Forests of Southeast and 

South-Central Alaska, 

2004–2008

Five-Year Forest Inventory 

and Analysis Report

United States

Department of

Agriculture

Forest 

Service

Pacific Northwest

Research Station

General Technical Report

PNW-GTR-835

March 2011

DEPAR TMENT

O

F

AG

RICULT URE
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of multiple 

use management of the Nation’s forest resources for sustained yields of wood, water, forage, 

wildlife, and recreation. Through forestry research, cooperation with the States and private forest 

owners, and management of the national forests and national grasslands, it strives—as directed by 

Congress—to provide increasingly greater service to a growing Nation.

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on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, 

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should contact USDA’s TARGET Center at (202) 720-2600 (voice and TDD). To file a complaint of 

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Washington, DC 20250-9410 or call (800) 795-3272 (voice) or (202) 720-6382 (TDD). USDA is an 

equal opportunity provider and employer.

Technical Editors

Tara M. Barrett is a research forester, Forestry Sciences Laboratory, 3301 C Street Suite 200, 

Anchorage, AK 99503; and Glenn A. Christensen is a forester, Forestry Sciences Laboratory, 

620 SW Main Street, Suite 400, Portland, OR 97205.

Contributing Authors

Hans-Erik Andersen is a research forester, Bethany K. Schulz is a research ecologist, and 

Kenneth Winterberger is a remote sensing specialist, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 3301 C Street, 

Suite 200, Anchorage, AK 99503; John Caouette (deceased) was a statistician, The Nature 

Conservancy, 416 Harris Street, Suite 301, Juneau, AK 99801; Lisa K. Crone is a research 

economist, formerly with U.S. Department of Agriculture, Forest Service, Pacific Northwest 

Research Station, Sitka Wood Utilization Center, 204 Siginaka Way, Sitka, AK 99835; Paul 

Hennon is a research pathologist, U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station, Forestry Sciences Laboratory, 11305 Glacier Highway, Juneau, AK 

99801; Connie Hubbard is an ecologist, Chugach National Forest, 3301 C Street, Suite 300, 

Anchorage, AK 99503; Theresa Jain is a research forester, U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station, Forestry Sciences Laboratory, 1221 S Main, 

Moscow, ID 83843; Sarah Jovan is a research ecologist, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 620 SW Main Street, 

Suite 400, Portland, OR 97205; John E. Lundquist is a forest entomologist, U.S. Department 

of Agriculture, Forest Service, Forest Health Protection Alaska Region and Pacific Northwest 

Research Station, 3301 C Street, Suite 202, Anchorage, AK 99503; Todd A. Morgan is director 

of forest industry research, Bureau of Business and Economic Research, University of Montana, 

32 Campus Drive, Missoula, MT 59812; Mark Schultz is an entomologist, and Dustin Wittwer 

is a biological technician, U.S. Department of Agriculture, Forest Service, Forest Health 

Protection Alaska Region, 2770 Sherwood Lane, Suite 2A, Juneau, AK 99801.

Cover photo by Jon Williams.
Abstract

Barrett, Tara M.; Christensen, Glenn A., tech. eds. 2011. Forests of southeast and 

south-central Alaska, 2004–2008: five-year forest inventory and analysis report. 

Gen. Tech. Rep. PNW-GTR-835. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 156 p.

This report highlights key findings from the most recent (2004–2008) data collected 

by the Forest Inventory and Analysis program across all ownerships in southeast 

and south-central Alaska. We present basic resource information such as forest area, 

ownership, volume, biomass, carbon sequestration, growth, and mortality; structure 

and function topics such as vegetation and lichen diversity and forest age distribution; 

disturbance topics such as insects and diseases, yellow-cedar decline, fire, and invasive 

plants; and information about the forest products industry in Alaska, the potential of 

young growth for timber supply, biofuels, and nontimber forest products. The appendixes describe inventory methods and design in detail and provide summary tables of 

data and statistical error for the forest characteristics sampled. 

Keywords: Coastal Alaska, temperate rain forests, boreal forests, climate change, 

carbon accounting, mistletoe, yellow-cedar, Kenai Peninsula, invasive species, timber 

volume, timberland, wood products.
Contents 

1 Chapter 1: Introduction

5 Chapter 2: Basic Resource Information

5 Forest Area, Composition, and Ownership

 Tara M. Barrett and Kenneth Winterberger

13 Biomass and Carbon 

 Hans-Erik Andersen

22 Change in Forests Between 1995–2003 and 2004–2008

 Tara M. Barrett

33 Chapter 3: Forest Structure and Function

33 Lichen Biodiversity

 Sarah Jovan

36 Vegetation Diversity 

 Bethany K. Schulz

45 Older Forests in Coastal Alaska 

 Tara M. Barrett

46 A Day in the Life of Alaska FIA Crew [sidebar]

 Connie Hubbard

57 Chapter 4: Disturbance and Stressors

57 Use of Forest Inventory Data to Document Patterns of Yellow-Cedar Occurrence, 

Mortality, and Regeneration in the Context of Climate 

 Paul Hennon, John Caouette, Tara M. Barrett, and Dustin Wittwer

62 Forest Insect Pests in Coastal Alaska Forests

 John E. Lundquist and Mark Schultz

67 Invasive Plants in Coastal Alaska

 Bethany K. Schulz 

70 The Western Kenai Peninsula: an Opportunity to Study Fire and Its 

Effects on Soils and Trees

 Theresa Jain and Tara M. Barrett

74 The Distribution of Hemlock Dwarf Mistletoe Suggests Influences of Climate 

 Paul Hennon, Tara M. Barrett, and Dustin Wittwer

79 Chapter 5: Forest Products

79 Young-Growth Timber Resource in Coastal Alaska

Glenn A. Christensen

84 Alaska’s Primary Forest Products Industry

 Todd A. Morgan

87 Forest Biomass as a Renewable Source of Energy in Coastal Alaska [sidebar]

 Hans-Erik Andersen

88 Nontimber Forest Products

 Lisa K. Crone
93 Chapter 6: Concluding Remarks

93 Glossary

98 Common and Scientific Names

102 Acknowledgments

103 Metric Equivalents

103 References

113 Appendix 1: Inventory Methods and Design

119 Appendix 2: Summary Data Tables
Summary

Forests in southeast and south-central Alaska are unique in many ways. The largest oldgrowth temperate rain forest on Earth extends through most of the region, with huge old 

Sitka spruce and western hemlock intermixed with yellow-cedar and western redcedar at 

lower elevations and mountain hemlock (see “Common and Scientific Names”) at higher 

elevations. But the south-central region also contains the southernmost extent of Alaska’s 

boreal forest, characterized by black spruce, white spruce, and hardwoods such as aspen, 

birch, and willow. This report shows that relative to the rest of the U.S. coastal regions, 

Alaska’s coastal region contains slower growing forest, older forest, less forest in private 

ownership, and much more forest per capita.

Although only approximately 12 percent of Alaska’s forest land is found in the 

southeast and south-central region, the area contains about half of the state’s timberland 

and half of the state’s population. Public agencies manage 88 percent of the 15.3 million 

acres of forest land in the coastal region, with most forest in the two largest national 

forests in the United States, the Tongass and Chugach National Forests. Although private 

ownership is only about 12 percent of the forested area in the region, it contains 24 percent 

of timberland.

Nearly 1.3 billion tons of biomass are stored within the live trees of coastal Alaska. 

Significant amounts of carbon and biomass are also contained in snags, particularly on 

the Kenai Peninsula where 47 percent of biomass is within dead white spruce, most likely 

killed by bark beetles (Dendroctonus rufipennis [Kirby]) in the 1990s. There was no net 

change of live-tree carbon or biomass between 1995–2003 and 2004–2008.

There has been speculation that the 2- to 3- °F average annual temperature increase 

that the coastal region has experienced over the past half century could lead to forestation through increasing elevation of treeline, drying of wetlands, and glacial recession. 

Although change in forest area was difficult to assess because of procedural changes to 

the Forest Inventory and Analysis (FIA) inventory, a rough estimate of a 0.7 percent 

increase in forest area per decade suggests that climate-related changes are not yet 

noticeably impacting biomass and carbon through forestation. 

The most common species in the region—western hemlock, Sitka spruce, mountain 

hemlock, and yellow-cedar—did not show any significant change in live tree biomass 

between 1995–2003 and 2004–2008. Because the range of western hemlock, mountain 

hemlock, Sitka spruce, and yellow-cedar is quite broad within the inventory region, it is 

possible that slow migration or more localized changes could be occurring without impact 

on total biomass for the region. Western redcedar showed a significant increase, as did 

hardwoods such as cottonwood, quaking aspen, and red alder. Lodgepole pine showed a 

significant decrease in biomass. Overall, harvest removals were 23 percent of mortality 

from other causes. Mortality ranged from a low of 0.22 (standard error [SE] = 0.06) percent 

of western redcedar dying annually to a high of about 2.57 percent (SE = 0.47) of white 

spruce trees dying annually.
By ownership, no statistically significant change in live tree carbon and biomass was 

detected on private lands or the Tongass National Forest, but state and local government 

had an estimated 8-percent increase and the Chugach National Forest had an estimated 

4-percent increase between the 1995–2003 and 2004–2008 inventories. Carbon in live trees 

increased in two higher elevation ecoregions, one in south-central Alaska and one in southeast Alaska, but no significant increase of live tree carbon was detected in lower elevation 

ecoregions containing most of the forest land.

Average lichen species richness is higher for southeast Alaska than compared to the 

similar Coast Range ecological region in Oregon or Washington. Unlike the Oregon and 

Washington Coast Range ecosystems, almost all sampled communities contained species 

within both the functionally important cyanolichen (nitrogen fixers) and wildlife forage 

groups. Widespread abundance of pollution-sensitive species suggests a lichen flora 

characteristic of forest ecosystems unstressed by the regional air-quality issues of the 

other U.S. Pacific Coast States.

The vegetation indicator is used to assess species composition and structure, which 

in turn help determine potential productivity and wildlife habitat. The average species 

richness on the 0.17-acre sample areas was 37.1 species. Of species found, 99.7 percent were 

native. Seven species were extremely common, found on over 70 percent of all plots. The 

highest species richness for the inventory area was found on sites dominated by yellowcedar, mountain hemlock, and western redcedar. 

The coastal Alaska region is unique within the United States because of the substantial 

amount of older forest. In the Temperate Rainforest ecoregion, 67 percent of federal forest 

land is in older forest, 35 percent of state and local government forest land is in older forest, 

and 34 percent of private land is in older forest. Of the 8.9 million acres (SE = 0.3) of older 

forest in the coastal inventory region, 35 percent is reserved. Older forests in the Temperate 

Rainforest ecoregion store substantial amounts of carbon. The greatest amounts of carbon 

mass were stored in older forests of the Sitka spruce forest type, estimated at 107.2 tons per 

acre (SE = 12.2) in live and dead trees in forest that was at least 200 years of age. 

A map of yellow-cedar decline, appearing as patches of high mortality, is included 

in the report with more than 2,000 locations and half a million acres of mapped mortality. Yellow-cedar decline is thought to be related to climate warming, with roots freezing 

when snow depth is not adequate to protect them from spring freezes. Overall, live tree 

biomass for yellow-cedar was unchanged between 1995–1998 and 2004–2008, suggesting 

that yellow-cedar may be migrating rather than declining, with regeneration and growth 

currently compensating for mortality. Surviving smaller trees are more common at higher 

elevations, whereas dead trees (and the associated yellow-cedar decline) are more common 

at lower elevations, supporting the migration hypothesis. An additional explanation for the 

carbon and biomass stability is that mortality associated with the decline could be episodic, 

with lower levels during the 1995–2003 to 2004–2008 remeasurement period than in 

preceding decades. 
Compared to those of the other 49 States, Alaska’s coastal forests are relatively free of 

introduced species. Less than 2 percent of forested plots had introduced or native noxious 

plants compared to 67 percent of plots in other parts of the United States. This means that 

Alaska has a unique window of opportunity to prevent establishment of invasive species 

in its forests, an opportunity no longer available to other states.

Forest insect pests are surveyed annually by the Forest Health Protection branch 

of the USDA Forest Service, and summarized results are available in Forest Conditions 

Reports from 1970 to the present. The most widespread insect pest in coastal Alaska since 

2003 has been the spruce needle aphid (Elatobium abietinum [Walker]). Another important insect within the region is the spruce beetle, which was responsible for the outbreaks 

of the 1990s, mostly on the Kenai Peninsula, which affected millions of acres of forest. 

The FIA inventory data collected in Alaska have been used to build predictive models of 

spruce beetle risk and hazard.

Another important impact on coastal forests is hemlock dwarf mistletoe 

(Arceuthobium tsugense [Rosendhal] G.N. Jones), a parasitic plant of older western 

hemlock trees throughout southeast Alaska. Using measurements from FIA plots, 

dwarf mistletoe is estimated to infect about 1 million acres of forest land. Analysis of 

inventory data reveals that mistletoe infection is sharply reduced above about 500 feet in 

elevation, compared to its host species western hemlock, which is found up to elevations 

of 2,000 feet or higher. Because the disease and host appear to have different climatic 

envelopes, the range of this disease may potentially expand with a warming climate.

Young-growth forests, defined in this report as a stand age of 150 years or less of 

any stand size on timberland, are currently estimated to cover 2.9 million acres. Private 

owners currently own 1.2 million acres of this young growth, whereas national forests 

manage about 1.0 million acres of these forests. However, more than half of the younggrowth volume (52 percent) is found on the national forests. By forest type, the majority 

of young-growth stands are classified as either Sitka spruce or western hemlock forests; 

these forest types also contain the majority of the larger sawtimber-sized stands. 

In 2005, the total timber harvest in Alaska was 268.2 million board feet Scribner. The 

harvest in southeast Alaska has dropped substantially in recent years from contributing 

factors such as lawsuits over timber sales on the Tongass National Forest, lower timber 

inventories on some native corporation lands, high operating costs throughout the region, 

and shifting global markets and competition. National forests supplied only 18 percent 

of total harvest volume in 2005, but they supplied 53 percent of the timber that went to 

Alaska mills. Forest industry employment was 1,049 workers in 2005, and product and 

mill residue sales were about $150 million, mostly as saw-log and pulpwood exports.

As the cost of traditional petroleum-based fuel products continues to rise, there is 

increasing interest in the use of forest biomass as an alternative fuel source for power 

generation in rural Alaska. The advantages include the renewable nature of the resource, 

reducing wildfire hazard near rural communities, and possibly creating local employment 
opportunities. Constraints on the realization of these opportunities for bioenergy include 

high transportation costs, currently inadequate harvesting systems, and limited information 

on available biomass supply.

Nontimber forest products, also known as special forest products, include a wide range 

of botanical resources of the forest, with each agency currently using different policies 

and regulations for management. Many of the plants that have been inquired about for 

commercial harvest on the Chugach and Tongass National Forests also serve documented 

subsistence or traditional uses, which raises concern among many Alaska Native tribes and 

organizations in the region. Products currently being produced in Alaska include crafts, 

artwork, dyes, floral greenery, berries and other wild fruits, syrups, teas, flavorings, edible 

and medicinal plants, native seeds, edible mushrooms, and medicinal fungi.
Jon Williams
1

Forests of Southeast and South-Central Alaska, 2004–2008

Chapter 1: Introduction

This report describes the status of and changes in forests in 

southeast and south-central Alaska along with some contemporary forest issues that can be addressed with inventory 

data. Most estimates of the characteristics of Alaska’s coastal 

forests are derived from measurements taken on field plots 

between 2004 and 2008 by the Forest Inventory and Analysis 

(FIA) program1 of the Pacific Northwest Research Station.

The FIA program is charged with the responsibility of 

monitoring status and trends of all the public and private 

forests of the United States. The FIA program was created 

within the U.S. Department of Agriculture in 1928 to 

conduct assessments of all the Nation’s forested lands for 

economic and forest management planning. In the 1970s 

through the 1990s, the program expanded from what 

had largely been a timber-orientated program to a more 

comprehensive suite of forest monitoring methods. During 

this process, many protocols became regionalized, making 

it difficult to address issues that cross state and regional 

boundaries.

The Agriculture Research and Education Reform Act 

of 1998 substantially changed the FIA program, creating 

a common set of core measurements that are used across 

the United States. As part of the revision, the FIA program 

switched to an annualized system, which means that a 

subsample of plots is measured in each state in each year. 

The new system, commonly referred to as “annual FIA,” was 

implemented on a state-by-state schedule starting in 2000. 

As of 2010, Alaska is the only U.S. state where the 

annual FIA system has not been fully implemented. Alaska 

is also unique among all U.S. States in never having had an 

FIA inventory of all forested lands, although a collection of 

outdated timber inventories exists for some areas of the state. 

This contrasts sharply with other states, many of which are 

on the 5th to 10th complete remeasurement of all forested 

lands. Alaska is believed to have about 16 to 17 percent of 

total U.S. forest land area.

Although most of the forest land in Alaska is not 

included in the current FIA program, in southeast and 

south-central Alaska, the annual FIA program was fully 

implemented in 2004. This annual FIA inventory is also 

remeasuring many of the plots established in comprehensive 

forest inventories taken in the southeast Alaska region from 

1995 through 2000 and taken in the south-central Alaska 

region from 1999 through 2003. This report summarizes 

data collected from 2004 through 2008, which is about 50 

percent of all plots that will be installed in the coastal region, 

and uses the remeasurement plots to estimate change for the 

region.

The region covered by this report includes the Tongass 

and Chugach National Forests, which contain most of the 

forest in the region, and also land owned by other public and 

private entities (fig. 1). In 2005, the inventory included all 

lands in the region excluding only Glacier Bay National Park. 

However, since 2006, the inventory has been excluded from 

the wilderness and wilderness study areas of the Tongass and 

Chugach National Forests (fig. 2). Although we have used the 

2005 data from the wilderness areas where possible throughout this report, it adds imprecision to estimates owing to the 

very small number of plots from those areas. Estimates of 

change and estimates from attributes measured in the 1995 

through 2003 inventories (e.g., hemlock dwarf mistletoe) 

do not include any data from the Tongass and Chugach 

wilderness and wilderness study areas. The base set of field 

plots (called phase 2 plots) are spaced at approximate 3-mile 

intervals on a hexagonal grid throughout forested lands in 

the coastal region (fig. 3). One out of every 16 phase 2 plots 

is also a phase 3 plot, where detailed information on forest 

health is collected, including measurement for vegetation 

diversity and lichens (fig. 4). Additional information on 

inventory techniques is provided in appendix 1. 

The report consists of 14 different issue-focused summaries of current topics in forest health and management. 

Each section includes background information, key findings 

from the FIA inventory data, and interpretation. Information 

is aggregated to levels that differ depending on the issue, 

such as ecoregions, forest types, or ownership groups. The 

issues addressed by this report cover only a small portion of 

possible analyses with the FIA data. Customized summaries 

can be made from tools at the national Web site, and the data 

can also be downloaded in standardized data tables from the 

Web site at http://www.fia.fs.fed.us.

1

 The Forest Inventory and Analysis unit is now part of the 

Resource Monitoring and Assessment Program at the Pacific 

Northwest Research Station.
2

GENERAL TECHNICAL REPORT PNW-GTR-835

Figure 1—Forested land by owner group for southeast and south-central Alaska. Projection: Alaska Albers NAD 83. Source: USDA 

Forest Service, Forest Inventory and Analysis program, 2004–2008 data. Ownership data was derived from various sources.

Kenneth C. Winterberger
3

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 2—Forest, nonforest, and inaccessible wilderness areas for the coastal Alaska inventory region (depicted plot locations 

are approximate).

Figure 3—Forest Inventory and Analysis (FIA) phase 2 plots on 

the Kenai Peninsula, south-central Alaska (plot locations shown 

have some deliberate spatial error added to protect privacy of 

private landowners).

Figure 4—Forest Inventory and Analysis (FIA) phase 3 plots on 

the Kenai Peninsula, south-central Alaska (plot locations shown 

have some deliberate spatial error added to protect privacy of 

private landowners).

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Phase 3 FIA plots

Nonforest plots

Forested plots

Kenneth C. Winterberger
4

GENERAL TECHNICAL REPORT PNW-GTR-835

Misha Yatskov
5

Forests of Southeast and South-Central Alaska, 2004–2008

Chapter 2: Basic Resource Information

This chapter provides an overview of the distribution, 

composition, and ownership of forests in the coastal 

Alaska inventory unit. The basic physical, ecological, 

and socioeconomic attributes of the region are 

described, providing context for later chapters in the 

report. Aboveground forest biomass and carbon are 

characterized by forest type, tree size, and ownership. 

The chapter concludes with a description of recent 

change in the inventory area, including changes in forest 

area, numbers of trees, biomass, carbon, and volume. 

1

 Authors: Tara M. Barrett and Kenneth Winterberger.

Forest Area, Composition, and Ownership1

Background

Ecological provinces—

The coastal Alaska region is characterized by outstanding 

natural beauty, with a rocky forested archipelago and 

coastline bordered by the Pacific Ocean on one side 

and glacier-studded mountains on the other. This report 

highlights the current status and trends of forests in the 

37.8-million-acre coastal inventory region, which stretches 

1,170 miles from Kodiak Island in the southwest to the 

Canadian border in the southeast. As the inventory unit 

crosses two different broad ecoregional divisions within 

Alaska (fig. 5), two groups of forest types are represented: 

boreal forest types on the western Kenai Peninsula and 

Cook Inlet region, and coastal temperate rain forests in 

the rest of the inventory unit.

Figure 5—Alaska division-level ecoregions: Marine, Subarctic (boreal), and Tundra (arctic) (division boundaries are based on Nowacki 

et al. 2002). Projection: Alaska Albers NAD 83.

Kenneth C. Winterberger
6

GENERAL TECHNICAL REPORT PNW-GTR-835

The Coastal Rainforests ecological province (fig. 6) that 

falls into the inventory region consists of the Kodiak Island, 

Gulf of Alaska Coast, and Alexander Archipelago ecological sections (Nowaki et al. 2002) (fig. 7). Wildlife includes 

bald eagles; otters; black bear; moose; Sitka black-tailed 

deer; the Alaskan brown bear; and pink, chum, king, and 

sockeye salmon (see “Common and Scientific Names”). The 

most characteristic tree species for the Coastal Rainforests 

province are Sitka spruce and western hemlock, with mountain hemlock at higher elevations. In the southeast portion 

of this province, yellow-cedar, western redcedar, and small 

amounts of lodgepole pine are found. Cottonwood grows 

along riparian areas, with alder, willow, and birch shrubs 

also occurring in wetland areas. 

On the western side of the Kenai Peninsula, boreal 

forest in the Cook Inlet Basin ecological section falls within 

the boundaries of the coastal inventory unit (Nowaki et 

al. 2002) (fig. 7). In this region, wetlands are common, 

as are numerous small lakes. Much of the lowland area is 

characterized by black spruce, with cottonwoods, alder, 

Figure 6—The Forest Inventory and Analysis coastal inventory unit. Most of the area consists of the Coastal Rainforest ecological 

province, but it also contains a small portion of the Alaskan boreal forests (Alaska Range Transition ecological province) on the western 

Kenai (division boundaries are based on Nowacki et al. 2002). Projection: Alaska Albers NAD 83.

and willow in riparian areas. More productive well-drained 

sites have white spruce, Lutz spruce (a Sitka spruce/white 

spruce hybrid), birch, and aspen. Wildlife includes dense 

populations of moose; black bear; beavers; and muskrats; 

and sockeye, king, and silver salmon are found in the rivers 

and streams of the area. 

These two ecological provinces are distinguished by 

differences in climate. Throughout the Coastal Rainforests 

ecological province, mean annual temperature is moderate 

(fig. 8), mean annual precipitation is high (fig. 9), and fire 

is very uncommon. The Cook Inlet ecological section on 

the western Kenai Peninsula has a lower average annual 

temperature and less annual precipitation than the Coastal 

Rainforests province. Historical fire occurrence on the 

western Kenai, while low when compared with that in much 

of the Western United States, is more frequent than in the 

Coastal Rainforests. Permafrost occurs on the western 

Kenai, although it is probably only present in small areas 

of the landscape and at substantial soil depths (Berg 2009).

Kenneth C. Winterberger
7

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 7—Ecological sections (ecoregions) in the Forest Inventory and Analysis coastal inventory unit (section boundaries based on 

Nowacki et al. 2002). Projection: Alaska Albers NAD 83.

Figure 8—Mean annual temperature in the inventory unit (data are from the PRISM Climate Group 2002).

Kenneth C. Winterberger Kenneth C. Winterberger
8

GENERAL TECHNICAL REPORT PNW-GTR-835

Figure 9—Mean annual precipitation in the inventory unit (data are from the PRISM Climate Group 2002). Source: Alaska Average 

Monthly or Annual Mean Temperature, 1961–90, Spatial Climate Analysis Service at Oregon State University (SCAS/OSU).

Four mountain ranges within the inventory unit affect 

temperature, precipitation, and the amount of precipitation that falls as snow. The Kenai Mountains divide the 

Coastal Rainforests province of Kenai Fjords and the Prince 

William Sound area from the boreal forests of the western 

Kenai. From west to east the Chugach, Saint Elias, and 

Coastal Mountain Ranges form the northern border of the 

inventory unit, with land cover of rock, ice, and alpine vegetation. The inventory unit is within a geologically active 

area, and subsidence and uplift following earthquakes is a 

significant source of disturbance. Other common types of 

disturbance include flooding, avalanches, and windstorms. 

Population and economy—

State residents often use the phrase “Things are different 

in Alaska.” Alaska has the lowest population density of 

the 50 U.S. States, with an estimated 1 person per square 

mile compared to the national average of more than 80 

people per square mile. It has a single major municipal 

area, Anchorage, founded during World War I. Most towns 

and villages are located either along the coast or along the 

limited road system. Alaska does not have counties, and 

internal boundaries used for political divisions and the 

U.S. census tend to change over time. The native flora and 

fauna has had relatively little impact from 20th-century 

development, in comparison to the native flora and fauna of 

other U.S. States.

The current coastal forest inventory unit contains the 

former southeast Alaska inventory unit (van Hees 2003) and 

the former south-central Alaska inventory unit (van Hees 

2005b). These two areas differ fairly substantially in population and economy. The southeast area contains the census 

areas of Haines, Hoonah-Angoon, Juneau, Ketchikan Gateway, Prince of Wales-Outer Ketchikan, Sitka, SkagwayYakutat-Angoon, Wrangell-Petersburg, and Yakutat. This 

southeast area has a population of less than 100,000 people, 

and growth has been slow or level for recent decades (fig. 

10). The south-central area contains portions of the Anchorage, Kenai Peninsula, Kodiak Island, and Valdez-Cordova 

census areas. The south-central area contains about one half 

of the state’s population, and growth has been increasing at 

about 11 percent per decade (fig. 10). 

The coastal inventory region of Alaska, like the rest 

of the state, has a rich and diverse cultural history. Native 

cultures in the inventory region included the Athabaskan 

Dena’ina on the western Kenai Peninsula and Cook Inlet 

region, the Koniag Alutiiq (Sugpiaq) on Kodiak Island, 

the Chugach Alutiiq in the Prince William Sound region, 

the Eyak in the Copper River Delta, and the Tlingit and 

Haida in southeast Alaska (Langdon 2008). Alaska Native 

Kenneth C. Winterberger
9

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 10—Population trends for the southeast and south-central census areas and for the state 

(U.S. Census Bureau 2009).

corporations are major forest landowners, both in the state 

as a whole and in the coastal inventory region. 

The economy of the state is somewhat independent 

from that of the rest of the United States, with relatively 

stable employment and housing prices during the U.S. 

recession of 2008–2010. However, rural areas have been 

negatively impacted by high energy and commodity prices 

in recent years. Subsistence use, hunting, fishing, tourism, 

mining, and energy development are economic uses 

within the coastal inventory region. Logging, commercial 

fishing, and state and local government employment are 

important components of the economy in the southeast 

region. The south-central region has a more diversified 

economy, including more manufacturing, retail, and service 

industries. 

Results

Smith et al. (2009) provided approximate values of 11.9 

million acres of timberland and 126.9 million acres of forest 

land in Alaska. Using those values for the state, the 6.2 

million acres of timberland in the coastal inventory unit 

would constitute 52 percent of timberland in Alaska, and the 

15.3 million acres of forest land in the coastal inventory unit 

would constitute 12 percent of forest land in Alaska. Forest 

is defined as land that is at least 10 percent stocked by forest 

trees of any size, or land formerly having such tree cover 

that is not developed for a nonforest use (see “Glossary” for 

a detailed definition). Timberland is defined as unreserved 

forest land that is capable of producing at least 20 cubic feet 

per acre per year. 

300

400

500

600

700

800

0

100

200

300

400

500

600

700

800

Population (thousands)

1975 1980 1985 1990 1995 2000 2005 2010

Year

Alaska

South-central Alaska

Southeast Alaska
10

GENERAL TECHNICAL REPORT PNW-GTR-835

Approximately 40 percent of the coastal inventory unit 

is forested (fig. 11). Most of the forest in the region is found 

in the southeast, on the western side of the Kenai Peninsula, 

or in relatively thin coastal strips located between the ocean 

and the mountains (fig. 12). Of the 15.3 million acres of forest, 34.6 percent is reserved. Reserved land is reserved from 

management for wood products, and in the inventory region 

includes national parks and monuments, national wildlife 

refuges, state parks and game refuges, and wilderness areas 

and wilderness study areas in national forests. Additional 

detailed estimates for forest land and timberland by ownership, forest type, stand size class, and site class are shown in 

appendix 2.

Figure 11—Percentage of area by land class category in coastal 

Alaska, 2004–2008.

Figure 12—Approximate location of forest within the inventory unit.

16.3%

59.5%

14.0%

10.2%

Timberland

Nonforest

Other forest—

 reserved

Other forest—

 unreserved

When classified by the predominant tree species, 

western hemlock forest is the most common forest type for 

the inventory area, followed by mountain hemlock, yellowcedar, Sitka spruce, and western redcedar (fig. 13). Sitka 

spruce and lodgepole are the forest types with the lowest 

proportion of area in reserve status; black spruce and aspen 

have the highest proportion of area in reserve status. 

Almost 88 percent of forest in the coastal inventory 

unit is in public ownership (fig. 14). On private land, 79 

percent of the forest is considered timberland. On lands 

owned by state and local government, 66 percent of the 

forest is considered timberland. On national forests, only 35 

percent of forest land on national forests is timberland. The 

two largest national forests in the United States are found in 

the coastal inventory region; the Tongass National Forest in 

southeast Alaska, and the Chugach National Forest in southcentral Alaska. Other federal land in the region includes 

national parks (Wrangell St. Elias, Kenai Fjords, and 

Glacier Bay National Parks) and national wildlife refuges 

(Kodiak and Kenai National Wildlife Refuges). Most of this 

other federal land is nonforest, and very little (5 percent) of 

the forest is timberland. 

Kenneth C. Winterberger
11

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 13—Forest types within the inventory area by reserve status. Lines at end of bars represent ± standard error.

0 1,000 2,000 3,000 4,000 5,000

Pacific silver fir

Subalpine fir

Red alder

Nonstocked

Aspen

Lodgepole pine

White spruce

Black spruce

Cottonwood

Paper birch

Western redcedar

Sitka spruce

Alaska yellow-cedar

Mountain hemlock

Western hemlock

Thousand acres

Forest type

Boreal (unreserved)

Boreal (reserved)

Rain forest (unreserved)

Rain forest (reserved)

Figure 14—Timberland and forest land by owner group in the coastal inventory unit.

Forest land (thousand acres)

3,747 

(61%)

68 (1%) 

879 

(14%)

1,481 

(24%)

Timberland (thousand acres)

10,781

(70%)

1,308 (9%)

1,331

(9%)

1,880

(12%)

National forest

Other federal

State and local

Private

Numbers of growing-stock trees (see “Glossary” for 

definition) on timberland by diameter size class are shown 

in appendix 2, along with volume statistics. Much higher 

volumes per acre of sawtimber on timberland are found 

on federal land than on private land. Total volume of 

growing-stock trees on timberland is 29 billion cubic feet 

or 157 billion board feet. Western hemlock has the highest 

growing-stock volume on timberland, followed by Sitka 

spruce.

The coastal forest in Alaska is part of a larger ecoregion including the coastal forests of British Columbia, 

Oregon, and Washington. However, even where forest 
12

GENERAL TECHNICAL REPORT PNW-GTR-835

types are similar, significant structural 

differences in the forest are noticeable. For 

example, yellow-cedar is not found in the 

coastal forests of south-central Alaska and 

is uncommon in coastal forests of Oregon 

and Washington (see chapter 4). Or, for 

example, western hemlock/Sitka spruce 

forests in Alaska show a dramatically 

different age class distribution than 

western hemlock/Sitka spruce forests in 

Oregon or Washington (fig. 15).

Interpretation

The relatively low human population 

density, the relatively high proportion 

of reserved land, and the relatively low 

potential productivity of forest land all 

contribute to the unique nature of the 

forests of coastal Alaska:

Estimate Coastal Alaska U.S. averagea

Percentage of land that 40 33 

is forested

People per forested acre 2 36

Percentage of forest land 12 56 

that is privately owned

Percentage of forested 35 10 

land that is reserved

Percentage of forest land 41 68 

that is timberland

Percentage of timberland 6 35 

that is capable of 

producing at least 

85 ft3 ∙ ac-1 ∙ yr-1

Percentage of timberland 39 2

that is at least 200 

years old

a

 U.S. average estimates were calculated from Smith et al. (2009).

The extent, composition, and structural characteristics 

of the forests in coastal Alaska are driven by a complex set 

of human and environmental factors. The environmental 

factors—soil, temperature, precipitation—control where 

different tree species are found, and wind, flooding, 

fire, insects, and disease affect succession, mortality, 

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

Forest area (thousand acres)

Stand age (years)

0–24

25–74

75–124

125–174175–224225–274275–324325–374

≥375

Alaska

Washington

Oregon

Figure 15—Distribution of western hemlock-Sitka spruce forest area in Alaska, 

Oregon, and Washington by age class (from Forest Inventory and Analysis data collected 2001–2008 for Oregon, 2002–2008 for Washington, and 2004–2008 for Alaska).

and regeneration. The human factors—timber harvest, 

development, and fire suppression—affect characteristics 

such as age structure, fuels, and fragmentation. In addition, 

unpredictable elements such as invasive species and climate 

change, which are driven by both environmental and human 

factors, can greatly affect how forests will change over time. 

In coastal Alaska, the human factors have had relatively 

less impact on forests than in many other parts of the United 

States or the world. For example, as shown in figure 15, in 

Oregon and Washington, management has dramatically 

shifted the distribution of hemlock-spruce coastal forest into 

younger age classes. In contrast, the age class distribution 

of Alaskan hemlock-spruce forests more closely resembles 

what would be expected for coastal forests of several centuries ago, with stand-replacing disturbance relatively rare. 

This does not mean that human impacts have been insignificant. In the later chapters of this report, we explore some of 

the ways that forests in coastal Alaska are changing. 

In summary, climate is the principal factor controlling 

where different tree species are found in coastal Alaska. 

Most of the inventory region has a maritime climate of 

high precipitation, moderate winters, and cool summers, 
13

Forests of Southeast and South-Central Alaska, 2004–2008

resulting in temperate coastal coniferous rain forest. Small 

portions of the inventory unit, in the western Kenai and the 

upper Copper River region, experience less precipitation, 

hotter summers, and colder winters, resulting in boreal 

forest species. The coastal forest region, although only 

10 percent of Alaska land area, contains about half of the 

timberland and about half of the people in the state. Relative 

to the rest of the United States, the coastal region contains 

slower growing forest, older forest, less forest in private 

ownership, and much more forest per capita. 

Biomass and Carbon2

Background

Forest biomass can be defined as the dry weight of all 

living, or recently living, plant material within a given area 

of forest. Aboveground vegetation biomass is a critical 

determinant of net primary productivity, and therefore 

the carbon budget, of a forest ecosystem (Chapin et al. 

2002). Forest carbon accumulates within a forest system 

through the process of photosynthesis, and leaves the 

system through the processes of respiration, decomposition, 

combustion (wildfire), and hydrological transport, as well as 

anthropogenic causes such as harvesting. Carbon makes up 

approximately 50 percent of forest biomass (Chapin 2002, 

Fahey et al. 2005). Assessment of the quantity and distribution of biomass and carbon resources using comprehensive 

forest inventory data is important in determining carbon 

stocks and monitoring the change of these stocks over time. 

The forest carbon resource in coastal Alaska—

Comprehensive inventory data can also be used to develop 

landscape- and regional-scale assessments of carbon stocks. 

With the emergence of international markets for carbon 

credits—the Chicago Climate Exchange in the United 

States had joined already active exchanges in Europe, Asia, 

and Australia—there is a critical need to quantify existing 

carbon stocks and evaluate possible outcomes of various 

alternative carbon management options under varying 

climatic and disturbance regimes (Leighty et al. 2006). The 

possibility of gaining carbon credits—where each credit 

equals 1 ton of carbon—could provide incentives for forest 

landowners to modify management practices (e.g., lengthen 

rotations, intensify reforestation and restoration activities) 

so as to increase levels of accumulated carbon beyond 

what would have been achieved under existing practices, 

thereby allowing them to manage carbon as an additional 

forest product (Wolfe 2008). In addition, managing to 

maximize forest carbon sequestration can complement 

other activities designed to improve forest health, maintain 

wildlife habitat, and increase forest productivity, including 

removal of hazardous fuels (reduces carbon emissions from 

wildfires), use of biomass for energy production (use of a 

renewable resource is carbon-neutral; see sidebar on page 

87), site preparation to improve growth of spruce seedlings 

in beetle-killed areas, and planting of mixed hardwood and 

spruce species to enhance wildlife habitat (Cole et al. 2004, 

Youngblood and Cole 2003). Carbon estimates obtained 

from a systematic, comprehensive inventory help to establish baseline information on where carbon is currently 

stored, while remeasurement data can provide information 

on rates of sequestration across the region. 

Methods

Data from the Forest Inventory and Analysis (FIA) plots, 

measured from 2004 to 2008, were used to develop the 

findings reported in this section. The sources for biomass 

equations used are listed in appendix 1 of this report. All 

calculations of carbon are based on the assumption that 

carbon = biomass × 0.50 (Fahey et al. 2005). The wilderness 

and wilderness study areas of the Chugach and Tongass are 

included with a 20-percent sampling intensity compared to 

other lands, and Glacier Bay National Park is excluded. 

Findings

There are over 1.3 billion tons of biomass stored within 

the live trees (≥ 1 inch diameter at breast height [d.b.h.]) of 

coastal Alaska (table 1, figs. 16 and 17). The overwhelming 

majority (83 percent) of the live forest biomass in coastal 

Alaska is on national forest land managed by the USDA 

Forest Service, with most of the remainder on privately 

owned land (7 percent) and land managed by state and local 

2 governments (7 percent) (table 2, fig. 18). Author: Hans-Erik Andersen.
14

Table 1—Estimated aboveground biomass of live trees by forest type and diameter class, coastal Alaska, 2004–2008 Diameter class (inches) 1.0–2.9 3.0–4.9 5.0–6.9 7.0–8.9 9.0–10.9 11.0–12.9 Forest type Total SE Total SE Total SE Total SE Total SE Total SE Thousand bone-dry tons Softwoods: Alaska yellow-cedar 8,413 1,566 5,426 791 8,439 1,127 12,217 1,654 14,955 2,033 16,136 2,266 Black spruce 2,174 313 2,513 450 1,518 358 585 168 170 68 184 93 Lodgepole pine 1,162 294 530 152 804 160 1,160 248 958 210 1,060 264 Mountain hemlock 2,747 347 4,314 739 7,287 910 10,834 1,261 16,247 2,326 16,280 2,201 Sitka spruce 4,865 1,001 3,658 556 5,109 715 7,155 919 9,271 1,267 11,093 1,566 Western hemlock 5,506 701 6,896 749 11,465 1,076 16,810 1,478 23,213 2,145 26,338 2,397 Western redcedar 1,704 306 1,537 300 3,655 608 5,402 973 6,947 1,283 9,068 1,656 White spruce 495 142 882 304 1,110 224 1,079 202 865 223 717 177 Total 27,066 1,962 25,757 1,391 39,389 1,575 55,242 2,096 72,624 3,038 80,876 3,400 Hardwoods: Cottonwood 987 297 832 257 1,081 308 2,031 723 2,139 553 1,819 544

Aspen 235 86 417 138 529 172 589 233 718 321 718 268

Paper birch 426 108 1,210 348 1,550 303 2,090 343 2,668 515 2,508 476

Red alder 89 77 194 148 245 151 165 102 180 141 — —

 Total 1,737 333 2,653 476 3,404 483 4,874 834 5,705 823 5,045 763

Nonstocked — — — — 3 3 — — — — — —

All forest types 28,803 1,979 28,409 1,440 42,796 1,593 60,117 2,143 78,329 3,079 85,922 3,427

Diameter class (inches)

13.0–14.9 15.0–16.9 17.0–18.9 19.0–20.9 21.0–28.9 29+ 

Forest type Total SE Total SE Total SE Total SE Total SE Total SE 

Thousand bone-dry tons

Softwoods:

Alaska yellow-cedar 18,761 3,180 15,573 2,817 16,606 2,892 9,838 1,979 24,063 5,545 19,285 5,884 Black spruce 82 60 17 19 30 33 — — 79 86 — — Lodgepole pine 843 271 529 187 511 186 312 180 718 338 — — Mountain hemlock 19,403 2,592 21,092 3,240 16,882 2,325 14,479 2,692 45,810 7,566 38,916 10,191 Sitka spruce 12,040 1,641 12,506 1,576 12,863 1,710 11,554 1,787 48,788 7,343 95,189 19,108 

Western hemlock 28,144 2,120 28,888 2,441 34,904 3,778 32,421 2,873 144,098 12,148 186,858 20,666 Western redcedar 9,297 1,792 7,588 1,717 8,551 2,622 6,909 1,752 24,118 7,241 42,595 13,573 White spruce 355 113 127 81 97 72 80 78 — — — — 

 Total 88,924 4,016 86,321 4,460 90,444 5,086 75,594 4,542 287,674 15,252 382,844 28,487 

Hardwoods:

Cottonwood 2,478 807 2,004 529 1,425 364 1,266 458 4,674 2,187 3,110 2,490 

Aspen 351 161 300 138 93 80 42 46 — — — — 

Paper birch 1,474 363 1,514 480 952 326 435 239 1,131 457 309 316 Red alder 95 99 49 45 — — — — — — — — 

 Total 4,398 902 3,868 721 2,470 494 1,743 518 5,806 2,233 3,419 2,510 

Nonstocked — — — — — — — — — — — — 

All forest types 93,321 4,045 90,188 4,497 92,914 5,098 77,337 4,558 293,480 15,388 386,263 28,578 

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 50,000 bone-dry tons.
15

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 16—Significant forest carbon and biomass has accumulated in the spruce and hemlock forests of southeast Alaska.

Figure 17—Distribution of forest biomass in coastal Alaska (biomass derivation procedure from Blackard et al. 2008). 

Kenneth C. Winterberger

Kathleen Galloway
16

GENERAL TECHNICAL REPORT PNW-GTR-835

Almost all (97 percent) of the live tree biomass in the 

coastal inventory unit is within conifer forests (fig. 19). 

The highest average biomass levels in coastal Alaska are 

concentrated in the Sitka spruce (120 tons/acre), western 

redcedar (122 tons/acre), and western hemlock (137 tons/

acre) forests of southeast Alaska (table 3). Most of the 

hardwood forest biomass is stored in black cottonwood 

and paper birch forests (fig. 19). In the northern and 

western areas of coastal Alaska, there are concentrations 

of relatively high forest biomass on Afognak Island, 

eastern Kenai Peninsula, and Prince William Sound, with 

lower average biomass levels within the white spruce and 

mixed-hardwood forests on the western lowlands of the 

Kenai Peninsula (fig. 17). Most (28 percent) of the live tree 

biomass for conifer forest is concentrated in trees greater 

than 29 inch d.b.h., whereas most (13 percent) of the live 

Figure 18—Aboveground live tree biomass by owner group on 

forest land in coastal Alaska, 2004–2008.

7%

83%

National forest

Other federal

State and local

 government

Private

3% 7%

Table 2—Estimated aboveground biomass of live trees by forest type and owner group, coastal Alaska, 

2004–2008

Owner group

National forest Other federal State and local Private All owners

Forest type Total SE Total SE Total SE Total SE Total SE

Thousand bone-dry tons

Softwoods:

Alaska yellow-cedar 163,206 23,139 — — 4,149 2,097 2,357 1,019 169,713 23,243

Black spruce — — 6,388 1,064 245 199 719 471 7,352 1,180

Lodgepole pine 7,114 1,544 — — 205 166 1,268 908 8,587 1,797

Mountain hemlock 192,957 26,090 4,343 1,553 10,431 3,286 6,559 2,314 214,291 26,418

Sitka spruce 150,659 29,083 2,838 1,772 41,236 6,621 39,357 8,298 234,090 30,980

Western hemlock 485,349 37,115 3,166 2,807 29,686 7,069 27,340 6,721 545,541 38,105

Western redcedar 123,089 28,078 — — 2,716 1,790 1,567 962 127,371 28,138

White spruce 520 340 3,248 762 480 263 1,559 684 5,807 1,108

 Total 1,122,896 41,310 19,983 3,678 89,149 9,967 80,727 10,399 1,312,755 42,844

Hardwoods:

Cottonwood 6,269 3,194 10,360 4,789 3,859 1,565 3,358 1,205 23,846 6,069

Aspen — — 3,734 1,245 — — 257 165 3,992 1,256

Paper birch 18 19 8,204 1,574 2,637 1,477 5,407 1,470 16,267 2,601

Red alder 1,016 543 — — — — — — 1,016 543

 Total 7,303 3,240 22,299 5,159 6,496 2,141 9,022 1,856 45,120 6,681

Nonstocked — — — — — — 3 3 3 3

All forest types 1,130,200 41,092 42,282 6,114 95,645 10,008 89,752 10,418 1,357,878 42,840

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 50,000 bone-dry tons.
17

Forests of Southeast and South-Central Alaska, 2004–2008

0 100 200 300 400 500 600

Red alder

Paper birch

Aspen

Cottonwood

Sitka spruce

Western redcedar

Western hemlock

Lodgepole pine

Alaska yellow-cedar

Mountain hemlock

Black spruce

White spruce

Biomass (million bone-dry tons)

Forest type

Figure 19—Aboveground live tree biomass by forest type on forest land in coastal Alaska, 2004–2008. Lines at end of 

bars represent ± standard error.

Table 3—Estimated average aboveground biomass of live trees by forest type and owner group, coastal 

Alaska, 2004-2008

Owner group

National forest Other federal State and local Private All owners

Forest type Mean SE Mean SE Mean SE Mean SE Mean SE

Bone-dry tons per acre

Softwoods:

Alaska yellow-cedar 73.1 6.3 — — 77.4 16.3 34.7 8.9 72.1 6.1

Black spruce — — 15.2 1.8 10.0 3.8 13.7 5.9 14.7 1.6

Lodgepole pine 18.9 2.5 — — 5.5 2.8 40.7 10.5 19.3 2.6

Mountain hemlock 70.3 7.0 39.4 8.4 73.2 10.9 39.6 10.0 67.8 6.1

Sitka spruce 161.4 15.1 112.3 23.3 91.7 10.6 71.7 12.7 119.6 9.6

Western hemlock 145.3 6.1 201.0 — 135.6 17.3 69.0 13.3 137.4 5.5

Western redcedar 132.1 17.4 — — 63.2 24.5 24.0 10.5 122.5 16.3

White spruce 21.8 4.1 14.0 2.3 7.8 3.3 12.0 3.9 13.0 1.8

 Total 106.0 3.8 24.8 3.9 86.5 7.3 55.4 6.2 94.5 3.0

Hardwoods:

Cottonwood 42.6 13.6 73.8 26.4 30.5 8.6 28.4 6.2 44.8 8.9

Aspen — — 30.4 6.2 — — 6.9 2.6 24.5 5.4

Paper birch 1.6 1.5 34.9 4.4 23.6 11.1 22.4 4.0 27.1 3.2

Red alder 34.0 9.4 — — — — — — 34.0 9.4

 Total 38.7 10.8 44.7 8.4 27.0 6.9 22.7 3.2 34.0 4.0

Nonstocked — — — — — — 0.1 0.1 0 0

All forest types 104.8 3.8 32.3 4.1 71.8 6.3 47.7 5.0 88.7 2.8

Note: Totals may be off because of rounding; data subject to sampling error; — = less than 50,000 bone-dry tons.
18

GENERAL TECHNICAL REPORT PNW-GTR-835

tree biomass in hardwood forest is in the 21.0 to 28.9-inch 

d.b.h. class (fig. 20). Relatively little of the total biomass 

in the coastal Alaska region is concentrated in the white 

spruce and black spruce forest types that characterize 

the southern extent of the boreal forest biome in Alaska 

(fig. 19). Most of the carbon accumulated in live (≥ 1 inch 

d.b.h.) and dead (≥ 5 inch d.b.h.) trees is also concentrated 

in western hemlock, mountain hemlock, and Sitka spruce 

forest types (table 4, fig. 21). A relatively high proportion of 

the carbon accumulated in conifer forest types is in standing 

dead material (snags)—especially in the white spruce type, 

where 47 percent of the carbon is stored in snags most likely 

killed by spruce bark beetle (Dendroctonus rufipennis 

[Kirby]) in south-central Alaska (table 5, figs. 22 and 23).

