all activities. By 

2060, rates are projected to decline between 13 and 72 percent, 

with much larger declines in the RPA scenarios with climate 

change (table 45). The climate effects are much more variable 

within RPA A1B than for RPA A2 or B2. Income growth in 

RPA A1B slows the decline in the projections without climate 

effects. Days per participant decline slightly when climate is 

not considered, but those declines are larger under all RPA 

scenario-climate combinations.

Hunting and Fishing

The adult hunting participation rate is projected to decline 

between 22 and 35 percent across RPA scenarios by 2060 

(table 46). The RPA A2 scenario shows the biggest decrease. 

Increased education levels, increased population density, 

diminishing availability of private and public land, and strong 

negative relationships between growing minority populations 

and hunting appear to be influencing the decline in participation 

rate. Days per hunter are also projected to decline, from 12 to 

14 percent across RPA scenarios. Models with climate effects 

resulted in a marginally negative effect across RPA scenarioclimate combinations.

The participation rate for fishing is projected to decline from 

3 to 10 percent, with the largest decline under RPA A2 (table 47). 

Fishing days per participant are projected to fall between 3 and 

8 percent. The effect of climate on fishing participation rates 

was negative, but the effect on days per participant was not 

consistent across RPA scenarios.

Nonmotorized Winter Activities

Developed skiing (including snowboarding) participation rates 

are projected to increase from 4 to 45 percent across RPA 

scenarios. Income growth is a strong driver in skiing participation, resulting in the largest increases in the RPA A1B scenario, 

whereas other scenarios show much more modest increases 

(table 48). Days per participant are projected to remain largely 

unchanged except under the RPA A1B scenario, in which a 

9- to 10-percent increase is projected. Climate effects on participation rates are not consistent across RPA scenario-climate 

combinations; climate effects on days per participant vary little 

from the effects with no climate.

Undeveloped skiing includes cross-country skiing and 

snowshoeing. With the exception of RPA A1B with no climate 

effects, participation rates are projected to decline up to 63 

percent (table 49). Climate effects markedly increase the 

decline in participation rates across all RPA scenario-climate 

combinations. Days per participant are projected to increase 

slightly with no climate effects, but decline in eight of the nine 

projections with climate effects, although the differences are 

relatively small.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.04 0.035 (13) (32) (49) (72)

RPA A2 0.04 0.031 (23) (60) (43) (69)

RPA B2 0.04 0.032 (21) (49) (46) (51)

days per participant percent increase (decrease) from 2008

RPA A1B 7.25 7.04 (3) (10) (24) (24)

RPA A2 7.25 6.95 (4) (9) (18) (22)

RPA B2 7.25 7.12 (2) (13) (14) (13)

 CC = climate change.

Table 45. Motorized snow activity projected participation and use by American adults, 2008–2060, by RPA scenario and related climate 

futures.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

Table 44. Motorized water projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.263 0.304 15 14 11 5

RPA A2 0.263 0.257 (2) (7) (4) (10)

RPA B2 0.263 0.265 1 (3) (2) (3)

days per participant percent increase (decrease) from 2008

RPA A1B 15.3 16.0 4 3 2 0

RPA A2 15.3 14.3 (6) (8) (7) (9)

RPA B2 15.3 14.9 (2) (4) (4) (4)

 CC = climate change.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3
162 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.119 0.093 (22) (24) (25) (28)

RPA A2 0.119 0.082 (31) (34) (33) (35)

RPA B2 0.119 0.092 (23) (25) (25) (24)

days per participant percent increase (decrease) from 2008

RPA A1B 19.1 16.8 (12) (12) (14) (14)

RPA A2 19.1 16.8 (12) (12) (12) (14)

RPA B2 19.1 16.8 (12) (13) (12) (12)

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.309 0.300 (3) (6) (8) (6)

RPA A2 0.309 0.277 (10) (17) (13) (8)

RPA B2 0.309 0.282 (9) (13) (12) (8)

days per participant percent increase (decrease) from 2008

RPA A1B 18.5 17.5 (5) (5) (6) (7)

RPA A2 18.5 17.2 (7) (6) (6) (8)

RPA B2 18.5 17.7 (4) (4) (4) (3)

 CC = climate change.

 CC = climate change.

Table 46. Hunting projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

Table 47. Fishing projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.101 0.147 45 44 43 43

RPA A2 0.101 0.114 11 11 9 4

RPA B2 0.101 0.115 13 8 17 14

days per participant percent increase (decrease) from 2008

RPA A1B 7.19 7.90 10 9 9 9

RPA A2 7.19 7.26 1 0 0 (1)

RPA B2 7.19 7.31 2 0 2 1

 CC = climate change.

Table 48. Developed skiing projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.033 0.035 6 (18) (36) (63)

RPA A2 0.033 0.029 (8) (50) (30) (60)

RPA B2 0.033 0.030 (6) (35) (34) (38)

days per participant percent increase (decrease) from 2008

RPA A1B 6.58 6.72 2 (3) (6) (5)

RPA A2 6.58 6.69 2 (4) (4) (7)

RPA B2 6.58 6.74 3 (4) 0 (2)

 CC = climate change.

Table 49. Undeveloped skiing projected participation and use by American adults, 2008–2060, by RPA scenario and related climate 

futures.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 163

Nonmotorized Water Activities

This category consists of various kinds of outdoor swimming, 

including related activities like snorkeling, surfing, diving, and 

visiting beaches or watersides. Swimming is the fourth most 

popular outdoor activity, with a 61-percent adult participation 

rate (table 50). Differences in projected participation-rate 

increases primarily reflect population growth differences across 

RPA scenarios. Climate variables had almost no effect on participation rate projections. Days per participant are projected to 

increase slightly under RPA A1B, whereas they decline slightly 

under both RPA A2 and B2. Climate change had a negative 

effect on days per participant.

Floating activities include canoeing, kayaking, and rafting. 

By 2060, the participation rate is projected to increase slightly 

under RPA A1B without climate effects and to have no change 

or decrease when climate effects are included. Participation 

rates decline for both RPA B2 and A2, with stronger declines 

projected when climate is considered (table 51). Days per 

participant were virtually unchanged across all RPA scenarioclimate combinations.

Conclusions

Public lands are crucial resources for nature-based outdoor 

recreation. Although the total land area owned by local governments is modest relative to State and Federal Governments, 

those lands are important for providing recreation opportunities 

in close proximity to where most of the population lives. The 

private sector also plays a significant role as both a provider 

and a facilitator of outdoor recreation opportunities, including 

as a partner with Federal and State agencies for the development and operation of concessions that supply visitor services.

The outlook for recreation resources is generally for declining 

opportunities per person. Assuming the public land base for 

outdoor recreation remains stable into the future, an increasing 

population will result in decreasing per-person opportunities 

for recreation across most of the United States. Although 

there are many other factors involved in recreation supply, it 

is likely that recreation resources will become less available 

as more people compete to use them. A major challenge for 

natural resource managers and planners will be to ensure that 

recreation opportunities remain viable and grow along with the 

population. This goal would more than likely be accomplished 

through management and site attribute inputs and plans, rather 

than through any major expansions or additions to the natural 

resource base for recreation.

Choices in outdoor recreation activities have changed over time 

in response to changing preferences, demographics, and recreation 

opportunities. Overall, there has been growth in nature-based 

outdoor recreation participation since the last RPA Assessment, 

continuing a long-term trend. At the same time, recreation 

visitation to State parks and Federal lands has not increased at 

similar rates, indicating that recreationists are also using other 

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.609 0.676 11 11 11 11

RPA A2 0.609 0.645 6 6 6 6

RPA B2 0.609 0.642 5 6 5 5

days per participant percent increase (decrease) from 2008

RPA A1B 24.0 25.1 5 3 1 1

RPA A2 24.0 23.7 (1) (4) (3) (4)

RPA B2 24.0 23.8 (1) (3) (3) (3)

adult per capita participation percent increase (decrease) from 2008

RPA A1B 0.169 0.171 3 0 (7) (20)

RPA A2 0.169 0.146 (11) (23) (15) (27)

RPA B2 0.169 0.155 (7) (15) (14) (16)

days per participant percent increase (decrease) from 2008

RPA A1B 6.50 6.50 0 0 (1) (1)

RPA A2 6.50 6.49 0 0 0 (1)

RPA B2 6.50 6.51 0 0 0 0

 CC = climate change.

 CC = climate change.

Table 50. Swimming projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

Table 51. Floating activity projected participation and use by American adults, 2008–2060, by RPA scenario and related climate futures.

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3

RPA scenario

2008 2060 2060

No CC No CC Climate 1 Climate 2 Climate 3
164 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

recreation resources. The change in recreation preferences at 

least partly reflects changing demographics in the American 

public. As the population ages and becomes more racially and 

ethnically diverse, it is unclear whether current recreation opportunities will meet future needs. Based on the available data, 

we still project future growth for most recreation activities.

The five outdoor recreation activities projected to have the fastest growth in participation rate across the three RPA scenarios 

are developed skiing, challenge activities, equestrian activities, 

motorized water activities, and day hiking. In contrast, the activities with the largest projected participation rate declines are 

motorized off-road activities, motorized snow activities, hunting, 

fishing, and floating activities. Participation rate changes for the 

remaining activities will be marginal. Several of the activities 

with projected participation rate growth, such as developed skiing and equestrian activities, tend to require substantial financial commitments. This factor partially explains the low current 

participation rates and may limit growth in participant numbers 

depending on the distribution of future income growth.

Population growth in all RPA scenarios is high enough that the 

total number of participants and the total number of days for 

most activities are projected to increase regardless of the direction of the trends in participation rates or days per participant 

(tables 52 and 53). Exceptions occur for the RPA B2 scenario 

for hunting, for which total days decline compared with 2008; 

snowmobiling, for which participant numbers and total days of 

participation drop substantially from 2008; and undeveloped 

skiing, for which the majority of RPA scenario-climate 

Table 52. Changes in total outdoor recreation participants, 2008–2060, for all activities across all RPA scenarios and climate futures.a 

Activity

2008

participantsb

2060

participant rangec

2060

average

participant changec

2060

participant ranged

2060

average

participant changed

millions millions percent millions millions percent millions

Visiting developed sites

Developed site use 194 273–346 42–77 + 116 271–339 40–75 + 112

Interpretive site use 158 231–294 48–84 + 106 231–289 46–83 + 104

Viewing and photographing nature

Birding 82 118–149 46–81 + 53 115–144 40–76 + 47

Nature viewing 190 267–338 42–76 + 114 268–333 41–75 + 112

Backcountry activities

Challenge 25 38–48 50–86 + 19 37–48 47–90 + 18

Equestrian 17 24–31 44–87 + 11 25–35 50–110 + 13

Day hiking 79 117–150 50–88 + 55 114–143 45–82 + 50

Primitive area use 91 120–152 34–65 + 47 119–145 31–60 + 42

Motorized activities

Off-road driving 48 62–75 29–56 + 21 62–76 28–58 + 21

Motorized water 62 87–112 41–81 + 40 84–111 35–78 + 35

Motorized snow (snowmobiling) 10 10–13 10–37 + 3 4–10 (56)–6 – 2.5

Consumptive

Hunting 28 30–34 8–23 + 5 29–34 5–21 + 4

Fishing 73 92–115 28–56 + 33 89–115 22–58 + 30

Nonmotorized winter

Developed skiing 24 38–54 58–127 + 23 36–54 50–126 + 21

Undeveloped skiing 8 10–13 32–67 + 4 5–10 (42)–28 – 1

Nonmotorized water

Swimming 144 210–268 47–85 + 99 212–266 47–85 + 99

Floating 40 52–65 30–62 + 20 47–62 18–56 +13

a

 Activities are individual or activity composites derived from the National Survey on Recreation and the Environment (NSRE). Participants are determined by the product of the average weighted frequency 

of participation by activity for NSRE data from 2005–2009 and the U.S. adult (> 16) population during 2008.

b

 Since initial values for 2008 differ across RPA scenarios, RPA A1B is used for a starting value.

c

 Participant range across A1B, A2, B2 without climate considerations.

d

 Participant range across the nine RPA scenario-climate combinations. 

Source: NSRE 2005–2009, Versions 1 to 4 (January 2005 to April 2009), N = 24,073
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 165

Table 53. Changes in total outdoor recreation days, 2008–2060, for all activities across all RPA scenarios and climate futures.a

Activity

2008

daysb

2060

days rangec

2060

average

days changec

2060

days ranged

2060

average

days changed

millions millions percent millions millions percent millions

Visiting developed sites

Developed site use 2,246 3,121–3,949 40–74 + 1,294 3,055–3,796 36–69 + 1,185

Interpretive site use 1,249 1,899–2,417 53–91 + 952 1,935–2,435 55–95 + 988

Viewing and photographing nature

Birding 2,162 3,008–3,798 40–74 + 1,246 2,941–3,654 36–69 + 1,141

Nature viewing 32,461 41,805–52,835 30–61 + 14,635 41,550–51,288 28–58 + 13,597

Backcountry activities

Challenge 121 178–219 49–83 + 86 179–232 48–92 + 89

Equestrian 263 388–503 49–92 + 196 369–482 40–83 + 166

Day hiking 1,835 2,901–3,682 59–98 + 1,470 2,825–3,541 54–93 + 1,366

Primitive area use 1,239 1,630–2,046 33–63 + 622 1,562–1,946 26–57 + 519

Motorized activities

Off-road driving 1,053 1,264–1,532 21–46 + 357 1,274–1,611 21–53 + 385

Motorized water 958 1,304–1,806 37–90 + 596 1,245–1,763 30–84 + 495

Motorized snow (snowmobiling) 69 74–91 8–33 + 16 23–65 (6)–(67) – 27

Consumptive

Hunting 538 506–576 (5)–8 + 14 494–575 (8)–7 – 8

Fishing 1,369 1,665–2020 23–46 + 514 1,602–1,958 17–41 + 397

Nonmotorized winter

Developed skiing 178 274–437 61–150 + 179 258–422 50–146 + 165

Undeveloped skiing 52 69–87 35–70 + 29 28–64 (45)–25 – 5

Nonmotorized water

Swimming 3,476 5,037–6,429 46–83 + 2,446 4,396–6,257 42–80 + 2,298

Floating 262 338–422 30–62 + 128 309–409 18–56 + 83

a

 Activities are individual or activity composites derived from the National Survey on Recreation and the Environment (NSRE). Participants are determined by the product of the average weighted frequency 

of participation by activity for NSRE data from 2005–2009 and the U.S. adult (> 16) population during 2008.

b

 Since initial values for 2008 differ across RPA scenarios, RPA A1B is used for a starting value.

c

 Participant range across A1B, A2, B2 without climate considerations.

d

 Participant range across the nine RPA scenario-climate combinations. 

Source: NSRE 2005–2009, Versions 1 to 4 (January 2005 to April 2009), N = 24,073

combinations indicate declines in both participant numbers 

and days. For most activities, the largest number of future 

participants and days of participation occur under RPA A2, 

the scenario with the highest population growth. Activities that 

are strongly influenced by income, however, were projected to 

grow the most under RPA A1B, including challenge, equestrian, motorized water, and developed skiing activities.

Climate can affect individual willingness to participate in recreation activities and/or affect recreation resource availability 

and quality. The climate variables used in the recreation models 

were limited to those coming directly from the RPA climate 

projections, or variables derived from those basic variables. 

Generally, the climate variables used in these recreation models 

were presumed to affect willingness to participate and frequency of participation directly. Despite the lack of existing data, it 

is reasonable to expect that climate change will affect resource 

availability. For example, in the case of hunting and fishing, 

increasing temperatures will likely affect the distribution of 

plant and animal species that are fundamental to maintaining 

fish and game populations. Moreover, changes in precipitation 

may influence local snow cover and thus affect seasonal availability for activities like snowmobiling and undeveloped skiing. 

Disentangling the effects of the climate variables on recreation 

participation is difficult. Further exploration of these direct and 

indirect relationships, at both local and macro levels, will be 

fundamental to improving forecasts of recreation behavior in 

the future.
166 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 167

Chapter 15. Future Resource Challenges and 

Opportunities

The 2010 Resources Planning Act (RPA) Assessment results indicate that America’s renewable natural 

resources will continue to be important in meeting diverse demands for goods and services. The growing population, coupled 

with economic growth, will put pressure on natural landscapes, 

and biophysical stressors, including climate change, will continue to influence the condition of natural ecosystems. This section draws on the RPA Assessment results to present examples 

of challenges facing policymakers and resource managers and 

to present opportunities to address these challenges so that we 

can continue to conserve the renewable natural resources of the 

United States to meet the needs of future generations.

Challenge: Conserving Natural Landscapes in the Face of Urbanization 

and Low-Density Development

The combination of U.S. population growth and economic 

growth will strongly influence future development patterns. 

We projected a loss of both forest and rangelands, primarily to 

urban and other developed uses, in all of the RPA scenarios. 

Development patterns differ in their effects on natural landscapes. Urbanization tends to expand from existing urbanized 

areas, usually converting entire landscapes by removing a large 

portion of natural vegetation. In contrast, lower density development in rural landscapes removes less natural vegetation, but 

the resulting pattern increases fragmentation. Fragmentation 

is mainly a local yet widely dispersed phenomenon; even a 

small area of resource loss can effectively fragment a large 

total resource area. Both types of development increase the 

susceptibility of the affected landscapes to multiple stresses, 

such as invasive species and altered fire regimes.

The challenges are twofold for urban and natural resource 

planners and managers: (1) maximizing the ecosystem services 

from a small natural resource base within the urban area and 

(2) sustaining ecosystem services from a diminishing natural 

resource base outside the urban zone. Urban resource planners 

will need to manage ecological processes so that ecosystem 

health and productivity adapts to and is sustained on the small 

urban resource base. At the broader scale, regional planners 

need to manage resources in the face of low-density development that is now occurring in nonurban areas across a large and 

diverse landscape that is managed by a variety of jurisdictions. 

Federal land managers are faced with the effects of development around and within public land boundaries (see sidebar, 

Housing Growth in and Around Public Lands), and State and 

local jurisdictions are faced with providing services such as fire 

protection to a more dispersed population.

Opportunities

Urban vegetation and its management can significantly influence human health and ecosystem services in and around cities. 

