Pacific Northwest

Research Station

Forest 

Service

United States 

Department of 

Agriculture

General Technical Report

PNW-GTR-648

September 2005

Status and Trends of Northern Spotted Owl 

Populations and Habitat

NORTHWEST

FOREST PLAN

THE FIRST 10 YEARS (1994–2003)
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.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and 

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

marital status, familial status, parental status, religion, sexual orientation, genetic information, 

political beliefs, reprisal, or because all or part of an individual’s income is derived from any 

public assistance program. (Not all prohibited bases apply to all programs.) Persons with 

disabilities who require alternative means for communication of program information (Braille, 

large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice 

and TDD). To file a complaint of discrimination, write USDA, Director, Office of Civil Rights, 

Room 1400 Independence Avenue, SW, Washington, DC 20250-9410 or call (800) 795-3272 

(voice) or (202) 720-6382 (TDD). USDA is an equal opportunity provider and employer.

Technical Coordinator

Joseph Lint is a wildlife biologist, U.S. Department of the Interior, Bureau of Land 

Management, Oregon State Office, 777 Garden Valley Blvd., NW, Roseburg, OR 

97470. He is the module leader for northern spotted owl effectiveness monitoring 

under the Northwest Forest Plan.

Cover photograph by Tom Iraci.
Northwest Forest Plan—the 

First 10 Years (1994–2003): 

Status and Trends of 

Northern Spotted Owl 

Populations and Habitat

Joseph Lint

Technical Coordinator

U.S. Department of Agriculture, Forest Service

Pacific Northwest Research Station

Portland, Oregon

General Technical Report PNW-GTR-648

September 2005
ii

Abstract

Lint, Joseph, tech. coord. 2005. Northwest Forest Plan—the first 10 years 

(1994–2003): status and trends of northern spotted owl populations and 

habitat. Gen. Tech. Rep. PNW-GTR-648. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research Station. 176 p. 

This report presents results from monitoring spotted owl (Strix occidentalis caurina) 

populations and habitat during the first 10 years of implementation of the Northwest 

Forest Plan (the Plan). Estimated population decline ranged from 0 to 10 percent across 

study areas (weighted average of 3.4 percent) annually. The average annual rate of decline for the four demographic areas in Washington was 7.1 percent. Presence of barred 

owls (Strix varia), weather, past and present harvest of habitat, and wildfire and insect 

infestations that alter habitat are all possible contributors to the noted decline.

Maps depicting the suitability of habitat-capable area were produced by using 

habitat models. Rangewide (range of the owl), about 74 percent of the federal land 

area was habitat-capable. Fifty-seven percent of the habitat-capable area was in a forest 

condition similar to the conditions where territorial owls were known to be present on 

the landscape. Fifty-one percent of the habitat-capable area fell in large, reserved blocks 

intended, under the Plan, to support clusters of reproducing owls. About 62 percent of 

the habitat-capable area inside the reserved blocks was in a forest condition similar to 

the conditions where owls were known to be present.

Owl movement was assessed in selected provinces by using data from banded owls. 

Movements with resighting locations inside reserved blocks accounted for 51 percent 

of juvenile movement records. Over 30 percent of the juvenile movements were into 

reserved blocks from outside points.

Predictive model development using demographic data showed a variety of factors that influence owl survival and productivity including precipitation, owl age, and 

habitat. 

The barred owl has expanded its range in past decades and currently is present 

throughout the range of the spotted owl. The likelihood of competitive interaction between spotted owls and barred owls raises concern on the future of spotted owl populations. Barred owls are high on the list of factors that may be contributing to spotted owl 

declines in northern portions of the spotted owl’s range. 

Barred owls, West Nile virus (Flavivirus sp.), and management of owl habitat in 

high-fire-risk areas are topics for future management consideration.

Keywords: Northwest Forest Plan, effectiveness monitoring, northern spotted owl, 

GIS, owl habitat, habitat suitability, demographic study, remote sensing, predictive 

model, habitat model.
iii

Preface

This report is one of a set of reports produced on this 10-year anniversary of the Northwest 

Forest Plan (the Plan). The collection of reports attempts to answer questions about the 

effectiveness of the Plan based on new monitoring and research results. The set includes 

a series of status and trends reports, a synthesis of all regional monitoring and research 

results, a report on interagency information management, and a summary report. 

The status and trends reports focus on establishing baselines of information from 1994, 

when the Plan was approved, and reporting change over the 10-year period. The status and 

trends series includes reports on late-successional and old-growth forests, northern spotted 

owl (Strix occidentalis caurina) population and habitat, marbled murrelet (Brachyrampus 

marmoratus) population and habitat, watershed condition, government-to-government 

tribal relationships, socioeconomic conditions, and monitoring of project implementation 

under Plan standards and guidelines. 

The synthesis report addresses questions about the effectiveness of the Plan by using 

the status and trends results and new research. It focuses on the validity of the Plan assumptions, differences between expectations and what actually happened, the certainty of the 

findings, and, finally, considerations for the future. The synthesis report is organized in two 

parts: Part I—introduction, context, synthesis and summary—and Part II—socioeconomic 

implications, older forests, species conservation, the aquatic conservation strategy, and 

adaptive management and monitoring.

The report on interagency information management identifies issues and recommends 

solutions for resolving data and mapping problems encountered during the preparation of 

the set of monitoring reports. Information management issues inevitably surface during 

analyses that require data from multiple agencies covering large geographic areas. The goal 

of this set of reports is to improve the integration and acquisition of interagency data for the 

next comprehensive report.
iv

1 Anthony, R.G.; Forsman, E.D.; Franklin, A.B.; Anderson, D.R.; Burnham, K.P.; White, G.C.; Schwarz, 

C.J.; Nichols, J.; Hines, J.; Olson, G.S.; Ackers, S.H.; Andrews, S.; Biswell, B.L.; Carlson, P.C.; Diller, 

L.V.; Dugger, K.M.; Fehring, K.E.; Fleming, T.L.; Gerhardt, R.P.; Gremel, S.A.; Gutierrez, R.J.; Happe, 

P.J.; Herter, D.R.; Higley, J.M.: Horn, R.B.; Irwin, L.L.; Loschl, P.J.; Reid, J.A.; Sovern, S.G. 2004. Status 

and trends in demography of northern spotted owls, 1985–2003. Final report to the Regional Interagency 

Executive Committee. Portland, Oregon. 

Executive Summary

The purpose of the northern spotted owl (Strix occidentalis caurina) effectiveness monitoring plan is to assess trends in spotted owl populations and habitat. Monitoring data will 

be used to evaluate the success of the Plan in arresting the downward trends in spotted owl 

populations and in maintaining and restoring the habitat conditions necessary to support 

viable owl populations on federally administered land throughout the range of the owl.

The monitoring objectives are to (1) assess changes in population trend and demographic performance of spotted owls on federally administered forest land within the 

range of the owl, and (2) assess changes in the amount and distribution of nesting, roosting, and foraging habitat (owl habitat) and dispersal habitat for spotted owls on federally 

administered forest land.

Analyses of population data1 indicated a 3.4 percent weighted average annual decline 

for owl populations in the 10 demographic areas associated with land managed under 

the Plan. Annual rate of population change was not calculated for the Marin study area 

because of the few years of data. The rates of decline differed between individual study 

areas. Populations were stationary (see “Glossary”) or increasing in 3 of the 10 areas (all 

in southern Oregon) based on estimates of realized population change. For those areas 

showing annual declines, those in Washington showed the highest rate of decline—an 

average decline of 7.1 percent per year. Possible reasons for the declines include high 

densities of barred owls (Strix varia) in Washington and parts of Oregon, loss of habitat to 

wildfire, harvest of owl habitat, poor weather conditions, and forest defoliation by insect 

infestations (see footnote 1). 

Analyses of habitat conditions on federal land were based on province-scale habitat 

suitability maps derived from vegetation base maps. About 74 percent of the federal land 

in the range of the owl was estimated as capable of producing owl habitat. The models 

used to derive the habitat condition maps depict a range of habitat-capable area from least 

similar to most similar to conditions of known territorial owl presence by using a scale 

of 0 to 100. Generally, habitat-capable area rated 41 to 100 has habitat conditions most 

similar to forest conditions known to be used by spotted owls. The habitat-capable area 

rated 0 to 40 has conditions dissimilar to known owl locations but is important because it 

has potential to be recruited into the range of conditions most similar to those with known 

owl presence. The monitoring results establish a baseline for tracking maintenance and 

restoration of habitat conditions related to owl presence.
v

Rangewide (throughout the spotted owl range), about 57 percent of habitat-capable 

area was rated 41 to 100 at the beginning of the monitoring period. About 51 percent of the 

habitat-capable area occurs inside large, reserved blocks of federal land intended to support 

clusters of reproducing owls. Inside the blocks, about 62 percent of the habitat-capable 

area was rated 41 to 100 for habitat condition. We were able to account for loss of habitat 

in the 41 to 100 range resulting from stand-replacing timber harvest and wildfire: about 1.5 

percent from both sources with wildfire accounting for over 80 percent of the habitat lost. 