450

400

350

300

250

200

150

100

50

0

Biomass (million bone-dry tons)

Diameter class (inches)

1.0–2.93.0–4.95.0–6.97.0–8.9

9.0–10.9

11.0–12.9

13.0–14.915.0–16.917.0–18.919.0–20.921.0–28.9

29.0+

Softwoods

Hardwoods

Figure 20—Aboveground live tree biomass by diameter class on forest land in coastal Alaska, 2004–2008. Lines 

at end of bars represent ± standard error.

Interpretation

The forests of coastal Alaska represent a significant pool 

of stored carbon. Notably, much (24 percent) of the carbon 

stored in conifer forests of southeast and south-central 

Alaska is contained in snags—which, unlike live trees, will 

represent a net carbon source, as they are not photosynthetically active and are eventually emitting carbon either by 

burning or decomposing. As markets for carbon credits 

continue to emerge and requirements (and incentives) for 

carbon accounting become increasingly stringent at the 

local, state, and national levels, the comprehensive FIA 

inventory will serve as a useful source of information on 

the status and dynamics of the forest carbon resource. 
19

Forests of Southeast and South-Central Alaska, 2004–2008

Table 4—Estimated carbon of live trees and snags by forest type and owner group, coastal Alaska, 2004–2008

Owner group

National forest Other federal State and local

Live trees Snags Live trees Snags Live trees Snags

Forest type Total SE Total SE Total SE Total SE Total SE Total SE

Thousand bone-dry tons

Softwoods:

Alaska yellow-cedar 81,603 11,570 24,145 5,025 81,603 11,570 24,145 5,025 2,075 1,049 376 189

Black spruce — — — — 3,194 532 351 167 122 99 11 10

Lodgepole pine 3,557 772 913 233 3,557 772 913 233 103 83 19 20

Mountain hemlock 96,479 13,045 13,330 2,083 98,650 13,067 13,631 2,088 5,216 1,643 949 384

Sitka spruce 75,330 14,542 13,553 3,968 76,749 14,568 14,026 4,000 20,618 3,310 2,799 728

Western hemlock 242,675 18,558 70,215 6,894 244,257 18,600 70,411 6,896 14,843 3,535 2,163 798

Western redcedar 61,544 14,039 16,320 3,129 61,544 14,039 16,320 3,129 1,358 895 374 298

White spruce 260 170 33 26 1,884 417 1,209 299 240 131 682 362

 Total 561,448 20,655 138,508 7,826 571,440 20,719 141,004 7,851 44,574 4,983 7,373 1,182

Hardwoods:

Aspen — — — — 1,867 622 622 214 — — — —

Cottonwood 3,134 1,597 120 80 8,315 2,878 228 107 1,929 782 444 289

Paper birch 9 9 — — 4,111 787 888 269 1319 738.3 792.8 368.9

Red alder 508 271 113 79 508 271 113 79 — — — —

 Total 3,652 1,620 233 113 14,801 3,046 1,852 355 3,248 1,071 1,237 469

Nonstocked — — — — — — — — — — 610 275

All forest types 565,100 20,546 138,741 7,824 586,241 20,745 142,856 7,852 47,822 5,004 9,220 1,225

Owner group

Private All owners

Live trees Snags Live trees Snags

Forest type Total SE Total SE Total SE Total SE

Thousand bone-dry tons

Softwoods:

Alaska yellow-cedar 1,179 510 268 133 84,857 11,621 24,789 5,030

Black spruce 360 236 27 22 3,676 590 388 169

Lodgepole pine 634 454 122 101 4,294 899 1,055 254

Mountain hemlock 3,279 1,157 520 241 107,145 13,209 15,100 2,136

Sitka spruce 19,679 4,149 2,705 683 117,045 15,490 19,530 4,120

Western hemlock 13,670 3,360 3,250 976 272,770 19,053 75,824 6,998

Western redcedar 783 481 184 111 63,686 14,069 16,877 3,142

White spruce 779 342 717 338 2,904 554 2,607 572

 Total 40,363 5,199 7,793 1,218 656,377 21,422 156,171 7,971

Hardwoods:

Aspen 129 83 4 4 1,996 628 625 214

Cottonwood 1,679 602 215 189 11,923 3,034 887 362

Paper birch 2703.6 734.85 851.04 275.1 8133.4 1301 2532.3 532.8

Red alder — — — — 508 271 113 79

 Total 4,511 928 1,069 328 22,560 3,340 4,158 669

Nonstocked 1 1 207 172 1 1 817 325

All forest types 44,876 5,209 9,070 1,243 678,939 21,420 161,146 7,978

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 50,000 bone-dry tons.
20

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 5—Estimated average carbon of live trees and snags by forest type and owner group, coastal Alaska, 

2004–2008

Owner group

National forest Other federal State and local

Live trees Snags Live trees Snags Live trees Snags

Forest type Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE

Bone-dry tons per acre

Softwoods:

Alaska yellow-cedar 36.5 3.2 10.8 1.8 36.5 3.2 10.8 1.8 38.7 8.2 7.0 1.5

Black spruce — — — — 7.6 0.9 0.8 0.4 5.0 1.9 0.5 0.2

Lodgepole pine 9.4 1.3 2.4 0.5 9.4 1.3 2.4 0.5 2.8 1.4 0.5 0.4

Mountain hemlock 35.2 3.5 4.9 0.7 34.6 3.4 4.8 0.7 36.6 5.5 6.7 1.9

Sitka spruce 80.7 7.5 14.5 3.3 80.1 7.4 14.6 3.2 45.9 5.3 6.2 1.4

Western hemlock 72.7 3.1 21.0 1.6 72.8 3.1 21.0 1.6 67.8 8.6 9.9 2.9

Western redcedar 66.0 8.7 17.5 2.3 66.0 8.7 17.5 2.3 31.6 12.3 8.7 5.1

White spruce 10.9 2.0 1.4 0.8 7.4 1.1 4.7 0.9 3.9 1.6 11.1 3.0

 Total 53.0 1.9 13.1 0.8 50.1 1.8 12.4 0.7 43.2 3.7 7.2 1.0

Hardwoods:

Aspen — — — — 15.2 3.1 5.1 1.1 — — — —

Cottonwood 21.3 6.8 0.8 0.6 28.9 7.8 0.8 0.4 15.3 4.3 3.5 2.0

Paper birch 0.8 0.8 — — 16.6 2.2 3.6 0.8 11.8 5.5 7.1 2.4

Red alder 17.0 4.7 3.8 2.4 17.0 4.7 3.8 2.4 — — — —

 Total 19.3 5.4 1.2 0.7 21.5 3.4 2.7 0.5 13.5 3.5 5.1 1.6

Nonstocked — — — — — — — — — — 10.2 1.4

All forest types 52.4 1.9 12.9 0.7 48.5 1.7 11.8 0.7 35.9 3.1 6.9 0.8

Owner group

Private All owners

Live trees Snags Live trees Snags

Forest type Mean SE Mean SE Mean SE Mean SE

Bone-dry tons per acre

Softwoods:

Alaska yellow-cedar 17.4 4.5 4.0 1.3 36.0 3.0 10.5 1.7

Black spruce 6.9 2.9 0.5 0.3 7.4 0.8 0.8 0.3

Lodgepole pine 20.3 5.2 3.9 1.9 9.6 1.3 2.4 0.4

Mountain hemlock 19.8 5.0 3.1 1.2 33.9 3.1 4.8 0.6

Sitka spruce 35.9 6.4 4.9 1.1 59.8 4.8 10.0 1.7

Western hemlock 34.5 6.6 8.2 2.1 68.7 2.7 19.1 1.4

Western redcedar 12.0 5.2 2.8 1.2 61.2 8.1 16.2 2.1

White spruce 6.0 2.0 5.5 2.0 6.5 0.9 5.8 1.0

 Total 27.7 3.1 5.3 0.8 47.3 1.5 11.2 0.6

Hardwoods:

Aspen 3.4 1.3 0.1 0.1 12.3 2.7 3.8 1.0

Cottonwood 14.2 3.1 1.8 1.5 22.4 4.4 1.7 0.7

Paper birch 11.2 2.0 3.5 0.9 13.5 1.6 4.2 0.7

Red alder — — — — 17.0 4.7 3.8 2.4

 Total 11.4 1.6 2.7 0.7 17.0 2.0 3.1 0.5

Nonstocked 0.1 0.0 8.0 3.4 0.0 0.0 9.1 1.5

All forest types 23.9 2.5 4.8 0.6 44.4 1.4 10.5 0.5

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 50,000 bone-dry tons.
21

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 21—Carbon mass of live trees and snags by forest type on forest land in coastal Alaska, 2004–2008; 

d.b.h. = diameter at breast height.

Figure 22—A significant amount of forest carbon in coastal Alaska is stored in standing dead trees, such as these 

white spruce killed by bark beetles on the Kenai Peninsula.

Marc Much

0 100 200 300 400

Carbon (million bone-dry tons)

Red alder

Paper birch

Aspen

Cottonwood

Sitka spruce

Western redcedar

Western hemlock

Lodgepole pine

Alaska yellow-cedar

Mountain hemlock

Black spruce

White spruce

Forest type

Live trees (greater than 1 inch d.b.h.)

Snags (greater than 5 inches d.b.h.)
22

GENERAL TECHNICAL REPORT PNW-GTR-835

Change in Forests Between 1995–2003 

and 2004–20083

Background

Change in forests can be caused by a variety of natural and 

anthropogenic sources. It is usually not possible to attribute 

observed change to a single cause. Climate, plant competition, seed dispersal, herbivory, land development, mineral 

extraction, roadbuilding, fire, windstorms, invasive species, 

timber harvest, disease, flooding, earthquakes, volcanoes, 

and insect outbreaks can all cause change in coastal Alaska 

forests. Changes can occur in species composition, forest 

structure (e.g., vegetation density, height, or canopy layering), or in processes, such as rates of nutrient uptake 

or evapotranspiration. 

Many of the factors that can cause change interact. 

For example, fire is more likely to be of high intensity if 

significant tree damage has previously occurred from some 

other cause such as insects or disease (e.g., Turner et al. 

1999). Saturated soils from a climate of high precipitation 

can prevent trees from establishing deep root systems, 

making them more susceptible to windthrow. Land 

development often increases the probability of invasive 

species in many areas. Late spring freezes or summer 

drought can stress plants, making them more susceptible 

to disease. Although FIA field crews assign a general cause 

of death to each individual tree that dies, in many cases, it 

is an educated guess rather than a known cause.

Interpretation of change should incorporate the spatial 

and temporal context for the region being analyzed. At 

smaller spatial scales, forest indicators show greater fluctuation. Forest planners use the concept of “natural range 

of variability’’ to identify if a change is within expected 

boundaries, as any indicator will normally fluctuate in 

response to small-scale disturbances and regrowth. The 

region being reported on here is very large, incorporating 

the entire northern portion of the coastal temperate rain 

forest as well as the boreal forest region of the Kenai Peninsula. Thus changes are more likely to reflect underlying 

trends, and less likely to reflect small-scale disturbance or 

succession. However, there are some vectors of change that 

could be large enough to possibly impact regional trends, 

such as climate, harvest, windthrow, and, for the Kenai 

region, post-epidemic response to mortality from beetles.

Figure 23—Average carbon mass of live trees and snags by forest type on forest land in coastal Alaska, 2004–2008; 

d.b.h. = diameter at breast height.

0 10 20 30 40 50 60 70 80 90 100

Average carbon mass per acre (bone-dry tons)

Red alder

Paper birch

Aspen

Cottonwood

Sitka spruce

Western redcedar

Western hemlock

Lodgepole pine

Alaska yellow-cedar

Mountain hemlock

Black spruce

White spruce

Forest type

Live trees (greater than 1 inch d.b.h.)

Snags (greater than 5 inches d.b.h.)

3 Author: Tara M. Barrett.
23

Forests of Southeast and South-Central Alaska, 2004–2008

Although expansions of glaciers in the coastal 

Alaska region probably occurred during the Little Ice Age 

(760–260 BP) (Calkin et al. 2000), the last maximum glacial 

extent for coastal Alaska is believed to have occurred in the 

late Wisconsin glaciation (about 20,000 year BP). Because 

the glaciers would have covered most of the land that is 

now forest, trees had to recolonize the coastal region, and it 

is possible that some tree species such as yellow-cedar are 

still responding to the general warming trend. Overlying 

this long-term process is a more recent climate trend, global 

warming that may be related to an increase in greenhouse 

gases. Climate data for Alaska show that average annual 

temperature has increased for many parts of the state over 

the past 60 years. Although the 0.9° to 3.7-°F increase in 

the temperate coastal rain forest area (fig. 24) is less than 

increases in the subarctic and boreal forest regions of 

Alaska, this is still a substantial change. The coastal region 

is expected to experience additional increases in average 

annual temperature in the coming decades, so current 

change can be helpful in providing early warning of future 

trends.

The largest recent human-related source of disturbance 

to coastal forests has been timber harvests. Although 

timber harvest began in the region in the late 1800s to 

support mining and local construction, significant harvest 

in the coastal region did not begin until the mid-1950s. 

Peak levels were reached in 1989–1990, and harvest in the 

region has declined since then; the average annual timber 

volume harvested from public and private forests in Alaska 

decreased to 369 million board feet in the period 1997–2006 

from an average annual volume of 896 million board feet 

in the period 1987–1996 (Brackley et al. 2009). Although 

those values reflect statewide estimates, a similar decline in 

harvest would be expected for the coastal Alaska inventory 

unit, particularly southeast Alaska, where most harvest 

takes place. Dates of initial plot measurements in southeast 

Figure 24—Change in mean annual temperature (°F) between 1954 and 2008 at long-term climate monitoring 

stations in southeast and south-central Alaska. Projection: Alaska Albers NAD 83.

0 2 120 K 40 480 ilometers

150°0'0"W 140°0'0"W 130°0'0"W

60°0'0"N

50°0'0"N

60°0'0"N

150°0'0"W 140°0'0"W

Talkeetna

5.0 Gulkana

2.6

Anchorage

3.3

Kodiak

0.9

Yakutat

2.5 Juneau

3.0

Annette

1.6

Homer

3.7

Talkeetna

5.0 Gulkana

2.6

Anchorage

3.3

Kodiak

0.9

Yakutat

2.5 Juneau

3.0

Annette

1.6

Homer

3.7
24

GENERAL TECHNICAL REPORT PNW-GTR-835

Alaska were from 1995 to 2000 with remeasurement taking 

place from 2004 to 2008 (table 6). Thus the change reported 

could reflect ingrowth into the 5 inches d.b.h. class at the 

second inventory as previously harvested areas regrow.

In southeast Alaska, windthrow is an important process 

driving forest development (Kramer et al. 2001). Previous 

studies have recorded windthrow events of 2,000 to 63,000 

acres in southeast Alaska (Nowacki and Kramer 1998). 

Ecoregions reported on here range from 0.5 million acres of 

forest on Kodiak Island to 10 million acres of forest within 

the Alexander Archipelago ecological section. Very large 

windthrow events, or a pulse of windthrow events clustered 

temporally, could cause detectable change in inventory 

estimates.

Methods

A set of 2,355 remeasured FIA plots, 930 of which had 

forested land, were used for estimates of change. All results 

reported in this chapter exclude Glacier Bay National Park 

and wilderness and wilderness study areas in national 

forests (see fig. 2 in chapter 1 of this report). A separate 

stratification was developed specifically for remeasurement 

analysis. Biomass and volume change estimates used initial 

volumes from the 1995 to 2003 inventory that were adjusted 

for changes in procedures and corrected for errors such as 

species misidentification. Biomass and volume change is 

calculated only from land that was defined as forest land at 

both periods. 

No information for trees less than 5 inches d.b.h. 

was used in most change estimates because the microplot 

location, used to sample small trees, was moved between 

the periodic and annual inventories. Mortality and harvest 

rate were calculated as an arithmetic average over the 

remeasurement period. “Harvest” refers to trees that were 

cut by direct human activity and that were assumed to 

have been utilized. “Mortality” refers to trees that died by 

any other cause, and can include trees that were killed by 

direct human activity (such as land clearing or silvicultural 

purposes) but not utilized for wood. For each individual 

plot, the remeasurement period was adjusted from calendar 

time by assuming that trees grew linearly during a growing 

season interval of May 1 to September 1. 

Statistical tests for difference were based on a onesample t-test, where the sample consisted of the observed 

difference for individual plots. The t-test assumes that 

sample means are normally distributed, an assumption that 

Table 6—Number of forested plots by years of 

measurement, southeast Alaska

First Second measurement year 

measurement

year 2004 2005 2006 2007 2008 Total

1995 19 23 26 30 28 126

1996 33 20 34 29 21 137

1997 27 26 39 41 36 169

1998 18 36 33 22 30 139

2000 4 2 4 1 3 14

 Total 101 107 136 123 118 585

One of the largest recent disturbances in the coastal 

region was a spruce bark beetle epidemic in the Kenai 

region that peaked around 1995 and that had greatly diminished by 1999 (Berg et al. 2006). Beetle epidemics in this 

region are thought to be linked to climate through longer 

warmer summers allowing the beetles to mature in 1 year 

instead of 2 years (Werner and Holsten 1985) and through 

fewer periods of severe low winter temperatures that reduce 

beetle populations. The first measurement dates of the 

south-central Alaska inventory used to estimate change in 

this report were made after the most recent beetle epidemic 

(table 7), and thus the estimates of change for south-central 

Alaska reported here should incorporate recovery from the 

previous epidemics and relatively little beetle mortality. 

Table 7—Number of forested plots by years of 

measurement, south-central Alaska

First Second measurement year 

measurement

year 2004 2005 2006 2007 2008 Total

1999 26 19 27 27 25 124

2001 49 5 38 12 34 138

2002 13 13 9 9 16 60

2003 4 6 5 3 5 23

 Total 92 43 79 51 80 345
25

Forests of Southeast and South-Central Alaska, 2004–2008

can be violated for biomass change when the number of 

sampled plots is small. Numerous procedural differences 

occurred between the periodic and annual inventories, and, 

in some cases, this has restricted the types of forest attributes that can be reliably assessed for change. 

Appendix 1 in this report contains additional information on procedures for both inventories, such as equations 

used for biomass estimates.

Results

Rates of growth, mortality, and harvest—

Overall average annual morality of trees with d.b.h. greater 

than 5 inches was 0.65 percent, or 1 tree out of 154 dying 

each year (table 8). The three species with the highest 

mortality rate were white spruce (1 tree out of 39 dying 

each year), black spruce (1 tree out of 47 dying each year), 

and paper birch (1 tree out of 77 dying each year). The two 

species with the lowest mortality rate were western redcedar 

(1 tree out of 457 dying each year) and yellow-cedar (1 tree 

out of 339 dying each year). 

By species, the trees with the highest numbers of trees 

harvested were Sitka spruce, western hemlock, paper birch, 

mountain hemlock, and yellow-cedar (table 8). Overall, harvest was 23 percent of mortality by numbers of trees killed. 

Harvest was 57 percent of mortality for Sitka spruce and 46 

percent of mortality for paper birch. Harvest was the lowest 

percentage of mortality for lodgepole pine (2 percent), white 

spruce (2 percent), and black spruce (3 percent). 

Using only the 699 plots that were fully forested at both 

periods, a paired t-test was used to look at whether there 

was a change in numbers of saplings for different species on 

these plots. Three species showed a net increase in number 

of saplings: yellow-cedar, with a 23 percent increase (standard error [SE] of 11 percent, p-value of 0.0059); mountain 

hemlock, with a 23 percent increase (SE 10 percent, p-value 

of 0.0033); and Sitka spruce, with a 16 percent increase 

(SE of 11 percent, p-value of 0.0840). These increases are 

likely to be driven by trees growing across the 1-inch d.b.h. 

threshold on previously clearcut acres in southeast Alaska 

(see forest products chapter of this report).

Table 8—Average annual mortality and harvest rates between 1995–2003 and 2004–2008 of trees in 

coastal Alaska

Average annual

Average annual mortality Average annual harvest harvest and mortality

Number of Percentage of live Percentage of live Percentage of live

live trees

Species 1995–2003 Trees Percent SEa Trees Percent SE Trees Percent SE

Alaska yellow-cedar 198,694,966 586,924 0.30 0.04 82,507 0.04 0.02 669,430 0.34 0.05

Black cottonwood 27,740,065 223,937 0.81 0.29 49,884 0.18 0.19 273,821 0.99 0.34

Black spruce 23,856,633 506,004 2.12 0.42 13,528 0.06 0.06 519,532 2.18 0.43

Lodgepole pine 54,749,764 411,391 0.75 0.12 6,258 0.01 0.01 417,649 0.76 0.13

Mountain hemlock 309,268,056 1,465,727 0.47 0.05 98,249 0.03 0.03 1,563,976 0.51 0.06

Paper birch 45,852,782 597,664 1.30 0.36 274,364 0.60 0.60 872,028 1.90 0.70

Sitka spruce 280,973,064 1,840,016 0.65 0.08 1,047,199 0.37 0.21 2,887,215 1.03 0.21

Western hemlock 557,201,633 3,117,581 0.56 0.04 810,335 0.15 0.05 3,927,916 0.70 0.06

Western redcedar 83,943,962 183,664 0.22 0.06 24,049 0.03 0.02 207,712 0.25 0.06

White spruce 58,620,673 1,507,347 2.57 0.47 26,779 0.05 0.03 1,534,126 2.62 0.47

Other speciesb 22,905,331 330,289 1.44 0.30 12,515 0.05 0.05 342,804 1.50 0.30

 Total of all species 1,663,806,929 10,770,542 0.65 0.04 2,445,667 0.15 0.05 13,216,209 0.79 0.06

Note: Data subject to sampling error; SE = standard error.

Note: Does not include Glacier Bay National Park or wilderness in national forests.

Note: Uses only live trees ≥ 5 inches diameter at breast height at first inventory on plots that were forested at both the first and second inventories.

a SE was calculated using a combined ratio estimator.

b Other species includes Pacific silver fir, Pacific yew, subalpine fir, quaking aspen, and red alder.
26

GENERAL TECHNICAL REPORT PNW-GTR-835

Change in carbon and biomass by species—

Net change in aboveground tree carbon per acre between 

the two measurement periods was not significantly different than zero over the whole inventory area. There was 

no significant change for the ecoregions of the Alexander 

Archipelego, Gulf of Alaska Coast, Cook Inlet Basin, or 

Kodiak Island ecological sections (see fig. 7, chapter 2 for 

a map of these regions). There was a significant increase 

for two ecoregions, the Chugach-St. Elias Mountains with 

an estimated 7-percent increase in carbon per forested 

acre, and the Northern Coast Mountains, with an estimated 

8-percent increase in carbon per forested acre (table 9).

By biomass or numbers of trees, the four most common species in the coastal region are western hemlock, 

Sitka spruce, mountain hemlock, and yellow-cedar. None 

of the four showed a significant change in biomass between 

1995–2003 and 2004–2008 (table 10). The next most common species, western redcedar, showed an overall biomass 

increase of 4.2 percent, which was significantly different 

from zero (p-value less than 0.01). 

Of the less common species, black cottonwood, quaking aspen, and red alder all showed significant increases 

in biomass. Lodgepole pine showed a significant decrease 

of 4.6 percent (SE of 2.1 percent). The other less common 

species—black spruce, paper birch, and white spruce—did 

not show any significant change in biomass. Overall, total 

live tree biomass remained constant in the coastal Alaska 

region (0.41 percent increase, which was not significantly 

different from zero).

Change by type of owner—

On the Chugach National Forest, carbon and biomass in 

aboveground trees increased by almost 4 percent between 

the two inventories (table 11). For other federal ownership, 

estimated change was almost the same as for the Chugach 

National Forest, but the change was not quite significantly 

different from zero (p-value 0.0849, alpha = 0.05). The 

Tongass National Forest did not have a significant change in 

carbon and biomass. State and local government carbon and 

biomass increased significantly, by an estimated 7.6 percent. 

Private land estimated carbon and biomass did not show a 

statistically significant change.

Between the first inventory in 1995–2003 and the 

second inventory in 2004–2008, there was a statistically 

significant increase in gross cubic foot volume on 

timberland for the Chugach National Forest and on stateowned land (table 12). There was no statistically significant 

difference in volume on timberland on the Tongass National 

Forest, other federal land, or on private land. Although 

procedural differences in cull deductions prevented 

comparisons of net volume, current procedures show net 

volumes that range from 90 to 95 percent of gross volume 

for the various owner groups (table 12). 

Table 9—Change in aboveground live tree carbon by ecoregion between 1995–2003 and 2004–2008, 

coastal Alaska

Ecoregion (section level) na nf

b 1995–2003 2004–2008 Change SE of change p-valuec

Ovendry tons per acre

Alexander Archipelago 631 621 50.88 50.82 (0.06) 0.64 0.93 

Gulf of Alaska Coast 279 159 36.58 37.47 0.89 0.71 0.22 

Cook Inlet Basin 197 158 7.54 7.70 0.09 0.18 0.60 

Kodiak Island 100 33 76.68 74.78 (1.90) 4.36 0.66 

Chugach-St. Elias Mountains 857 70 19.48 21.06 1.38 0.39 0.00

Northern Coast Mountains 291 60 49.68 53.49 3.81 0.99 0.00

Note: Data subject to sampling error; SE = standard error.

Note: Does not include Glacier Bay National Park or wilderness in national forests.

Note: Uses only trees ≥ 5 inches diameter at breast height at first inventory on plots that were forested at both the first and second inventories.

a n = number of plots in the ecoregion.

b nf

 = number of plots with forest.

c p-value for test where H0 is change = 0; HA is change ≠ 0. 
27

Forests of Southeast and South-Central Alaska, 2004–2008

Table 10—Net change of live tree biomass by species between 1995–2003 and 2004–2008, coastal Alaska

Biomass Survivor

1995–2003 Mortality Harvest Ingrowth growth Net change

Species Value SE Value SE Value SE Value SE Value SE Value SE nspeciesa

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Thousand tons - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Number

Alaska yellow-cedar 82,836 6,319 2,485 855 1,064 811 359 47 3,651 353 525 1,332 266

Black cottonwood 8,379 1,859 121 66 50 50 90 28 1,017 89 952 265 49

Black spruce 1,881 498 205 70 3 3 221 49 134 219 147 94 54 

Lodgepole pine 12,123 1,351 1,001 240 8 10 90 18 344 90 (561) 258 130

Mountain hemlock 105,414 7,795 3,589 636 421 381 464 47 4,341 458 859 970 432

Paper birch 11,651 1,754 742 193 144 144 206 90 1,046 205 377 343 104

Quaking aspen 2,765 813 221 101 — — 70 43 312 70 162 95 28 

Red alder 2,162 575 273 133 11 13 533 163 617 517 872 299 36

Sitka spruce 242,935 16,653 12,320 3,152 6,374 2,695 2,622 454 17,017 2,612 1,155 5,516 545

Western hemlock 376,590 20,677 20,067 3,298 9,025 4,897 2,724 349 22,903 2,688 (3,166) 6,243 556

Western redcedar 47,820 5,992 951 480 73 55 214 48 2,812 210 2,025 598 141

White spruce 7,706 1,009 1,394 436 9 7 410 65 1,030 406 48 434 121

Other speciesb 242 127 33 36 — — 2 2 19 2 (12) 33

 Total 902,505 28,930 43,402 4,731 17,183 6,944 8,007 741 55,244 7,919 3,384 9,598 895

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 tons was estimated.

Note: Does not include Glacier Bay National Park or wilderness in national forests.

Note: Uses only trees ≥ 5 inches diameter at breast height at first inventory on plots that were forested at both the first and second inventories.

a

 nspecies denotes number of remeasured plots where the species occurred; sample size was 2,355 remeasured plots.

b Other species include Pacific silver fir, Pacific yew, and subalpine fir.

Table 11—Change in aboveground live tree carbon on forest land by owner group between 1995–2003 and 

2004–2008, coastal Alaska

Survivor Net

Carbon time 1 Mortality Harvest Ingrowth growth change

Owner Value SE Value SE Value SE Value SE Value SE Value SE

Thousand tons

Chugach National Forest 22,577 3,163 671 241 — — 135 27 1,411 350 875 410

Tongass National Forest 333,026 12,835 17,019 2,276 3,222 2,504 2,322 315 18,693 2,068 773 3,978 

Other federal 11,304 1,840 911 185 — — 314 57 1,040 223 443 212

State and local government 45,418 5,318 1,407 397 28 30 550 120 4,345 640 3,461 790 

Private 38,928 5,162 1,693 446 5,342 2,405 683 148 2,492 365 (3,860) 2,529

 Total 451,253 14,465 21,701 2,366 8,592 3,472 4,003 371 27,981 2,203 1,692 4,799

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 tons was estimated.

Note: Does not include Glacier Bay National Park or wilderness in national forests.

Note: Uses only trees ≥ 5 inches diameter at breast height at first inventory on plots that were forested at both the first and second inventories.
28

GENERAL TECHNICAL REPORT PNW-GTR-835

Change in forest-land area—

The biomass, volume, and carbon change estimates reported 

here do not reflect any increase or decrease associated with 

land that is deforested owing to development or natural 

causes or land that becomes forested through such causes 

as increased elevation of treeline or drying of wetlands. The 

alteration of the forest definition between the two inventories prevented tracking biomass changes that were due to 

such reversion or diversions. To try to get a rough estimate 

of area of land that diverted to or reverted from forest land, 

an experienced field crew member, working in the office 

and using notes taken in the field, classified every change 

from forest to nonforest (or vice versa) at the center of the 

four subplots for every plot as either (a) procedural change 

or (b) real change.

The results of this procedure are that an estimated 

11,000 acres changed from forest to nonforest, and an estimated 55,000 acres changed from nonforest to forest. This 

is a net gain of 44,000 acres, out of the 10,406,000 forested 

acres in the remeasurement inventory. Overall, this is an 

increase of less than one-half of one percent for the period 

1995–2003 to 2004–2008. Expressed as an annual rate, 

this is a net increase of 7,000 acres per year for the coastal 

Alaska region. Because of the very small number of plots 

where change was detected, and the difficulty of separating 

out real change from procedural change, there is a great deal 

of imprecision in this estimate. Plot notes where real change 

occurred indicate such reasons as a flood plain slowly being 

colonized by trees and forest creeping into muskeg.

Interpretation

Of four early-succession species (paper birch, black 

cottonwood, quaking aspen, and red alder), three showed 

significant increases in biomass, which suggests some 

change in biophysical conditions favorable to pioneering 

species (fig. 25). The increase in tree biomass in the two 

higher elevation ecoregions (the Chugach-St. Elias Mountain ecological section in south-central Alaska and the 

Northern Coast Mountain ecological section in southeast 

Alaska) would not be accounted for by increase in treeline 

elevation, as only areas defined as forest at both inventories 

were used in calculations of biomass change. However, it 

could indicate a general increase in tree density or size in 

higher elevation forests, which would be consistent with the 

increasing treeline elevation noted by Dial et al. (2007) and 

improving conditions for tree growth in higher elevation 

Table 12—Change in gross volume on timberland by owner group between 1995–2003 and 2004–2008, 

coastal Alaska

Volume Survivor Volume

time 1 Mortality Harvest Ingrowth growth time 2 Net change

Owner Value SE Value SE Value SE Value SE Value SE Value SE Value SE

Million cubic feet

Chugach 1,623 295 51 22 — — 7 2 113 32 1,692 301 70 35

 National Forest

Tongass 23,001 1,384 1,066 182 262 231 157 29 1,376 208 23,244 1,377 205 362

 National Forest

Other federal 188 144 11 10 — — — — 23 18 200 153 12 9

State and local 3,289 485 83 29 2 3 37 10 358 58 3,598 521 309 65 

 government

Private 3,286 496 154 44 499 220 48 12 232 37 2,913 478 (373) 232 

 Total 31,387 1,535 1,364 191 764 319 250 21 2,102 220 31,648 1,536 223 436 

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet was estimated.

Note: Uses only live trees ≥ 5 inches diameter at breast height at first inventory on plots that were forested at both the first and second inventories.

Note: Does not include Glacier Bay National Park or wilderness in national forests.

Note: Gross volume does not include deductions for rotten or missing cull. In the 2004–2008 inventory, the ratio of net cubic-foot volume to 

gross cubic-foot volume on timberland was 91 percent on the Chugach National Forest, 90 percent on the Tongass National Forest, 95 percent 

on other federal land, 92 percent on state land, and 93 percent for privately owned land.
29

Forests of Southeast and South-Central Alaska, 2004–2008

areas. All of the changes noted in this report—an increase 

in biomass of pioneering species, an increase in biomass 

in higher elevation ecoregions, and the increase in forested 

area from drying wetlands—would not contribute greatly 

to total biomass in the region because they are areas of low 

biomass to begin with. 

The overall stability of biomass and carbon in live trees 

in southeast and south-central Alaska during the period 

of monitoring is intriguing. The species of the coastal 

temperate rain-forest biome are at the northern limits of 

the species range. We might expect, given general trends of 

climate warming, that these species might be expanding in 

numbers and biomass. Tree ring studies suggest individual 

tree growth has been responding to climate trends in this 

region (Wiles et al. 2004).

However, the three most common species—western 

hemlock (fig. 26), Sitka spruce, and mountain hemlock, 

which together make up 80 percent of total tree biomass 

in the coastal region are very stable—with no statistically 

significant change in biomass and carbon. In contrast, two 

of the minor components of this biome, lodgepole pine and 

western redcedar, are showing trends in opposite directions, 

Figure 25—Paper birch did not show a significant change in 

carbon and biomass, but it had one of the highest rates of harvest 

mortality. The three other hardwood species in the region (aspen, 

red alder, and black cottonwood) all showed a significant increase 

in biomass between 1995–2003 and 2004–2008.

Figure 26—Western hemlock 

forest near Ketchikan, Alaska. 

Western hemlock is the most 

common tree species in the 

temperate rain-forest biome 

and was found on 556 of 895 

inventory plots. However, it 

showed no significant change 

in biomass between 1995–2003 

and 2004–2008.

Marc Much

Nicole Amato
30

GENERAL TECHNICAL REPORT PNW-GTR-835

with lodgepole pine decreasing by 4.7 percent and western 

redcedar increasing by 4.2 percent. Overall net change in 

biomass and carbon was less than one half of one percent, 

which was not significantly different than zero.

There are three possible explanations for the apparent 

stability of carbon and biomass in the coastal temperate 

rain-forest biome. One is that there are multiple vectors of 

change, which can push forest change in different directions. If the biomass of harvested trees was included with 

net change in the remaining forest, biomass increase in the 

region would shift from 3.7 million tons to 15.5 million 

tons. Thus there is an increase in carbon and biomass in the 

region from increased growth that is being counterbalanced 

by harvesting. Carbon accounting for forests is notoriously 

difficult (Cornwell et al. 2009). For carbon accounting, 

some portion of carbon in harvested trees would likely be 

considered as sequestered, but there is little empirical data 

for carbon in dead wood, peat, soil, and water in forest ecosystems, which can contain significant amounts of carbon 

and where the impact of disturbance on carbon fluxes is 

largely unknown.

The second explanation for stability of carbon and 

biomass is the size of the inventory region. The common 

species in the region—western hemlock, Sitka spruce, and 

mountain hemlock—may be undergoing the same type 

of slow migration that is hypothesized for yellow-cedar. 

(See chapter 4 for additional discussion on yellow-cedar). 

However, because the coastal temperate rain-forest biome 

spans a wide range of elevation gradients and distance, tree 

species could be dying in some parts of their range and 

thriving in other parts of their range, resulting in relatively 

little net change for the greater temperate rain-forest biome 

in Alaska.

The third contributing factor is the relatively short time 

between remeasurements. The mean remeasurement period 

for stands was 8.9 years in southeast Alaska and 5.4 years 

in south-central Alaska. Potential growth in the coastal 

temperate rain-forest biome is lower than in the comparable 

coastal biomes of Oregon and Washington, requiring a 

longer period to detect increases or decreases in growth 

rates. In the boreal forests of the western Kenai, the main 

known disturbance, beetle outbreaks, had subsided before 

the initial measurements.

Changes in forest area could also potentially contribute 

to changes in tree carbon and biomass. Glacial recession 

in southeast and south-central Alaska has frequently been 

pointed to as a sign of warming climate (Larsen et al. 2007). 

As glaciers recede, shrub species move in, often followed 

by tree species. Warmer temperatures could lead to drying 

of wetland areas (fig. 27) and allow forest area to increase. 

Our ability to track forest increases and decreases was negatively impacted by procedural changes for the inventory. 

However, the magnitude of observed change was on the 

order of a 7-percent-per-century increase in forest land in 

the coastal region, assuming that the rate remains constant; 

this is not conclusive, but it suggests that glacial recession, 

treeline elevation increases, and other sources of forestation will take some time to have noticeable impact on total 

forest area or make significant contributions to biomass and 

carbon accumulation. Future inventory assessments may be 

able to detect more types of change, as the length between 

remeasurement increases and the number of remeasured 

plots doubles by 2014. 
31

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 27—Warmer temperatures could lead to drying of wetlands and an increase of forested areas as tree species move in, but the 

most recent inventory data suggest that afforestation rates are small. 

Jon Williams
32

GENERAL TECHNICAL REPORT PNW-GTR-835

Jon Williams
33

Forests of Southeast and South-Central Alaska, 2004–2008

Although trees are the most noticeable vegetation in 

forests, other life forms are also important components of 

forest systems. This section provides results on lichen, an 

important forest health indicator, from a lichen biodiversity 

assessment conducted by USDA Forest Service Forest 

Inventory and Analysis (FIA). The next section provides 

results on vascular plant diversity and composition, which 

is also a forest health indicator of FIA. The chapter concludes with a section describing some characteristics of 

older forests within the inventory region.

Lichen Biodiversity1

Background

Lichens are included among the FIA suite of forest health 

indicators (Jovan 2008). These organisms serve many basic 

and vital functions in forest ecosystems: they provide wildlife sustenance and habitat (Sharnoff and Rosentreter 1998), 

moderate hydrologic flux in the forest interior, and affect 

stand mineral budgets by enhancing inputs of important 

plant nutrients like nitrogen (N), calcium, and magnesium 

(Knops et al. 1996). Certain groups of lichen species are 

intimately linked to forest health. For instance, two critical 

functional groups of lichens in southeast Alaska include the 

cyanolichens and long beard-like “forage” lichens primarily 

from the genera Alectoria, Bryoria, and Usnea (see table 57 

in app. 2 for species names). Cyanolichens are a specialized 

group of native lichens that fix N and make substantial contributions to forest fertility in N-limited stands of the Pacific 

Northwest (PNW) (Antoine 2004) (fig. 28). Forage lichens 

(fig. 29) are common nesting material for a variety of birds 

and rodents and are critical winter forage for southeast 

Alaska’s big game ungulates like the Sitka black-tailed deer 

(Odocoileus hemionus sitkensis), moose (Alces alces gigas), 

and the coastal mountain goat (Oreamnos americanus). 

Chapter 3: Forest Structure and Function

1 Author: Sarah Jovan.

Figure 28—Nephroma isidiosum (Pepper paw), an uncommon 

cyanolichen found in southeast Alaska.

Figure 29—Usnea longissima (Methuselah's beard), a forage 

lichen that is popular among birds and rodents as nesting material. 

Heather Root Sarah Jovan
34

GENERAL TECHNICAL REPORT PNW-GTR-835

Methods 

Between 2004 and 2008, FIA crews surveyed for epiphytic 

(tree-dwelling) lichens on all phase 3 (app. 1) plots (n = 132) 

in southeast and south-central Alaska. Surveyors spent a 

minimum of 30 minutes and a maximum of 2 hours recording the abundance of each lichen species occurring within 

a 0.94-ac area centered on the typical FIA phase 2 plot 

(app. 1).

Abundance codes used in lichen community surveys. 

A lichen body is known as a thallus:

Code Abundance

1 Rare (1 to 3 thalli)

2 Uncommon (4 to 10 thalli)

3 Common (>10 thalli; species occurring on less 

than 50 percent of all boles and branches in plot)

4 Abundant (>10 thalli; species occurring on greater 

than 50 percent of boles and branches in plot)

Results

Species richness of epiphytic lichen communities varied 

widely across the study area, and no prominent diversity 

hotspots were detected (fig. 30) (table 13). A total of 142 

lichen species were recorded among all surveyed plots in 

southeast and south-central Alaska, which is a considerable 

portion (54 percent) of total diversity found across all plots 

surveyed in California, Oregon, and Washington (Jovan 

2008). Most Alaska plots are located in a belt of temperate rain forests (n = 115) known as the Coastal Rainforest 

ecoregion (Nowacki et al. 2002). Although cooler, these 

rain forests are characterized by a wet, mild climate very 

similar to conditions affecting PNW rain forests to the 

south in the Oregon and Washington Coast Range (Bailey 

1983). Overall alpha richness in Alaska rain forests was 

high: plots had an average of 18 lichen species, significantly 

higher than the average in PNW rain forests (14 species; 

t-test p < 0.01). About 65 percent of plots in Alaska rain 

forests contained 16 species or more compared to 45 percent 

of PNW rain-forest plots. Where intact forest habitat is 

available, the moist, maritime climate of the Pacific west 

coast will support a rich and complex epiphytic lichen flora. 

Only 19 of 115 Alaska rain-forest plots hosted a depauperate 

community of 10 species or fewer; most of these sites had 

been recently clearcut, and the remainder were young stands 

recovering from natural disturbance. In contrast, more than 

a third of PNW rain-forest plots were depauperate (27 of 73 

Figure 30—Forest Inventory and Analysis (FIA) plots sampled for epiphytic lichen communities. Color indicates categories of total 

lichen species richness (depicted plot locations are approximate). Source: USDA Forest Service, FIA program, 2004–2008 data. Ecological section boundaries from Nowacki et al. (2002).

Kenneth C. Winterberger
35

Forests of Southeast and South-Central Alaska, 2004–2008

plots) likely owing in large part to the landscape being more 

impacted by timber harvest, fire, and air quality stressors. 

In both Alaska and PNW rain forests, about 39 percent 

of regionwide diversity consists of the ecologically important N-fixing cyanolichen and forage lichen functional 

groups. Unlike PNW rain forests, however, nearly all 

sampled Alaska rain forests contained several species from 

both functional groups. An average of 28 and 22 percent of 

species richness at Alaska rain-forest plots was composed 

of cyanolichens and forage species, respectively. Cyanolichens were not widely distributed in PNW rain forests and 

made a significantly smaller contribution to community 

richness (7 percent; t-test p = < 0.0001). Forage lichens, 

predominantly from the genus Usnea, were comparatively 

widespread in the PNW plots, and there was no significant 

difference in the contribution of forage lichens to PNW 

versus Alaska rain forests (t-test p = 0.08).

In air quality studies, most forage species are classified as N-sensitive indicator species known as oligotrophs 

(i.e., acidophytes) (Jovan 2008). The nonforage genera 

that dominate southeast and south-central Alaska’s lichen 

communities (i.e., Cladonia, Hypogymnia, and Platismatia) 

also contain many oligotrophic species (app. 2, table 57). 

Abundance of cyanolichens is likewise a clean air indicator (McCune and Geiser 2009). By comparison, species 

positively indicating N pollution, called eutrophs (i.e., 

nitrophytes) were very rare. Eutrophs were detected at only 

about 7 percent of all surveyed sites in Alaska and were 

minor contributors to communities, averaging less than 0.5 

percent of plot-level richness. These weedy lichens were 

likely primarily associated with localized natural sources 

of N-like guano, their natural niche, versus anthropogenic 

sources of N pollution that boost eutroph populations in 

urban and agriculturally affected forests (Jovan 2008). 

The widespread abundance of pollution-sensitive 

species found in this preliminary analysis indicates a lichen 

flora characteristic of healthy forest ecosystems largely 

unstressed by the regional air quality issues detected by FIA 

biomonitoring studies in the more populated Pacific States 

to the south (Fenn et al. 2008, Geiser and Neitlich 2007, 

Table 13—Summary of lichen community indicator species richness, 2004–2008 

Alaska Oregon and 

Coastal Alaska Range coastal Washington

Parameter Alaska transition rain forests Coast Ranges

Number of plots 132 17 115 73

Number of plots by lichen species richness category:a b

<6 species 3 0 3 13

6–15 species 44 7 37 27

16–25 species 71 7 64 27

>25 species 14 3 11 6

Median species richness 18 17 18 15

Range of species richness per plot (Low–High) 2–33 8–28 2–33 0–32

Range of forage species richness 0–22 2–10 0–22 0–8

Range of cyanolichen species richness 0–20 0–10 0–20 0–8

Average species richness per plot (alpha diversity) 18 18 18 14

Average forage species richness 4 4 4 3

Average cyanolichen species richness 5 4 5 1

Standard deviation of species richness per plot 6 6 6 8

Species turnover rate (beta diversity)c 8 4 8 2

 Total number of species per area (gamma diversity) 142 75 142 120

a

 Categories are based on a cumulative distribution function of plot species richness for the Southeastern U.S. gradient model.

b

 Plots with no lichens are included.

c

 Beta diversity is calculated as gamma diversity divided by alpha diversity. 
36

GENERAL TECHNICAL REPORT PNW-GTR-835

Jovan 2008). Pollution risks in southeast and south-central 

Alaska are likely in close association with point sources 

and urban areas, a finding corroborated by a previous 

large-scale lichen inventory by Geiser et al. (1998). A more 

intensive FIA analysis of lichen communities is underway 

that will allow detection of more subtle and localized 

pollution effects on species composition. Results will be 

available in 2011. 

Vegetation Diversity2

Background

The coastal Alaska inventory encompasses extremely 

rugged topography in a land of dynamic geomorphic 

processes and climatic conditions that strongly influence the 

vegetation diversity of the area. Vegetation composition and 

structure are often assessed to determine current and potential productivity for wildlife habitat or forest products, and a 

site’s likely response to disturbance. Some species occur in 

certain situations so commonly that they are used as “indicators” of certain site conditions; others are known for their 

value for providing wildlife food or shelter. Understanding 

which species grow where and with what other species can 

inform many management-related questions. Groups of 

plant species occur together across the landscape where site 

conditions are favorable; these groups are described as plant 

community types or associations. 

Plant community classifications are useful 

communication tools for managers responsible for 

sustainable ecosystem management. Land managers 

operating within the area of the coastal Alaska inventory 

have several local classification systems (table 14) to 

reference when planning management activities or 

developing conservation strategies. These classifications are 

based on species composition data from thousands of plots 

in areas nested within the larger coastal Alaska inventory 

area and describe communities and associations in great 

detail, incorporating information on successional processes, 

indicator species, and resource management implications. 

With these classifications, the vegetation indicator species 

composition data from the forest health plots, a 1/16th

subsample of the regular FIA inventory plots, can further 

inform us about the vegetation diversity in coastal Alaska.

The vegetation indicator is designed to provide 

information on regional and national scales using standard 

methods on permanently located plots that will be revisited 

in the future (Schulz et al. 2009). We can establish baseline 

conditions with estimates concerning species richness 

and species distribution that we will monitor in the future 

for changes beyond those expected. By comparing FIA’s 

broader sample with the detailed local associations from 

areas nested within the inventory, similarities between 

neighboring classifications can be identified while providing a means to compare the local region with other areas 

of the country. These data greatly enhance the local plant 

association classifications by quantifying patterns of species 

richness and distribution that can be monitored over time, 

providing a measure of temporal change and successional 

direction. 

2

 Author: Bethany K. Schulz.