A number of decisionmaking tools have been designed to aid 

with optimal vegetation design and management practices 

to improve the ability of trees and urban forests to provide 

ecosystem services. These tools enable users to collect local 

data and analyze urban forest composition, ecosystem services, 

and values. i-Tree is a suite of urban forest analysis software 

to aid managers and the public in assessing their urban forest 

ecosystem and street tree populations (http://www.itreetools.

org/). Urban and community forestry programs also provide 

opportunities to educate urban residents about the contribution 

of trees to their welfare.

At the broader landscape scale, where low-density development 

threatens the integrity of natural ecosystems, a variety of analytical tools exist that can be used to evaluate management and 

policy options to maintain intact natural ecosystems. The measures of landscape pattern and fragmentation presented in chapter 6 provide an essential starting point for such evaluations by 

identifying broad regional patterns and trends. These measures 

can be used to identify the most efficient way to increase intact 

environments or minimize further fragmentation, or to evaluate 

opportunities to improve overall connectivity across landscapes 

for any of the land cover types. Habitat connectivity has long 

been discussed in the context of forest-dependent species and 

may also be important for the migration of tree species in 

response to climate change. The wetlands predictive model, 

also described in chapter 6, could be used to (1) predict wetland 

conversion risk, (2) prioritize wetland areas for conservation 

based on conservation value and risk of conversion, (3) evaluate wetland habitat connectivity, and (4) aid planning decisions 

for projected urban development to lessen wetland conversion 

potential. Smart Growth principles (http://www.epa.gov/dced/

index.htm) can be used in both rural and urban environments to 

evaluate development decisions that affect natural landscapes. 

Although these many tools and approaches can be used to 

evaluate and design management options to conserve natural 

landscapes, accomplishing these goals will require cooperation 

across a variety of ownership and jurisdictions.
168 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Housing Growth in and Around Public Lands

The effects of housing development around public lands were 

described previously (sidebar, Housing Growth Near Public Lands, 

chapter 6). Four case studies provide more specific examples of 

the ecological consequences attributable to housing development 

adjacent to Federal protected areas (Radeloff et al. 2010). 

Figure 147 shows the change in housing density between 

1940 and 2000 in proximity to these four sites.

Rocky Mountain

North

South

Pacific Coast

Cleveland

National Forest

Mount Evans

Wilderness Great Smoky Mountains

National Park

Huron-Manistee

National Forest

1940

2000

Housing density (units/square mile)

0 0–5 5–10 10–20 20–50 50–100 100–200 > 200

0 25 50 100

mi

0 15 30 60

mi

0 25 50 100

mi

0 50 100 200

mi

Figure 147. Changes in housing density, 1940–2000, within 15 and 30 miles of four case study areas: the Cleveland National 

Forest, Mount Evans Wilderness Area, Great Smoky Mountains National Park, and the Huron-Manistee National Forest.

miles miles miles miles
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 169

Case 1: Cleveland National Forest in 

Southern California

Housing growth, and the associated road network, is limiting 

dispersal of mammalian carnivores such as mountain lion, bobcat, 

and badger among protected area units that support native 

habitats (Crooks 2002). Furthermore, the increasing presence 

of humans in this landscape has increased fire frequency and 

predisposed the landscape to invasion by exotic grasses (Talluto 

and Suding 2008).

Case 2: Mount Evans Wilderness Area in 

Colorado

Exurban development linked to Denver, CO, is encroaching from 

the east. Isolation effects are less of a concern here given the 

substantial area of other public lands to the west. The Mount 

Evans Wilderness Area, however, has experienced increasingly 

high recreational use that has altered vegetation and game population demography (Braun et al.1993; Wilderness.net undated).

Case 3: Great Smoky Mountains National 

Park in Tennessee

Protected areas located in the Eastern United States have been 

subject to a greater intensity of encroachment because they tend 

to be smaller and less buffered by large expanses of public land, 

as found in the Western United States. Housing densities around 

the park have increased substantially since the 1940s, nearly 

enveloping the park by 2000. This pattern of housing growth has 

contributed to the degeneration of air quality within the park 

(Shaver et al. 1994) and has been associated with increasing 

poaching pressure on wild ginseng (e.g., see http://www.

nationalparkstraveler.com/2009/02/gingseng-poachers-greatsmoky-mountains-national-park-receive-jail-time).

Case 4: Huron-Manistee National Forest in 

Michigan

The prevalence of private in-holdings (48 percent of the land 

within the administrative boundary of the national forest is 

privately owned) within the Huron-Manistee presents special 

conservation challenges. Fire suppression activities to protect 

homes in the vicinity of the national forest have altered the 

historic disturbance regime. The altered regime has reduced 

habitat availability for the endangered Kirtland’s warbler that, for 

breeding, prefers the young jack pine forests established after fire 

(Mayfield 1993). Moreover, housing growth within the forest’s 

boundary has increased warbler exposure to nest parasitism from 

brown-headed cowbirds that forage in residential areas, further 

eroding warbler reproduction (Kelly and DeCapita 1982).

These case studies all illustrate that housing development, even 

in rural settings, can cause multiple and interacting conservation 

threats (Pidgeon et al. 2007) and therefore present resource 

managers with challenging problems to solve if these threats 

are to be mitigated. Within the Forest Service, Research and 

Development could monitor and understand resource effects 

traceable to an increasing human footprint in and near the lands 

for which it has resource stewardship responsibility. State and 

Private Forestry could educate the public at the level of individual 

landowners to increase awareness of home development effects 

to natural resources. The National Forest System (NFS) could 

implement regulations and management directed at ameliorating 

effects within their boundaries and could acquire or exchange 

lands that will serve to buffer ecosystems from the expanding 

footprint of human development. Furthermore, the Forest Service 

could promote existing, while helping to develop new, Federal, 

State, and local tax incentive programs designed to reduce 

the cost of private resource stewardship activities, leading to 

biodiversity conservation and sustainable resource development 

(Robles et al. 2008; Stein et al. 2010a, 2010b).

Challenge: Enhancing the Market 

Value of Wood Resources

The RPA scenarios point to a challenge of enhancing future 

market value of wood resources for both forest management 

and technology research and development. The annual U.S. 

timber harvest peaked in the late 1980s. Timber prices and 

aggregate market value of wood resources have since declined. 

Going forward, future real timber prices and timber revenues in 

the United States will remain relatively static for RPA scenarios 

with anticipated demands for solidwood and paper products 

and only modest expansion in wood energy consumption 

(e.g., the RPA B2 and RPA historical fuelwood scenarios). In 

those scenarios, projected timber demands for forest products 

are sufficient to sustain, but not substantially enhance, timber 

revenues. At the other extreme, in the RPA A1B scenario, 

timber prices and revenues are projected to escalate along with 

expansion in U.S. and global wood biomass consumption for 

energy. Technologies and market conditions that would facilitate economic conversion of higher value biomass into higher 

value energy, chemicals, or biofuels do not yet exist, however. 

Thus, enhancing market value of wood resources remains a 

challenge faced by forest managers and the forest product and 

biomass energy research and development community.

Opportunities

Studies show that the lowest rates of deforestation and forest 

carbon emissions occur in global regions with the highest rates 

of forest product output and industrial roundwood harvest, 
170 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

such as North America and Europe (Ince 2010). Enhancing the 

flow of timber revenues helps to sustain forest management and 

provides an economic rationale for policies favoring sustainable 

forests and good forestry practices. If future technology development and wood demands provide enhanced timber revenues, 

then historic experience suggests that forests and forest management will thrive. If the value of timber declines, however, 

through low-value use, limited demand, or insufficient forest 

product technology development, the future sustainability of 

forests will be compromised.

A range of strategies exists to meet the challenge of enhancing 

market values for wood resources. One such strategy is to grow 

wood resources that have properties anticipated as desirable for 

future products or energy needs in the 21st century (Wegner et 

al. 2010). Another strategy is to orient wood product development and marketing to take advantage of inherent green characteristics of wood as a raw material and enhance market value of 

wood products. For example, there are potential “game changers” in green building codes and standards that could result in 

a fundamental market shift favoring higher value wood use in 

building construction (Bowyer et al. 2010). Another strategy 

is to merge forest product technology with other technological 

developments that offer higher value or more revenue. Examples include (1) integrated forest product biorefining, such as 

production of biofuel and biochemicals at existing pulp mills 

(Belin et al. 2008; Thorp and Murdock-Thorp 2008); (2) the 

use of electronic information and communication technology to 

create more useful and higher value applications for paper

board packaging (Ince et al. 2005); and (3) the use of cellulose 

nanofibers or cellulose nanocrystals to enhance the value of 

existing products by increasing their strength or durability, or 

to provide new products, such as thinner electronic display 

screens (Agenda 2020 Technology Alliance 2010). These areas 

of technology development should be supported by coordinated 

research and development strategies aimed at creating higher 

market value for wood resources and maintaining global competitiveness for forest products, thus enhancing the flow of 

revenues to forest owners. An ancillary benefit of such policies 

would be to help give long-range forest planners and managers 

a better basis for assessing the future for forest resources in the 

overall economy, so they can justify the planting of trees and 

management of forests today knowing there will be adequate 

revenues to generate profitable returns when the trees are 

harvested decades from now.

Challenge: Adapting to Expected 

Water Shortages in an Uncertain 

World

The assessment of the vulnerability of U.S. water supplies to 

shortage indicates that large areas of the West face the prospect of increasing water shortages as the century progresses, 

as a result of both increasing demand and decreasing supply. 

Other, more localized areas of the United States may also face 

increasing shortages that were not detected at the large spatial 

scale of the assessment. As indicated in figure 148, however, 

considerable uncertainty remains about the level of vulnerability. Figure 148 shows the minimum and maximum levels 

of vulnerability for 2060 for each assessment subregion (ASR) 

across the nine RPA scenario-climate combinations evaluated. 

If the ASRs experience the minimum projected change by 

2060, only 28 ASRs are projected to face a positive probability of shortage, and only 5 have a probability above 0.5 

(figure 148a). At the other extreme, using the maximum 

projected change, 74 ASRs are projected to face a positive 

probability of shortage, and 21 of those ASRs have a probability above 0.5 (figure 148b).

Figure 148. Composite of vulnerability (probability of shortage), by ASR, projected for 2060 based on the nine RPA scenario-climate combinations: 

(a) minimum and (b) maximum.

Upper end 

of category

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

(a) (b)
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 171

The vulnerability analysis shows what would happen if water 

demand were to progress as if water shortages were not becoming ever more common and serious—i.e., no adaptive actions 

are taken during the projection period. Therefore, the vulnerability results show the areas at greatest risk of future shortages, and thus those areas with the greatest need to consider 

adaptation options. The primary forces behind this increasing 

vulnerability—human population growth and climate-driven 

decline in water yield—are very difficult to alter, but other 

options exist for addressing the projected shortages. The challenge for society, in light of the uncertainties about the levels of 

vulnerability, is to carefully consider each option and begin to 

facilitate adoption of the most promising ones.

Opportunities

Although increases in reservoir storage may help address water 

shortages in some locations, the RPA Assessment indicates 

that large storage increases generally are not the answer. Three 

other options should be carefully evaluated. These options 

are particularly pertinent because they can be implemented 

incrementally, and thus are well suited to an uncertain world. 

The first option is to improve water use efficiency. As the 

analysis of water demand shows, great strides have already 

been made in several water use sectors in lowering the amount 

of water used per demand unit. The analysis assumes continued 

progress in this area, but options probably exist for even greater 

improvements, especially in irrigated agriculture in the West. 

A second option, related to the first, is to use water pricing to 

encourage conservation. This option is already in use in many 

locations, especially in the municipal sector, and will certainly 

play an increasing role in water-short areas. A third option, 

one that has great potential, is increased use of water trading, 

allowing water to be shifted through voluntary trades to higher 

valued uses. Although water trading and water banking are already common in some areas, significant institutional and legal 

constraints limit expansion of water trading, especially across 

State lines. Of course, these options bring their own formidable 

challenges, but if the projected shortages prove to be realistic, 

the incentives for change will be compelling.

Challenge: Designing Integrated 

Management Strategies To Conserve 

Biodiversity

Biodiversity in the United States continues to erode. Conserving biodiversity will require strategies that consider the role of 

both private and public land. Habitat on privately owned land 

is more fragmented than on public land, and given the increasing development pressure on private land, public land will 

serve a growing role in the conservation of imperiled species. 

For example, NFS lands provide habitat for more listed and 

imperiled species than do those of any other Federal agency 

(Stein et al. 2008). Private land will also serve a crucial role 

in biodiversity conservation, however, if only because most 

U.S. land is privately owned. Robles et al. (2008) found that 60 

percent of forest species of conservation concern occurred on 

private forests in the conterminous United States.

Private land is particularly crucial for conserving biodiversity 

in the Eastern United States, where public lands account for a 

much smaller proportion of the land base. For example, many 

private lands in the Southeastern United States not only support concentrations of at-risk species, but are also considered 

to have a high risk of forest conversion (Stein et al. 2010a, 

2010b). The homogenization of habitats that often occurs with 

intensive land management reduces biodiversity and must be 

counterbalanced by preserving the integrity and diverse features of forest, grassland, shrubland, and agricultural habitats.

Opportunities

Collaborative efforts across public and private lands are vital to 

maintaining the ecosystem services from the Nation’s flora and 

fauna. Failure to take a broad programmatic and policy view 

of biodiversity conservation will risk further erosion of our 

biological heritage in the future. Monitoring designs, discovery 

of habitat relationships, and completion of viability assessments will require increasing research investments to document 

population trends, identify emerging at-risk species, design 

management actions to recover at-risk species, and determine 

when key populations have been restored—all of which serve 

to increase our conservation capacity.

Within the Forest Service, NFS and State and Private Forestry 

managers can work together to implement complementary 

actions to preserve and restore habitats through (1) land acquisition or conservation easements that will target priority areas 

via public-private partnerships, (2) design of cost-reduction and 

tax incentive programs that facilitate species conservation (e.g., 

Forest Legacy and Forest Stewardship Programs), (3) development of resource certification systems that require biodiversity 

conservation standards (Robles et al. 2008), (4) training on 

forestry best management practices that have been shown to 

minimize water quality effects (Ince 2010), and (5) development of market-based instruments to reward landowners for 

biodiversity conservation (Bishop et al. 2008).
172 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Challenge: Information To Conduct 

Broad-Scale Resource Assessments

Assessments of the current and future conditions of renewable 

resources rely on data from a variety of public and private 

sources. Given its national focus, the RPA Assessment must 

draw on data from Federal, State, and nongovernmental sources. The availability of credible, unbiased, and well-documented 

data is vital to underpinning this work and other broad-scale 

assessments. The availability and quality of data varies widely 

across resource areas and ownerships.

Data limitations from several resource areas are illustrative 

of these challenges. Data on recreationally important wildlife 

and fish populations and harvest are problematic because State 

jurisdiction over the management of resident wildlife and fish 

makes it difficult to merge inventories across State borders. 

This data limitation has been a long-noted impediment to the 

Forest Service’s ability to conduct comprehensive assessments 

of status and trends among most aquatic species—a constraint 

that, in turn, hinders the evaluation of large-scale factors 

that may be affecting aquatic populations. In many cases, 

this challenge is defined by data access, not data existence. 

Estimating populations and harvests of terrestrial and aquatic 

species across large geographic areas is conceptually simple; 

the inventories upon which those estimates are based, however, 

are logistically difficult and expensive to implement (Morellet 

et al. 2007). Data describing the status and trends of rangeland 

vary widely across ownership categories and are generally not 

comparable among agencies or even within different regions 

in the same agency. Although weather and climate data appear 

abundant, most of the data are collected close to weather 

stations, so large areas of the United States, particularly in 

the West and at high elevation, are not served by any type of 

weather data collection.

Natural disturbances, human development, and climate all interact in their effects upon natural resources. Understanding these 

interactions requires data designed to support analysis across 

multiple resources. Data to support analysis of resource interactions are very challenging, as they need to be linked temporally, 

spatially, and by common definitions. Integrated data are vital 

to supporting analyses that can link changes in socioeconomic 

and biophysical characteristics (e.g., human population, land 

use, climate, and landscape pattern) to changes in forest and 

rangeland resources, water, recreation, and biodiversity.

Opportunities

There are opportunities for coordinated approaches to data 

collection and monitoring among Federal, State, nongovernmental, and academic institutions that could meet the needs 

of resources managers at all scales. Ameliorating many of the 

pressures on wildlife resources stemming from habitat loss and 

degradation, land use intensification, and climate change will 

require multijurisdictional and regional efforts that would benefit from monitoring data that can be aggregated easily across 

broad geographic areas. In some cases, the data limitation 

issue can be solved by mechanisms that facilitate data sharing 

through distributed information systems that will accept diverse 

data input but have designed standardization to permit merging 

across sources (Nate and Loftus 2012).

The disjointed, sparse, and incomplete data situation for 

rangeland presents a unique opportunity for land management 

agencies to agree on and implement interagency standards, 

guidelines, and protocols. Such coordination would reduce 

redundancy, improve cost effectiveness, and provide a common 

data structure that could be used to make meaningful inferences 

regardless of ownership at the national level. The joint Forest 

Service-Natural Resources Conservation Service-Bureau of 

Land Management agreement to use a common definition of 

ecological/range sites to describe rangeland vegetation is an 

example of one interagency approach.

Land cover monitoring through remote sensing is now well 

established in the United States, but research is needed to learn 

better ways to combine that information with other data sources 

and to interpret their meaning. Synoptic monitoring of land 

cover is now well coordinated among Federal agencies because 

of initiatives during the past decade. There has also been substantial research progress in understanding the effects of land 

cover patterns on natural ecosystem functioning during the past 

three decades. The major opportunities now are to improve the 

use of available synoptic data by combining it with other available data (such as ground-based forest and resource inventories), 

to improve our ability to predict changes in patterns resulting 

from land use changes, and to articulate the consequences of 

changes in terms of the sustainability of natural resources and 

the ecological functions that depend on them.

Challenge: Meeting Future Outdoor 

Recreation Demands

Increases are projected for total outdoor recreation participants 

and total days of participation. These increases will put 

additional pressure on what is expected to be a largely fixed 

public land and water base. The largest growth in number of 

participants is projected to occur for activities associated with 

visiting developed sites and nature viewing, for which more 

than 100 million participants may be added by 2060. Outdoor 

recreation activities projected to have the fastest growth in 

participation rates included developed skiing, day hiking, and 

motorized water activities. Developed infrastructure is necessary to accommodate many of these needs, and extensive trail 

systems are needed for others.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 173

Recreation preferences may also change in response to the 

changing demographic composition of the U.S. population. 

