33

Increased Housing Density and Other Pressures on Private Forest Contributions

MANY ECOLOGICAL and socioeconomic forces—including wildfires, native and 

exotic (nonnative) insects and other pests, extreme weather events, and timber harvest—help keep forests dynamic and constantly changing. The frequency, severity, 

and magnitude of these forces and their impacts on forest conditions can be heavily 

influenced by housing density increases and associated development. For example, 

as described in this section, increased housing development has been associated 

with an increase in wildfire ignitions (Syphard et al. 2007), roads have been linked 

to the spread of invasive plants (Meekins and McCarthy 2001), and urbanization 

can lead to the spread of forest insect pests (Poland and McCullough 2006). 

This section displays and describes maps that indicate where U.S. private 

forests are affected by the pressures of wildfire, insect pests and diseases, or air 

pollution. It also provides a ranking of watersheds according to the prospects 

that increased housing density and one of these factors will occur in the same 

watershed. 

Presented here are only a few of the many possible maps that identify areas 

across the country where pressures in addition to increased housing density 

may affect private forest benefits and contributions, and where housing density 

increases in turn may exacerbate the effects of insect pests, diseases, wildfire, 

and air pollution. For additional maps and to create individualized maps, please 

visit the Forests on the Edge Web site, http://www.fs.fed.us/openspace/fote.

Additional Pressures

Ted Wood/Aurora Photos
34

PRIVATE FORESTS, PUBLIC BENEFITS

Insect Pests and Diseases

Assessment of Current Situation

Forest insects and diseases play critical roles in forest ecosystems but can also 

have adverse impacts on forest health (Tkacz et al. 2007). An estimated 117 

species of exotic insect species have been introduced to U.S. forests since the 

1800s (Stolte and Darr 2006); the spread and impacts of 

some of these—such as the hemlock woolley adelgid (Adelges 

tsugae) and the emerald ash borer (Agrilus planipennis)—

are well-documented (Frelich 2003, Liebhold et al. 1995, 

Tkacz et al. 2007). Another exotic pest, the gypsy moth 

(Lymantria dispar), has spread to 17 states and the District 

of Columbia (Tkacz et al. 2007). In addition to exotic pests, 

U.S. forests have also been affected by native pests. For 

example, outbreaks of mountain pine beetle (Dendroctonus 

ponderosae), native to North America, have been increasing 

throughout the Western United States since 2003.

Among the dozens of diseases that affect U.S. forests each year, chestnut blight 

(Cryphonectria parasitica (Murrill) Barr), Dutch elm disease (Ophiostoma ulmi 

(Buisman) Nannf.), and beech bark disease (Neonectria faginata (Lohman et al.) 

Castl.) alone have led to the near elimination of important tree species in many 

areas (Frelich 2003, Liebhold et al. 1995). A recently introduced disease called sudden oak death (Phytophthora ramorum) is responsible for the deaths of thousands 

of native oak trees (Quercus spp.) in coastal California (Tkacz et al. 2007). Native 

and exotic insects and diseases can cause substantial damage to roots, stems, and 

leaves of plants (Nair and Sumardi 2000), which overall can affect forest condition 

and productivity (USDA FS 2005). In 2006 alone, more than 5 million acres of tree 

mortality in the United States was due to insects and diseases (USDA FS 2007a).

Figure 14 displays watersheds according to the average percentages of basal 

area

2

 expected to be lost owing to insects and diseases. The map is based on data 

compiled by the Forest Service’s Forest Health Monitoring Program (Krist et 

al. 2007). Watersheds in the 90th percentile are scattered across the country and 

include many watersheds in the interior West. Large concentrations of watersheds in the 90th percentile are also found in and around West Virginia, eastern 

Washington and Oregon, and northern California. 

2 Basal area is the cross-section area of a tree stem in square feet, commonly measured at 

breast height (4.5 feet in the United States). Basal area is often used as an indicator of plot 

or stand attributes because it combines the number of trees and the sizes of those trees.

Eric G. Vallery

Western pine beetle 

(Dendroctonus brevicomis) Ray Roper, 

©iStockphoto.com

Bark beetle damage.

Insect pests and 

diseases have caused 

up to 41 percent 

basal area loss in 

watersheds across 

the United States.
35

Increased Housing Density and Other Pressures on Private Forest Contributions

What Is Forest Health?

WHAT DO we mean by “forest health?” The U.S. 

Forest Service forest health definition is: “A condition 

wherein a forest has the capacity across the landscape for renewal, for recovery from a wide range 

of disturbances, and for retention of its ecological 

resiliency while meeting current and future needs of 

people for desired levels of values, uses, products, 

and services” (USDA FS 2003).

Determining the health or condition of a forest 

is not an easy task. Forest ecosystems are complex 

and forest health can be influenced by the 

interaction of many natural and human-caused 

factors over time—including climate change, 

timber harvesting and other types of forest 

management, fire, windstorms, diseases, and 

insects. The determination as to whether a forest 

is healthy or not can also differ considerably 

depending on the interests and needs of the 

person or organization making the determination. 

Figure 14—Watersheds by susceptibility and vulnerability of private forests to insect pests and diseases.
36

PRIVATE FORESTS, PUBLIC BENEFITS

Identifying Areas Where Insect Pests and 

Diseases Could Compound the Impacts of 

Future Housing Density Increases

Figure 15 indicates where nationwide the greatest percentages 

of private forests might be most affected by insects and diseases 

as well as by increased housing density. Eastern watersheds 

ranking in the 90th percentile are concentrated in Connecticut, 

Rhode Island, New Jersey, Pennsylvania, western regions of 

North Carolina, South Carolina, and Michigan. In the West, 

watersheds in the 90th percentile are found in western Montana, 

Washington, California, Colorado, New Mexico, and Arizona. 

Whitney Cranshaw, Colorado State University

Figure 15—Watersheds by overlap of projected housing density increases on private forest lands and susceptibility to insect 

pests and diseases.
37

Increased Housing Density and Other Pressures on Private Forest Contributions

Table 5—The 15 highest ranking watersheds projected to experience exacerbated impacts from increased 

housing density on private forest lands because of additional pressures from insects and diseases

Private forest to

Numerical Average basal experience increased

rank Watershed State(s) areaa loss housing density

Percent

1 Middle Fork Payette Idaho 29 41

2 Des Plaines Illinois, Wisconsin 20 47

3 Charlotte Harbor Florida 18 60

4 Upper Spokane Idaho, Washington 19 49

5 Clearwater-Elk Minnesota 25 39

6 Clinton Michigan 18 52

7 North Galveston Bay Texas 21 40

8 Shenandoah Virginia, West Virginia 23 37

9 Huron Michigan 17 47

10 Driftwood Indiana 39 34

11 Blackstone Massachusetts, Rhode Island 24 34

12 Clear Colorado 20 37

13 Daytona–St. Augustine Florida 15 65

14 Upper Fox Illinois, Wisconsin 17 43

15 Lower St. Johns Florida 16 44

a The cross-section area of a tree stem in square feet, commonly measured at breast height (4.5 feet in the United States). 

Table 5 presents the 15 highest ranking watersheds projected to experience 

exacerbated impacts from increased housing density because of additional pressures from insects and diseases; 6 are located in the Midwest and 4 in the West; 

3 of the 4 located in the South are in Florida.

Implications

Outbreaks from insects and diseases can lead to damaging levels of defoliation or 

mortality, and forests weakened from insects and diseases are likely to be more 

susceptible to further stress from development (Tkacz et al. 2007). 

Urbanized areas are more likely than rural areas to be points of entry for many 

exotic insect pests. One reason for this phenomenon is that urbanized areas receive 

a greater volume of international shipments, many of which contain wood packing 

materials harboring exotic insects. Furthermore, some tree species most popular 

for urban plantings, such as maple (Acer spp.) or ash (Fraxinus spp.), have also 

been frequent hosts for exotic insect species such as emerald ash borer and Asian 

longhorned beetle (Anoplophora glabripennis). In addition, urban trees are often 

planted in settings such as parking lots and roadsides that do not promote healthy 

tree growth, and weaker trees are more susceptible to insect attack (Poland and 

McCullough 2006). Urbanized areas also contain greater numbers of ornamental 

David Cappaert, Michigan State University

Emerald ash borer.

Watersheds ranking 

highest for increased 

housing density and 

insect/pest damage are 

scattered from Idaho 

to Florida.
38

PRIVATE FORESTS, PUBLIC BENEFITS

plantings, which are another source of insect and pest invasion. For example, 

rhododendrons (Rhododendron spp.) and camellias (Camellia spp.)—popular 

nursery plants widely used in home landscaping—can be hosts to the pathogen 

that causes sudden oak death (Stokstad 2004, Tooley et al. 2004). 

Although this section focuses on insect pests and diseases, the condition 

of private forests can also be affected by invasive plants; roads often serve as a 

primary entry point for invasive plants (Meekins and McCarthy 2001, Parendes 

and Jones 2000).

Insects and diseases cause varying degrees of damage to host tree species 

and forests through defoliation, death, or other injuries and stresses. Sudden oak 

death causes lethal trunk infestations in oaks and other species (Stokstad 2004).

Joseph O’Brien, USDA Forest Service, Bugwood.org
39

Increased Housing Density and Other Pressures on Private Forest Contributions

Three decades ago, Lon 

Merrifield returned from 

overseas and purchased the 

320-acre property that had 

been his childhood home. 

The property is nestled at 

an elevation of 2,350 feet, at 

the base of Clifty Mountain 

in Boundary County, Idaho, 

between the Cabinet and 

Selkirk mountain ranges. 

Bordering the Panhandle 

National Forest and Bureau 

of Land Management lands, 

the area is under heavy 

development pressures. With a strong desire to keep the property and surrounding 

land in its current undeveloped state, Lon and his wife Donna purchased an additional 

1,300 acres of adjacent land, piece by piece, over the past 30 years—an achievement 

that required considerable effort, patience, and strategic planning. In some cases, they 

purchased land a considerable distance away and then traded it with timber companies 

for land adjacent to their property. The Merrifields financed their efforts by operating the 

Clifty View Nursery, by living frugally, by getting help from “... some very understanding 

bankers...,” and by selling a conservation easement through the Forest Legacy program 

operated by the U.S. Forest Service. About 200 acres of the property are managed as 

part of the nursery; many of the remaining acres are managed for timber and wildlife. 