Table 14—Plant community classifications and associations relevant for the coastal area inventory, 

ordered from southeast to northwest

Reference Author year Area covered Approach

1 DeMeo et al. 1992 Ketchikan area, Tongass National Forest Climax vegetation

2 Pawuk and Kissinger 1989 Stikine area, Tongass National Forest Climax vegetation

3 Martin et al. 1995 Chatham area, Tongass National Forest Climax vegetation

4 Boggs et al. 2009 Glacier Bay National Park Existing vegetation

5 Shephard 1995 Yukatat Foreland, Tongass National Forest Existing vegetation

6 Boggs 2000 Copper River Delta Existing vegetation

7 Boggs et al. 2008 Kenai Fjords National Park Existing vegetation

8 DeVelice et al. 1999 Chugach National Forest Existing vegetation
37

Forests of Southeast and South-Central Alaska, 2004–2008

Methods

A complete inventory of the vascular plants was taken 

on the forest health plots for the Vegetation Diversity and 

Structure (VEG) indicator (USDA FS 2008a). The FIA 

plot design includes four circular subplots of about 1/24th

acre each (fig. 31). On each subplot, three 1-m2 (10.76 ft2) 

quadrats were established. Each vascular plant species 

occurring on the quadrat was recorded using plant symbols 

referencing the PLANTS Database (USDA NRCS 2000). 

The subplot was then surveyed and all vascular plant 

species present and their canopy covers (a measure of 

abundance) were recorded. Only accessible forest lands 

were measured; on some plots, fewer than four subplots 

were assessed (Schulz et al. 2009). From plot species data, 

species composition was summarized for each plot. Species 

richness was compiled for each sample unit; quadrat, 

subplot, and plot. Site conditions, elevation and slope, along 

with measures of forest structure—live basal area and stand 

age—were summarized for each plot. 

There were 135 forested plots in the forest health 

subsample, randomly distributed throughout the accessible 

forest lands of southeast and south-central Alaska. Some 

forested lands within the inventory area were inaccessible (wilderness areas) and could not be included in data 

summaries or results. To examine plant community types 

within the inventory area, only plots that were determined 

to be a single “forest type” (app. 1) that represented 

homogenous stands (Schulz et al. 2009, Tart et al. 2005) 

were considered. Plots were first grouped according to 

the ecological province where they occurred. Ecological 

provinces were determined by climatic and geomorphic 

characteristics and define areas with similar attributes that 

influence plant growth (Bailey 1995, Cleland et al. 2005). 

Plots were then grouped according to the forest type they 

were assigned by using a standard FIA computer algorithm 

based on tree species present (app. 1).

Each forest type within an ecological province with at 

least five plots was assessed (table 15). Forest types were 

summarized in terms of species richness, plant species 

composition, site conditions, and 

structural characteristics following procedures given in Schulz 

et al. (2009). Species richness 

was reported as average number 

of species per unit area and 

estimated at three scales: quadrat 

(10.76 square feet or 1 square 

meter), subplot area (0.0417 acre), 

and plot (0.167 acre).

Plant species composition 

data were compiled to describe 

the forest types in terms of the 

abundance and distribution 

of species commonly encountered. Plot-level plant species 

composition data were used to 

classify plots to locally described 

associations (table 14). Each 

classification system was unique Figure 31—Forest Inventory and Analysis plot design with vegetation quadrats.
38

GENERAL TECHNICAL REPORT PNW-GTR-835

to the area where it was developed; however, no attempt 

was made to use the plot location to limit classification to 

the key for that area. Instead, classification keys were used 

whenever the dominant tree species was included in the 

key, providing a better understanding of the distribution of 

the described forest type or association. Species and site 

attributes were compared to detailed descriptions of local 

plant associations. 

Results

Over the entire inventory area, a total of 442 unique species 

(including some plants not identified to species) were 

recorded on the 135 forest health plots; 151 of those species 

were recorded only once. Ninety-one percent of all plants 

observed were identified to species—of those, 99.7 percent 

were native species. The average number of species on fully 

forested plots was 37.1, with a standard error (SE) of 1.3, 

and a range of 12 to 86 species. The average on subplots 

was 23.9 (SE = 0.57), and on quadrats the average was 8.9 

(SE = 0.08). 

Table 15—Number of fully forested, single condition forest health plots per forest type by ecological province 

for coastal Alaskaa

Forest

Inventory

and Analysis Number

Ecological province code Forest type of plots

Alaska Range Transition 122 White spruce (Picea gluaca (Moench) Voss) 1

Alaska Range Transition 703 Cottonwood (Populus balsamifera L. ssp. trichocarpa 1 

(Torr. & A. Gray ex Hook.) Brayshaw)

Alaska Range Transition 125 Black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb). 4

Alaska Range Transition 270 Mountain hemlock (Tsuga mertensiana (Bong.) Carrière) 1

Alaska Range Transition 901 Aspen (Populus tremuloides Michx) 1

Alaska Range Transition 902 Birch (Betula papyrifera Marsh.) 5

Coastal Rainforest Mountains 270 Mountain hemlock 19

Coastal Rainforest Mountains 122 White spruce 1

Coastal Rainforest Mountains 271 Yellow-cedar (Chamaecyparis nootkatensis (D. Don) Spach) 15

Coastal Rainforest Mountains 301 Western hemlock (Tsuga heterophylla (Raf.) Sarg.) 28

Coastal Rainforest Mountains 305 Sitka spruce (Picea sitchensis (Bong.) Carr.) 10

Coastal Rainforest Mountains 703 Cottonwood 7

Coastal Rainforest Mountains 281 Lodgepole pine (Pinus contorta Douglas ex Louden) 2

Coastal Rainforest Mountains 304 Western redcedar (Thuja plicata Donn ex D. Don) 6

a

 Only forest types in bold are included in estimates by ecological province and forest type.

A list of the most commonly encountered species 

across the entire inventory was compiled (table 16), along 

with their indicator properties, constancies (the percentage 

of visited plots where the species was recorded), and 

average cover when encountered. Many species were 

ubiquitous throughout the area—seven species were 

found on over 70 percent of all plots. Some individual 

species were indicators of site conditions (DeMeo et al. 

1990, Martin et al. 1995). The 2 most common species are 

important deer forage and 3 of the top 12 are indicators 

of nutrient-rich soils. Nonnative species were recorded on 

the forested portions of eight plots and on the nonforest 

portion of two additional plots. (See “Invasive Plants in 

Coastal Alaska.”) 

The area of the coastal Alaska inventory extended 

into two ecological provinces; 117 plots were within the 

Coastal Rainforest Mountains ecological province, and 

18 were within the Alaska Range Transition province (fig. 

32). Of the 135 plots visited, 101 were located entirely 

within single-forest-type stands. On those plots, 391 
39

Forests of Southeast and South-Central Alaska, 2004–2008

Table 16—Most common species encountered on the forest health plots of the coastal Alaska inventory

Indicator Mean

Scientific name Common name valuea Constancy SEb cover SE

- - - - - - - - - - Percent - - - - - - - - -

Cornus canadensis L. Bunchberry df 79.26 0.035 5.73 0.533

Rubus pedatus Sm. Fiveleaf bramble df 78.52 .035 4.60 .491

Menziesia ferruginea Sm. Rusty menziesia hlght 76.3 .037 8.09 0.734

Vaccinium ovalifolium Sm. Early blueberry dwe 75.56 .037 21.59 1.756

Picea sitchensis (Bong.) Carrière Sitka spruce fld, sd, sspr 74.81 .037 12.49 1.623

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 74.81 .037 1.07 .115

Gymnocarpium dryopteris (L.) Newman Western oakfern 70.37 .039 6.75 .644

Athyrium filix-femina (L.) Roth Common ladyfern wms, nr 67.41 .04 4.76 .746

Listera cordata (L.) R. Br. ex Ait. f. Heartleaf twayblade 65.19 .041 0.85 .108

Dryopteris expansa (C. Presl) Spreading woodfern wdr, nr 62.96 .042 3.40 .548

Fraser-Jenkins & Jermy

Tsuga heterophylla (Raf.) Sarg. Western hemlock wdr 61.48 .042 33.74 2.717

Oplopanax horridus (Sm.) Miq. Devil’s club wms, nr 59.26 .043 6.00 1.501

Blechnum spicant (L.) Sm. Deer fern 58.52 .043 2.55 .357

Rubus spectabilis Pursh Salmonberry fld,sd 53.33 .043 6.82 1.211

Tiarella trifoliate L. Threeleaf foamflower df 53.33 .043 2.20 .221

Tsuga mertensiana (Bong.) Carr. Mountain hemlock cld, dsr 53.33 .043 20.69 2.037

Coptis aspleniifolia Salisb. Fernleaf goldthread df 52.59 .043 4.67 .56

Lycopodium annotinum L. Stiff clubmoss 49.63 .043 1.68 .355

Lysichiton americanus Hultén & St. John American skunkcabbage bf, pdr 48.15 .043 5.17 .63

Veratrum viride Aiton Green false hellebore 42.96 .043 1.75 .2

Alnus viridis (Chaix) DC. ssp. sinuata Sitka alder 40.74 .042 22.77 3.41 

(Regel) A. Löve & D. Löve

Nephrophyllidium crista-galli (Menzies Deercabbage cld, np, pdr 39.26 .042 9.10 1.093 

ex Hook.) Gilg

Streptopus lanceolatus (Aiton) Reveal Twistedstalk 36.30 .041 2.74 .508

var. roseus (Michx.) Reveal

Clintonia uniflora (Menzies ex J.A. & Bride's bonnet 35.56 .041 .97 .226

J.H. Schultes) Kunth

Vaccinium parvifolium Sm. Red huckleberry 35.56 .041 3.50 .618

Moneses uniflora (L.) A. Gray Single delight 34.81 .041 .57 .128

Equisetum arvense L. Field horsetail 34.07 .041 8.22 2.7

Chamaecyparis nootkatensis (D. Don) Alaska yellow-cedar pdr 31.85 .04 22.27 2.358

Spach

Phegopteris connectilis (Michx.) Watt Long beechfern 31.11 .04 2.83 .479

Vaccinium vitis-idaea L. Lingonberry 30.37 .04 3.84 .807

a

 Indicator values: bf = bear forb; cld = cold soils; df = deer forage; dsr = deer summer range; dwe = deer winter energy; fld = flooding; 

hlght = high light; np = nutrient poor; nr = nutrient rich; pdr = poorly drained; sd = site disturbance; sspr = salt spray; wdr = well-drained soils; 

wms = water moving through soils.

b SE = standard error.
40

GENERAL TECHNICAL REPORT PNW-GTR-835

species were recorded, with 145 species recorded only once. 

The 101 plots were summarized by forest types within each 

ecological province, resulting in 14 ecological province/

forest type combinations (table 15). One-half of the 

combinations were sampled with at least 5 plots; these seven 

forest types included 90 of the 101 plots. 

Forest type species richness assessed for the standard 

sample areas highlighted the patterns of species-area 

relationships (fig. 33). The figure is arranged in order of 

decreasing plot species richness. Yellow-cedar, western 

redcedar, and mountain hemlock stands had the highest 

species richness; their average subplot species richness was 

similar to plot-level richness in western hemlock, Alaska 

paper birch, and Sitka spruce stands. The three scales 

of species richness revealed a pattern in the birch forest 

type suggesting a high percentage of species were evenly 

distributed throughout these stands; subplot alpha was about 

75 percent of plot alpha, and quadrat alpha about 40 percent 

of the birch subplot alpha.

The plot with the greatest plot species richness (86) 

was a mountain hemlock forest type. The plot with the 

least number of species (12) was a cottonwood forest type. 

Both were in the Coastal Rainforest Mountains province. 

Figure 32—Distribution of forest health plots by forest type (depicted plot locations are approximate).

Interestingly, the plot with the second highest number of 

plot species (73) was a cottonwood forest type, the single 

sample of this type in the Alaska Range Transition ecological province (table 15).

Species composition of most individual plots could 

be classified to an association or cover type by using at 

least one of the local classification systems. Plot data of 

well-distributed forest types matched descriptions from 

multiple systems. The species composition of a few plots 

defied classification to a described association or cover type. 

Conversely, the forest health subsample of the FIA inventory plots did not sample every association or cover type 

described in each classification.

Structural (stand age and live basal area) and physical 

(elevation and percentage of slope) characteristics for forest 

types are shown in table 17. Ranges are included with average values; each forest type includes a wide range of values, 

but averages describe “typical” values that help us visualize 

where the communities occur over the wider landscape. 

Summarized forest types are described below, along 

with a listing of plant associations matched to the plots 

within each type referenced to the classifications found in 

table 14.

Kenneth C. Winterberger
41

Forests of Southeast and South-Central Alaska, 2004–2008

60

50

40

30

20

10

0

Average number of species

Forest type

Alaska yellow-cedar

Western redcedar

Mountain hemlock

Cottonwood

Western hemlock

Alaska paper birch

Sitka spruce

Plot (0.167 acre)

Subplot (0.0417 acre)

Quadrat (0.000247 acre)

Figure 33—Average number of species per sample unit area by forest type. Lines at end of 

bars represent ± standard error.

Table 17—Physical and structural attributes of summarized forest community types

Stand age Live basal area Elevation Slope

Forest type Average (SE) Range Average (SE) Range Average (SE) Range Average (SE) Range

- - - - - - Years - - - - - - - - Square feet/acre - - - - - - - - - Feet - - - - - - - - - - - Percent - - - -

Western hemlock 195 (16.25) 0–369 201 (17.10) 0–350 536 (80.56) 100–1700 37 (5.60) 0–115

Mountain hemlock 213 (20.23) 48–329 124 (16.64) 9–273 1511 (131.59) 500–2700 35 (3.93) 0–65

Yellow-cedar 255 (30.68) 63–544 153 (25.46) 22–352 793 (108.57) 300–1700 33 (7.28) 0–125

Sitka spruce 95 (19.08) 26–191 150 (23.11) 0–231 420 (114.33) 100–1100 11 (3.63) 0–35

Western redcedar 138 (40.98) 15–281 183 (62.65) 4–410 300 (94.69) 100–700 16 (7.43) 0–55

Cottonwood 53 (6.16) 25–74 72 (23.66) 6–186 414 (176.22) 100–1500 9 (5.78) 0–40

Birch 74 (24.54) 25–166 62 (23.59) 6–146 460 (135.26) 100–900 13 (6.46) 0–40

Note: SE = standard error.
42

GENERAL TECHNICAL REPORT PNW-GTR-835

The western hemlock forest type was the most 

common within the inventory area. Common species 

present indicate a wide variety of sites, from nutrient poor 

to nutrient rich (app. 2, table 58). Deer forage species were 

common. This forest type had lower values for species 

richness, but had the highest basal area. Stands were 

often on slopes exceeding 30 percent, and were found at 

an elevation of about 500 feet (table 17). This species and 

forest type extended as far north as Prince William Sound, 

but the sampled forest health plots of western hemlock 

forest type were all located in the southern third of the 

inventory area (fig. 32). The best represented associations 

were western hemlock/blueberry (eight plots), western 

hemlock-western redcedar/blueberry (three plots), and 

mixed conifer/blueberry (three plots). The moderately 

productive western hemlock/blueberry association is 

the most abundant association in the Chatham area, is 

common in the Stikine and Ketchikan areas (Martin et al. 

1995), and is also widespread in eastern Prince William 

Sound (DeVelice et al. 1999). 

Western hemlock/devil’s club (2, 3, 4)

Western hemlock/blueberry (1, 2, 3, 5, 6, 8)

Western hemlock/blueberry-devil’s club (1, 2, 3, 5, 6, 8)

Western hemlock/blueberry/American skunk cabbage 

 (1, 2, 3, 8)

Western hemlock-western redcedar/blueberry (1)

Western hemlock-western redcedar/blueberry-Salal (1)

Western hemlock-yellow-cedar/early blueberry (1, 2, 3)

Western hemlock-yellow-cedar/early blueberry/

 American skunk cabbage (1, 2, 3)

Mixed-conifer/blueberry (1, 2, 3, 4, 5)

Mixed-conifer/blueberry/American skunk cabbage 

 (1, 2, 4, 5)

Mixed-conifer/blueberry/deer cabbage (1, 3)

Sitka spruce-(western hemlock)/blueberry (1, 3, 8)

Mountain hemlock/early blueberry (1, 2, 4, 5)

The mountain hemlock forest type communities 

were dominated by mountain hemlock, the second most 

common tree species within the entire inventory area. 

These were some of the older stands, second only to 

yellow-cedar in age, but with lower live basal area than 

most other types. These stands were found at the highest 

elevations (on average, 1,500 feet or more), and slopes (35 

to 40 percent) (table 17). Plant species also indicated colder, 

higher elevation sites, more open canopy, and a variety of 

microsites (app. 2, table 58). The mountain hemlock type 

was among the most species rich within the inventoried area 

(fig. 33); the single individual plot with the most species 

(86) was a mountain hemlock type (mountain hemlock/

blueberry). The 19 plots classified to 19 types, with the 

mountain hemlock/cassiope association most frequently 

observed. The long list of associations was partially due to 

different naming conventions; many plots keyed to similar 

associations in different classifications with different 

names. It also reflected the overall diversity of plant 

associations in the mountain hemlock forest type, which 

was sampled throughout the inventory area (fig. 32).

Mountain hemlock/Sitka alder (7, 8)

Mountain hemlock/cassiope (1, 3, 4, 7, 8)

Mountain hemlock/cassiope-deer cabbage (2)

Mountain hemlock/rusty menziesia/sparse (8)

Mountain hemlock/deer cabbage (7)

Mountain hemlock/devil’s club (7)

Mountain hemlock/early blueberry (1, 2, 3, 4, 5, 7, 8)

Mountain hemlock/early blueberry/deer cabbage (2, 3, 4, 8)

Mountain hemlock/early blueberry-copperbush (7, 8)

Mountain hemlock/early blueberry-copperbush/

 deer cabbage (1, 2, 3)

Mountain hemlock/early clueberry/Steller’s cassiope (7)

Mountain hemlock/copperbush (1, 3, 5)

Mountain hemlock/copperbush-cassiope (1, 3)

Mountain hemlock-Sitka spruce/early blueberry- 

 devil’s club (1, 3, 4, 8)

Mountain hemlock-western hemlock/early blueberry/

 deer cabbage (8)

Mountain hemlock-western hemlock/early blueberry-

 rusty menziesia (8)

Mixed-conifer/blueberry (1, 3, 5)

Mixed-conifer/blueberry/American skunk cabbage (1, 3, 5)

Sitka spruce-mountain hemlock/blueberry (1, 3, 7)

The yellow-cedar forest type had the highest average 

species richness (fig. 33), as the oldest of all forest types 

(table 17), and always had a mix of tree species present (app. 

2, table 58). These stands were often at 800 feet elevation 
43

Forests of Southeast and South-Central Alaska, 2004–2008

or higher, on slopes of about 30 percent and ranged from 

sea level to the mountain hemlock zone, most often on 

poorly drained sites (Martin et al. 1995). Common species 

indicated a variety of site conditions, deer forage and habitat 

(table 58). Yellow-cedar wood is highly valued (DeMeo et 

al. 1992), and its decline has been the focus of intense study 

(refer to yellow-cedar chapter).

The 15 plots assigned to the yellow-cedar forest type 

classified to nine described associations, listed below. Most 

common were mixed-conifer/blueberry/deer cabbage and 

mixed-conifer/blueberry/American skunk cabbage, with 

five plots each. According to Martin et al. (1995), the deer 

cabbage type provides better wildlife habitat, has a more 

open canopy, and has lower productivity, whereas the skunk 

cabbage type is moderately productive, but provided lower 

overall quality for habitat. Yellow-cedar occurred as far 

north as Prince William Sound, but sampled plots were all 

located in the southern half of the inventory area (fig. 32). 

Western hemlock-yellow-cedar/early blueberry (1, 3, 4)

Mixed conifer/blueberry (1, 3, 5) 

Mixed conifer/blueberry/American skunk cabbage (1, 3, 4)

Mixed conifer/blueberry/deer cabbage (1, 2, 3, 4)

Shore pine/crowberry (1, 2, 3, 4) 

Mixed conifer/copperbush (3, 4) 

Mixed conifer/copperbush/deer cabbage (1)

Mountain hemlock/early blueberry (1, 3)

Mountain hemlock/early blueberry/deer cabbage (2)

The Sitka spruce forest type plots were among both 

the youngest and most productive in the inventory (table 17) 

when the ratio of live basal area to age is considered. Sitka 

spruce forest type also had the lowest species richness (fig. 

33). Common species (including Sitka spruce) indicated 

sites with a high incidence of disturbance, usually flooding 

or water moving through the soil on steep slopes prone to 

landslides, and nutrient-rich conditions (app. 2, table 58). 

Management implications discussed in local classifications 

note potential conflicts when large, highly valued Sitka 

spruce trees grow in riparian zones bordering salmon 

streams. Stands of large trees also provide critical habitat 

for terrestrial wildlife (DeMeo et al. 1992, Martin et al. 

1995). 

Pure Sitka spruce stands were uncommon, but Sitka 

spruce is the most ubiquitous tree species in the inventory 

(table 16). Only 10 plots had Sitka spruce stands, but these 

plots were distributed from Kodiak to Ketchikan (fig. 32). 

Sitka spruce stands occurred in relatively small patches generated by a site disturbance over a discrete area, landscape 

features that are often missed by the FIA sampling grid. 

Many associations were not captured in the sample of forest 

health plots. The 10 plots classified to 11 associations; some 

plots fit to several different associations owing to subtle differences in keys and different naming conventions among 

the classifications. DeMeo et al. (1992) acknowledged many 

upland types remain undescribed because of the steep, 

hazardous terrain they occupy.

Sitka spruce/Sitka alder (3, 4, 5, 6, 8)

Sitka spruce-red alder/salmonberry (1, 3)

Sitka spruce/blueberry (1, 3, 4, 5, 7)

Sitka spruce/blueberry-devil’s club (1, 3, 5, 7)

Sitka spruce/devil’s club (1, 3, 4, 5, 6, 8)

Sitka spruce/devil’s club-salmonberry (1, 3, 5, 8) 

Sitka spruce/moss (Bryophyte) (5, 6, 7)

Sitka spruce-cottonwood/Sitka alder (5, 8) 

Sitka spruce-mountain hemlock/blueberry (1, 3, 7)

Sitka spruce-mountain hemlock/early blueberry- 

 devil’s club (7)

The western redcedar forest type had the second 

highest species richness of all the forest type communities 

(fig. 33). Found at lower elevations, western redcedar forest 

type had the second highest average basal area (table 17). 

This type was confined to the southern portion of the inventory area, which is the northern limit of western redcedar’s 

range (fig. 32). The western redcedar forest type had eight 

species that occur on all plots (app. 2, table 58), including 

co-dominant western hemlock. It was distinguished by the 

frequent occurrence of red huckleberry and salal, which 

were both uncommon in other forest types (app. 2, table 

58). Salal is not found north of the southern Stikine area; its 

range limitations are similar to western redcedar. Blueberry 

understories were associated with higher productivity 

(DeMeo et al. 1992). This forest type has the most limited 

range of all forest types in the Coastal Rainforest Mountain 
44

GENERAL TECHNICAL REPORT PNW-GTR-835

ecological province, and is only included in classifications 

for the Ketchikan and Stikine areas of the Tongass National 

Forest. Six plots were classified to six different associations.

Western hemlock-western redcedar/salal (1)

Western hemlock-western redcedar/blueberry (1, 2)

Western hemlock-western redcedar/blueberry-salal (1)

Western hemlock-western redcedar/blueberry-salal/

 American skunk cabbage (1)

Western hemlock-western redcedar/blueberry-

 American skunk cabbage (1)

Western hemlock/early blueberry/American 

 skunk cabbage (1)

The cottonwood forest type plots had variable, midrange species richness (fig. 33) and were relatively young 

stands (table 17). Species composition indicated recently or 

frequently disturbed sites (app. 2, table 58), similar to the 

Sitka spruce forest type. Cottonwood was found throughout 

the entire inventory area, but because it is not considered a 

climax species, this forest type was not included in classifications based on climax or potential vegetation communities. Cottonwood forest type plots were distributed from 

Kodiak to Yakutat (fig. 32). It is the only forest type in the 

Coastal Rainforest Mountains ecological province without 

early blueberry on the list of common species, and the only 

type over the entire inventory area that lacked bunchberry, 

the most ubiquitous species in the inventory (app. 2, table 

58).

Cottonwood/Sitka alder (4, 6, 8) 

Cottonwood/willow (5)

Sitka spruce-cottonwood/Sitka alder (5, 8) 

The birch forest type was sampled in the colder 

Alaska Range Transition ecological province, on the 

western Kenai Peninsula (fig. 32). It was the second youngest forest type, and had the lowest basal area (table 17). 

Although the FIA forest type specified paper birch, the 

species observed is more likely to be Alaska paper birch 

(Viereck and Little 2007). The five birch forest type plots 

produced a species list with six species present on all plots, 

providing a good description of this forest type within its 

limited extent of the coastal inventory. Two of those species 

were found throughout the inventory area in other forest 

types (table 16); three were much less common to the other 

summarized forest types (fireweed, arctic starflower, and 

twinflower) (app. 2, table 58). The most common species 

list was similar to the more general Society of American 

Forester’s description of paper birch forest cover type (Erye 

1980). Birch forest types were only covered in DeVelice 

et al. (1999), and all plots were located to the west of the 

Chugach National Forest. The species composition of 

several individual plots did not match any cover type on the 

Chugach National Forest. Some plots may have occurred 

in stands that had been assigned to the white spruce type 

prior to the spruce beetle outbreak. These stands are likely 

to continue to change in structure and composition as the 

forests regenerate in the coming years. 

Paper birch/bluejoint reedgrass (8)

Paper birch/rusty menziesia (8)

Interpretation

Plots were summarized by forest type within the two 

ecological provinces to facilitate comparisons to larger 

regions or other parts of the country as more FIA Vegetation Indicator data become available for analysis. The 

Coastal Rainforest Mountains ecological province is more 

thoroughly sampled than the Alaska Range Transition province, which only extends slightly into the area of the Alaska 

coastal inventory. Highest species richness for the inventory 

area is found on sites dominated by yellow-cedar, mountain 

hemlock, and western redcedar; the lowest species richness 

is found on sites dominated by Sitka spruce, birch, or western hemlock. In coastal Alaska species, richness is higher 

on sites that are of lower value for timber production; these 

stands have less overstory cover and a variety of microsites 

to support higher species diversity.

Reporting species richness for three standard sample 

areas gives a multiscale perspective of how species occupy 

the forest stands of each type. Although many species are 

ubiquitous throughout the inventory, some species and 

community types have limited distributions over the area. 

The procedures provide a standard method for assessing the 

differences in species richness and species and community 

distribution over time and space as permanent plots are 

remeasured.
45

Forests of Southeast and South-Central Alaska, 2004–2008

The FIA’s forest types, computed by algorithm, are 

roughly analogous to the Society of American Forester’s 

forest cover types (Erye 1980), forest (tree) cover types 

(DeVelice et al. 1999, Shephard 1995), forest series (DeMeo 

et al. 1992, Martin et al. 1995, Pawuk and Kissinger 1989), 

alliances (Tart et al. 2005), or landcover classes (Boggs et 

al. 2008, 2009). The local classifications then further distinguish each forest type to more specific community types 

or associations. Associations are usually differentiated 

along gradients, such as drainage, elevation, or frequency of 

disturbance; the presence of certain indicator species often 

reflects the differences in site conditions. Authors caution 

that each classification is developed for a particular area; 

for this study, species composition data were applied to a 

number of classifications to assess distribution of associations across the entire inventory.

Most plots could be classified to a described plant 

association or community type. The opportunity to compare 

individual plot species composition with locally developed 

classifications increases our understanding of the plant association distributions within the inventory area. Each FIAdefined forest type included a wide variety of described 

associations and community types, indicating the greater 

vegetation diversity each forest type represents. Evaluation 

of the assembled common species list for plants indicating 

site conditions or habitat values along with basic stand 

structure and physical setting attributes revealed similarities in site characteristics and helped predict where the plant 

communities could be found across the inventory area. 

The classification systems used differed in the types of 

vegetation assessed. DeMeo et al. (1992), Pawuk and Kissinger (1989), and Martin et al. (1995) all described potential 

or climax forest vegetation; the other authors described 

existing vegetation. Vegetation sampling for developing 

classifications usually occurred on temporary plots that 

would be difficult to relocate and remeasure. Although 

sampling methods were similar for each classification, 

sample plots differed in size and configuration. Therefore, 

it was not possible to assess or compare species richness of 

the plant associations based on the information provided by 

the classifications. 

The ability to describe plant communities enhances 

communication between land managers planning and 

performing management activities. The forest plant communities described from analyses of the VEG data provide 

a bridge between generalized descriptions of forest types 

(Erye 1980, Viereck et al. 1992) and the locally relevant, 

detailed vegetation plant association descriptions (table 14) 

developed by using many more, albeit temporary, plots. The 

forest health grid of plots is sparse compared to the thousands of plots that were sampled for the classifications. However, the grid provides permanently established plots that 

can be linked via species composition data of the vegetation 

indicator to the detailed locally defined plant associations 

and community types. The standardized data collection and 

sampling procedures used here help to describe the distribution of the more detailed associations within the inventory 

area and facilitate quantified comparisons of species richness and species distribution with other regions. 

Older Forests in Coastal Alaska3

Background

Older forests have been the subject of great controversy in 

the past decades. Generally, old-growth forests are characterized by old, large trees, snags, and downed logs (Franklin 

and Spies 1991). Additional characteristics can include 

diverse lichens, mosses, fungi, spatially uneven structure 

with occasional gaps where large trees have fallen, and large 

quantities of standing biomass (fig. 36). In most forest ecosystems, these old-growth characteristics only emerge many 

decades, even centuries, after a stand-replacing disturbance. 

In the United States overall, only a small proportion of the 

old-growth area that existed prior to European settlement 

remains in old-growth conditions; how small that proportion is has been a subject of considerable debate. Alaska is 

unique among U.S. states owing to the substantial proportion of forest that remains relatively unaffected by management activities such as land conversion, fire suppression, or 

timber harvest; this is true both for the coastal temperate 

rain forest and the boreal forests of interior Alaska.

3 Author: Tara M. Barrett.
46

GENERAL TECHNICAL REPORT PNW-GTR-835

A Day in the Life of Alaska FIA crew4

Data collection for Forest Inventory and Analysis 

(FIA) is done quite differently in coastal Alaska than 

in other U.S. states. A mile or so from the coastline, 

it is extremely difficult, if not impossible, to access 

plot locations from the shore by foot. Most of our plots 

are accessed via a helicopter from the back deck of an 

86-foot-long boat (fig. 34). We depart from Ketchikan 

in early June, work our way around and through the 

islands in southeast Alaska, out along the Gulf of 

Alaska, through Prince William Sound, out to Kodiak 

Island, and finally finish up on the Kenai Peninsula 

in August. Letting the boat or helicopter sit idle is 

expensive, so we operate 7 days a week for the full field 

season by keeping crews on rotating shifts. With a shift 

change every 5 days, and transport to and from the 

boat involving a complex system of helicopter, skiff, 

float plane, and commercial flights through southeast 

Alaska’s changeable weather, logistics are difficult.

A Good Day

Rise and shine at 6:30 am. After a brief session of 

calisthenics or yoga on the heli deck, pack your 

lunch and field gear. Contact dispatch to make sure 

they have the flight schedule for the day. Check on 

radio repeaters and the weather. We have a full crew 

briefing at 8:00 am. In our briefings we go over the 

plan for the day, review safety and any data quality 

issues, and conduct the helicopter briefing. By 8:15, 

the first crews, packs, survival bags, and shotguns 

are loaded into the helicopter. All crews are out in 

the field by 10:30 (fig. 35). Our captain does the flight 

following and keeps track of crew locations in the 

field. By mid to late afternoon, the crews start to call 

back to the boat; their plots are finished and they’re at 

their landing zone, ready for a ride back to the boat. 

Figure 34—Most plots have to be accessed by using a helicopter and boat.

4 Author: Connie Hubbard.

Brita Janson
47

Forests of Southeast and South-Central Alaska, 2004–2008

Back on the boat, plot data are downloaded, edited, 

and plot writeups are done. After dinner, the next day’s 

plan is made and posted. The schedule is e-mailed via 

satellite phone to dispatch, and load calculations for 

the helicopter are in the pilot’s hand. Quality assurance 

staff review the plot folders for our day’s work and back 

up our databases. 

A Not-So-Good Day

Some days the weather is not so good. Rain doesn’t 

really matter, but we need a minimum 500-foot 

cloud-free ceiling and a half mile of visibility, as well 

as less than 30 knot winds to fly. If the weather doesn’t 

allow us to fly to the plots we planned to visit, we’ll go 

right into plan B: identify plots at lower elevations that 

aren’t inhibited by low clouds, or plots we can access 

with a skiff. Sometimes there isn’t much we can do 

besides call it a weather day and do other things: clean 

firearms, catch up on writeups and checking data, plan 

for the next crew swap. When the weather is bad or on 

a rough crossing, 18 people on a small boat can feel 

very crowded. Personal space is limited to a bunk and 

a single drawer. But bad weather on the boat is better 

than the alternative. One time after a few weather days, 

we had a small break in the weather. The forecast for 

the late afternoon was not good, but we thought we 

could beat it ... and we left 12 folks in the field that 

night. Our crews were spread between Montague, 

Hitchenbrook, and Hawkins Islands in Prince William 

Sound. We retrieved the last crew just before it got 

dark on the second day. With the unpredictable coastal 

weather, it’s pretty standard for a couple of crews to 

spend a night out each season. 

Training, Attitude, and Safety

We couldn’t do FIA in Alaska without aircraft. Each 

crew member takes Aviation User Training, and gets 

to wear a bright orange Nomex flight suit, float vest, 

flight helmet and other personal protective equipment. 

Everyone is considered a crew member on each flight 

and needs to watch for other aircraft, birds, or other 

obstacles, as well as voice any concern about weather 

or other pertinent flying conditions. Crew leaders sit in 

the front seat and navigate to the landing zone closest to 

their plot by using maps, aerial photography, and satellite imagery. Landing zone selection can be a challenge, 

and often there is still an arduous hike to the plot. It is 

somewhat difficult to imagine being aboard a helicopter 

taking off and landing on a 20- by 20-foot deck on the 

back of an 86-foot boat. The mast is only about 15 feet 

from the tip of the rotors. Our pilots are good! 

Our training is about half data collection protocols 

and half safety training. Many safety topics are unique 

to Alaska: we have bear awareness, survival training 

(for those unplanned nights out in the woods), firearms 

safety and range qualifications, aviation user training, 

and helicopter manager training. Many of our employees also are certified Wilderness First Responders. 

Attitude is everything with Alaska FIA crews. 

Physical and mental challenges abound, as do the 

rewards for a job well done. Good attitude and flexibility are required to get the work done and have fun 

doing it. With the boat, helicopter, bears, firearms, 

rugged terrain, and remoteness of our operation, safety 

is our number one priority. If it can’t be done safely, we 

don’t go. We have fostered a safety culture that involves 

everyone at every step of the way. We look out for each 

other and learn to manage our many risks. 

Figure 35—Crews carry packs, survival bags, and safety gear. 

Eric Straley
48

GENERAL TECHNICAL REPORT PNW-GTR-835

Figure 36—Western hemlock forest near Hawk Mine, Alaska. Old-growth forests in the temperate rain forest of Alaska are often 

characterized by large trees, snags, and down logs.

Results presented here focus solely on tree-related 

vegetation characteristics, but it is important to recognize 

that old-growth forests consist of a complex web of interacting biotic and abiotic elements. Old-growth forests play an 

important role in sustaining quality of streams and rivers 

in Alaska (fig. 37). One of the primary concerns about loss 

of old-growth is the impact on birds, mammals, and other 

wildlife species. In the coastal Alaska temperate rain forest, 

loss of old-growth area is complicated by the isolation created by the distribution of the forest across an archipelago. 

Analysis of mitochondrial genetic information suggests that 

isolation among islands has caused some genetic divergence 

and endemism among mammals in southeast Alaska (Cook 

et al. 2001). Although concern over loss of biodiversity 

has often focused on fauna, other elements of old-growth 

forests can also be impacted by conversion to younger age 

structures, but the effects tend to be less well understood. 

For example, experience in the temperate rain forests of 

Oregon and Washington suggests that there are associations 

between fungi and old-growth conditions, but understanding and identifying those associations can be very difficult 

and expensive (Molina 2008). 

The proportion of forest in old-growth conditions will 

depend on the frequency of stand-initiating disturbances. 

In the coastal temperate rain forest, wind is an important 

source of disturbance (Kramer et al. 2001). Nowacki and 

Kramer (1998) compiled data from case studies to provide 

blowdown rates for forest on southeast Chichagof Island 

Jon Williams
49

Forests of Southeast and South-Central Alaska, 2004–2008

(2.6 percent per century), northeast Chichagof Island (3.4, 

3.4, and 1.3 percent per century), Kuiu Island (0.9 and 3.9 

percent per century), and Prince of Wales Island (7.3 percent 

and 14.3 percent per century, based on data from 1961 to 

1972). Avalanches, insect outbreaks, and flooding also can 

cause stand initiation, as can timber harvest. The most 

recent FIA inventory data show that in the coastal temperate rain-forest region, outside of national forest wilderness, 

harvest occurred on an estimated 0.25 percent of forest land 

per year (see “change” section, chapter 2).

Most of the controversy in Alaska over harvesting old 

growth has focused on the temperate rain forest. However 

90 percent of Alaska’s forests, and 15 percent of forest in 

the inventory region, is in boreal forest types (see fig. 5, 

chapter 2). Stand-replacing disturbance such as fires or 

beetle outbreaks are much more common in the boreal 

forest, so that the resulting age distribution of forest stands 

is younger than in the temperate rain forest. DeVolder 

(1999) reported that the black spruce forests on the Kenai 

Peninsula have fire-return intervals ranging from 25 to 185 

years, with a mean return interval of 89 years. Spruce beetle 

(Dendroctonus rufipennis [Kirby]) outbreaks on the Kenai 

in white, Sitka, and Lutz spruce forests have occurred on 

average every 52 years (Berg et al. 2006). The most recent 

FIA inventory data show that in the boreal forest region, 

harvest occurred on an estimated 0.26 percent of forest 

land per year (see “change” section in chapter 2).

“Old growth” is a term that has been defined in many 

different ways. Each definition will produce different 

amounts of forest land that is classified as old growth. The 

two national forests in coastal Alaska currently use ecological definitions described in Capp et al. (1992a 1992b). 

Elements in that system that are used to indicate old growth 

include the number of large trees per acre, the minimum 

Figure 37—Older forests sustain quality and quantity of water in coastal Alaska streams.

Misha Yatskov
50

GENERAL TECHNICAL REPORT PNW-GTR-835

diameter at breast height of large trees, the minimum age 

of large trees, canopy layering, number of snags, number of 

large pieces of down wood, and forb cover.

That system cannot be used directly with the inventory 

for a variety of reasons. The inventory plots are smaller 

than the plots used to develop the system, which affects the 

amount of classified old growth that would meet criteria 

such as minimum age or size of trees. The inventory currently does not include plant association, downed woody 

material, canopy layering, or forb cover on all plots, which 

would be necessary for using the Capp et al. classification. 

Instead, in this report, a single metric, stand age, is used 

to describe forest conditions. Most results that follow are 

reported as distributions across age classes. When results 

are presented by using a single metric, the term ‘’older 

forest’’ is used to avoid confusion with existing old-growth 

definitions. 

Methods

On each inventory plot, boundaries are delineated between 

stands that differ by reserved status, owner group, forest 

type, stand size class, regeneration status, and tree density. 

On most stands, three live trees that represent the plurality 

of the predominant stand size class are then selected and an 

increment borer is used to find tree age (fig. 38). For bored 

trees, a number of years are added to estimated age to 

approximate the amount of time for a tree to grow to breast 

height: 2 years for alder, aspen, or cottonwood; 5 years for 

paper birch; and 8 years for other species. For 99.7 percent 

of trees with measured ages, age is determined by counting 

rings; in 0.03 percent of cases, the bore misses the center of 

the tree and age is extrapolated. By using the ages of the 

selected trees, the field crew then assign an age to the stand. 

If the age of three trees were measured during the previous 

1995–2003 inventory, and no change to stand age appears to 

have occurred, additional ages are not taken and age is 

computed by adding the number of years between field 

measurements to the previous age. Where trees are too 

small to bore, such as in a seedling or sapling stand, the 

field crew estimate an age. Tree age is subject to some 

measurement error, through false growth rings, absent 

rings, and inaccurate counting. A greater source of 

uncertainty in estimating stand age is the choice of which 

trees to select for aging, as ages can differ by very large 

amounts even among trees of the same size

For this report, “older forest” is defined as forest that 

meets a minimum stand age by forest type. Although the 

minimum ages used for the Sitka spruce, western hemlock, 

white spruce, aspen, and lodgepole pine forest types correspond to tree ages described in Capp et al. (1992a, 1992b), 

that system refines minimum ages by plant association 

subseries and uses different minimum ages for Sitka spruce 

on the Chugach and Tongass National Forests. Although the 

system used here is a gross simplification of Capp et al., it 

has the advantage of being repeatable in future FIA inventories, which will be helpful in monitoring changes in forest 

characteristics over time. Minimum ages used for each 

forest type to define older forest were 170 years for yellowcedar, 50 years for aspen, 100 years for cottonwood, 170 

years for lodgepole pine, 160 years for mountain hemlock, 

50 years for paper birch, 200 years for Sitka spruce, 160 

years for subalpine fir, 180 years for western hemlock, 170 

years for western redcedar, and 150 years for white spruce. 

Capp et al. used black spruce tree ages of 200 years as one 

indicator of old growth, but we did not find trees of that age 

and instead used the mean fire interval (89 years) plus standard deviation (43 years) from De Volder (1999) to obtain 

a minimum older forest stand age of 132 years. Red alder, 

Pacific silver fir, and subalpine fir forest types were not 

included in the analysis because only very small numbers of 

these forest types were found within the inventory region. Figure 38—Trees are aged in the field by taking an increment bore 

and counting rings.

Misha Yatskov
51

Forests of Southeast and South-Central Alaska, 2004–2008

Because the disturbance regimes in the boreal forests 

on the western Kenai are substantially different from the 

temperate rain forests in the remainder of the inventory 

region, some results are presented separately for those two 

areas. Forests in the Alaska Range Transition ecological 

province (shown in fig. 6 of chapter 2) are referred to here 

as the boreal region, and forests in the Coastal Rainforest Mountains ecological province are referred to as the 

temperate rain-forest region. These ecoregion boundaries 

are based on Nowacki et al. (2002), and plots were assigned 

to regions based on best available coordinates. 

Results

Based on the minimum age class definition for older forest, 

the temperate rain-forest ecoregion contains 8.4 million 

acres of older forest, which is 61.2 percent of all forest in 

the region. The boreal forest region on the Kenai contains 

0.5 million acres of older forest, which is 31.2 percent of all 

forest in the region. Some 47 percent of older forest in the 

boreal region on the Kenai is reserved, and 35 percent of 

the older forest in the temperate rain-forest ecoregion is 

reserved (see glossary for definition of “reserved” forest 

land). 

The age class distribution of different forest types differs substantially (fig. 39). Structural composition of older 

forest differs among forest types, as shown by numbers 

of live and dead trees per acre by diameter class (tables 

18 and 19). In order of descending area, most older forest 

is found in the western hemlock, mountain hemlock, and 

yellow-cedar forest types (table 20).

The age distribution of forest also differs substantially 

by ownership (fig. 40). In the boreal region, 75 percent of 

the older forest is in public ownership. In the temperate 

rain-forest region, 89 percent of the older forest is in public 

ownership. In the temperate rain-forest ecoregion, 67 

percent of federal forest land is in older forest, 35 percent 

of state and local government forest land is in older forest, 

and 34 percent of private land is in older forest. In the 

boreal ecoregion, 27 percent of federal forest land is in 

Figure 39—Percentage of forest type area by age class for coastal Alaska, 2004–2008.

Age (years)

0 20 40 60 80 100

Percentage of forest type

0–49

50–99

100–149

150–199

200–249

250–299

300+

Alaska yellow-cedar

Western redcedar

Western hemlock

Mountain hemlock

Lodgepole pine

Sitka spruce

Paper birch

Cottonwood

White spruce

Black spruce

Aspen

Forest type
52

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 18—Estimated live trees per acre of older forests by diameter class and forest type, coastal Alaska, 

2004–2008

Diameter class (inches)

0–4.9 5.0–9.9 10.0–14.9 15.0–19.9 20.0–24.9 25.0–29.9 30.0–34.9 35.0+

Minimum

Forest type stand age Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE

Years - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Trees per acre - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Alaska yellow-cedar 170 581 66 144 9 60 5 22 2 6 1 2 — 1 — 1 —

Lodgepole pine 170 459 79 83 10 22 5 5 1 2 1 — — — — — —

Mountain hemlock 160 293 26 102 5 52 4 26 2 10 1 4 1 1 — 2 1

Paper birch 50 159 36 80 9 26 3 6 1 1 — — — — — — —

Sitka spruce 200 193 63 74 14 40 8 14 2 9 2 8 2 7 2 6 2

Western hemlock 180 250 19 89 4 40 2 22 1 13 1 7 1 4 1 3 1

Western redcedar 170 465 39 147 9 66 7 26 5 11 2 7 2 4 1 4 1

Note: Data subject to sampling error; SE = standard error; — = less than 0.5 trees per acre were estimated. Note: Glacier Bay National Park is excluded.

Table 19—Estimated snags per acre in older forests by diameter class and forest type, coastal Alaska, 

2004–2008

Diameter class (inches)

5.0–9.9 10.0–14.9 15.0–19.9 20.0–24.9 25.0–29.9 30.0–34.9 35.0+

Minimum

Forest type stand age Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE

Years - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Trees per acre - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Alaska yellow-cedar 170 37 3 18 2 6 1 3 1 1 — 1 1 — —

Lodgepole pine 170 32 7 7 2 1 1 — — — — — — — —

Mountain hemlock 160 13 2 6 1 3 1 2 — 1 — 1 1 — —

Paper birch 50 27 4 13 2 2 1 — — — — — — — —

Sitka spruce 200 17 4 14 4 7 2 4 1 3 1 2 1 1 1

Western hemlock 180 16 2 8 1 6 1 3 — 2 — 1 1 1 1

Western redcedar 170 52 4 19 2 9 1 3 1 1 — 1 1 1 1

Note: Data subject to sampling error; SE = standard error; — = less than 0.5 trees per acre were estimated. Note: Glacier Bay National Park is excluded.

Table 20—Area of older forest by forest type, owner group, and reserve status, coastal Alaska, 2004–2008

U.S. Forest Service Other public Private All ownerships

Minimum

Forest type stand age Unreserved Reserved Total Unreserved Reserved Total Unreserved Area SE

Years - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Thousand acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Alaska yellow-cedar 170 1,041 760 1,801 39 — 39 46 1,886 215 

Aspen 50 — — — 3 82 85 10 95 29 

Cottonwood 100 18 — 18 12 15 28 24 70 28 

Lodgepole pine 170 231 20 251 9 — 9 23 284 55 

Mountain hemlock 160 994 661 1,655 115 42 157 101 1,912 191 

Paper birch 50 0 — 0 139 177 316 208 524 70 

Sitka spruce 200 117 210 327 43 21 64 53 444 111 

Western hemlock 180 1,680 846 2,525 104 — 104 152 2,781 227 

Western redcedar 170 454 337 790 35 — 35 42 867 143 

White spruce 150 — — — — 5 5 12 17 14 

 Total 4,535 2,834 7,369 499 343 842 670 8,881 278

Note: Data subject to sampling error; data subject to sampling error; SE = standard error; — = less than 500 acres were estimated. 

Note: Glacier Bay National Park is excluded.
53

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 40—Forest land area by stand age class and reserve status for four owner groups, coastal Alaska 2004–2008.

1,600

1,400

1,200

1,000

800

600

400

200

 0

Acres (thousands)

Stand age (years)

Chugach National Forest Tongass National Forest

Unreserved

Reserved

0–49

50–99

100–149

150–199

200–249

250–299

300–349

350–399

400–449

450–499

500+

1,600

1,400

1,200

1,000

800

600

400

200

 0

Acres (thousands)

Stand age (years)

0–49

50–99

100–149

150–199

200–249

250–299

300–349

350–399

400–449

450–499

500+

1,600

1,400

1,200

1,000

800

600

400

200

 0

Acres (thousands)

Stand age (years)

Other public Private

0–49

50–99

100–149

150–199

200–249

250–299

300–349

350–399

400–449

450–499

500+

1,600

1,400

1,200

1,000

800

600

400

200

 0

Acres (thousands)

Stand age (years)

0–49

50–99

100–149

150–199

200–249

250–299

300–349

350–399

400–449

450–499

500+
54

GENERAL TECHNICAL REPORT PNW-GTR-835

older forest, 35 percent of state and local government forest 

land is in older forest, and 33 percent of private forest land 

is in older forest. Amounts of older forest by forest type, 

owner group, and reserve status are shown in table 20.