A population with an increasing average age may require additional opportunities for less physically challenging activities. 

Different racial and ethnic groups currently prefer different activities and recreation settings (see sidebar, Addressing Outdoor 

Recreation Needs of Latinos). Shifts in preferences are reflected 

in traditional activities such as hunting and fishing activities, 

which have seen declining participation rates since the early 

1990s. Climate change may also affect opportunities in terms of 

the physical resource base (e.g., effects on regional snow cover) 

and of climatic conditions for participating in different activities (e.g., participating fewer days because of heat).

As the number of recreationists continues to increase, public 

recreation managers will face problems of infrastructure deterioration and deferred maintenance. These problems are repeated 

at the national, State, and local levels, and the economic downturn has hindered the ability of public agencies to maintain their 

recreation facilities. The effects of climate change could further 

exacerbate this problem, e.g., sea level rise threatening coastal 

recreation facilities (Walls et al. 2009).

Opportunities

Researchers and recreation managers have a wealth of knowledge about recreationists and their preferences. Although not 

complete in every regard, synthesizing and better communicating what is known could provide useful guidance for recreation 

managers at local, State, and Federal recreation sites. Synthesizing information often helps highlight gaps in knowledge, 

which could be used to prioritize information needs to address 

future recreation management challenges.

Most public-sector recreation providers are expecting continued 

tight budgets into the future. There has been major growth in 

recent years in conservation land trusts and innovative conservation financing tools that includes partnerships with government agencies, however (Walls et al. 2009). Although outdoor 

recreation has not been the focus of these conservation efforts, 

it would be worth exploring a larger role for outdoor recreation.

Concerns about the physical fitness of Americans and about their 

connections with nature as the population becomes increasingly 

urban present opportunities to create new management and 

research partnerships. Outdoor recreation provides numerous 

opportunities for exercise, ranging from physically challenging 

activities such as mountain climbing to less strenuous activities 

such as visiting interpretative sites. Recreationists are usually 

simultaneously exposed to natural environments while pursing 

these activities. There are opportunities to design education 

programs that address both health and nature education needs 

and to design recreation opportunities that encourage people to 

use outdoor recreation to improve their health and well-being.

Addressing Outdoor Recreation Needs of Latinos

The ethnic and racial profile of the United States is undergoing a 

major shift. In the decades ahead, people of color will constitute 

a majority of the population. Few racial or ethnic groups have 

had as great an effect on the demography of the United States as 

Latinos. Research conducted over 15 years in southern California 

has helped better understand the recreation needs of Latinos 

(Chavez 2012).

Many Latinos report having only one day off from work per week, 

thus they are primarily “day use” visitors. This knowledge is crucial in determining when use will be heaviest and what sites may 

require concentration of resources. Site design should consider 

the strong desire for family time and family bonding through large 

family group outings. Meeting the development needs of Latino 

visitors may require renovation or equipment upgrades, such as 

installing larger picnic tables, placing groups of tables together, 

and providing several trash receptacles to accommodate larger 

visitor groups. Some consideration should be made for the longer 

period Latinos tend to stay at sites—perhaps having services 

and facilities such as group play areas for volleyball or soccer, 

drinking water, and toilets.

Communication is key to serving Latinos at outdoor recreation 

sites. Translating materials into Spanish is recommended, and 

providing materials that have been back-translated (wherein a 

message is translated to Spanish, then translated back to English) 

would be even better. In regards to translation, traditional use of 

brochures at the site entrances, signs along the road, and notes 

on bulletin boards are acceptable. Alternate communication 

strategies, such as onsite bilingual hosts and interpretations, also 

can be helpful.
174 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Challenge: Sustainable Management 

of Natural Resources Under Climate 

Change

This assessment has explored the implications of climate 

change on renewable resources. In these analyses, climate 

change influences forest growth, water availability, quality and 

quantity of wildlife habitat, tree cover area, and participation in 

various recreation activities across the United States. Although 

much is known about the potential effects of climate change 

on renewable resources, much is still to be learned (U.S. CCSP 

2008a; Hibbard et al. 2010). For example, although wildfires 

are a natural process that structures ecosystems, recent analyses 

suggest that climate is altering historical fire dynamics and 

may alter the patterns beyond that to which the biota is adapted 

(Westerling et al. 2006, 2011). Plants and animals will thus be 

affected directly by the changes in climate and indirectly by 

changes to natural disturbances such as fire, insect outbreaks, 

and disease. Furthermore, feedbacks between the land surface 

and climate will complicate our ability to anticipate ecosystem 

responses. For example, recent studies have shown that land 

use changes affect local temperature and precipitation (Fall et 

al. 2009), and these effects, in turn, can alter vegetation dynamics and future land use decisions. Thus, resource response 

projections are more complicated than what may be suggested 

based simply on changes in elevated greenhouse gases and 

corresponding changes in temperature and precipitation. These 

interactions are important to consider in renewable resource 

management.

Opportunities

Sustainable management of natural resources in the face of 

climate change will require the implementation of existing 

management actions and the development of novel management strategies. The mix of management actions to address 

climate change, what are often referred to as adaptive strategies, have been categorized into resistance options (forestall 

effects and protect highly valued resources), resilience options 

(improve the capacity of ecosystems to return to desired 

conditions after disturbance), and response options (facilitate 

transition of ecosystems from current to new conditions) 

(Millar et al. 2007). As environmental conditions continue to 

change from the effects of climate change, sustaining the current or historical landscape will be challenging and may have 

to be abandoned, in the longer term, as a traditional resource 

management goal (U.S. CCSP 2008b). Continued implementation of current management actions designed to remedy insect 

and disease outbreaks, air and water quality issues, wildfire, 

habitat alteration and fragmentation, and legacy effects of land 

management will support the ability of plants and animals 

to resist the near-term effects of climate change. Moreover, 

restoration of degraded ecosystems will return ecosystems to 

their characteristic structure and function, thus enhancing their 

resilience to some climate change effects. Finally, proactive 

response management could help retain important ecosystem 

services that would otherwise erode under climate change if not 

for targeted novel actions that promote landscape connectivity, 

enhance natural regeneration, increase habitat diversity and 

redundancy, facilitate species transitions, and expand the 

genetic guidelines for planting (Joyce et al. 2009; Millar et al. 

2007). Environmental variability, the inevitability of surprise, 

and the range of management objectives across the United 

States imply that no single approach will fit all situations. 

Therefore, sustainable management of natural resources in the 

face of climate change will require a commitment to monitoring environmental, social, and economic systems if society is to 

adjust its management adaptively. The choices for sustainable 

management of natural resources will be influenced by the 

availability of information, vulnerability of ecological and 

socioeconomic systems, complexity of resource interactions, 

and the inherent uncertainties associated with climate change.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 175

Chapter 16. Conclusions

The United States has abundant natural resources, but there is little doubt that demands for forest, rangeland, 

and water resources will increase in the future in response to a 

growing population. We expect the resource base to be able to 

meet some future demands, but the outlook for other goods and 

services is more uncertain. Increasing demands on a shrinking 

natural land base and increased competition for water set the 

stage for continued conflicts in the use and management of 

renewable resources.

The findings of this assessment are largely consistent with 

previous Resources Planning Act (RPA) Assessments. Population growth continues to be a main driver of resource change. 

The recent recession and slow recovery have slowed the pace 

of development, but a return to stronger growth in the long term 

is expected.

Urbanization and low-density development will reduce forest 

and rangeland area, reshape landscape conditions, and alter 

wildlife communities in the absence of concerted action to 

reduce development effects. As urban area expands, urban 

forests can play an increased role in providing an array of 

ecosystem services to urban residents and in minimizing effects 

on surrounding landscapes.

Development pressures are projected to affect forest land more 

than rangelands because population growth is more concentrated in regions where forests occur. Incentives to convert 

forest land are highly influenced by the expected economic 

returns among competing land uses. The effects of economic 

globalization on forest product markets have been tracked 

through successive assessments, and they remain important. 

Currently, global demands are providing export growth for 

some forest products, whereas domestic markets continue to be 

negatively affected by the U.S. housing market. The future of 

forest product markets across the 2010 RPA scenarios is highly 

sensitive to assumptions about biomass use for bioenergy domestically and globally. In the absence of major new demands, 

such as bioenergy, the outlook continues to be for relatively 

flat timber prices, indicating little incentive either to retain 

forest land or to invest in forest management. This outlook is 

consistent with the last RPA Assessment, which raised similar 

concerns about sufficient incentives for sustainable forest management. Although a large increase in biomass energy would 

significantly increase timber prices and returns to landowners, 

it could also lead to competition with agricultural uses and 

changes in forest composition, particularly the accelerated 

expansion of pine plantations at the expense of natural pine 

in the Southern United States. Rangelands are less threatened 

by conversion than forest land. The effects of fragmentation 

from activities such as housing development and oil and gas 

development, however, pose threats to rangeland integrity.

The outlook for carbon storage in this RPA Assessment is not 

as positive as in the previous assessment, which reported that 

although the size of the annual addition to carbon stocks was 

declining in the future, the forest was expected to remain a 

carbon sink. In this assessment, the forest becomes a source 

of emissions in all RPA scenarios, a result of forest loss and 

changes in carbon storage per acre of forest.

The outlook for water in the last RPA Assessment was relatively 

positive because of slow increases in withdrawal rates compared 

with population growth, but potential effects on instream flows 

were raised as a concern. In this assessment, the effects of climate change on water yield and water use were found likely to 

greatly exacerbate water use conflicts and increase the vulnerability of the U.S. water supply to shortage. Although water use 

efficiency has improved substantially, further improvements are 

not likely to be sufficient to avoid tradeoffs between water uses. 

There are already well-developed water markets, particularly 

in the West. Expansion of water trading and water banking has 

potential if legal and institutional constraints can be overcome.

This assessment continues to find reasons for concerns about 

biodiversity—including increasing numbers of at-risk species 

and threats to habitat from land conversion and fragmentation. 

Given the relatively high incidence of at-risk species among 

those organisms that inhabit aquatic ecosystems, the resolution 

of water use conflicts will need to consider the potential biodiversity effects associated with water supply vulnerabilities.

Increases in the number of participants in outdoor recreation 

are projected, consistent with previous RPA Assessments. A 

continuing concern is whether a stable public land base will be 

able to meet increasing demands without exacerbating conflicts 

among users and causing increasing congestion. A large share 

of Federal recreation resources are not located in close proximity to population centers, putting additional pressure on local, 

State, and Federal facilities that are in close proximity.
176 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

The future outlook for natural resources is complicated by 

climate change. Climate change, particularly in concert with 

other natural and human stressors, will increasingly affect the 

future condition of the Nation’s forests and rangelands and 

their ability to provide the goods and services demanded by the 

American public. Although RPA Assessments have included 

analyses of the effects of climate change since 1990, in this 

assessment, we expanded our ability to incorporate climate 

effects across various resource areas. We explored the effects 

of projected temperature and precipitation changes on water 

availability, forest growth, terrestrial habitats, tree cover, 

and recreation activities. These analyses suggest that climate 

change will change natural ecosystems and human choices in 

ways that we understand, but also in ways that will surprise us.

Future Challenges

The use of scenarios in the 2010 RPA Assessment enabled us 

to link socioeconomic assumptions with climate projections 

to provide a consistent set of alternative futures in which we 

could explore potential effects on renewable resources across 

the United States. The range of results across RPA scenarios 

stresses the need to develop forest and rangeland policies that 

are flexible enough to be effective under a wide range of future 

socioeconomic and climate conditions. Given the geographic 

variation in results, it will also be important to develop local 

and regional solutions to resource management issues. The outcomes portrayed in the assessment projections are not inevitable. 

These outcomes are based on a continuation of current policies. 

During the 2010 RPA Assessment update cycle, we will evaluate the effects of policy options on resource outcomes.

Many policies and management strategies can be used to change 

the direction of future trends. Changes in markets, technology, 

trade flows, government policies, and public values will all play 

key roles in shaping responses to changing resource conditions. 

Although markets are quite effective at providing incentives 

for commodity products, incentives to provide other ecosystem 

services are limited. Increased use of payments for ecosystem 

services could provide incentives to landowners to maintain a 

wide array of services, but much progress remains to be made 

in this area. Other types of programs, such as land retirement 

programs, conservation easements, and tradable development 

permits are all options that can contribute to sustaining forest 

and rangelands. Timely actions from policymakers and resource 

managers are needed. The results from this assessment provide 

a scientific foundation for their actions.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 177

References

Adair, C. 2010. Structural panel and engineered wood yearbook 

2010 (E176). Tacoma, WA: APA—The Engineered Wood 

Association. 80 p. 

Agenda 2020 Technology Alliance. 2010. Forest products 

industry technology roadmap. http://www.agenda2020.org/

uploads/1/1/4/1/11419121/fpi_roadmap_2010.pdf. (2012 

August 6).

Aiken, R.; Harris, A. 2012. Preserving the hunting heritage: rise 

in youth hunting. In: Cordell, H.K. Outdoor recreation trends 

and futures: a technical document supporting the Forest Service 

2010 RPA Assessment. Gen. Tech. Rep. SRS-150. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station: 89–90.

Allan, J.D.; Erickson, D.L.; Fay, J. 1997. The influence of 

catchment land use on stream integrity across multiple spatial 

scales. Freshwater Biology. 37: 149–161.

Andelt, W.F.; Phillips, R.L.; Schmidt, R.H.; Gill, R.B. 1999. 

Trapping furbearers: an overview of the biological and social 

issues surrounding a public policy controversy. Wildlife Society Bulletin. 27: 53–64.

Anderson A.; Henifin K.; Supples, C. 2010. PAD–US 1.1. CBI 

Edition: standards and procedures. Corvallis, OR: The Conservation Biology Institute. 24 p.

Ankney, C.D. 1996. An embarrassment of riches: too many 

geese. Journal of Wildlife Management. 60: 217–223.

Ansley, R.J.; Rasmussen, G.A. 2005. Managing native invasive 

juniper species using fire. Weed Technology. 19: 517–522.

Archer, S.; Boutton, T.W.; Hibbard, K.A. 2001. Trees in 

grasslands: biogeochemical consequences of woody plant 

expansion. In: Schulze, E-D.; Heimann, M.; Harrison, S.; [et 

al.], eds. Global biogeochemical cycles in the climate system. 

San Diego: Academic Press: 115–138.

Bachelet, D.; Lenihan, J.M.; Daly, C.; [et al.]. 2001. MC1: a dynamic vegetation model for estimating the distribution of vegetation and associated carbon, nutrients, and water—technical 

documentation. Version 1.0. Gen. Tech. Rep. PNW–GTR–508. 

Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 95 p.

Baker, K.K.; Higgins, K.F. 2009. Planted grasslands and native 

sod prairie: Equivalent habitat for grassland birds? Western 

North American Naturalist. 69: 235–242.

Balmford, A.; Bond, W. 2005. Trends in the state of nature and 

their implications for human well-being. Ecology Letters. 8: 

1218–1234.

Barry, J. J.; Hellerstein, D. 2004. Farm recreation. In: Cordell, 

H.K., ed. Outdoor recreation for 21st century America. State 

College, PA: Venture Publishing, Inc.: 149–167.

Belin, T.; Brown, C.; Connor, E. [et al.]. 2008. Adding biofuel/

bioproduct capacity to existing U.S. mills; Part 1: Options. 

Paper 360o (TAPPI/PIMA). 3(4): 33–37. Part 2: The business 

case. Paper 360o (TAPPI/PIMA). 3(6): 24–28.

Bidwell, T.G.; Engle, D.M.; Moseley, M.E.; Masters, R.E. 

1995. Invasion of Oklahoma rangelands and forests by eastern 

redcedar and ashe juniper. Circular E-947. Stillwater, OK: 

Oklahoma Cooperative Extension Service. 14 p.

Birdsey, R.A.; Pregitzer, K; Lucier, A. 2006. Forest carbon 

management in the United States: 1600–2100. Journal of 

Environmental Quality. 35: 1461–1469.

Bishop, J.; Kapila, S.; Hicks, F.; Mitchell, P.; Vorhies, F. 

2008. Building biodiversity business. London, England; Gland, 

Switzerland: Shell International Limited and the International 

Union for Conservation of Nature. 164 pp.

Bowker, J.M.; Askew, A. 2012. U.S. Outdoor recreation participation projections 2010 to 2060. In Cordell, H.K. Outdoor 

recreation trends and futures: a technical document supporting 

the Forest Service 2010 RPA Assessment. Gen. Tech. Rep 

SRS-150. Asheville, NC: U.S. Department of Agriculture, 

Forest Service, Southern Research Station: 105–124.

Bowker, J.M.; Askew, A.; Cordell, H.K.; [et al.]. 2012. Outdoor recreation participation in the United States—projections 

to 2060: a technical document supporting the Forest Service 

2010 RPA Assessment. Gen. Tech. Rep. SRS-160. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 34 p.

Bowyer, J.; Bratkovich, S.; Howe, J.; Fernholz, K. 2010. New 

developments signal a fundamental shift and perhaps significant opportunity for building materials suppliers. Dovetail 

Partners, Inc. 9 p.
178 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Braun, C.E.; Martin, K.; Robb, L.A. 1993. White-tailed 

ptarmigan (Lagopus leucura). Issue No. 068. In: Poole, A., ed. 

The birds of North America Online. Ithaca, NY: Cornell Lab 

of Ornithology. http://bna.birds.cornell.edu/bna/species/068. 

(2009 September 24).

Brennan, L.A.; Kuvlesky, W.P. 2005. North American 

grassland birds: an unfolding conservation crisis? Journal of 

Wildlife Management. 69: 1–13.

Briggs, J.M.; Knapp, A.K.; Blair, J.M. [et al.]. 2005. An ecosystem in transition: causes and consequences of the conversion 

of mesic grasslands to shrubland. BioScience. 55: 243–254.

Brown, D.M.; Reeder, R.J. 2007. Farm-based recreation: a 

statistical profile. ERR–53. Washington, DC: U.S. Department 

of Agriculture, Economic Research Service. 28 p.

Brown, T.C. 2000. Projecting U.S. freshwater withdrawals. 

Water Resources Research. 36: 769–780.

Brown, T.C.; Binkley, D. 1994. Effect of management on water 

quality in North American forests. Gen. Tech. Rep. RM–GTR–

248. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Forest and Range Experiment Station 

Station. 27 p.

Brown, T.C.; Froemke, P. 2010. Risk of impaired condition of 

watersheds containing national forest lands. Gen. Tech. Rep. 