We were unable to account for ingrowth of habitat. 

Movement distances were calculated for 1,210 juvenile movement records and 1,388 

nonjuvenile records in relation to the Plan’s reserve network. Juvenile movements occurred 

from one reserved block to another (142 juveniles), from outside a reserved block to inside a 

reserved block (247 juveniles), and within a single reserved block (232 juveniles). All these 

movements had recovery points inside the reserved blocks and accounted for 51 percent of 

juvenile movement records.

Juvenile movements with recovery points outside the reserved blocks included 268 

juveniles moving from a reserved block to outside of the block and another 321 that moved 

from outside a reserved block to another point outside a reserved block. These movements 

composed about 49 percent of the juvenile movement records.

Research on predicting owl survival and productivity in relation to habitat amount and 

arrangement has given us insight and understanding, but we are not in a position to shift 

from mark-recapture studies to increased reliance on habitat monitoring by using predictive 

models to indirectly estimate the demographic performance of spotted owls. The study 

of the relation of occupancy (occurrence) to habitat amount and arrangement is a work in 

progress with initial results expected in fall 2005.

Emerging issues that may need future management consideration are the apparent 

negative interaction between barred owls and spotted owls and the potential loss of individual owls to West Nile virus (Flavivirus sp.). Management of owl habitat in the drier, 

fire-prone provinces will continue to provide a challenge in the face of increasing risk of 

catastrophic fire and the need to assess short-term versus long-term effects on owl populations in these areas.
vi

Contents

 1 Chapter 1: Background, by Joseph Lint and Jon Martin

 3 References

 7 Chapter 2: Population Status and Trend, by Joseph Lint

 7 Introduction

 9 Data Sources and Methods

 9 Collecting Field Data

 9 Analyzing Population Status and Trend

 10 Developing Data Files for Survival Analysis

 10 Developing Data Files for Fecundity Analysis

 10 Checking for Errors in the Survival and Fecundity Database Files

 10 Developing Data Files for Analyzing Rates of Population Change

 11 Estimating Apparent Annual Survival

 11 Estimating Fecundity

 12 Estimating Annual Rate of Population Change

 12 Estimating Realized Population Change

 12 Results

 12 Survival

 13 Fecundity

 14 Annual Rate of Population Change

 16 Realized Population Change

 16 Discussion and Conclusions

 18 References

 21 Chapter 3: Habitat Status and Trend, by Raymond Davis and Joseph Lint

 21 Introduction

 23 Habitat, Habitat Suitability, and Habitat Modeling

 24 Data Sources 

 24 Vegetation Map Data

 25 Land Use Allocation Data

 26 Spotted Owl Presence Data

 26 Change Detection Data

 28 Wildfire Occurrence Data

 28 Historical Forest Maps

 28 Current Vegetation Survey Data

 30 Other Ancillary Data Sources

30 Analysis Methods

30 Estimating Federal Land Area

30 Estimating “Forest-Capable” Federal Area

 30 Estimating “Habitat-Capable” Federal Area

 32 Estimating Habitat Suitability

 37 Calibrating the Model

 38 Validating the Model

 38 Model Limitations

 39 An Example of Model Application

 41 Estimating Habitat Conditions in the Spotted Owl Reserve Network

 43 Estimating Habitat Change
vii

 43 Estimating Habitat Fragmentation and Patch Metrics

 46 Estimating Dispersal-Capable Federal Area

 46 Estimating Dispersal Habitat Conditions

 46 Estimating Owl Habitat Quality From Plot Data

 47 Results

 47 Spatial Analyses

 68 Nonspatial Analyses

 68 Limitations of Analysis

 75 Discussion

 77 Conclusions

 77 References

 83 Chapter 4: Owl Movement, by Joseph Lint, Jason Mowdy, Janice Reid, 

 Eric Forsman, and Robert Anthony

 83 Introduction

 83 Data Sources and Methods

 85 Results

 87 Discussion and Conclusions

 87 References

 89 Chapter 5: Related Research, by Joseph Lint

 89 Introduction

 89 Barred Owl

 89 Data Sources and Methods

 89 Results

92 Effect of the Barred Owl

 93 Predictive Model Development

 94 Data Sources and Methods

 95 Results

97 What Did We Learn From the Model Work?

 98 References

101 Chapter 6: Emerging Issues and Monitoring Program Needs, by Joseph Lint

 101 Emerging Issues

 101 Monitoring Program Needs

102 References

 104 Acknowledgments

 104 Metric Equivalents

 105 Glossary

 109 Appendix A: Senior Analysts for 2004 Northern Spotted Owl Population 

 Status and Trend Workshop 

 110 Appendix B: Nearest Neighbor Analysis of Owl Presence Data

 114 Appendix C: Habitat Variables Used in BioMapper Modeling 

 115 Appendix D: Spotted Owl Habitat Suitability Maps 

 125 Appendix E: BioMapper Habitat Model Output Statistics Summary 

 135 Appendix F: Model Validation With Independent Data Sets

 137 Appendix G: Spotted Owl Habitat Suitability Histograms 

 152 Appendix H: Timber Harvest and Wildfire Change Histograms

 167 Appendix I: Spotted Owl Dispersal Habitat Maps 
viii

GENERAL TECHNICAL REPORT PNW-GTR-648

Frank Oliver
1

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Chapter 1: Background

Joseph Lint 1 and Jon Martin2

In the early 1990s, controversy over harvest of old-growth 

forests led to sweeping changes in management of federal 

forests in western Washington and Oregon and northwest 

California. These changes were prompted by a series of 

lawsuits in the late 1980s and early 1990s, which effectively 

shut down federal timber harvest in the Pacific Northwest. In response, President Clinton convened a summit 

in Portland, Oregon, in 1993. At the summit, President 

Clinton issued a mandate for federal land management 

and regulatory agencies to work together to develop a plan 

to resolve the conflict. The President’s guiding principles 

followed shortly after the summit in his Forest Plan for 

a Sustainable Economy and Sustainable Environment 

(Clinton and Gore 1996).

Immediately after the summit, a team of scientists and 

technical experts was convened to conduct an assessment 

of options (FEMAT 1993). This assessment provided the 

scientific basis for the Environmental Impact Statement 

and Record of Decision (ROD) (USDA USDI 1994) to 

amend Forest Service and Bureau of Land Management 

planning documents within the range of the northern 

spotted owl (Strix occidentalis caurina). 

The ROD, covering 24 million federal ac, put in 

place a new approach to federal land management. Key 

components of the ROD included a new set of land use 

allocations—late-successional reserves, matrix, riparian 

reserves, adaptive management areas, and key watersheds. 

Plan standards and guidelines provided specific management direction regarding how these land use allocations 

were to be managed. In addition, the Plan put in place a 

variety of strategies and processes to be implemented. 

These included adaptive management, an aquatic conservation strategy, late-successional reserve and watershed 

assessments, a survey and manage program, an interagency 

organization, social and economic mitigation initiatives, 

and monitoring. 

Monitoring provides a means to address the uncertainty 

of our predictions and compliance with forest management 

laws and policy. The ROD stated that monitoring is essential 

(USDA USDI 1994): 

Monitoring is an essential component of the selected 

alternative. It ensures that management actions 

meet the prescribed standards and guidelines and 

that they comply with applicable laws and policies. 

Monitoring will provide information to determine 

if the standards and guidelines are being followed, 

verify if they are achieving the desired results, and 

determine if underlying assumptions are sound. 

Judge Dwyer (1994) reinforced the importance of monitoring in his decision declaring the Plan legally acceptable: 

Monitoring is central to the [Northwest Forest 

Plan’s] validity. If it is not funded, or done for any 

reason, the plan will have to be reconsidered.

The ROD monitoring plan provided a very general 

framework to begin development of an interagency monitoring program. It identified key areas to monitor, initial sets 

of questions, types and scope of monitoring, the need for 

common protocols and quality assurance, and the need to 

develop a common design framework. In 1995, the effectiveness monitoring program plan (Mulder et al. 1995) and 

initial protocols for implementation monitoring (Alegria 

et al. 1995) were approved by the Regional Interagency 

Executive Committee. Approval of the effectiveness 

monitoring plan led to the formation of technical teams to 

develop the overall program strategy and design (Mulder et 

al. 1999) and monitoring protocols for late-successional and 

old-growth forests (Hemstrom et al. 1998), northern spotted 

owls (Lint et al. 1999), marbled murrelets (Brachyrampus 

marmoratus) (Madsen et al. 1999), tribal issues (USDA 

USDI 2002), and watershed condition (Reeves et al. 2004). 

Socioeconomic monitoring protocols continue to be tested 

(Charnley, in press).