Older forests in the temperate rain forest ecoregion 

store substantial amounts of carbon. Overall, for example, 

the amount of carbon in live and dead trees on forest land 

in the Tongass National Forest is estimated at 68 tons per 

acre. But that same statistic increases to 75 tons per acre for 

older forests. The Chugach National Forest has an estimated 

38 tons per acre of carbon in live and dead trees over all 

forest land, but 56 tons per acre in older forest. The greatest 

amounts of carbon per acre are found in older forest of the 

Sitka spruce forest type (table 21). Overall, carbon in live 

trees compared to carbon in snags was about a 4-to-1 ratio 

in older forest. Carbon content in older forests was highest 

in the southeast portion of the temperate rain forest. Moving 

westward from the Alexander Archipelago ecological section to the Gulf of Alaska Coast ecological section, carbon 

dropped from 53.8 to 43.5 tons per acre for the mountain 

hemlock forest type, from 100.0 to 75.3 tons per acre for the 

western hemlock forest type, and from 137.9 to 57.2 tons per 

acre for the Sitka spruce forest type.

Interpretation

In the temperate coastal rain forest that stretches along the 

west coast of North America, from Alaska through British 

Columbia, Washington, Oregon, and northern California, 

stand-replacing natural disturbance is relatively rare, and 

most native tree species are long-lived. The result is that 

absent significant human disturbance, the median stand age 

for most common forest types is hundreds of years of age. 

Outside of Alaska, the distribution of stand ages has shifted 

to much younger age classes throughout much of the coastal 

temperate rain-forest region, through forest management 

and harvesting. Within Alaska, large portions of the forest 

are in parks or wilderness, and even in unreserved land 

much of the forest has never been harvested. Thus Alaska 

has a much higher proportion of older forest than other 

U.S. states. 

Most of the older forest on public land in the region is 

not reserved from being managed for wood products utilization through statute or administrative designation. However, 

management plans may still specify that unreserved lands 

be managed for purposes other than timber. In addition, 

logistical and economic constraints can preclude harvest of 

timber. A single ownership, the Tongass National Forest, 

Table 21—Carbon per acre stored in live and dead trees in older forest, coastal Alaska, 2004–2008

Carbon in live trees Carbon in snags Total

Forest type Minimum stand age Mean SE Mean SE Mean SE

Years - - - - - - - - - - - - - - - - Tons per acre - - - - - - - - - - - - - - - -

Alaska yellow-cedar 170 40.3 3.5 12.0 2.1 52.3 5.3

Aspen 50 17.8 3.6 6.5 1.1 24.3 4.0

Cottonwood 100 56.8 27.2 3.7 2.4 60.5 26.6

Lodgepole pine 170 10.3 1.7 2.6 0.6 12.9 2.0

Mountain hemlock 160 44.6 3.7 6.9 0.8 51.5 3.9

Paper birch 50 14.5 1.7 4.8 0.8 19.3 1.7

Sitka spruce 200 86.2 9.0 21.0 4.8 107.2 12.2

Western hemlock 180 77.5 3.6 19.9 1.5 97.4 4.0

Western redcedar 170 60.8 9.4 15.5 1.7 76.3 9.8

White spruce 150 10.8 4.0 1.2 0.6 12.0 4.6

 All forest 54.4 1.9 13.3 0.7 67.7 2.3

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error.

Note: Glacier Bay National Park is excluded.
55

Forests of Southeast and South-Central Alaska, 2004–2008

contains 77 percent of the older forest in the coastal Alaska 

region. 

Older forest is a substantially smaller proportion of 

forest land for privately owned forests than for publicly 

owned forest. Most of the young forest in the coastal region 

is in private ownership. Both of these findings about private 

forests are also true of most other U.S. states. Because the 

rotation lengths that maximize economic returns tend to be 

short, these trends are likely to continue in the future. 

Other factors than timber harvest can alter the age 

distribution of coastal forests. Severe bark beetle outbreaks, 

such as occurred on the Kenai Peninsula in the 1980s and 

1990s, can shift large areas of forest to younger age classes. 

There is anecdotal evidence that a rare large-area windstorm occurred in the coastal forest in the 1880s, creating a 

shift to younger age classes (Hennon and McClellan 2003). 

There is some evidence that yellow-cedar is experiencing 

elevated mortality that could be due to climate change (see 

yellow-cedar section in chapter 4 in this report). If the same 

methods are used to monitor stand age in future inventories, 

it will be possible to evaluate change in forest age distribution from these and other causes.
56

GENERAL TECHNICAL REPORT PNW-GTR-835

Walter Foss
57

Forests of Southeast and South-Central Alaska, 2004–2008

There are many natural disturbances—wind, flooding, 

insects, fire, disease, avalanches—that affect the functioning of forest ecosystems in southeast and southcentral Alaska. In this chapter, we analyze a few of the 

disturbances that can act as stressors on forests in the 

region. In the first section, dynamics of yellow-cedar (see 

“Common and Scientific Names”) are discussed in the 

context of climate change. The next section describes some 

of the insects that affect forests in this area, followed by a 

short analysis of results of monitoring for invasive plants on 

Forest Inventory and Analysis (FIA) plots. A new postfire 

monitoring system being used on FIA plots on the Kenai 

Peninsula is described, along with the type of fire effects 

that will be monitored. The last section discusses hemlock 

dwarf mistletoe, and its host tree western hemlock, in the 

context of climate change. 

Use of Forest Inventory Data to Document 

Patterns of Yellow-Cedar Occurrence, 

Mortality, and Regeneration in the 

Context of Climate1

Background

Yellow-cedar has great cultural and economic value but 

has experienced widespread mortality for about 100 years 

in southeast Alaska (Hennon and Shaw 1997). The tree 

mortality, known as yellow-cedar decline, appears as dense 

concentrations of dead yellow-cedar trees (fig. 41), which 

are readily detectable by aerial survey or other forms of 

remote sensing. Yellow-cedar decline occurs primarily in 

Chapter 4: Disturbance and Stressors

1

 Authors: Paul Hennon, John Caouette, Tara M. Barrett, 

and Dustin Wittwer.

Figure 41—Yellow-cedar decline results in over 70 percent mortality of yellow-cedar, which can be detected by aerial surveys and other 

forms of remote sensing. Evaluating healthy yellow-cedar forests requires the use of inventory data.

Dustin Wittwer
58

GENERAL TECHNICAL REPORT PNW-GTR-835

unmanaged forests on wet soils where trees of various sizes 

and ages die and remain standing long after death. The 

cause of yellow-cedar decline appears to be freezing injury 

of shallow fine roots when they are not protected by snow 

in late winter or spring (Hennon et al. 2008, Schaberg et 

al. 2008). The U.S. Forest Service Forest Health Protection 

team has developed a fairly complete distribution map for 

yellow-cedar decline in Alaska; it occurs in more than 

2,000 locations totaling over 500,000 acres (Lamb and 

Winton 2010). Producing maps and geographic information 

system (GIS) layers for healthy yellow-cedar forests has 

proven more difficult, however, because cedar trees are 

not easily distinguished from hemlocks and other trees in 

mixed-species forests. Thus, there is no reliable information 

on the current distribution of healthy yellow-cedar forests to 

place the decline issue into some spatial context.

Methods

We used FIA and other inventory plot data and personal 

field observations in selected areas to produce a coarse distribution map of yellow-cedar forests for southeast Alaska. 

For this coarse map, entire watersheds were designated 

as having yellow-cedar if yellow-cedar was present in an 

inventory plot or was observed there. We overlaid the map 

of dead cedar forests generated from the forest health detection aerial survey to determine how yellow-cedar decline 

fits into the general distribution of the tree. 

We also analyzed FIA inventory data on the occurrence 

of live, dead, and regenerating yellow-cedar by elevation 

classes to evaluate our observations that the tree is dying at 

low elevations but thriving and regenerating at higher elevations in the region. We calculated ratios of live trees:dead 

trees and live trees:live saplings by using a 5-inch-diameter 

threshold to separate trees and saplings, and then charted 

these ratios by elevation to explore trends of how yellowcedar populations may be changing by elevation. Results 

that relate numbers of yellow-cedar trees to elevation used 

FIA plots that were measured between 1995 and 1998; 

there were 625 forested plots with yellow-cedar trees in that 

inventory. For information about net change of yellow-cedar 

in the region, we used 307 of these plots that were remeasured in 2004 through 2008. 

Results

The map showing the occurrence of yellow-cedar in southeast Alaska (fig. 42) is the most detailed view of yellowcedar’s natural range within the region. Yellow-cedar is 

present throughout most of southeast Alaska, but there are 

areas where it is rare or absent. For example, yellow-cedar 

is apparently missing from large areas in the northeastern 

portion of the panhandle even though there is abundant suitable habitat present in the form of bog and forested wetland 

complexes. This regional map of yellow-cedar is useful for 

a variety of purposes; for example, it has already been used 

to illustrate where yellow-cedar is present as a resource for 

bark and wood collection by Native people near each of the 

villages or towns. Also, we used this map as the basis for 

sampling in a new regionwide population genetics study for 

yellow-cedar. 

Overlaying the yellow-cedar decline on this map 

indicates that the intensive mortality problem covers only 

part of yellow-cedar’s regional distribution. Yellow-cedar 

decline is present in the southern and northwestern portions 

of the panhandle, but yellow-cedar growing in the northeastern portion of the panhandle appears to be free of the 

intensive mortality. 

Although yellow-cedar can be found from shoreline 

forests to timberline in southeast Alaska, inventory data 

reveal that the abundance of this tree peaks at mid-elevation 

range (fig. 43). Tree death and regeneration of yellow-cedar 

show somewhat of a departure by elevation from this 

pattern of live trees. The ratio of dead trees:live trees was 

greatest at lower elevation and then diminished upslope, 

but the ratio of live trees:live saplings showed the opposite 

relationship with a greater proportion of regeneration at 

higher elevations (fig. 44). 

Overall, change in net live tree biomass of yellow-cedar 

between the 1995–1998 and 2004–2008 inventories showed 

an increase of 0.61 percent, which was not significantly different from 0 (p-value 0.4064). Average annual mortality of 

trees greater than 5 inches diameter at breast height (d.b.h.) 

was 0.30 percent (standard error [SE] = 0.04 percent). Average annual harvest rate was 14 percent of average annual 

mortality, for a total tree death rate of 0.34 percent. Total 
59

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 42—Occurrence map of yellow-cedar in southeast Alaska (yellow) from Forest Inventory and Analysis and other inventory data 

and several personal observations, and the distribution of yellow-cedar decline (red) mapped during forest health aerial detection surveys. Note that yellow-cedar decline occurs within most, but not all, of the range of yellow-cedar in southeast Alaska. The speckled areas 

along the outer west coast of the region indicate glacial refugia during the late Pleistocene Epoch (Carrara et al. 2007) and may represent 

the origins for yellow-cedar for subsequent Holocene migration.

Dustin Wittwer
60

GENERAL TECHNICAL REPORT PNW-GTR-835

Figure 43—Numbers of yellow-cedar trees (live trees ≥ 5 inches diameter at breast height (d.b.h.), dead trees ≥ 5 

inches d.b.h., and live saplings < 5 inches d.b.h.) by elevation. Lines at end of bars represent ± standard error.

Figure 44—Ratio of yellow-cedar dead trees:live trees and live saplings:live trees for 

200-foot elevation classes from Forest Inventory and Analysis inventory plot data. 

90

80

70

60

50

40

30

20

10

0

Trees (millions)

Elevation (feet)

Live trees

Dead trees

Saplings

0–199

200–399

400–599

600–799

800–999

1,000–1,199

1,200–1,399

1,400–1,599

1,600–1,799

1,800–1,999

2,000–2,199

2,200+

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0

1

2

3

4

5

6

7

Ratio of dead trees to live trees

Ratio of saplings to live trees

Dead:live

Sapling:live

Elevation (feet)

0–199

200–399

400–599

600–799

800–999

1,000–1,199

1,200–1,399

1,400–1,599

1,600–1,799

1,800–1,999

2,000–2,199

2,200+
61

Forests of Southeast and South-Central Alaska, 2004–2008

number of live yellow-cedar trees ≥ 5 inches d.b.h. did not 

show significant change in biomass between the 1995–1998 

and 2004–2008 inventories (p-value = 0.7443).

Interpretation

The general occurrence of yellow-cedar in southeast Alaska 

may be the result of long-term climate change in the region 

combined with yellow-cedar’s low reproductive capacity. 

We hypothesize that yellow-cedar survived the late Pleistocene Epoch in forested refugia along the outer western 

coast of the panhandle when most of the region was covered 

by ice sheets (Carrara et al. 2007). Yellow-cedar likely 

began to colonize much of southeast Alaska during favorable climate conditions in the last 4,000 years, but our data 

suggest that the species is still actively migrating toward the 

northeast. This scenario may explain the absence or rarity 

of the tree in the northeast portion of southeast Alaska. The 

above-mentioned population genetics study based on our 

yellow-cedar map is designed to evaluate this hypothesis. 

Yellow-cedar also grows farther to the northwest in Prince 

William Sound. Populations are so small there that we were 

able to produce a map of the tree’s range by observations 

from a boat (Hennon and Trummer 2001). 

The pattern of yellow-cedar decline within the general 

distribution of yellow-cedar is consistent with our interpretation that seasonal snow depth is a controlling factor for 

yellow-cedar decline. The lowest snow zone on a regional 

snow map shows a remarkably close association with 

yellow-cedar decline (Hennon et al. 2008). Areas with more 

annual accumulation of snow are generally those that have 

healthy yellow-cedar populations. Snow protects yellowcedar from the proximal injury leading to tree death—

freezing injury of shallow-growing fine roots in late winter 

(Schaberg et al. 2008). 

Yellow-cedar death in FIA plots is more common at 

low elevations. This is consistent with the same finding 

from aerial surveys, where the acreage of yellow-cedar 

decline mapped was clearly skewed toward lower elevations 

(Lamb and Winton 2010). The association of yellow-cedar 

decline with lower elevations is consistent with the role of 

snow in protecting yellow-cedar from the freezing scenario 

mentioned above. 

Tree species that show different elevational patterns 

of occurrence among live trees, dead trees, and regeneration may be considered relatively unstable with regard to 

climate. More stability for a tree species would be exhibited 

by a pattern where live trees, dead trees, and regeneration 

had similar elevational trends. Yellow-cedar appears to be a 

species in flux, however, as our data indicate, the trees are 

dying at low elevation, surviving as live trees at mid elevation, and regenerating at higher elevation. Thus, under the 

recent-past and current climate, yellow-cedar populations 

appear to be shifting to higher elevations. 

The relatively low rate of recent tree mortality is 

interesting given the large acreage of dead yellow-cedar 

in southeast Alaska. Our reconstruction of yellow-cedar 

mortality through the 1900s shows that tree death peaked 

during the 1970s and 1980s (Hennon and Shaw 1994). We 

have observed recent mortality in specific areas, despite the 

fairly low regional mortality rate. Although pollen records 

clearly show that tree species migrate over time in response 

to climate, little is known about the process of migration. 

The long-term occurrence of yellow-cedar is not well 

known because the species was omitted from the classic 

pollen profile studies conducted in the region (Heusser 

1960). Spatially differentiated mortality and regeneration 

could occur either gradually or in pulses. If mortality is 

caused by the combination of low snow cover and spring 

freezing, both episodic events, then mortality would occur 

in pulses. However, yellow-cedar trees may take a long time 

to die after they are injured, as root damage from weather 

can have cumulative effects on tree growth and health 

(Beier et al. 2008). These possibilities suggest that monitoring needs to be long-term to capture temporal variability as 

well as spatial variability. 

These findings can contribute to development of an 

adaptive strategy for the conservation and management of 

yellow-cedar (Hennon et al. 2008). Continued analysis of 

inventory plot data for yellow-cedar habitat preferences 

would aid in the construction of a high-resolution distribution map. A distribution map could be combined with aerial 

surveys of dead cedar and snow modeling to partition the 

landscape of coastal Alaska into areas that are unsuitable 

and suitable for yellow-cedar. Inventory plot data should be 
62

GENERAL TECHNICAL REPORT PNW-GTR-835

queried to evaluate successional trends in forests impacted 

by yellow-cedar decline to project the future composition 

and productivity of these forests. Inventory data could also 

be used to document the resource (e.g., diameter classes, 

volume) of dead yellow-cedar that might be available for 

salvage recovery on the roughly 1⁄2 million acres of yellowcedar decline. 

Forest Insect Pests in Coastal 

Alaska Forests2

The Alaska coastal forest occupies a vast, largely inaccessible area. Thorough disturbance detection and assessment 

surveys are scarce. Known disturbances include strong 

winds, flooding, insect outbreaks, diseases, avalanches, 

wildfires, ice and snow events, tree harvesting, and many 

others.

Because only a small percentage of coastal Alaska is 

accessible by roads, estimates of pest extent, distribution, 

and impacts are mostly made from data collected by visual 

observations from airplanes. The area covered during these 

surveys can only be a small fraction of the actual forested 

land acreage. Undoubtedly, the incidence of insect pests in 

coastal Alaska forests is much greater than the aerial surveys estimate. Much about the status of forest insect pests 

and other damaging agents is probably unknown. Better 

methods of estimating these agents are needed.

Common Forest Insect Pests in Coastal Alaska

Forest insect pests have been surveyed annually by the 

Forest Health Protection branch of the U.S. Department of 

Agriculture (USDA) Forest Service since the early 1970s. 

Results of these surveys have been reported in the USDA 

Forest Service Forest Conditions Report since 1970. 

A list of the most common insect pests of major coastal 

tree species in Alaska is summarized in table 22. Tree 

species are limited to the species most commonly found in 

southeast Alaska, including western hemlock, Sitka spruce, 

mountain hemlock, red alder, western redcedar, and yellowcedar. Other less common tree species include white spruce, 

black spruce, Lutz spruce, alder, aspen, birch, and willow. 

Table 23 shows aerial survey estimates of the distribution of the four most commonly surveyed insect pests that 

occur in coastal Alaska forests.

Distribution and Damage 

According to estimates in table 23, the most widespread 

insect pest in coastal southeast Alaska since 2003 has 

been the spruce aphid (see table 22 for scientific names). 

This defoliator has had a long history in coastal Alaska. 

Like many defoliators, the blackheaded budworm varies 

in severity of infestation and defoliation from year to year. 

Infestation history of this insect since 1960 is displayed in 

figure 45. The last major infestation occurred in 1993. The 

last 5 years have seen relatively low levels of infestation.

Spruce aphid has been relatively active in recent 

years. The infestation history of this insect is displayed 

in figure 46. 

Not all of the insects listed in table 22 cause damage 

significant enough to affect consumptive or nonconsumptive forest uses. It might be argued that some actually cause 

positive impacts. Spruce beetles, Ips beetles, and other bark 

beetles, for example, facilitate successional dynamics when 

infestations levels are low. They also help maintain diverse 

stand structures and enhance habitat conditions for certain 

bird species. Not surprisingly, disturbances are a necessary 

part of the ecology of all natural forests.

The potential for impact (hazard) is related to feeding 

behavior. In this regard, the insects listed in table 22 fall 

within several categories depending on their feeding habits 

or guilds: leaf feeders (folivorous); sap-suckers; phloem 

feeders (phloeophagous); wood borers (xylophagous); and 

cone, seed and bud feeders. Insects within different guilds 

affect tree growth and development, stand structure and 

composition, and landscape patterns in different ways (table 

24). Leaf feeders, for example, primarily cause reduced 

growth rates. Phloem feeders commonly cause mortality. 

Level of impact, of course, depends on severity of 

infestation, timing, duration, periodicity, and other factors. 

Few studies have been done to determine the growth or 

mortality-based impacts of insect pests in Alaska coastal 

forests, but this kind of work has undoubtedly been done in 

2

 Authors: John E. Lundquist and Mark Schultz.
63

Forests of Southeast and South-Central Alaska, 2004–2008

Table 22—Insects common to the coastal forests of Alaska

Host

Alder (Alnus spp.)Aspen (Populus tremuloides) 

Balsam poplar (Populus balsmifera)

Birch (Betula spp.)Black spruce (Picea mariana)

Cottonwood (Populus trichocarpa)Fir (Abies lasiocarpa)Larch (Larix laricina)Lodgepole pine (Pinus contorta)Lutz spruce (Picea 

X lutzii)

Mountian hemlock (Tsuga mertensiana)

Redcedar (Thuja plicata)Sitka spruce (Picea sitchensis)

Western hemlock (Tsuga heterophylla)

White spruce (Picea glauca)Willow (Salix spp.)Yellow-cedar (Chamaecyparis nootkatensis)

Insect guild

Bark beetles:

Cedar bark beetle (Phloeosinus cupressi Hopkins, X X

 P. sequoia Hopkins)

Dryocoetes (Dryocoetes affaber [Mannerheim]; X X X X X

 Dryocoetes confusus Swaine)

Engravers (Ips sp.) X X X X X 

Hemlock hylesinus (Pseudohylesinus tsugae [Swaine]) X

Spruce beetle (Dendroctonus rufipennis [Kirby]) X X X X

Willow bark beetle (Trypophloeus striatulus [Mannerheim]) X

Bud and shoot insects:

Larch budmoth (Zeiraphera sp.) X 

Spruce bud midge (Rhabdophaga swainei Felt) X X X X

Spruce budmoth (Zeiraphera spp.) X X X

Cone and seed insects: 

Spruce cone-axis midge (Rhabdophaga rachiphaga [Tripp]) X X

Spruce seed moth (Cydia youngana [Kearfott]) X X X X

Yellow-cedar gall midge (Chamaediplosis nootkatensis X X X

Gagne and Duncan)

Defoliators:

Alder woolly sawfly (Eriocampa ovata [L.]) X

Aspen leaf miner (Phyllocnistis populiella [Chambers]) X X

Birch leaf miner (Profenusa thomsoni [Konow], Fenusa pusilla X 

 Leach, Heterarthrus nemoratus Klug)

European green alder sawfly (Monsoma pulveratum [Retzius]) X

Green-striped forest looper (Melanolophia imitate [Walker]) X X X

Hemlock sawfly (Neodiprion tsugae [Middleton]) X X

Larch budmoth (Zeiraphera sp.) X 

Larch sawfly (Pristiphora erichsonii [Hartig]) X

Leaf beetles (Chrysomela spp.) X X X 

Leaf blotch miner (Phyllonorcyter nipigon [Freeman] X X X

 and Lyonetia sp.)

Leaf roller (Epinotia solandriana [L.]) X X X X

Saddle-backed looper (Ectropis crepuscularia X X X

[Denis & Schiffermuller])
64

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 22—Insects common to the coastal forests of Alaska (continued)

Host

Alder (Alnus spp.)Aspen (Populus tremuloides)

Balsam poplar (Populus balsmifera)

Birch (Betula spp.)Black spruce (Picea mariana)

Cottonwood (Populus trichocarpa)Fir (Abies lasiocarpa)Larch (Larix laricina)Lodgepole pine (Pinus contorta)Lutz spruce (Picea 

X lutzii)

Mountian hemlock (Tsuga mertensiana)

Redcedar (Thuja plicata)Sitka spruce (Picea sitchensis)

Western hemlock (Tsuga heterophylla)

White spruce (Picea glauca)Willow (Salix spp.)Yellow-cedar (Chamaecyparis nootkatensis)

Insect guild

Spear-marked black moth (Rheumaptera hastata [L.]) X X X

Spotted tussock moth (Lophocampa maculata [Harris]) X

Spruce budworm (Choristoneura spp.) X X X 

Striped alder sawfly (Hemichroa crocea [Geoffroy]) X

Western blackheaded budworm (Acleris gloverana X X X X X 

[Walsingham])

Western hemlock looper (Lambdina fiscellaria X

lugubrosa [Hulst])

Willow leaf blotch miner (Micrurapteryx salicifoliella X

[Chambers])

Sap suckers: 

Birch aphid (Euceraphis betulae [Koch]) X

Eriophyid mites (Eriophyes sp.) X X X

Hemlock woolly aphid (Adelges tsugae Annand) X 

Giant conifer aphid (Cinara sp.) X X X 

Spruce aphid (Elatobium abietinum Walker) X X X 

Spruce gall aphid (Adelgis abietis [L.]; Adelgis cooleyi X X X 

[Gillette]; Pineus similis [Gillette])

Wooly adelgids (Adelges tsugae Annand; A. cooleyi [Gillette]; X X X 

 Pineus similis [Gillette])

Wood borers: 

Ambrosia beetles (Trypodendron lineatum [Olivier], X X X X X X X

Trypodendron retusum [LeConte], Trypodendron

 betulae Swaine)

Bronze birch borer (Agrilus anxius [Gory]) X X

Flat-headed woodborers (Melanophila drummondi [Kirby], X X

 Melanophila fulvoguttata [Harris), Chrysobothris trinervia

[Kirby], and Buprestis nuttalli [Kirby])

Horntail (Sirex juvencus [L.]) X X X X X

White-spotted sawyer (Monochamus scutellatus [Say]) X X X X
65

Forests of Southeast and South-Central Alaska, 2004–2008

Table 23—Estimate of area of infestation or infection of major insect pests 

and diseases in southeast Alaskaa

Year

Insect 2003 2004 2005 2006 2007 2008

Acres

Blackheaded budworm 15,050 1,483 1,401 1,463 10,344 2,741

Spruce aphid 30,627 7,758 14,982 9,120 3,433

Western balsam bark beetle 190 785 498 92

Hemlock sawfly 155 131

a Estimates from USDA Forest Service Forest Conditions Reports for Alaska Region for 2003 to 2008.

8.0

6.0

4.0

2.0

0

Log of acres defoliated

Year

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Figure 45—Log of acres of western hemlock (with or in combination with a few acres of Sitka spruce) 

defoliated by blackheaded budworm since 1945.

Table 24—Types of impacts associated with various insect guilds

 Leaf Sap Phloem Wood Cone, seed, 

Type of impact feeder sucker seeder borer bud feeder

Growth loss x x

Mortality x

Wood degradation x

Reproduction losses x

Reduced stand diversity x 

Increased stand structural heterogeneity x

Decreased spatial continuity x 

Increased spatial diversity x

Increased fuels x x x

Increased susceptibility to other x x x

disturbance agents
66

GENERAL TECHNICAL REPORT PNW-GTR-835

northern-reaching forests elsewhere. Importance depends 

on the management objectives defined by decisionmakers. 

Increasingly it is recognized that these disturbances 

are best presented as a complex network where some agents 

interact directly with their host trees, but most concurrently 

or sequentially interact indirectly with other disturbances 

and environmental factors forming complex multipath linkages. As a consequence, predicting future outbreaks, and 

even explaining past outbreaks is difficult. 

Previous Use of FIA Data in Forest Insect 

Pest Studies

Perhaps the most extensive work on predictive models using 

FIA data is that done on predicting spruce beetle risk and 

hazard. The spruce beetle (Dendroctonus rufipennis) is 

well-recognized as the most important insect pest in Alaska. 

It is not normally considered a coastal species, but during 

the 1990s, this phloem-feeding bark beetle was responsible 

for some of the most damaging natural biotic disturbance 

events ever when it killed more than 4.0 million acres of forest, mostly on the Kenai Peninsula. Much was written about 

the impacts of this outbreak on the remaining forests and 

resources (Werner 1996). Even more interesting perhaps, 

this outbreak spawned a number of studies that made use 

of FIA data ranging from sociological investigations (Flint 

2004, 2006) to the wood utilization of beetle-killed trees 

(Boucher and Mead 2006, Schulz 2003, van Hees 2005, van 

Hees and Larson 1991). 

Reynolds and Holsten (1994, 1996) used FIA data to 

develop hazard (amount of mortality expected to occur) and 

risk (probability of infestation) predictive models for spruce 

beetle on Lutz and Sitka spruce on the Kenai Peninsula. 

Reynolds and Holsten used expert opinions organized into 

a hierarchical model to estimate the relative importance 

of various risk factors. In developing their models, they considered the following potential risk factors: stand hazard, 

size and trend of spruce beetle populations in neighboring 

stands, daily ambient air temperature maxima in previous 

June, total rainfall in the past summer, and abundance of 

breeding materials (windthrow, right-of-way, and logging). 

Results indicate that infestation is a function of total stand 

basal area, percentage of total stand basal area infected, 

percentage of total spruce basal area of trees greater than 

25 centimeters d.b.h., stand elevation and stand aspect. 

These models were subsequently presented as a userfriendly decision-support system called Sbexpert 

(Reynolds and Holsten 1997). 

60

40

20

0

Sitka spruce (thousands of acres)

Year

194019451950195519601965197019751980198519901995200020052010

Figure 46—Acres of Sitka spruce defoliated by spruce aphid since 1940.
67

Forests of Southeast and South-Central Alaska, 2004–2008

Invasive Plants in Coastal Alaska3

Background

The composition and structure of the Nation’s forests have 

been influenced by a number of historical events involving 

introduced species. The gypsy moth (Lymantria dispar

[L.]), chestnut blight (caused by the fungus Cryphonectria 

parasitiea), and kudzu are all examples of organisms 

accidentally or intentionally released in the United States 

with dire consequences. Most species introduced to areas 

where they are not native have had little apparent negative 

impact; some, however, become invasive as they adapt 

to their new environment. The impacts of invasive plant 

species on forested ecosystems have become even better 

known in the past few decades. Invasive plants can dominate habitats by competing for nutrients, water, and light, 

displacing native plants. By changing the composition of 

invaded plant communities, introduced plants can reduce 

the quality of wildlife habitat, alter soil chemistry, and 

increase the frequency and intensity of wildfires. Millions 

of dollars are spent annually to control invasive plants in the 

United States. Experience has shown that the most effective way to manage invasive plants is to prevent them from 

becoming naturalized at the site of introduction. Knowledge 

about where introduced or invasive plants have become 

established is essential for prioritizing management efforts. 

Compared to the other 49 U.S. states, Alaska is 

relatively free of introduced invasive plants. For many 

years, it was assumed that climatic conditions limited the 

potential for many of the most troublesome weed pests from 

becoming established. However, we now know that Alaska 

is not immune to invasive plants. Although roadside weeds 

may appear to be pretty flowers to the casual observer, 

displays of bird vetch (fig. 47) and white sweetclover cause 

alarm for those who understand the damage they can cause. 

These species are just two examples of crops introduced by 

early pioneer agriculturists that have escaped and spread far 

beyond their initial introduction sites. Bird vetch spreads 

from roadsides via seeds spread by wildlife into adjacent 

forested areas, and white sweetclover, a nitrogen-fixing 

legume, has become established along glacial riverbeds, 

especially where roads cross rivers. Plants brought to 

Alaska for stabilizing disturbed areas or as ornamentals 

have also proved troublesome. Japanese knotweed and 

reed canary grass are found throughout southeast Alaska 

along roadsides and spreading into adjacent streams and 

waterways. Volunteer European bird cherry seedlings have 

spread away from cultivation and into wild areas in southcentral and interior Alaska (Lamb and Winton 2010). The 

early detection of garlic mustard, spotted knapweed, and 

purple loosestrife have reduced or eliminated small populations, but vigilant monitoring must continue to ensure 

timely response to new infestations. Although many species 

that have become problematic in other parts of the country 

may never survive in Alaska’s extreme climate, other 

species are well adapted. The invasiveness of bird vetch 

and European bird cherry is unique to Alaska; they do not 

exhibit invasive tendencies in the Lower 48 States (Carlson 

et al. 2008).

3

 Author: Bethany K. Schulz.

Figure 47—Bird vetch (Vicia cracca). 

Tricia Wurtz
68

GENERAL TECHNICAL REPORT PNW-GTR-835

Introduced and invasive plants are assessed by FIA as 

part of both the standard field (phase 2) and forest health 

plots (phase 3, see app. 1) but there are some differences 

between methods. On all phase 2 plots, crews search for 

a list of 44 taxa deemed to be either important nonnative 

invasive species or native species known to have noxious 

qualities (app. 2, table 49). Plants were chosen based on 

expert opinion, early weed ranking efforts for Alaska 

(Carlson et al. 2008), likelihood of plants establishing in 

forests, and ease of identification in the field. Crews search 

nonforested portions of plots in addition to the forested 

portions, and are encouraged to collect other species if they 

appear to be introduced or displaying invasive tendencies, 

such as an unfamiliar plant with high canopy cover. On 

phase 3 plots, crews record all vascular plants present on 

forested portions of plots, but do not record any species on 

nonforested portions of plots. List-based methods on the 

phase 2 plots limit analyses of impact but capture invasive 

species on the edge of forested areas; whereas phase 3 data 

allow us to report the cover of introduced species relative to 

the native species present. This measure of impact can be 

compared with other regions and the rest of the Nation. 

Table 25—Invasive and introduced plant species recorded on 1,196 plots in coastal Alaska

Number

of plots Invasive ranka Scientific name Common name

12 58 Taraxacum officinale G.H. Weber ex Wiggers Common dandelion

7 53–59 Trifolium L. Clover

5 83 Phalaris arundinacea L. Reed canarygrass

4 44 Plantago major L. Common plantain

3 32 Matricaria discoidea DC. Pineapple weed

2 (nr)b Agrostis capillaris L. Colonial bentgrass

2 56 Phleum pratense L. Timothy

1 (nr) Erysimum cheiranthoides L. Wormseed wallflower

1 54 Crepis tectorum L. Narrowleaf hawksbeard

1 40 Galeopsis L. Hempnettle

1 63 Hordeum jubatum L. Foxtail barley

1 61 Leucanthemum vulgare Lam. Oxeye daisy

1 (nr) Lolium arundinaceum (Schreb.) S.J. Darbyshire Tall fescue

1 80 Melilotus alba Medikus White sweetclover

1 54 Ranunculus repens L. Creeping buttercup

1 42 Stellaria media (L.) Vill. Common chickweed

1 73 Vicia cracca L. ssp. cracca Bird vetch

a

 From Carlson et al. 2008.

b

 (nr) = not ranked.

Results 

Of the total 1,196 plots visited from 2004 to 2008, introduced or native noxious plants were recorded on only 19 

plots (less than 2 percent), compared to some regions in the 

Lower 48 where 67 percent of all forested plots have at least 

one introduced species present (Gray and Azuma 2005; 

Schulz, in press). Of the eight phase 3 plots where introduced species were recorded, only one plot has a relative 

cover of introduced plants of 1 percent—the others were 

less than 1 percent. 

A total of 17 species of interest were recorded. These 

species are summarized in table 25. Dandelion was the most 

frequently recorded, followed by clover and reed canary 

grass. Of the 17 species recorded, 14 were on the phase 2 

invasive list, with 3 additional introduced species recorded 

in the phase 3 surveys. 

A disproportionate number of plots with introduced 

plants were located within 500 feet of the nearest improved 

road (table 26, fig. 48). Of those 19 plots with introduced or 

invasive plants present, 45 percent had a portion of the plot 

occupied by a nonforested condition such as a right-of-way 

or developed residential area compared to only 12 percent 
69

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 48—Forest Inventory and Analysis plots with introduced plants recorded (depicted plot locations are approximate).

of all sampled plots. Canary reed grass, common dandelion, 

and colonial bentgrass were recorded on plots more than 5 

miles from improved roads, which do not include logging 

roads or decommissioned logging roads. Reed canary 

grass was once used to stabilize logging roads; dandelion 

and colonial bentgrass could have been introduced around 

homestead sites. Only 7 of the 19 plots were infested with 

species considered highly invasive (Invasive rank greater 

than 70 percent) (table 25).

Table 26—Percentage of plots within distance 

classes from improved roads

Plots with at least 

one invasive/

Distance from All plots introduced plant 

improved road (n = 1,196) species (n = 19)

Percent

0–500 feet 7.5 63

More than 500 feet, 19 16 

less than 1 mile

One mile or more 74 21

Interpretation 

Compared to other areas of the United States, Alaska’s 

forest areas are, for the most part, free of potentially 

invasive plants. The areas where introduced plants have 

been found are usually not too far from the road system. 

To date, these species have had only a minor impact to 

the native plant populations. 

However, although Alaska’s forests have yet to be 

seriously affected by invasive plant species, recent observations in urban areas and along road corridors are unsettling 

and stress the need for continued monitoring (AKEPIC 

2005, Bargeron 2009). Invasive plants are spread primarily 

by humans, and are commonly encountered in urban areas, 

but human settlements in remote areas are also prone to 

invasion. Birds and other wildlife can extend the reach of 

invasion by delivering seeds of well-established species 

beyond cultivated areas. 

European bird cherry (fig. 49) has proven to be highly 

invasive in and around urban areas of Alaska. For many 

years this species was promoted as an ornamental species 

Kenneth C. Winterberger
70

GENERAL TECHNICAL REPORT PNW-GTR-835

Figure 49— European bird cherry (Prunus padus). 

Tricia Wurtz

with a high survival rate. Indeed, it has done very well in 

the region, and local birds are spreading viable seeds. In 

Anchorage and Fairbanks, seedlings are abundant along 

river and stream corridors, near parent trees, and were 

found in a recently burned forest stand in Anchorage. In 

some urban park zones, this is the only hardwood tree 

species found regenerating in the understory (Lamb and 

Winton 2010), replacing native species that moose feed on. 

Control efforts are underway in some areas, but this species 

has the potential to seriously affect Alaskan forests if they 

spread out of urban areas. That Alaska’s coastal forests 

currently have a low level of invasive species means that 

Alaska has an opportunity to prevent establishment of many 

invasive species, an opportunity not available to other U.S. 

states where so many invasives are well-established.

The Western Kenai Peninsula: an 

Opportunity to Study Fire and Its 

Effects on Soils and Trees4

Most of the coastal Alaska inventory unit is part of the temperate rain-forest biome, and fire is an extremely rare event. 

However, for the western side of the Kenai Peninsula and 

the Cook Inlet region, fire is a common source of natural 

disturbance (fig. 50). Although wildfires are a normal part 

of the disturbance regime for this region, urban growth 

and associated infrastructure raises the risk of increasing 

potential loss from fire. 

4 Authors: Theresa Jain and Tara M. Barrett.
71

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 50—Fires on the Kenai Peninsula, 1946–2009. Source: Monitoring Trends in Burn Severity database (http://mtbs.gov/). 

Projection: Alaska Albers NAD 83.

Kenneth C. Winterberger
72

GENERAL TECHNICAL REPORT PNW-GTR-835

Understanding how the forest environment is affected 

by fire is helpful for predicting how the ecosystems in the 

Cook Inlet and western Kenai may change in the future. 

Fire effects include impacts on the soil as well as impacts on 

the trees and other vegetation. A soil’s physical (appearance 

and mineralogy), chemical (nutritients), and biological (soil 

fauna) states after a fire are dependent upon the amount 

and duration of heating, prefire environmental conditions, 

landscape attributes, and fire-suppression tactics. The 

primary soil physical properties affected by fire are water 

repellency, structure stability, texture, color, temperature, 

and abundance of surface organic matter (Certini 2005). 

The chemical properties include the abundance of organic 

carbon, cation exchange capacity, nutrient levels and 

availability, and pH. However, the quantity, composition, 

and quality of the surface and mineral soil organic layers 

(leaves, needles, humic substances) are also important for 

influencing chemical properties. The biological properties 

include fungi, bacteria, soil-dwelling invertebrates, plant 

seed, roots, bulbs, root crowns, and other plant materials 

needed for sprouting or regeneration. All these components 

influence factors such as erosion, wildlife habitat, soil 

productivity, and vegetation recovery.

Soil physical properties—

Erosion is a common postfire effect because of its influence 

on water quality and the potential for flooding. In most 

circumstances, erosion does not occur unless the surface 

organic matter (litter, root mat) cover is less than 30 percent 

(Lewis et al. 2006). However, if the appropriate prefire 

soils exist (such as granitics), water repellency (inability 

for the soil to absorb water) can occur at relatively low 

temperatures that are quite common in wildfires (177 to 

204 °C) when duration is greater than 15 minutes (Debano 

et al. 1976). Lewis et al. (2006) reported that grey ash is 

more characteristic of potential water repellency than the 

presence of black ash. Morever, if the overstory is killed 

in the fire and the forest floor is no longer present, soil 

temperatures can increase. 

Soil chemistry—

The highest proportion of soil nutrients exist within the 

surface organic matter, upper mineral soil layers, and wood 

residue (surface and buried rotten wood). Harvey et al. 

(1989) reported that up to 56 percent of the nitrogen occurs 

within the litter and humic layers, upper mineral in the first 

2 inches, and wood residue. During a fire, these components 

are also the most vulnerable to loss through combustion. 

For example, nitrogen, a nutrient that is frequently limiting 

in forest environments, can volatilize at 752 °F (Hungerford 

et al. 1991). However, nutrients such as potassium do not 

volatilize until temperatures exceed 1293 °F (Hungerford 

et al. 1991). 

Soil biology—

Many organisms do not survive temperatures greater than 

140 °F. Pollen, seeds, spores, lichens, and mosses do not 

survive above 284 °F when exposed to these temperatures 

for more than 30 minutes. Even lower temperatures (158 

°F) can cause mortality if exposure exceeds 4 hours (Levitt 

1980). Wingless insects die when exposed to temperatures 

greater than 104 °F. Mammals, when exposed to the 

temperatures directly, cannot survive when temperatures 

exceed 140 °F. Ninety percent of the microbial carbon in 

soils is volatized when exposed to temperatures of 572 °F 

for longer than 15 minutes (Levitt 1980).

A fire can kill vegetation two ways, through direct 

combustion, such as in a crown fire, or through radiant heat, 

which causes needle or crown scorch (red needles). Radiant 

heat from smoldering fires can kill roots or damage the 

tree cambium; this type of mortality is typically delayed 

and therefore a tree that appears alive after the fire can die 

over time. In addition, if a tree survives a fire, it sometimes 

can become stressed, allowing for mortality to occur from 

insects or disease. 

How quickly a forest recovers after a fire is dependent 

on three survival mechanisms: whether vegetation survives 

a fire (e.g., ponderosa pine), recolonization either from 

seed banks in the soil, or transported to the site (e.g., wind, 

animals, floods), or sprouting new growth from roots. Each 
73

Forests of Southeast and South-Central Alaska, 2004–2008

forest is different as to how it recovers after a wildfire. If 

a forest is able to recover quickly with much of its original 

plant composition, the forest is considered resilient. However, if after a wildfire, the soils no longer have seed, or an 

introduced species occupies the site, the forest may be less 

resilient (fig. 51). The level to which a forest can recover 

after a fire is dependent on the soils, the forest composition, and other factors that may influence how a fire burns 

a particular site. For example, white spruce produces large 

seed crops after a hot dry summer; black spruce has semiserotinous cones that open when exposed to heat; aspen, 

with its copious root systems, can sprout after a fire; and 

birch has seeds that disperse long distances. However, all 

forests are unique—thus what we learn in other parts of the 

United States does not always transfer to Alaskan forests. 

Therefore, it is critical that each forest type be evaluated as 

to the relation between the postfire environment and how 

quickly a forest recovers.

The FIA program of the Pacific Northwest Research 

Station maintains a permanent sample of plots located on 

a grid throughout the United States; this provides unique 

information on the extent and severity of fires in forest lands 

as well as on postfire recovery and succession. In the Western 

United States, FIA has begun measuring postfire impacts 

on soil and vegetation the year after a fire. This information, 

when combined with long-term remeasurements over 

succeeding decades, can help us understand the trends and 

variation in fire effects for different forest types ranging from 

ponderosa pine forests of the interior West, to mixed-conifer 

forests of the Sierra Nevada, to the boreal forests of Alaska. 

Figure 51—Liverworts, mosses, horsetails, and an invasive cherry seedling (Prunus padus, circled) sprout during the spring following 

the 2008 Piper Fire in Anchorage, Alaska. 

Tara M. Barrett
74

GENERAL TECHNICAL REPORT PNW-GTR-835

In Alaska, we used postfire protocols that were first 

implemented on FIA plots following the 2007 Caribou Hills 

wildfire, a human-caused fire that burned approximately 

50,000 acres on the Kenai Peninsula. This was an area that 

had previously been affected by a bark beetle epidemic on 

the Kenai Peninsula that began in the late 1980s and continued into the 1990s. Warmer temperatures had increased 

overwinter survival and shortened maturation time for 

beetles (Berg et al. 2006), and increased summer temperatures may have also caused drought stress in trees, favoring 

successful beetle attacks. Preliminary results from the FIA 

plots burned in the Caribou Hills wildfire show that most 

trees that had survived the bark beetle epidemic were killed 

by the fire (fig. 52). The Caribou Hills wildfire provides 

an example of how complex interacting factors determine 

how forests change over time in the south-central region of 

Alaska. Warmer temperatures encourage beetle outbreaks, 

increasing the abundance of dead fuel, which in turn may 

increase the frequency of wildfires. The frequency and 

extent of wildfires is also affected by the increasing development in the region. Active fire suppression in populated 

areas contributes to fuel buildup, and fire ignitions increase 

with human activities. Changes in the fire regime resulting 

from these interactions may potentially alter the path of 

succession in these areas (Kasischke et al. 2000). 

Figure 52—Dead white spruce and regenerating understory 

vegetation on the Kenai Peninsula 1 year after the 2007 

Caribou Hills Fire.

Figure 53—Western hemlock killed by hemlock dwarf mistletoe, 

resulting in a canopy gap in an old-growth forest. Note the numerous witches’ broom infections on the branches of the dead tree.

The Distribution of Hemlock Dwarf 

Mistletoe Suggests Influences of Climate5

Background

Hemlock dwarf mistletoe (Arceuthobium tsugense

[Rosendhal] G.N. Jones) is an important disease of 

western hemlock in old-growth forests throughout 

southeast Alaska (fig. 53). The disease is caused by 

infection from a small higher plant, which is well-adapted 

to life as a parasite. When intensely infected, western 

5 Authors: Paul Hennon, Tara M. Barrett, and Dustin Wittwer.

hemlock trees suffer significant growth loss, reduced wood 

quality, and higher rates of mortality. Branch infections 

result in abnormal growth proliferations known as “witches’ 

brooms” that can serve as important wildlife habitat. 

Depending on management goals, foresters can achieve 

various levels of infected trees in managed stands by 

manipulating harvest regimes, ranging from eradication to 

numerous infected trees (Muir and Hennon 2007). Clearcut 

harvests greatly reduce or eliminate the disease, whereas 

retention harvests that maintain large, dispersed infected 

trees encourage it. 

Paul Hennon

Eric Straley
75

Forests of Southeast and South-Central Alaska, 2004–2008

Hemlock dwarf mistletoe is difficult to detect during 

aerial surveys; thus its extent and intensity have never 

been adequately quantified in Alaska. We have observed 

that dwarf mistletoe is more common at low elevations 

even though the primary host, western hemlock, is rather 

ubiquitous at all but the highest forested elevations. We used 

forest inventory plots to document the broad occurrence 

and elevational distribution of dwarf mistletoe and then 

compared it to areas where western hemlock grows in 

coastal Alaska. 

Methods

The presence or absence of mistletoe was recorded for all 

live trees in the 1995–2000 periodic inventory of southeast 

Alaska as described in appendix 1. There were 3,955 plots 

in that inventory, 1,278 of which were forested, and the 

inventory did not include wilderness on national forests or 

Glacier Bay National Park. Although no information on 

measurement accuracy exists for the inventory, it is likely 

that numbers of trees infected are an underestimate because 

field crews are more likely to miss an infected tree than 

note one that does not exist. Where results are reported as 

numbers of acres with mistletoe present, it means that at 

least one infected tree was recorded within forested conditions on the plot.

Results

Inventory data indicated that hemlock dwarf mistletoe is 

present in only part of the range of western hemlock (fig. 

54). The disease occurs in most of southeast Alaska from 

Dixon Entrance north to Haines and Glacier Bay National 

Park. Western hemlock grows approximately 400 miles farther to the northwest on the coastline of the Gulf of Alaska, 

but the disease is completely absent in these forests. 

Ignoring the inaccessible wilderness not sampled, 

hemlock dwarf mistletoe occurred on approximately 

955,000 acres (standard error = 68,000 acres). Within the 

Alexander Archipelago ecoregion (Nowacki et al. 2002, fig. 