RMRS–GTR–251. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 57 p.

Brown, T.C.; Froemke, P. 2012. Nationwide assessment of nonpoint source threats to water quality. Bioscience. 62: 136–146.

Brown, T.C.; Hobbins, M.T.; Ramirez, J.A. 2008. Spatial 

distribution of water supply in the coterminous United States. 

Journal of the American Water Resources Association. 44: 

1474–1487.

Buck, E. H.; Upton, H.F. 2010. Pacific salmon and steelhead 

trout: managing under the Endangered Species Act. Report 

98–666. Washington, DC: Congressional Research Service. 13 p.

Buongiorno, J.; Zhu, S.; Raunikar, R.; Prestemon, J. 2012. 

Outlook to 2060 for world forests and forest industries: a 

technical document supporting the Forest Service 2010 RPA 

Assessment. Gen. Tech. Rep. SRS-151. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, Southern Research 

Station. 119 p.

Butler, B.J. 2008. Family forest owners of the United States, 

2006. Gen. Tech. Rep. NRS–27. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, Northern Research 

Station. 72 p.

Butler, B.J. 2009. Forest ownership. In: Smith, W.B.; Miles, 

P.D.; Perry, C.H.; Pugh, S.A. Forest resources of the United 

States, 2007: a technical document supporting the 2010 RPA 

Assessment. Gen. Tech. Rep. WO-78. Washington, DC: U.S. 

Department of Agriculture, Forest Service, Washington Office: 

19–21.

California Department of Water Resources. 1998. California 

water plan update. Bulletin 160–98. Sacramento, CA: California Department of Water Resources. [Paginated by chapter].

Chambers, J.C.; McArthur, E.D.; Monson, S.B. [et al.]. 2005. 

Sagebrush steppe and pinyon-juniper ecosystems—effects 

of changing fire regimes, increased fuel loads, and invasive 

species. Final Report to the Joint Fire Science Program, Project 

#00-1-1-03. 66 p.

Chavez, D.J. 2012. Latinos and outdoor recreation. In: Cordell, 

H.K. Outdoor recreation trends and futures: a technical document supporting the Forest Service 2010 RPA Assessment. 

Gen. Tech. Rep. SRS-150. Asheville, NC: U.S. Department of 

Agriculture, Forest Service, Southern Research Station: 74–77.

Chudy, R. P. 2011. European Union wood biomass demand for 

energy purposes and its influence on U.S. southeastern forest 

market and carbon storage. Raleigh, NC: North Carolina State 

University. 110 p. Thesis.

Colorado Water Conservation Board [CWCB]. 1998. River 

basin facts. Colorado Division of Water Resources. http://cwcb.

state.co.us/Home/RiverBasinFacts. (2011 February 7).

CWCB. 2010. CDSS memoranda. Colorado Division of Water 

Resources. http://cdss.state.co.us/Pages/CDSSHome.aspx. 

(2012 August 6).

Comer, P.; Faber-Langendoen, D.; Evans, R. [et al.]. 2003. Ecological systems of the United States: a working classification of 

U.S. terrestrial systems. Arlington, VA: NatureServe. 75 p.

Comer, P.J.; Schulz, K.A. 2007. Standardized ecological classification for mesoscale mapping in the southwestern United 

States. Rangeland Ecology and Management. 60: 324–335.

Conover, M.R. 2001. Effect of hunting and trapping on wildlife 

damage. Wildlife Society Bulletin. 29: 521–532.

Conservation Biology Institute. 2010. PAD-US 1.1 (CBI 

Edition). Corvallis, OR: Conservation Biology Institute.

Cordell, H.K. [In press]. The diversity of wilderness: ecosystems 

represented in the national wilderness preservation system. 

International Journal of Wilderness.

Cordell, H.K. 2012. Outdoor recreation trends and futures: a 

technical document supporting the Forest Service 2010 RPA 

Assessment. Gen. Tech. Rep. SRS-150. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, Southern Research 

Station. 167 p.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 179

Cordell, H.K.; Betz, C.J.; Zarnoch, S.J. [In press]. Recreation 

and protected land resources in the United States: a technical 

document supporting the Forest Service 2010 RPA Assessment. 

Gen. Tech. Rep. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station.

Cordell, H.K.; Murphy, D.; Riitters, K.; Harvard, J.E. 2005. 

The human context and natural character of wilderness lands. 

In: Cordell, H.K.; Bergstrom, J.C.; Bowker, J.M., eds. The 

multiple values of wilderness. State College, PA: Venture 

Publishing, Inc.: 57–89.

Côté, S.D.; Rooney, T.P.; Tremblay, J-P. [et al.] 2004. Ecological impacts of deer overabundance. Annual Review of Ecology, 

Evolution, and Systematics. 35: 113–147.

Coulson, D.P.; Joyce, L.A.; Price, D.T. [et al.]. 2010a. Climate 

scenarios for the conterminous United States at the county 

spatial scale using SRES scenarios A1B and A2 and PRISM 

climatology. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. http://dx.doi.

org/10.2737/RDS-2010-0008. (2012 August 6).

Coulson, D.P.; Joyce, L.A.; Price, D.T. [et al.]. 2010b. Climate 

scenarios for the conterminous United States at the county spatial 

scale using SRES scenario B2 and PRISM climatology. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. http://dx.doi.org/10.2737/

RDS-2010-0009. (2012 August 6).

Council of Economic Advisors. 2011. Economic report of the 

President. 2011. Washington, DC: United States Government 

Printing Office. 316 p.

Crooks, K.R. 2002. Relative sensitivities of mammalian 

carnivores to habitat fragmentation. Conservation Biology. 16: 

488–502.

Cumming, A.B.; Twardus, D.B.; Nowak, D.J. 2008. Urban 

forest health monitoring: large scale assessments in the United 

States. Arboriculture and Urban Forestry. 34: 341–346.

Dahl, T.E. 1990. Wetland losses in the United States, 1780s to 

1980s. Washington, DC: U.S. Department of Interior, U.S. Fish 

and Wildlife Service. 22 p.

Dahl, T.E. 2006. Status and trends of wetlands in the conterminous 

United States 1998 to 2004. Washington, DC: U.S. Department 

of the Interior, U.S. Fish and Wildlife Service. 112 p.

Daily, G.C. 1997. Nature’s services: societal dependence on 

natural ecosystems. Washington, DC: Island Press. 392 p.

Dale, V.H. 1997. The relationship between land use change and 

climate change. Ecological Applications. 7: 753–769.

Daniels, A.E.; Cumming, G.S. 2008. Conversion or conservation? Understanding wetland change in northwest Costa Rica. 

Ecological Applications. 18: 49–63.

David, E.L. 1990. Manufacturing and mining water use in the 

United States, 1954–83. In: Carr, J.E.; Chase, E.B.; Paulson, 

R.W.; Moody, D.W., eds. National water summary 1987: 

Hydrologic events and water supply and use. Denver, CO: U.S. 

Geological Survey, Water-Supply Paper 2350: 81–92.

Dawson, C.P.; Hendee, J.C. [In press]. The National Wilderness Preservation System and its stewardship. In: Cordell, 

H.K.; et al. Recreation and protected land resources in the 

United States: a technical document supporting the Forest 

Service 2010 RPA Assessment. Gen. Tech. Rep. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station.

DeVink, J-M.; Berezanski, D.; Imrie, D. 2011. Comments on 

Brodie and post: harvest effort: the missing covariate in analyses of furbearer harvest data. Population Ecology. 53: 261–262.

DiTomaso, J.M.; Masters, R.A.; Peterson, V.F. 2010. Rangeland invasive plant management. Rangelands. 32: 43–47.

du Moulin, A.; Alford, M.B. [In press]. State and local government financing for land conservation. In: Cordell, H.K.; et al. 

Recreation and protected land resources in the United States: 

a technical document supporting the Forest Service 2010 RPA 

Assessment. Gen. Tech. Rep. Asheville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research Station.

Duda, M.A.; Jones, M.F.; Criscione, A. 2010. The sportsman’s 

voice: hunting and fishing in America. State College, PA: 

Venture Publishing. 259 p.

Duncan, C.A.; Jachetta, J.J.; Brown, M.L. [et al.]. 2004. Assessing the economic, environmental, and societal losses from 

invasive plants on rangeland and wildlands. Weed Technology. 

18: 1411–1416.

Eagleson, P.S. 1978. Climate, soil, and vegetation. Water 

Resources Research. 14(5): 705–776.

Epstein, H.E.; Lauenroth, W.K.; Burke, I.C.; Coffin, D.P. 1997. 

Productivity patterns of C3 and C4 functional types in the U.S. 

Great Plains. Ecology. 78: 722–731.

Fall, S.; Niyogi, D.; Gluhovsky, A. [et al.]. 2009. Impacts of 

land use land cover on temperature trends over the continental 

United States: assessment using the North American Regional 

Reanalysis. International Journal of Climatology. http://www.

interscience.wiley.com. doi:10.1002/joc.1996.

Feldman, D.L. 2009. Preventing the repetition: Or, what Los 

Angeles’ experience in water management can teach Atlanta 

about urban water disputes. Water Resources Research. 45: 

W04422, doi:10.1029/2008WR007605.
180 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Flather, C.H.; Brady, S.J.; Knowles, M.S. 1999. Wildlife 

resource trends in the United States: a technical document 

supporting the 2000 USDA Forest Service RPA Assessment. 

Gen. Tech. Rep. RMRS–GTR–33. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 79 p.

Flather, C.H.; Hayward, G.D.; Beissinger, S.R.; Stephens, P.A. 

2011. Minimum viable populations: is there a ‘magic number’ 

for conservation practioners? Trends in Ecology and Evolution. 

26: 307–316.

Flather, C.H.; Hoekstra, T.W. 1989. An analysis of the wildlife 

and fish situation in the United States: 1989–2040. Gen. 

Tech. Rep. RM–178. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Forest and Range 

Experiment Station. 146 p.

Flather, C.H.; Joyce, L.A.; Bloomgarden, C.A. 1994. Species 

endangerment patterns in the United States. Gen. Tech. Rep. 

RM–241. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Forest and Range Experiment 

Station. 42 p.

Flather, C.H.; Knowles, M.S.; Jones, M.F.; Schilli, C. [In 

press a]. Wildlife population and harvest trends in the United 

States: a technical document supporting the Forest Service 

2010 RPA assessment. Gen. Tech. Rep. RMRS–GTR. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station.

Flather, C.H.; Knowles, M.S.; Kendall, I.A. 1998. Threatened 

and endangered species geography: characteristics of hot spots 

in the conterminous United States. BioScience. 48: 365–376.

Flather, C.H.; Knowles, M.S.; McNees, J. 2008. Geographic 

patterns of at-risk species: a technical document supporting 

the USDA Forest Service interim update of the 2000 RPA 

assessment. Gen. Tech. Rep. RMRS–GTR–211. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 21 p.

Flather, C.H.; Knowles, M.S.; McNees, J. [In press b]. Criterion 

1: Conservation of biological diversity. Indicator 5: Number and 

status of native forest associated species at risk, as determined by 

legislation or scientific assessment. In: Data report: a supplement 

to the national report on sustainable forests—2010. Washington, 

DC: U.S. Department of Agriculture, Forest Service.

Flather, C.H.; Sauer J.R. 1996. Using landscape ecology to test 

hypotheses about large-scale abundance patterns in migratory 

birds. Ecology. 77: 28–35.

Flather, C.H.; Sieg, C.H. 2007. Species rarity: definition, classification, and causes. In: Raphael, M.G.; Molina, R., eds. Conservation of rare or little-known species: biological, social, and 

economic considerations. Washington, DC: Island Press: 40–66.

Flather, C.H.; Wilson, K.R.; Shriner, S.A. 2009. Geographic 

approaches to biodiversity conservation: implications of scale 

and error to landscape planning. In: Millspaugh, J.J.; Thompson, F.R., eds. Models for planning wildlife conservation in 

large landscapes. Burlington, MA: Academic Press: 85–122.

Food and Agriculture Organization of the United Nations 

[FAO]. 2010. Global forest resources assessment 2010: main 

report. FAO Forestry Paper 163. Rome, Italy: Food and Agriculture Organization of the United Nations. 340 p.

FAO. 2011. The state of the world’s land and water resources 

for food and agriculture, summary report. Rome, Italy: Food 

and Agriculture Organization of the United Nations. 47 p.

Forman, R.T.T.; Alexander, L.E. 1998. Roads and their major 

ecological effects. Annual Review of Ecology, Evolution, and 

Systematics. 29: 207–231.

Foti, R.; Ramirez, J.A.; Brown, T.C. [In press]. Vulnerability of 

U.S. water supply to shortage: a technical document supporting 

the Forest Service 2010 RPA Assessment. Gen. Tech Rep. 

RMRS–GTR. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Fowler, H. J.; Kilsby, C.G.; O’Connell, P.E. 2003. Modeling 

the impacts of climate change and variability on the reliability, 

resilience, and vulnerability of a water resource system. Water 

Resources Research. 39(8): 1222, doi:10.1029/2002WR001778.

Füssel, H-M. 2007. Vulnerability: a generally applicable 

conceptual framework for climate change research. Global 

Environmental Change. 17: 155–167.

Gaston, K.J.; Fuller, R.A. 2008. Commonness, population 

depletion, and conservation biology. Trends in Ecology and 

Evolution. 23: 14–19.

Gedney, D.R.; Azuma, D.L.; Bolsinger, C.L.; McKay, N. 1999. 

Western juniper in eastern Oregon. Gen. Tech. Rep. PNWGTR-464. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 64 p.

Gleick, P.H. 1990. Vulnerability of water systems. In: Waggoner, P.E., ed. Climate change and U.S. water resources. New 

York: John Wiley and Sons: 233–240.

Glick, P.; Stein, B.A.; Edelson, N.A., eds. 2011. Scanning the 

conservation horizon: a guide to climate change vulnerability 

assessment. Washington, DC: National Wildlife Federation. 

168 p.

Good, T.P.; Waples, R.S.; Adams, P., eds. 2005. Updated status 

of Federally listed ESUs of West Coast salmon and steelhead. 

Washington, DC: U.S. Department of Commerce, NOAA 

Tech. Memo. NMFS–NWFSC–66. 598 p.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 181

Green, G.T.; Bowker, J.M.; Wang, X.; Cordell, H.K.; Johnson, 

C.Y. 2012. A national study of constraints to participation 

in outdoor recreational activities. In: Cordell, H.K. Outdoor 

recreation trends and futures: a technical document supporting 

the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. 

SRS-150. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 79–82.

Greenfield, E.J.; Nowak, D.J. [In preparation]. Tree cover and 

aridity projections to 2060: a technical document supporting the 

Forest Service 2010 RPA Assessment. Gen. Tech. Rep. NRS. 

Newtown Square, PA: U.S Department of Agriculture, Forest 

Service, Northern Research Station.

Gutzwiller, K.J.; Flather, C.H. 2011. Wetland features and 

landscape context predict the risk of wetland habitat loss. 

Ecological Applications. 21: 968–982.

Habich, E.F. 2001. Ecological site inventory. Denver, CO: U.S. 

Department of the Interior, Bureau of Land Management. 112 p.

Haley, M.M. 2001. Changing consumer demand for meat: 

the U.S. example, 1970–2000. In: Regni, A., ed. Changing 

structure of global food consumption and trade. Washington, 

DC: Economic Research Service: 41–48.

Hansen, A.J.; Neilson, R.P.; Dale, V.H. [et al.]. 2001. Global 

change in forests: responses of species, communities, and 

biomes. BioScience. 51: 765–779.

Hansen, L. 2006. Wetland status and trends. In: Wiebe, K.; 

Gollehon, N., eds. Agricultural resources and environmental 

indicators, 2006 edition. Economic Information Bulletin No. 

16. Washington, DC: U.S. Department of Agriculture, Economic Research Service. 42–49.

Hardy, S.D.; Koontz, T.M. 2010. Collaborative watershed partnership in urban and rural areas: different pathways to success? 

Landscape and Urban Planning. 95: 79–90.

Harper, K.A.; MacDonald, S.E.; Burton, P.J. [et al.]. 2005. Edge 

influence on forest structure and composition in fragmented 

landscapes. Conservation Biology. 19: 768–782.

Havel, J.E.; Lee, C.E.; Vander Zanden, M.J. 2005. Do reservoirs 

facilitate invasions into landscapes? BioScience. 55: 518–525.

Hawbaker, T.J.; Radeloff, V.C.; Clayton, M.K.; [et al.]. 2006. 

Road development, housing growth, and landscape fragmentation in northern Wisconsin: 1937–1999. Ecological Applications. 16: 1222–1237.

Heath, L.S.; Smith, J.E.; Skog, K.E.; [et al.]. 2011. Managed 

forest carbon estimates for the U.S. Greenhouse Gas Inventory, 

1990–2008. Journal of Forestry. April/May: 167–173.

Heinz Center. 2008. Landscape pattern indicators for the 

Nation: a report from the Heinz Center’s landscape pattern 

task group. Washington, DC: The H. John Heinz III Center for 

Science, Economics, and the Environment. 108 p.

Hennon, P.; D’Amore, D.; Wittwer, D. [et al.]. 2006. Climate 

warming, reduced snow, and freezing injury could explain 

the demise of yellow-cedar in southeast Alaska, USA. World 

Resource Review. 18: 427–450.

Herrick, J.E.; Lessard, V.C.; Spaeth, K.E. [et al.]. 2010. National 

ecosystem assessments supported by scientific and local knowledge. Frontiers in Ecology and the Environment. 8: 403–408.

Hibbard, K.; Janetos, A.; Van Vuuren, D.P. [et al.]. 2010. 

Research priorities in land use and land cover change for the 

Earth system and integrated assessment modeling. International 

Journal of Climatology. 30: 2118–2128.

Homer, C.; Dewitz, J.; Fry, J. [et al.]. 2007. Completion of the 

2001 national land cover database for the conterminous United 

States. Photogrammetric Engineering and Remote Sensing. 

73: 337–341.

Howard, J.L. [In preparation] U.S. timber production, trade, 

consumption, and price statistics 1965 to 2010. Research Paper 

FPL–RP. Madison, WI: U.S. Department of Agriculture, Forest 

Service, Forest Products Laboratory.

Hughes, F.R.; Archer, S.R.; Asner, G.P. [et al.]. 2006. Changes 

in aboveground primary production and carbon and nitrogen 

pools accompanying woody plant encroachment in a temperate 

savanna. Global Change Biology. 12: 1733–1747.