The property provides many public services, including habitat for lynx and grizzly bear, 

two at-risk species that rely on a mosaic of private and public lands. 

Clifty View Nursery

Lon Merrifield and his family proudly hold a copy of an article 

in the local newspaper on the new conservation easement that is 

helping to conserve 1,642 acres of their forest land.

Faces of Private Forest Owners

Courtesy of Merrifield family
40

PRIVATE FORESTS, PUBLIC BENEFITS

Wildfire

Assessment of Current Situation

Wildfire is an important component of some forest ecosystems and can provide 

beneficial effects under particular circumstances; however, wildfire can also be a 

threat to forest land and landowners because of diverse and complex impacts on 

aquatic and terrestrial ecosystems depending on the specific situation (Rieman et al. 

2005). Immediate or long-term effects can include increased soil erosion (Kocher 

et al. 2001); reduced carbon sequestration (Hurteau et al. 2009); and a host of other 

impacts including death or displacement of fish and wildlife, changes to stream 

temperature and chemistry, altered sediment levels, modification of vegetation, 

and increased access by off-highway vehicles (Rieman et al. 2005). Activities 

to suppress wildfires can also lead to soil damage (Rieman et al. 2005), water 

quality degradation, and damage to aquatic life in environmentally sensitive 

areas (Kalabokidis 2000).

Wildfire suppression is costly; in 2000, for the first time ever, federal wildfire 

suppression expenditures exceeded $1 billion (Donovan and Brown 2007). Other 

economic implications include a potential decrease in timber supply over the 

long term and a consequent stimulus to salvage more timber from damaged areas 

(Prestemon et al. 2005). Increased wildfire events can also lead to decreases in 

tourism; in 2002, for example, visitor numbers in Colorado dropped by 40 percent 

in some areas owing to wildfire and drought (Scott 2003).

Over 90 percent of 

private forests in 

many southwestern 

watersheds have high 

wildfire potential.

Tom Iraci
41

Increased Housing Density and Other Pressures on Private Forest Contributions

Figure 16 depicts watersheds according to the potential for wildfire on private 

forests. The rankings are derived from the Wildland Fire Potential Model produced 

by the Forest Service’s Fire Modeling Institute (http://www.fs.fed.us/fmi/). For 

watersheds in the 90th percentile, more than 90 percent of the private forests have 

a high wildfire potential. Large concentrations of these watersheds are found 

throughout the interior West and, in fact, most Western U.S. watersheds are in the 

upper 50th percentile or higher. Clusters of high-ranking watersheds (upper 75th

percentile or higher) are also found in California, Georgia, North Carolina, South 

Carolina, and Florida. 

Figure 16—Watersheds by susceptibility and vulnerability of private forests to wildfire.
42

PRIVATE FORESTS, PUBLIC BENEFITS

Identifying Areas Where Wildfire Could Compound 

the Impacts of Future Increases in Housing Density

Figure 17 displays watersheds where projected increases in 

housing density and susceptibility to wildfire may overlap. 

Watersheds where wildfire threat and future housing density 

increases are highest (in the 90th percentile) are scattered 

throughout the West and parts of the South. Western areas include 

central Colorado, New Mexico, and Arizona, as well as parts 

of Idaho, Washington, Oregon, and California. Southern areas 

include Florida, a cluster of watersheds in north Georgia, North 

Carolina, and South Carolina, as well as parts of eastern Texas.

Figure 17—Watersheds by projected overlap of increased housing density on private forest lands and susceptibility to wildfire.
43

Increased Housing Density and Other Pressures on Private Forest Contributions

Table 6 presents the 15 highest ranking watersheds projected to experience 

exacerbated impacts from increased housing density because of additional pressures from wildfire; 11 are located in the South, including 3 in Florida and 4 in 

North Carolina and South Carolina; the 4 Western watersheds are located in 

Utah, Wyoming, Colorado, and California.

Table 6—The 15 highest ranking watersheds projected to experience exacerbated impacts from increased 

housing density on private forest lands because of additional pressures from wildfire

Private forest Private forest to

Numerical with high fire experience increased

rank Watershed State(s) susceptibility housing density

Percent

1 Charlotte Harbor Florida 99 60

2 Daytona–St. Augustine Florida 97 65

3 Rocky North Carolina, South Carolina 99 48

4 Mobile Bay Alabama 97 48

5 Jordan Utah 99 40

6 Lower St. Johns Florida 98 44

7 New North Carolina 94 52

8 Upper Cape Fear North Carolina 94 51

9 East Fork San Jacinto Texas 95 46

10 West Fork San Jacinto Texas 95 46

11 Greys–Hobock Wyoming 90 56

12 Perdido Bay Alabama, Florida 89 52

13 South Platte headwaters Colorado 92 46

14 Lower Catawba North Carolina, South Carolina 100 33

15 Ventura California 98 36

Many watersheds 

with highest fire 

susceptibility and 

projected housing 

density increases 

are in the South.

© Connie Bransilver, http://www.ConnieBransilver.com
44

PRIVATE FORESTS, PUBLIC BENEFITS

Implications

A proliferation of houses in areas at risk for wildfire increases the risk to human 

property and life and at the same time makes wildfire management and suppression more complicated, controversial, and expensive (Stein et al. 2007, Syphard et 

al. 2007, US GAO 2007). Increased numbers of houses and people are associated 

with more frequent wildfire ignitions (Hammer et al. 2007, Stein et al. 2007, 

Syphard et al. 2007), and wildfire size and spread are influenced by the presence 

and flammability of houses (Spyratos et al. 2007).

Nationwide, current patterns of housing growth in the wildland-urban interface—where homes, businesses, and other structures abut or are intermingled 

with wildland vegetation—are exacerbating wildfire problems and costs (Grulke 

et al., in press; Hammer et al. 2007; Syphard et al. 2007). In areas where fire risk 

is already high, increased housing is exacerbating the problem. The number of 

people and structures located in the wildland-urban interface has increased by 

11 percent (20,458 square miles) in west coast states in the 1990s (Hammer et al. 

2007) and by more than 50 percent nationwide from 1970 to 2000, to a total of 

179,727 square miles (Theobald and Romme 2007). Nearly 90 percent (30,293 

square miles) of the wildland-urban interface in the West occurs in high-severity 

wildfire regimes (Theobald and Romme 2007). Wildfire associated with housing density increases in the wildland-urban interface is also a critical concern 

in the South, where more than 5 million acres of land are at high risk of wildfire 

(Andreu and Hermansen-Baez 2008). 
45

Increased Housing Density and Other Pressures on Private Forest Contributions

said 63-year-old 

North Carolina landowner Albert Beatty. 

The original farm was 

purchased by Mr. 

Beatty’s ancestors at 

the beginning of the 

20th century; he has 

lived on his land his 

entire life and plans to 

keep it in his family for 

many years to come. 

But the 360-acre 

Beatty farm—located 

midway between 

Fayetteville and Wilmington—is in high demand for 

development. Many properties nearby have already 

been sold for this purpose.

Efforts by Albert Beatty and his wife Ada have 

contributed to the conservation of working farm 

and forest land in their area. Over the years, they 

have increased their property to its current size 

by purchasing adjoining land. The diverse farm 

includes more than 200 acres of managed loblolly 

(Pinus taeda L.) and longleaf pine (Pinus palustris

Mill.), a confined swine operation, grain production, 

and 55 head of cattle. The land is bisected by state 

highway 210 and is bordered by the South River.

The Beattys are approached several times 

each month by real estate brokers, who are 

primarily interested in developing riverfront 

properties for beach houses—ranging in size 

“You’ve got 

to really love 

your land to 

stay on it,”

from small cottages to 3,000-square-foot homes. 

Many are willing to buy the entire property just for 

the waterfront land.

Although finances have always been an issue 

in holding on to the property, the Beatty family 

has managed well and is now on solid financial 

footing. But management challenges continue, 

such as last summer’s severe drought, when they 

lost two wells, the crops were dry, and they had to 

reroute water for the cattle. In part to ensure the 

farm’s longevity, Mr. Beatty involves his daughter 

in its operations. Although she lives and works a 

distance away, she does the farm’s taxes and “...

knows about the lands....” This year, the Beattys 

incorporated the farm and named their daughter 

vice president.

Faces of Private Forest Owners

Andrew Kornylak/Aurora Photos
46

PRIVATE FORESTS, PUBLIC BENEFITS

Air Pollution

Assessment of Current Situation

Air pollution—especially ozone and nitrogen compounds from vehicle exhausts 

and other fossil fuel combustion—has increased substantially since World War II 

(Grulke et al., in press). This section focuses on ozone because it is known to be 

transported long distances from source metropolitan areas to rural forested areas. 

Ozone concentrations have risen substantially since pre-industrial times, especially 

in the South (Tkacz et al. 2007), and models predict that concentrations will continue to increase over the next 50 years (Chappelka and Samuelson 1998). Ozone 

can reduce tree seedling growth and photosynthesis by 20 percent or more and 

can also affect the growth of mature trees (Pye 1988). Oak-hickory forests in the 

southern Midwest have the highest risk of changes to the structure and function 

of the ecosystem from ozone damage (USDA FS 2004).

Other air pollutants such as nitrates and sulfates are major components of acid 

rain (National Acid Precipitation Assessment Program 1990). Rapid increases in 

urbanization, population, and distances driven in the West have contributed to 

a steady increase in nitrogen deposition (Fenn et al. 2003). Atmospheric sulfate 

deposition to the Northeastern United States has increased more than fivefold in 

the past 150 years, and most acid-sensitive ecosystems have been exposed to high 

inputs of strong acids for many decades (Driscoll et al. 2001). Although acid rain 

deposition has decreased in recent years, emissions still need to be reduced to 

maintain ecological recovery (US EPA 2009a).

iStockphoto.com/rusm
47

Increased Housing Density and Other Pressures on Private Forest Contributions

Figure 18—Watersheds by susceptibility and vulnerability of private forests to ozone pollution.