1 Joseph Lint is a wildlife biologist, U.S. Department of the 

Interior, Bureau of Land Management, Oregon State Office, 777 

Garden Valley Blvd., NW, Roseburg, OR 97470. He is the module 

leader for northern spotted owl effectiveness monitoring under the 

Northwest Forest Plan.

2 Jon Martin is the Assistant Director for Resource Planning and 

Monitoring in the Pacific Northwest Region, U.S. Department 

of Agriculture, Forest Service, 333 SW First Avenue, Portland, 

OR 97208. He is the Interagency Regional Monitoring Program 

Manager.
2

GENERAL TECHNICAL REPORT PNW-GTR-648

Periodic analysis and interpretation of monitoring data 

is essential to completing the monitoring task critical to 

completing the adaptive management cycle. This important 

step was described in the overall monitoring strategy 

(Mulder et al. 1999) and approved by the Regional Interagency Executive Committee. This 10-year report is the 

first comprehensive analysis and interpretation of monitoring data since the ROD.

The northern spotted owl was a species of interest when 

the Northwest Forest Plan (the Plan) was being developed in 

1993–94. It was listed in 1990 as a threatened species under 

the Endangered Species Act of 1973, as amended (the act). 

The listing action invoked section 7(a)(1) of the act, which 

directed federal agencies to carry out programs to conserve 

endangered and threatened species. The Plan’s record of 

decision reflected the direction in land use allocations and 

standards and guidelines intended to constitute the contributions of the USDA Forest Service (FS) and the USDI 

Bureau of Land Management (BLM) to the recovery of the 

northern spotted owl (USDA USDI, 1994: 31). The owl’s 

recovery depends on improved status of the species so that 

listing under one or more of the criteria in section 4 (a)(1) 

of the act would no longer be appropriate. Maintaining and 

restoring habitat under the Plan are keys to improving the 

owl’s status and placing it on a trajectory toward recovery 

and delisting. 

The need to meet expectations for owl habitat and 

populations made monitoring an essential component of 

managing owls under the Plan. The ROD posed a primary 

evaluation question on spotted owls: Is the population 

stable or increasing? Monitoring results will aid federal 

managers in evaluating whether the Plan has succeeded in 

arresting the downward trend in the owl population and 

in maintaining and restoring habitat conditions needed to 

support viable populations on federally administered forest 

lands throughout the range of the owl (Lint et al. 1999). 

Monitoring will gauge the agencies’ progress in providing 

the federal contribution to owl recovery.

Spotted owl monitoring (Lint et al. 1999) has incorporated a mix of monitoring actions and research studies to 

investigate the status and trend of the owl population and 

its habitat. The cornerstone of the strategy for effectiveness 

monitoring was assessing the owl’s population and habitat 

(Lint et al. 1999). The monitoring program developed by 

Lint et al. (1999) comprises studying owl demographics, 

assessing its habitat, and developing predictive models to 

relate demographic variability to habitat variation at scales 

of the landscape and the owl’s home range. Lint et al. (1999) 

provided this general description of the approach to monitoring the spotted owl: 

The plan [monitoring strategy] will be implemented 

in two phases. In Phase I, the ongoing demographic 

monitoring of the territorial portion of owl populations will continue in selected areas using markrecapture techniques. These methods yield detailed 

information on demographic performance and 

annual rate of population change (Burnham et al. 

1996). In the latter part of Phase I, data on the abundance and demographic performance of owls will be 

combined with information on habitat characteristics 

(structural and composition aspects of the dominant 

vegetation) from the demographic study areas. The 

combined data sets will be used to develop predictive 

models of owl occurrence and demographic performance based on observed habitat characteristics.

Habitat conditions will also be determined throughout the range of the owl to provide a means for tracking changes in habitat condition at the landscape 

scale. These estimates will require the compilation 

of vegetation conditions across the range of the owl 

into a GIS format. Vegetation data layers will be 

overlaid to create a forest-class map from which an 

owl habitat map can be derived. The forest-class map 

will be a product from the Late-Successional/OldGrowth Effectiveness Monitoring Plan. 

 Phase I will culminate with model validation based 

on data collected outside of the demographic study 

areas, in selected validation sites. Validation sites 

will compare the observed distribution and population numbers of owls with those predicted from the 

habitat models. Habitat condition will be initially 
3

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

estimated for selected model-validation test areas by 

a probability-based selection of sites from the range 

wide habitat map. The models will be refined and 

evaluated for operational monitoring during Phase II. 

If the predictive models produced in Phase I prove 

adequate, (i.e., provide predictions with an associated 

risk that is acceptable to decision-makers) they will 

replace the intensive owl surveys and multi-scale 

habitat work of Phase I. As a result, Phase II monitoring will rely less than Phase I on mark-recapture 

techniques to estimate demographic performance. 

At least four of the demographic studies would be 

continued beyond Phase I, however, to maintain a 

direct link to the population status and trend through 

an annual check of demographic parameters. If the 

habitat-based models do not predict owl occurrence 

with acceptable reliability, Phase I activities will 

continue. That is, monitoring of population trends 

of spotted owls in the demographic areas would 

continue as the primary effectiveness monitoring strategy until the models are improved and 

the desired prediction accuracy is achieved, or 

an alternative monitoring strategy is developed. 

Likewise, if the range-wide habitat monitoring effort 

does provide data of satisfactory resolution to track 

trend, then the habitat monitoring element will track 

changing conditions by demographic study area until 

such time that the range-wide effort is operational or 

another habitat monitoring strategy is adopted. 

Since the Plan’s adoption in 1994, the spotted owl 

monitoring program has implemented the components of 

Phase I. Population monitoring gathered information on owl 

occupancy, survival, and reproduction in demographic study 

areas in the owl’s range. Habitat monitoring used owl habitat 

maps for each physiographic province (fig. 1-1) derived 

by modeling data on known owl locations and landscape 

vegetation condition. Phase I efforts also investigated the 

possibilities of predicting owl survival, productivity, and 

occupancy by using habitat characteristics. Our report 

provides the results of monitoring owl habitat and population monitoring, along with an update of the ongoing work 

on developing predictive models. The monitoring results 

measured changes in owl habitat and populations since the 

Plan’s inception and established benchmarks for measuring 

changes from this point forward. 

This report and its supporting documents, incorporated 

by reference, describe the key monitoring questions, data 

sources, data-compilation processes, analyses methods, 

and results for the three areas of interest: habitat, population, and predictive models. Guidance for data analysis and 

reporting in Lint et al. (1999) were to provide estimates of:

• Survival, reproductive success, and annual rate of 

population change for each demographic study area, 

as well as cumulatively, through a rangewide metaanalysis.

• Change in the amount and distribution of spotted 

owl habitat, by tabulating information about acres 

of habitat by land use allocation and examining the 

percentage of change over time.

References

Alegria, J.; Hyzer, M.; Mulder, B.; Schnoes, B.; Tolle, 

T. 1995. Guidance for implementation monitoring for 

management of habitat for late-successional and oldgrowth related species within the range of the northern 

spotted owl. Unpublished report. On file with: Regional 

Ecosystem Office, 333 SW First Avenue, Portland, OR 

97204.

Burnham, K.P.; Anderson, D.R.; White, G.C. 1996.

Meta-analysis of vital rates of the northern spotted owl. 

Studies in Avian Biology. 17: 92–101.

Charnley, S., tech. coord. [In press]. Northwest Forest 

Plan—the first 10 years (1994–2003): socioeconomic 

monitoring results. Gen. Tech. Rep. PNW-GTR-649. 

Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 6 vol.
4

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure 1-1—Physiographic provinces in the range of the northern spotted owl.

Boundary of the

Northwest Forest Plan

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers

Lakes and rivers

Urban areas

Interstate highway o
5

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Clinton, W.J.; Gore, A., Jr. 1996. The forest plan for a 

sustainable economy and a sustainable environment. 

In: Tuchmann, E.T.; Connaughton, K.E; Freedman, 

L.E.; Moriwaki, C.B. The Northwest Forest Plan: a 

report to the President and Congress. Portland, OR: 

U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. Appendix A.

Dwyer, W.L. 1994. Seattle Audubon Society, et al. v. James 

Lyons, Assistant Secretary of Agriculture, et al. Order 

on motions for Summary Judgment RE 1994 Forest Plan. 

Seattle, WA: U.S. District Court, Western District of 

Washington.

Forest Ecosystem Management Assessment Team 

[FEMAT]. 1993. Forest ecosystem management: an 

ecological, economic, and social assessment. Portland, 

OR: U.S. Department of Agriculture; U.S. Department of 

the Interior [and others]. [Irregular pagination].

Hemstrom, M.; Spies, T.; Palmer, C.; Kiester, R.; 

Teply, J.; McDonald, P.; Warbington, R. 1998.

Late-successional and old-growth forest effectiveness 

monitoring plan for the Northwest Forest Plan. Gen. 