Hurley, M.A.; Unsworth, J.W.; Zager, P. [et al.]. 2011. 

Demographic response of mule deer to experimental reduction 

of coyotes and mountain lions in southeastern Idaho. Wildlife 

Monographs. 178: 1–33.

Huston, M.A. 2005. The three phases of land use change: 

Implications for biodiversity. Ecological Applications. 15: 

1864–1878.

Hutson, S.S.; Barber, N.L.; Kenny, J.F. [et al.]. 2004. Estimated 

use of water in the United States in 2000. Circular 1268. 

Reston, VA: U.S. Geological Survey. 76 p.

Ibáñez, I.; Clark, J.S.; Dietze, M.C. [et al.]. 2006. Predicting 

biodiversity change: outside the climate envelope, beyond the 

species-area curve. Ecology. 87: 1896–1906.

Ince, P.; Kalliranta, S.; Vlosky, R. 2005. ICT and the paperboard and packaging industry. In: Information technology and 

the forest sector, Volume 18. Vienna, Austria: International 

Union of Forestry Research Organizations: 105–129.
182 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Ince, P.J. 2010. Global sustainable timber supply and demand. 

Sustainable development in the forest products industry, Chapter 2. Porto, Portugal: Universidade Fernando Pessoa, 2010: 

29–41. http://www.treesearch.fs.fed.us/pubs/37326.

Ince, P.J.; Kramp, A.D.; Skog, K.E.; Spelter, H.N.; Wear, D.N. 

2011. U.S. Forest Products Module: a technical document 

supporting the Forest Service 2010 RPA Assessment. Research 

Paper FPL–RP–662. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 61 p.

Inkley, D.B.; Anderson, M.G.; Blaustein, A.R. [et al.]. 2004. 

Global climate change and wildlife in North America. 

Bethesda, MD: The Wildlife Society. 26 p.

Intergovernmental Panel on Climate Change [IPCC]. 2006. 

2006 IPCC guidelines for national greenhouse gas inventories, 

agriculture, forestry, and other land uses (AFOLU). In: Eggelston, S.; Buendia, L.; Miwa, K.; Ngara, T.; Tanabe, K., 

eds. Volume 4. Hayama, Japan: Prepared by the National 

Greenhouse Gas Inventories Programme, Institute for Global 

Environmental Strategies, Hayama. [Paginated by chapter]. 

IPCC. 2007a. Climate change 2007: Synthesis report. Contribution of working groups I, II and III to the fourth assessment. Report of the Intergovernmental Panel on Climate Change 

[Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)]. 

Geneva, Switzerland: IPCC, 104 p.

IPCC. 2007b. Climate change 2007: The physical science basis. 

Summary for policymakers. Geneva: IPCC Secretariat. 18 p. 

http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-

spm.pdf. (2008 July 15).

International Joint Commission. 2011. 15th biennial report on 

Great Lakes water quality. Washington, DC: International Joint 

Commission, Canada and United States. 59 p.

International Monetary Fund. 2011. World economic outlook: 

April 2011. Washington, DC: International Monetary Fund. 

242 p.

International Union for the Conservation of Nature [IUCN]. 

1994. Guidelines for protected area management categories. 

Cambridge, UK: International Union for the Conservation of 

Nature. 94 p.

Irons, K.S.; Sass, G.G.; McClelland, M.A.; Stafford, J.D. 2007. 

Reduced condition factor of two native fish species coincident 

with invasion of non-native Asian carps in the Illinois River, 

U.S.A. Is this evidence for competition and reduced fitness? 

Journal of Fish Biology. 71 (suppl D): 258–273.

Iverson, L.R.; Prasad, A.M.; Matthews S.N.; Peters, M. 2008. 

Estimating potential habitat for 134 eastern U.S. tree species 

under six climate scenarios. Forest Ecology and Management. 

254: 390–406.

Jelks, H.L.; Walsh, S.J.; Burkhead, N.M. [et al.]. 2008. Conservation status of imperiled North American freshwater and 

diadromous fishes. Fisheries. 33: 372–407.

Jones, K.B. 2004. Trends in the U.S. sheep industry. AIB–787. 

Washington, DC: U.S. Department of Agriculture, Economic 

Research Service. 40 p.

Jordan, N.; Boody, G.; Broussard, W. [et al.]. 2007. Sustainable 

development of the agricultural bio-economy. Science. 316: 

1570–1571.

Joyce, L.A. 1989. An analysis of the range forage situation in 

the United States: 1989–2040. Gen. Tech. Rep. RM–GTR–180. 

Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Forest and Range Experimental 

Station. 136 p.

Joyce, L.A.; Blate, G.M.; Littell, J.S. [et al.]. 2009. Managing 

for multiple resources under climate change. Environmental 

Management. 44: 1022–1032.

Joyce, L.A.; Cross, M.; Girvetz, E. 2011. Addressing uncertainty in vulnerability assessments. In: Glick, P.; Stein, B.A.; 

Edelson, N.A., eds. Scanning the conservation horizon: a guide 

to climate change vulnerability assessment. Washington, DC: 

National Wildlife Federation: 68–73.

Joyce, L.A.; Flather, C.H.; Koopman, M.E. 2008. Analysis 

of potential impacts of climate change on wildlife habitats in 

the U.S. Final Report to the National Council for Science and 

the Environment. Washington, DC: Wildlife Habitat Policy 

Research Program. 69 p.

Joyce, L.A.; Mitchell, J.E.; Loftin, S.R. 2000. The applicability 

of Montreal Process Criterion 3—maintenance of ecosystem 

health—to rangelands. International Journal of Sustainable 

Development and World Ecology. 7: 107–127.

Joyce, L.A.; Price; D.T.; Coulson, D.P. [et al.]. [In preparation]. 

Projecting climate change in the United States: a technical 

document supporting the Forest Service 2010 RPA Assessment. 

Gen. Tech. Rep. RMRS. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station.

Kelly, S.T.; DeCapita, M.E. 1982. Cowbird control and its 

effect on Kirtland’s warbler reproductive success. The Wilson 

Bulletin. 94: 363–365.

Kennen, J.G.; Chang, M.; Tracy, B.H. 2005. Effects of landscape change in fish assemblage structure in a rapidly growing 

metropolitan areas in North Carolina, USA. American Fisheries 

Society Symposium. 47: 39–52.

Kenny, J.F.; Barber, N.L.; Hutson, S.S. [et al.]. 2009. Estimated 

use of water in the United States in 2005. Circular 1344. 

Reston, VA: U.S. Geological Survey. 52 p.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 183

Knapp, A.K.; Briggs, J.M.; Koelliker, J.K. 2001. Frequency 

and extent of water limitation to primary production in a mesic 

grassland. Ecosystems. 4: 19–28.

Krist, F.J. [et al.]. 2007. Mapping risk from forest insects and 

diseases, 2006. FHTET 2007–06. U.S. Department of Agriculture, Forest Service, Forest Health Protection, Forest Health 

Technology Enterprise Team. 116 p.

Kuo, F.E.; Sullivan, W.E. 2001. Environment and crime in 

the inner city: does vegetation reduce crime? Environmental 

Behavior. 33: 343–365.

Kupfer, J.A.; Miller, J.D. 2005. Wildfire effects and post-fire 

responses of an invasive mesquite population: the interactive 

importance of grazing and non-native herbaceous species invasion. Journal of Biogeography. 32: 453–466.

Labadie, J.W.; Pineda, A.M.; Bode, D.A. 1984. Network 

analysis of raw water supplies under complex water rights and 

exchanges: Documentation for Program MODSIM3. Fort Collins, CO: Colorado Water Institute, Colorado State University. 

96 p.

Land Trust Alliance. 2006. 2005 National Land Trust Census 

Report: http://www.northolympiclandtrust.org/Documents/2005

LandTrustCensusReport.pdf. (2012 August 6).

Laurance, W.F. 2008. Theory meets reality: how habitat fragmentation research has transcended island biogeography theory. 

Biological Conservation. 141: 1731–1744.

Levick, L.R.; Goodrich, D.C.; Hernandez, M. [et al.]. 2008. 

The ecological and hydrological significance of ephemeral and 

intermittent streams in the arid and semi-arid American southwest. Washington, DC: U.S. Environmental Protection Agency, 

EPA/600/R–08/134. 102 p.

Levin, P.; Levin, D. 2002. The real biodiversity crisis. American Scientist. 90: 6–8.

Levy, S. 2006. A plague of deer. BioScience. 56: 718–721.

Litke, D.W.; Appel, C.L. 1989. Estimated use of water in Colorado, 1985. Water-Resources Investigations Report 88–4101. 

Denver, CO: U.S. Geological Survey. 157 p.

Litvaitis, J.A. 2003. Shrubland and early-successional forests: 

critical habitats dependent on disturbance in the northeastern 

United States. Forest Ecology and Management. 185: 1–4.

Loftus, A.J., ed. 2006. Proceedings of the national fisheries data 

summit. Bethesda, MD: American Fisheries Society, Computer 

User Section. 56 p.

Loftus, A.J.; Flather, C.H. 2000. Fish and other aquatic 

resource trends in the United States: A technical document supporting the 2000 USDA Forest Service RPA Assessment. Gen. 

Tech. Rep. RMRS–GTR–53. Ft. Collins, CO. U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research 

Station. 50 p.

Loftus, A.J.; Flather, C.H. 2012. Fish and other aquatic resource 

trends in the United States: a technical document supporting the 

Forest Service 2010 RPA assessment. Gen. Tech. Rep. RMRS–

GTR–283. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 79 p.

Logan, J. A.; MacFarlane, W.W.; Willcox, L. 2010. Whitebark 

pine vulnerability to climate-driven mountain pine beetle 

disturbance in the Greater Yellowstone Ecosystem. Ecological 

Applications. 20: 895–902.

Lubowksi, R.N.; Vesterby, M.; Bucholtz, S.; Baez, A.; Roberts, 

M.J. 2006. Major uses of land in the United States, 2002. Economic Information Bulletin Number 14. Washington, DC: U.S. 

Department of Agriculture, Economic Research Service. 47 p.

Lund, G.H. 2007. Accounting for the world’s rangelands. 

Rangelands 29: 3–10.

Mac, M.J.; Opler, P.A.; Puckett Haecker, C.E.; Doran, P.D. 

1998. Status and trends of the Nation’s biological resources. 

2 vols. Reston, VA: U.S. Department of the Interior, U.S. 

Geological Survey. 543 p.

Mahoney, S.P.; Cobb, D. 2010. Future challenges to the model: 

why collapse is possible and alteration inevitable. Wildlife 

Professional. 4(3): 83–85.

Malcolm, S.A.; Aillery, M.; Weinberg, M. 2009. Ethanol 

and a changing agricultural landscape. Economic Research 

Report 86. Washington, DC: U.S. Department of Agriculture, 

Economic Research Service. 64 p.

Margules, C.R.; Pressey, R.L. 2000. Systematic conservation 

planning. Nature. 405: 243–253.

Martin, W. 2001. Trade policies, developing countries, and 

globalization. World Bank. http://siteresources.worldbank.

org/INTPRRS/Resources/2866_trade_martin.pdf. (2011 

November 1).

Mayfield, H.F. 1993. Kirtland warblers benefit from large forest tracts. Wilson Bulletin. 105: 351–353.

McKeever, D.B.; Howard, J.L. 2011. Solid wood timber 

products consumption in major end uses in the United States, 

1950–2009: a technical document supporting the Forest Service 

2010 RPA Assessment. Gen. Tech. Rep. FPL–GTR–199. 

Madison, WI: U.S. Department of Agriculture, Forest Service, 

Forest Products Laboratory. 20 p.
184 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

McKenzie, D.; Heinsch, F.A.; Heilman, W.E. 2011. Wildland 

fire and climate change. U.S. Department of Agriculture, Forest 

Service, Climate Change Resource Center. http://www.fs.fed.

us/ccrc/topics/wildlfire/. (2011 November 4).

Menu, S.; Gauthier, G.; Reed, A. 2002. Changes in survival 

rates and population dynamics of greater snow geese over a 

30-year period: implications for hunting regulations. Journal of 

Applied Ecology. 39: 91–102.

Millar, C.I.; Stephenson, N.L.; Stephens, S.L. 2007. Climate 

change and forests of the future: managing in the face of uncertainty. Ecological Applications. 17: 2145–2151.

Millennium Ecosystem Assessment [MEA]. 2005. Ecosystems 

and human well-being: synthesis. Washington, DC: Island 

Press. 137 p.

Miller, R.F.; Bates, J.D.; Svejcar, T.J.; Pierson, F.B.; Eddleman, L.E. 2005. Biology, ecology, and management of western 

juniper. Tech. Bull. 152. Corvallis, OR: Oregon State University, Agricultural Experiment Station. 82 p.

Miller, R.F.; Rose, J. 1999. Fire history and western juniper 

encroachment in sagebrush steppe. Journal of Range Management. 52: 550–559.

Miller, R.F.; Tausch, R.J.; McArthur, E.D.; Johnson, D.D.; 

Sanderson, S.C. 2008. Age structure and expansion of piñonjuniper woodlands: a regional perspective in the Intermountain 

West. Res. Pap. RMRS-RP-69. Fort Collins, CO: U.S. Department of Agriculture, Forest Service. 15 p.

Minor, E.S.; Urban, D.L. 2007. Graph theory as a proxy for 

spatially explicit population models in conservation planning. 

Ecological Applications. 17: 1771–1782.

Mitchell, J.E. 2000. Rangeland resource trends in the United 

States: a technical document supporting the 2000 USDA Forest 

Service Assessment. Gen. Tech. Rep. RMRS-GTR-68. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station. 84 p.

Mitsch, W.J.; Gosselink, J.G. 2007. Wetlands. Fourth edition. 

Hoboken, NJ: John Wiley & Sons. 295 p.

Mockrin, M.H.; Aiken, R.A.; Flather, C.H. [In press]. Wildlifeassociated recreation trends in the United States: a technical 

document supporting the Forest Service 2010 RPA assessment. 

Gen. Tech. Rep. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Mooney, H.; Mace, G. 2009. Biodiversity policy challenges. 

Science. 325: 1474.

Moore, M.R.; Crosswhite, W.M.; Hostetler, J.E. 1990. Agricultural water use in the United States, 1950–85. In: Carr, J.E.; 

Chase, E.B.; Paulson, R.W.; Moody, D.W., eds. National water 

summary 1987—hydrologic events and water supply and use, 

2350 ed. Washington, DC: U.S. Geological Survey: 93–108.

Mooty, W.B.; Jeffcoat, H.H. 1986. Inventory of interbasin 

transfers of water in the eastern United States. Open-File 

Report 86–148. Tuscaloosa, AL: U.S. Geological Survey. 53 p.

Morellet, N.; Gaillard J-M.; Hewison, A.J.M. [et al.]. 2007. 

Indicators of ecological change: new tools for managing 

populations of large herbivores. Journal of Applied Ecology. 

44: 634–643.

Morgan, J. A.; LeCain, D. R.; Pendall, E.; [et al.]. 2011. C4 

grasses prosper as carbon dioxide eliminates desiccation in 

warmed semi-arid grassland. Nature 476: 202-206.

Morgan, J.A.; Milchunas, D.G.; LeCain, D.R.; West, M.; 

Mosier, A.R. 2007. Carbon dioxide enrichment alters plant 

community structure and accelerates shrub growth in the shortgrass steppe. Proceedings of the National Academy of Sciences 

USA. 104: 14724–14729.

Morgan, J.A.; Mosier, A.R.; Milchunas, D.G. [et al.]. 2004. 

CO2

 enhances productivity, alters species composition, and 

reduces digestibility of shortgrass steppe vegetation. Ecological 

Applications. 14: 208–219.

Mule Deer Working Group. 2004. North American mule deer 

conservation plan. Western Association of Fish and Wildlife 

Agencies. 17 p.

Murcia, C. 1995. Edge effects in fragmented forests: implications for conservation. Trends in Ecology and Evolution. 10: 

58–62.

Nakicenovic, N.; Swart, R., eds. 2000. Special report on emissions scenarios. Prepared for the Intergovernmental Panel on 

Climate Change. Cambridge, England: Cambridge University 

Press. 570 p. http://www.ipcc.ch/ipccreports/sres/emission/. 

(2007 July 16).

Nate, N.; Loftus, A. 2012. Exploring trends in largemouth 

bass relative abundance: a MARIS case study. In: Loftus, A.J.; 

Flather, C.H. Fish and other aquatic resource trends in the United States: a technical document supporting the Forest Service 

2010 RPA assessment. Gen. Tech. Rep. RMRS-GTR-283. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station: 39–43.

National Association of State Park Directors. 2009. Annual 

information exchange report. http://www.naspd.org/. (2012 

February 18).
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 185

National Fish Habitat Board. 2010. Through a fish’s eye: the 

status of fish habitats in the United States 2010. Washington, 

DC: Association of Fish and Wildlife Agencies: 68 p.

National Fish Habitat Action Plan. 2006. http://www.fishhabitat.

org/documents/plan/National_Fish_Habitat_Action_Plan.pdf. 

(2010 October 20).

National Interagency Fire Center. 2011. Online statistics. 

http://www.nifc.gov/fireInfo/fireInfo_statistics.html. (2011 

November 3).

National Research Council. 1994. Rangeland health: new methods to classify, inventory and monitor rangelands. Washington, 

DC: National Academy Press. 180 p.

NatureServe. 2010. NatureServe central databases. Metadata 

on file with Michael S. Knowles, Rocky Mountain Research 

Station, Fort Collins, CO. Arlington, VA: NatureServe. (2010 

October 13).

NatureServe. 2011. NatureServe central databases. Metadata 

on file with Michael S. Knowles, Rocky Mountain Research 

Station, Fort Collins, CO. Arlington, VA: NatureServe. (2010 

May 8).

Nichols, J.D.; Johnson, F.A.; Williams, B.K. 1995. Managing 

North American waterfowl in the face of uncertainty. Annual 

Review of Ecology and Systematics. 26: 177–199.

Nichols, J.D.; Runge, M.C.; Johnson, F.A.; Williams, B.K. 

2007. Adaptive harvest management of North American waterfowl populations: a brief history and future prospects. Journal 

of Ornithology. 148(Suppl 2): S343–S349.

North American Bird Conservation Initiative, U.S. Committee. 

2010. The state of the birds 2010 report on climate change, 

United States of America. Washington, DC: U.S. Department 

of the Interior. 32 p.