6 in chapter 2), this is 14.8 percent of forest land. Mistletoe 

infected an estimated 7.6 percent of western hemlock trees 

≥ 5 in d.b.h. in the Alexander Archipelago ecoregion. On 

plots where at least one tree had mistletoe, 46.0 percent 

Figure 54—Distribution of western hemlock and hemlock dwarf mistletoe in coastal Alaska. 

Dustin Wittwer
76

GENERAL TECHNICAL REPORT PNW-GTR-835

of western hemlock trees were infected. Hemlock dwarf 

mistletoe is clearly concentrated at low elevations in 

southeast Alaska (fig. 55). There is an apparent threshold at 

approximately 500 feet, above which the parasite can occur 

but is much less frequent. The principal host, western hemlock, is distributed well above this threshold, suggesting 

that some factor limits the distribution of hemlock dwarf 

mistletoe at higher elevations. 

Interpretation

Results reflect the patchy distribution of the disease, even 

within the pathogen’s preferred range. The elevational 

and regional limits for the range of the pathogen might be 

explained by two possible scenarios, both dealing with climate factors. First, there has been speculation that hemlock 

Figure 55—Occurrence of western hemlock and hemlock dwarf mistletoe by elevation in southeast Alaska. Note the drop in 

the percentage of infected western hemlocks above 500 feet elevation. Lines at end of bars represent ± standard error.

5 

0

10 

15 

20 

25 

0

10

20

30

40

50

60

70

80

Mistletoe infection (percent)

Trees (millions)

Trees with mistletoe

Western hemlock trees

Percentage of mistletoe infection

Elevation (feet)

0–99

200–299

400–499

600–699

800–999

1,000–1,099

1,200–1,299

1,400–1,499

1,600–1,699

1,800–1,899

2,000–2,099

2,200–2,299

2,400–2,499

dwarf mistletoe fruits need an adequate growing season to 

mature to the stage where seeds can disperse. If a fall freeze 

comes before fruit maturation, the fruits are killed before 

seeds disperse, eliminating reproduction. 

Second, snow, which is more abundant at higher 

elevation and in coastal forests around the Gulf of Alaska, 

may be responsible for removing dwarf mistletoe seeds 

from hemlock tissues during winter. Dwarf mistletoe seeds 

disperse from fruits in late summer but do not germinate 

until the following spring. Thus, they overwinter on 

hemlock shoots or needles, stuck to them with a viscous 

coating. Heavy snow, or repeated snow-rain cycles, could 

slough off many of these overwintering seeds. Longer 

growing seasons and reduced snow are among the climate 

change scenarios projected for coastal Alaska (IPCC 2007). 
77

Forests of Southeast and South-Central Alaska, 2004–2008

Thus, hemlock dwarf mistletoe is expected to increase its 

range and its impact on western hemlock, a tree that is also 

favored by a warmer climate. The FIA data provide a means 

to monitor the upslope spread and northwest migration of 

dwarf mistletoe, a process that is expected to be relatively 

slow. The distribution of the hemlock dwarf mistletoe 

disease appears to be an excellent situation to model as 

range expansions of the host tree and the parasite are likely 

to occur with climate change. 
78

GENERAL TECHNICAL REPORT PNW-GTR-835

Sasha Sulia
79

Forests of Southeast and South-Central Alaska, 2004–2008

People benefit from the forests in southeast and southcentral Alaska in different ways: forests are used for 

recreation and tourism, as a source of clean water, and as 

home to a large variety of fish and wildlife. In this chapter, 

we provide some analysis related to use of the forests for 

products. There is increasing interest in young-growth 

timber for wood supply in southeast Alaska, and the first 

section describes some of the attributes of young growth in 

the coastal region. The second section describes the primary 

forest products industry in Alaska, using a Forest Inventory 

and Analysis (FIA) survey of mills in the region. A new 

potential use, using forest biomass for energy, is briefly 

described in the following section. The chapter concludes 

with a section describing the use of coastal Alaska forests 

for nontimber forest products (NTFPs). 

Young-Growth Timber Resource in 

Coastal Alaska1

Introduction 

Timber has been harvested from the forests of coastal 

Alaska since before the state was purchased more than 140 

years ago. Prior to the proclamation creating the Tongass 

and Chugach National Forests in 1907, small amounts 

of timber were harvested mostly for local consumption 

(Brackley et al. 2009, Harris and Farr 1974). Leading up 

to the 1940s, annual harvest volumes along coastal Alaska 

remained fairly low but began increasing over time. With 

the demand for high-quality spruce (see “Common and 

Scientific Names”) for the manufacture of aircraft in 

support of World War II, annual timber harvest picked up 

steadily in the following decades (Hutchison 1968). The 

1950s saw two important events affecting the young-growth 

resource: harvest levels significantly increased with the 

introduction of the pulp industry in southeast Alaska, and 

harvesting practices changed from partial harvest methods 

to the near exclusive use of clearcutting and even-age 

management (Deal et al. 2010, Harris and Farr 1974). 

Legislation has brought changes in ownership since the 

1950s also affecting this resource: the Alaska Statehood 

Act of 1958 (Public Law 85-508), the Alaska Native Claims 

Settlement Act of 1971 (ANCSA) (Public Law 92-203), 

and the Alaska National Interest Lands Conservation Act 

(ANILCA) (Public Law 96-487) (van Hees 1987). Harvests 

from national forests peaked in the early 1970s, and from 

all forested ownerships in the late 1980s (Brackley et al. 

2009). In addition to removal of trees for timber, the forests 

of coastal Alaska are also subject to stand-replacing events 

such as windthrow, wildfire, landslides, and, recently, 

insect infestation such as spruce bark beetle (Dendroctonus 

rufipennis [Kirby]) on the Kenai Peninsula (Campbell et 

al. 2005, Harris and Farr 1974). In some cases it has been 

decades since these young stands regenerated following 

harvest and other disturbances. What do they look like 

today? Inventory results are presented in this section that 

describe and estimate the current extent of young-growth 

forests throughout southeast and south-central Alaska 

(fig. 56). These forests are summarized by their estimated 

acreage, current tree sizes, regenerated mix of forest species 

types, potential productivity, and current standing volume.

Methods

Young-growth timber is defined for this report as forested 

stands on unreserved timberland where the majority of 

volume is in trees with an estimated stand age of 150 years 

or less. Using the 150-year age limit is useful as it includes 

any stand that has regenerated since significant timber 

harvest activities began in the region during the 1950s. 

Traditionally, this definition for young-growth forests has 

been used throughout coastal Alaska resource and planning 

publications and includes the tree stocking and size criteria 

for sawtimber stands (stands stocked with half or more of 

trees at least 11.0 inches diameter at breast height [d.b.h.] for 

hardwoods and 9.0 inches d.b.h. for softwoods) (Campbell et 

al. 2004, 2005; Hutchison 1968; Hutchison and LaBau 1975; 

USDA FS 2008a; van Hees 1988, 2003). All stand sizes—

seedling (< 1.0 inch d.b.h.) and sapling (1.0 to 4.9 inches 

d.b.h.), poletimber (5.0 to 11.0 inches d.b.h. for hardwoods 

and 9.0 inches d.b.h. for softwoods), and sawtimber (> 11.0 

inches d.b.h. for hardwoods and > 9.0 inches d.b.h. for softwoods)—are reported here to provide a better assessment of 

Chapter 5: Forest Products

1

 Author: Glenn A. Christensen.
80

GENERAL TECHNICAL REPORT PNW-GTR-835

the entire current young-growth resource. Results presented 

here are summarized by stand size for consistency with past 

reporting.

This report relies heavily on the stand age of younggrowth forests. These young stands tend to have a less 

complex structure than typical old-growth, multiaged 

forests in the region and are predominantly even aged 

(Alaback 1984, Deal et al. 2010). Stand age is determined 

by averaging the ages of several live trees that best represent the stand’s predominate size classes. Individual tree 

ages are determined by counting the annual growth rings 

of a core extracted from the tree with an increment borer 

and adjusted by adding the number of years required to 

reach breast height; see the section on old-growth forests 

in coastal Alaska (chapter 3) for details. Stand age is subject 

to error owing to measurement of tree age (from false 

growth rings or miscounting) or error in selection of trees 

that best represent the current stand.

Results

Area of young-growth timber in coastal Alaska—

Throughout coastal Alaska, there are 15.3 (standard error 

[SE] = 0.3) million acres of forest land, with about 6.1 (SE 

= 0.3) million acres having a current stand age of 150 years 

or less. Timberland, forest land having a growth capacity 

of at least 20 cubic feet per acre per year and currently not 

set aside as reserved forest, makes up about 6.2 (SE = 0.2) 

million acres throughout the region. Young-growth timber 

stands, forested stands on timberland with a current standage of less than or equal to 150 years, currently makes up 

Figure 56—Young-growth stand in southeast Alaska.

Kathleen Galloway
81

Forests of Southeast and South-Central Alaska, 2004–2008

47 percent, or 2.9 (SE = 0.1) million acres of these 

timberland acres. By region, there is 1.5 (SE = 

0.1) million acres of young-growth timberland in 

the south-central region and 1.4 (SE = 0.1) million 

acres of young-growth timberland in the southeast 

region. These estimates include all stand sizes 

from seedling and sapling-sized stands to larger 

sawtimber-sized stands of young-growth forest 

(table 27). 

Much (41 percent) of young-growth timberland 

is found on privately owned land (a total of 1.2 

(SE = 0.1) million acres). The national forests 

manage about 34 percent and other federal or state 

and local governments manage the remaining 25 

percent of young-growth timber acreage. Standage class distribution of young-growth timberland 

differs by ownership (fig. 57). The distribution 

by acre on privately owned lands is fairly even across all 

classes except the 10-to 30-year stand age class, which 

covers significantly more acres than any other age class. 

National forests tend to have more young-growth acreage 

in either the younger or the older age classes throughout 

coastal Alaska. 

Figure 57—Estimated young-growth timberland area by owner group and stand age class in coastal 

Alaska, 2004–2008. Lines at end of bars represent ± standard error.

Table 27—Timberland acreage by stand-age class for 

south-central, southeast, and all coastal Alaska

South-central Southeast Coastal

Stand-age class Total SE Total SE Total SE

Years - - - - - - - - - Thousand acres - - - - - - - - -

< 10 119 36 105 33 223 48

10–30 122 35 357 59 480 68

31–50 212 49 282 57 494 74

51–70 245 53 57 25 302 58

71–90 256 52 92 31 348 61

91–110 223 48 73 28 297 55

111–130 149 41 238 52 387 65

131–150 163 44 200 47 363 64

> 151 394 58 2,887 143 3,281 153

All young growth 1,489 107 1,404 116 2,894 153

All classes 1,884 110 4,290 161 6,174 185

Note: SE = standard error.

0

50

100

150

200

250

300

350

<10 

10–30

31–50

51–70

71–90

91–110

111–130

131–150

Young-growth timberland (thousand acres)

Stand age (years)

National forest

Private

Other federal

State and local government

About 1.5 million acres (SE = 0.1), or 51 percent, of the 

young growth on timberland consists of sawtimber-sized 

stands. A little over half of these stands (27 percent of all 

young-growth timberland) is found within the southeast 

region (about 785,000 [SE = 92,000] acres) (fig. 58). About 

687,000 (SE = 80,000) acres of these sawtimber-sized 
82

GENERAL TECHNICAL REPORT PNW-GTR-835

stands lie within the south-central region (24 percent of the 

young growth).

Coastal Alaska has many different forests, but Sitka 

spruce and western hemlock are the dominant forest types 

Figure 58—Estimated young-growth timberland area by region 

and stand size class in coastal Alaska, 2004–2008. Lines at end 

of bars represent ± standard error.

0

100

200

300

400

500

600

700

800

900

1,000

Seedlings 

and saplings

Poletimber Sawtimber

Timberland (thousand acres)

Stand size class

South-central

Southeast

0 200 400 600 800 1,000 1,200

Timberland (thousand acres)

Sitka spruce

Western hemlock

Cottonwood

Paper birch

White spruce

Mountain hemlock

Western redcedar

Lodgepole pine

Alaska yellow-cedar

Forest type

Seedlings and saplings

Poletimber

Sawtimber

Figure 59—Estimated young-growth timberland area by forest type and stand size in coastal Alaska, 2004–2008. Lines at 

end of bars represent ± standard error.

in the region (see “Forest type” in glossary). Sitka spruce 

young-growth makes up 36 percent, or over 1 million acres 

(SE = 0.1); nearly 60 percent of these stands are sawtimber 

size (fig. 59). Western hemlock stands are also common and 

make up about 27 percent (nearly 800,000 [SE = 93,000] 

acres) of the young-growth. These western hemlock forests 

currently consist of nearly 60 percent sawtimber-sized 

stands. 

Assessment of productivity could be helpful to determine how soon these young stands could reach merchantable size and replace the older forests currently being 

harvested for wood products. Productivity is a measure of 

the annual potential for growth as measured in cubic feet 

per acre of wood grouped into classes (see “Site class” in 

glossary). Throughout coastal Alaska, highly productive 

stands tend to be a minor component of timberland area. 

About 216,000 acres (SE = 50,000) (7 percent) are in the 

highest productivity class (120 to 164 cubic feet per acre 

per year) (fig. 60), more than half (51 percent) of the young 

growth stands within the south-central region are in the 

most marginally productive class (20 to 49 cubic feet per 

acre per year).
83

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 60—Estimated young-growth timberland area by region and productivity 

class in coastal Alaska, 2004–2008. Lines at end of bars represent ± standard error.

Volume of young-growth timber in coastal Alaska—

The volume of wood found in the young-growth resource 

throughout coastal Alaska is a small portion of the total 

wood volume found on all timberland in the region. About 

27 percent of all net cubic-foot growing-stock volume on 

timberland currently comes from these young-growth trees, 

0

100

200

300

400

500

600

700

800

900

20–49 50–84 85–119 120–164

Timberland (thousand acres)

Productivity class (cubic feet per acre per year)

South-central

Southeast

estimated at about 8 billion cubic feet (SE 

= 0.8). Generally, there is a positive correlation between acreage and tree volume. 

A notable exception is with volume by 

ownership where the national forests 

have over half (52 percent) of all the 

current young-growth cubic-foot volume 

but only about a third (34 percent) of the 

young-growth acreage. As is found when 

evaluating acreage by forest type, most of 

the current volume is in Sitka spruce and 

western hemlock trees; together these two 

species make up about 83 percent of the 

young-growth volume. Individually, Sitka 

spruce is 54 percent (4.3 billion cubic feet 

[SE = 0.5]) and western hemlock is 29 

percent (2.3 billion cubic feet [SE = 0.3]) 

of the young-growth volume. Figure 61 shows how this 

volume is distributed in softwood and hardwood trees 

by 2-inch diameter classes. For softwoods, the largest 

diameter classes (those larger than 21.0 inches) contribute 

about 42 percent of the softwood young-growth cubicfoot volume. 

Figure 61—Estimated young-growth timberland cubic-foot volume by tree diameter class in coastal Alaska, 2004–2008. Lines at end 

of bars represent ± standard error.

0

500

1,000

1,500

2,000

Timberland (million cubic feet)

Diameter class (inches)

Softwood tree species

Hardwood tree species

5.0–6.9

7.0–8.9

9.0–10.9

11.0–12.9

13.0–14.9

15.0–16.9

17.0–18.9

19.0–20.9

29.0+

21.0–28.9
84

GENERAL TECHNICAL REPORT PNW-GTR-835

Interpretation

The young-growth forests of coastal Alaska today are 

diverse and productive. Following logging or major disturbances in western hemlock or Sitka spruce forests, they 

respond by growing back with a postharvest regeneration 

that is abundant with the development of a dense new cohort 

of similar species (Deal 2007). These young stands have a 

simple structure that is uniform in tree height and diameter 

distribution, lacking the more complex stand structures 

found in old-growth stands (Deal and Tappeiner 2002). 

Because of these differences in stand structure and development, these stands will have a different wood product 

potential than the older forests currently supplying nearly 

all of the wood products in the region.

Overall, 41 percent of the young-growth acres falls on 

private ownerships, and these tend to be weighted more 

toward the younger age classes. These younger stands on 

private ownerships tend to have less overall volume, which 

explains why the national forests actually manage fewer 

overall acres of young-growth timberland but have over half 

(52 percent) of the current volume. Potential productivity 

will be helpful to determine how soon these young-growth 

stands could reach merchantable size and replace the older 

forests currently being harvested. However, larger forces 

such as economic and social considerations, management 

objectives, and public land management policy may have an 

even greater impact on the future of young-growth forests 

in coastal Alaska. Information presented here provides an 

initial summary of inventory results; additional analyses of 

these data are being compiled for forthcoming publication.

Alaska’s Primary Forest 

Products Industry2

Background

Alaska’s primary forest products industry provides 

social and economic benefits to Alaska communities by 

supplying wood products such as lumber and log homes, 

and by providing employment and income associated with 

land management, timber harvesting, and wood products 

manufacturing. Availability of timber for harvest and 

2

 Author: Todd A. Morgan.

retention of forest products infrastructure to use timber are 

important issues facing Alaska’s forest products industry. 

In cooperation with USDA Forest Service Pacific 

Northwest Forest Inventory and Analysis (PNW-FIA), the 

Bureau of Business and Economic Research at the University of Montana conducts a periodic census of Alaska’s 

primary forest products industry (i.e., timber processors and 

mill residue users). This census is the source of information 

presented below and provides detail on timber harvest and 

flow, as well as comprehensive information about the state’s 

timber processing sectors, product volumes, sales values, 

and mill residue (Halbrook et al. 2009).

Results

The 2005 harvest in Alaska was 268.2 million board feet 

(MMBF) Scribner, but the volume processed by Alaska 

facilities was significantly less. Excluding raw logs that 

were received by Alaska mills and subsequently exported, 

Alaska facilities received slightly over 44.8 MMBF for 

processing in-state, approximately 16.7 percent of the 

harvest. In stark contrast to the timber harvest volume, of 

which national forests accounted for just 17.5 percent, the 

largest share (53 percent) of timber received by mills in 

Alaska came from national forests, followed by state and 

other public lands (38 percent), private and Native lands 

(8 percent), with Canadian imports representing about 

0.5 percent of the timber received by Alaska processors. 

Quite unlike the industry in the Lower 48 States, Alaska’s 

primary forest products industry almost exclusively utilizes 

timber harvested in-state. 

Primary timber processors in Alaska produced an 

array of products including dimension lumber, board and 

shop lumber, timbers, finished house logs, log homes, log 

furniture, pulp chips from roundwood, cedar products, 

tonewood (for musical instruments), and novelty items such 

as bowls, spoons, and mugs. A total of 78 primary forest 

products facilities operated in Alaska during 2005 (fig. 

62). The sawmill sector was the largest with 50 facilities, 

and the log home sector had 20, and eight other facilities 

actively purchased or used timber. During 2005, production 

of lumber and other sawn products was approximately 55 

MMBF lumber tally, of which 89 percent was produced in 
85

Forests of Southeast and South-Central Alaska, 2004–2008

Figure 62—Alaska’s resource areas and primary forest products manufacturers, calendar year 2005 (Source: Halbrook et al. 2009).

southeast Alaska. Production of house logs totaled more 

than 805,000 lineal feet, and pulp chip production from 

harvested trees exceeded 94,000 bone-dry tons.

Alaska’s primary forest products sectors had product 

and mill residue sales of nearly $150 million during 2005. 

The largest share ($119 million) of sales was in the form of 

saw-log and pulpwood exports, primarily to Pacific Rim 

countries with about $10 million in sales to the Lower 48 

States. Lumber accounted for about $20 million in sales, 

and house logs, mill residue, and other products combined 

accounted for slightly less than $10 million. Approximately 

$13 million in sales were within Alaska and consisted 

of a mix of products including lumber, log homes, cedar 

products, log furniture, tonewood, and novelty items. 

Shown below are 2005 sales from Alaska’s primary 

forest products industry sectors:

Products 2005 sales value

Thousands of U.S. dollars

Saw-log and pulpwood exports $119,171

Lumber $20,460

House logs $4,171

Other products3 $4,320

Mill residue $1,416

Total products $149,537

3

 Other products include cedar products, log furniture, tonewood, 

and novelty items.
86

GENERAL TECHNICAL REPORT PNW-GTR-835

Alaska’s forest products industry employed over 3,000 

workers in the 1970s. After dropping during the 1980–1982 

recession, harvest and employment peaked in 1989 and 1990 

at over 1 billion board feet and more than 4,600 workers. 

By 2005, with a 90-percent decline in national forest timber 

harvest and an 80-percent fall in Native/private harvest, 

total harvest had dropped by more than 80 percent. This 

precipitous decline in harvest led to a substantial loss in 

infrastructure with the closure of the two pulp mills in 

the 1990s, and forest industry employment fell to just 

1,049 workers in 2005 (USDC Bureau of Economic 

Analysis 2007). 

In addition to the steep employment declines, there was 

also a shift in the proportions of employment in various 

components of Alaska’s industry. Prior to mill closures 

in the 1990s, approximately one-half the employment 

was in the logging sector. Currently, with the export of 

approximately 80 percent of Alaska’s timber harvest, over 

two-thirds of the workforce is in private sector logging and 

forest management, and the remaining one-third is in wood 

products manufacturing (USDC BEA 2007). 

Interpretation

Recent timber harvest levels in Alaska are on par with the 

mid-1950s but considerably below harvest levels seen from 

1960 through 1999 (fig. 63). Changes in harvest over the 

last 60 years have been driven by timber output from two 

ownership categories: Native/private and national forests. 

Beginning in the 1950s with the establishment of pulp 

mills in the region, the annual volume of timber harvested 

in Alaska began to increase rapidly owing to increased 

harvest on national forests. Through 1973, national forests 

provided more than 90 percent of Alaska’s timber harvest. 

A major shift in the ownership source of timber took place 

in the 1970s. The Alaska Native Claims Settlement Act of 

1971 transferred ownership of approximately 44 million 

acres of federal land to Alaska Natives. With the shift in 

land ownership, the combined Native and private harvest 

increased from near zero prior to 1980 to a peak of 617 

MMBF in 1989. 

Total timber harvest volume in Alaska fell from 

1,033 MMBF in 1990 to 268 MMBF in 2005. During 

this period, national forest harvest dropped by 90 percent 

Figure 63—Alaska’s timber harvest by owner group, 1909–2005. (Source: Brackley et al. 2009).

200

0

400

600

800

1,000

1,200

19101915192019251930193519401945195019551960196519701975198019841990199520002005

Annual timber harvest (MMBF, Scribner)

Year

Native and private

State and other public

National forests
87

Forests of Southeast and South-Central Alaska, 2004–2008

4

 Author: Hans-Erik Andersen.

Forest Biomass as a Renewable 

Source of Energy in Coastal Alaska4

There is increasing interest in the use of forest biomass 

as an alternative fuel source for energy supply in 

rural areas of Alaska, where the ever-increasing cost 

of petroleum-based fuel products, specifically diesel, 

are putting tremendous economic pressure on these 

communities (Crimp et al. 2008). In coastal Alaska, 

the fuel supply for bioenergy production will likely be 

obtained as a byproduct of a range of forest management and utilization activities, including forest thinnings, sawmill operations, removal of hazardous fuels 

in fire-prone areas (such as the Kenai Peninsula), and 

habitat improvement/restoration (Nicholls and Miles 

2009, Parrent 2007). Although bioenergy systems do 

release carbon dioxide and other products of combustion (gases and particulate matter) into the atmosphere, 

they offer several significant advantages over the use 

of fossil fuels for heating and power generation in rural 

Alaska: (1) forest biomass is a renewable source of fuel, 

and can be regrown in the near future, possibly offsetting greenhouse emissions from bioenergy production; 

(2) reduction of hazardous wildfire fuels near rural 

communities reduces risk to humans and property; 

and (3) managing the forest resource for sustainable, 

continuous biomass supply can create employment 

opportunities in nearby rural communities (unemployment rates in remote Native communities in southeast 

Alaska often reach 60 to 90 percent) (Wolfe 2008). As 

an example, the city of Craig in southeast Alaska has 

recently developed a bioenergy system that provides 

heat for the local school, by using a gasifier system 

with very low emissions, at a cost savings of $20,000 

per year for the school (Peterson and Bruns 2006). 

Other options being considered include the use of 

wood pellets for home heating, electrical generation 

from wood biomass, use of beetle-killed spruce in the 

production of wood-plastic composite building materials, and export of forest biomass to foreign markets 

(Nicholls 2007). 

Although there is tremendous potential for deriving energy from the forest biomass resource in Alaska, 

there are still significant obstacles to the efficient 

utilization of forest biomass, including prohibitive 

transportation costs and inadequate harvesting systems 

for small-diameter trees (Nicholls and Miles 2009). 

Another constraint on the development of bioenergy 

systems in rural Alaska is inadequate information on 

available biomass supply. Although Forest Inventory 

and Analysis plots can provide information on the 

distribution of biomass resources over large areas, the 

sampling intensity is not adequate to provide information on the biomass resources over smaller areas, for 

example, within the vicinity of a rural village. Research 

is currently underway to investigate how highresolution remotely sensed data, such as airborne laser 

scanning, can be used in combination with FIA field 

plots to provide detailed information on the quantity 

and spatial distribution of biomass resources over 

smaller geographic areas (Andersen 2009). 

The tree species present in coastal Alaska 

differ somewhat in terms of their value as a biofuel 

resource. For example, wood fuel heating values are as 

follows—western hemlock 13.9 million British thermal 

units (BTUs)/cord, Sitka spruce 13.4 million BTUs/

cord, western redcedar 12.8 million BTUs/cord, and 

white spruce 13.7 million BTUs/cord (Nicholls and 

Miles 2009). The potential savings from converting 

to a wood-fired bioenergy system can be substantial. 

For example, at 2006 prices, the cost for biofuel (e.g., 

white spruce cordwood, 30 percent moisture content, 

$150/cord) was $16.98/million BTU, significantly lower 

than the cost of fuel oil ($31.70/million BTU and $3.50/

gallon) (Nicholls and Miles 2009, Parrent 2007). With 

the cost of fossil fuels unlikely to decrease in the near 

future, the demand for forest biomass as an alternative 

fuel source for energy production in Alaska will likely 

continue to grow. 
88

GENERAL TECHNICAL REPORT PNW-GTR-835

and Native/private by about 80 percent. Numerous factors 

contributed to this decline, including: 

• The Tongass Timber Reform Act of 1990 and 

Tongass timber program lawsuits

• Substantially lower timber inventories on some 

Native corporation lands 

• Shifting markets and market conditions—especially 

weakness in Asian wood markets and increased 

competition from other producing regions 

• Relatively high operating costs in Alaska

Going forward, availability of timber from Native lands 

and national forests will be a key factor in the retention of 

Alaska’s forest products industry as well as the social and 

economic benefits provided by the industry. 

Nontimber Forest Products5

Nontimber forest products (also known as special forest 

products [SFPs]) are products or services derived from 

the botanical and fungal resources of the forest other than 

timber. A precise definition of what is included as an NTFPdiffers by land ownership and land management agency. In 

the Forest Service proposed rule on the “Sale and Disposal 

of National Forest System Timber; Special Forest Products 

and Forest Botanical Products” the following definition was 

given (USDA FS 2007: 59502):

Products collected from National Forest System 

lands for commercial, personal, tribal, educational, 

or scientific purposes, including without limitation: 

bark, berries, boughs, bryophytes, bulbs, burls, 

Christmas trees, cones, ferns, firewood, forbs, 

fungi (including mushrooms), grasses, mosses, 

nuts, pinestraw, roots, sedges, seeds, transplants, 

tree sap, wildflowers, fence material, mine props, 

posts and poles, shingle and shake bolts, and rails. 

The term special forest products does not include 

sawtimber, pulpwood, non-sawlog material removed 

in log form, cull logs, small roundwood, house logs, 

telephone poles, derrick poles, minerals, animals, 

animal parts, insects, worms, rocks, water, and soil.

Many NTFPs are derived from vascular plant species. 

The FIA inventory surveys vascular plant diversity and 

abundance on a 1/16th-systematic subsample of all field 

plots. In the coastal Alaska inventory unit, this systematic 

subsample resulted in 135 forested plots surveyed for 

vegetation diversity between 2004 and 2008. Each vascular 

species was recorded on twelve 1-meter (3.3 foot) quadrants 

on each plot in the subsample (see Schulz et al. 2009 for 

additional detail on methods). This section of the report 

uses these data to provide some information on abundance 

of vascular plant species that are used as NTFPs.

As discussed in chapter 2, land ownership and management in Alaska’s coastal inventory unit includes federal, 

state, local, and private entities. Each owner or land 

management agency has their own policies and regulations 

regarding the harvest of NTFPs. On most federal and state 

lands, uses of NTFPs are separated into noncommercial 

use and commercial use. In their interim SFP resource 

management policy, the Alaska Region of the Forest Service 

provides the following category of uses (USDA Forest 

Service 2000: 1):

NON-COMMERCIAL USE—this category includes: 

Subsistence Use—Customary and traditional uses 

by rural residents, as defined in ANILCA Title VIII, 

for direct personal or family use or consumption, 

barter, sharing, or customary trade that does not 

constitute a significant commercial enterprise. 

Personal Use—Gathering of small quantities of 

materials for personal or family use or consumption; not intended for selling or resale. This includes 

cultural and traditional use by nonrural residents. 

Other Non-Commercial Use—This includes 

collections of plants by non-commercial entities 

for research not directed toward development of 

a commercial product. Educational use is also 

considered non-commercial when the class or 

workshop is affiliated with a college, university, 

Tribe or other recognized organization. The class 

5

 Author: Lisa K. Crone.
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Forests of Southeast and South-Central Alaska, 2004–2008

instructor may be compensated for his/her time 

in gathering and preparing the materials for the 

classroom, either by the organization or by the 

students. 

COMMERCIAL USE —Materials harvested for the 

primary purpose of sale, resale, or use in a manufacturing process resulting in a product that will be sold 

or used for business activities. Research collections 

directed at development of a commercial product are 

also treated as commercial use.

The order of priority for harvesting NTFPs on national 

forest lands in Alaska are subsistence use, personal use, 

educational and noncommercial research use, and commercial use. Permits are not required for subsistence and 

personal use unless there is a specific local impact to be 

mitigated, but permits are required for educational, noncommercial research, and commercial use. Noncommercial 

research and educational use can be authorized through an 

administrative use permit or free-use permit. The procedures for obtaining a commercial permit are much more 

extensive and must comply with national, regional, forest, 

and district direction, while also allowing for adequate 

review by tribes and interested groups or individuals. 

Fees are charged for commercial permits with the fees 

determined by the specific NTFP resources requested, the 

amounts allowed for harvest, and the locations of harvests. 

The primary areas of consideration for commercial NTFP 

harvest are areas where planned management activities such 

as timber harvest, road and trail construction, etc., would 

otherwise damage or destroy the product. Areas of known 

traditional subsistence and personal use are prioritized for 

subsistence and personal use.

The national parks and national wildlife refuges in 

Alaska have their own regulations concerning NTFP 

harvest (which can differ by specific national park or 

wildlife refuge). Woolwine (2003) provided a brief 

summary of their general policies. 

6 These lands do not include state parks, nor land managed by other 

state agencies such as the University of Alaska, Alaska Mental 

Health Trust, Department of Transportation and Public Facilities, 

or the Alaska Railroad. Pilz et al. (2006) provided a discussion of 

NTFP regulations for some of the state parks in the region.

On general state-owned lands6 managed by the 

Division of Mining, Land and Water (DMLW), the 

harvesting of a small number of wild plants, mushrooms, 

berries, and other plant materials for personal, 

noncommercial use is generally allowed with no permit 

required (Alaska DNR 2009). For commercial harvest of 

NTFP on these lands, a permit is required. A limited NTFP 

commercial harvest permit may be obtained from the 

DMLW for many NTFP resources, without public review. 

The specific product and species allowed under this type of 

permit, as well as their quantity limits, harvest seasons and 

required harvest methods, are included in the Alaska NonTimber Forest Products Harvest Manual for Commercial 

Harvest on State-Owned Land (Alaska DNR 2008). Any 

commercial harvest activities or quantities not specifically 

addressed in that manual must be applied for and authorized 

through the state’s standard Land Use Permit, including 

all associated public review periods. All commercial use 

permits have fees, which are determined by the specific 

products and quantities harvested.

Pilz et al. (2006) provided an excellent overview of 

NTFP resources and their uses in Alaska. Included in their 

list of products currently being produced in the state are 

crafts, artwork, dyes, floral greenery, berries and other wild 

fruits, syrups, teas, flavorings, edible and medicinal plants, 

native seeds, edible mushrooms, and medicinal fungi. 

They also include a table that lists plants with documented 

historical or contemporary subsistence use in Alaska, and 

another table that lists NTFP resources that customers have 

inquired about for commercial harvest at ranger districts 

of the Chugach or Tongass National Forests since 1995. 

In table 60 (app. 2) plants that were found in at least one 

phase 3 FIA plot in the Alaska coastal inventory unit that 

also appear in one or both of the Pilz et al. (2006) tables are 

listed along with their subsistence uses and their intended 

commercial use/parts harvested as listed in the Pilz et al. 

(2006) tables. Moss and mushrooms are not inventoried in 

the FIA plots, so they are not included in this table. 
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GENERAL TECHNICAL REPORT PNW-GTR-835

The frequency attribute in the table indicates the 

percentage of plots on which these species were found using 

FIA vegetation survey methods (Schulz et al. 2009) and 

provides an indication of how common these plants are in 

southeast and south-central Alaska forests.

Table 60 indicates that many of the plants that were 

inquired about for commercial harvest on the Chugach and 

Tongass, also have documented subsistence or traditional 

uses. Schroeder (2002) wrote that the Alaska National 

Interest Lands Conservation Act has made federal agencies 

“loath to encourage commercial exploitation of nontimber 

forest products where conflict with existing subsistence uses 

might result.” Lee (2002) noted the importance of nonmonetary exchange networks centered on fish, game, and wild 

plants in maintaining links between rural and urban Alaska 

Natives, with the gathering and processing of NTFP contributing to the development and maintenance of social ties 

needed for long-term cultural sustainability. Through oral 

interviews, Newton and Moss (2005) documented the manner in which many important NTFP subsistence resources 

were traditionally collected and used by the Tlingits.

Many Alaska Native tribes and organizations in southeast and south-central Alaska have expressed concern over 

the commercial harvest of NTFP on national forest lands 

in the Alaska Region. Some of these concerns are captured 

by Mater (2000) who summarized tribal comments on the 

Alaska Region’s draft SFP policy.

Some tribal groups submitted lists of the species or 

plants they wanted excluded from commercial harvest, 

whereas others were opposed to any commercial use of 

NTFP. These concerns were addressed in the regional 

interim SFP resource management policy (USDA FS 2000) 

through the prioritization of uses, as well as an explicit 

guideline that local tribes must be directly notified of 

any proposed commercial SFP resource harvest activities 

within their use areas. Through the consultation process, 

tribal officials may identify any possible areas of conflict 

with traditional uses such as traditional gathering areas or 

traditional-use plants associated with the harvest. Because 

of its cultural and spiritual significance to Alaska Natives, 

commercial harvest of devil’s club is not allowed on the 

Tongass. Other NTFP resources with special restrictions 

for commercial harvest from the Tongass include red and 

yellow-cedar bark, moss and lichens, and burls. Requests 

for bioprospecting7 and medicinal plant harvests were 

also of concern to Alaska Native tribes and organizations. 

Scoping for these requests, whether they are requesting 

commercial or administrative permits, must involve Alaska 

Native tribes and organizations.

There have been very few commercial permits issued 

in the Alaska Region over the past 5 years. On the Chugach 

National Forest, there were two permits issued in calendar 

year 2004, for 300 tree transplants (trees that are dug up and 

replanted elsewhere for purposes such as landscaping) and 

100 pounds of mushrooms. There were no commercial permits issued on the Chugach for calendar years 2005 through 

2008. Table 28 lists the number of commercial permits 

and harvest quantities for the Tongass National Forest for 

calendar years 2004 through 2008.

To date, most NTFP resources in the coastal inventory 

region of Alaska have been harvested at sustainable levels. 

However, the continued involvement of Alaska Natives and 

other stakeholder groups, in designing NTFP management 

policies (including inventory and monitoring) is essential 

for both sustaining these resources and ensuring their 

contribution to the social, cultural, and economic well-being 

of individuals, tribes, businesses, groups, and communities 

in the region.

7 The Tongass National Forest Interim Special Forest Products 

Resource Management Policy document, available online at 

http://www.fs.fed.us/r10/tongass/projects/sfp/, gives the following 

definition of the term bioprospecting: “This activity involves the 

sampling of small amounts of plants, animals and microorganisms 

(often from soil). The samples are used in laboratory testing for 

compounds that might be active for use in cancer drugs or other 

medical cures, as well as potential microorganisms for use in 

environmental cleanup.”
91

Forests of Southeast and South-Central Alaska, 2004–2008

Table 28—Nontimber forest products commercial 

permits and harvest quantities on the Tongass 

National Forest, 2004–2008

Number 

Year of permits Products and quantities

2004 2 20 Christmas trees, 20 lbs of other 

plants, 15 lbs of bark, 39 lbs of 

foliage, 20 lbs of nuts and seeds, 

20 gal of fruit and berries

2005 6 44 Christmas trees, 2,400 lbs of 

limbs and boughs, 30 lbs of bark, 

64 lbs of foliage, 20 lbs of nuts 

and seeds, 55 gal of fruit and 

berries, 10 roots

2006 1 400 lbs of limbs and boughs

2007 2 50 lbs of fruit and berries, 

400 lbs of limbs and boughs

2008 2 40 lbs of fruit and berries, 60 lbs 

of limbs and boughs, 20 lbs 

of foliage

Source: Timber Information Management database, Alaska Region, 

USDA Forest Service.
92

GENERAL TECHNICAL REPORT PNW-GTR-835

Emily Bernhardt
93

Forests of Southeast and South-Central Alaska, 2004–2008

We hope this report has provided a better understanding of 

status and change of Alaska’s coastal forests, highlighting 

information that is new as well as confirming things you 

may already know from personal experience or from other 

data and publications. Because this report is an overview, 

touching briefly on many topics, we expect some readers 

will be eager to see more indepth research and analysis on 

selected topics to fully understand current status, change, 

and relationships in Alaska’s coastal forests. Some possible 

areas of future work may include more comprehensive 

analysis and reporting of issues such as carbon dynamics; forest productivity; climate-related disturbances and 

ecosystem resiliency; and forest health issues such as risk 

from disease, insects, and invasives.

We expect that researchers from the Pacific Northwest 

Research Station, U.S. Forest Service, as well as researchers 

and analysts from other organizations and institutes will 

investigate many of the questions that can be addressed with 

the annual inventory data, especially once a full cycle of 

data has been collected.

The annual Forest Inventory and Analysis (FIA) 

inventory, as currently designed, will continue into the 

future, provided funding and support for it are maintained. 

As directed by the 1998 Farm Bill (section 253(c) of the 

Agricultural Research, Extension, and Education Reform 

Act of 1998), findings from the inventory will be published 

every 5 years. For southeast and south-central Alaska, the 

next report should be written in 2015, after all FIA plots 

have been visited and the first full cycle of data collection 

is completed.

Glossary 

abiotic—Pertaining to nonliving factors such as temperature, water, and mineral soil.

anthropogenic—Of human origin or influence (Helms 

1998).

aspect—Compass direction that a slope faces.

basal area—The cross-sectional area of a tree’s trunk.

biodiversity—Variety and variability among living organisms and the ecological complexes in which they occur. 

Diversity can be defined as the number of different items 

and their relative frequencies. http://www.epa.gov/OCEPAterms/bterms.html. (March 21, 2008).

bioenergy—Renewable energy made available from materials derived from biological sources. http://en.wikipedia.org/

wiki/Bioenergy. (March 21, 2008).

biomass—The aboveground weight of wood and bark in 

live trees 1.0 inch diameter at breast height (d.b.h.) and 

larger from the ground to the tip of the tree, excluding 

all foliage. The weight of wood and bark in lateral limbs, 

secondary limbs, and twigs under 0.5 inch in diameter at 

the point of occurrence on sapling-size trees is included in 

the measure, but on poletimber- and sawtimber-sized trees 

this material is excluded. Biomass is typically expressed as 

green or oven-dry weight in tons (USDA FS 2006). 

biotic—Pertaining to living organisms and their ecological 

and physiological relations (Helms 1998).

board foot—A volume measure of lumber 1 foot wide, 

1 foot long, and 1 inch thick (12 inches × 12 inches × 1 inch 

= 144 cubic inches). http://www.ccffa-oswa.org/B.html. 

(March 21, 2008).

bole—Trunk or main stem of a tree (USDA FS 2006).

carbon mass—The estimated weight of carbon stored 

within wood tissues. On average, carbon mass values are 

about half of biomass values for trees, and are summarized 

as thousand tons or mean tons per acre.

carbon sequestration—Incorporation of carbon dioxide 

into permanent plant tissues (Helms 1998).

coarse woody material—Down dead tree and shrub boles, 

large limbs, and other woody pieces that are severed from 

their original source of growth. Coarse woody material also 

includes dead trees that are supported by roots, severed 

from roots, or uprooted, and leaning > 45 degrees from 

vertical (USDA FS 2006).

crown—The part of a tree or woody plant bearing live 

branches or foliage (Helms 1998).

Chapter 6: Concluding Remarks
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GENERAL TECHNICAL REPORT PNW-GTR-835

crown fire—Fire that spreads across the tops of trees or 

shrubs more or less independently of a surface fire. Crown 

fires are sometimes classed as running (independent or 

active) or dependent (passive) to distinguish the degree of 

independence from the surface fire (Helms 1998).

cyanolichens—Lichen species containing cyanobacteria, 

which fix atmospheric nitrogen into a form that plants 

can use. 

damage—Damage to trees caused by biotic agents such 

as insects, diseases, and animals or abiotic agents such as 

weather, fire, or mechanical equipment.

diameter at breast height (d.b.h.)—The diameter of a tree 

stem, located at 4.5 feet above the ground (breast height) on 

the uphill side of a tree. The point of diameter measurement 

may differ on abnormally formed trees (USDA FS 2006).

disturbance—Any relatively discrete event in time that 

disrupts ecosystem, community, or population structure 

and changes resources, substrate availability, or the physical 

environment (Helms 1998). 

down woody material (DWM)—Dead material on the 

ground in various stages of decay, including coarse and fine 

woody material. Previously named down woody debris. The 

DWM indicator for Forest Inventory and Analysis includes 

measurements of depth of duff layer, litter layer, and overall 

fuelbed; fuel loading on the microplot; and residue piles 

(USDA FS 2006).

ecological region (ecoregion)—A defined geographic 

region that is similar in climate, geology, or other environmental attributes. This report uses ecoregions defined 

by Nowaki et al. (2002) and arranged by the hierarchical 

classification of Cleland et al. (1997). The hierarchical 

classification divides Alaska into three broad ecological 

divisions (Marine, Subarctic [boreal], and Tundra [arctic]). 

Divisions are subdivided into ecological provinces, which 

are further subdivided into ecological sections. 

ecological section (ecosection)—A level in a hierarchical 

classification of ecological units for a geographic area delineated on the basis of similar climate, geomorphic processes, 

stratigraphy, geologic origin, topography, and drainage 

systems (Cleland et al. 1997).

ecosystem—A spatially explicit, relatively homogeneous 

unit of the Earth that includes all interacting organisms and 

components of the abiotic environment within its boundaries. An ecosystem can be of any size: a log, a pond, a field, a 

forest, or the Earth’s biosphere (Helms 1998).

elevation—Height above a fixed reference point, often 

the mean sea level. http://en.wikipedia.org/wiki/Elevation. 

(March 21, 2008).

epidemic—(1) Entomology: pertaining to populations of 

plants, animals, and viruses that build up, often rapidly, to 

unusually and generally injuriously high levels. Synonym: 

outbreak. Many insect and other animal populations cycle 

periodically or irregularly between endemic and epidemic 

levels. (2) Pathology: a disease sporadically infecting a large 

number of hosts in an area and causing considerable loss 

(Helms 1998). 

erosion—The wearing away of the land surface by running 

water, wind, ice, or other geological agents (USDA FS 

2006).

federal forest land—Public lands owned by the U.S. 

government (USDA FS 2006).

fine woody material (FWM)—Down dead branches, 

twigs, and small tree or shrub boles <3 inches in diameter 

not attached to a living or standing dead source (USDA FS 

2006).

fixed-radius plot—A circular sampled area with a specified 

radius in which all trees of a given size, shrubs, and other 

items, are tallied (USDA FS 2006).

forb—A broad-leaved herbaceous plant, as distinguished 

from grasses, shrubs, and trees (USDA FS 2006).

forest land—Land that is at least 10 percent stocked by 

forest trees of any size, or land formerly having such tree 

cover, and not currently developed for a nonforest use. The 

minimum area for classification as forest land is 1 acre. 

Roadside, streamside, and shelterbelt strips of timber must 

be at least 120 feet wide to qualify as forest land (USDA FS 

2006).

forest type—A classification of forest land based on and 

named for the tree species that forms the plurality of livetree stocking (USDA FS 2006).
95

Forests of Southeast and South-Central Alaska, 2004–2008

forestation—The process of nonforest land (e.g., shrubland, 

wetland, grassland, cropland) becoming forested as conditions change and trees are planted or naturally regenerate.

greenhouse gas—A gas, such as carbon dioxide or methane, that contributes to potential climate change. http://

www.epa.gov/OCEPAterms/gterms.html. (March 21, 2008).

growing stock—All live trees 5 inches d.b.h. or larger that 

are considered merchantable in terms of saw-log length, 

and grade; excludes rough and rotten cull trees (USDA FS 

2006).

hardwood—Tree species belonging to the botanical subdivision Angiospermae, class Dicotyledonous, usually broad 

leaved and deciduous (USDA FS 2006).

herbivory—The consumption of herbaceous vegetation by 

organisms ranging from insects to large mammals such as 

deer, elk, or cattle. http://www.biology-online.org/dictionary/Herbivory. (March 21, 2008).

increment borer—An auger-like instrument with a hollow 

bit and an extractor, used to extract thin radial cylinders of 

wood (increment cores) from trees having annual growth 

rings, to determine increment or age (Helms 1998). 

invasive plant—Plants that are not native to the ecosystem 

under consideration and that cause or are likely to cause 

economic or environmental harm or harm to human, 

animal, or plant health. http://www.invasivespeciesinfo.gov/

docs/council/isacdef.pdf. (March 21, 2008).

lichen—An organism consisting of a fungus and an alga 

or cyanobacterium living in symbiotic association. Lichens 

look like masses of small, leafy, tufted or crustlike plants 

(USDA FS 2006).

live trees—All living trees, including all size classes, all 

tree classes, and both commercial and noncommercial species for tree species listed in the FIA field manual (USDA 

FS 2006).

mean annual increment (MAI)—A measure of the produc-tivity of forest land expressed as the average increase 

in cubic feet of wood volume per acre per year. For a given 

species and site index, the mean is based on the age at 

which the MAI culminates for fully stocked natural stands. 

The MAI is based on the site index of the plot (Azuma 

et al. 2004).

MMBF—A million board feet of wood in logs or lumber 

(Helms 1998). 

model—(1) An abstract representation of objects and events 

from the real world for the purpose of simulating a process, 

predicting an outcome, or characterizing a phenomenon. 

(2) Geographic information system data representative of 

reality (e.g., spatial data models), including the arc-node, 

georelational model, rasters or grids, polygon, and triangular irregular networks (Helms 1998).

mortality—The death of trees from natural causes, or 

subsequent to incidents such as storms, wildfire, or insect 

and disease epidemics (Helms 1998). 

national forest lands—Federal lands that have been 

designated by Executive order or statute as national forest or 

purchase units and other lands under the administration of 

the U.S. Department of Agriculture, Forest Service, including experimental areas and Bankhead-Jones Title III lands 

(Azuma et al. 2004).

native species—Plant species that were native to Alaska 

prior to Russian and Euro-American settlement. For 

vascular plants, these are the species that are not present 

on the USDA Natural Resources Conservation Service list 

of nonnative species (see nonnative species) (USDA NRCS 

2000).

net volume—Gross volume less deductions for sound and 

rotten defects. Growing-stock net volume is gross volume 

(in cubic feet) less deductions for rot and missing bole 

sections on poletimber and sawtimber growing-stock trees. 