Tech. Rep. PNW-GTR-438. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. 37 p.

Lint, J.; Noon, B.; Anthony, R.; Forsman, E.; Raphael, 

M.; Collopy, M.; Starkey E. 1999. Northern spotted 

owl effectiveness monitoring plan for the Northwest 

Forest Plan. Gen. Tech. Rep. PNW-GTR-440. Portland, 

OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 43 p.

Madsen, S.; Evans, D.; Hamer, T.; Henson, P. ; Miller, 

S.; Nelson, S.K.; Roby, D.; Stapanian, M. 1999. 

Marbled murrelet effectiveness monitoring plan for the 

Northwest Forest Plan. Gen. Tech. Rep. PNW-GTR-439. 

Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 51 p.

Mulder, B.; Alegria, J.; Czaplewski, R.; Ringold, P.; 

Tolle, T. 1995. Effectiveness monitoring: an interagency 

program for the Northwest Forest Plan with an emphasis 

on late-successional forest, northern spotted owl, 

marbled murrelet, survey and manage, and riparian 

and aquatic. Portland, OR: Research and Monitoring 

Committee, Regional Ecosystem Office. 51 p + 

appendices.

Mulder, B.; Noon, B.; Spies, T.; Raphael, M.; Palmer, 

C.; Olsen, A.; Reeves, G.; Welsh, H. 1999. The 

strategy and design of the effectiveness monitoring 

program for the Northwest Forest Plan. Gen. Tech. Rep. 

PNW-GTR-437. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research 

Station. 138 p.

Reeves, G.; Hohler, D.; Larsen, D.; Busch, D.; Kratz, 

K.; Reynolds, K.; Stein, K.; Atzet, T.; Hays, P.; 

Tehan, M. 2004. Effectiveness monitoring for the 

aquatic and riparian component of the Northwest Forest 

Plan: conceptual framework and options. Gen. Tech. 

Rep. PNW-GTR-577. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research 

Station. 71 p.

U.S Department of Agriculture, Forest Service; 

U.S. Department of the Interior, Bureau of Land 

Management. 1994. Final supplemental environmental 

impact statement on management of habitat for latesuccessional and old-growth forest related species 

within the range of the northern spotted owl. 

Volumes 1–2 + Record of Decision. 

U.S Department of Agriculture, Forest Service; 

U.S. Department of the Interior, Bureau of Land 

Management. 2002. Tribal monitoring under the 

Northwest Forest Plan. Interagency executive letter. 

On file with: Regional Ecosystem Office, 333 SW 

First Avenue, Portland, OR 97204.
6

GENERAL TECHNICAL REPORT PNW-GTR-648

Scott Graham
7

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Joseph Lint 1

Introduction

The population component of the owl monitoring plan 

tackled this question: Will implementing the Northwest 

Forest Plan (the Plan) reverse the declining population trend 

and maintain the historical geographic range of the northern 

spotted owl (Strix occidentalis caurina)? 

Population information to address this question came 

primarily from eight demographic study areas that were 

part of the federal effectiveness monitoring plan for the 

northern spotted owl (the monitoring plan) (Lint et al. 

1999). Three additional study areas, independent of the 

monitoring plan, also provided relevant population data; 

their data are used because they include lands managed under the Plan. These 11 study areas were spread throughout 

the owl’s range (fig. 2-1 and table 2-1). Data on owl occupancy, survival, and productivity were gathered annually 

from each study area to answer the question: What are the 

trends in rates of demographic performance in relation to 

adult survival, reproduction, and the annual rate of change 

of owl populations?

Results of the data analyses from the demographic 

study areas were reported by Anthony et al. (2004). The 

objectives of the population status and trend analyses 

were to:

• Estimate age-specific survival probabilities and 

fecundity rates, and their sampling variances, for 

individual study areas.

• Determine if any trends in adult female survival and 

fecundity exist across study areas.

• Estimate annual rates of population change (λRJS) and 

their sampling variances for individual study areas 

and across study areas.

• Examine the demographic performance of spotted owl 

populations on the eight areas that are the basis of the 

monitoring plan on federal lands (Lint et al. 1999).

Other pertinent questions examined by Anthony 

et al. (2004) were as follows:

• Were owl populations stationary (see “Glossary”) 

during the period of study?

• Were rates of survival and reproduction changing 

over time?

Chapter 2: Population Status and Trend

1 Joseph Lint is a wildlife biologist,U.S. Department of the 

Interior, Bureau of Land Management, Oregon State Office, 

777 Garden Valley Blvd., NW, Roseburg, OR 97470. He is the 

module leader for northern spotted owl effectiveness monitoring 

under the Northwest Forest Plan.

Table 2-1—Eleven demographic study areas associated with land managed under the Northwest Forest Plan

 Land Study Captures

Physiographic Years of management area Owls banded plus

Study area State province study class size Juveniles Nonjuveniles Total detections

Square

miles - - - - - - - - - - - - Number - - - - - - - - - - - 

Wenatchee Washington Eastern and 1990–2003 Mixed 1,795 752 448 1,200 2,556

 Western Cascades

Cle Eluma

 Washington Eastern Cascades 1989–2003 Mixed 689 502 222 724 1,570

Rainier Washington Western Cascades 1992–2003 Mixed 824 97 120 217 530

Olympica

 Washington Olympic Peninsula 1987–2003 Federal 1,270 516 469 985 3,568

Coast Rangea Oregon Coast Range 1990–2003 Mixed 1,513 574 451 1,025 3,386

Tyeea

 Oregon Coast Range 1985–2003 Mixed 672 610 422 1,032 3,293

H.J. Andrewsa

 Oregon Western Cascades 1987–2003 Federal 589 602 493 1,095 3,151

Southern Oregon Western Cascades 1991–2003 Federal 1,303 411 470 881 2,141

 Cascadesa

Klamatha Oregon Klamath 1985–2003 Mixed 534 698 449 1,147 2,964

NW Californiaa

 California Klamath 1985–2003 Federal 691 609 417 1,026 2,865

Marin California Coast 1998–2003 Federal 84 41 55 96 225

 Total 9,964 5,412 4,016 9,428 26,249

a Study area is one of eight monitored under the Northern Spotted Owl Effectiveness Monitoring Program for the Northwest Forest Plan.

Source: adapted from Anthony et al. 2004.
8

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure 2-1—Location and boundaries of the northern spotted owl demographic study areas (from Anthony et al. 2004). 

Northern spotted owl

demographic study areas

Washington

Wenatchee

Cle Elum

Rainier

Olympic Peninsula

Oregon

Oregon Coast Range

H.J. Andrews

Warm Springs

Tyee

Klamath

Southern Oregon Cascades

California

Northwest California

Hoopa Tribal Area

Simpson Resource Area

Marin

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

Lakes and rivers

Urban areas

Interstate highway o

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers
9

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

• Did barred owls (Strix varia) influence spotted owl 

survival or fecundity?

Spotted owl population status and trend were analyzed 

in 1991, 1993, 1998, and 2004. After 1993, the data were 

analyzed whenever additional 5-year increments were 

available. The 2004 analyses incorporated the complete data 

sets for the eight demographic areas under the monitoring 

plan as well as for six other independent demographic study 

areas in the owl’s range (Anthony et al. 2004). Results from 

the subset of the 11 study areas associated with federal 

lands were used in our monitoring report. Annual rate of 

population change was not calculated for the Marin study 

area, one of the three additional study areas, because of the 

few years of data.

Data Sources and Methods

The analysis of status and trends of the owl populations 

on federal lands in the Plan area relied on 11 demographic 

study areas (fig. 2-1). Eight of the 11 study areas—the 

Olympic Peninsula and Cle Elum in Washington; the H.J. 

Andrews, Southern Cascades, Coast Range, Klamath, and 

Tyee in Oregon; and Northwest California—were part of 

this monitoring plan (Lint et al. 1999). The other three study 

areas—Rainier and Wenatchee in Washington and Marin in 

California (fig. 2-1)—were independent of the monitoring 

plan. Because they are on federal lands in the Plan area, 

they provide additional data to assess the Plan’s effectiveness. The three remaining study areas are the Warm Springs 

and Hoopa on tribal lands and the Simpson located on 

private timber company lands. These three areas, along with 

the 11 mentioned above were part of the rangewide status 

and trend analyses by Anthony et al. (2004). Because they 

had no land managed under the Plan, they were not included 

in this monitoring report except when results for all 14 

study areas are provided. 

The size of the 11 study areas ranged from 84 to 1,795 

mi2. The median study area size was 691 mi2 and the mean 

size was 906 mi2 (table 2-1). Together, the study areas 

covered 9,964 mi2, or about 11 percent of the owl’s range. 

The periods covered by the data sets from the study 

areas ranged from 6 to 19 years. The study areas were primarily federal lands managed by the USDA Forest Service 

(FS), USDI Bureau of Land Management (BLM), and the 

USDI National Park Service (NPS). In some study areas, 

nonfederal lands, mainly owned by private timber companies, were intermingled with the federal lands, forming a 

checkerboard pattern of mixed ownership. 