North American Waterfowl Management Plan. 1994. North 

American waterfowl management plan, 1994 update: expanding the commitment. Washington, DC: U.S. Fish and Wildlife 

Service. 47 p.

Noss, R.F.; LaRoe III, E.T.; Scott, J.M. 1995. Endangered 

ecosystems of the United States: a preliminary assessment of 

loss and degradation. Washington, DC: National Biological 

Service. 58 p.

Nowak, D.J.; Cumming, A.; Twardus, D.; [et al.]. 2011. Urban 

forests of Tennessee. Gen. Tech. Rep. SRS-149. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 52 p.

Nowak, D.J.; Crane, D.E.; Stevens, J.C. 2006. Air pollution 

removal by urban trees and shrubs in the United States. Urban 

Forestry and Urban Greening. 4: 115–123.

Nowak, D.J.; Dwyer, J.F. 2007. Understanding the benefits 

and costs of urban forest ecosystems. In: Kuser, J., ed. Urban 

and community forestry in the northeast. New York: Springer: 

25–46.

Nowak, D.J.; Greenfield, E.J. 2008. Urban and community forests of New England: Connecticut, Maine, Massachusetts, New 

Hampshire, Rhode Island, Vermont. Gen. Tech. Rep. NRS-38. 

Newtown Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. 62 p.

Nowak, D.J.; Greenfield, E.J. 2010. Evaluating the National 

Land Cover Database Tree Canopy and Impervious Cover 

Estimates across the conterminous United States: a comparison 

with photo-interpreted estimates. Environmental Management. 

46: 378–390.

Nowak, D.J.; Greenfield, E.J. 2012a. Tree and impervious 

cover in the United States. Landscape and Urban Planning. 

107: 21–30.

Nowak, D.J.; Greenfield, E.J. 2012b. Tree and impervious 

cover change in U.S. cities. Urban Forestry and Urban Greening. 11: 21–30.

Nowak, D.J.: Greenfield, E.J.; Hoehn, R.; LaPoint, E. [In prep.]. 

Carbon storage and sequestration by trees in urban areas of the 

United States.

Nowak, D.J.; Noble, M.H.; Sisinni, S.M.; Dwyer, J.F. 2001. 

Assessing the U.S. urban forest resource. Journal of Forestry. 

99: 37–42.

Nowak, D.J.; Rowntree, R.A.; McPherson, E.G. [et al.]. 1996. 

Measuring and analyzing urban tree cover. Landscape and 

Urban Planning. 36: 49–57.

Nowak, D.J.; Stein, S.M.; Randler, P.B. [et al.]. 2010. Sustaining America’s urban trees and forest: a forest on the edge 

report. Gen. Tech. Rep. NRS–62. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, Northern Research 

Station. 27 p.

Nowak, D.J.; Walton, J.T.; Dwyer, J.F.; Kaya, L.G.; Myeong, 

S. 2005. The increasing influence of urban environments on 

U.S. forest management. Journal of Forestry. 103: 377–382.

Nusser, S.M.; Goebel, J.J. 1997. The National Resources 

Inventory: a long-term multi-resource monitoring programme. 

Environmental and Ecological Statistics. 4: 181–204.

O’Gara, B.W.; Morrison, B. 2004. Managing the harvest. 

In: O’Gara, B.W.; Yoakum, J.D., eds. Pronghorn: ecology 

and management. Boulder, CO: University Press Colorado: 

673–704.
186 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Organ, J.F.; Decker, T.; Langlois, S.; Mirick, P.G. 2001. 

Trapping and furbearer management in North American 

wildlife conservation. Northeast Furbearer Resources Technical 

Committtee. Coordinated by the Massachusetts Division of 

Fisheries and Wildlife and the U.S. Fish and Wildlife Service, 

Division of Federal Aid. 41 p.

Organ, J.F.; Mahoney, S.P.; Geist, V. 2010. Born in the hands 

of hunters: the North American model of wildlife conservation. 

Wildlife Professional. 4(3): 22–27.

Ostrom, E. 1990. Governing the commons: The evolution of 

institutions for collective action. Cambridge, England: Cambridge University Press. 281 p.

The Outdoor Foundation. 2009. Outdoor recreation participation report 2009. http://www.outdoorfoundation.org/research.

participation.2009.html. (2010 September 13).

Padding, P. 1996. [Personal communication]. (May 29). U.S. 

Department of the Interior, U.S. Fish and Wildlife Service, 

Office of Migratory Bird Management, Arlington, VA.

Parmesan, C.; Yohe, G. 2003. A globally coherent fingerprint 

of climate change impacts across natural systems. Nature. 421: 

37–42.

Paruelo, J.M.; Lauenroth, W.K. 1996. Relative abundance of 

plant functional types in grasslands and shrublands of North 

America. Ecological Applications. 6: 1212–1224.

Pellant, M.; Pyke, D.A.; Shaver, P.; Herrick, J.E. 2005. 

Interpreting indicators of rangeland health: version 4. Technical 

Reference 1734–6. Denver, CO: Bureau of Land Management: 

119 p.

Petsch, Jr., H.E. 1985. Inventory of interbasin transfers of water 

in the western conterminous United States. Open-File Report 

85–166. Lakewood, CO: U.S. Geological Survey. 65 p.

Piccolo, J.J.; Adkison, M.D; Rue, F. 2009. Linking Alaskan 

salmon fisheries management with ecosystem-based escapement goals: a review and prospectus. Fisheries. 34: 124–132.

Pidgeon, A.M.; Radeloff, V.C.; Flather, C.H. [et al.]. 2007. 

Associations of forest bird species richness with housing and 

landscape patterns across the USA. Ecological Applications. 

17: 1989–2010.

Polley, H.W.; Johnson, H.B.; Derner, J.D. 2003. Increasing 

CO2

 from 4 subambient to superambient concentrations alters 

species composition and increases aboveground biomass in a 

C3/C4 grassland. New Phytologist. 160: 319–327.

Polley, H.W.; Tischler C.R.; Johnson, H.B. 2006. Elevated 

atmospheric CO2 magnified intra-specific variation in seedling 

growth of honey mesquite: an assessment of relative growth 

rates. Rangeland Ecology & Management. 59: 128–134.

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I. [et al.]. 2005. 

The wildland-urban interface in the United States. Ecological 

Applications. 15: 799–805.

Radeloff, V.C.; Stewart, S.I.; Hawbaker, T.J. [et al.]. 2010. 

Housing growth in and near United States protected areas limits 

their conservation value. Proceedings of the National Academy 

of Sciences of the United States of America. 107: 940–945.

Raftovich, R. 2009. [Personal communication]. (July 13). U.S. 

Department of the Interior, U.S. Fish and Wildlife Service, 

Division of Migratory Bird Management, Arlington, VA.

Rahel, F.J. 2002. Homogenization of freshwater faunas. Annual 

Review of Ecology and Systematics. 33: 291–315.

Rands, M.R.; Adams, W.M.; Bennun, L. [et al.]. 2010. Biodiversity conservation: challenges beyond 2010. Science. 329: 

1298–1303.

Rapport, D.J.; Regier, H.A.; Hutchinson, T.C. 1985. Ecosystem 

behavior under stress. American Naturalist. 125: 617–640.

Rasmussen, J.L.; Regier, H.A.; Sparks, R.E.; Taylor, W.W. 

2011. Dividing the waters: the case for hydrologic separation of 

the North American Great Lakes and Mississippi River basins. 

Journal of Great Lakes Research. 37: 588–592.

Rau, B. M.; Tausch, R.; Reiner, A. [et al.]. 2010. Influence of 

prescribed fire on ecosystem biomass, carbon, and nitrogen in a 

pinyon juniper woodland. Rangeland Ecology & Management. 

63: 197–202.

Recreation Information Database. 2009. On-line database. 

http://explore.data.gov/Geography-and-Environment/

Recreation-Information-Database-RIDB/m8bg-wv5v. (2009 

November 18).

Reeves, M.; Ryan, K.C.; Rollins, M.G.; Thompson, T. 2009. 

Spatial fuel data products of the LANDFIRE Project. International Journal of Wildland Fire. 18: 250–267.

Reeves, M.C.; Zhao, M.; Running, S.W. 2006. Applying 

improved estimates of MODIS productivity to characterize 

grassland vegetation dynamics. Journal of Rangeland Ecology 

and Management. 59: 1–10.

Reeves, M.C.; Mitchell, J.E. 2011. Extent of conterminous 

U.S. rangelands: quantifying implications of differing agency 

perspectives. Rangeland Ecology and Management. 64(6): 

585–595.

Reeves, M.C.; Mitchell, J. E. 2012. A synoptic review of U.S. 

rangelands: a technical document supporting the Forest Service 

2010 RPA Assessment. Gen. Tech. Rep. RMRS-GTR-288. Fort 

Collins, CO: U.S. Department of Agriculture, Forest Service, 

Rocky Mountain Research Station.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 187

Regan, R.J. 2010. Priceless, but not free: why all nature lovers 

should contribute to conservation. Wildlife Professional. 4(3): 

39–41.

Rehfeldt, G.E.; Crookston, N.L.; Warwell, M.V.; Evans, J.S. 

2006. Empirical analyses of plant-climate relationships for the 

western United States. International Journal of Plant Science. 

167: 1123–1150.

Reich, P.B.; Tilman D.; Craine, J. [et al.]. 2001. Do species and 

functional groups differ in acquisition and use of C, N and water under varying atmospheric CO2

 and N availability regimes? 

A field test with 16 grassland species. New Phytologist. 150: 

435–448.

Ricketts, T.; Dinerstein, E.; Olson, D. [et al.]. 1999. Terrestrial 

ecoregions of North America: a conservation assessment. 

Washington, DC: Island Press. 558 p.

Ries L.; Fletcher, R.J.; Battin, J.; Sisk, T.D. 2004. Ecological 

responses to habitat edges: mechanisms, models, and variability 

explained. Annual Review of Ecology, Evolution and Systematics. 35: 491–522.

Riitters, K.H. 2011. Spatial patterns of land cover in the United 

States: a technical document supporting the Forest Service 2010 

RPA Assessment. Gen. Tech. Rep. SRS–136. Asheville, NC: 

Department of Agriculture Forest Service, Southern Research 

Station. 64 p.

Riitters K.H.; Coulston, J.W.; Wickham, J.D. 2012. Fragmentation of forest communities in the eastern United States. Forest 

Ecology and Management. 263: 85–93.

Riitters, K.H.; Wickham, J.D. 2003. How far to the nearest 

road? Frontiers in Ecology and Environment. 1: 125–129.

Riitters, K.H.; Wickham, J.D.; Wade, T.G. 2009. An indicator 

of forest dynamics using a shifting landscape mosaic. Ecological Indicators. 9: 107–117.

Robbins, C.S.; Bystrak, D.; Geissler, P.H. 1986. The Breeding 

Bird Survey: its first fifteen years, 1965–1979. Resource Publication 157. Washington, DC: U.S. Department of the Interior, 

U.S. Fish and Wildlife Service. 196 p.

Robles, M.D.; Flather, C.H.; Stein, S.M.; Nelson, M.D.; Cutko, 

A. 2008. The geography of private forests that support at-risk 

species in the conterminous United States. Frontiers in Ecology 

and the Environment. 6: 301–307.

Rollins, M. 2009. LANDFIRE: a nationally consistent vegetation, wildland fire and fuel assessment. International Journal of 

Wildland Fire. 18: 235–249.

Ruefenacht, B.; Finco, M.V.; Nelson, M.D. [et al.]. 2008. 

Conterminous U.S. and Alaska forest type mapping using forest 

inventory and analysis data. Photogrammetric Engineering and 

Remote Sensing. 74: 1379–1388.

Running, S.W.; Nemani, R.R.; Heinsch, F.A. [et al.]. 2004. 

A continuous satellite-derived measure of global terrestrial 

primary production. Bioscience. 54: 547–560.

Sala, O.E.; Chapin, F.S.; Armesto, J.J. [et al.]. 2000. Biodiversity—global biodiversity scenarios for the year 2100. Science. 

287: 1770–1774.

Sala, O.E.; Jackson, R.B. 2006. Determinants of biodiversity 

change: ecological tools for building scenarios. Ecology. 87: 

1875–1876.

Sathre, R.; O’Connor, J. 2008. A synthesis of research on wood 

products and greenhouse gas impacts. Technical report TR–19. 

Vancouver, BC, Canada: FPInnovations—Forintek Division: 

74.

Sathre, R.; O’Connor, J. 2010. Meta-analysis of greenhouse gas 

displacement factors of wood product substitution. Environmental Science & Policy. 13: 104–114.

Sauer, J.R. 2010. [Personal communication]. (July 3). U.S. 

Geological Survey, Biological Resources Division, Patuxent 

Wildlife Research Center.

Sauer, J.R.; Hines, J.E.; Fallon, J.E. [et al.]. 2009. The North 

American Breeding Bird Survey, results and analysis 1966–

2009. Laurel, MD: U.S. Geological Survey, Patuxent Wildlife 

Research Center. ftp://ftpext.usgs.gov/pub/er/md/laurel/BBS/

Archivefiles/Version2009v1. (2010 June 9)

Sauer, J.R.; Link, W.A. 2002. Hierarchical modeling of population stability and species group attributes from survey data. 

Ecology. 83: 1743–1751.

Schadberg, P.G.; Hennon, P.E.; D’Amore D.V.; Hawley, G. 

2008. Influence of simulated snow cover on the cold tolerance 

and freezing injury of yellow-cedar seedlings. Global Change 

Biology. 14: 1282–1293.

Schefter, J.E. 1990. Domestic water use in the United States, 

1960–85. In: Carr, J.E.; Chase, E.B.; Paulson, R.W.; Moody, 

D.W., eds. National Water Summary 1987—hydrologic events 

and water supply and use. Washington, DC: U.S. Geological 

Survey: 71–80.

Schlesinger, W.H.; Reynolds, J.F.; Cunningham, G.L. [et al.]. 

1990. Biological feedbacks in global desertification: explains 

why nutrient cycling is so slow in desert. Science. 247: 

1043–1048.

Schmidt, T.L.; Leatherberry, E.C. 1995. Expansion of eastern 

red-cedar in the lower midwest. Northern Journal of Applied 

Forestry. 12: 180–183.
188 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Schneider, S.H.; Semenov, S.; Patwardhan, A. [et al]. 2007. 

Assessing key vulnerabilities and the risk from climate change. 

Climate change 2007: impacts, adaptation and vulnerability. 

Contribution of Working Group II to the Fourth Assessment 

Report of the Intergovernmental Panel on Climate Change. 

Cambridge, England: Intergovernmental Panel on Climate 

Change: [Paginated by chapter].

Schwandt, J. 2006. Whitebark pine in peril: A case for restoration. Missoula, MT: U.S. Department of Agriculture, Forest 

Service, Northern Region, R1–06–28. 26 p.

Scodari, P.F. 1997. Measuring the benefits of Federal wetland 

programs. Washington, DC: Environmental Law Institute. 107 p.

Shaver, C.L.; Tonnessen, K.A.; Maniero, T.G. 1994. Clearing 

the air at Great Smoky Mountains National Park. Ecological 

Applications. 4: 690–701.

Shifley, S.R. [In press]. Criterion 1: Conservation of biological diversity. Indicator 6: Status of in situ and ex situ efforts 

focused on conservation of species diversity. In: Data report: a 

supplement to the national report on sustainable forests—2010. 

Washington, DC: U.S. Department of Agriculture, Forest 

Service.

Skog, K.E. 2008. Sequestration of carbon in harvested wood 

products for the United States. Forest Products Journal. 58: 56–72.

Skog, K.E.; McKeever, D.B.; Ince, P.J.; [et al.]. 2012. Status 

and trends for the U.S. forest products sector: a technical 

document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. FPL–GTR–207. Madison, WI: U.S. 

Department of Agriculture, Forest Service, Forest Products 

Laboratory. 35 p.

Smith, W.B., tech. coord.; Miles, P.D., data coord.; Perry, C.H., 

map coord.; Pugh, S.A., data CD coord. 2009. Forest resources 

of the United States, 2007. Gen. Tech. Rep. WO–78. Washington, DC: U.S. Department of Agriculture, Forest Service. 336 p.

Society for Range Management [SRM]. 1998. A glossary of 

terms used in range management. Denver, CO: Society for 

Range Management. 20 p.

Soille P.; Vogt P. 2009. Morphological segmentation of binary 

patterns. Pattern Recognition Letters. 30: 456–459.

Solley, W.B.; Merk, C.F.; Pierce, R.R. 1988. Estimated use of 

water in the United States in 1985. Circular 1004. Denver, CO: 

U.S. Geological Survey. 82 p.

Solley, W.B.; Pierce, R.R.; Perlman, H.A. 1993. Estimated use 

of water in the United States in 1990. Circular 1081. Denver, 

CO: U.S. Geological Survey. 76 p.

Solley, W.B.; Pierce, R.R.; Perlman, H.A. 1998. Estimated use 

of water in the United States in 1995. Circular 1200. Denver, 

CO: U.S. Geological Survey. 71 p.

Southwick, R.; Woolley, A.; Leonard, D.; Rushton, S. 2005. 

Potential costs of losing hunting and trapping as wildlife management methods. Washington, DC: International Association 

of Fish and Wildlife Agencies, Animal Use Issues Committee. 

52 p.

Spelter, H.N.; Toth, D. 2009. North America’s wood pellet 

sector. Research Paper FPL–RP–656. Madison, WI: U.S. 

Department of Agriculture, Forest Service, Forest Products 

Laboratory. 21 p.

Spreitzer, P.N. 1985. Transitory range. A new frontier. Rangelands. 7: 33–34.

Stein, B.A.; Kutner, L.S.; Hammerson, G.A.; Master, L.L.; 

Morse, L.E. 2000. State of the states: geographic patterns of 

diversity, rarity, and endemism. In: Stein, B.A.; Kutner, L.S.; 

Adams, J.S., eds. Precious heritage: the status of biodiversity in 

the United States. Oxford, England: Oxford University Press: 

119–157.

Stein, B.A.; Scott, C.; Benton, N. 2008. Federal lands and 

endangered species: the role of military and other Federal land 

in sustaining biodiversity. BioScience. 50: 339–347.

Stein, S.M.; McRoberts, R.E.; Alig, R.J. [et al.]. 2005. Forests 

on the edge: housing development on America’s private 

forests. Gen. Tech. Rep. PNW–GTR–636. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 16 p.