Figure 18 shows watersheds with private forests that are susceptible or vulnerable to ozone damage, based on observations by FIA field crews. Watersheds 

containing the highest percentages (67 to 100 percent) of private forests vulnerable 

to ozone damage are displayed in dark red. These watersheds are concentrated in 

specific areas across the country, including a large area covering much of South 

Carolina and Georgia; an area extending along the Atlantic Coast from Massachusetts through Maryland and along the southern border of Pennsylvania; an 

area overlapping northern Kentucky and southern Indiana; most watersheds in 

the vicinity of central California; and several watersheds in Michigan.

Forests with highest 

ozone vulnerability 

are found in central 

California and the East. 
48

PRIVATE FORESTS, PUBLIC BENEFITS

For maps showing watersheds with private forests with elevated levels of 

nitrates and sulfates, see the FOTE Web site, http://www.us.fed.us/openspace/fote. 

Identifying Areas Where Ozone Pollution Could Compound 

the Impacts of Future Increases in Housing Density

Figure 19 displays watersheds where projected increases in housing density and 

high or moderate levels of ozone damage are expected to overlap. Although 

increased housing density may not directly result in an increase in ground-level 

ozone, it can place additional stresses on forests that are already susceptible to 

ozone damage. Watersheds colored red on the map contain the highest percentages 

of private forest susceptible to both ozone damage and increased housing density. 

These watersheds follow a pattern similar to that depicted in figure 17, but they 

are grouped in smaller clusters scattered across the country.

Figure 19—Watersheds by projected overlap of increased housing density on private forest lands and susceptibility to ozone pollution.
49

Increased Housing Density and Other Pressures on Private Forest Contributions

Table 7 presents the 10 highest ranking watersheds3 projected to experience 

exacerbated impacts from increased housing density because of additional pressures from ozone damage. Nine of the 10 are located in the East, including the 

highest ranking watershed, the Middle Potomac-Catoctin, which stretches across 

the District of Columbia, Maryland, Virginia, and West Virginia. The only Western 

watershed in this category is in California. 

Table 7—The 10a highest ranking watersheds projected to experience exacerbated impacts from increased 

housing density on private forest lands because of additional pressures from ozone pollution

Private forest Private forest to

Numerical vulnerable to experience increased

rank Watershed State(s) ozone damage housing density

Percent

1 Middle Potomac-Catoctin DC, Maryland, Virginia, West Virginia 90 63

2 Upper Chowchilla– California 100 38

 Upper Fresno

3 Salt Kentucky 96 45

4 Mullica-Toms New Jersey 94 43

5 North Fork Edisto South Carolina 79 39

6 Great Egg Harbor New Jersey 78 44

7 Pawcatuck-Wood Connecticut, Rhode Island 63 46

8 Rocky North Carolina, South Carolina 62 48

9 Monocacy Maryland, Pennsylvania 58 53

10 Patuxent Maryland 33 47

a This table displays only 10 highest ranking watersheds, rather than 15 as in other sections of this report. Because of the way in which 

we combined the two layers, the scores for the next 25 watersheds all have the same value.

Implications

The relationship between increased housing density and ozone production 

is complex. Higher levels of housing density and associated development 

can result in increased fossil fuel combustion, electricity generation, and 

use of lawn and garden equipment, each of which can contribute to ozone 

production (Driscoll et al. 2003). Although the level of ozone production 

may be negligible in areas of low housing density, in more urbanized areas 

the addition of more housing and related infrastructure could have a more 

substantial effect.

Ozone specifically causes foliar lesions and rapid leaf aging, alters 

species composition, weakens pest resistance, and reduces root biomass 

Ozone damage.

3 It was not possible to distinguish 15 highest ranking watersheds for the ozone 

analysis, as was done for other sections in this report, because the scores for the 

next 25 watersheds after the 10th are identical.

Paul A. Mistretta, USDA Forest Service, Bugwood.org
50

PRIVATE FORESTS, PUBLIC BENEFITS

(Chappelka and Samuelson 1998, Grulke et al. 1998, Karnosky et al. 2005). 

Several recent studies have shown that ozone affects forest growth and that the 

level of impact varies by tree species and forest type (Chappelka and Samuelson 

1998, Karnosky et al. 2005). Ozone and elevated nitrogen deposition cause 

changes in forest tree carbon, nitrogen, and water balance that increase tree 

susceptibility to drought and insect attack, and these changes make the whole 

ecosystem more susceptible to wildfire (Grulke et al., in press; Karnosky et al. 

2005). Ozone and acid precipitation are known to have damaging effects on 

vegetation, soil chemistry, aquatic and terrestrial species, and human health 

(National Acid Precipitation Assessment Program 1990, Unger 2006). Decreases 

in tree growth and excessive tree mortality from ozone and nitrogen pollution also 

can potentially lead to decreases in timber and agricultural yields (US EPA 1997, 

Wallenstein 2004). The growing body of research on ozone effects indicates that 

the degree of change in plant growth varies substantially with each species and 

according to the duration and level of exposure to ozone (Pye 1988).

O.J. Byrne
51

Increased Housing Density and Other Pressures on Private Forest Contributions

HOUSING DENSITY is expected to continue to increase on rural private forests 

across the country, and the resulting changes will continue to affect numerous 

public goods and services. In many areas, the impacts of increased housing density 

are likely to be exacerbated by other threats. 

Watersheds with the greatest percentages of forest land under private ownership 

are concentrated in the East—not a surprising finding because much of the forest 

land in the West is in public ownership. Watersheds where private forests make the 

greatest contributions to the goods and services analyzed—water quality, timber 

volume, at-risk species habitat, and interior forest—follow a similar pattern. 

This pattern is particularly true for water quality but less so for other contributions. Most of the California watersheds included in this analysis, as well as 

most watersheds in western Oregon and Washington, also contain private forests 

making substantial contributions to U.S. timber volume. Although private forest 

contributions to at-risk species habitat and interior forest are high in the East, they 

are quite high in many Western U.S. watersheds as well; in fact, most watersheds in 

the Southwest, California, and western Oregon that were included in this analysis 

contain substantial numbers of at-risk species. Many Western U.S. watersheds also 

rank fairly high in terms of contributions to interior forest.

Interestingly, some watersheds in Florida, the Southwest, Washington state, 

and coastal California contain low percentages of private forest but harbor high 

numbers of at-risk species associated with private forest. These areas also contain 

several watersheds with low percentages of private forest that contain high percentages of interior forest. 

Watersheds with the greatest percentages of private forests under pressure 

from insect pests and diseases are distributed across the West and East. With the 

exception of watersheds in western Oregon and western Washington, a high percentage of private forests in most western watersheds are classified as having high 

wildfire potential. This is also true of a swath of watersheds across the Southeast. 

Watersheds with private forests exposed to ozone pollution are found primarily in 

the East but also in California. 

What happens when any of the threat layers (insect pests and diseases, wildfire, 

or ozone) are combined with the housing density layer? In each case, more watersheds in the 90th percentile are found in the East. Nevertheless, in every region of 

the United States, private forests are experiencing increases in housing density, and 

these increases are associated with numerous economic and ecological changes. 

Private forest lands across the country are also at risk from other threats—including 

insect pests and diseases, wildfire, and air pollution—that compound the effects of 

increased housing density. 

Summary and Conclusions

As the U.S. population 

increases by 80 million 

people, increases in 

housing density will 

continue to affect 

many public benefits 

of private forests.
52

PRIVATE FORESTS, PUBLIC BENEFITS

The projections in this report assume a continuation of economic conditions 

in the country as they exist in the early part of this century. Although the future of 

housing markets is uncertain at this time, residential development in rural landscapes in the United States has been occurring for a long time. With the U.S. population projected to increase by at least another 80 million people between 2000 and 

2030, forest resource managers should expect and plan for continued development 

pressure on many of the Nation’s private forests. Monitoring land-use changes and 

relationships to economic conditions—as well as monitoring changes in population 

and personal income levels—will therefore be important. Forests on the Edge will 

periodically update data and projection methods as appropriate. 

Future research should also address the need to anticipate and understand the 

implications of housing density increases within the rural I category (fewer than 16 

housing units per square mile). Additionally, analysis of woody vegetation types 

that do not qualify as forest in the NLCD but that are critically important and 

under development pressures, should also be a focus of future research. This would 

include woodlands and narrow riparian forests common in the interior West and the 

Plains States. 

The results of this analysis are based on national-level data and are best suited 

for use at national levels, but the information can provide context and inspiration for 

local discussions about forest land development. It is at the local level where scientists, resource managers, landowners, and communities contend with the challenges 

of planning for growth while conserving the potential for private forests to provide 

valuable goods, services, and economic opportunities far into the future. 

Forest Service Tools for Open Space Conservation

THE FOREST SERVICE has a number of resources and tools designed 

to address open space conservation needs. Visit http://www.fs.fed.us/

openspace/index.html for a list of tools and resources and for additional 

information on open space conservation.
53

Increased Housing Density and Other Pressures on Private Forest Contributions

Acknowledgments

Many people have made important contributions to the production of this report. 

The project would not have been possible without the data provided by many scientists from across the country, including: Jason McNees (at-risk species), NatureServe; Mark Hatfield (water quality) and Frank Krist (insect pests and diseases), 

Forest Service Northern Research Station; Jim Menakis (fire), Rocky Mountain 

Research Station; and John Coulston (ozone), Southern Research Station. Kurt 

Riitters, Southern Research Station, had created an interior forest data layer used 

in previous FOTE papers and kindly advised us on the update and re-creation of 

that layer for this publication. Each of these scientists has also provided invaluable 

advice in the production of this and other Forests on the Edge products. 

We greatly appreciate the input and advice of Brett Butler, Forest Service 

Northern Research Station. Members of the Forest Service Washington Office 

Cooperative Forestry staff who have provided very helpful input include: Al Todd, 

Kathryn Conant, and Sarah Hilbert, a summer intern from Virginia Tech. Throughout this process we have appreciated the support and counsel of Cooperative 

Forestry Assistant Director Ted Beauvais, as well as the invaluable advice provided 

by Director Paul Reis. We also thank Jamie Barbour, program manager, Pacific 

Northwest Research Station, for his interest and support.