Collecting Field Data 

Population data were collected annually in the field in each 

of the 11 demographic study areas. Biologists attempted 

to find and mark all spotted owls in each study area. They 

observed the marked owls by using standardized techniques to determine the age, sex, and reproductive status 

of individuals and to estimate the reproductive output by 

counting the number of young fledged (Franklin et al. 

1996). For additional information on the field methods for 

surveying, determining of sex and age, capturing, marking, 

and estimating reproductive output, refer to Franklin et al. 

(1996) and Lint et al. (1999).

Analyzing Population Status and Trend 

The status and trends of northern spotted owl populations 

in the demographic study areas were analyzed during an 

8-day (January 4 through January 11, 2004) workshop in 

Corvallis, Oregon. Some followup analyses and a workshop 

report were completed after the workshop. The workshop’s 

format followed the protocol for analyzing empirical data 

related to natural resource issues described by Anderson 

et al. (1999). Senior analysts with recognized expertise in 

population dynamics, statistics, and the analysis of capturerecapture data led the workshop and guided the analysis 

(app. A). The final, peer-reviewed results of the analyses 

are reported in Anthony et al. (2004).

Before the workshop, the empirical data from each 

of the demographic study areas were summarized and 

compiled into three data files for analysis: one was used for 

estimating age- and sex-specific survival, one for estimating 

age- and sex-specific fecundity, and the third for estimating 

population rates of change from marked, territorial owls.
10

GENERAL TECHNICAL REPORT PNW-GTR-648

The next four sections on developing data files and 

error-checking of data files, were summarized from the 

pre-workshop directions and guidelines sent to the principal investigators of the demographic study areas by the 

workshop’s organizers (Forsman 2003).

Developing Data Files for Survival Analysis 

The data file for survival-rate analysis was derived from a 

detailed capture-history matrix. Each record in the database 

represented a capture history for an individual owl. The 

capture histories for individual owls were built by using a 

system of ones and zeros. A “1” in the column for a specific 

year denoted an owl identified in that year, and a “0” in the 

column for the following year indicated the owl was not 

identified that year. The resulting capture strings of 1s and 

0s (such as 1 0 0 0 0 1 1 1 1 0) showed the capture history 

for successive years after the initial capture. 

Developing Data Files for Fecundity Analysis 

The fecundity database provided annual information on the 

occupancy status of the owl territory (site), the age of the 

owls present at the site, and the number of young fledged. 

Occupancy status reflects whether the site was occupied by 

a single male, single female, or a pair of owls. One record 

was created for each occupied site for each year, and the age 

of each owl was recorded. 

Spotted owl sites were checked annually, and the 

number of young seen out of the nest tree (fledged) was 

recorded. Documentation of the number of young fledged 

was based on a minimum of two visits to count the young 

after the date young were generally known to leave the nest 

tree in that geographic area. Techniques used to characterize the reproductive output of individual territorial owls are 

described in Franklin et al. (1996) and Lint et al. (1999). 

Checking for Errors in the Survival and 

Fecundity Database Files 

Compilation of the survival and fecundity database files 

was designed to ensure that all files were built correctly 

and that the field data supported the data in the digital files. 

All study-area leaders were provided with instructions 

for entering data in the analysis files. When the files were 

completed, they were checked for errors by members of the 

workshop-organizing team not associated with that specific 

data set.

The capture-history files were error-checked by randomly drawing 10 capture histories from each study-area 

file. The principal investigators were required to provide 

paper copies of the field-data forms that supported each 

capture history. 

For the fecundity-file error check, 10 records were 

selected at random from each study-area file. Again, the 

principal investigators were required to provide paper copies of the field-data forms that supported the data record of 

reproductive success in a given year for a specific female. 

If errors were found in the first round of error checking, 

another sample of 10 records was selected and the process 

was repeated. If errors were found in the second round of 

error checking, the principal investigator was directed to 

review the whole data file for errors. The sequence of error 

checking and data review was repeated until a set of 10 

randomly drawn records were without error. Copies of the 

error-checked records along with copies of the field-data 

forms submitted to confirm those records, were archived. 

Principal investigators signed statements before the data 

analyses to certify their data were accurate and ready for 

analysis.

Developing Data Files for Analyzing 

Rates of Population Change 

The analyses of rates of population change (λ) used a 

modified version of the capture-history matrix derived 

from the certified database file for survival analysis. The 

Reparameterized Jolly-Seber method (λRJS) was used to 

estimate the annual rates of population change (λ) (Nichols 

et al. 2000, Pradel 1996).

The analysis of apparent survival (φ) was restricted to 

territorial individuals (that is, nonjuveniles) and included 

both subadults and adults. Estimates of juvenile survival 

and fecundity were not needed to estimate λRJS; thus the 

problems associated with estimating those parameters 

in previous analyses were eliminated. Other limitations 

existed, however, such as spatial variability in the survey 

effort. For example, some portions of a study area may not 
11

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

have been as completely surveyed as others. If many owls 

were initially missed in these areas and later found, λ can 

be biased high because the individuals would be considered 

new recruits when they were already present in the population. Because of the temporal variability of spatial survey 

effort, the capture-history matrices used in this analysis 

came from a fixed area with relatively constant survey 

effort. 

Conventions for selecting the study area for analysis 

included the rule that sites or geographic areas could not 

be removed from the study area over time. Sites could have 

been added or the land area expanded during the course 

of the study, but only once, and on the condition that the 

expanded area was surveyed consistently for the remainder 

of the study. 

The capture histories used in the analysis of λRJS were 

from territorial individuals, either male or female. This data 

file was developed from the original capture histories. Owls 

that were initially captured as juveniles, but never recaptured, or were first recaptured off the study area of interest, 

were eliminated from the data file. For those birds initially 

captured as juveniles and later recaptured on the study area 

of interest, the juvenile portion of the capture history was 

eliminated and only the portion after the first recapture was 

retained. 

The principal investigators submitted descriptions of 

the sampling periods, the study areas, and a study area 

boundary map. The description also explained why the 

particular years and study areas were chosen and why 

they represented an area with reasonably consistent survey 

effort. Any expansion of study areas was also described 

with the approximate number of sites added. The maps 

showed the geographic location of each study area with 

original and expanded boundaries of the study areas 

delineated, where appropriate.

Two final preparatory steps—testing for goodness-offit to the Cormack-Jolly-Seber model and over-dispersion 

on the capture-recapture data and establishing agreed-upon 

analysis protocols among the principal investigators and 

analysts—were completed before the beginning of the 

analyses (Anthony et al. 2004).

Estimating Apparent Annual Survival 

The general approach (Anthony et al. 2004) used to analyze 

the capture-recapture data for survival estimates was to: 

• Determine a set of a priori models to analyze.

• Evaluate goodness-of-fit and estimate an overdispersion parameter (c) for each data set.

• Estimate the capture probabilities and apparent 

survival for each capture-recapture data set with the 

models developed in the first step by using program 

MARK (White and Burnham 1999).

• Adjust the covariance matrices and corrected 

Akaike’s information criterion (AICc) values with 

the over-dispersion parameter to obtain corrected 

quasi-Akaike’s information criterion (QAICc) values 

(White et al. 2001).

• Select an appropriate model for inference based on 

QAICc model selection (Burnham and Anderson 

2002). 

Discussions of the approach to estimating survival 

for individual study areas, as well as the meta-analysis of 

trends in survival, were presented in Anthony et al. (2004) 

and Franklin et al. (1996).

Anthony et al. (2004) also conducted meta-analyses of 

apparent survival by using ownership, geographic region, 

ownership with region interaction, and latitude in 27 model 

combinations. The criteria for the groups were explained in 

Anthony et al. (2004).

Estimating Fecundity 

The estimation of fecundity used annual data from each 

demographic study area to determine the number of young 

fledged per territorial female. These data included the number of young fledged, study area, the owl territory name, the 

year, and age of the female (Anthony et al. 2004). 

The fecundity data were analyzed under a maximum 

likelihood framework by using mixed models. This step 

was needed to account for a possible lack of independence 

in the data caused by females breeding on the same territory 

for many years (Anthony et al. 2004). The models applied 

to each study-area data set included the effects of age class, 

time, barred owls, even-odd years, land ownership, and 
12

GENERAL TECHNICAL REPORT PNW-GTR-648

geographic region (Anthony et al. 2004). The even-odd-year 

effect was the result of a cyclic pattern in the number of 

young fledged, with more in even years. (Anthony et al. 

2004).