Sawtimber net volume is gross volume (in board feet) less 

deductions for rot, sweep, crook, missing bole sections, 

and other defects that affect the use of sawtimber trees for 

lumber (Azuma et al. 2004).

nitrophyte—One of a group of lichen species that grows in 

nitrogen-rich habitats.

nonnative species—Plant species that were introduced to 

Alaska subsequent to Russian and Euro-American settlement. For vascular plants, nonnative species are those on 
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GENERAL TECHNICAL REPORT PNW-GTR-835

the USDA Natural Resources Conservation Service (USDA 

NRCS 2000) list of nonnative species. 

nonstocked areas—Timberland that is less than 10 percent 

stocked with live trees. Recent clearcuts scheduled for planting are classified as nonstocked area (Azuma et al. 2004).

nontimber forest products (NTFP)—Species harvested 

from forests for reasons other than production of timber 

commodities. Vascular plants, lichens, and fungi are the 

primary organisms included in NTFPs.

old-growth forest—Old-growth forest is differentiated 

from younger forest by its structure and composition, and 

often by its function. Old-growth stands are typified by the 

presence of large older trees; variety in tree species, sizes, 

and spacing; multiple canopy layers; high amounts of standing and down dead wood; and broken, deformed, or rotting 

tops, trunks, and roots (Franklin et al. 1986). 

overstory—That portion of the trees, in a forest of more 

than one story, forming the uppermost canopy layer (Helms 

1998). 

owner group—A variable that combines owner classes into 

the following groups: Forest Service, other federal agency, 

state and local government, and private. Differing categories of owner group on a plot require different conditions 

(USDA FS 2006).

ownership—A legal entity having an ownership interest 

in land, regardless of the number of people involved. An 

ownership may be an individual; a combination of persons; 

a legal entity such as a corporation, partnership, club, or 

trust; or a public agency. An ownership has control of a 

parcel or group of parcels of land (USDA FS 2006).

pathogen—Parasitic organism directly capable of causing 

disease (Helms 1998). 

productive forest land—Forest land that is producing or 

capable of producing in excess of 20 cubic feet per acre 

per year of wood at culmination of mean annual increment 

(MAI) without regard to reserved status (USDA FS 2006).

public land—An ownership group that includes all federal, 

state, county, and municipal lands (USDA FS 2006).

pulpwood—Whole trees, tree chips, or wood residues used 

to produce wood pulp for the manufacture of paper products. Pulpwood is usually wood that is too small, of inferior 

quality, or the wrong species for the manufacture of lumber 

or plywood (adapted from Helms 1998).

quadrat—The basic 3.28 square feet sampling unit for the 

phase 3 Vegetation Indicator (USDA FS 2006).

regeneration (artificial and natural)—The established 

progeny from a parent plant, seedlings or saplings existing 

in a stand, or the act of renewing tree cover by establishing 

young trees naturally or artificially. May be artificial (direct 

seeding or planting) or natural (natural seeding, coppice, or 

root suckers) (Adapted from Helms 1998).

remote sensing—Capture of information about the Earth 

from a distant vantage point. The term is often associated 

with satellite imagery but also applies to aerial photography, 

airborne digital sensors, ground-based detectors, and other 

devices. http://www.nsc.org/ehc/glossar2.htm. (March 21, 

2008).

reserved forest land—Land permanently reserved from 

wood products utilization through statute or administrative 

designation. Examples include national forest wilderness 

areas and national parks and monuments (USDA FS 2006). 

richness—The number of different species in a given area, 

often referred to at the plot scale as alpha diversity and at 

the region scale as gamma diversity (USDA NRCS 2000).

riparian—Related to, living in, or associated with a 

wetland, such as the bank of a river or stream or the edge of 

a lake or tidewater. The riparian biotic community significantly influences and is influenced by the neighboring body 

of water (Helms 1998). 

salvage cutting—Removal of dead trees, or trees damaged 

or dying because of injurious agents other than competition, 

to recover economic value that would otherwise be lost. 

Synonym: salvage recovery, salvage logging (Helms 1998). 

sampling error—Difference between a population value 

and a sample estimate that is attributable to the sample, as 

distinct from errors owing to bias in estimation, errors in 

observation, etc. Sampling error is measured as the standard 

error of the sample estimate (Helms 1998). 
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Forests of Southeast and South-Central Alaska, 2004–2008

sapling—A live tree 1.0 to 4.9 inches in diameter 

(USDA FS 2006).

saw log—A log meeting minimum standards of diameter, 

length, and defect for manufacture into lumber or plywood. 

The definition includes logs with a minimum diameter outside bark for softwoods of 7 inches (9 inches for hardwoods) 

(Azuma et al. 2004). 

sawtimber trees—Live softwood trees of commercial 

species at least 9.0 inches in d.b.h. and live hardwood trees 

of commercial species at least 11.0 inches in d.b.h. At least 

25 percent of the board-foot volume in a sawtimber tree 

must be free from defect. Softwood trees must contain at 

least one 12-foot saw log with a top diameter of not less 

than 7 inches outside bark; hardwood trees must contain at 

least one 8-foot saw log with a top diameter of not less than 

9 inches outside bark (Azuma et al. 2004).

seedlings—Live trees <1.0 inch d.b.h. and at least 6 inches 

in height (softwoods) or 12 inches in height (hardwoods) 

(USDA FS 2006).

shrub—Perennial, multistemmed woody plant, usually 

less than 13 to 16 feet in height, although under certain 

environmental conditions shrubs may be single-stemmed or 

taller than 16 feet. Includes succulents (e.g., cacti) (USDA 

FS 2007). 

site class—A classification of forest land based on its 

capacity to grow wood.

slope—Measure of change in surface value over distance, 

expressed in degrees or as a percentage (Helms 1998). 

snag—Standing dead tree ≥5 inches d.b.h. and ≥4.5 feet in 

length, with a lean of <45 degrees. Dead trees leaning more 

than 45 degrees are considered to be DWM. Standing dead 

material shorter than 4.5 feet are considered stumps (USDA 

FS 2007).

stand—An area of relatively homogenous forest, often distinguished from surrounding forest by similarity in species 

composition, size, density, or structure.

stand age—Average age of the trees (plurality of all live 

trees not overtopped) in the predominant stand size class of 

the condition.

state land—An ownership class of public lands owned 

by states or lands leased by states for more than 50 years 

(USDA FS 2006).

stocked/nonstocked—In the Forest Inventory and Analysis 

Program, a minimum stocking value of 10 percent live trees 

is required for accessible forest land (USDA FS 2007).

stocking—(1) At the tree level, the density value assigned 

to a sampled tree (usually in terms of numbers of trees or 

basal area per acre), expressed as a percentage of the total 

tree density required to fully use the growth potential of the 

land. (2) At the stand level, the sum of the stocking values 

of all trees sampled (Bechtold and Patterson 2005).

stratification—A statistical tool used to reduce the variance of the attributes of interest by partitioning the population into homogenous strata (Bechtold and Patterson 2005). 

succession—The gradual supplanting of one community of 

plants by another (Helms 1998).

timberland—Forest land that is producing or capable 

of producing >20 cubic feet per acre per year of wood at 

culmination of MAI. Timberland excludes reserved forest 

lands (USDA FS 2006).

transect—A narrow sample strip or a measured line laid 

out through vegetation chosen for study (Helms 1998). 

tree—A woody perennial plant, typically large, with a 

single well-defined stem carrying a more or less definite 

crown; sometimes defined as attaining a minimum diameter 

of 3 inches and a minimum height of 15 feet at maturity. For 

FIA, any plant on the tree list in the current field manual is 

measured as a tree (USDA FS 2006).

understory—All forest vegetation growing under an 

overstory (Helms 1998).

unproductive forest land—Forest land that is not capable 

of producing in excess of 20 cubic feet per acre per year 

of wood at culmination of MAI without regard to reserved 

status (USDA FS 2006).

unreserved forest land—Forest land that is not withdrawn 

from harvest by statute or administrative regulation. 

Includes forest lands that are not capable of producing in 

excess of 20 cubic feet per acre per year of industrial wood 

in natural stands (Smith et al. 2009).
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GENERAL TECHNICAL REPORT PNW-GTR-835

vascular plant—A plant possessing a well-developed 

system of conducting tissue to transport water, mineral 

salts, and sugars. http://www.biology-online.org/dictionary/

Vascular_plant. (March 21, 2008).

wilderness—(1) According to the Wilderness Act of 1964, 

“a wilderness, in contrast with those areas where man and 

his works dominate the landscape, is hereby recognized 

as an area where the earth and its community of life are 

untrammeled by man, where man himself is a visitor who 

does not remain.” (2) A roadless land legally classified as 

a component area of the National Wilderness Preservation 

System and managed to protect its qualities of naturalness, 

solitude, and opportunity for primitive recreation. Wilderness areas are usually of sufficient size to make maintenance in such a state feasible (Helms 1998).

Wilderness First Responder—An individual trained 

to respond to emergency situations in remote settings, 

with certification typically obtained through an 80-hour 

training course.

wildfire—Any uncontained fire, other than prescribed fire, 

occurring on wildland. Synonym: wildland fire (Adapted 

from Helms 1998).

wildland—Land other than that dedicated for uses such 

as agriculture, urban, mining, or parks (Helms 1998).

wildland-urban interface (WUI)—A term used to 

describe an area where various structures (most notably 

private homes) and other human developments meet or are 

intermingled with forest and other vegetative fuel types. 

http://www.borealforest.org/nwgloss13.htm. (March 21, 

2008).

Common and Scientific Names1

Common name Scientific name

Trees:

Alaska paper birch2 Betula neoalaskana Sarg.

Barclay's willow Salix barclayi Anderss.

Black cottonwood Populus balsamifera L. ssp. trichocarpa (Torr. & A. Gray ex Hook.) 

Brayshaw

Black spruce Picea mariana (Mill.) Britton, Sterns & Poggenb.

European bird cherry Prunus padus L.

Lodgepole pine Pinus contorta Dougl. ex Loud.

Lutz spruce3 Picea X lutzii Little

Mountain hemlock Tsuga mertensiana (Bong.) Carr.

Pacific silver fir Abies amabilis (Douglas ex Louden) Douglas ex Forbes

Pacific yew Taxus brevifolia Nutt.

Paper birch Betula papyrifera Marsh.

Ponderosa pine Pinus ponderosa P. & C. Lawson

Quaking aspen Populus tremuloides Michx.

Red alder Alnus rubra Bong.

Sitka spruce Picea sitchensis (Bong.) Carrière

Subalpine fir Abies lasiocarpa var. lasiocarpa (Hook.) Nutt.

Western hemlock Tsuga heterophylla (Raf.) Sarg.

Western mountain ash Sorbus sitchensis M. Roemer

Western redcedar Thuja plicata Donn ex D. Don

White spruce Picea glauca (Moench) Voss

Yellow-cedar, Alaska yellow-cedar Chamaecyparis nootkatensis (D. Don) Spach

Vines:

Kudzu Pueraria montana (Lour.) Merr.
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Forests of Southeast and South-Central Alaska, 2004–2008

Common name Scientific name

Shrubs:

American red raspberry Rubus idaeus L.

Blueberry Vaccinium L.

Bog Labrador tea Ledum groenlandicum Oeder

Bog laurel Kalmia polifolia Wangenh.

Common juniper Juniperus communis L.

Common snowberry Symphoricarpos albus (L.) Blake

Copperbush Elliottia pyroliflorus (Bong.) S.W. Brim & P.F. Stevens

Devil’s club Oplopanax horridus (Sm.) Miq.

Early blueberry Vaccinium ovalifolium Sm.

Highbush cranberry Viburnum edule (Michx.) Raf.

Japanese knotweed Polygonum cuspidatum Sieb. & Zucc.

Kamchatka rhododendron Rhododendron camtschaticum Pallas

Red currant Ribes triste Pallas

Red elderberry Sambucus racemosa L.

Red fruit bearberry Arctostaphylos rubra (Rehd. & Wilson) Fern.

Red huckleberry Vaccinium parvifolium Sm.

Russet buffaloberry Shepherdia canadensis (L.) Nutt.

Rusty menziesia Menziesia ferruginea Sm.

Salal Gaultheria shallon Pursh

Salmonberry Rubus spectabilis Pursh

Scotch broom Cytisus scoparius (L.) Link

Siberian peashrub Caragana arborescens Lam.

Sitka alder Alnus viridis (Chaix) DC. ssp. sinuata (Regel) A. Löve & D. Löve

Stink currant Ribes bracteosum Dougl. ex Hook.

Sweetgale Myrica gale L.

Thimbleberry Rubus parviflorus Nutt.

Trailing black currant Ribes laxiflorum Pursh

Willow Salix L.

Subshrubs:

Alaska bellheather Harrimanella stelleriana (Pallas) Coville

Alpine bearberry Arctostaphylos alpina (L.) Spreng.

Alpine clubmoss Lycopodium alpinum L.

Black crowberry Empetrum nigrum L.

Bog blueberry Vaccinium uliginosum L.

Bog rosemary Andromeda polifolia L.

Bunchberry dogwood Cornus canadensis L.

Cloudberry Rubus chamaemorus L.

Dwarf bilberry Vaccinium caespitosum Michx.

Fiveleaf bramble Rubus pedatus Sm.

Hemlock dwarf mistletoe Arceuthobium tsugense (Rosendahl) G.N. Jones

Lapland cornel Cornus suecica L.

Lingonberry Vaccinium vitis-idaea L.

Liverleaf wintergreen Pyrola asarifolia Michx.

Mountainheath Phyllodoce Salisb.

Nagoonberry Rubus arcticus L.

Partridgefoot Luetkea pectinata (Pursh) Kuntze

Running clubmoss Lycopodium clavatum L.
100

GENERAL TECHNICAL REPORT PNW-GTR-835

Common name Scientific name

Sidebells wintergreen Orthilia secunda (L.) House

Small cranberry Vaccinium oxycoccos L.

Stiff clubmoss Lycopodium annotinum L.

Tree groundpine Lycopodium dendroideum Michx.

Twinflower Linnaea borealis L.

Forbs/herbs:

Alfalfa Medicago L.

Alpine mountainsorrel Oxyria digyna (L.) Hill

American skunkcabbage Lysichiton americanus Hultén & St. John

Arctic starflower Trientalis europaea L.

Beachhead iris Iris setosa Pallas ex Link

Bird vetch Vicia cracca L. ssp. cracca

Bride's bonnet Clintonia uniflora (Menzies ex J.A. & J.H. Schultes) Kunth

Bride's feathers Aruncus dioicus (Walt.) Fern.

Brightgreen spleenwort Asplenium trichomanes-ramosum L.

Broadpetal gentian Gentiana platypetala Griseb.

Bull thistle Cirsium vulgare (Savi) Ten.

Butter and eggs Linaria vulgaris P. Mill.

Canadian burnet Sanguisorba canadensis L.

Canada thistle Cirsium arvense (L.) Scop.

Claspleaf twistedstalk Streptopus amplexifolius (L.) DC.

Clover Trifolium L.

Common chickweed Stellaria media (L.) Vill.

Common cowparsnip Heracleum maximum Bartr.

Common dandelion Taraxacum officinale G.H. Weber ex Wiggers

Common groundsel Senecio vulgaris L.

Common ladyfern Athyrium filix-femina (L.) Roth

Common mare's-tail Hippuris vulgaris L.

Common plantain Plantago major L.

Common tansy Tanacetum vulgare L.

Common yarrow Achillea millefolium L.

Creeping buttercup Ranunculus repens L.

Deer fern Blechnum spicant (L.) Sm.

Deercabbage Nephrophyllidium crista-galli (Menzies ex Hook.) Gilg

Dwarf fireweed Chamerion latifolium (L.) Holub

European stickseed Lappula squarrosa (Retz.) Dumort.

False lily of the valley Maianthemum dilatatum (Wood) A. Nels. & J.F. Macbr.

Fernleaf goldthread Coptis aspleniifolia Salisb.

Field horsetail Equisetum arvense L.

Fireweed Chamerion angustifolium (L.) Holub

Garlic mustard Alliaria petiolata (Bieb.) Cavara & Grande

Green false hellebore Veratrum viride Ait.

Hairy catsear Hypochaeris radicata L.

Hairy rockcress Arabis hirsuta (L.) Scop.

Heartleaf twayblade Listera cordata (L.) R. Br. ex Ait. f.

Hempnettle Galeopsis L.

Horsetail Equisetum L.

Kamchatka fritillary Fritillaria camschatcensis (L.) Ker-Gawl.
101

Forests of Southeast and South-Central Alaska, 2004–2008

Common name Scientific name

Largeleaf avens Geum macrophyllum Willd.

Ledge stonecrop Rhodiola integrifolia ssp. integrifolia Raf.

Licorice fern Polypodium glycyrrhiza D.C. Eat.

Little yellowrattle Rhinanthus minor L.

Long beechfern Phegopteris connectilis (Michx.) Watt

Lyrate rockcress Arabis lyrata L.

Meadow hawkweed Hieracium caespitosum Dumort.

Monkshood Aconitum delphiniifolium DC.

Mouseear hawkweed Hieracium pilosella L.

Narrowleaf hawksbeard Crepis tectorum L.

Narrowleaf hawkweed Hieracium umbellatum L.

Nootka lupine Lupinus nootkatensis Donn ex Sims

Northern groundcone Boschniakia rossica (Cham. & Schlecht.) Fedtsch.

Northern maidenhair Adiantum pedatum L.

Northwestern twayblade Listera caurina Piper

Orange hawkweed Hieracium aurantiacum L.

Ornamental jewelweed Impatiens glandulifera Royle

Oxeye daisy Leucanthemum vulgare Lam.

Perennial sowthistle Sonchus arvensis L.

Pineapple weed Matricaria discoidea DC.

Prostrate knotweed Polygonum aviculare L.

Red baneberry Actaea rubra (Ait.) Willd.

Roundleaf sundew Drosera rotundifolia L.

Scentbottle Platanthera dilatata (Pursh) Lindl. ex Beck

Scottish licorice-root Ligusticum scoticum L.

Shepherd's purse Capsella bursa-pastoris (L.) Medik.

Single delight Moneses uniflora (L.) Gray

Slender bog orchid Platanthera stricta Lindl.

Small twistedstalk Streptopus streptopoides (Ledeb.) Frye & Rigg

Spotted knapweed Centaurea biebersteinii DC.

Spreading woodfern Dryopteris expansa (K. Presl) Fraser-Jenkins & Jermy

Sticky tofieldia Tofieldia glutinosa (Michx.) Pers.

Subalpine fleabane Erigeron peregrinus (Banks ex Pursh) Greene

Swamp gentian Gentiana douglasiana Bong.

Sweetclover, white Melilotus alba Medikus

Sweetclover, yellow Melilotus officinalis (L.) Lam.

Tansy ragwort Senecio jacobaea L.

Threeleaf foamflower Tiarella trifoliata L.

Threeleaf goldthread Coptis trifolia (L.) Salisb.

Twistedstalk Streptopus lanceolatus (Aiton) Reveal var. roseus (Michx.) Reveal

Violet Viola L.

Western brackenfern Pteridium aquilinum (L.) Kuhn

Western columbine Aquilegia formosa Fisch. ex DC.

Western oakfern Gymnocarpium dryopteris (L.) Newman

Western salsify Tragopogon dubius Scop.

Wild chives Allium schoenoprasum L.

Wormseed wallflower Erysimum cheiranthoides L.

Yellow marsh marigold Caltha palustris L.
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GENERAL TECHNICAL REPORT PNW-GTR-835

Common name Scientific name

Grass-like plants:

Bluejoint Calamagrostis canadensis (Michx.) Beauv.

Cheatgrass Bromus tectorum L.

Colonial bentgrass Agrostis capillaris L.

Foxtail barley Hordeum jubatum L.

Orchardgrass Dactylis glomerata L.

Pacific reedgrass Calamagrostis nutkaensis (J. Presl) J. Presl ex Steud.

Quackgrass Elymus repens (L.) Gould

Reed canarygrass Phalaris arundinacea L.

Rush Juncus L.

Ryegrass Lolium L.

Sedge Carex L.

Smooth brome Bromus inermis Leyss.

Tall cottongrass Eriophorum angustifolium Honckeny

Tall fescue Lolium arundinaceum (Schreb.) S.J. Darbyshire

Timothy Phleum pratense L.

Tufted bulrush Trichophorum caespitosum (L.) Hartman

Water whorlgrass Catabrosa aquatica (L.) Beauv.

1 Scientific names are from the USDA PLANTS database at http://plants.usda.gov. (January 2000).

2

 Alaska paper birch, Kenai paper birch, and western paper birch are not recorded as different species 

by FIA and are included together as “paper birch” in this report.

3

 On the Kenai Peninsula, Sitka spruce and white spruce hybridize as Lutz spruce. The FIA field crew 

classify each individual Lutz spruce tree to whichever of the two species it most resembles.

Acknowledgments

John Cauoette, one of the authors of this report, died 

unexpectedly in the autumn of 2010. John’s energy and 

enthusiasm contributed much to this report and to other 

forest research in southeast Alaska, and he will be greatly 

missed by his colleagues.

We wish to acknowledge the Pacific Northwest 

Research Station Forest Inventory and Analysis (FIA) 

field crew for collecting the high-quality field data on 

which this report was based: Stacey Allen, Nicole Amato, 

Gerald Bednarczyk, Kristen Bednarczyk, Zachary 

Bergen, Tina Boucher, Allan Braun, Will Bunten, 

Brandon Burke, Janelle Cossey, Erica Engle, Water Foss, 

Kathleen Galloway, Marion Glasser, Walter Grabowiecki, 

Richard Hannah, Connie Hubbard, John Kelley, Ann 

Klinefelter, Ray Koleser, Nicholas Lisuzzo, Marc Much, 

Brendt Mueller, Laura Mujica-Crapanzano, Eric Murphy, 

Shawn Osborn, Pierre Parisien, Elizabeth Patterson, Ben 

Pfohl, Andrew Richards, Eric Straley, Sasha Sulia, Andy 

Tasler, Chris Teutsch, Steve Trimble, Julie Vorachek, 

Jonathan Williams, and Mikhail Yatskov. In addition, 

many other people contributed significantly to this report. 

We greatly appreciate all the work done by Jane Reid and 

Kevin Dobelbower, who compiled the data that were the 

foundation for this report. We thank Ron Wanek and Kurt 

Campbell who provided troubleshooting for data issues, 

Chuck Veneklase who programmed the field data recorder, 

and Dale Weyermann and Elena Graham for geographic 

information system support. We appreciate the help from 

Olaf Kuegler, who contributed substantially on stratification 

and statistical issues. We thank Bill van Hees, Ray Koleser, 

and George Breazeale, who all contributed substantially 

to the Alaska inventory. We appreciate the efforts of Sue 

Willits and Sally Campbell in supporting FIA in Alaska 

over the past decade, and for providing review comments. 
103

Forests of Southeast and South-Central Alaska, 2004–2008

Thanks also to the other reviewers that provided helpful 

comments on a draft version of this report: Phyllis Adams, 

Keith Boggs, Tina Boucher, Roger Burnside, Bob Deal, 

Rob Develice, Mike McClellan, Carol McKenzie, Amy 

Miller, Barb Schrader, and Lori Winton. We thank 

Carolyn Wilson, Keith Routman, Lynn Sullivan, and Ann 

Pruszenski for helping to prepare the report for publication.

Metric Equivalents

When you know: Multiply by: To find:

Inches 2.54 Centimeters

Feet .3048 Meters

Miles 1.609 Kilometers

Acres .405 Hectares

Board feet .0024 Cubic meters

Cubic feet .0283 Cubic meters

Cubic feet per acre .06997 Cubic meters 

 per hectare

Square feet .0929 Square meters

Square feet per acre .229 Square meters 

 per hectare

Ounce 28349.5 Milligrams

Gallon 3.79 Liters

Pound .453 Kilograms

Pounds per cubic foot 16.018 Kilograms per 

 cubic meter

Tons per acre 2.24 Megagrams per 

 hectare

Degrees Fahrenheit .56(°F – 32) Degrees Celsius

British thermal units .000293 Kilowatt hours 

(Btu)

Pounds per cubic foot .016 Grams per cubic 

 centimeter

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Anchorage, AK. http://westcarb.org/Anchorage_pdfs/

Wolfe_SE-AK-Forest%20Carbon.pdf. (April 14, 2010). 

Woolwine, P.A. 2003. Summary information on access to 

nontimber forest products on federal lands in Alaska. 

In: Alaska Boreal Forest Council, comps. Proceedings: 

hidden forest values. The first Alaska-wide nontimber 

forest products conference and tour. Gen. Tech. Rep. 

PNW-GTR-579. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research 

Station: 38–40.

Youngblood, A.; Cole, E.C. 2003. White spruce and 

moose enhancement project [Brochure]. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. 
113

Forests of Southeast and South-Central Alaska, 2004–2008

Field Design and Sampling Method

Data used in this report came from two inventories conducted by the Forest Inventory and Analysis (FIA) program 

of the Pacific Northwest Research Station (PNW).1 The 

first inventory (the “periodic inventory”) includes data 

from southeast Alaska collected between 1995 and 2001 

and data from south-central Alaska collected between 1999 

and 2003. The second inventory (the “annual inventory”) 

includes data from 2004 through 2008 for both the southeast and south-central areas, which were combined into a 

single coastal Alaska inventory unit. Most, but not all, of 

the plots in the second inventory were remeasurements of 

plots established during the first inventory. However, there 

were some differences in sampling design and inventory 

methods that are discussed in the following sections. All 

estimates of current (2004–08) conditions in this report are 

produced with the annual inventory, with the exception of 

mistletoe (Arceuthobium tsugense), which used the 1995 

through 2003 inventory. All estimates of change were 

produced with the remeasured plots in both inventories.

The Annual Inventory

In the 2004 through 2008 annual inventory system in 

coastal Alaska, a random subsample of 10 percent of the 

plots in the inventory unit is measured each year. This 

annual subsample is referred to as a panel. Because there 

are very few roads in the coastal Alaska region, most 

plots must be accessed by a helicopter/boat combination. 

Owing to limitations on the use of helicopters, Glacier Bay 

National Park and wilderness areas in the Chugach and 

Tongass National Forests are currently excluded from the 

inventory (see fig. 2). With those exceptions, the plots measured in a single panel span all forest land in the inventory 

unit, including all public and privately owned forests. One 

panel of data was collected from wilderness areas in the 

national forests in 2005 and was used in this report.

Estimates of forest attributes made from a single panel 

are imprecise because one panel represents only 10 percent 

Appendix 1: Inventory Methods and Design

of the full inventory sample. More precise statistics are 

obtained by combining data from multiple panels. Estimates 

from sampled plots in the five panels measured from 2004 

to 2008 were combined to produce most of the statistics in 

this report. One extra panel of plots was collected and used 

for this report on the Kenai National Wildlife refuge. When 

all panels have been measured once (2013), panels will be 

remeasured, creating a 10-year remeasurement interval. 

The PNW-FIA program collects information in three 

phases. In phase 1, the area in the inventory unit is interpreted from remotely sensed imagery, classifying land into 

broad relatively homogenous groups. In phase 2, field plots 

are measured for a variety of indicators that describe forest 

composition, structure, and the physical geography of the 

landscape. Phase 2 plots are spaced at approximate 3-mile 

intervals on a hexagonal grid throughout the forest. In phase 

3, one of every 16 phase 2 field plots is visited and a variety 

of forest health measurements are taken.

Phase 1 

The goal of phase 1 is to reduce the variance associated 

with estimates of various forest attributes. For the phase 

1 interpretation of the coastal Alaska unit, we used the 

National Land Cover Dataset 2001 (Homer et al. 2007) 

to stratify the land by forest, water, and nonforest and by 

canopy density class within forest land. We also incorporated an elevation grouping from Digital Elevation Models 

(USGS 1999), broad ownership information from a variety 

of sources, and climate data representing a spatial model 

of 1961 through 1990 average annual temperature and 

precipitation (PRISM Climate Group 2002). The resulting 

strata are evaluated for each estimation unit, and collapsed 

as necessary to ensure that at least four plots are in each 

stratum. Stratified estimation is applied by assigning each 

plot to one of these collapsed strata and by calculating the 

area of each collapsed stratum in each estimation unit. The 

estimates from stratified data are usually more precise than 

those from unstratified estimates. Because of the statistical 

methods used, misclassification errors in the phase 1 data 

may cause imprecision but do not create bias in the estimators used. More information on sample design and estimators is available in Bechtold and Patterson (2005).

1 The Forest Inventory and Analysis unit is now part of the 

Resource Monitoring and Assessment Program at the Pacific 

Northwest Research Station.
114

GENERAL TECHNICAL REPORT PNW-GTR-835

Phase 2 

Phase 2 plots are a systematic sample with a random 

component selected from the entire area within the inventory unit, including both land and water. Plots are visited 

in the field if an air photo indicates that they might contain 

forest. The plot installed at each forested phase 2 location 

is a cluster of four subplots spaced 120 feet apart (fig. 64). 

Subplot 1 is in the center, with subplots 2 through 4 uniformly distributed radially around it. Each point serves as 

the center of a 1/24-acre circular subplot used to sample all 

trees at least 5.0 inches in diameter at breast height (d.b.h.). 

A 1/300-acre microplot, with its center located just east of 

each subplot center, is used to sample trees 1.0 to 4.9 inches 

d.b.h., as well as seedlings (trees less than 1.0 inch d.b.h.). 

Field crews delineate areas that are relatively homogenous by using (1) reserved status, (2) owner group, (3) 

forest type, (4) stand size class, (5) regeneration status, 

and (6) tree density; these areas are described as condition 

classes. The process of delineating these condition classes 

on a fixed-radius plot is called mapping. All measured trees 

are assigned to the mapped condition class in which they 

are located. 

On phase 2 plots, crews assess physical characteristics 

such as slope, aspect, and elevation; stand characteristics 

such as age, size class, forest type, disturbance, site productivity, and regeneration status; and tree characteristics 

such as tree species, diameter, height, and vertical crown 

dimensions. They also collect general descriptive information such as proximity to roads, and the geographic position 

of the plot in the larger landscape. 

Figure 64—The Forest Inventory and Analysis plot design used in the Alaska annual inventory, 2004–2008; d.b.h. = diameter at 

breast height.

Microplot (6.8-ft radius):

Tally seedlings and saplings.

Collect fuels data. 

Subplot (24.0-ft radius):

Tally all trees 5-in d.b.h.

or greater. Measure 

understory vegetation.

Transects:

Tally course and fine

woody debris. Collect

ground cover data.

o

0 25 50 100

Feet
115

Forests of Southeast and South-Central Alaska, 2004–2008

The PNW-FIA program measured 1,193 forested phase 

2 plots in Alaska between 2004 and 2008. Estimates of 

tree biomass and other forest attributes were derived from 

tree measurements and classifications made at each plot. 

Volumes for individual tally trees were computed with 

equations for each of the major species in Alaska. Estimates 

of growth, removals, and mortality were determined from 

measurements taken at a subset of 975 forested plots that 

were also measured during the 1994 through 2003 periodic 

inventory. 

Phase 3 

Some additional forest health measurements are collected 

on a subset (1/16) of phase 2 sample locations. At the phase 

3 plots, measurements are taken on tree crowns, lichens, 

downed woody material, and understory vegetation, in addition to the phase 2 variables. Although one half of the phase 

3 plots were not scheduled to be visited until 2009 through 

2013, we accelerated the measurement schedule for phase 

3 plots and visited the full 1/16 subsample between 2003 

and 2008. This provided 135 forested phase 3 plots in the 

coastal Alaska inventory unit. One forest health measurement, ozone injury, was monitored in 2004 and 2005 with 

four specially selected plots near Anchorage and Juneau; no 

ozone injury was detected. The relatively small number of 

phase 3 samples is intended to serve as a broad-scale detection monitoring system for forest health problems. 

Core, Core-Optional, and Regional Variables

Almost all of the FIA variables collected in Alaska are 

identical to those collected by FIA elsewhere in the United 

States—these are national “core” or “core optional” 

variables. Core optional, as the name suggests, are variables that are optional for any state but, if collected, must 

be collected using the standard national method. A small 

number of other variables are unique to PNW-FIA. These 

are “regional” variables and include such items as the 

species and cover of any invasive plants found on the plot 

and some variables used to help link measurements between 

the periodic and annual inventories. The database and 

documentation of variables used in this report are available 

from the Web site http://fiatools.fs.fed.us/fiadb-downloads/

datamart.html. Field manuals can also be useful for understanding how variables were collected and are available in 

electronic form from http://www.fs.fed.us/pnw/fia/publications/fieldmanuals.shtml.

Data Processing

The annual data used for this report are stored in the FIA 

National Information Management System (NIMS). It provides a means to input, edit, process, manage, and distribute 

FIA data. The NIMS includes a process for data loading, 

a national set of edit checks to ensure data consistency, an 

error correction process, approved equations and algorithms, code to compile and compute calculated attributes, a 

table report generator, and routines to populate the presentation database. The NIMS applies numerous algorithms and 

equations to calculate, for example, stocking, forest type, 

stand size, volume, and biomass. The NIMS generates 

estimates and associated statistics based on the combined 

phase 1 and phase 2 information. Additional FIA statistical 

design and estimation techniques are further reviewed in 

Bechtold and Patterson (2005). 

Periodic Inventory

The periodic inventory (1995–2003) of southeast and 

south-central Alaska used the same plot layout as the 

annual inventory with the exception of the microplot used to 

measure trees less than 5 inches d.b.h. This microplot was 

located at subplot center in the periodic inventory and was 

offset in the annual inventory, resulting in no remeasurement information for trees less than 5 inches d.b.h. 

The periodic inventory differed from the annual 

inventory in not being a paneled inventory. Instead, data 

collection began in 1995 in southeast Alaska, and gradually 

moved up the coast finishing in south-central Alaska in 

2003. Although most estimates of change were converted 

to annual values before being used to calculate change, in 

general the time period between measurements was greater 

in southeast Alaska than in south-central Alaska. 

There were also some differences in the sampling 

methods. Although both inventories used a spatially balanced design to locate samples, plots are located by using 

hexagons in the current inventory but were located within a 
116

GENERAL TECHNICAL REPORT PNW-GTR-835

gridded area in the periodic inventory. This resulted in some 

of the periodic plots being dropped in the annual inventory 

and reduced the number of remeasured plots available for 

analysis. In addition, Kodiak Island was sampled at 50 

percent intensity in the periodic inventory, resulting in less 

information about change for that area. 

The forest-land definition changed between the periodic 

and annual inventories, as did a number of items affecting 

the definition of a tree. These changes in definition had a 

profound effect on being able to detect change. Forestation 

(when nonforest land becomes forested) and deforestation 

(when forested land is converted to nonforest) are indistinguishable from procedural differences. This also means 

that real differences in carbon, biomass, numbers of trees, 

and other estimates produced from plots that were in both 

inventories are masked by procedural differences in the 

definition of forest land. 

We have attempted to compensate for these problems 

by limiting analysis to plots and portions of plots that 

were classified as forest in both inventories. Thus reported 

change applies to land that was considered forest by 

using one definition in 1995 through 2003 and by using a 

different definition of forest land in 2004 through 2008. To 

develop an approximate estimate of land area reverting to 

or diverting from forest, an experienced field crew person 

did review each change between forest and nonforest 

that occurred at the center of each remeasured subplot 

to categorize whether the change had been caused by 

real or procedural differences. Although this allowed an 

approximation for forest area change, no attempt to quantify 

volume, biomass, forest type, or carbon change associated 

with reversions and diversions was possible.

Estimates produced from the periodic inventory data 

used in this report will differ slightly from those reported 

in van Hees (2003, 2005) for additional reasons. Errors 

in species identification or d.b.h. made at the first visit 

to the plot were often correctable at the second measurement. Additional errors found, such as missing data and 

misidentification of ownership or reserve status for a few 

plots and imprecise metric-to-English conversions for small 

trees were also corrected before using the periodic data. A 

substantial change to the definition of forest type, and slight 

alterations to site index, volume, and biomass equations, 

were also made to match methods in the annual inventory. New information on boundaries and ownership was 

incorporated into the annual inventory.

Change calculation used the Beers and Miller (1964) 

method, which is the method currently used by the national 

FIA program.2 It fixes the inclusion probability of trees at 

time 1, or as they were measured in the periodic inventory. 

Change by ownership category, forest type, or site class are 

also reported by the category assigned in the periodic inventory. Because plots that were inaccessible during either the 

periodic or the annual inventory are not included in change 

calculations, the potential for bias from missing data is 

higher than for current (2004–08) estimates.

Site Index, Volume, and Biomass Estimates

Site index is used to measure potential productivity of forest 

land and to distinguish timberland from forest land. For 

Pacific silver fir, subalpine fir, Sitka spruce, western hemlock, and mountain hemlock (See “Common and Scientific 

Names”) more than 200 years of age, site index curves from 

Hegyi et al. (1979) were used. For trees of these species less 

than 200 years old, a site index equation was fitted to data 

from Taylor (1934) by using data from Payandeh (1974). For 

yellow-cedar, lodgepole pine, western redcedar, paper birch, 

aspen, and red alder, Hegyi et al. (1979) was used. For white 

spruce and black spruce, Farr (1967) was used.

Volume equations used include Bracket (1973), DeMars 

(1996a and 1996b), Embry and Haack (1965), Larson and 

Winterberger (1988, 1990), and Bruce (1984). Application to 

individual trees differed depending on the height class and 

diameter class of the tree and what type of volume (cubic 

foot, Scribner board foot, international board foot) was 

being estimated. For application to specific cases, contact 

the FIA office.

2

 PNW-FIA is one of four regional programs within the national 

FIA program.
117

Forests of Southeast and South-Central Alaska, 2004–2008

Biomass calculations come from direct biomass 

equations, as shown in the following tablulation.

Tree type Biomass citation

Seedlings Alemdag (1984)

Balsam popular Singh (1984)

Birch Alemdag (1984)

Black spruce Manning et al. (1984)

Cottonwood Singh (1984)

Lodgepole pine Manning et al. (1984)

Mountain hemlock Shaw (1979)

Pacific silver fir Krumlik and Kimmins (1973)

Red alder Standish (1983)

Sitka spruce Standish (1983)

Tamarack Singh (1984)

Western hemlock Shaw (1979)

Western redcedar Shaw (1979)

White spruce Manning et al. (1984)

Yellow-cedar Standish (1983)

Statistical Estimates 

Throughout this report, we have published standard errors 

(SE) for most of our estimates. These standard errors 

account for the fact that we measured only a small sample 

of the forest (thereby producing a sample-based estimate) 

and not the entire forest (which is the population parameter 

of interest). Because of small sample sizes or high 

variability within the population, some estimates can be 

very imprecise. The reader is encouraged to take the SE into 

account when drawing any inference. One way to consider 

this type of uncertainty is to construct confidence intervals. 

Customarily, 66- or 95-percent confidence intervals are 

used. A 95-percent confidence interval means that one can 

be 95 percent confident that the interval contains the true 

population parameter of interest. For more details about 

confidence intervals, please consult Moore and McCabe 

(1989) or other statistical literature.

It is relatively easy to construct approximate 66- or 

95-percent confidence intervals by multiplying the SE 

by 1.0 (for 66-percent confidence intervals) or 1.96 (for 

95-percent confidence intervals) and subtracting and 

adding this to the estimate itself. For example, in table 2 

of appendix 2, we estimated the total forest area in the 

coastal Alaska inventory unit to be 15.301 million acres, 

with a SE of 0.268 million acres. A 95-percent confidence 

interval for the total forest area ranges from 14.765 to 

15.837 million acres.

The reader may want to assess whether or not two 

estimates are significantly different from each other. The 

statistically correct way to address this is to estimate the 

SE of the difference of two estimates and either construct 

a confidence interval or use the equivalent z-test. However, 

this requires the original inventory data. It is often reasonable to assume that two estimates are nearly uncorrelated. 

For example, plots usually belong to one and only one 

owner. The correlation between estimates for different 

owners will be very small. If both estimates are assumed to 

be nearly uncorrelated, the standard error of the difference 

can be estimated by 

2 2 SE SE SE Difference = + Estimate 1 Estimate 2

Using the SE of the difference, a confidence of the 

difference can be constructed with this method.

If two estimates are based on data that occur on the 

same plot at the same time, the above equation should 

not be used. For example, table 44 in appendix 2 contains 

estimates of tree volume by diameter class. If one wants to 

compare the volume of trees in the diameter class 9.0 to 

10.9 d.b.h. with that of trees in the diameter class 21.0 to 

22.9 d.b.h., the covariance between the estimates is not zero, 

and this equation should not be used. For more complex and 

indepth analysis, the reader may contact the PNW-FIA.

All estimates—means, totals and their associated 

SE—are based on the poststratification methods described 

in detail by Bechtold and Patterson (2005).

Access Denied, Hazardous, or Inaccessible Plots

Although every effort was made to visit all field plots that 

were entirely or partially forested, some were not sampled 

for a variety of reasons. Field crews may have been unable 

to obtain permission from the landowner to access the plot 

(“denied access”). In coastal Alaska, it is common to have 

plots that are temporarily inaccessible owing to snow presence at the time that the boat is in that section of the coast. 
118

GENERAL TECHNICAL REPORT PNW-GTR-835

Many of the plots that were inaccessible owing to snow in 

2003 through 2008 were measured in 2009, owing to an 

unusually mild winter and warm summer, but were not able 

to be included in this report. Coastal Alaska also has many 

areas with extreme topography that can be very hazardous 

or impossible to reach, and plots in those areas will never be 

field measured.

This kind of missing data can introduce bias into the 

estimates if the nonsampled plots tend to be different from 

the entire population. The field crew rarely visit plots that 

are nonforested, and, therefore, the proportion of deniedaccess, hazardous, or inaccessible plots is significantly 

smaller for them than it is for forested plots.

The poststratification approach outlined in Bechtold 

and Patterson (2005) removes nonsampled plots from the 

sample. Estimates are adjusted for plots that are partially 

nonsampled by increasing the estimates by the nonsampled 

proportion within each stratum. To reduce the possible bias 

introduced by nonsampled plots, the phase 1 data are used 

to group plots in similar climate, vegetation, and ownership 

strata. Table 29 in appendix 2 shows the estimated land area 

in the categories of denied-access, temporarily inaccessible, 

and hazardous/inaccessible plots that were scheduled to be 

measured in 2004 through 2008.

By inventory subunit, 80 percent of plots (four panels) 

in the Chugach and Tongass National Forest Wilderness and 

Wilderness study areas were nonsampled, 100 percent of 

plots in Glacier Bay National Park and Preserve were nonsampled, and 1.3 percent of plots in the remaining coastal 

inventory area were nonsampled (table 29). Because no 

information was available for Glacier Bay National Park and 

Preserve, that area of land was excluded from all estimates 

throughout this report. 

For the Tongass and Chugach National Forest wilderness and wilderness study areas, the 2005 panel of plots 

was used in some summary estimates. The process of 

stratification redistributed the 6.188 million unsampled 

acres to the sampled area, resulting in an estimate of 4.057 

million forested acres and 5.806 million nonforested acres 

in the national forest wilderness areas. Because the ratio of 

plots to land area is very low (one plot per 58,000 acres), all 

estimates produced for the national forest wilderness are 

very imprecise.
119

Forests of Southeast and South-Central Alaska, 2004–2008

Appendix 2: Summary Data Tables

The following tables contain basic information about the 

forest resources of southeast and south-central Alaska. 

The tables aggregate data to a variety of levels (owner, 

forest type), along the Pacific Northwest Research Station 

Forest Inventory and Analysis (FIA) inventory results to 

be applied at various scales and used for various anises. 

Many other tables could be generated from the FIA data 

but are not included here because of limited space. Data 

are also available in nonsummarized form at http://www.fia.

fs.fed.us.

The national FIA Web site (http://www.fia.fs.fed.us/

tools-data) contains some tools for querying the coastal 

Alaska annual data and generating custom tables or maps. 

Some of the tables in this appendix contain summaries of 

regional variables; data for regional variables are not currently included in the national FIA database (FIADB) but 

can be requested from the regional FIA office http://www.

fs.fed.us/pnw/fia/. 

Please note that information in tables presented here 

and those generated from the FIADB may differ. As new 

data are added each year to FIADB, any tables generated 

from it will be based on the current full set of data in 

FIADB (e.g., 2004–2009, 2004–2010, etc.), whereas tables 

in this publication contain only data from 2004 through 

2008. The user can take a snapsot of data from FIADB by 

selecting the desired years and generating tables that are 

similar, but probably not identical, to those presented here.

List of Tables in Appendix

Table 29—Number of Forest Inventory and Analysis plots 

measured by inventory subunit and measurement status, coastal 

Alaska, 2004–2008

Table 30—Estimated area by land class, owner group, and 

reserved component, coastal Alaska, 2004–2008

Table 31—Estimated area of forest land by owner group, 

reserved status, and forest type, coastal Alaska, 2004–2008

Table 32—Estimated area of timberland by owner group and 

forest type, coastal Alaska, 2004–2008

Table 33—Estimated area of forest land, by forest type and 

stand-size class, coastal Alaska, 2004–2008

Table 34—Estimated area of forest land, by forest type and 

stand-age class, coastal Alaska, 2004–2008

Table 35—Estimated area of timberland by owner group and 

stand-size class, coastal Alaska, 2004–2008

Table 36—Estimated area of forest land, by forest type and 

productivity class, coastal Alaska, 2004–2008

Table 37—Estimated area of timberland by owner group and 

cubic foot site class, coastal Alaska, 2004–2008

Table 38—Estimated area of timberland by forest type and 

stand-size class, coastal Alaska, 2004–2008 

Table 39—Estimated number of live trees on forest land, by 

species and diameter class, coastal Alaska, 2004–2008 

Table 40—Estimated number of growing-stock trees on timberland 

by species and diameter class, coastal Alaska, 2004–2008

Table 41—Estimated net volume of all live trees, by owner class 

and forest land status, coastal Alaska, 2004–2008

Table 42—Estimated net volume of all live trees on forest land, 

by forest type and stand size class, coastal Alaska, 2004–2008

Table 43—Estimated net volume of all live trees on forest land, 

by species and ownership group, coastal Alaska, 2004–2008

Table 44—Estimated net volume of all live trees on forest land, 

by species and diameter class, coastal Alaska, 2004–2008

Table 45—Estimated net volume of growing stock and sawtimber 

on timberland by class of timber, owner group, and species group, 

coastal Alaska, 2004–2008

Table 46—Estimated net volume of growing-stock trees on 

timberland by species and diameter class, coastal Alaska, 

2004–2008

Table 47—Estimated net volume of sawtimber on timberland by 

species and diameter class, coastal Alaska, 2004–2008

Table 48—Estimated net volume of growing stock on timberland 

by species and owner group, coastal Alaska, 2004–2008 

Table 49—Estimated net volume of sawtimber trees on timberland, 

by species and owner group, coastal Alaska, 2004–2008

Table 50—Estimated net volume of sawtimber on timberland by 

species and owner group, coastal Alaska, 2004–2008

Table 51—Estimated net volume of growing stock on timberland 

by forest type and stand size class, coastal Alaska, 2004–2008

Table 52—Estimated net volume of sawtimber on timberland by 

forest type and stand size class, coastal Alaska, 2004–2008

Table 53—Estimated net volume of growing stock on timberland 

by forest type and owner group, coastal Alaska, 2004–2008

Table 54—Estimated net volume of sawtimber on timberland by 

forest type and owner group, coastal Alaska, 2004–2008

Table 55—Estimated average biomass, volume, and density of 

snags on forest land, by forest type and diameter class, coastal 

Alaska, 2004–2008

Table 56—Estimated biomass and carbon mass of snags on forest 

land, by forest type and owner, coastal Alaska, 2004–2008

Table 57—Lichen species found across all coastal Alaska plots, 

2004–2008

Table 58—Most common species by forest type, coastal Alaska, 

2004–2008

Table 59—Introduced species that are searched for on standard 

phase 2 plots, coastal Alaska, 2004–2008

Table 60—Plants found in the coastal Alaska FIA inventory unit 

phase 3 plots with documented subsistence use or inquired about 

for commercial use on the Chugach or Tongass National Forests

Table 61—Estimated aboveground biomass of all live trees 

on forest land, by species and diameter class, coastal Alaska 

2004–2008
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GENERAL TECHNICAL REPORT PNW-GTR-835

Table 30—Estimated area by land class, owner group, and reserved component, coastal Alaska, 2004–2008a

Other Total Nonforest Noncensus Census

Timberlandb forest landc forest land land water water All land

Owner group Total SE Total SE Total SE Total SE Total SE Total SE Total SE

Thousand acres

National forest:

All 3,747 155 7,034 268 10,781 253 — — — — — — 10,781 253

Reserved — — 4,123 232 4,123 232 — — — — — — 4,123 232

Other federal:

All 68 27 1,240 61 1,308 65 — — — — — — 1,308 65

Reserved — — 1,168 55 1,168 55 — — — — — — 1,168 55

State and local 879 84 453 67 1,331 90 — — — — — — 1,331 90

Private 1,481 102 399 63 1,880 106 — — — — — — 1,880 106

All owners 6,174 185 9,126 287 15,301 268 22,483 267 90 24 16,539 152 37,783 183

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.

a

 Area includes wilderness in the Tongass and Chugach National Forests and excludes Glacier Bay National Park.

b

 Forest land that is capable of producing in excess of 20 cubic feet per acre per year of wood at culmination of mean annual increment.

c

 Forest land that is not capable of producing in excess of 20 cubic feet per acre per year of wood at culmination of mean annual increment.