Close to 20 people generously contributed their time to read and comment 

on the final draft. Erik Nordman, Grand Valley State University, Michigan, gave 

detailed comments as a peer-reviewer, as did peer-reviewers from the Forest 

Service: Krista Gebert, Rocky Mountain Research Station; John Greis, State and 

Private Forestry, Southern Region; Sally Campbell, Pacific Northwest Research 

Station; Will McWilliams, Northern Research Station; and Wayne Zipperer, 

Southern Research Station. Several people from Forest Service regions and 

state forestry agencies across the country also responded to our call for reviews. 

These include Forest Service employees Robin Morgan, Northeast Area; John 

Dondero, Southern Region; Claire Harper, Rocky Mountain Region; Dee Sessions, 

Southwestern and Intermountain Regions; and James Geiger, Pacific Southwest 

Region. State forestry employees providing comments include: Don Mansius, 

Maine Forest Service; Gary Michael and Andrew Arends, Minnesota Department 

of Natural Resources; Gerald Andritz, New York Bureau of Land Services; Carl 

Garrison, Virginia Department of Forestry; and Geoff McNaughton, representing 

the Western Forestry Leadership Coalition. We also are grateful for the thoughtful 

and detailed input of Eric White, Pacific Northwest Research Station, who is a 

valued member of the Forests on the Edge team. 
54

PRIVATE FORESTS, PUBLIC BENEFITS

Metric Equivalents

When you know: Multiply by: To find:

Feet 0.3048 Meters

Cubic feet 0.0283 Cubic meters

Yards 0.914 Meters

Acres 0.405 Hectares

Miles 1.609 Kilometers

Square feet 0.0929 Square meters

Square miles 2.59 Square kilometers

References

Alig, R. 2007. A United States view on changes in land use and land values 

affecting sustainable forest management. Journal of Sustainable Forestry. 

24(2/3): 209–227.

Alig, R.; Butler, B.J. 2004. Projecting large-scale area changes in land use 

and land cover for terrestrial carbon analyses. Environmental Management. 

33(4): 443–456.

Andreu, A.; Hermansen-Baez, A. 2008. Fire in the South 2: the southern wildfire 

risk assessment. Asheville, NC: U.S. Department of Agriculture, Forest Service, 

Southern Research Station. 30 p. www.southernwildfirerisk.com/reports/

FireInTheSouth2.pdf. (17 February 2009).

Brett, M.T.; Arhonditsis, G.B.; Mueller, S.E.; Hartley, D.M.; Frodge, J.D.; 

Funke, D.E. 2005. Non-point-source impacts on stream nutrient concentrations 

along a forest to urban gradient. Environmental Management. 35(3): 330–342.

Brown, T.C.; Hobbins, M.T.; Ramirez, J.A. 2005. The source of water supply 

in the United States. RMRS-RWU-4851 Discussion Paper. 57 p. Unpublished 

report. On file with: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station, Fort Collins, CO 80526.

Butler, B. 2008. Private forest owners of the United States, 2006. Gen. Tech. 

Rep. NRS-27. Newtown, PA: U.S. Department of Agriculture, Forest Service, 

Northern Research Station. 37 p.

Chappelka, A.H.; Sameulson, L.J. 1998. Ambient ozone effects on forest trees 

of the Eastern United States: a review. New Phytologist. 139: 91–108.

Clutter, M.; Mendell, B.; Newman, D.; Wear, D.; Greis, J. 2005. Strategic 

factors driving timberland ownership changes in the U.S. South. Asheville, NC: 

U.S. Department of Agriculture, Forest Service, Southern Research Station. 15 p.
55

Increased Housing Density and Other Pressures on Private Forest Contributions

Coleman, J.S.; Temple, S.A. 1993. Rural residents’ free-ranging domestic cats: 

a survey. Wildlife Society Bulletin. 21(4): 381–390.

Conservation Biology Institute [CBI]. 2007. Protected areas database v4.6. 

[Unpublished data set]. http://www.consbio.org/what-we-do/protected-areasdatabase-pad-version-4. (15 September 2009).

Coulston, J.W.; Smith, G.C.; Smith, W.D. 2003. Regional assessment of ozone 

sensitive tree species using bioindicator plants. Environmental Monitoring and 

Assessment. 83: 113–127.

Crosbie, B.; Chow-Fraser, P. 1999. Percentage land use in the watershed 

determines the water and sediment quality of 22 marshes in the Great Lakes 

basin. Canadian Journal of Fisheries and Aquatic Sciences. 56(10): 1781–1791.

Daniels, T. 1999. When city and country collide: managing growth in the 

metropolitan fringe. Washington, DC: Island Press. 361 p. 

Debinski, D.M.; Holt, R.D. 2000. A survey and overview of habitat fragmentation 

experiments. Conservation Biology. 14: 342–355.

DellaSala, D.A.; Staus, N.L.; Strittholt, J.R.; Hackman, A.; Iacobelli, A. 2001.

An updated protected areas database for the United States and Canada. Natural 

Areas Journal. 21: 124–135.

Donavan, G.H.; Brown, T.C. 2007. Be careful what you wish for: the legacy of 

Smokey Bear. Frontiers in Ecology. 5(2): 73–79.

Donovan, T.M.; Flather, C.H. 2002. Relationships among North American 

songbird trends, habitat fragmentation, and landscape occupancy. Ecological 

Applications. 12: 364–374.

Driscoll, C.T.; Lawrence, G.B.; Bulger, A.J.; Butler, T.J.; Cronan, C.S.; 

Eager, C.; Lambert, K.F.; Likens, G.E.; Stoddard, J.L.; Weathers, K.C. 

2001. Acidic deposition in the Northeastern United States: sources and inputs, 

ecosystem effects, and management strategies. BioScience. 51: 180–198.

Driscoll, C.T.; Whitall, D.; Aber, J.; Boyer, E.; Castro, M.; Cronan, C.; 

Goodale, C.L.; Groffman, P.; Hopkinson, C.; Lambert, K.; Lawerence, G.; 

Ollinger, S. 2003. Nitrogen pollution in the Northeastern United States: sources, 

effects, and management options. BioScience. 53(4): 357–374.
56

PRIVATE FORESTS, PUBLIC BENEFITS

Eastman, J.R.; Emani, S.; Hulina, S.; Johnson, A.; Jiang, H.; Ramachandran, 

M. 1997. Applications of geographic information systems (GIS) technology 

in environmental risk assessment and management. In: United Nations 

Environment Programme (UNEP), ed. The Idrisi project—Clark labs for 

cartographic technology. Worcester, MA: Clark Labs/Clark University, in 

cooperation with UNEP: 1–10. 

Egan, A.; Taggart, D.; Annis, I. 2007. Effects of population pressures on wood 

procurement and logging opportunities in northern New England. Northern 

Journal of Applied Forestry. 24(2): 85–90.

Engels, T.; Sexton, C. 1994. Negative correlation of blue jays and golden-cheeked 

warblers near an urbanizing area. Conservation Biology. 8: 286–290.

Fenn, M.E.; Haeuber, R.; Tonnesen, G.S.; Baron, J.S.; Grossman-Clarke, S.; 

Hope, D.; Jaffe, D.A.; Copeland, S.; Geiser, L.; Rueth, H.M.; Sickman, J.O. 

2003. Nitrogen emissions, deposition, and monitoring in the Western United 

States. BioScience. 53: 1–13.

Findlay, C.S.; Houlahan, J.E. 1997. Anthropogenic correlates of species richness 

in southeastern Ontario wetlands. Conservation Biology. 11: 1000–1009.

FitzHugh, T. 2001. Watershed characteristics and aquatic ecological integrity: 

a literature review. The Nature Conservancy Freshwater Initiative. http://

conserveonline.org/docs/2001/06/watershed.pdf. (10 January 2006).

Forest Inventory and Analysis [FIA]. 2007. Forest Inventory and Analysis 

national program: we are the Nation’s forest census. U.S. Department of 

Agriculture, Forest Service. http://fia.fs.fed.us/. (24 April 2007). 

Frelich, L.E. 2003. Global warming and forest impacts in MN—a background 

paper to guide discussion. 1 p. Unpublished report. On file at: University of 

Minnesota, St. Paul, MN 55108. 

Graham, K.L. 2007. Human influences on forest wildlife habitat. In: Southern 

forest resource assessment. Chapter 3 (terra-3), final online report. Asheville, 

NC: U.S. Department of Agriculture, Southern Region and Southern Research 

Station; U.S. Environmental Protection Agency; U.S. Fish and Wildlife Service; 

Tennessee Valley Authority; and state forestry agencies of the Southern United 

States: 63–90. http://www.srs.fs.usda.gov/sustain/report/terra3/terra3.htm. 

(17 February 2009).

Grulke, N.E.; Andersen, C.P.; Fenn, M.E.; Miller, P.R. 1998. Ozone exposure 

and nitrogen deposition lowers root biomass of ponderosa pine in the San 

Bernardino Mountains, California. Environmental Pollution. 103(1): 63–73.
57

Increased Housing Density and Other Pressures on Private Forest Contributions

Grulke, N.E.; Paine, T.; Riggan, P. [In press]. Air pollution increases forest 

susceptibility to wildfires: a case study for the San Bernadino Mountains in 

southern California. In: Pye, J.M.; Rauscher, H.M.; Sands, Y.; Lee, D.C.; Beatty, 

J.S., eds. Advances in threat assessment and their application to forest and 

rangeland management. Gen. Tech. Rep. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station.

Hammer, R.B.; Radeloff, V.C.; Fried, J.S.; Stewart, S.I. 2007. Wildlandurban interface housing growth during the 1990s in California, Oregon, and 

Washington. International Journal of Wildland Fire. 16: 255–265.

Hartley, M.J.; Hunter, M.L. 1998. A meta-analysis of forest cover, edge effects, 

and artificial nest predation rates. Conservation Biology. 12: 465–469.

Haynes, R.; Adams, D.; Alig, R.; Brooks, D.; Durbak, I.; Howard, J.; Ince, 

P.; McKeever, D.; Mills, J.; Skog, K.; Zhou, X. 2001. Projections of the U.S. 

timber supply and demand situation to 2050—draft findings from the USDA 

Forest Service 2000 RPA timber assessment. In: Proceedings, Society of 

American Foresters 2000 national convention. http://www.fpl.fs.fed.us/

documnts/pdf2000/hayne00a.pdf. (17 July 2007).