Separate meta-analysis of the fecundity data used all 

eight monitoring plan demographic study areas. In the 

meta-analysis, the relations between geographic regions 

and land-ownership categories were also included (Anthony 

et al. 2004). The authors noted that a key feature of the 

meta-analysis was that the experimental units were year and 

region, not the individual females. The birds were subsamples in region-with-year interaction treatments applied 

to each demographic study area, so the study areas are the 

experimental units. A discussion of the methods used for 

estimating fecundity is provided in Anthony et al. (2004).

Estimating Annual Rate of Population Change 

The population change analyses determined whether 

spotted owl populations in the demographic study areas 

were stationary, increasing, or decreasing. If they were 

increasing or decreasing, the annual rate of change was of 

interest. The Reparameterized Jolly-Seber method (λRJS) 

was used to estimate the annual rates of population change 

(λ) (Nichols et al. 2000, Pradel 1996). A time-specific λRJS

was estimated from annual capture-history data from the 

individual demographic study areas. This estimate, based 

on survival and recruitment, reflected changes in population 

size resulting from reproduction, mortality, and movement 

(Anthony et al. 2004). Additional discussions of the method 

are in Anthony et al. (2004) and Pradel (1996). 

Estimating Realized Population Change 

Anthony et al. (2004) estimated the trajectory of the population, over time, by translating the annual estimates of λ for 

each study area into estimates of the realized change. This 

analysis provides understanding of how annual estimates of 

λ affect the trends in populations over time (Anthony et al. 

2004). The realized change was expressed as the percentage 

of the initial population present at the beginning of each 

study that was present in 2003. They provided the example 

that for year-specific lambdas of 0.9 (1993), 1.2 (1994), and 

0.7 (1995). The realized change would be 0.9 × 1.2 × 0.7 = 

0.756. This product indicated that the population in 1995 

was 75.6 percent of the starting population in 1993. It also 

shows that a λ greater than 1.0 (increasing population) in 

one year (1994) may not compensate for lambdas in other 

years that are below 1.0 (decreasing population) resulting 

in a population decline over time. 

Results

These results are a summary of the findings from the 

analyses of survival rates, fecundity rates, annual rate 

of population change, and estimated realized population 

change for the northern spotted owl reported by Anthony 

et al. (2004). Anthony et al. (2004) noted that the results 

of their analyses cannot be considered representative of 

demographic trends of spotted owls throughout the range 

because the study areas were not randomly selected and 

did not include all portions of the range. However, they 

stated the belief that their results are representative of most 

populations on federal land in the United States because 

the study areas were large, covered much of the owl’s 

geographic range, and included a variety of land ownerships and management strategies. Unless otherwise stated, 

the information reported pertains to the 11 demography 

study areas associated with federal land managed under 

the Plan. The report by Anthony et al. (2004), in particular, 

the discussion section, complements this summary. 

Survival 

Estimates of survival rates for spotted owls in the individual study areas differed by age class, and they were 

slightly higher for older owls. Estimated survival rates for 

1-year-old owls ranged from 0.42 to 0.86, and for 2-yearolds from 0.63 to 0.89. For adult owls >2 years old, the 

estimated survival rates ranged from 0.75 to 0.91 (table 

2-2). For the Plan study areas, survival for adults was 

>0.85 in all areas except Wenatchee and Rainier, and for 

adult female owls in the Marin study area. 

Declines in survival, over time, were detected in the 

Wenatchee, Cle Elum, Rainier, Olympic Peninsula, and 

Northwest California study areas. These declines are 

important because annual rates of population change have 
13

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

been found most sensitive to changes in adult survival. 

One of the keys to stationary populations is having high 

(>0.85), nondeclining, adult survival.

Results from the meta-analysis for the eight monitoring-plan demography areas failed to show any effect on 

survival from sex, ownership, or latitude. The survival 

effects noted were due to geographic region (for example, 

Washington Douglas-fir areas or Oregon/California 

mixed-conifer areas) and time. Study areas in the mixedconifer and Douglas-fir zones in Washington showed a 

major downward trend in survival, which was consistent 

with the declines in survival noted for all four of the 

individual study areas in Washington. 

The exploratory barred owl covariate was not a good 

predictor of apparent survival. Although strong evidence 

of a negative effect of barred owls on survival appeared 

in some study areas in Washington, a positive effect was 

found for the Southern Cascades study area in Oregon. 

The Southern Cascades finding was opposite the authors’ 

hypothesis and may be a spurious result, as barred owls 

occurred at less than 10 percent of spotted owl territories 

in the study area during most of the study period.

Fecundity 

Most of the fecundity information was from spotted owl 

territories occupied by adult females, with low frequency 

of occupation and breeding attempts by young females. The 

instances of 1- and 2-year-old female owls on territories 

accounted for only about 10 percent of the females sampled. 

Fecundity for 1-year-olds was 0.074 and for 2-year-olds, 

only 0.208. Age was the primary factor affecting fecundity, 

with adults having the highest fecundity of 0.372. 

Of the 11 study areas in the Plan’s area, the four with 

the highest adult fecundity were Cle Elum (0.574), Marin 

(0.530), Wenatchee (0.491), and Klamath (0.445) (table 

2-3). The lowest estimated adult fecundity rates were in the 

Rainier (0.253), Oregon Coast (0.260), and Olympic Peninsula (0.293) study areas. For the eight monitoring-plan study 

areas, Cle Elum and Klamath had the highest estimated 

fecundity rates, and the Olympic and Oregon Coast Range 

study areas had the lowest.

Variability in fecundity was partially explained by age 

and the even-odd year effects. Fecundity was high in evennumbered years and low in odd-numbered years. This effect 

was strongest in the decade after 1990 and less evident after 

Table 2-2—Survival rates for northern spotted owls by age class in the 11 Northwest Forest Plan demographic 

study areas 

Age class

Landowner 1 year old 2 years old ≥3 years old

Study area State class Survival Std. error Survival Std. error Survival Std. error

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

Wenatchee Washington Mixed 0.626 0.073 0.626 0.073 0.750 0.026

Cle Eluma Washington Mixed .860 .017 .860 .017 .860 .017

Rainier Washington Mixed .832 .020 .832 .020 .832 .020

Olympica Washington Federal .570 .117 .855 .011 .855 .011

Coast Rangea Oregon Mixed .721 .107 .886 .010 .886 .010

Tyeea Oregon Mixed .817 .042 .878 .011 .878 .011

H.J. Andrewsa Oregon Federal .415 .111 .883 .010 .883 .015

Southern Cascadesa Oregon Federal .725 .079 .725 .079 .854 .014

Klamatha Oregon Mixed .849 .009 .849 .009 .849 .009

NW Californiaa California Federal .810 .027 .810 .027 .869 .011

Marin California Federal .913b .035 .913b .035 .913b .035

 .824c .045 .824c .045 .824c .045

a Study area is one of eight monitored under the Northern Spotted Owl Effectiveness Monitoring Program for the Northwest Forest Plan.

b Male survival rate for Marin study area; survival rates for male and female are the same for other study areas.

c

 Female survival rate for Marin study area; survival rates for male and female are the same for other study areas.

Source: adapted from Anthony et al. 2004.
14

GENERAL TECHNICAL REPORT PNW-GTR-648

2001. The fecundity trend for the 11 Plan-related study areas 

was stationary for 6 study areas, decreasing for 4 areas, and 

increasing on 1 of the areas (table 2-4). For the eight monitoring-plan study areas, four had a decreasing trend and 

the other four were either stationary (3) or increasing (1).

The analyses using the barred owl covariate did not 

show any negative effects of barred owls on spotted owl 

fecundity, but the authors pointed out that the barred owl 

variable used in the analyses was year-specific, so it was 

coarse-grained and lacked the specificity to individual 

nest sites that may have been needed to fully evaluate 

the effect of barred owls on spotted owl fecundity.

The meta-analysis for the eight demographic areas 

under the monitoring plan indicated that the additive effect 

of geographic region and time was important in explaining 

the fecundity data. Land-ownership effect had little weight, 

as did the barred owl covariate. Fecundity for the eight 

monitoring-plan areas was highest in the mixed-conifer 

zone in Washington (0.596) followed, in descending order, 

by the Oregon/California mixed conifer (0.380), the Oregon 

Cascades Douglas-fir (0.373), the Washington Douglas-fir 

(0.310), and the Oregon Coastal Douglas-fir (0.306) regions. 

For the expanded analysis using all 14 study areas, the highest fecundity was, once again, in the mixed-conifer zone 

in Washington (0.590). The second-highest fecundity was 

in the California Coast region (0.442) on Simpson Timber 

Company lands. None of the 11 Plan-related data sets was 

in the California Coast region, however.

Annual Rate of Population Change 

The annual rate of population change (λRJS) was calculated 

for 10 of the 11 study areas associated with Plan-managed 

federal lands. Because of the few years of data for the 

Marin study area, no annual rate of population change was 

calculated. Estimates of λRJS, for the remaining 10 Planrelated study areas ranged from 0.896 to 1.005 (table 2-4 

and fig. 2-2). 