Table 29—Number of Forest Inventory and Analysis plots by inventory subunit 

and measurement status, coastal Alaska, 2004–2008a

Inventory subunit and measurement status Plots Area

Number Acres

Coastal inventory area:

Accessible

 Forest 1,050 13,723,099

Nonforest 3,128 36,413,062

Total accessible 4,178 50,136,161

Inaccessible

 Denied access 3 34,793

 Hazardous 22 241,947

Delayed access (weather) 30 348,956

Total inaccessible 55 625,696

Total coastal inventory area 4,233 50,761,857

Tongass and Chugach Wilderness and Wilderness Study Areas:

Accessible, 2004 panel

 Forest 53 752,509

Nonforest 87 1,191,530

Total accessible 140 1,944,039

Inaccessible, 2004 panel 4 42,088

Unknown, 2005–2008 plots dropped from the inventory 554 6,146,356

Total—Tongass and Chugach Wilderness 698 8,132,484

Glacier Bay National Park and Preserve:

Unknown, 2004–2008 plots dropped from the inventory 282 2,694,819

Total for all categories 5,213 61,589,160

a

 Totals in this table will not match other tables that follow. During the process of stratification, acres associated 

with inaccessible plots and dropped plots (excluding Glacier Bay, which is not included in this report) are 

redistributed across measured plots.
121

Table 31—Estimated area of forest land by owner group, reserved status, and forest type, coastal Alaska, 2004–2008a

U.S. Forest Service Other federal

Reserved Nonreserved Total Reserved Nonreserved Total State and local Privat

Forest type Total SE Total SE Total SE Total SE Total SE Total SE Total SE Total 

Thousand acres

Softwoods:

Alaska yellow-cedar 995 208 1,239 111 2,233 235 — — — — — — 54 24 68 

Black spruce — — 1 1 1 1 407 49 15 13 422 51 24 17 52 

Lodgepole pine 20 16 356 59 377 60 — — — — — — 37 19 31 Mountain hemlock 1,333 231 1,410 110 2,743 255 75 27 35 20 110 33 143 40 166 

Sitka spruce 319 115 615 80 933 139 23 16 3 3 25 16 450 58 549 Western hemlock 988 197 2,352 136 3,340 236 — — 16 14 16 14 219 47 396 

Western redcedar 382 131 550 70 932 148 — — — — — — 43 21 65 

White spruce — — 24 15 24 15 223 39 9 11 232 40 62 26 129 

Total 4,036 236 6,556 145 10,592 256 728 57 77 29 805 63 1,031 85 1,457 1

Hardwoods:

Aspen — — — — — — 123 32 — — 123 32 3 3 37 Cottonwood 86 63 61 26 147 68 110 33 31 17 140 37 126 38 118 

Paper birch — — 12 12 12 12 203 36 33 20 235 41 112 36 242 

Total 86 63 103 33 189 71 435 55 63 26 498 61 241 51 397 

Nonstocked — — — — — — 5 6 — — 5 6 60 26 26 

Total 4,123 232 6,658 146 10,781 253 1,168 55 140 39 1,308 65 1,331 90 1,880 1

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.a Area includes wilderness in the Tongass and Chugach National Forests and excludes Glacier Bay National Park.
122

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 32—Estimated area of timberland by owner group and forest type, coastal Alaska, 2004–2008

U.S. Forest Service Other federal State and local Private All owners

Forest type Total SE Total SE Total SE Total SE Total SE

Thousand acres

Softwoods:

Alaska yellow-cedar 323 61 — — 12 11 14 11 350 62

Black spruce — — — — 24 17 19 15 43 23

Lodgepole pine 45 19 — — 30 18 — — 75 27

Mountain hemlock 298 56 — — 56 25 29 17 383 63

Sitka spruce 553 76 3 3 305 54 541 65 1,402 113

Western hemlock 2,061 130 16 14 196 45 366 61 2,639 147

Western redcedar 379 62 — — 11 12 17 15 407 64

White spruce 17 14 9 11 41 21 109 35 176 44

Total 3,677 154 28 18 676 77 1,096 90 5,477 181

Hardwoods:

Aspen — — — — — — 37 21 37 21

Cottonwood 39 21 31 17 96 33 118 36 284 55

Paper birch — — 10 11 58 26 204 46 272 53

Total 69 26 41 20 154 42 359 59 624 77

Nonstocked — — — — 48 23 26 17 74 29

Total 3,747 155 68 27 879 84 1,481 102 6,174 185

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.

Table 33—Estimated area of forest land, by forest type and stand size class, coastal Alaska, 2004–2008

Large-diameter Medium-diameter Small-diameter

standsa standsb standsc All size classes

Forest type Total SE Total SE Total SE Total SE

Thousand acres

Softwoods:

Alaska yellow-cedar 1,816 217 116 55 423 100 2,355 237

Black spruce 23 14 63 26 413 54 499 59

Lodgepole pine 146 41 53 20 247 48 445 66

Mountain hemlock 2,269 224 155 49 738 144 3,162 263

Sitka spruce 1,401 150 171 43 382 63 1,957 164

Western hemlock 3,523 235 50 25 399 84 3,971 245

Western redcedar 979 150 — — 61 25 1,040 152

White spruce 71 27 141 35 235 49 447 63

Total 10,227 256 752 100 2,902 214 13,885 273

Hardwoods:

Aspen 37 18 39 19 87 29 163 38

Cottonwood 300 55 142 68 90 30 532 93

Paper birch 232 48 220 47 149 41 601 74

Total 569 74 431 86 326 57 1,325 121

Nonstocked — — — — — — 90 31

Total 10,796 262 1,183 130 3,227 219 15,301 268

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.

a

 Stands in which the majority of trees are at least 11.0 inches diameter at breast height (d.b.h.) for hardwoods and 9.0 inches d.b.h. for 

softwoods.

b

 Stands in which the majority of trees are at least 5.0 inches d.b.h. but not as large as large-diameter trees.

c

 Stands in which the majority of trees are less than 5.0 inches d.b.h.
123

Forests of Southeast and South-Central Alaska, 2004–2008

Table 34—Estimated area of forest land, by forest type and stand age class, coastal Alaska, 2004–2008

Stand age class (years)a

1–20 21–40 41–60 61–80 81–100 101–120

Forest type Total SE Total SE Total SE Total SE Total SE Total SE

Thousand acres

Softwoods:

Alaska yellow-cedar — — 23.9 14.9 — — 12.5 11.5 10.4 11.4 11.7 11.2

Black spruce — — 76.5 25.4 109.0 31.1 87.2 28.7 75.5 25.5 99.8 30.9

Lodgepole pine 10.9 9.6 7.8 10.0 5.8 7.5 42.2 19.0 18.8 14.6 7.0 9.4

Mountain hemlock 95.7 70.2 48.0 46.8 89.1 57.2 110.6 67.2 167.4 43.1 173.0 70.1

Sitka spruce 182.4 41.9 201.8 48.0 168.2 43.4 161.4 41.5 206.6 46.4 148.7 61.0

Western hemlock 177.6 44.2 201.4 47.1 43.6 23.7 31.5 19.4 44.4 20.4 171.1 43.7

Western redcedar 17.4 14.5 5.9 8.9 — — — — 13.1 12.5 20.7 14.4

White spruce 9.1 10.8 49.0 22.8 151.9 38.0 99.6 31.8 40.3 20.0 51.9 22.8

 Total 493.1 92.9 619.7 90.8 567.6 89.7 545.1 94.2 576.5 76.9 687.6 110.6

Hardwoods:

Aspen 12.1 12.4 30.3 16.5 32.1 17.3 35.3 18.9 21.1 14.3 25.1 14.5

Cottonwood 10.7 7.6 134.0 66.6 168.6 43.3 86.8 31.3 62.0 25.5 27.5 17.2

Paper birch — — 56.0 24.1 58.6 25.3 99.9 33.1 124.4 36.1 131.7 36.8

 Total 22.8 14.6 239.7 74.0 262.2 53.1 222.0 49.3 207.6 46.2 184.4 42.9

Nonstocked — — — — — — — — — — — —

Total 515.8 94.0 859.4 116.4 829.8 103.7 767.1 105.7 784.1 89.0 872.0 118.2

Stand age class (years)a

All accessible

121–140 141–160 161–180 181–200 201+ forest land

Forest type Total SE Total SE Total SE Total SE Total SE Total SE

Thousand acres

Softwoods:

Alaska yellow-cedar 82.1 29.7 162.3 71.0 298.9 101.5 75.2 28.6 1,677.8 210.8 2,354.7 237.2

Black spruce 41.9 20.9 9.3 9.0 — — — — — — 499.2 59.3

Lodgepole pine 27.5 17.1 32.0 19.1 43.6 20.7 28.6 16.6 221.0 48.6 445.2 65.5

Mountain hemlock 336.1 117.6 258.6 77.0 215.5 74.1 289.4 78.8 1,378.2 170.9 3,161.7 263.3

Sitka spruce 124.5 37.7 168.9 43.2 90.6 31.7 60.3 26.9 443.7 111.5 1,957.1 163.9

Western hemlock 135.9 40.1 135.1 39.7 323.3 98.3 320.8 81.9 2,386.3 213.7 3,970.9 244.7

Western redcedar 15.2 12.3 64.8 47.3 74.2 29.1 144.6 40.7 684.1 137.9 1,040.1 151.8

White spruce 12.8 11.1 15.4 12.1 17.0 13.7 — — — — 447.0 62.8

 Total 775.9 136.3 846.4 130.7 1,063.2 164.7 918.8 125.7 6,791.2 285.7 13,885.0 272.8

Hardwoods:

Aspen 6.6 8.1 — — — — — — — — 162.7 38.4

Cottonwood 10.7 11.6 15.7 14.1 — — — — 15.9 12.5 532.0 92.9

Paper birch 69.9 26.8 40.5 22.3 13.0 9.8 — — 6.6 8.1 600.6 74.0

 Total 87.3 30.2 63.8 27.3 13.0 9.8 — — 22.5 14.9 1,325.3 120.7

Nonstocked — — — — — — — — — — 90.3 31.4

Total 863.2 139.3 910.2 133.4 1,076.2 165.0 918.8 125.7 6,813.7 285.9 15,300.6 267.6

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres were estimated.

a

 The age of the stand is unknown on some plots because no trees were available for boring.
124

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 35—Estimated area of timberland by owner group and stand size class, coastal Alaska, 2004–2008

U.S. Forest Service Other federal State and local Private All owners

Stand size class Total SE Total SE Total SE Total SE Total SE

Thousand acres

Seedling and sapling 251 50 18 13 145 40 579 73 993 97

Poletimber 150 40 — — 83 30 149 40 382 64

Sawtimber 3,330 145 50 24 571 72 662 76 4,613 170

Nonstocked 16 10 — — 79 29 91 32 186 44

All classes 3,747 155 68 27 879 84 1,481 102 6,174 185

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.

Table 37—Estimated area of timberland by owner group and cubic foot site class, coastal Alaska, 2004–2008

U.S. Forest Service Other federal State and local Private All owners

Site classa Total SE Total SE Total SE Total SE Total SE

ft3 ∙ ac-1 ∙ yr-1 Thousand acres

20–49 1,910 133 21 14 445 67 579 74 2,955 161

50–84 1,240 111 35 19 323 57 666 76 2,263 144

85–119 365 65 — — 62 27 173 44 600 82

120–164 162 43 12 12 27 18 63 28 264 55

All classes 3,747 155 68 27 879 84 1,481 102 6,174 185

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.

a

 Site productivity class refers to the potential productivity of forest land expressed as the mean annual increment (in cubic feet 

per acre per year) at culmination in fully stocked stands.

Table 36—Estimated area of forest land, by forest type and productivity class, coastal Alaska, 2004–2008

Site productivity class (cubic feet per acre per year)a All productivity 0–19 20–49 50–84 85–119 120–164 165–224 225+ classes

Forest type Total SE Total SE Total SE Total SE Total SE Total SE Total SE Total SE

Thousand acres

Softwoods:

Alaska yellow-cedar 1,631 198 553 114 170 89 — — — — — — — — 2,355 237

Black spruce 272 47 211 43 16 11 — — — — — — — — 499 59

Lodgepole pine 370 61 67 26 8 5 — — — — — — — — 445 66

Mountain hemlock 2,549 250 560 120 53 23 — — — — — — — — 3,162 263

Sitka spruce 102 33 468 82 805 119 292 57 228 68 33 20 29 18 1,957 164

Western hemlock 577 125 1,416 161 1,576 174 286 57 107 35 — — 9 9 3,971 245

Western redcedar 393 101 429 102 218 73 — — — — — — — — 1,040 152

White spruce 105 32 234 46 100 31 9 9 — — — — — — 447 63

Total 6,009 320 3,937 254 2,946 219 587 82 335 77 33 20 37 21 13,885 273

Hardwoods:

Aspen 15 11 102 32 45 21 — — — — — — — — 163 38

Cottonwood 22 16 121 35 209 47 150 69 20 15 9 11 — — 532 93

Paper birch 174 43 284 53 110 31 21 16 — — — — 12 13 601 74

Total 211 48 523 69 379 61 171 71 20 15 9 11 12 13 1,325 121

Nonstocked 11 12 26 16 42 23 — — 11 12 — — — — 90 31

Total 6,232 323 4,486 261 3,367 227 758 107 366 79 42 23 50 25 15,301 268

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.

a

 Site productivity class refers to the potential productivity of forest land expressed as the mean annual increment (in cubic feet 

per acre per year) at culmination in fully stocked stands.
125

Forests of Southeast and South-Central Alaska, 2004–2008

Table 38—Estimated area of timberland by forest type and stand size class, coastal Alaska, 2004–2008

Seedling and All stand 

sapling Poletimber Sawtimber Nonstocked size classes

Forest type Total SE Total SE Total SE Total SE Total SE

Thousand acres

Softwoods:

Alaska yellow-cedar 6 6 — — 344 62 — — 350 62

Black spruce 28 18 15 14 — — — — 43 23

Lodgepole pine 67 26 2 2 4 3 3 3 75 27

Mountain hemlock 23 16 10 11 343 60 8 7 383 63

Sitka spruce 269 54 133 38 942 95 58 24 1,402 113

Western hemlock 300 57 47 24 2,271 137 21 15 2,639 147

Western redcedar 22 15 — — 385 62 — — 407 64

White spruce 105 35 37 19 25 17 10 11 176 44

Total 820 88 243 52 4,313 164 100 31 5,477 181

Hardwoods:

Aspen 37 21 — — — — — — 37 21

Cottonwood 43 20 56 26 174 43 12 12 284 55

Paper birch 93 33 53 24 126 38 — — 272 53

Total 173 44 139 39 300 56 12 12 624 77

Nonstocked — — — — — — 74 29 74 29

Total 993 97 382 64 4,613 170 186 44 6,174 185

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 acres.
126

Table 39—Estimated number of live trees on forest land, by species and diameter class, coastal Alaska, 2004–2008 Diameter class (inches) 1.0–2.9 3.0–4.9 5.0–6.9 7.0–8.9 9.0–10.9 11.0–12.9 

Species Total SE Total SE Total SE Total SE Total SE Total SE 

Thousand trees Softwoods: Alaska yellow-cedar 782,923 121,669 202,655 31,436 117,281 14,606 76,215 9,509 58,380 7,828 38,639 4,552 

Black spruce 463,328 60,750 119,980 17,977 24,177 4,749 2,882 899 283 163 77 67 

Lodgepole pine 114,308 25,601 38,760 8,837 22,686 2,842 15,838 1,924 13,231 2,454 9,568 2,059 

Mountain hemlock 661,742 72,991 355,995 50,101 188,735 18,347 128,244 12,649 93,653 11,218 59,704 6,492 Sitka spruce 443,293 53,818 151,498 17,243 95,776 8,919 65,033 5,844 48,619 3,991 34,761 3,551 Western hemlock 1,379,355 101,783 476,716 36,852 280,680 16,073 181,420 10,471 122,191 6,884 81,630 5,236 

Western redcedar 192,755 35,793 55,570 14,251 32,900 4,466 24,757 3,685 18,540 3,116 16,514 2,545 

White spruce 148,497 28,419 71,456 13,158 35,679 4,283 19,156 2,123 7,963 1,215 4,077 672 Other softwood 508 706 — — 623 296 294 150 47 61 62 69 

 Total 4,186,709 225,235 1,472,630 74,569 798,538 30,050 513,838 19,974 362,907 15,123 245,032 10,255 

Hardwoods: Black cottonwood 58,007 23,397 20,089 6,655 9,270 2,354 12,188 5,240 6,966 1,770 4,233 1,220 

Paper birch 109,125 27,746 31,665 9,579 17,476 3,621 12,511 2,247 9,232 1,588 5,786 951 Quaking aspen 25,095 13,644 2,869 2,195 3,010 1,331 2,477 1,110 2,621 1,203 2,331 761 

Red alder 10,639 4,336 14,945 6,550 9,093 2,755 5,182 1,106 3,577 1,110 1,067 338 

 Total 202,866 38,295 69,568 13,478 38,850 5,346 32,358 5,914 22,397 2,878 13,417 1,768 

Total 4,389,575 229,099 1,542,198 75,804 837,388 30,386 546,196 20,166 385,304 15,323 258,449 10,315 

Diameter class (inches)

17.0–18.9 19.0–20.9 21.0–24.9 25.0–28.9 29.0–32.9 33.0–36.9 

Species Total SE Total SE Total SE Total SE Total SE Total SE 

Thousand trees

Softwoods:

Alaska yellow-cedar 16,497 2,615 8,926 1,459 9,484 1,649 4,564 921 2,963 666 1,400 498 

Black spruce — — — — — — — — — — — — 

Lodgepole pine 2,317 726 568 208 748 231 69 71 — — — — 

Mountain hemlock 21,672 2,534 12,872 2,194 13,725 2,030 6,987 1,487 3,361 897 1,340 471 

Sitka spruce 17,275 1,702 11,709 1,392 19,000 1,942 12,080 1,492 8,906 2,053 4,813 1,081 

Western hemlock 26,936 2,593 19,165 1,657 29,532 2,685 17,124 1,694 10,453 1,342 5,922 886 

Western redcedar 7,943 1,575 4,363 831 7,871 1,531 5,807 2,099 3,830 1,053 1,084 292 

White spruce 278 177 80 77 — — — — — — — — Other softwood 95 84 — — — — — — — — — — 

Total 93,013 5,079 57,684 3,479 80,360 4,322 46,631 3,493 29,514 2,926 14,559 1,520 

Hardwoods:

Black cottonwood 2,060 479 1,170 357 1,888 667 566 257 73 75 65 68 Paper birch 750 290 241 132 250 119 — — — — — — Quaking aspen 164 103 93 73 — — 46 52 — — — — 

Red alder — — 71 72 79 75 — — — — — — 

Total 2,974 573 1,575 394 2,217 688 612 262 73 75 65 68 

Total 95,987 5,100 59,259 3,497 82,577 4,398 47,242 3,505 29,587 2,930 14,624 1,522 

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 trees.
127

Forests of Southeast and South-Central Alaska, 2004–2008

Table 40—Estimated number of growing-stocka trees on timberland by species and diameter class, coastal 

Alaska, 2004–2008

Diameter class (inches at breast height)

All classes 5.0–6.9 7.0–8.9 9.0–10.9 11.0–12.9

Species Total SE Total SE Total SE Total SE Total SE

Thousand trees

Softwoods:

Alaska yellow-cedar 65,267 8,133 13,984 2,380 10,429 1,633 9,079 1,646 6,478 1,023

Black spruce 3,589 1,756 3,271 1,610 318 187 — — — —

Lodgepole pine 8,022 2,059 2,318 763 1,929 540 1,190 436 906 325

Mountain hemlock 101,814 11,387 29,020 3,662 20,517 2,790 14,840 2,063 10,025 1,402

Sitka spruce 228,558 17,866 57,529 6,940 37,179 4,117 30,025 2,918 22,365 2,717

Western hemlock 457,087 23,522 134,225 8,411 88,361 5,578 65,111 4,528 41,636 2,972

Western redcedar 49,112 7,292 9,199 1,833 7,306 1,439 5,309 1,018 4,786 1,096

White spruce 23,596 4,348 10,161 2,358 6,887 1,304 3,330 861 1,972 512

Other softwood 586 402 218 183 103 89 47 61 62 69

 Total 937,632 39,042 259,925 13,648 173,028 8,801 128,929 6,945 88,228 4,910

Hardwoods:

Black cottonwood 20,882 4,712 4,950 1,525 4,680 1,603 2,925 864 1,562 492

Paper birch 16,296 3,473 4,222 1,125 3,303 961 3,884 1,028 2,783 742

Quaking aspen 1,138 837 748 622 183 218 — — — —

Red alder 16,768 4,028 8,014 2,696 4,778 1,095 1,922 632 1,004 331

 Total 55,083 7,210 17,935 3,383 12,944 2,179 8,731 1,491 5,350 958

All species 992,715 40,139 277,859 14,233 185,972 9,234 137,660 7,166 93,578 5,040

Diameter class (inches at breast height)

13.0–14.9 15.0–16.9 17.0–18.9 19.0–20.9 21.0–28.9 29.0+

Species Total SE Total SE Total SE Total SE Total SE Total SE

Thousand trees

Softwoods:

Alaska yellow-cedar 6,742 1,138 4,755 916 3,659 644 2,682 495 5,190 876 2,270 495

Black spruce — — — — — — — — — — — —

Lodgepole pine 773 317 471 210 67 70 159 109 210 125 — —

Mountain hemlock 7,545 1,180 5,549 1,048 5,246 881 2,394 504 4,775 802 1,904 530

Sitka spruce 16,603 1,880 14,704 1,641 10,937 1,202 8,003 1,020 19,529 1,953 11,685 1,245

Western hemlock 32,308 2,346 21,032 1,649 16,491 1,456 13,380 1,310 30,537 2,531 14,007 1,454

Western redcedar 4,398 798 3,589 780 2,801 602 1,869 476 6,137 1,058 3,720 770

White spruce 633 255 392 193 221 168 — — — — — —

Other softwood 62 69 — — 95 84 — — — — — —

 Total 69,063 3,933 50,492 2,970 39,515 2,423 28,487 1,977 66,377 3,733 33,586 2,341

Hardwoods:

Black cottonwood 1,924 652 1,662 521 1,096 342 532 229 1,478 752 73 75

Paper birch 641 273 940 436 326 231 195 121 — — — —

Quaking aspen 133 100 — — 73 75 — — — — — —

Red alder 497 313 403 200 — — 71 72 79 75 — —

 Total 3,196 772 3,005 711 1,495 430 799 269 1,557 755 73 75

All species 72,259 4,013 53,497 3,039 41,010 2,446 29,286 1,997 67,934 3,855 33,660 2,354

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500 trees.

a

 Growing-stock trees are live trees of commercial species that meet certain merchantability standards; excludes trees that are 

entirely cull (rough or rotten tree classes).
128

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 41—Estimated net volume of all live trees, by owner class and forest land status, coastal Alaska, 

2004–2008

Unreserved forests Reserved forests

Other Other All forest

Timberlanda forest b Total Productive a forest b Total land

Owner class Total SE Total SE Total SE Total SE Total SE Total SE Total SE

Million cubic feet

U.S. Forest Service:

National forest 22,430 1,176 6,161 480 28,591 1,098 13,256 1,709 5,103 833 18,359 1,498 46,950 1,768

 Total 22,430 1,176 6,161 480 28,591 1,098 13,256 1,709 5,103 833 18,359 1,498 46,950 1,768

Other federal government:

National Park Service — — — — — — 231 98 43 36 275 103 275 103

Bureau of Land Management 390 261 60 30 449 262 — — — — — — 449 262

U.S. Fish and Wildlife Service — — — — — — 585 96 173 47 759 99 759 99

Departments of Defense 20 22 2 2 22 22 — — — — — — 22 22

 and Energy

 Total 410 262 61 30 471 263 817 137 217 59 1,034 143 1,505 295

State and local government:

State 2,900 423 350 110 3,250 424 626 215 1 1 627 215 3,877 440

Local 100 55 150 86 250 102 — — — — — — 250 102

 Total 3,000 425 500 135 3,500 429 626 215 1 1 627 215 4,127 445

Private: 3,287 460 409 93 3,696 461 — — — — — — 3,696 461

 Total 3,287 460 409 93 3,696 461 — — — — — — 3,696 461

All owners 29,127 1,319 7,131 505 36,258 1,237 14,699 1,728 5,321 835 20,020 1,520 56,278 1,858

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.

a

 Forest land that is capable of producing in excess of 20 cubic feet per acre per year of wood at culmination of mean annual increment.

b

 Forest land that is not capable of producing in excess of 20 cubic feet per acre per year of wood at culmination of mean annual increment.
129

Forests of Southeast and South-Central Alaska, 2004–2008

Table 42—Estimated net volume of all live trees on forest land, by forest type and stand size class, coastal 

Alaska, 2004–2008

Large-diameter Medium-diameter Small-diameter

stands a standsb stands c All size classes

Forest type Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 6,456 996 173 105 160 78 6,788 993

Black spruce 8 9 44 20 39 10 91 23

Lodgepole pine 204 59 34 15 53 16 291 63

Mountain hemlock 8,355 1,034 183 62 145 30 8,683 1,035

Sitka spruce 10,357 1,417 218 72 83 23 10,658 1,416

Western hemlock 22,215 1,566 73 39 183 92 22,472 1,564

Western redcedar 5,317 1,193 — — 18 10 5,335 1,193

White spruce 44 21 83 23 57 18 184 35

 Total 52,956 1,901 809 150 738 128 54,503 1,854

Hardwoods:

Aspen 50 27 81 43 12 7 144 50

Cottonwood 830 272 210 108 8 3 1,049 292

Paper birch 350 94 170 44 30 10 551 99

 Total 1,230 288 495 125 51 13 1,776 311

All forest types 54,186 1,914 1,303 193 789 129 56,278 1,858

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.

a

 Stands with a majority of trees at least 11.0 inches diameter at breast height (d.b.h.) for hardwoods and 9.0 inches d.b.h. for softwoods.

b

 Stands with a majority of trees at least 5.0 inches d.b.h. but not as large as large-diameter trees.

c

 Stands with a majority of trees less than 5.0 inches d.b.h.
130

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 43—Estimated net volume of all live trees on forest land, by species and owner group, coastal Alaska, 

2004–2008

Owner group

State and local 

U.S. Forest Service Other federal government Private All owners

Species Total SE Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 5,727 593 — — 95 45 78 23 5,899 594

Black spruce — — 49 11 4 3 11 8 64 14

Lodgepole pine 587 98 2 2 26 11 49 21 665 100

Mountain hemlock 7,512 784 148 53 445 121 299 92 8,405 800

Sitka spruce 11,475 1,211 410 179 2,274 318 1,765 327 15,923 1,301

Western hemlock 17,387 1,001 116 89 958 209 1,103 264 19,564 1,042

Western redcedar 3,855 678 — — 90 36 45 19 3,990 679

White spruce 46 19 188 25 42 16 84 21 360 41

Other softwood 13 7 — — 4 4 — — 17 8

 Total 46,601 1,768 913 228 3,937 440 3,434 458 54,885 1,850

Hardwoods:

Black cottonwood 194 103 260 92 147 56 97 33 697 152

Paper birch 5 4 205 38 34 12 144 39 388 55

Quaking aspen — — 128 38 2 2 6 5 136 38

Red alder 150 37 — — 7 5 15 12 172 39

 Total 349 109 592 108 190 60 262 52 1,393 172

All species 46,950 1,768 1,505 295 4,127 445 3,696 461 56,278 1,858

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.
131

Table 44—Estimated net volume of all live trees on forest land, by species and diameter class, coastal Alaska, 2004–2008

Diameter class (inches)

5.0–6.9 7.0–8.9 9.0–10.9 11.0–12.9 13.0–14.9 15.0–16.9 

Species Total SE Total SE Total SE Total SE Total SE Total SE 

Million cubic feet

Softwoods:

Alaska yellow-cedar 220 27 379 47 502 65 534 66 632 101 587 93 

Black spruce 49 10 12 4 2 1 1 1 — — — — 

Lodgepole pine 32 4 68 9 104 20 135 31 113 29 51 12 

Mountain hemlock 312 34 532 51 783 100 847 93 886 95 932 138 

Sitka spruce 224 23 379 36 532 46 654 71 828 84 871 84 

Western hemlock 648 39 1,025 61 1,379 88 1,449 95 1,523 97 1,350 95 

Western redcedar 58 8 114 17 151 27 207 33 218 42 214 42 

White spruce 75 9 96 11 72 12 59 10 24 6 21 9 

Other softwood 1 1 2 1 1 1 1 1 4 3 3 3  Total 1,620 63 2,606 98 3,526 145 3,887 163 4,228 191 4,030 209 

Hardwoods:

Black cottonwood 12 4 57 27 62 17 65 23 104 39 76 20 

Paper birch 35 8 57 10 83 15 79 14 44 10 42 15 Quaking aspen 8 4 15 7 29 14 36 12 22 8 11 5 Red alder 21 6 33 8 36 11 22 7 16 9 28 13  Total 76 12 162 30 210 28 203 30 187 42 157 29 

All species 1,697 64 2,769 100 3,736 147 4,089 165 4,415 192 4,187 210 

Diameter class (inches)

19.0–20.9 21.0–24.9 25.0–28.9 29.0–32.9 33.0–36.9 37.0+ 

Species Total SE Total SE Total SE Total SE Total SE Total SE 

Million cubic feet

Softwoods:

Alaska yellow-cedar 413 72 645 116 467 103 463 124 239 84 155 57 Black spruce — — — — — — — — — — — — 

Lodgepole pine 28 11 44 14 6 6 — — — — — — Mountain hemlock 621 106 930 139 698 156 434 114 249 84 345 155 Sitka spruce 831 105 1,857 193 1,762 227 1,793 432 1,272 285 3,993 635 

Western hemlock 1,329 118 2,670 248 2,287 248 1,879 261 1,490 247 1,168 193 

Western redcedar 172 36 475 105 461 165 404 113 137 39 1,104 362 White spruce 3 3 — — — — — — — — — — Other softwood — — — — — — — — — — — — 

 Total 3,397 207 6,620 370 5,681 422 4,973 563 3,386 384 6,765 748 

Hardwoods:

Black cottonwood 51 16 125 51 51 24 15 15 9 10 — — 

Paper birch 9 5 12 6 — — — — — — — — Quaking aspen 4 3 — — 3 4 — — — — — — Red alder 5 5 12 11 — — — — — — — — 

 Total 69 17 149 52 55 24 15 15 9 10 — — 

All species 3,466 208 6,769 378 5,736 423 4,988 564 3,395 384 6,765 748 

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.
132

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 45—Estimated net volume of growing-stock and sawtimber on timberland by class of timber, 

owner group, and species group, coastal Alaska, 2004–2008

Volume per acre Total net volume

Species group

All species Softwoods Hardwoods All species

Forest type Mean SE Total SE Total SE Total SE

Units per acre - - - - - - - - - - - - - - - Million units - - - - - - - - - - - - - - -

Growing-stocka (cubic feet)

National forest 5,961 217 22,277 1,173 55 35 22,332 1,174

Other federal 6,006 2,732 328 205 82 73 410 262

State and local 3,407 369 2,868 420 125 56 2,993 424

Private 2,213 278 3,178 459 100 32 3,278 460

 Total 4,699 166 28,652 1,306 362 102 29,014 1,317

Sawtimberb (board feet)c

National forest 32,796 1,395 122,593 6,959 278 190 122,871 6,961

Other federal 30,654 15,792 1,663 1,089 430 398 2,093 1,436

State and local 17,245 2,134 14,492 2,311 658 319 15,150 2,337

Private 11,274 1,659 16,335 2,670 363 125 16,698 2,670

 Total 25,397 1,035 155,082 7,704 1,729 554 156,811 7,767

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error.

a

 Growing-stock trees are live trees of commercial species that meet certain merchantability standards; excludes trees that are entirely cull 

(rough or rotten tree classes).

b

 Sawtimber trees have merchantability limits that differ for softwood and hardwood species as follows: ≥9 inches diameter at breast height 

(d.b.h.) for softwoods and ≥11 inches d.b.h. for hardwoods.

c

 International 1/4-inch rule.
133

Forests of Southeast and South-Central Alaska, 2004–2008

Table 46—Estimated net volume of growing-stocka trees on timberland by species and diameter class, 

coastal Alaska, 2004–2008

Diameter class (inches at breast height)

All classes 5.0–6.9 7.0–8.9 9.0–10.9 11.0–12.9 13.0–14.9

Species Total SE Total SE Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 1,813 218 29 5 56 9 90 17 102 16 158 27

Black spruce 7 4 6 3 1 1 — — — — — —

Lodgepole pine 108 29 4 1 11 3 12 4 17 6 19 8

Mountain hemlock 1,946 243 54 7 98 14 135 20 156 22 178 28

Sitka spruce 10,048 771 140 18 224 25 341 35 445 59 486 58

Western hemlock 12,709 777 332 23 540 37 770 60 813 63 934 71

Western redcedar 1,555 231 18 4 36 7 46 9 69 16 93 17

White spruce 148 30 20 5 34 7 31 8 29 8 12 5

Other softwood 10 6 1 0 1 1 1 1 1 1 2 2

 Total 28,343 1,301 605 35 1,001 54 1,425 84 1,632 98 1,882 113

Hardwoods:

Black cottonwood 354 102 5 2 19 7 24 7 22 7 38 13

Paper birch 166 43 9 3 17 5 39 10 43 12 13 6

Quaking aspen 8 5 2 2 1 1 — — — — 3 2

Red alder 142 35 19 6 30 8 24 8 21 7 14 9

 Total 670 116 36 7 67 11 87 15 85 16 68 17

All species 29,014 1,317 641 36 1,068 56 1,511 86 1,717 101 1,950 114

Diameter class (inches at breast height)

15.0–16.9 17.0–18.9 19.0–20.9 21.0–28.9 29+

Species Total SE Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 161 31 167 31 149 29 474 81 426 99

Black spruce — — — — — — — — — —

Lodgepole pine 17 8 2 3 8 5 16 10 — —

Mountain hemlock 178 34 224 38 127 27 411 70 385 113

Sitka spruce 589 69 599 68 587 81 2,322 239 4,316 521

Western hemlock 829 67 884 83 967 97 3,439 303 3,202 344

Western redcedar 105 24 103 23 80 21 417 74 588 128

White spruce 13 6 9 7 — — — — — —

Other softwood — — 5 4 — — — — — —

 Total 1,892 114 1,993 130 1,918 141 7,078 427 8,917 701

Hardwoods:

Black cottonwood 48 17 35 11 27 12 121 63 15 15

Paper birch 27 14 11 8 7 4 — — — —

Quaking aspen — — 3 3 — — — — — —

Red alder 17 9 — — 5 5 12 11 — —

 Total 92 23 49 14 39 14 133 64 15 15

All species 1,984 117 2,042 130 1,957 142 7,211 438 8,932 703

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.

a

 Growing-stock trees are live trees of commercial species that meet certain merchantability standards; excludes trees that are entirely cull 

(rough or rotten tree classes).
134

Table 47—Estimated net volume of sawtimbera on timberland by species and diameter class, coastal Alaska, 2004–2008

Diameter class (inches at breast height)

All classes 9.0-10.9 11.0-12.9 13.0-14.9 15.0-16.9 17.0-18.9 19.0-20.9 21.0-28.9 

Species Total SE Total SE Total SE Total SE Total SE Total SE Total SE Total SE 

Million board feet (International 1/4-inch rule)

Softwoods:

Alaska yellow-cedar 9,562 1,211 341 64 448 72 760 130 823 161 890 166 824 162 2,796 47

Lodgepole pine 470 134 47 17 85 31 100 41 93 44 13 13 41 28 91 5Mountain hemlock 9,587 1,306 522 78 676 99 849 135 884 170 1,176 206 688 149 2,359 40

Sitka spruce 57,262 4,703 1,411 148 2,109 292 2,472 305 3,120 375 3,314 386 3,361 475 13,910 1,45Western hemlock 68,908 4,555 3,294 266 3,895 312 4,790 374 4,465 370 4,966 473 5,624 571 21,268 1,88

Western redcedar 7,904 1,225 159 33 279 68 403 78 495 117 482 110 412 112 2,202 39

White spruce 424 107 120 31 128 34 57 22 69 35 50 38 — — — — 

Other softwood 44 28 3 4 6 6 7 8 — — 27 24 — — — —  Total 154,160 7,684 5,898 362 7,626 477 9,438 579 9,949 614 10,918 729 10,950 823 42,626 2,62

Hardwoods:

Black cottonwood 1,697 554 — — 111 35 203 69 258 91 193 61 158 68 689 36

Paper birch 558 181 — — 201 56 74 33 162 81 72 53 48 30 — — 

Quaking aspen 33 26 — — — — 15 12 — — 18 18 — — — — Red alder 364 143 — — 92 32 74 48 98 50 — — 26 26 74 7

 Total 2,651 603 — — 404 74 366 91 518 133 283 86 232 79 763 36

All species 156,811 7,767 5,898 362 8,030 488 9,804 587 10,467 627 11,201 732 11,182 828 43,389 2,68

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 board feet.a Sawtimber trees have merchantability limits that differ for softwood and hardwood species as follows: ≥9 inches diameter at breast height (d.b.h.) for softwoods and ≥11 inches d.b.h. for hardwoods.
135

Forests of Southeast and South-Central Alaska, 2004–2008

Table 48—Estimated net volume of growing-stocka on timberland by species and owner group, coastal 

Alaska, 2004–2008

U.S. Forest Service Other federal State and local Private All owners

Owner group Total SE Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 1,752 215 — — 36 34 25 16 1,813 218

Black spruce — — — — 4 3 3 2 7 4

Lodgepole pine 96 29 2 2 5 4 5 4 108 29

Mountain hemlock 1,592 217 — — 221 88 134 68 1,946 243

Sitka spruce 6,496 621 204 155 1,597 292 1,751 327 10,048 771

Western hemlock 10,681 715 102 88 876 207 1,050 264 12,709 777

Western redcedar 1,502 229 — — 54 33 — — 1,555 231

White spruce 29 14 1 1 38 16 79 21 148 30

Other softwood 6 5 — — 4 4 — — 10 6

 Total 22,153 1,168 309 204 2,834 418 3,047 456 28,343 1,301

Hardwoods:

Black cottonwood 55 35 82 73 124 56 94 32 354 102

Paper birch 3 4 19 21 28 12 116 36 166 43

Quaking aspen — — — — 2 2 6 5 8 5

Red alder 120 33 — — 7 5 15 12 142 35

 Total 179 48 101 76 160 60 231 49 670 116

All species 22,332 1,174 410 262 2,993 424 3,278 460 29,014 1,317

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.

a

 Growing-stock trees are live trees of commercial species that meet certain merchantability standards; excludes trees that are entirely cull 

(rough or rotten tree classes).
136

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 49—Estimated net volume of sawtimber treesa on timberland, by species and owner group, coastal 

Alaska, 2004–2008

Owner group

State and local 

U.S. Forest Service Other federal government Private All owners

Species Total SE Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 1,633 205 — — 33 33 20 13 1,687 208

Lodgepole pine 80 25 1 1 3 4 4 3 88 25

Mountain hemlock 1,438 208 — — 179 73 121 62 1,739 228

Sitka spruce 6,271 613 197 152 1,456 277 1,601 314 9,524 753

Western hemlock 9,772 684 71 61 769 190 944 251 11,556 740

Western redcedar 1,421 220 — — 46 32 — — 1,467 222

White spruce 22 11 — — 23 12 48 15 94 22

Other softwood 6 4 — — 3 3 — — 8 5

 Total 20,643 1,124 269 182 2,513 390 2,738 436 26,163 1,250

Hardwoods:

Black cottonwood 51 34 78 72 115 54 62 23 306 98

Paper birch 1 3 13 14 12 8 75 27 101 31

Quaking aspen — — — — 2 2 4 4 5 4

Red alder 49 21 — — 3 2 9 9 60 23

 Total 101 40 91 73 130 57 150 36 472 106

All species 20,744 1,128 359 244 2,643 396 2,888 438 26,635 1,265

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.

a

 Sawtimber trees have merchantability limits that differ for softwood and hardwood species as follows: ≥9 inches diameter at breast 

height (d.b.h.) for softwoods and ≥11 inches d.b.h. for hardwoods.
137

Forests of Southeast and South-Central Alaska, 2004–2008

Table 50—Estimated net volume of sawtimbera on timberland by species and owner group, coastal Alaska, 

2004–2008

Owner group

State and local 

U.S. Forest Service Other federal government Private All owners

Species Total SE Total SE Total SE Total SE Total SE

Million board feet (International 1/4-inch rule)

Softwoods:

Alaska yellow-cedar 9,274 1,194 — — 193 197 95 62 9,562 1,211

Lodgepole pine 429 132 5 5 17 20 19 16 470 134

Mountain hemlock 7,990 1,200 — — 971 404 626 327 9,587 1,306

Sitka spruce 38,606 3,916 1,187 933 8,257 1,609 9,212 1,882 57,262 4,703

Western hemlock 58,292 4,210 387 334 4,514 1,128 5,715 1,571 68,908 4,555

Western redcedar 7,563 1,205 — — 340 247 — — 7,904 1,225

White spruce 104 55 — — 106 60 214 70 424 107

Other softwood 30 24 — — 13 15 — — 44 28

 Total 122,288 6,940 1,579 1,085 14,411 2,303 15,882 2,656 154,160 7,684

Hardwoods:

Black cottonwood 278 190 430 398 649 319 339 124 1,697 554

Paper birch 6 11 83 90 67 52 401 148 558 181

Quaking aspen — — — — 9 10 24 24 33 26

Red alder 298 132 — — 14 11 52 54 364 143

 Total 583 232 513 408 739 334 816 196 2,651 603

All species 122,871 6,961 2,093 1,436 15,150 2,337 16,698 2,670 156,811 7,767

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 board feet.

a

 Sawtimber trees have merchantability limits that differ for softwood and hardwood species as follows: ≥9 inches diameter at breast 

height (d.b.h.) for softwoods and ≥11 inches d.b.h. for hardwoods.
138

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 51—Estimated net volume of growing-stocka on timberland by forest type and stand size class, coastal 

Alaska, 2004–2008

Seedling All stand

and sapling Poletimber Sawtimber Nonstocked size classes

Forest type Total SE Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 2 2 — — 1,564 315 — — 1,565 315

Black spruce 3 3 7 6 — — — — 9 7

Lodgepole pine 13 8 0 0 15 13 0 0 28 15

Mountain hemlock 14 10 13 16 1,628 327 — — 1,656 327

Sitka spruce 62 21 153 54 6,626 833 5 3 6,845 833

Western hemlock 82 25 72 39 15,763 1,087 2 2 15,919 1,087

Western redcedar 1 1 — — 1,967 353 — — 1,969 353

White spruce 32 14 22 12 25 18 — — 79 26

 Total 209 38 267 69 27,587 1,303 8 4 28,070 1,300

Hardwoods:

Aspen 3 2 — — — — — — 3 2

Cottonwood 3 2 71 38 563 258 1 1 638 260

Paper birch 21 9 33 17 215 82 — — 269 84

 Total 27 9 137 45 778 270 1 1 943 272

Nonstocked — — — — — — 0 0 0 0

All species 235 39 404 82 28,365 1,322 9 4 29,014 1,317

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.

a

 Growing-stock trees are live trees of commercial species that meet certain merchantability standards; excludes trees that are entirely cull 

(rough or rotten tree classes).
139

Forests of Southeast and South-Central Alaska, 2004–2008

Table 52—Estimated net volume of sawtimbera on timberland by forest type and stand 

size class, coastal Alaska, 2004–2008

Seedling All stand

and sapling Poletimber Sawtimber size classes

Forest type Total SE Total SE Total SE Total SE

Million board feet (International 1/4-inch rule)

Softwoods:

Alaska yellow-cedar 3 3 — — 7,658 1,627 7,661 1,627

Black spruce — — 9 8 — — 9 8

Lodgepole pine 37 31 1 2 76 68 115 75

Mountain hemlock 69 49 23 27 8,272 1,749 8,364 1,750

Sitka spruce 185 75 313 126 37,272 4,985 37,770 4,983

Western hemlock 222 115 138 77 88,576 6,367 88,936 6,366

Western redcedar — — — — 9,506 1,809 9,506 1,809

White spruce 81 40 40 25 88 64 208 79

 Total 598 153 524 154 151,448 7,669 152,569 7,660

Hardwoods:

Aspen 10 11 — — — — 10 11

Cottonwood 7 7 124 70 3,007 1,496 3,138 1,497

Paper birch 61 34 48 29 943 419 1,052 419

 Total 78 36 214 81 3,950 1,552 4,242 1,552

All species 675 157 738 173 155,398 7,779 156,811 7,767

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 

board feet.

a

 Sawtimber trees have merchantability limits that differ for softwood and hardwood species as follows: ≥9 inches diameter at 

breast height (d.b.h.) for softwoods and ≥11 inches d.b.h. for hardwoods.
140

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 53—Estimated net volume of growing-stocka on timberland by forest type and owner group, coastal 

Alaska, 2004–2008

U.S. Forest Service Other federal State and local Private All owners

Forest type Total SE Total SE Total SE Total SE Total SE

Million cubic feet

Softwoods:

Alaska yellow-cedar 1,472 308 — — 56 59 37 28 1,565 315

Black spruce — — — — 7 6 2 3 9 7

Lodgepole pine 22 14 — — 6 7 — — 28 15

Mountain hemlock 1,300 300 — — 243 110 112 71 1,656 327

Sitka spruce 3,811 680 5 6 1,241 290 1,788 387 6,845 833

Western hemlock 13,636 1,023 156 138 1,120 277 1,007 268 15,919 1,087

Western redcedar 1,905 348 — — 63 66 1 1 1,969 353

White spruce 13 11 0 0 19 11 47 21 79 26

 Total 22,159 1,174 161 138 2,757 419 2,993 459 28,070 1,300

Hardwoods:

Aspen — — — — — — 3 2 3 2

Cottonwood 140 110 229 222 135 65 134 48 638 260

Paper birch 0 1 20 22 102 67 147 46 269 84

 Total 173 112 249 223 236 93 285 65 943 272

Nonstocked — — — — — — 0 0 0 0

All species 22,332 1,174 410 262 2,993 424 3,278 460 29,014 1,317

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 cubic feet.

a

 Growing-stock trees are live trees of commercial species that meet certain merchantability standards; excludes trees that are entirely cull 

(rough or rotten tree classes).
141

Forests of Southeast and South-Central Alaska, 2004–2008

Table 54—Estimated net volume of sawtimbera on timberland by forest type and owner group, coastal Alaska, 

2004–2008

U.S. Forest Service Other federal State and local Private All owners

Forest type Total SE Total SE Total SE Total SE Total SE

Million board feet (International 1/4-inch rule)

Softwoods:

Alaska yellow-cedar 7,216 1,591 — — 291 323 154 116 7,661 1,627

Black spruce — — — — 9 8 — — 9 8

Lodgepole pine 91 69 — — 24 28 — — 115 75

Mountain hemlock 6,749 1,643 — — 1,107 513 508 335 8,364 1,750

Sitka spruce 21,899 4,154 19 21 6,308 1,575 9,545 2,269 37,770 4,983

Western hemlock 76,978 6,031 669 594 5,869 1,519 5,419 1,559 88,936 6,366

Western redcedar 9,095 1,764 — — 411 431 — — 9,506 1,809

White spruce 26 23 — — 46 33 136 68 208 79

 Total 122,054 6,965 688 594 14,065 2,298 15,762 2,668 152,569 7,660

Hardwoods:

Aspen — — — — — — 10 11 10 11

Cottonwood 775 642 1,321 1,305 608 324 433 162 3,138 1,497

Paper birch — — 83 90 477 372 492 177 1,052 419

 Total 817 643 1,405 1,308 1,085 493 935 233 4,242 1,552

All species 122,871 6,961 2,093 1,436 15,150 2,337 16,698 2,670 156,811 7,767

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 500,000 board feet.