Homer, C.; Dewitz, J.; Fry, J.; Coan, M.; Hossain, N.; Larson, C.; Herold, 

N.; McKerrow, A.; VanDriel, J.N.; Wickham, J. 2007. Completion of 

the 2001 national land cover database for the conterminous United States. 

Photogrammetric Engineering & Remote Sensing. 73(4): 337–341.

Homer, C.; Huang, C.; Yang, L.; Wylie, B.; Coan, M. 2004. Development of 

a 2001 national land cover database for the United States. Photogrammetric 

Engineering and Remote Sensing. 70(7): 829–840.

Houlahan, J.E.; Findlay, C.S. 2003. The effects of adjacent land use on wetland 

amphibian species richness and community composition. Canadian Journal of 

Fisheries and Aquatic Sciences. 60: 1078–1094.

Houlahan, J.E.; Findlay, C.S. 2004. Estimating the “critical” distance at which 

adjacent land-use degrades wetland water and sediment quality. Landscape 

Ecology. 19: 677–690.

Houlahan, J.E.; Keddy, P.A.; Makkay, K.; Findlay, C.S. 2006. The effects of 

adjacent land use on wetland species richness and community composition. 

Wetlands: the Journal of the Society of the Wetland Scientists. 26(1): 79–96.

Hurteau, M.D.; Hungate, B.A.; Koch, G.W. 2009. Accounting for risk in 

valuing forest carbon offsets. Carbon Balance and Management. 4: 1. 

http://www.cbmjournal.com/content/4/1/1. (4 February 2009).
58

PRIVATE FORESTS, PUBLIC BENEFITS

Im, S.; Brannan, K.M.; Mostaghimi, S. 2003. Simulating hydrologic and water 

quality impacts in an urbanizing watershed. Journal of the American Water 

Resources Association. 39(6): 1465–1479.

Jacobson, S. 2006. The increasing threat of highway-caused wildlife mortality and 

barrier impacts on U.S. public lands. [Poster/abstract.] Presented at Advances 

in threat assessment and their application to forest and rangeland management, 

Boulder, CO, July 2006. http://www.forestryencyclopedia.net/p/p26/p52. 

(29 April 2008).

Johnson, M.; Stone, B. 2008. Minnesota forests for the future: strategic report of 

the DNR commissioner’s advisory team on the Minnesota Forests for the Future 

program. St. Paul, MN: Minnesota Department of Natural Resources. 53 p. 

www.mndnr.gov/forestlegacy. (25 February 2009).

Jones, K.B.; Neale, A.C.; Nash, M.S.; Riitters, K.H.; Wickham, J.D.; O’Neill, 

R.V.; van Remortel, R.D. 2000. Landscape correlates of breeding bird richness 

across the United States Mid-Atlantic region. Environmental Monitoring and 

Assessment. 63(1): 159–174.

Kalabokidis, K.D. 2000. Effects of wildfire suppression chemicals on people and 

the environment: a review. Global Nest. 2(2): 129–137.

Karnosky, D.F.; Pregitzer, K.S.; Zak, D.R.; Kubiske, M.E.; Hendrey, G.R.; 

Weinstein, D.; Nosal, M.; Percy, K.E. 2005. Scaling ozone responses of forest 

trees to the ecosystem level in a changing climate. Plant, Cell and Environment. 

28: 965–981.

Kerr, J.T.; Deguise, I. 2004. Habitat loss and the limits to endangered species 

recovery. Ecology Letters. 7: 1163–1169.

Keyser, A.J. 2002. Nest predation in fragmented forests: landscape matrix by 

distance from edge interactions. Wilson Bulletin. 114(2): 186–191.

Kline, J.D.; Alig, R.J. 2005. Forestland development and private forestry with 

examples from Oregon (USA). Forest Policy and Economics. 7: 709–720.

Kline, J.D.; Azuma, D.L.; Alig, R.J. 2004. Population growth, urban expansion, 

and private forestry in western Oregon. Forest Science. 50(1): 33–43.

Kocher, S.; Harris, R.; Nakamura, G. 2001. Recovering from wildfire: a 

guide for California’s forest landowners. Agricultural and Natural Resources 

Publication 21603. Oakland: University of California. 16 p.
59

Increased Housing Density and Other Pressures on Private Forest Contributions

Krist, F.J.; Sapio, F.; Tkacz, B. 2007. Mapping risk from forest insects and 

diseases. FHTET-2007 06. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Forest Health Technology Enterprise Team. 115 p. 

Kurki, S.; Nikula, A.; Helle, P.; Linde, H. 2000. Landscape fragmentation and 

forest composition effects on grouse breeding success in boreal forests. Ecology. 

81(7): 1985–1997. 

Lampila, P.; Monkkonen, M.; Desrochers, A. 2005. Demographic responses by 

birds to forest fragmentation. Conservation Biology. 19(5): 1537–1546.

LeVan, S. 1995. Life cycle assessment: measuring environmental impact. 

Proceedings, 49th annual meeting of the Forest Products Society. Madison, 

WI: Forest Products Society: 7–11. http://www.fpl.fs.fed.us/documnts/pdf1998/

levan98b.pdf. (17 February 2009).

LeVert, M.; Colgan, C.; Lawton, C. 2008. Are the economics of a sustainable 

Maine forest sustainable? Maine Policy Review. 16(2): 26–36.

Liebhold, A.M.; MacDonal, W.L.; Bergdahl, D.; Mastro, V.C. 1995. Invasion 

by exotic pests: a threat to forest ecosystems. Forest Science Monographs 30. 

Bethesda, MD: Society of American Foresters. 49 p.

Mahan, C.G.; Yahner R.H. 1999. Effects of forest fragmentation on behaviour 

patterns in the eastern chipmunk (Tamias striatus). Canadian Journal of Zoology. 

77: 1991–1997.

Matteo, M.; Randhir, T.; Bloniarz, D. 2006. Watershed-scale impacts of forest 

buffers on water quality and runoff in urbanizing environment. Journal of Water 

Resources Planning and Management. 132 (3): 144–152.

McWilliam, W.J.; Brown, R.D. 2001. Effects of housing development on bird 

species diversity in a forest fragment in Ontario, Canada. Landscape Research. 

26(4): 407–419. 

Meekins, J.F.; McCarthy, B.C. 2001. Effect of environmental variation on the 

invasive success of a nonindigenous forest herb. Ecological Applications. 

11(5): 1336–1348. 

Mendell, B.; Clutter, M.; Newman, D.; Wear, D.; Greis, J. 2005. Under new 

management: a case study of southern timberland ownership changes. Asheville, 

NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 

13 p. 
60

PRIVATE FORESTS, PUBLIC BENEFITS

Munn, I.A.; Barlow, S.A.; Evans, D.L.; Cleaves, D. 2002. Urbanization’s 

impact on timber harvesting in the South-Central United States. Journal of 

Environmental Management. 64: 65–66.

Nair, K.S.S.; Sumardi. 2000. Insect pests and diseases of major plantation species. 

In: Insect pests and diseases in Indonesian forests: an assessment of the major 

threats, research efforts and literature. Bogor, Indonesia: Center for International 

Forestry Research: 15–38. http://www.cifor.cgiar.org/publications/pdf_files/

Insect-pests.pdf. (17 February 2009).

National Acid Precipitation Assessment Program. 1990. Acidic deposition: 

episodic acidification of surface waters due to acidic deposition. State of 

Science and Technology Report 12. Washington, DC. 100 p.

Parendes, L.A.; Jones, J.A. 2000. Role of light availability and dispersal in exotic 

plant invasion along roads and streams in the H.J. Andrews Experimental Forest, 

Oregon. Conservation Biology. 14: 64–75.

Phelps, M.G.; Hoppe, M.C. 2002. New York–New Jersey Highlands regional 

study: 2002 update. NA-TP-02-03. Newtown, PA: U.S. Department of Agriculture, Forest Service, Northeastern Area State and Private Forestry. 209 p.

Poland, T.M.; McCullough, D.G. 2006. Emerald ash borer: invasion of the urban 

forest and the threat to North America’s ash resource. Journal of Forestry. 

104(3): 118–124.

Prestemon, J.P.; Wear, D.N.; Stewart, F.J.; Holmes, T.P. 2005. Wildfire, 

timber salvage, and the economics of expediency. Forest Policy and Economics. 

8(3): 312–322.

Pye, J.M. 1988. Impact of ozone on the growth and yield of trees: a review. 

Journal of Environmental Quality. 17: 347–360.

Ratner, S.; Lande, R.; Roper, B.B. 1997. Population viability analysis of spring 

Chinook salmon in the South Umpqua River, Oregon. Conservation Biology. 

11(4): 879–889.

Rieman, B.; Luce, C.; McIver, J.D.; Kershner, J.; Webster, M.; Robichaud, 

P.; Cook, R.; Swanson, R., contributors. 2005. Earth...water...fire: the role 

and management of fire in aquatic ecosystems. FS-828. Wildland Waters. 

Washington, DC: U.S. Department of Agriculture, Forest Service. 24 p. 

http://www.fs.fed.us/wildlandwaters/newsletters/wildlandwaters_summer05.pdf. 

(18 March 2009).

Riitters, K.H.; Wickham, J.D. 2003. How far to the nearest road? Frontiers in 

Ecology and Environment. 1(3): 125–132.
61

Increased Housing Density and Other Pressures on Private Forest Contributions

Riitters, K.H.; Wickham, J.D.; Coulston, J.W. 2004. A preliminary assessment 

of Montréal Process indicators of forest fragmentation for the United States. 

Environmental Monitoring and Assessment. 91(1–3): 257–276.

Riitters, K.H.; Wickham, J.D.; O’Neill, R.V.; Jones, K.B.; Smith, E.R.; 

Coulston, J.W.; Wade, T.G.; Smith, J.H. 2002. Fragmentation of continental 

United States forests. Ecosystems. 5(8): 815–822.

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(6): 301–307. 

http://www.esajournals.org/doi/pdf/10.1890/070106. (17 February 2009).

Sampson, N.; DeCoster, L. 2000. Forest fragmentation; implications for 

sustainable private forests. Journal of Forestry. 98(3): 4–8.

Scott, D. 2003. Climate change and tourism in the mountain regions of North 

America. Presentation to first international conference on climate change and 

tourism, Djerba, Tunisia, 9–11 April 2003. 9 p. http://www.linkbc.ca/torc/

downs1/danielscott.pdf?PHPSESSID=e936ec3c4637a5da5a8523f12ed665ae. 