The point estimates of λ for all areas but one, Tyee, 

were under 1.0. Populations in the Tyee, Klamath, and 

Southern Cascades study areas in Oregon were stationary 

during the study period, based upon the 95-percent confidence interval. These three study areas were in the southern 

half of the spotted owl’s range. The two study areas with 

λRJS values significantly <1.0 (declining population) were 

Wenatchee and Cle Elum in Washington. The Rainier and 

Olympic Peninsula study areas in Washington, the Oregon 

Coast and H.J. Andrews study areas in Oregon, and the 

Northwest California study area also showed population 

declines. Six of these seven study areas were in the northern 

half of the range of the owl.

Table 2-3—Fecundity (young fledged per territorial female) rates of northern spotted owls by age class in the 

11 demographic study areas for the Northwest Forest Plan 

Age class

Landowner 1 year old 2 years old ≥3 years old

Study area State class Mean Std. error Mean Std. error Mean Std. error

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

Wenatchee Washington Mixed 0.050 0.050 0.290 0.085 0.491 0.058

Cle Eluma Washington Mixed .136 .097 .467 .117 .574 .069

Rainier Washington Mixed .000 .000 .000 .000 .253 .061

Olympica Washington Federal .071 .050 .267 .098 .293 .057

Coast Rangea Oregon Mixed .000 .000 .111 .045 .260 .050

Tyeea Oregon Mixed .054 .032 .201 .047 .319 .040

H.J. Andrewsa Oregon Federal .109 .091 .113 .060 .321 .045

Southern Cascadesa Oregon Federal .061 .046 .223 .082 .377 .059

Klamatha Oregon Mixed .070 .028 .285 .052 .445 .040

NW Californiaa California Federal .101 .066 .205 .052 .333 .032

Marin California Federal .275 .195 .271 .159 .530 .056

a

 Study area is one of eight monitored under the Northern Spotted Owl Effectiveness Monitoring Program for the Northwest Forest Plan.

Source: adapted from Anthony et al. 2004.
15

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure 2-2—Estimates of mean lambda from reparameterized Jolly-Seber analysis for 

northern spotted owls on 13 study areas in Washington, Oregon, and California (from 

Anthony et al. 2004, fig. 7).

1.10

1.05

1.00

0.95

0.90

0.85

0.80

0.75Wenatchee

Study area

Mean 

�

Cle Elum

Rainier

Olympic Peninsula

Warm Springs Reservation

Coast Range

H.J. Andrews

Tyee

Klamath

South Cascades

Northwest California

Hoopa Reservation

Simpson Timber

 

Table 2-4—Trends of fecundity, survival, and population for the 11 demographic study areas associated with 

land managed under the Northwest Forest Plan 

Population trend 

(from estimates

Landowner

λRJS of realized 

Study area State class Fecundity Survival Mean SE 95% CI population change)

Wenatchee Washington Mixed Stable Declining 0.917 0.018 0.882–0.952 Declining

Cle Eluma Washington Mixed Declining Declining .938 .019 .901–.976 Declining

Rainier Washington Mixed Stable Declining .896 .055 .788–.003 Declining

Olympica

 Washington Federal Stable Declining .956 .032 .893–1.018 Declining

Coast Rangea Oregon Mixed Declining Stable .968 .018 .932–1.004 Declining

Tyeea Oregon Mixed Increasing Stable 1.005 .019 .967–1.043 Stationary

H.J. Andrewsa

 Oregon Federal Stable Stable .978 .014 .950–1.005 Declining

Southern Cascadesa Oregon Federal Declining Stable .974 .035 .906–1.042 Stationary

Klamatha Oregon Mixed Stable Stable .997 .034 .930–1.063 Stationary

NW Californiaa

 California Federal Declining Declining .985 .013 .959–1.011 Declining

Marin California Federal Stable Stable NA NA NA NA

Weighted mean λRJS for all areas 0.966b

 0.0097

Weighted mean λRJS for 8 monitoring plan areas 0.976 0.007

Note: NA= not available.

λRJS = reparameterized Jolly-Seber estimate of rate of population change.

a

 Study area is one of eight areas monitored under the Northern Spotted Owl Effectiveness Monitoring Program for the Northwest Forest Plan.

b Weighted mean for λ calculated by using same method as Anthony et al. 2004 (Olson 2004).
16

GENERAL TECHNICAL REPORT PNW-GTR-648

The weighted mean λRJS for the 10 areas was 0.9660 

(SE = 0.0097), which equated to an average decline of 

about 3.4 percent annually (Olson 2004). The subset of 

eight study areas under the monitoring plan showed an 

estimated weighted average annual decline of 2.4 percent. 

Of the eight monitoring plan areas, Cle Elum, Olympic Peninsula, Oregon Coast Range, H.J. Andrews, and Northwest 

California showed declines, whereas the other three were 

relatively stationary (table 2-4). The weighted mean λRJS

for all 14 of the study areas in the owl’s range indicated a 

3.7-percent average annual decline per year for the study 

period.

Realized Population Change 

The realized population change estimate represented the 

trend in the proportion of the population remaining each 

year, given the initial population based on changes in λRJS

over time (Anthony et al. 2004, fig. 11a–11c). Population 

trends for the 10 areas (Marin not included) under the Plan 

are summarized in table 2-4. Populations in 7 of the 10 (Cle 

Elum, Wenatchee, Olympic, Rainier, H.J. Andrews, Oregon 

Coast Range, and Northwest California) study areas declined over the past decade. The decline was most prevalent 

in Cle Elum, Wenatchee, and Rainier, where only 40 to 60 

percent of the initial populations remain. Declines in the 

Olympic, H.J. Andrews, and Oregon Coast Range were not 

as steep but still resulted in the loss of 20 to 30 percent of 

the populations present in those areas. The populations in 

the Klamath, Tyee, and Southern Cascades were stationary.

Discussion and Conclusions

The Plan’s final supplemental environmental impact statement (FSEIS) (USDA USDI 1994: 3&4–212) discussed 

results from the 1993 spotted owl population status and 

trend analysis. The 1993 analyses estimated the owl population was declining at about 4.5 percent per year (λ = 0.9548, 

95 percent CI = 0.9162 to 0.9934). The FSEIS stated these 

results were “not surprising” given that the 1990 federal 

listing of the northern spotted owl was due to declining 

habitat with a strong inference that populations were also 

declining. The FSEIS went on to state “Given this history, 

it would be surprising if the rate of population growth of 

owls was equal to or greater than 1.0 with a stable population structure.” Both Thomas et al. (1990) and the Northern 

Spotted Owl Recovery Team (USDI 1992) projected that 

“habitat and owls would continue to decline for up to 50 

years before reaching a new equilibrium.” Like these 

strategies, the prognosis of population trend under the Plan 

was general, providing a qualitative rather than quantitative 

expectation of population trajectory. 

The results from the first 10 years of population monitoring under the Plan were both expected and unexpected. 

First, results from the realized population change analysis 

for 3 of the 10 study areas, all in southern Oregon, indicated 

stationary populations in those study areas. The fact that 

owl populations in some portions of the range were stationary was not expected just 10 years into the Plan given the 

general prediction of continued declines in the population 

in the first several decades of implementation. However, the 

average rate of population change across the range for Planmanaged lands showed an average decline of 3.4 percent. 

Anthony et al. (2004) pointed out that the rate of decline in 

some of the study areas was noteworthy, particularly the 

precipitous declines for the four study areas in Washington. 

The 7.1 percent average annual rate of decline for the four 

study areas in Washington was not gradual. The average 

annual rate of decline for study areas outside of Washington 

was 1.7 percent and more representative of a gradual rate 

of decline. The Plan FSEIS (USDA USDI 1994: 3&4–233) 

stated:

The actual rate of decline should be considered when 

thinking about the likelihood of decline continuing 

into the future. If the average decline projected from 

the demographic, about 4.5 percent per year, were to 

continue into the future, then the population would 

be reduced by half in about 15 years. This rate of 

decline could have very serious consequences in the 

near future.

 A population declining at 7 percent per year will have 

only about 34 percent of the initial population remaining 

after 15 years. A population declining at 1.7 percent per year 

will be about 77 percent of the initial level after 15 years. 

Anthony et al. (2004) stated that any predictions about past 
17

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

or future population trajectories for the populations they 

studied are not recommended. But, as discussed in the 

FSEIS in 1994, the actual rates of decline should be considered when thinking about the likelihood that decline may 

continue. In light of the annual rate of decline observed in 

Washington (7.1 percent) compared to the Plan-related study 

areas in the remainder of the range (1.7 percent), concern for 

the magnitude and direction of future population changes is 

greatest in Washington. 