Stein, S.M.; Alig, R.J.; White, E.M.; [et al.]. 2007. National 

forests on the edge: development pressures on America’s national forests and grasslands. Gen. Tech. Rep. PNW-GTR-728. 

Portland, OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 26 p.

Stein, S.M.; Carr, M.A.; McRoberts, R.E.; Mahal, L.G.; Comas, S.J. 2010a. Threats to at-risk species in America’s private 

forests. Gen. Tech. Rep. NRS–73. Newtown Square, PA: U.S. 

Department of Agriculture, Forest Service, Northern Research 

Station. 20 p.

Stein, S.M.; McRoberts, R.E.; Mahal, L.G. [et al.]. 2009. 

Private forests, public benefits: Increased housing density and 

other pressures on private forest contributions. Gen. Tech. Rep.

PNW–GTR–795. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 74 p.

Stein, S.M.; McRoberts, R.E.; Nelson, M.D. [et al.]. 2010b. 

Private forest habitat for at-risk species: where is it and where 

might it be changing. Journal of Forestry. 108: 61–70.

Stokstad, E. 2010. Despite progress, biodiversity declines. 

Science. 329: 1272–1273.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 189

Strand, E.K.; Vierling, L.A.; Smith, A.M.S.; Bunting, S.C. 

2008. Net changes in aboveground woody carbon stock in 

western juniper woodlands, 1946–1998. Journal of Geophysical 

Research.113: G01013, doi:10.1029/2007JG000544.

Sullivan, P.; Hellerstein, D.; Hansen, L. [et al.]. 2004. The 

Conservation Reserve Program: economic implications for rural 

America. Agricultural Economic Report No. 834. Washington, 

DC: U.S. Department of Agriculture, Economic Research 

Service. 112 p.

Talluto, M.V.; Suding, K.N. 2008. Historical change in coastal 

sage scrub in southern California, USA in relation to fire 

frequency and air pollution. Landscape Ecology. 23: 803–815.

Tennant, D.L. 1976. Instream flow regimens for fish, wildlife, 

recreation and related environmental resources. Fisheries. 1: 

6–10.

Thorp, B.A. (IV); Murdock-Thorp, L.D. 2008. A compelling 

case for integrated biorefineries; Part I. Paper 360o (TAPPI/

PIMA). 3(3): 14–15; Part II. Paper 360o (TAPPI/PIMA) 3(4): 

20–22; Part IV. Paper 360o (TAPPI/PIMA). 3(5): 12–14 and 

16–17.

Tomback, D.F.; Arno, S F.; Keane, R.E., eds. 2001. Whitebark 

pine communities: ecology and restoration. Washington, DC: 

Island Press. 441 p.

Tomosy, M.S.; Thompson, F.R.; Boyce, D.A. 2011. Role of the 

U.S. Forest Service: helping forests, grasslands, and wildlife 

adapt to shifts in climate. Wildlife Professional. 5(3): 42–46.

Torgerson, D. 2007. U.S. macroeconomic projections to 2060. 

On file with the U.S. Forest Service Quantitative Sciences Staff, 

Washington, DC.

Trust for Public Lands. 2011a. City park facts. Center for 

City Park Excellence. http://www.tpl.org/publications/booksreports/ccpe-publications/city-park-facts-report-2011.html.

(2011 October 17).

Trust for Public Lands. 2011b. Conservation Almanac. http://

www.conservationalmanac.org/secure. (2011 September 14).

Tubiello, F.N.; Soussana, J.F.; Howden, S.M. 2007. Crop and 

pasture response to climate change. Proceedings of the National 

Academies of Sciences of the USA. 104 (50): 19686–19690.

Ulrich, R.S. 1986. Human response to vegetation and landscapes. Landscape and Urban Planning. 13: 29–44.

United Nations. 2009. World population prospects: the 2008 

revision. New York: U.N. Department of Economic and Social 

Affairs, Population Division. Extended Dataset on CD-ROM, 

ST/ESA/SER.A/283, Sales No. 09.XII6. (2011 May 14).

United Nations. 2010. World urbanization prospects: the 2009 

revision highlights. New York: U.N. Department of Economic 

and Social Affairs, Population Division. ESA/P/WP/215 March 

2010. 56 p.

U.S. Army Corps of Engineers [USACE]. 2009. National 

Inventory of Dams. https://nid.usace.army.mil. (2009 June 18).

U.S. Census Bureau [USCB]. 2001. 2000 Census summary file 

1. Washington, DC: U.S. Census Bureau.

USCB. 2004. U.S. interim projections by age, sex, race and 

hispanic origin. http://www.census.gov/population/www/

projections/usinterimproj/natprojtab01a.pdf. (2008 May 8).

USCB. 2007. U.S census data. http://www.census.gov. (2008 

January 15).

USCB. 2008. Statistical abstract of the United States. http://

www.census.gov/compendia/statab/2008/2008edition.html. 

(2008 August 14).

USCB. 2010. Historical census of housing tables: vacation 

homes. http://www.census.gov/hhes/www/housing/census/

historic/vacation.html. (2011 March 7).

USCB. 2011. U.S. census data. http://2010.census.

gov/2010census/data/. (2011 May 16).

U.S. Climate Change Science Program [CCSP]. 2008a. The 

effects of climate change on agriculture, land resources, water 

resources, and biodiversity. A report by the U.S. Climate 

Change Science Program and the Subcommittee on Global 

Change Research. Synthesis and assessment product 4.3. 

Washington, DC: U.S. Department of Agriculture. 362 p.

U.S. CCSP. 2008b. Preliminary review of adaptation options 

for climate-sensitive ecosystems and resources. A report by the 

U.S. Climate Change Science Program and the Subcommittee 

on Global Change Research. Synthesis and assessment product 

4.4. Washington, DC: U.S. Environmental Protection Agency. 

873 p.

U.S. Department of Agriculture [USDA]. 2011. USDA 

agricultural projections to 2020. Prepared by the Interagency 

Agricultural Projections Committee. Long-term projections 

report OCE–2011–1. World Agricultural Outlook Board, Office 

of the Chief Economist. Washington, DC: U.S. Department of 

Agriculture. 100 p.

USDA Forest Service. 1989. An analysis of the land base situation in the United States: 1989–2040: a technical document 

supporting the 1989 USDA Forest Service RPA Assessment. 

Gen. Tech. Rep. RM–181. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Forest and 

Range Experiment Station. 76 p.
190 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

USDA Forest Service. 2000-2008. Grazing Statistical Summary 

Reports. Washington, DC: US Department of Agriculture, Forest Service, Washington Office, Rangeland Management. http://

www.fs.fed.us/rangelands/reports/index/shtml. (2009 June 9).

USDA Forest Service. 2004. Forest type groups of the United 

States [map]. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Remote Sensing Applications Center. 

http://fsgeodata.fs.fed.us/rastergateway/forest_type/. (2011 

April 28).

USDA Forest Service. 2008. Land areas report as of September 

30, 2008. Washington, DC: US Department of Agriculture, 

Forest Service, Washington Office, Lands and Realty Management. http://www.fs.fed.us/land/staff/lar/index.html. (2009 

February 9).

USDA Forest Service. 2009a. America’s forests: 2009 health 

update. AIB–804. Washington, DC: U. S. Department of 

Agriculture, Forest Service: 17 p.

USDA Forest Service. 2009b. State park systems database 

compiled from published State literature and State park Web 

sites. On file with RWU-4953. Athens, GA: U.S. Department 

of Agriculture, Forest Service, Southern Research Station.

USDA Forest Service. 2010. The forest inventory and analysis 

database: database description and user’s manual for phase 2, 

version 4.0, revision 3. Washington, DC: U.S. Department of 

Agriculture, Forest Service, Forest Inventory and Analysis 

Program: 368 p. http://fia.fs.fed.us/library/databasedocumentation/. (2011 February 18).

USDA Forest Service. 2011. National report on sustainable 

forests—2010. FS–979. Washington, DC: U.S. Department of 

Agriculture, Forest Service, Washington Office. 212 p.

USDA Forest Service. 2012. Future scenarios: a technical 

document supporting the Forest Service 2010 RPA Assessment. 

Gen. Tech. Rep. RMRS–GTR–272. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain 

Research Station. 34 p.

USDA National Agricultural Library. 2011. National invasive 

species information center: species profiles. http://www.

invasivespeciesinfo.gov/animals/main.shtml. (2011 

November 10).

USDA National Agricultural Statistics Service [NASS]. 

2009. 2007 census of agriculture: United States summary and 

State data. Washington, DC: U.S. Department of Agriculture, 

National Agricultural Statistics Service. 739 p.

USDA Natural Resources Conservation Service [NRCS]. 2000. 

Summary report: 1997 national resources inventory (revised 

December 2000). Washington, DC: Natural Resources Conservation Service; Ames, IA: Iowa State University, Statistical 

Laboratory. 89 p.

USDA NRCS. 2009. Summary report: 2007 national resources 

inventory. Washington, DC: Natural Resources Conservation 

Service; Ames, IA: Iowa State University, Statistical Laboratory. 123 p.

USDA NRCS. 2011. RCA appraisal: soil and water resources 

conservation act. Washington, DC: U.S. Department of Agriculture. 112 p.

U.S. Department of Commerce [USDC] Bureau of Economic 

Analysis. 2008a. National and income product accounts table 

1.15. Gross domestic product. Version January 30, 2008.

USDC Bureau of Economic Analysis. 2008b. National and 

income product accounts table 2.1. Personal income and its 

disposition. Version January 30, 2008.

USDC Bureau of Economic Analysis. 2012. National Economic Accounts, Gross Domestic Product. http://www.bea.gov/

national/index.htm#gdp. (2012 February 9).

USDC National Marine Fisheries Service [NMFS]. 2009a. 

2008 status of U.S. fisheries. Annual report to congress on 

the status of U.S. fisheries—2008. Silver Spring, MD: U.S. 

Department of Commerce, NOAA, National Marine Fisheries 

Service. 23 p.

USDC NMFS. 2009b. Fisheries of the United States. Silver 

Spring, MD: U.S. Department of Commerce, Office of Science 

and Technology, National Marine Fisheries Service. 118 p.

USDC NMFS. 2010. 2010 Report to Congress: Pacific coastal 

salmon recovery fund FY2000–FY2009. Silver Spring, MD: 

U.S. Department of Commerce, National Marine Fisheries 

Service. 16 p.

USDC National Oceanic and Atmospheric Administration 

[NOAA]. 2010. Online database: http://www.st.nmfs.noaa.gov/

st1/commercial/index.html. (2011 February 23).

U.S Department of Energy [DOE] Energy Information 

Administration [EIA]. 2009. Annual energy outlook 2008. 

DOE/EIA–0383(2010). Washington, DC: U.S. Department of 

Energy, Energy Information Administration. 447 p.

U.S. DOE EIA. 2010. Annual energy review 2010. DOE/

EIA–0384(2008). Washington, DC: U.S. Department of 

Energy, Energy Information Administration. 407 p.

U.S. Department of the Interior [USDI] Bureau of Land Management [BLM]. 2001. Rangeland health standards. Healthy 

Rangeland Initiative, BLM Manual H–4180–1. Washington, 

DC: U.S. Department of the Interior, Bureau of Land Management. 51 p.

USDI BLM. 2001-2010. Public land statistics. http://www.blm.

gov/public_land_statistics/. (2009 June 15).
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 191

USDI BLM. 2009. Payment in lieu of taxes (PILT) acres by 

agency. http://www.doi.gov/pilt/index.cfm. (2012 August 6).

USDI Bureau of Reclamation. 2008. Recreation Fast Facts. 

http://www.usbr.gov/recreation. (2009 February 19).

USDI U.S. Fish and Wildlife Service [FWS]. 2010. Waterfowl 

population status, 2010. Washington, DC: U.S. Department of 

the Interior, U.S. Fish and Wildlife Service. 79 p.

USDI FWS; Environment Canada Canadian Wildlife Service; 

Secretaria De Desarrollo Social Mexico. 1994. 1994 update to 

the North American waterfowl management plan: expanding 

the commitment. Washington, DC: U.S. Department of the 

Interior, U.S. Fish and Wildlife Service. 30 p.

USDI FWS; USCB. 2006. National survey of fishing, hunting, 

and wildlife-associated recreation. Washington, DC: U.S. 

Government Printing Office: 164 p.

USDI National Park Service. 2009. Listing of acreage by State 

and county as of 12/31/2008. Washington, DC: U.S. Department of the Interior, National Park Service, Land Resources 

Division. http://www.nature.nps.gov/stats/acreagemenu.cfm. 

(2009 February 26).

USDI U.S. Geological Survey [USGS]. 2007. Strategic plan 

for the North American Breeding Bird Survey: 2006–2010. 

U.S. Geological Survey Circular 1307. Washington, DC: U.S. 

Department of the Interior, U.S. Geological Survey. 30 p.

USDI USGS 2010. Great Lakes science center. http://www.

glsc.usgs.gov/. (2011 November 4).

U.S. Environmental Protection Agency [U.S. EPA]. 2006. 

Wadeable streams assessment: a collaborative survey of the 

Nation’s streams. EPA 841–B–06–002. Washington, DC: U.S. 

Environmental Protection Agency, Office of Water and Office 

of Research and Development. 98 p.

U.S. EPA. 2008. National coastal condition report III. Office of 

Water EPA/842–R–002. Washington, DC: U.S. Environmental 

Protection Agency. 329 p.

U.S. EPA. 2009. National lakes assessment: a collaborative 

survey of the Nation’s lakes. EPA 841–R–09–001. Washington, 

DC: U.S. Environmental Protection Agency, Office of Water 

and Office of Research and Development. 102 p.

U.S. EPA. 2011a. Clean lakes. http://water.epa.gov/type/lakes/. 

(2011 October 24).

U.S. EPA. 2011b. Conservation of biological diversity in the Great 

Lakes basin ecosystem: issues and opportunities. http://www.

epa.gov/ecopage/glbd/issues/intro.html. (2011 October 24).

U.S. EPA. 2011c. Inventory of U.S. greenhouse gas emissions 

and sinks: 1990–2009. EPA 430–R–11–005. Washington, DC: 

U.S. Environmental Protection Agency. 459 p.

U.S. EPA 2011d. Watershed assessment, tracking, and environmental results. http://iaspub.epa.gov/waters10/attains_

nation_cy.control#total_assessed_waters. (2011 March 14).

U.S. EPA; Environment Canada. 2009. State of the Great 

Lakes highlights 2009. http://binational.net/solec/sogl2009/

sogl_2009_h_en.pdf. (2011 October 30).

U.S. Water Resources Council. 1978. The Nation’s water 

resources 1975–2000. Washington, DC: U.S. Government 

Printing Office. 72 p.

Unsworth, J.W.; Pac, D.F.; White, G.C.; Bartmann, R.M. 1999. 

Mule deer survival in Colorado, Idaho, and Montana. Journal 

of Wildlife Management. 63: 315–326.

Van Auken, O.W. 2000. Shrub invasions of North American 

semiarid grasslands. Annuals Review of Ecology, Evolution, 

and Systematics. 31: 197–215.

Van Tassell, L.W.; Bartlett, E.T.; Mitchell, J.E. 2001. Projected 

use of grazed forages in the United States 2000 to 2050: a 

technical document supporting the 2000 USDA Forest RPA 

Assessment. Gen. Tech. Rep. RMRS–GTR–82. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 73 p.

Vitousek, P. M. 1997. Human domination of Earth’s ecosystems. Science. 278: 494-499.

Vogelmann, J.E.; Howard, S.M.; Yang, L. [et al.]. 2001. 

Completion of the 1990s national land cover data set for the 

conterminous United States from landsat thematic mapper data 

and ancillary data sources. Photogrammetric Engineering and 

Remote Sensing. 67: 650–662.

Vörösmarty, C.J.; Green, P.; Salisbury, J.; Lammers, R.B. 

2000. Global water resources: vulnerability from climate and 

population growth. Science. 289: 284–288.

Vulnerability and Impacts of North American Forests to 

Climate: Ecosystem Responses and Adaptation (VINCERA). 

2011. http://www.environment.uwaterloo.ca/research/vincera/.

(2007 May 11).

Wall, T.G.; Miller, R.F.; Svejcar, T.J. 2001. Juniper encroachment into aspen in the northwest great basin. Journal of Range 

Management. 54: 691–698.

Walls, M.; Darley, S.; Siikamaki, J. 2009. The state of the great 

outdoors. Washington, DC: Resources for the Future. 97 p.

Wear, D.N. 2010. USFAS—the United States Forest Assessment System: analysis to support forest assessment and 

strategic analysis. Proposal and project plan (version 3). On file 

with David Wear at USDA Forest Service, Research Triangle 

Park Forestry Sciences Laboratory, Research Triangle Park, 

NC, 27709. 12 p.
192 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Wear, D.N. 2011. Forecasts of county-level land uses under 

three future scenarios: technical document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. SRS–141. 

Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station. 41 p.

Wear, D.N.; Greis, J.G., eds. [In press]. The Southern Forest 

Futures Project: Technical Report. Gen. Tech. Rep. SRS. 

Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station.

Wear, D.N.; Huggett, R.; Li, R.; Perryman, B.; Liu, S. [In 

press]. Forecasts of forest conditions in U.S. regions under 

future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment. Gen. Tech. Rep. SRS. 

Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station.

Wegner, T.H.; Ince P.J.; Skog, K.E.; Michler, C.J. 2010. Uses 

and desirable properties of wood in the 21st Century. Journal of 

Forestry. 108: 165–173.

Westerling, A.L.; Hidalgo H.G.; Cayan D.R.; Swetnam T.W. 

2006. Warming and earlier spring increase western U.S. forest 

wildfire activity. Science. 313: 940–943.

Westerling, A.L.; Turner, M.G.; Smithwick, E.A.H.; [et al.]. 

2011. Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. Proceedings of the 

National Academies of Sciences of the United States. 108(32): 

13165–13170.

Westphal, L.M. 2003. Urban greening and social benefits: a 

study of empowerment outcomes. Journal of Arboriculture. 

29(3): 137–147.

White, B. 2010. [Personal communication]. (December 6). 

Association of Fish and Wildlife Agencies and Missouri 

Department of Conservation.

White, R.P.; Murray, S.; Rohweder, M. 2000. Pilot analysis of 

global ecosystems: grassland ecosystems. Washington, DC: 

World Resources Institute: 81 p.