(17 February 2009).

Sieving, K.E.; Willson, M.F. 1999. A temporal shift in Steller’s jay predation on 

bird eggs. Canadian Journal of Zoology. 77: 1829–1834.

Smail, R.A.; Lewis, D.J. 2009. Forest land conversion, ecosystem services, and 

economic issues for policy: a review. Gen. Tech. Rep. PNW-GTR-797. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station. 40 p.

Smith, G.C.; Coulston, J.W.; Jepsen, E.; Prichard, T. 2003. A national ozone 

biomonitoring program—results from field surveys of ozone sensitive plants in 

Northeastern forests (1994–2000). Environmental Monitoring and Assessment. 

87: 271–291. 

Smith, W.B.; Miles, P.D.; Vissage, J.S.; Pugh, S.A. 2004. Forest resources of the 

United States, 2002. Gen. Tech. Rep. NC-241. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central Research Station. 137 p. http://www.

ncrs.fs.fed.us/pubs/gtr/gtr_nc241.pdf. (17 February 2009).

Spyratos, V.; Bourgeron, P.S.; Ghil, M. 2007. Development at the wildlandurban interface and the mitigation of forest-fire risk. Proceedings of the 

National Academy of Sciences. 104(36): 14272–14276.
62

PRIVATE FORESTS, PUBLIC BENEFITS

Steeves, P.A.; Nebert, D.D. 1994. Hydrological unit maps of the conterminous 

United States [database]. U.S. Geological Survey [USGS], open-file dataset 

“huc250,” ed. 1. Reston, VA: U.S. Geological Survey. http://water.usgs.gov/

GIS/metadata/usgswrd/XML/huc250k.xml. (10 January 2006).

Stein, S.; Alig, R.J.; White, E.M.; Comas, S.J.; Carr, M.; Eley, M.; Elverum, 

K.; O’Donnell, M.; Theobald, D.M.; Cordell, K.; Haber, J.; Beauvais, T.W. 

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.; Butler, B. 2004. On the frontline: private forests and water resources. 

FS-790. Wildland Waters. Washington, DC: U.S. Department of Agriculture, 

Forest Service. 24 p.

Stein, S.; McRoberts, R.E.; Alig, R.J.; Nelson, M.D.; Theobald, D.; Eley, M.; 

Dechter, M.; Carr, M. 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.; Hatfield, M.H.; McRoberts, R.E.; Meneguzzo, D.M.; Comas, S.J. 

[In press]. Threats to private forest lands in the USA. In: Pye, J.M.; Rauscher, 

H.M.; Sands, Y.; Lee, D.C.; Beatty, J.S., eds. Advances in threat assessment and 

their application to forest and rangeland management. Gen. Tech. Rep. Portland, 

OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research 

Station.

Stein, S.M.; McRoberts, R.E.; Nelson, M.D.; Theobald, D.; Eley, M.; Dechter, 

M. 2006. Forests on the edge: a GIS-based approach to projecting housing 

development on private forests. In: Aguirre-Bravo, C.; Pellicane, P.J.; Burns, 

D.P.; Draggan, S., eds. Monitoring science and technology symposium in the 

western hemisphere. RMRS-P-42CD. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station: 736–743. http://

www.fs.fed.us/rm/pubs/rmrs_p042/rmrs_p042_736_743.pdf. (17 February 2009).

Stokstad, E. 2004. Nurseries may have shipped sudden oak death pathogen 

nationwide. Science. 303(26): 1959.

Stolte, K.; Darr, D. 2006. Major stressors, effects, and risks to forest ecosystems 

throughout the United States. [Abstract.] Presented at Advances in threat 

assessment and their application to forest and rangeland management, Boulder, 

CO, July 2006. http://www.forestryencyclopedia.net/p/p124. (29 April 2008).
63

Increased Housing Density and Other Pressures on Private Forest Contributions

Syphard, A.D.; Radeloff, V.C.; Keeley, J.E.; Hawbaker, T.J.; Clayton, M.K.; 

Stewart, S.I.; Hammer, R.B. 2007. Human influence on California fire regimes. 

Ecological Applications. 17(5): 1388–1402.

Taylor, P.D.; Merriam, G. 1995. Habitat fragmentation and parasitism of a forest 

damselfly. Landscape Ecology. 11: 181–189.

Theobald, D. 2005. Landscape patterns of exurban growth in the USA from 

1980 to 2020. Ecology and Society. 10(1): 32. http://www.ecologyandsociety.org/

vol10/iss1/. (13 September 2006).

Theobald, D. 2007. Unprotected private, protected private, public, tribal lands in 

the U.S. [Unpublished data set]. On file with: David M. Theobald, Colorado State 

University, Natural Resource Ecology Laboratory, Fort Collins, CO 80523-1499.

Theobald, D.; Romme, W.H. 2007. Expansion of the U.S. wildland-urban 

interface. Landscape Urban Planning. 83(4): 340–354.

Thorne, S.; Sundquist, D. 2001. New Hampshire’s vanishing forests: conversion, 

fragmentation and parcelization of forests in the granite state. Report of the New 

Hampshire Forest Land Base Study. Concord, NH: Society for the Protection of 

New Hampshire Forests. 164 p. 

Tkacz, B.; Moody, B.; Castillo, J.V. 2007. Forest health status in North America. 

Proceedings: Impacts of air pollution and climate change on forest ecosystems. 

The Scientific World Journal. 7(S1): 28–36. 

Tooley, P.W.; Kyde, K.L.; Englander, L. 2004. Susceptibility of selected 

ericaceous ornamental host species. Plant Disease. 88(9): 993–999.

Unger, N. 2006. Interaction of ozone and sulfate in air pollution and climate 

change. Goddard Institute for Space Studies Science Brief. http://www.giss.nasa.

gov/research/briefs/unger_01/. (18 July 2007).

U.S. Department of Agriculture, Forest Service [USDA FS]. 2003. America’s 

forests: 2003 health update. AIB-776. Washington, DC. 15 p. http://www.fs.fed.

us/foresthealth/publications/foresthealthupdate2003.pdf. (5 February 2009).

U.S. Department of Agriculture, Forest Service [USDA FS]. 2004. U.S. forest 

resource facts and historical trends. FS-801-M. Washington, DC. 37 p.

U.S. Department of Agriculture, Forest Service [USDA FS]. 2005. Forest insect 

and disease conditions in the United States, 2004. Washington, DC: Forest 

Health Protection. http://www.fs.fed.us/foresthealth/current_conditions.shtml. 

(22 April 2008).
64

PRIVATE FORESTS, PUBLIC BENEFITS

U.S. Department of Agriculture, Forest Service [USDA FS]. 2007a. Forest 

insect and disease conditions in the United States, 2006. Washington, DC: 

Forest Health Protection. 176 p. 

U.S. Department of Agriculture, Forest Service [USDA FS]. 2007b. Interim 

update of the 2000 Renewable Resource Planning Act (RPA) assessment. FS874. Washington, DC. 114 p. http://www.fs.fed.us/research/rpa/2005rpa/RPA_

Interim_Update_April2007_low_resolution.pdf. (17 February 2009). 

U.S. Department of Agriculture, Forest Service [USDA FS]. [N.d.]. Welcome to 

the Eastern Forest Threat Center! http://www.forestthreats.org/. (07 June 2007).

U.S. Department of Agriculture, Natural Resources Conservation Service 

[USDA NRCS]. 2007. Effects of exurban development on wildlife and 

plant communities. Tech. Note 75. Washington, DC: Agricultural Wildlife 

Conservation Center. 4 p.

U.S. Department of Commerce, Census Bureau [U.S. Census Bureau]. 2001. 

Census 2000 Summary file 1. Washington, DC.

U.S. Environmental Protection Agency [US EPA]. 1997. EPA’s revised ozone 

standard. Research Triangle Park, NC: Office of Air Quality Planning 

and Standards. 4 p. http://www.epa.gov/ttn/oarpg/naaqsfin/o3fact.html. 

(17 February 2009).

U.S. Environmental Protection Agency [US EPA]. 2009a. Acid rain and related 

programs: 2007 progress report. EPA-430-K-08-010. 52 p. http://www.epa.gov/

airmarkets/progress/docs/2007ARPReport.pdf. (12 January 2009).

U.S. Environmental Protection Agency [US EPA]. 2009b.Climate change. 

http://www.epa.gov/climatechange/. (02 February 2009).

U.S. Environmental Protection Agency [US EPA]. [N.d.]. Multi-resolution land 

characteristics consortium (MRLC): 1992 national land cover data (NLCD). 

http://www.epa.gov/mrlc/nlcd.html. (09 April 2008).

U.S. Geological Survey [USGS]. 2000. The national hydrology dataset. 

http://nhd.usgs.gov/. (16 December 2005).

U.S. Geological Survey [USGS]. 2006. Habitat establishment, enhancement 

and management for forest and grassland birds in Illinois: area requirements 

of forest and grassland birds. http://www.npwrc.usgs.gov/resource/birds/

manbook/areareq.htm. (17 July 2007).
65

Increased Housing Density and Other Pressures on Private Forest Contributions

U.S. Geological Survey [USGS]. [N.d.]. The USGS land cover institute: USGS 

land cover. http://landcover.usgs.gov/usgslandcover.php. (09 April 2008).

U.S. Government Accountability Office [US GAO]. 2007. Wildland fire 

management: lack of clear goals or a strategy hinders federal agencies’ efforts 

to contain the costs of fighting fires. GAO-07-655. Washington, DC. 47 p. 

Wallenstein, M.D. 2004. Effects of increased nitrogen deposition on forest soil 

nitrogen cycling and microbial community structure. Durham, NC: Duke 

University, University Program in Ecology. 111 p. Ph.D. dissertation.

Wear, D.N.; Liu, R.; Foreman, J.M.; Sheffield, R.M. 1999. The effects of 

population growth on timber management and inventories in Virginia. Forest 

Ecology and Management. 118: 107–115. 

Whelan, C.J.; Maina, G.G. 2005. Effects of season, understorey vegetation 

density, habitat edge and tree diameter on patch-use by bark foraging birds. 

Functional Ecology. 19: 529–536.