a Sawtimber trees have merchantability limits that differ for softwood and hardwood species as follows: ≥9 inches diameter at breast 

height (d.b.h.) for softwoods and ≥11 inches d.b.h. for hardwoods.
142

Table 55—Estimated average biomass, volume, and density of snags on forest land, by forest type and diameter class, coa

Biomass Volume 

Diameter class (d.b.h. inches) Diameter class (d.b.h. inches) 

5 to 19 20 to 39 ≥40 Total 5 to 19 20 to 39 ≥40 Total 5 to 1

Forest type Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE Mean 

- - - - - - - - - - Bone-dry tons per acre - - - - - - - - - - - - - - - - - - - - - - - - - Cubic feet per acre - - - - - - - - - - - - - - - - - - - - 

Softwoods:

Alaska yellow-cedar 12.3 1.2 8.4 3.3 0.3 0.3 21.1 3.5 372.6 34.4 156.0 47.5 5.1 6.9 533.8 59.2 58.3 Black spruce 1.6 0.7 — — — — 1.6 0.7 51.5 23.5 — — — — 51.5 23.5 14.8 Lodgepole pine 4.6 0.8 0.2 0.2 — — 4.7 0.9 152.8 29.0 1.6 1.8 — — 154.5 29.1 36.2 Mountain hemlock 3.9 0.5 5.1 1.0 0.5 0.4 9.6 1.2 95.2 13.9 75.9 19.0 8.9 8.1 180.0 27.1 16.5 

Sitka spruce 8.4 1.5 9.0 1.9 2.6 1.4 20.0 3.4 212.2 44.2 105.3 31.6 5.6 4.7 323.0 71.0 30.6 

Western hemlock 10.8 0.8 19.6 1.8 7.8 2.0 38.2 2.7 229.8 20.3 189.2 21.5 52.6 23.6 471.6 41.3 32.1 Western redcedar 16.5 1.3 11.2 2.2 4.7 3.7 32.5 4.2 522.5 46.6 207.8 46.5 19.7 19.6 750.1 77.2 77.2 

White spruce 10.8 1.7 0.8 0.5 — — 11.7 1.9 373.7 63.3 36.8 20.7 — — 410.5 72.8 56.8 

 Total 9.0 0.4 10.3 0.8 3.1 0.7 22.5 1.2 238.4 12.1 129.5 12.0 20.2 7.3 388.1 20.3 36.4 

Hardwoods:

Aspen 7.7 2.0 — — — — 7.7 2.0 297.7 80.4 — — — — 297.7 80.4 49.9 

Cottonwood 2.8 1.0 0.6 0.3 — — 3.3 1.3 99.3 39.4 19.4 11.9 — — 118.7 50.4 12.9 

Paper birch 8.0 1.3 0.4 0.3 — — 8.4 1.4 251.3 49.0 11.9 11.4 — — 263.1 49.9 36.4 

 Total 5.9 0.8 0.4 0.2 — — 6.3 0.9 193.7 31.0 13.2 7.1 — — 206.9 33.8 28.3 

Nonstocked 11.6 3.7 6.5 3.0 — — 18.1 3.1 358.5 129.7 86.6 44.5 — — 445.1 129.7 63.1 

All species 8.8 0.4 9.5 0.8 2.8 0.6 21.1 1.1 235.2 11.3 119.1 10.9 18.4 6.6 372.7 18.7 35.9 

Note: Means are calculated using a ratio of means formula across plots within forest type groups; data subject to sampling error; SE = standard error; — = less than 0.05 bor 0.05 snags per acre was estimated; includes snags ≥5 inches in diameter at breast height (d.b.h.).
143

Table 56—Estimated biomass and carbon mass of snags on forest land, by forest type and owner, coastal Alaska, 2004–20

U.S. Forest Service Other federal State and local government Private A

Biomass Carbon Biomass Carbon Biomass Carbon Biomass Carbon Bioma

Forest type Total SE Total SE Total SE Total SE Total SE Total SE Total SE Total SE Total 

Million (bone-dry) tons

Softwoods:

Alaska yellow-cedar 48.3 10.1 24.1 5.0 — — — — 0.8 0.4 0.4 0.2 0.5 0.3 0.3 0.1 49.6 1

Black spruce — — — — 0.7 0.3 0.4 0.2 — — — — 0.1 0.0 — — 0.8 

Lodgepole pine 1.8 0.5 0.9 0.2 — — — — — — — — 0.2 0.2 0.1 0.1 2.1 

Mountain hemlock 26.7 4.2 13.3 2.1 0.6 0.3 0.3 0.2 1.9 0.8 0.9 0.4 1.0 0.5 0.5 0.2 30.2 Sitka spruce 27.1 7.9 13.6 4.0 0.9 1.0 0.5 0.5 5.6 1.5 2.8 0.7 5.4 1.4 2.7 0.7 39.1 Western hemlock 140.4 13.8 70.2 6.9 0.4 0.3 0.2 0.2 4.3 1.6 2.2 0.8 6.5 2.0 3.3 1.0 151.6 1

Western redcedar 32.6 6.3 16.3 3.1 — — — — 0.7 0.6 0.4 0.3 0.4 0.2 0.2 0.1 33.8 

White spruce 0.1 0.1 — — 2.4 0.6 1.2 0.3 1.4 0.7 0.7 0.4 1.4 0.7 0.7 0.3 5.2 

 Total 277.0 15.7 138.5 7.8 5.0 1.3 2.5 0.6 14.7 2.4 7.4 1.2 15.6 2.4 7.8 1.2 312.3 1

Hardwoods:

Aspen — — — — 1.2 0.4 0.6 0.2 — — — — — — — — 1.3 

Cottonwood 0.2 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.9 0.6 0.4 0.3 0.4 0.4 0.2 0.2 1.8 

Paper birch — — — — 1.8 0.5 0.9 0.3 1.6 0.7 0.8 0.4 1.7 0.6 0.9 0.3 5.1 

 Total 0.5 0.2 0.2 0.1 3.2 0.7 1.6 0.3 2.5 0.9 1.2 0.5 2.1 0.7 1.1 0.3 8.3 

Nonstocked — — — — — — — — 1.2 0.6 0.6 0.3 0.4 0.3 0.2 0.2 1.6 

All species 277.5 15.6 138.7 7.8 8.2 1.3 4.1 0.7 18.4 2.4 9.2 1.2 18.1 2.5 9.1 1.2 322.3 1

Note: Totals may be off because of rounding; data subject to sampling error; — = less than 50,000 (bone-dry) tons; includes snags ≥5 inches in diameter at breast height.
144

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 57—Lichen species found across all coastal Alaska plots, 2004–2008 

Species Common name Frequency Functional groupa

Alectoria imshaugii Spiny witch’s hair 0.76

Alectoria nigricans Grey witch’s hair 1.52 F

Alectoria sarmentosa Common witch’s hair 68.94 F

Anaptychia setifera Hanging fringe lichen 0.76

Bryocaulon pseudosatoanum Foxhair lichen 6.06

Bryoria bicolor Electric horsehair lichen 18.18 F

Bryoria capillaris Grey horsehair lichen 7.58 F

Bryoria cervinula Spiny horsehair 6.06 F

Bryoria fuscescens Pale-footed horsehair 15.15 F

Bryoria glabra Shiny horsehair 29.55 F

Bryoria pseudofuscescens Mountain horsehair 6.06 F

Bryoria tenuis Pied horsehair 18.94 F

Bryoria trichodes Elegant horsehair 54.55 F

Bunodophoron melanocarpum Northern fancoral 2.27

Cavernularia hultenii Powdered saguaro 39.39

Cavernularia lophyrea Eyed saguaro 9.85

Cetraria chlorophylla Shadow ruffle 33.33

Cetraria orbata Variable ruffle 1.52

Cetraria pinastri Powdered sunshine lichen 3.79

Cetraria sepincola Eyed ruffle 1.52

Cetraria subalpina Iceland moss 3.03

Cetrelia cetrarioides Speckled rag 2.27

Cladonia albonigra Sordid pixie cup 11.36

Cladonia amaurocraea Quill clad 0.76

Cladonia bellidiflora British soldiers 42.42

Cladonia carneola Crowned pixie cup 18.18

Cladonia cenotea Miner’s funnel 6.06

Cladonia chlorophaea Mealy pixie cup 14.39

Cladonia coniocraea Lesser powderhorn 15.91

Cladonia cornuta Common bighorn 41.67

Cladonia crispata Lesser organpipe 2.27

Cladonia deformis Lesser sulfur cup 5.3

Cladonia digitata Fingered pixie cup 0.76

Cladonia ecmocyna Greater frost soldiers 2.27

Cladonia fimbriata Powdered trumpet 13.64

Cladonia furcata Many-forked clad 1.52

Cladonia gracilis Black-footed soldiers 5.3

Cladonia gracilis ssp. turbina Bronzed pixie cup 0.76

Cladonia macilenta Lipstick powderhorn 1.52

Cladonia metacorallifera Dragon pixie cup 2.27

Cladonia multiformis Slotted clad 1.52

Cladonia ochrochlora Greater powderhorn 38.64

Cladonia pleurota Mind-altering pixie cup 9.85

Cladonia portentosa Reindeer lichen 1.52

Cladonia pyxidata Pebbled pixie cup 5.3

Cladonia rangiferina Reindeer lichen 1.52

Cladonia scabriuscula Many-winged clad 1.52

Cladonia squamosa Dragon funnel 54.55

Cladonia squamosa var. subsquamo Dragon funnel 9.85
145

Forests of Southeast and South-Central Alaska, 2004–2008

Species Common name Frequency Functional groupa

Cladonia stygia Black-footed reindeer lichen 0.76

Cladonia sulphurina Greater sulfur cup 6.82

Cladonia transcendens Magic pebblehorn 24.24

Cladonia umbricola Shaded cladonia 15.91

Cladonia verruculosa Greater pebblehorn 5.3

Collema furfuraceum Blister tarpaper lichen 2.27 C

Erioderma sorediatum Treepelt 0.76 C

Fuscopannaria sp. Mouse lichen 1.52 C

Fuscopannaria laceratula Cushion mouse 3.03 C

Fuscopannaria leucostictoides Petalled mouse 1.52 C

Fuscopannaria pacifica 1.52 C

Hypogymnia apinnata Beaded bone 45.45

Hypogymnia austerodes Powdered bone 3.79

Hypogymnia bitteri Powdered bone 6.06

Hypogymnia duplicata Tickertape bone 50

Hypogymnia enteromorpha Beaded bone 44.7

Hypogymnia inactiva Forking bone 0.76

Hypogymnia metaphysodes Deflated bone 3.03

Hypogymnia occidentalis Lattice bone 15.91

Hypogymnia oceanica Lattice bone 7.58

Hypogymnia physodes Monk’s hood 21.97

Hypogymnia pulverata Tube lichen 0.76

Hypogymnia rugosa Puckered bone 0.76

Hypogymnia tubulosa Dog bone 1.52

Hypogymnia vittata Monk’s hood 11.36

Hypotrachyna sinuosa Green loop 1.52

Imshaugia aleurites Salted starburst 1.52

Leptogium brebissonii Jellied vinyl 0.76 C

Leptogium burnetiae Peppered vinyl 2.27 C

Leptogium saturninum Peppered vinyl 6.06 C

Lobaria hallii Iron lung 2.27 C

Lobaria linita Cabbage lung 50 C

Lobaria oregana Lettuce lung 41.67 C

Lobaria pulmonaria Lungwort 10.61 C

Lobaria scrobiculata Textured lung 15.15 C

Melanohalea exasperatula Lustrous brown lichen 2.27

Melanohalea multispora Eyed brown lichen 6.06

Melanohalea septentrionalis Northern brown lichen 0.76

Melanohalea subolivacea Eyed brown lichen 1.52

Nephroma arcticum Green paw lichen 5.3 C

Nephroma bellum Cat paw lichen 30.3 C

Nephroma helveticum Dog paw lichen 18.18 C

Nephroma isidiosum Pepper paw lichen 3.79 C

Nephroma laevigatum Seaside paw lichen 1.52 C

Nephroma parile Powder paw lichen 10.61 C

Nephroma resupinatum Blister paw lichen 6.06 C

Nodobryoria oregana Pendent foxtail 0.76 F

Parmelia hygrophila Salted shield 29.55

Parmelia pseudosulcata Salted shield 25

Parmelia saxatilis Salted shield 3.03
146

GENERAL TECHNICAL REPORT PNW-GTR-835

Species Common name Frequency Functional groupa

Parmelia squarrosa Salted shield 0.76

Parmelia sulcata Powdered shield 34.09

Parmeliopsis ambigua Green starburst lichen 5.3

Parmeliopsis hyperopta Grey starburst lichen 48.48

Peltigera aphthosa Freckle pelt 1.52 C

Peltigera britannica Freckle pelt 34.85 C

Peltigera canina Dog pelt 0.76 C

Peltigera collina Tree pelt 15.15 C

Peltigera elisabethae Concentric pelt 0.76 C

Peltigera leucophlebia Freckle pelt 0.76 C

Peltigera membranacea Dog pelt 12.12 C

Peltigera neopolydactyla Frog pelt 40.15 C

Peltigera pacifica Frog pelt 0.76 C

Peltigera scabrosa Toad pelt 7.58 C

Physcia aipolia Grey-eyed rosette 3.03 E

Physcia caesia Powdered rosette 1.52 E

Physcia tenella Fringed rosette 0.76 E

Physconia americana Fancy frost lichen 0.76 E

Physconia perisidiosa Bordered frost lichen 0.76 E

Platismatia glauca Ragbag lichen 81.06

Platismatia herrei Tattered rag lichen 37.12

Platismatia lacunosa Crinkled rag lichen 34.09

Platismatia norvegica Laundered rag lichen 58.33

Polychidium contortum Maritime woollybear 10.61 C

Pseudocyphellaria anomala Netted specklebelly 5.3 C

Pseudocyphellaria crocata Yellow specklebelly 3.79 C

Pseudocyphellaria perpetua Yellow specklebelly 3.79 C

Ramalina dilacerata Punctured bush lichen 7.58

Ramalina farinacea Powdery ramalina 11.36

Ramalina obtusata Hooded bush lichen 0.76

Ramalina roesleri Frayed bush lichen 12.12

Ramalina thrausta Angelhair 3.03 F

Sphaerophorus globosus Clustered coral lichen 73.48

Stereocaulon sp. Foam lichen 1.52 C

Sticta fuliginosa Peppered moon lichen 7.58 C

Usnea chaetophora Articulated beard lichen 0.76 F

Usnea cornuta Crab’s beard 3.03 F

Usnea filipendula Fishbone beard 7.58 F

Usnea lapponica Powdered beard 6.82 F

Usnea longissima Methuselah’s beard 3.03 F

Usnea scabrata Straw beard 0.76 F

Xanthoria candelaria Shrubby orange lichen 3.03 E

a

 Functional groups are F = forage lichen, C = cyanolichen, and E = eutroph. 
147

Forests of Southeast and South-Central Alaska, 2004–2008

Table 58—Most common species by forest type, coastal Alaska, 2004–2008

Average

Scientific name Common name Indicator codes Constancy cover SE

- - - - - - - Percent - - - - - - -

Western hemlock forest type (n = 28):

Tsuga heterophylla (Raf.) Sarg. Western hemlock wds, wrm 100 51.29 4.05

Picea sitchensis (Bong.) Carr. Sitka spruce sspr, fld, sd. hlght 100 7.83 1.20

Menziesia ferruginea Sm. Rusty menziesia hlght, rdr 100 7.52 1.34

Vaccinium ovalifolium Sm. Early blueberry dwe 96.4 25.30 3.15

Cornus canadensis L. Bunchberry df 89.3 6.52 1.49

Blechnum spicant (L.) Sm. Deer fern 89.3 1.94 0.35

Gymnocarpium dryopteris (L.) Newman Western oakfern 85.7 4.56 .89

Athyrium filix-femina (L.) Roth Common ladyfern wms, nr 82.1 4.64 1.59

Rubus pedatus Sm. Five-leaf bramble df 82.1 4.54 1.01

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 82.1 0.75 .15

Listera cordata (L.) R. Br. ex Ait. f. Heartleaf twayblade 82.1 .53 .13

Dryopteris expansa (C. Presl) Spreading woodfern wms, nr 78.6 2.64 1.07

Fraser-Jenkins & Jermy

Lysichiton americanus Hultén & American skunk cabbage pdr, np, bf, df 75 5.55 1.22

H. St. John

Coptis aspleniifolia Salisb. Fernleaf goldthread df 71.4 5.73 1.34

Tiarella trifoliata L. Threeleaf foamflower df 67.9 1.93 .41

Vaccinium parvifolium Sm. Red huckleberry 64.3 3.56 .78

Oplopanax horridus Miq. Devil’s club wms, nr 60.7 4.87 1.43

Moneses uniflora (L.) Gray Single delight 60.7 .39 .11

Rubus spectabilis Pursh Salmonberry sd, fld 57.1 5.61 1.74

Mountain hemlock forest type (n = 19):

Tsuga mertensiana (Bong.) Carr. Mountain hemlock cld, pdr 100 28.22 4.02

Rubus pedatus Sm. Five-leaf bramble df 100 5.50 1.27

Vaccinium ovalifolium Sm. Early blueberry dwe 94.7 26.12 4.67

Menziesia ferruginea Sm. Rusty menziesia hlght, rdr 94.7 7.4 1.33

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 84.2 1.23 0.3

Nephrophyllidium crista-galli

(Menzies ex Hook.) Gilg Deercabbage very pdr, np, cld, df 78.9 9.12 1.53

Cornus canadensis L. Bunchberry df 78.9 5.04 1.01

Veratrum viride Aiton Green false hellebore 78.9 2.26 0.43

Listera cordata (L.) R. Br. ex Ait. f. Heartleaf twayblade 78.9 1.29 0.24

Athyrium filix-femina (L.) Roth Common ladyfern nr, wms 73.7 6.43 2.06

Picea sitchensis (Bong.) Carr. Sitka spruce sspr, fld, sd, hlght 73.7 5.73 1.11

Gymnocarpium dryopteris (L.) Western oakfern 73.7 4.57 1.22

Newman

Elliottia pyroliflorus (Bong.) S.W. Copperbush cld, pdr, snpk 68.4 10 3.82

Brim & P.F. Stevens

Oplopanax horridus (Sm.) Miq. Devil’s club wms, nr 68.4 3.88 2.04

Blechnum spicant (L.) Sm. Deer fern 68.4 2.74 0.65

Tiarella trifoliata L. Threeleaf foamflower df 68.4 2.26 0.74

Rubus spectabilis Pursh Salmonberry sd, fld 63.2 6.26 2.5
148

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 58—Most common species by forest type, coastal Alaska, 2004–2008 (continued)

Average

Scientific name Common name Indicator codes Constancy cover SE

- - - - - - - Percent - - - - - - -

Dryopteris expansa (K. Presl) Spreading woodfern wms, nr 63.2 1.66 0.27

Fraser-Jenkins & Jermy

Sorbus sitchensis M. Roemer Western mountain ash 63.2 1.5 0.41

Harrimanella stelleriana (Pall.) Coville Alaska bellheather cld, helev 57.9 7.65 2.32

Coptis aspleniifolia Salisb. Fernleaf goldthread df 57.9 6.03 1.21

Streptopus lanceolatus (Aiton) Reveal Twistedstalk 57.9 2.92 0.69

 var. roseus (Michx.) Reveal

Lysichiton americanus Hultén & American skunk cabbage pdr, np, bf, df 52.6 2.85 0.65

H. St. John

Yellow-cedar forest type (n = 15):

Chamaecyparis nootkatensis (D. Don) Yellow-cedar pdr, helev 100 27.00 3.90

Tsuga heterophylla (Raf.) Sarg. Western hemlock wds, wrm 100 21.23 3.34

Tsuga mertensiana (Bong.) Carr. Mountain hemlock cld, pdr 100 12.41 2.14

Cornus canadensis L. Bunchberry dogwood df 100 5.22 1.06

Rubus pedatus Sm. Five-leaf bramble df 100 2.47 0.46

Blechnum spicant (L.) Sm. Deer fern 100 1.87 0.21

Listera cordata (L.) R. Br. ex Ait. f. Heartleaf twayblade 100 0.96 0.22

Vaccinium ovalifolium Sm. Early blueberry dwe 93.3 20.57 4.06

Menziesia ferruginea Sm. Rusty menziesia hlght, rdr 93.3 8.80 1.22

Picea sitchensis (Bong.) Carr. Sitka spruce sspr, fld, sd, hlght 93.3 4.82 0.91

Coptis aspleniifolia Salisb. Fernleaf goldthread df 93.3 3.69 0.75

Nephrophyllidium crista-galli Deercabbage very pdr, np, cld df 86.7 11.48 2.30

(Menzies ex Hook.) Gilg

Lysichiton americanus Hultén & American skunk cabbage pdr, np bf, df 86.7 7.85 1.58

H. St. John

Lycopodium annotinum L. Stiff clubmoss 86.7 1.40 0.43

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 86.7 0.94 0.14

Tiarella trifoliata L. Threeleaf foamflower df 80 1.68 0.43

Platanthera stricta Lindl. Slender bog orchid 80 0.58 0.21

Coptis trifolia (L.) Salisb. Threeleaf goldthread muskeg, very pdr 73.3 1.84 0.72

Veratrum viride Aiton Green false hellebore 73.3 1.76 0.31

Trichophorum caespitosum (L.) Hartm. Tufted bulrush 60 7.94 1.72

Pinus contorta Dougl. ex Loud. Lodgepole pine pdr, ptl 60 7.52 1.16

Gymnocarpium dryopteris (L.) Newman Western oakfern 60 5.35 0.94

Vaccinium parvifolium Sm. Red huckleberry 60 2.54 0.80

Streptopus lanceolatus (Aiton) Reveal Twistedstalk 60 2.04 0.38

 var. roseus (Michx.) Reveal

Drosera rotundifolia L. Roundleaf sundew 60 0.88 0.41

Trientalis europaea L. Arctic starflower 53.3 1.73 0.61

Vaccinium vitis-idaea L. Lingonberry 53.3 1.68 0.36

Athyrium filix-femina (L.) Roth Common ladyfern nr, wms 53.3 1.57 0.35

Maianthemum dilatatum (Alph.Wood) False lily of the valley df 53.3 1.43 0.20

A. Nels. & J.F. Macbr.
149

Forests of Southeast and South-Central Alaska, 2004–2008

Table 58—Most common species by forest type, coastal Alaska, 2004–2008 (continued)

Average

Scientific name Common name Indicator codes Constancy cover SE

- - - - - - - Percent - - - - - - -

Erigeron peregrinus (Banks ex Pursh) Subalpine fleabane 53.3 1.24 0.34

Greene

Oplopanax horridus (Sm.) Miq. Devil’s club wms, nr 53.3 1.00 0.16

Western redcedar forest type (n = 6):

Tsuga heterophylla (Raf.) Sarg. Western hemlock wdr, wrm 100 24.27 9.21

Thuja plicata Donn ex D. Don Western redcedar 100 21.25 5.03

Vaccinium ovalifolium Sm. Early blueberry dwe 100 15.19 8.91

Menziesia ferruginea Sm. Rusty menziesia hlght, rdr 100 6.75 2.77

Cornus canadensis L. Bunchberry df 100 6.13 2.10

Lysichiton americanus Hultén & American skunk cabbage pdr, np, bf, df 100 3.89 0.67

H. St. John

Blechnum spicant (L.) Sm. Deer fern 100 2.32 0.83

Vaccinium parvifolium Sm. Red huckleberry 100 1.81 0.25

Gaultheria shallon Pursh Salal pdr, np, hlght 83 17.06 4.83

Coptis aspleniifolia Salisb. Fernleaf goldthread df 83 4.05 1.55

Picea sitchensis (Bong.) Carr. Sitka spruce sspr, fld, sd, hlght 83 3.89 1.37

Rubus pedatus Sm. Five-leaf bramble df 83 3.69 1.81

Maianthemum dilatatum (Alph.Wood) False lily of the valley df 83 2.35 0.49

A. Nels. & J.F. Macbr.

Nephrophyllidium crista-galli Deer cabbage very pdr, np, cld df 83 2.07 0.56

(Menzies ex Hook.) Gilg

Linnaea borealis L. Twinflower 83 1.37 0.40

Veratrum viride Aiton Green false hellebore 83 1.09 0.14

Chamaecyparis nootkatensis Yellow-cedar pdr, helev 67 10.00 3.35

(D. Don) Spach

Athyrium filix-femina (L.) Roth Common ladyfern nr, wms 67 5.00 2.36

Tsuga mertensiana (Bong.) Carr. Mountain hemlock cld, pdr 67 3.50 0.99

Vaccinium vitis-idaea L. Lingonberry 67 2.85 1.24

Listera cordata (L.) R. Br. ex Ait. f. Heartleaf twayblade 67 1.33 0.26

Listera caurina Piper Northwestern twayblade 67 0.78 0.18

Platanthera stricta Lindl. Slender bog orchid 67 0.78 0.20

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 67 0.75 0.19

Polypodium glycyrrhiza D.C. Eaton Licorice fern 67 0.50 0.25

Trichophorum caespitosum (L.) Hart. Tufted bulrush 50 20.25 5.92

Carex anthoxanthea J.& C. Presl Grassyslope arctic sedge 50 11.38 3.06

Carex pluriflora Hultén Manyflower sedge 50 9.00 4.48

Pinus contorta Dougl. ex Loud. Lodgepole pine pdr, ptl 50 6.64 1.32

Ledum groenlandicum Oeder Bog Labrador tea muskeg, very pdr 50 6.10 1.06

Phegopteris connectilis (Michx.) Watt Long beechfern 50 4.00 1.48

Streptopus lanceolatus (Aiton) Reveal Twistedstalk 50 2.57 0.84

 var. roseus (Michx.) Reveal

Rubus spectabilis Pursh Salmonberry sd, fld 50 2.40 0.51

Gentiana douglasiana Bong. Swamp gentian 50 1.63 0.40

Coptis trifolia (L.) Salisb. Threeleaf goldthread muskeg, very pdr 50 1.14 0.26

Tiarella trifoliata L. Threeleaf foamflower df 50 0.86 0.10
150

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 58—Most common species by forest type, coastal Alaska, 2004–2008 (continued)

Indicator Average

Scientific name Common name codes Constancy cover SE

- - - - - - - Percent - - - - - - -

Trientalis europaea L. Arctic starflower 50 0.70 0.34

Streptopus streptopoides (Ledeb.) Small twistedstalk 50 0.67 0.28

Frye & Rigg

Tofieldia glutinosa (Michx.) Pers. Sticky tofieldia 50 0.50 0.30

Sitka spruce forest type (n = 8):

Picea sitchensis (Bong.) Carr. Sitka spruce sspr, fld, sd, hlght 100 34.92 6.89

Vaccinium ovalifolium Sm. Early blueberry dwe 80 18.44 4.69

Oplopanax horridus Miq. Devil’s club wms, nr 80 15.00 7.38

Rubus spectabilis Pursh Salmonberry sd, fld 80 10.21 2.98

Gymnocarpium dryopteris (L.) Newman Western oakfern 80 10.00 1.85

Rubus pedatus Sm. Five-leaf bramble df 80 8.45 2.42

Dryopteris expansa (C. Presl) Fraser- Spreading woodfern wms, nr 80 6.23 1.52

Jenkins & Jermy

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 80 1.48 0.34

Cornus canadensis L. Bunchberry df 60 6.48 1.49

Menziesia ferruginea Sm. Rusty menziesia hlght, rdr 60 6.24 2.16

Athyrium filix-femina (L.) Roth Common ladyfern nr, wms 60 5.43 1.35

Phegopteris connectilis (Michx.) Watt Long beechfern 60 3.77 1.76

Tiarella trifoliata L. Threeleaf foamflower df 60 3.42 0.43

Listera cordata (L.) R. Br. ex Ait. f. Heartleaf twayblade 60 1.30 0.71

Calamagrostis canadensis (Michx.) Bluejoint 60 0.75 0.27

 Beauv.

Equisetum arvense L. Field horsetail 50 23.09 16.36

Alnus viridis (Chaix) DC. ssp. sinuata Sitka alder sd, fld, all elevations 50 15.10 2.23

(Regel) A. Löve & D. Löve

Lycopodium annotinum L. Stiff clubmoss 50 2.18 1.09

Moneses uniflora (L.) Gray Single delight 50 1.07 0.63

Cottonwood forest type (n = 7):

Populus balsamifera ssp. trichocarpa L. Black cottonwood 100.0 24.04 5.79

Alnus viridis (Chaix) DC. ssp. sinuata Sitka alder sd, fld 85.7 50.21 5.73

(Regel) A. Löve & D. Löve

Athyrium filix-femina (L.) Roth Common ladyfern nr, wms 85.7 3.39 1.20

Pyrola asarifolia Michx. Liverleaf wintergreen 85.7 1.38 0.43

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 85.7 0.60 0.09

Dryopteris expansa (C. Presl) Fraser- Spreading woodfern wms, nr 71.4 1.60 0.52

Jenkins & Jermy

Orthilia secunda (L.) House Sidebells wintergreen 71.4 1.34 0.52

Picea sitchensis (Bong.) Carr. Sitka spruce sspr, fld, sd, hlght 57.1 19.00 7.90

Salix barclayi Andersson Barclay's willow 57.1 14.22 2.84

Rubus spectabilis Pursh Salmonberry sd, fld 57.1 9.25 6.74

Equisetum arvense L. Field horsetail 57.1 8.43 5.60

Oplopanax horridus (Sm.) Miq. Devil’s club wms, nr 57.1 2.72 0.95

Heracleum maximum Bartram Common cowparsnip 57.1 2.34 1.43

Gymnocarpium dryopteris (L.) Newman Western oakfern 57.1 1.70 0.99
151

Forests of Southeast and South-Central Alaska, 2004–2008

Table 58—Most common species by forest type, coastal Alaska, 2004–2008 (continued)

Indicator Average

Scientific name Common name codes Constancy cover SE

- - - - - - - Percent - - - - - - -

Carex L. Sedge 57.1 0.34 0.33

Geum macrophyllum Willd. Largeleaf avens 57.1 0.29 0.17

Boschniakia rossica (Cham. & Schlecht.) Northern groundcone 57.1 0.17 0.16

 Fedtsch.

Birch forest type (n = 5):

Betula neoalaskana Sarg. Alaska paper birch 100 30.25 13.12

Chamerion angustifolium (L.) Holub Fireweed 100 20.2 7.37

 ssp. angustifolium

Gymnocarpium dryopteris (L.) Newman Western oakfern 100 14.63 1.64

Cornus canadensis L. Bunchberry df 100 7.7 2.36

Trientalis europaea L. Arctic starflower 100 2.26 0.55

Linnaea borealis L. Twinflower 100 1.67 0.73

Calamagrostis canadensis (Michx.) Bluejoint 80 40.38 15.15

 Beauv.

Equisetum sylvaticum L. Woodland horsetail 80 14.0 6.86

Equisetum arvense L. Field horsetail 80 8.36 6.08

Dryopteris expansa (C. Presl) Fraser- Spreading woodfern wms, nr 80 7.54 2.46

Jenkins & Jermy

Rubus pedatus Sm. Five-leaf bramble df 80 5.38 2.58

Spiraea stevenii (C.K. Schneid.) Rydb. Beauverd spirea 80 2.83 0.98

Orthilia secunda (L.) House Sidebells wintergreen 80 1.01 0.43

Picea glauca (Moench) Voss White spruce 60 11.3 3.30

Rubus idaeus L. American red raspberry 60 8.27 1.19

Viburnum edule (Michx.) Raf. Highbush cranberry 60 3.2 0.83

Sambucus racemosa L. Red elderberry 60 2.88 0.48

Vaccinium vitis-idaea L. Lingonberry 60 2.78 1.22

Streptopus amplexifolius (L.) DC. Claspleaf twistedstalk 60 2.4 0.94

Ribes triste Pall. Red currant 60 2.37 0.79

Lycopodium annotinum L. Stiff clubmoss 60 2.25 1.3

Athyrium filix-femina (L.) Roth Common ladyfern nr, wms 60 2 0.21

Pyrola asarifolia Michx. Liverleaf wintergreen 60 1.38 0.39

Galium triflorum Michx. Fragrant bedstraw 40 0.83 0.08

Stellaria L. Starwort 400 0.80 0.23

Aconitum delphiniifolium DC. Larkspurleaf monkshood 40 0.50 0.36

a

 Indicator values: bf = bear forb; cld = cold soils; df = deer forage; dsr = deer summer range; dwe = deer winter energy; fld = flooding; 

helev = high elevation; hlght = high light; muskeg = muskeg species; np = nutrient poor; nr = nutrient rich; pdr = poorly drained; ptl = peatland; 

sd = site disturbance; snpk = snow pack areas; sspr = salt spray; wdr = well-drained soils; wms = water moving through soils; wrm = warm site.
152

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 59—Introduced species that are searched for on standard phase 2 plots, 

coastal Alaska, 2004–2008 

CODE Scientific name Common name

ALPE4 Alliaria petiolata (M. Bieb.) Cavara & Grande Garlic mustard

BRIN2 Bromus inermis Leyss. Smooth brome

BRTE Bromus tectorum L. Cheatgrass

CABU2 Capsella bursa-pastoris (L.) Medik. Shepherd’s purse

CAAR18 Caragana arborescens Lam. Siberian peashrub

CEBI2 Centaurea biebersteinii DC. Spotted knapweed

CIAR4 Cirsium arvense (L.) Scop. Canada thistle

CIVU Cirsium vulgare (Savi) Ten. Bull thistle

CRTE3 Crepis tectorum L. Narrowleaf hawksbeard

CYSC4 Cytisus scoparius (L.) Link Scotch broom

DAGL Dactylis glomerata L. Orchardgrass

ELRE4 Elymus repens (L.) Gould Quackgrass

GALEO Galeopsis sp. L. Hempnettle

HIAU Hieracium aurantiacum L. Orange hawkweed

HIPI Hieracium pilosella L. Mouseear hawkweed

HICA10 Hieracium caespitosum Dumort. Meadow hawkweed

HIUM Hieracium umbellatum L. Narrowleaf hawkeed

HOJU Hordeum jubatum L. Foxtail barley

HYRA3 Hypochaeris radicata L. Hairy catsear

IMGL Impatiens glandulifera Royle Ornamental jewelweed

LASQ Lappula squarrosa (Retz.) Dumort. European stickseed

LEVU Leucanthemum vulgare Lam. Oxeye daisy

LIVU2 Linaria vulgaris P. Mill. Butter and eggs

LOLIU Lolium sp. L. Ryegrass

MADI6 Matricaria discoidea DC. Pineapple weed

MEDIC Medicago sp. L. Alfalfa

MEAL12 Melilotus alba Medikus Sweetclover, white

MEOF Melilotus officinalis (L.) Lam. Sweetclover, yellow

PHAR3 Phalaris arundinacea L. Reed canarygrass

PHPR3 Phleum pratense L. Timothy

PLMA2 Plantago major L. Plantain

POAV Polygonum aviculare L. Prostrate knotweed

POCU6 Polygonum cuspidatum Sieb. & Zucc. Japanese knotweed

PRPA5 Prunus padus L. European bird cherry

RARE3 Ranunculus repens L. Creeping buttercup

SEJA Senecio jacobaea L. Tansy ragwort

SEVU Senecio vulgaris L. Common groundsel

SOAR2 Sonchus arvensis L. Perennial sowthistle

STME2 Stellaria media (L.) Vill. Common chickweed

TAOF Taraxacum officinale G.H. Weber ex Wiggers Dandelion

TAVU Tanacetum vulgare L. Common tansy

TRDU Tragopogon dubius Scop. Western salsify

TRIFO Trifolium sp. Clover

VICRC Vicia cracca L. ssp cracca Bird vetch
153

Forests of Southeast and South-Central Alaska, 2004–2008

Table 60—Plants found in the coastal Alaska Forest Inventory and Analysis inventory unit phase 3 plots with 

documented subsistence use or inquired about for commercial use on the Chugach or Tongass National Forests

 

Intended

commercial use/ Constancya b

Scientific name Common name Subsistence use parts harvested Percent SE

Abies lasiocarpa var. lasiocarpa Subalpine fir Transplants 0.74 0.74

Aconitum delphiniifolium Larkspurleaf monkshood Foliage 9.63 2.55

Achillea millefolium Common yarrow Medicinal 3.70 1.63

Actaea rubra Red baneberry Foliage 5.93 2.04

Adiantum pedatum Northern maidenhair Transplants 1.48 1.04

Alnus rubra Red alder Fuel, wood articles, Transplants 6.67 2.15

 dye, medicinal

Allium schoenoprasum Wild chives Edible leaves 0.74 0.74

Andromeda polifolia Bog rosemary Whole plant 7.41 2.26

Aquilegia formosa Western columbine Foliage, seeds 3.70 1.63

Arctostaphylos alpina Alpine bearberry Edible berry 0.74 0.74

Aruncus dioicus Bride's feathers Whole plant 8.15 2.36

Arabis hirsuta Hairy rockcress Edible leaves or shoots 0.74 0.74

Arabis lyrata Lyrate rockcress Edible leaves or shoots 0.74 0.74

Arctostaphylos rubra Red fruit bearberry Edible berry 0.74 0.74

Asplenium trichomanes-ramosum Brightgreen spleenwort Whole plant 2.22 1.27

Athyrium filix-femina Common ladyfern Transplants 67.41 4.05

Caltha palustris Yellow marsh marigold Edible leaves 1.48 1.04 

(with caution)

Carex Sedge Seeds, transplants 25.93 3.79

Chamerion angustifolium Fireweed Cord (fishing nets), seed 2.22 1.27

fluff in padding or clothes, 

edible leaves or shoots

Chamerion latifolium Dwarf fireweed Seed fluff in padding or 2.22 1.27

clothes, edible leaves

 or shoots

Chamaecyparis nootkatensis Alaska cedar Fuel, wood articles, dye, 31.85 4.02

 medicinal, clothing, 

baskets, bows, tea

Cornus canadensis Bunchberry dogwood Edible berry 79.26 3.50

Cornus suecica Lapland cornel Edible berry 17.78 3.30

Dryopteris expansa Spreading woodfern Medicinals, bedding and 62.96 4.17

padding; edible fiddleheads

and roots (with caution)

Empetrum nigrum Black crowberry Edible berry 27.41 3.85

Equisetum Horsetail Abrasives for polishing; 0.74 0.74

 basketry, possible

medicinal; edible root 

(with caution)

Eriophorum angustifolium Tall cottongrass Edible root 15.56 3.13

Fritillaria camschatcensis Kamchatka fritillary Edible bulb 3.70 1.63

Gaultheria shallon Salal Edible berries; Berries 11.85 2.79 

 medicinal leaves

Geum macrophyllum Largeleaf avens Whole plant 5.93 2.04
154

GENERAL TECHNICAL REPORT PNW-GTR-835

Table 60—Plants found in the coastal Alaska Forest Inventory and Analysis inventory unit phase 3 plots with 

documented subsistence use or inquired about for commercial use on the Chugach or Tongass National Forests 

(continued)

 

Intended

commercial use/ Constancya b

Scientific name Common name Subsistence use parts harvested Percent SE

Gentiana platypetala Broadpetal gentian Whole plant 2.22 1.27

Hippuris vulgaris Common mare's-tail Edible leaves or stalk 0.74 0.74

Iris setosa Beachhead iris Foliage 2.96 1.46

Juniperus communis Common juniper Tea, edible berry Transplants, 2.22 1.27

(with caution) boughs, seeds

Juncus Rush Seeds, transplants 2.22 1.27

Kalmia polifolia Bog laurel Whole plant 8.15 2.36

Ledum groenlandicum Bog Labrador tea Foliage 15.56 3.13

Ligusticum scoticum Scottish licorice-root Edible leaves or stalk 1.48 1.04

Lupinus nootkatensis Nootka lupine Seeds 2.96 1.46

Lycopodium alpinum Alpine clubmoss Whole plant 2.22 1.27

Lysichiton americanus American skunkcabbage Food preparation, 48.15 4.32

 famine food

Lycopodium annotinum Stiff clubmoss Whole plant 49.63 4.32

Lycopodium clavatum Running clubmoss Whole plant 9.63 2.55

Lycopodium dendroideum Tree groundpine Whole plant 0.74 0.74

Maianthemum dilatatum False lily of the valley Edible leaves, berries, 25.93 3.79

 medicinal

Oplopanax horridus Devil’s club Medicinal 58.52 4.26

Oxyria digyna Alpine mountainsorrel Edible leaves or shoots 0.74 0.74

(with caution)

Pinus contorta Lodgepole pine Christmas trees, 12.59 2.87 

 seeds, transplants

Picea sitchensis Sitka spruce Fuel, wood articles, food, Cones 74.81 3.75

medicinal, roots for 

basketry and crafts

Platanthera dilatata Scentbottle Whole plant 11.85 2.79

Pteridium aquilinum Western brackenfern Edible rhizomes, 2.22 1.27

fiddleheads (carcinogenic)

Ribes bracteosum Stink currant Edible berry 11.85 2.79

Ribes laxiflorum Trailing black currant Edible berry Berries 7.41 2.26

Ribes triste Red currant Edible berry Berries 10.37 2.63

Rubus arcticus Nagoonberry Edible berry 11.11 2.71

Rubus chamaemorus Cloudberry Edible berry, shoots, 11.11 2.71

medicinal use of roots

Rubus idaeus American red raspberry Edible berry Berries 3.70 1.63

Rubus parviflorus Thimbleberry Edible shoots, berry 2.22 1.27

Rubus spectabilis Salmonberry Edible berry Berries 53.33 4.31

Sanguisorba canadensis Canadian burnet Whole plant 17.78 3.30

Salix Willow Edible leaves, medicinal use, Transplants, 8.89 2.46

basketry, dye nets, rope cuttings, seeds

Sambucus racemosa Red elderberry Edible berry 16.30 3.19
155

Forests of Southeast and South-Central Alaska, 2004–2008

Table 60—Plants found in the coastal Alaska Forest Inventory and Analysis inventory unit phase 3 plots with 

documented subsistence use or inquired about for commercial use on the Chugach or Tongass National Forests 

(continued)

 

Intended

commercial use/ Constancya b

Scientific name Common name Subsistence use parts harvested Percent SE

Shepherdia canadensis Russet buffaloberry Edible berry 0.74 0.74

Sorbus sitchensis Western mountain ash Seeds, transplants 20.00 3.46

Streptopus amplexifolius Claspleaf twistedstalk Edible leaves, berries, Berries 74.81 3.75

 medicinal use

Symphoricarpos albus Common snowberry Transplants 0.74 0.74

Thuja plicata Western red cedar Wood items, fuel, bark Bark 17.78 3.30 

and roots in clothing 

 and basketry

Tsuga heterophylla Western hemlock Wood items, fuel, tanning Boughs 61.48 4.20 

agent, dye, bedding, 

substrate for herring eggs

Vaccinium caespitosum Dwarf bilberry Edible berries, shoots 22.22 3.59

Vaccinium Blueberry Transplants, berries 0.74 0.74

Vaccinium ovalifolium Early blueberry Edible berry Berries 75.56 3.71

Vaccinium oxycoccos Small cranberry Edible berry Berries 18.52 3.36

Vaccinium parvifolium Red huckleberry Edible berry Berries 35.56 4.14

Vaccinium uliginosum Bog blueberry Edible berries, shoots 22.22 3.59

Vaccinium vitis-idaea Lingonberry Edible berry Berries 30.37 3.97

Viburnum edule Highbush cranberry Edible berry Berries 20.74 3.50

Note: SE = standard error.

a Constancy is the percentage of the 135 phase 3 FIA plots where the species was found. See Schultz et al. (2009) for details on data 

collection methods and sampling error calculations.

b Accuracy of estimates may be affected by date plots were visited relative to phenology, partially forested plots, and species misidentification. 

Plots were sampled from a 38-million-acre region and thus do not provide information on local abundance or rarity.
156

Table 61—Estimated aboveground biomass of all live trees on forest land, by species and diameter class, coastal Alaska, 2

Diameter class (inches) 5.0–6.9 7.0–8.9 9.0–10.9 11.0–12.9 13.0–14.9 15.0–16.9 

Species Total SE Total SE Total SE Total SE Total SE Total SE 

Million (bone-dry) tons Softwoods: Alaska yellow-cedar 6.2 0.8 8.2 1.0 10.7 1.4 11.4 1.4 13.6 2.2 12.8 1.9 

Black spruce 1.6 0.3 0.4 0.1 0.1 0.0 — — — — — — 

Lodgepole pine 1.0 0.1 1.6 0.2 2.3 0.4 3.0 0.7 2.6 0.7 1.2 0.3 

Mountain hemlock 7.5 0.8 11.6 1.1 16.7 2.1 18.3 2.0 19.5 2.1 21.0 3.1 

Sitka spruce 6.0 0.6 7.9 0.7 10.2 0.9 12.2 1.3 15.4 1.6 16.4 1.6 

Western hemlock 15.0 0.9 22.2 1.3 29.0 1.8 31.2 2.0 33.4 2.1 30.4 2.1 

Western redcedar 1.1 0.2 1.9 0.3 2.5 0.4 3.6 0.6 3.9 0.8 4.2 0.8 

White spruce 1.9 0.2 2.2 0.2 1.6 0.3 1.3 0.2 0.6 0.1 0.5 0.2 Other softwood — — — — — — — — 0.1 0.1 0.1 0.1 

 Total softwood 40.3 1.6 55.9 2.1 73.2 3.0 81.1 3.4 89.1 4.0 86.5 4.5 

Hardwoods:

Aspen 0.2 0.1 0.4 0.2 0.6 0.3 0.8 0.3 0.5 0.2 0.2 0.1 Cottonwood 0.5 0.1 1.4 0.7 1.4 0.4 1.4 0.5 2.1 0.8 1.5 0.4 

Paper birch 1.2 0.3 1.7 0.3 2.3 0.4 2.2 0.4 1.3 0.3 1.3 0.4 Red alder 0.6 0.2 0.7 0.2 0.8 0.2 0.5 0.1 0.3 0.2 0.6 0.3  Total hardwood 2.5 0.4 4.2 0.8 5.1 0.7 4.8 0.7 4.2 0.9 3.7 0.7 

Total 42.8 1.6 60.1 2.1 78.3 3.1 85.9 3.4 93.3 4.0 90.2 4.5 

Diameter class (inches)

19.0–20.9 21.0–24.9 25.0–28.9 29.0–32.9 33.0–36.9 37.0+ 

Species Total SE Total SE Total SE Total SE Total SE Total SE 

Million (bone-dry) tons

Softwoods:

Alaska yellow-cedar 9.7 1.6 15.9 2.8 11.6 2.5 11.4 3.0 6.5 2.1 5.9 2.3 

Black spruce — — — — — — — — — — — — Lodgepole pine 0.7 0.2 1.1 0.4 0.1 0.2 — — — — — — Mountain hemlock 14.6 2.4 22.7 3.3 18.5 4.1 12.9 3.6 7.3 2.3 13.9 7.2 

Sitka spruce 15.8 2.0 36.0 3.7 34.9 4.5 36.1 8.7 26.3 5.9 91.8 14.8 Western hemlock 31.2 2.8 66.4 6.2 60.0 6.4 51.6 7.0 40.3 6.5 35.0 5.8 Western redcedar 3.7 0.7 10.4 2.4 11.2 4.0 10.8 2.9 3.9 1.1 32.0 9.8 

White spruce 0.1 0.1 — — — — — — — — — — 

Other softwood — — — — — — — — — — — —  Total softwood 75.8 4.5 152.7 8.6 136.3 10.0 122.9 12.7 84.4 9.0 178.6 19.3 

Hardwoods:

Aspen 0.1 0.1 — — 0.1 0.1 — — — — — — Cottonwood 1.1 0.3 2.6 1.0 1.1 0.5 0.3 0.3 0.2 0.2 — — 

Paper birch 0.3 0.1 0.4 0.2 — — — — — — — — 

Red alder 0.1 0.1 0.2 0.2 — — — — — — — — 

 Total hardwood 1.5 0.4 3.3 1.1 1.2 0.5 0.3 0.3 0.2 0.2 — — 

Total 77.3 4.6 156.0 8.7 137.5 10.0 123.2 12.7 84.5 9.0 178.6 19.3 

Note: Totals may be off because of rounding; data subject to sampling error; SE = standard error; — = less than 50,000 (bone-dry) tons was estimated.
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