Wilby, R.; Miller, K. 2009. Technical briefing paper (5): climate 

vulnerability assessment: Climate Change Clearing House. 

http://www.theclimatechangeclearinghouse.org/Resources/

TechBrief/Technical%20Briefings%20Document%20Library/

(5)%20Climate%20Vulnerability%20Assessment.pdf. (2010 

July 7).

Wilderness.net. undated. Mount Evans Wilderness. http://www.

wilderness.net/index.cfm?fuse=NWPS&sec=wildView&W

ID=373. (2009 September 24).

Wildlife Management Institute. 1997. Organization, authority 

and programs of state fish and wildlife agencies. Washington 

DC: Wildlife Management Institute. 164 p.

Williams, C.K.; Guthery, F.S.; Applegate, R.D.; Peterson, M.J. 

2004. The northern bobwhite decline: scaling our management 

for the twenty-first century. Wildlife Society Bulletin. 32: 

861–869.

Williams, D.R. 2005. Agricultural programs. In: Connolly, 

K.D.; Johnson, S.M.; Williams, D.R., eds. Wetlands law and 

policy: understanding section 404. Chicago, IL: American Bar 

Association, Section of Environment, Energy, and Resources: 

463–499.

Wilmoth, J.R. 1998. The future of human longevity: a demographer’s perspective. Science. 280: 395–397.

Wilson, J.R.; Brown, R.H. 1983. Influence of leaf anatomy on 

the dry matter digestibility of C4, C3 and C3/C4 intermediate 

types of Panicum species. Crop Science. 23: 141–146.

Wilson, L.M.; Randall, C.B. 2005. Biology and biological 

control of knapweed. USDA Forest Service/UNL Faculty 

Publications. Paper 113. http://digitalcommons.unl.edu/

usdafsfacpub/113. (2011 February 15).

Winslow, J.C.; Hunt, E.R.; Piper, S.C. 2003. The influence of 

seasonal water 1 availability on global C3 versus C4 grassland 

biomass and its implications for climate change research. 

Ecological Modelling. 163: 153–173.

Wolf, K.M. 2003. Public response to the urban forest in innercity business districts. Journal of Arboriculture. 29: 117–126.

Woods & Poole Economics, Inc. 2007. The 2007 complete 

economic and demographic data source on CD-ROM. Washington, DC.

World Bank. 2012. GDP. http://data.worldbank.org/indicator/

NY.GDP.MKTP.CD. (2012 February 9).

Zarnoch, S.J.; Cordell, H.K.; Betz, C.J.; Langner, L. 2010. Projecting county-level populations under three future scenarios: 

a technical document supporting the Forest Service 2010 RPA 

Assessment. e-Gen. Tech. Rep. SRS–128. Asheville, NC: U.S. 

Department of Agriculture, Forest Service, Southern Research 

Station. 8 p.

Zhao, M.; Running, S.W. 2010. Drought induced reduction in 

global terrestrial net primary production from 2000 to 2009. 

Science 329: 940–943.

Zhu, Z.; Ohlen, D.; Kost, J.; Chen, X.; Tolk, B. 2006. Mapping 

existing vegetation composition and structure for the LANDFIRE Prototype project. In: The LANDFIRE Prototype Project: 

nationally consistent and locally relevant geospatial data and 

tools for wildland fire management. Gen. Tech. Rep. RMRS–

GTR–175. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station: 197–215.
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 193

Appendix A: Projected Tree Canopy Cover Change

Table A-1. Projected change in tree canopy cover by State between 2000 and 2060 using land use change projections for three Resource 

Planning Act (RPA) scenarios.

State Number of Counties

RPA A1B RPA A2 RPA B2

Change Relative

Change2 Change Relative

Change2 Change Relative Change2

Total Analyzed1 % % % % % %

Alabama 67 67 – 3.2 – 4.5 – 2.4 – 3.4 – 2.0 – 2.8

Arizona 15 15 0.1 0.3 0.1 0.4 0.0 0.2

Arkansas 75 75 – 4.7 – 8.5 – 3.6 – 6.5 – 3.2 – 5.8

California 58 57 – 1.4 – 4.0 – 1.5 – 4.1 – 1.0 – 2.9

Colorado 63 62 – 1.7 – 6.8 – 1.5 – 6.2 – 1.2 – 4.7

Connecticut 8 8 – 2.8 – 3.9 – 3.3 – 4.5 – 1.7 – 2.3

Delaware 3 3 – 2.4 – 5.9 – 2.6 – 6.4 – 1.6 – 4.0

District of Columbia 1 0 NA NA NA NA NA NA

Florida 67 67 – 4.6 – 8.5 – 4.9 – 9.1 – 3.6 – 6.7

Georgia 159 159 – 6.6 – 9.9 – 5.1 – 7.6 – 4.5 – 6.8

Idaho 44 44 – 0.4 – 0.9 – 0.3 – 0.7 – 0.2 – 0.5

Illinois 102 102 – 0.5 – 3.3 – 0.3 – 1.9 – 0.4 – 2.6

Indiana 92 92 – 2.4 – 8.6 – 1.6 – 5.7 – 1.4 – 5.2

Iowa 99 99 – 0.2 – 1.5 – 0.1 – 0.8 – 0.1 – 1.1

Kansas 105 105 – 0.1 – 1.0 – 0.1 – 1.0 – 0.1 – 0.8

Kentucky 120 120 – 5.0 – 9.0 – 3.9 – 7.0 – 3.1 – 5.6

Louisiana 64 64 – 3.0 – 5.9 – 2.3 – 4.4 – 1.9 – 3.6

Maine 16 16 – 1.5 – 1.7 – 1.2 – 1.4 – 0.9 – 1.0

Maryland 24 23 – 6.3 – 12.2 – 5.9 – 11.6 – 4.5 – 8.8

Massachusetts 14 14 – 4.1 – 5.9 – 4.4 – 6.4 – 2.7 – 3.9

Michigan 83 83 – 1.9 – 3.3 – 1.6 – 2.7 – 1.2 – 2.1

Minnesota 87 87 – 1.6 – 4.6 – 1.3 – 3.7 – 1.0 – 3.1

Mississippi 82 82 – 3.0 – 4.9 – 2.2 – 3.5 – 1.9 – 3.1

Missouri 115 114 – 3.0 – 7.1 – 2.2 – 5.3 – 1.9 – 4.7

Montana 56 56 – 1.2 – 4.5 – 1.0 – 3.6 – 0.8 – 3.0

Nebraska 93 93 – 0.2 – 3.6 – 0.1 – 2.4 – 0.1 – 2.1

Nevada 17 17 – 0.4 – 2.8 – 0.3 – 2.5 – 0.3 – 2.2

New Hampshire 10 10 – 3.4 – 4.0 – 3.0 – 3.6 – 2.3 – 2.7

New Jersey 21 21 – 7.3 – 13.1 – 8.4 – 15.1 – 4.9 – 8.8

New Mexico 33 33 – 0.2 – 1.0 – 0.1 – 0.8 – 0.1 – 0.7

New York 62 62 – 1.4 – 2.1 – 1.2 – 1.8 – 0.8 – 1.1

North Carolina 100 100 – 3.9 – 6.1 – 3.3 – 5.1 – 2.7 – 4.2

North Dakota 53 53 – 0.1 – 1.7 0.0 – 1.5 0.0 – 1.6

Ohio 88 88 – 0.9 – 2.3 – 0.8 – 2.0 – 0.5 – 1.3

Oklahoma 77 77 – 1.3 – 4.8 – 1.0 – 4.0 – 0.9 – 3.3

Oregon 36 36 – 1.0 – 2.4 – 1.0 – 2.4 – 0.7 – 1.7

Rhode Island 5 5 – 9.2 – 14.8 – 10.0 – 16.1 – 5.5 – 8.9

South Carolina 46 46 – 4.3 – 6.6 – 3.7 – 5.8 – 2.9 – 4.4

South Dakota 66 66 0.3 6.1 0.2 4.4 0.2 4.3

Tennessee 95 95 – 4.3 – 7.3 – 3.9 – 6.5 – 2.8 – 4.7

Texas 254 254 – 1.2 – 5.4 – 0.8 – 3.6 – 0.8 – 3.8

Utah 29 29 – 1.2 – 6.4 – 1.2 – 6.2 – 0.9 – 4.9

Vermont 14 14 – 3.5 – 4.5 – 2.5 – 3.2 – 2.3 – 3.0

Virginia 135 97 – 3.1 – 4.6 – 2.4 – 3.5 – 2.2 – 3.2

Washington 39 39 – 1.6 – 3.3 – 1.7 – 3.6 – 1.1 – 2.4

West Virginia 55 55 – 4.6 – 5.6 – 2.9 – 3.6 – 2.7 – 3.3

Wisconsin 72 72 – 0.9 – 1.9 – 0.7 – 1.5 – 0.6 – 1.2

Wyoming 23 23 – 0.2 – 1.6 – 0.2 – 1.2 – 0.2 – 1.1

Total 3,109 3,066 – 1.6 – 4.7 – 1.3 – 3.9 – 1.1 – 3.2

1

 Number of counties analyzed in analysis.

2

 Relative change = (cover year 2060 to cover year 2000)/cover year 2000.

NA = not applicable.
194 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Appendix B. List of Acronyms

ASR assessment subregion (98 water basins in the 

conterminous United States)

AUM animal unit month

BBS North American Breeding Bird Survey

bgd billion gallons per day (water)

BLM Bureau of Land Management

CRP Conservation Reserve Program

DOE Department of Energy

DOI Department of the Interior

DPI disposable personal income

EPA U.S. Environmental Protection Agency

ESA Endangered Species Act of 1973

FA forage availability

FD forage demand

FHM Forest Health Monitoring

FIA Forest Inventory and Analysis

FWS U.S. Fish and Wildlife Service

GCM general circulation model

GFPM Global Forest Products Model

GHG greenhouse gas

GDP gross domestic product

HFW historical fuelwood

HWP harvested wood products

IPCC Intergovernmental Panel on Climate Change

IPCC AR4 IPCC Fourth Assessment Report

IPCC TAR IPCC Third Assessment Report

IUCN International Union for the Conservation of 

Nature

kWh kilowatt hour

MARIS Multi-State Aquatic Resources Information System

MODIS Moderate Resolution Imaging Spectroradiometer

MSPA Morphological Spatial Pattern Analysis

NASS National Agricultural Statistics Service

NFHAP National Fish Habitat Action Plan

NFS National Forest System

NLCD National Land Cover Database

NKS National Kids Survey

NPP net primary productivity

NRA National Recreation Area

NRT National Recreation Trail

NRCS Natural Resources Conservation Service

NRI National Resources Inventory

NSRE National Survey on Recreation and the 

Environment

NWPS National Wilderness Preservation System

NWSR National Wild and Scenic River

OSB oriented strand board

PAD-US Protected Areas Database of the United States

PI personal income

RFS renewable fuel standard

RPA Resources Planning Act

SRWC short-rotation woody crops

TCSI Terrestrial Climate Stress Index

USDA U.S. Department of Agriculture

USFAS U.S. Forest Assessment System

USFPM U.S. Forest Products Module

USGS U.S. Geological Survey

WRR water resource region
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 195

Appendix C. List of Scientific Names

Birds American wigeon Anas americana

Blue-winged teal Anas discors

Brant Branta bernicla

Canada goose Branta canadensis

Canvasback Aythya valisineria

Chukar Alectoris chukar

Dusky Canada goose Branta canadensis occidentaiis

Emperor goose Chen canagica

Forest grouse, including

Blue grouse Dendropagus obscurus

Ruffed grouse Bonasa umbellus

Spruce grouse Falcipennis Canadensis

Gadwall Anas strepera

Green-winged teal Anas crecca

Grey partridge Perdix perdix

Mallard Anas platyrhynchos

Mourning dove Zenaida macroura

Northern bobwhite Colinus virginianus

Northern pintail Anas acuta

Northern shoveler Anas clypeata

Prairie grouse, including

Greater prairie-chicken Tympanuchus cupido

Lesser prairie-chicken Tympanuchus pallidicinctus

Sage grouse Centrocercus urophasianus

Sharp-tailed grouse Tympanuchus phasianellus

Quail, including

California quail Callipepla californica

Gambel’s quail Callipepla gambelii

Montezuma quail Cyrtonyx montezumae

Mountain quail Oreortyx pictus

Scaled quail Callipepla squamata

Redhead Aythya americana

Ring-necked pheasant Phasianus colchicus

Ross’ goose Chen rossii

Scaup, including

Greater scaup Aythya marila

Lesser scaup Aythya affinis

Snow goose Chen caerulescens

Tundra swans Cygnus columbianus

White-fronted goose Anser albifrons

Wild turkey Meleagris gallopavo

Woodcock Scolopax minor

Mammals American black bear Ursus americanus

Cottontail, including

Appalachian cottontail Sylvilagus obscures

Desert cottontail Sylvilagus audubonii

Eastern cottontail Sylvilagus floridanus

Marsh rabbit Sylvilagus palustris

New England cottontail Sylvilagus transitionalis

Nuttall’s cottontail Sylvilagus nuttallii

Swamp rabbit Sylvilagus aquaticus

Elk Cervus elaphus

Mule deer Odocoileus hemionus

Pronghorn Antilocapra americana

Squirrel, including

Abert’s squirrel Sciurus aberti

Arizona gray squirrel Sciurus arizonensis

Fox squirrel Sciurus niger

Gray squirrel Sciurus carolinensis

Common Name Scientific Name
196 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment

Appendix C. List of Scientific Names (continued)

Mammals Mexican fox squirrel Sciurus nayaritensis

Red squirrel Tamiasciurus hudsonicus

White-tailed deer Odocoileus viriginianus

Aquatic Species Asian carp Hypophthalmichthys nobilis or Hypophthalmichthys molitrix

Black bass Micropterus salmoides or Micropterus dolomieu

Bullhead Ameiurus spp.

Catfish Ictalurus spp.

Crappie Pomoxis spp.

Lake trout Salvelinus namaycush

Panfish a generic name referring to a small fish suitable for frying, in 

this case generally Perca sp., Pomoxis sp, or Lepomis sp.

Quagga mussel Dreissena rostriformis bugensis

Round goby Neogobius melanostomus

Salmon (Pacific, western, northwestern) Oncorhynchus spp.

Sea lamprey Petromyzon marinus

Sea-run cutthroat trout Oncorhynchus clarkii clarkii

Spiny water flea Bythotrephes longimanus

Steelhead trout Oncorhynchus mykiss

Trout Any of several chiefly freshwater game fish of the genera 

Oncorhynchus, Salvelinus, or Salmo

Walleye Sander vitreus

Yellow perch Perca flavescens

Zebra mussel Dreissena polymorpha

Plants Alaska yellow-cedar Chamaecyparis nootkatensis

American elm Ulmus americana

Atlantic white-cedar Chamaecyparis thyoides

Ash Fraxinus spp.

Ashe juniper Juniperus ashei

Aspen Populus spp.

Bald cypress Taxodium Rich.

Balsam fir Abies balsamae

Balsam poplar Populus balsamifera

Basswood Tilia spp.

Beech Fagus grandifolia

Black ash Fraxinus nigra

Black cherry Prunus serotina

Black locust Robinia pseudoacacia

Black oak Quercus velutina

Black spruce Picea mariana

Black walnut Juglans nigra

Bur oak Quercus macrocarpa

Cheatgrass Bromus tectorum

Cherry Prunus spp. 

Cherrybark oak Quercus falcate var. pagodifolia

Chestnut oak Quercus prinus

Cottonwood Populus spp.

Crested wheatgrass Agropyron cristatum

Dalmatian toadflax Linaria dalmatica

Douglas-fir Pseuditsuga menziesii

Eastern hemlock Tsuga canadensis

Eastern redcedar Juniperis virginiana

Eastern white pine Pinus strobus

Eurasian watermilfoil Myriophyllum spicatum

Gray birch Betula populifolia

Hickory Carya spp.

Hackberry Celtis occidentalis

Intermediate wheatgrass Thinopyrum intermedium

Jack pine Pinus banksiana

Juniper Juniperis spp

Knapweed Centaurea solstitialis, C. diffusa, C. maculosa, Acroptilon 

repens

Common Name Scientific Name
Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment 197

Appendix C. List of Scientific Names (continued)

Plants Leafy spurge Euphorbia esula

Loblolly pine Pinus taeda

Lodgepole pine Pinus contorta

Longleaf pine Pinus palustris

Mesquite Prosopis spp

Northern red oak Quercus rubra

Northern white-cedar Thuja occidentalis

Nuttall oak Quercus nutallii

Overcup oak Quercus lyrata

Palms Palmae spp.

Paper birch Betula papyrifera

Pecan Carya illinoinensis

Persimmon Diospyros virginiana

Pin cherry Prunus pensylvanica

Pinyon pine Pinus edulis

Pitch pine Pinus rigida

Pond cypress Taxodium ascendens

Pond pine Pinus serotina

Ponderosa pine Pinus ponderosa

Red brome Bromus rubens

Red maple Acer rubrum

Red oak Quercus falcata

Red pine Pinus resinosa

Red spruce Picea rubens

River birch Betula nigra

Sand pine Pinus clausa

Sassafras Sassafras albidum

Scarlet oak Quercus coccinea

Scotch pine Pinus sylvestris

Shortleaf pine Pinus echinata

Silver maple Acer saccharinum

Slash pine Pinus elliottii

Starthistles Centaurea spp.

Sugar maple Acer sacchurum

Sugarberry Celtis laevigata

Swamp chestnut oak Quercus michauxii

Sweetbay Laurus nobilis

Sweetgum Liquidambar styraciflua

Sycamore Platanus occidentalis

Tamarack Larix laricina

Virginia pine Pinus virginiana

Water hickory Carya aquatica

Water tupelo Nyssa sylvatica

Western juniper Juniperus occidentalis

White ash Fraxinus amiricana

White oak Quercus alba

White spruce Picea glauca

Whitebark pine Pinus albicaulis

Willow Salix spp.

Willow oak Quercus phellos

Yellow birch Betula alleghaniensis

Yellow poplar Liriodendron tulipifera

Other Asian longhorned beetle Anoplophora glabripennis

Emerald ash borer Agrilus planipennis

Gypsy moth Lymantria dispar

Ips beetles Ips spp.

Mountain pine beetle Dendroctonus ponderosae

Southern pine beetle Dendroctonus frontalis

White pine blister rust Cronartium ribicola

Common Name Scientific Name
198 Future of America’s Forests and Rangelands: Forest Service 2010 Resources Planning Act Assessment
United States Department of Agriculture

Forest Service

General Technical Report WO-87

August 2012