White, E.M.; Alig, R.J.; Stein, S.M.; Mahal, L.G.; Theobald, D.M. 2009. A 

sensitivity analysis of “Forests on the edge: housing development on America’s 

private forests.” Gen. Tech. Rep. PNW-GTR-792. Portland, OR: U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station. 35 p.

White, E.M.; Mazza, R. 2008. A closer look at forests on the edge: future 

development on private forests in three states. Gen. Tech. Rep. PNW-GTR-758. 

Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station. 20 p.

Wilcove, D.S.; Rothstein, D.; Dubow, J.; Phillips, A.; Losos, E. 2000. Leading 

threats to biodiversity: what’s imperiling U.S. species. In: Stein, B.A.; Kutner, 

L.S.; Adams, J.S., eds. Precious heritage. Oxford, United Kingdom: Oxford 

University Press: 239-254.

Woodbury, P.B.; Smith, J.E.; Heath, L.S. 2007. Carbon sequestration in the U.S. 

forest sector from 1990 to 2010. Forest Ecology and Management. 241: 14–27.

Woods, M.; McDonald, R.A.; Harris, S. 2003. Predation of wildlife by domestic 

cats Felis catus in Great Britain. Mammal Review. 33(2): 174–188.
66

PRIVATE FORESTS, PUBLIC BENEFITS

Methodology 

The objectives of this Forests on the Edge analysis are to (1) obtain or construct 

nationally consistent data layers depicting the spatial location of private forest lands 

and their contributions, (2) construct similar layers depicting future increases in 

housing density and other threats to private forest land, and (3) identify watersheds 

where private forest contributions coincide with increased housing density and 

other threats.

All data layers were obtained as or constructed to be nationally consistent and 

were summarized at the spatial scale of 4th-level hydrologic units (referred to as 

watersheds or 8-digit watersheds in this report) (Steeves and Nebert 1994). The conterminous United States contains 2,108 fourth-level watersheds averaging 1 million 

acres and ranging from 22,000 to 13 million acres. Watersheds were selected as the 

analytical units because they highlight the important connections between private 

forests and ecological processes. Only watersheds with at least 10 percent overall 

forest cover and containing at least 10,000 acres of private forest were considered 

for the study.

For each contribution and threat layer, the distribution of watershed values was 

determined and a percentile ranking was assigned to each watershed. Each layer 

was then combined with the layer for projected housing density. This was accomplished by re-ranking each watershed according to the average of two percentile 

categories (for example, the percentile categories for at-risk species and future 

housing density).

Several data layers lacked data for some watersheds. These watersheds were not 

ranked but were assigned to a separate “no data” category. However, if any of them 

satisfied the initial filtering criteria of 10 percent overall forest cover and 10,000 

acres of private forest, their number was included in the total number of watersheds 

for purposes of calculating percentiles. Three results follow: (1) the relative order of 

the watersheds in the ranking does not change, regardless of the total number used 

to calculate percentiles; (2) the number of watersheds corresponding to a given percentile is always the same, regardless of the particular attributes assessed; and (3) 

the particular watersheds assigned to percentile categories is approximate because 

the watersheds for which data were not available were not included in the rankings.

Appendix
67

Increased Housing Density and Other Pressures on Private Forest Contributions

Private Forest Contributions

Area of Private Forest Land

A 100- by 100-meter (328.1- by 328.1-foot) resolution forest ownership layer was 

constructed by aggregating the classes of the National Land Cover Dataset (NLCD) 

(Homer et al. 2007) into forest and nonforest classes and using the Protected Areas 

Database (PAD v4.6) (CBI 2007, DellaSalla et al. 2001, Theobald 2007) to distinguish ownership and protection categories. The emphasis for this study was private 

forest land—defined as all land not coded as federal, state, or city in the protected 

areas database. Stein et al. (2006) provided detailed information on this layer.

Water Quality

The water quality layer depicts the contribution of private forest land to the 

production of clean water and is based on three underlying assumptions: 

• Water bodies near the heads of hydrologic networks are more sensitive 

to the loss of forest buffers than are water bodies near the bases of the 

networks. 

• The presence or absence of upstream forest buffers influences water quality 

downstream in the networks. 

• The total amount of private forest land in each watershed is considered a 

better indicator of the contributions of private forest land to water quality 

than the location of that private forest land in the watershed (for example, 

location of forested buffers) (FitzHugh 2001). 

The water quality layer was constructed from two underlying layers—the forest 

ownership layer and the National Hydrography Dataset (USGS 2000), which depicts 

water bodies in the 48 contiguous states. The layer was constructed in four steps: 

(1) a 30-meter (98.4-foot) buffer was constructed around all water bodies, (2) the 

buffers were intersected with the private forest land class of the forest ownership 

layer to quantify the amount of private forest land close to water bodies, (3) each 

buffer segment was assigned to one of four categories based on the relative position 

of the segment to the head of its hydrologic network, and (4) for each watershed, 

the percentage in each of the four categories was determined. Water quality index 

(WQI) was then calculated for each watershed as

WQI = 0.6[A1 + (A1A2)] + 0.4(0.48B1 + 0.24B2 + 0.16B3 + 0.12B4) (1)

where A1 = percentage of watershed in private forest land, A2 = percentage of total 

forest land in watershed that is privately owned, B1 = percentage of private forest 

land buffer in the first category (nearer head of hydrologic network headwater), 
68

PRIVATE FORESTS, PUBLIC BENEFITS

B2 = percentage of buffer in the second category, B3 = percentage of buffer in the 

third category, and B4 = percentage of buffer in the fourth category (farthest downstream from the head of hydrologic network). Variables A1 and A2 represent private 

forest coverage throughout the watershed, and variables B1 through B4 represent 

private forest coverage in the buffers. In WQI, A1 and A2 are collectively weighted 

0.6, and variables B1 through B4 are collectively weighted 0.4 to reflect the third 

assumption above. Watershed boundaries for this and all other layers were determined through Steeves and Nebert (1994). 

Forest Inventory and Analysis (FIA): 

The Nation’s Forest Census

The Forest Service’s FIA Program has been collecting field data for more than 

70 years and offers the largest source of forest data in the United States. The 

FIA conducts annual surveys of all forest land in the country and remeasures 

plots on 5- to 10-year cycles. The FIA reports on status and trends in forest area and location; on the species, size, and health of trees; on total tree 

growth, mortality, and removals by harvest; on wood production and utilization 

rates by various products; and on forest land ownership (FIA 2007). 

Stacy Allen
69

Increased Housing Density and Other Pressures on Private Forest Contributions

Timber Volume

The timber volume layer depicts the ranking of watersheds relative to the amount of 

growing-stock volume (cubic feet) found on privately owned timberland within the 

watershed, based on Forest Inventory and Analysis (FIA) plot data (http://fia.fs.fed.

us/tools-data/default.asp). Timberland is defined by the FIA Program as forest land 

that is a minimum of 1 acre, a minimum width of 120 feet, has a minimum stocking 

of 10 percent, has not been withdrawn from timber production, and is capable of 

producing 20 cubic feet per year of industrial wood per acre. Growing stock volume 

is defined as the volume of trees of commercial species with diameters of at least 

five inches d.b.h. (diameter at breast height) growing on forest land.

At-Risk Species 

NatureServe and its member Natural Heritage Programs and Conservation Data 

Centres prepared a geographic data set depicting the number of at-risk species 

occurring on private forest lands within 8-digit watersheds in the contiguous 48 

states. At-risk species are defined as species with element occurrences (EO) that 

have been observed by an authoritative source within at least the past 50 years and 

are either (1) federally designated under the Endangered Species Act (endangered, 

threatened, candidate, proposed) or (2) designated as critically imperiled, imperiled, 

or vulnerable according to the NatureServe Conservation Status Ranking system 

(G1/T1 to G3/T3) (http://www.natureserve.org/explorer/ranking.htm). An EO is an 

area of land or water in which a species or natural community is, or was, present.

NatureServe selected only those populations that occur on private forested 

lands by conducting a geographic analysis comparing the location of at-risk populations with private forest locations (both protected and nonprotected). These species 

are labeled as forest-associated rather than forest-obligated because a separate 

analysis to refine this species list using knowledge of species habitat requirements 

and preferences was not conducted. Known data gaps include (1) no at-risk species data available in Arizona, Massachusetts, and the District of Columbia; (2) 

no at-risk fish data available for Idaho; and (3) incomplete at-risk animal data in 

Washington. Private forest lands were determined using the data layer described 

above under Area of Private Forest Land.

Interior Forest

Habitat contiguity can be measured in terms of the amount of interior forest 

cover that is functionally distinct from forest edge. The interior forest layer was 

constructed through a fragmentation analysis in which the 30- by 30-meter 

(98.4- by 98.4-foot) NLCD forest pixels were labeled as interior forest if they 
70

PRIVATE FORESTS, PUBLIC BENEFITS

were forested and at least 90 percent of the area in a surrounding 65-hectare 

(160.6-acre) window was also forest. The forest cover layer described above 

under Area of Private Forest Land was used to identify forest/nonforest pixels. 

Each watershed was divided into pixels of 30- by 30-meter (98.4- by 98.4-foot) 

resolution. The results were used to determine the fragmentation values for all the 

NLCD pixels in each watershed. The pixel-level fragmentation values were then 

masked by private forest land and summarized by watershed. This method was used 

to determine the proportions of private forest pixels identified as interior forest in 

each watershed. Note that the proportion is not the same as area; a watershed could 

have very little forest overall but a high proportion that met the threshold.

Threats to Private Forest Lands

Future Housing Density

The housing density layer depicts projected pressures on private forest lands resulting from housing density increases from 2000 to 2030. Three categories of housing 

density are used: rural I (fewer than 16 housing units per square mile), rural II (16 

to 64 housing units per square mile), and exurban/urban (more than 64 housing 

units per square mile). The future housing density layer identifies forest lands 

projected to convert from rural I to exurban/urban or rural II categories, as well as 

from rural II to exurban/urban categories (Stein et al. 2007). The housing density 

layer is based on estimates of current population and housing density data obtained 

from the 2000 U.S. Census (U.S. Census Bureau 2001) and projections of housing 

density increases. A spatially explicit model was used to predict the full urban-torural spectrum of housing densities (Theobald 2005). The model uses an allocation 

approach where population requirements are met and is based on the assumption 

that future growth patterns will be similar to those in the past decade. 

Current housing density patterns are estimated using housing unit counts from 

census blocks and a dasymetric modeling approach, which eliminates public lands 

and water bodies and is weighted toward NLCD 2001 urban land cover classes and 

higher road densities. For rural II areas in the 11 Western States, residential groundwater well densities were also used to better allocate housing units.

Future patterns are projected on a decadal basis in four steps: (1) the number 

of new housing units in the next decade is forced to meet the quantity demanded of 

the projected county-level populations; (2) a location-specific average population 

growth rate from the previous to current time step is computed for each of three 

density classes: exurban/urban (less than 0.6 acre per unit to 10 acres per unit), rural 

II (10 to 40 acres per unit), and rural I (more than 40 acres per unit); (3) the spatial 
71

Increased Housing Density and Other Pressures on Private Forest Contributions

distribution of projected new housing units is adjusted with respect to accessibility 

as computed by travel time along the road infrastructure to the nearest urban core 

area; and (4) projected new housing density is added to the current housing density 

under the assumption that housing densities do not decline over time. 

For these analyses, projected housing density increases were not permitted 

to occur on protected private land as indicated by the PAD v4.6 (DellaSalla et al. 

2001). The spatially explicit housing density projections were combined with the 

forest ownership layer to identify watersheds with the greatest projected conversion 

of private forest land to rural II and exurban/urban uses between the years 2000 

and 2030. Stein et al. (2006) provided detailed information on this layer. Note that 

analyses using census urban and developed data would not necessarily have the 

same result as the analyses conducted for this study.

Insect Pests and Disease

A measure of the impact of forest insect pests and diseases on forest health is the 

reduction of tree basal area, which is defined as the cross-section area of a tree 

stem in square feet at breast height (4.5 feet in the United States). The Forest Health 

Monitoring Program of the U.S. Forest Service formed a risk map integration team 

(RMIT) to coordinate the development of a nationally consistent database for mapping insect pest and disease risk. The RMIT developed a GIS-based multicriteria 

risk modeling framework based on Eastman’s risk assessment process (Eastman 

et al. 1997).

A five-step multicriteria process was used to construct a 1- by 1-kilometer 

(0.62- by 0.62-mile)-resolution map depicting risk (Krist et al. 2007): 

(1) Identify a list of forest pests (risk agents) and their target host species; 

this is conducted at the regional level with models constrained to selected 

geographic areas. 

(2) Identify, rank, and weight criteria (factors and constraints) that determine 

the susceptibility and vulnerability to each risk agent. 

(3) Standardize risk agent criteria values, and combine the resultant criteria 

maps in a final risk assessment using a series of weighted overlays. 

(4) Convert predicted values of potential risk of mortality for each pest to predicted basal area loss over a 15-year period; this is accomplished for each 

risk agent/forest host species pair included in the national risk assessment. 

(5) Compile the resultant values from step 4 and identify areas (1- by 

1-kilometer [0.62- by 0.62-mile] raster grid cells) on a national base 

map that are at risk of encountering a 25 percent or greater loss of 

total basal area in the next 15 years. 
72

PRIVATE FORESTS, PUBLIC BENEFITS

Wildfire

Projecting wildfire risk is extremely complex and relies on a variety of regional 

models using regional variables. The wildfire layer depicts the amount of private 

forest land with wildfire potential deemed high or very high based on the Wildland 

Fire Potential Model produced by the U.S. Forest Service Fire Modeling Institute 

(http://www.fs.fed.us/fmi/). The model is based on the following three components:

1. Fuels Potential: surface fuel potential (rate of spread and surface flame 

length from the national Fuel Characterization Classification Systems) 

and crown fire potential (by assigning relative classes of fire intensity to 

a current vegetation cover type map).

2. Weather Potential: weather zones; average number of days with high relative energy release component (above the 95th percentile from 1980 to 

2005); and number of days of extreme fire weather (based on thresholds 

of temperature, wind, and humidity from 1982 to 1997).

3. Fire Occurrence Potential: average number of fires greater than 0.25 acres 

per 1 million acres, average number of fires greater than 500 acres per 1 

million acres, and average number of fires by weather zone; all inputs from 

1986 to 1996.

Note that the period selected is somewhat different for each of the factors 

described above. Time and resource constraints precluded the use of data with 

exactly the same period for each. More detail on this model is provided on the 

following Web site: http://svinetfc4.fs.fed.us/RS2008/j_menakis/index.htm.

The Wildland Fire Potential Model ranks the Nation’s wildlands for fire 

potential on the following scale: very low, low, moderate, high, and very high. 

This analysis focused on the amount of private forest land rated as high or very 

high fire potential.

Air Pollution: Ozone

The ozone layer depicts private forest land threatened by ground-level ozone and 

was based on late-summer observations by FIA field crews of ozone damage to 

bioindicator species known to be sensitive to ground-level ozone. Bioindicator 

species are used to monitor the health of the environment; changes in the health of 

bioindicator species may indicate a problem for the ecosystem they inhabit. Each 

FIA plot was assigned a biosite value based on a subjective assessment by trained 

observers of the quantity and severity of damages (Coulston et al. 2003, Smith et al. 

2003). Inverse distance-weighted interpolation was used to create a map of ozone 

damage for each data set from FIA plots for the years 2000 through 2005. These 
73

Increased Housing Density and Other Pressures on Private Forest Contributions

maps were then combined by averaging ozone damage with all available data years 

for this study. The resulting map was then combined with the forest ownership 

layer to identify private forest land with elevated levels of ozone damage. For each 

watershed, the percentage of private forest land with bioindicator evidence of ozone 

damage was calculated. 

For the ozone classification, four classes were constructed by selecting percentiles of affected area as class boundaries as follows: 1 (95th to 100th percentile), 

2 (90th to 94th percentile), 3 (85th to 89th percentile), and 4 (0 to 84th percentile). The 

same percentiles were used as class boundaries for the housing density layer before 

combining this with the ozone layer to identify areas with high projected housing 

density increase as well as high percentage of ozone damage.

Sensitivity Analyses

These analyses involved decisions regarding the selection of screening criteria and 

specific housing density categories. The screening criteria used for this study were 

that a watershed had to have at least 10 percent forest cover and that at least 10,000 

acres of forest had to be under private ownership (screening criteria were used in 

order to avoid highlighting entire watersheds with very low amounts of private forest as being at great risk of change from increased housing density). We conducted 

two sets of sensitivity analyses to objectively assess the effects of these decisions on 

the results of our analyses. 

First, we assessed the impact of our screening criteria on the analysis of 

watersheds containing substantial expanses of private forest that are projected to 

be affected by increases in housing density between 2000 and 2030. The analyses 

indicated that eliminating the screening criteria altogether had little effect on watersheds in higher percentile categories for increased housing density or interior forest; 

most of the additional watersheds that were included after eliminating screening 

criteria were in the lower percentile categories. Although elimination of the criteria 

did identify new watersheds west of the Rocky Mountains in higher percentile 

categories for insects and disease and for wildfire, most of these watersheds had 

less than 5 percent and very frequently less than 1 percent private forest land.

The second set of analyses focused on the effects of using 16 housing units per 

square mile as the upper threshold for the least dense housing density category. In 

particular, the issue was whether the rural I category should be subdivided into two 

categories: rural Ia with 8 or fewer houses per square mile, and rural Ib with 9 to 16 

houses per square mile. 
74

PRIVATE FORESTS, PUBLIC BENEFITS

To address this issue, we split the rural I category into rural Ia and rural Ib 

categories and evaluated watersheds with respect to amounts of their private 

forest lands that changed among any of the now four housing density categories. 

We then ranked the watersheds in the same manner as was done when using only 

three housing density categories and determined that the use of our original three 

categories was appropriate for this study. The analyses indicated a slight increase in 

the overall number of watersheds in the East with high percentile rankings but no 

such increase for the watersheds in the West. We attribute this result to subdivision 

of a greater number of small parcels in the East than in the West. These results are 

consistent with those of a sensitivity analysis conducted in conjunction with the 

2005 Forests on the Edge report (White et al. 2009).
Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw/

Telephone (503) 808-2592

Publication requests (503) 808-2138

FAX (503) 808-2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution

Pacific Northwest Research Station

P.O. Box 3890

Portland, OR 97208-3890
Forests on the Edge 

Forests on the Edge is a project of the U.S. 

Department of Agriculture, Forest Service, State 

and Private Forestry, Cooperative Forestry staff, 

in conjunction with Forest Service Research and 

Development. The project has produced a series 

of reports to increase public understanding of the 

contributions of and pressures on America’s forests. 

The first report, Forests on the Edge: Housing 

Development on America’s Private Forests (Stein et 

al. 2005), identified development pressures on private 

forested watersheds in the conterminous United 

States. Subsequent reports have identified national 

forests and grasslands most likely to be affected by 

increased housing density along their borders (Stein et 

al. 2007); studied future development on private forests 

in Georgia, Washington, and Maine (White and Mazza 

2008); and reported on sensitivity analyses conducted 

for the first Forests on the Edge report. (White et al. 

2009).

This report focuses on watersheds where pressure 

from development may affect private forest contributions to water quality, timber volume, at-risk species 

habitat, and interior forest nationwide, and where 

development impacts may be exacerbated by additional pressures of wildfire, insect pests and diseases, 

and air pollution.

Future Forests on the Edge work will include assessments of additional contributions and risks and construction of an Internet-based system that permits 

users to select particular contribution and threat layers, 

options for combining them, and options for depicting 

the results.

Specific studies currently underway examine: 

• Housing development on private forests 

providing habitat for at-risk species.

• Pressures on urban forests across the Nation.

• Development projections for private forest 

lands in Alaska, Hawaii, Puerto Rico, the Virgin 

Islands, and the Pacific Islands.

• Implications of development on ecosystem 

goods and services.

For further information on Forests on the Edge, 

contact Susan Stein, U.S. Department of Agriculture, 

Forest Service, Cooperative Forestry staff, 1400 

Independence Avenue, SW, Mailstop 1123, Washington, DC 20250-1123. (202) 205-0837. sstein@fs.fed.us. 

http://www.fs.fed.us/openspace/fote/.