Anthony et al. (2004) postulated that possible causes 

for declines in owl survival and populations may include 

high density of barred owls in study areas in Washington 

and parts of Oregon, loss of habitat from wildfire and 

harvest, poor weather conditions, and forest defoliation 

from insect outbreaks. They gave examples of harvest of 

owl habitat on nonfederal lands in the Cle Elum study area 

between 1990 and 2003 and wildfires in the Wenatchee 

study area during the same period. They also pointed out 

that population declines may have been due to the lag effect 

from loss of habitat in earlier decades from timber harvest 

and wildfire. The available information allows only speculation on the factors that may be involved and permits no 

assignment of degree or magnitude of effect on the population. Heading the list of possible factors in Washington is 

the presence of the barred owl throughout the spotted owl’s 

range in the state. Evidence of displacement of spotted owls 

by barred owls has been anecdotally noted in Washington 

and Oregon, but the territory-specific analyses and competitive interaction studies recommended by Anthony et 

al. (2004) may be needed to better assess the influence of 

barred owls on spotted owls.

The Plan FSEIS noted that the basis was not as strong 

for believing that owl populations have passed or will soon 

pass some threshold that would result in extirpation of the 

species from large parts of its range. The primary support 

for that conclusion was the evidence of population declines 

from the demography studies. Caution was given not to 

project these declines into the future because habitat loss, 

which was believed to have caused the population decline, 

would be dramatically slowed by any of the alternatives 

analyzed in the FSEIS. In addition, personal communications with the principal investigators in the demography 

areas indicated that none had seen evidence either in the 

form of available data or field knowledge to indicate that 

the owl populations in their study areas have passed or will 

soon pass a threshold leading to irreversible decline.

Ten years have passed since the Plan FSEIS assessment 

on spotted owls. The first decade of the future they wrote 

about is now the decade of Plan implementation that was 

monitored. Their concern for projecting into the future 

for the first decade has passed; the results are known. The 

FSEIS offered a number of reasons for believing the accelerating rates of population decline detected in the demographic studies in 1993 should not have been projected into 

the future. First, there was evidence that population trend 

was not the same across the range, and some portions of the 

range were thought to be closer to stability than indicated by 

the combined results for all the study areas. Ten years later, 

we see this prognosis was correct. Anthony et al. (2004) 

stated that populations appeared to be stationary in four 

study areas (three associated with federal lands) as a result 

of high survival and stable fecundity rates. Second, the 

FSEIS offered that late-successional reserves might be valuable as source areas for owl populations even if populations 

declined during the transition period. After the first decade, 

the reserve network prescribed under the Plan remains in 

place and has been effective in maintaining and restoring 

habitat as evidenced by the fact that about 98 percent of the 

habitat-capable area in the large reserved blocks was unaffected by stand-replacing timber harvest or wildfire (table 

3-13, chapter 3 of this report). Third, the reserves were distributed across the full range of environmental conditions. 

This distribution remains an integral part of the Plan. Some 

loss has occurred in the reserves owing to wildfire, but it 

was limited to local areas in a few provinces. Habitat loss 

on federal land was markedly reduced in the first 10 years of 

the Plan, but population declines in Washington during the 

same period may be a signal of an uncertain future despite 

efforts to maintain and restore habitat.

The magnitude of population declines in Washington 

does not immediately translate into a failure of the Plan. 

The Plan is habitat-based. The maintenance and restoration of habitat is the action element of the Plan. Although 

management of forest stands in the reserves is a proactive 
18

GENERAL TECHNICAL REPORT PNW-GTR-648

approach to habitat restoration, the long-term gains in 

habitat restoration will be produced by forest succession, 

a passive agent of change. Habitat is a key element in the 

management of spotted owls, but it may not be the primary 

factor affecting population stability in either the short or 

long term. The Plan is maintaining and restoring habitat, 

but habitat is also being removed under the Plan. It is likely 

too soon to detect the benefits of habitat management under 

the plan from demographic data. There is also the additional 

complication that owl population response is influenced by 

a combination of factors, not just habitat. Even so, habitat 

remains a key foundation element for the conservation and 

recovery of the spotted owl. 

Will implementing the Plan reverse the declining population trend and maintain the historical geographic range of 

the northern spotted owl? Based on the results of the first 

decade of monitoring we cannot answer this question because not enough time has passed to provide the necessary 

measure of certainty. However, the results from the first 

decade of monitoring do not provide any reason to depart 

from the objective of habitat maintenance and restoration as 

described under the Plan. The Plan’s contribution to habitat 

management remains a cornerstone of the conservation 

and recovery of the spotted owl, but future spotted owl 

conservation efforts may need to address more than habitat 

management. The “maintain and restore [habitat] and they 

will come” approach seemed to be the straightforward solution a decade ago when habitat loss was a primary reason 

for listing the owls as a threatened species. We recognize 

that other stressors, some already in action (barred owl) and 

some yet to be realized (West Nile virus), may complicate 

the conservation and recovery of the spotted owl. Habitat 

maintenance and restoration, as currently envisioned under 

the Plan, remain essential to owl recovery and will continue, 

but in the near term, new partners, old partners with new 

roles, new discussions, and new initiatives must address 

the other stressors and how they may change our goals and 

objectives for spotted owl conservation.

References

Anderson, D.R.; Burnham, K.P.; Franklin, A.B.; 

Gutiérrez, R.J.; Forsman, E.D.; Anthony, R.G.; 

White, G.C.; Shenk, T.M. 1999. A protocol for 

conflict resolution in analyzing empirical data related 

to natural resource controversies. Wildlife Society 

Bulletin 27: 1050–1058.

Anthony, R.G.; Forsman, E.D.; Franklin, A.B.; 

Anderson, D.R.; Burnham, K.P.; White, G.C.; 

Schwarz, C.J.; Nichols, J.; Hines, J.; Olson, G.S.; 

Ackers, S.H.; Andrews, S.; Biswell, B.L.; Carlson, 

P.C.; Diller, L.V.; Dugger, K.M.; Fehring, K.E.; 

Fleming, T.L.; Gerhardt, R.P.; Gremel, S.A.; 

Gutierrez, R.J.; Happe, P.J.; Herter, D.R.; Higley, 

J.M.: Horn, R.B.; Irwin, L.L.; Loschl, P.J.; 

Reid, J.A.; Sovern, S.G. 2004. Status and trends in 

demography of northern spotted owls, 1985–2003. Final 

report to the Regional Interagency Executive Committee. 

On file with: Regional Ecosystem Office, 333 SW First 

Avenue, Portland, OR 97204. 

Burnham, K.P.; Anderson, D.R. 2002. Model selection 

and multimodel inference: a practical informationtheoretical approach. 2nd ed. New York, NY: SpringerVerlag. 488 p.

Forsman, E. 2004. Personal communication. Research 

wildlife biologist. USDA Forest Service, Pacific 

Northwest Research Station, 3200 SW Jefferson Way, 

Corvallis, OR 97331.

Franklin, A.B.; Anderson, D.R.; Forsman, E.D.; 

Burnham, K.P.; Wagner, F.W. 1996. Methods for 

collecting and analyzing demographic data on the 

northern spotted owl. Studies in Avian Biology. 

17: 12–20.

Lint, J.; Noon, B.; Anthony, R.; Forsman, E.; Raphael, 

M.; Collopy, M.; Starkey E. 1999. Northern spotted 

owl effectiveness monitoring plan for the Northwest 

Forest Plan. Gen. Tech. Rep. PNW-GTR-440. Portland, 

OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 43 p.
19

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Nichols, J.D.; Hines, J.E.; Lebreton, J.-D.; Pradel R. 

2000. Estimation of contributions to population growth: 

a reverse-time capture-recapture approach. Ecology. 

81: 3362–3376. 

Olson, G. 2004. Personal communication. Wildlife 

biometrician. Nash Hall, Oregon State University, 

Corvallis, OR 97330.

Pradel, R. 1996. Utilization of capture-mark-recapture for 

the study of recruitment and population growth rate. 

Biometrics. 52: 703–709.

Thomas, J. W.; Forsman, E.D.; Lint, J.B.; Meslow, E.C.; 

Noon, B.R.; Verner, J. 1990. A conservation strategy 

for the northern spotted owl: a report of the Interagency 

Scientific Committee to address the conservation of the 

northern spotted owl. Portland, OR: U.S. Department 

of Agriculture, Forest Service; U.S. Department of the 

Interior, Bureau of Land Management, Fish and Wildlife 

Service, National Park Service. 427 p. 

U.S Department of Agriculture, Forest Service; 

U.S. Department of the Interior, Bureau of Land 

Management [USDA USDI]. 1994. Final supplemental 

environmental impact statement on management of 

habitat for late-successional and old-growth forest related 

species within the range of the northern spotted owl. 

Volumes 1-2 + Record of Decision. 

U.S. Department of the Interior. 1992. Final draft 

recovery plan for the northern spotted owl. Unpublished 

report. U.S. Department of the Interior. Washington, 

D.C. On file with Region 1 office of the U.S. Fish and 

Wildlife Service, Portland, OR.

White, G.C.; Burnham, K.P. 1999. Program MARK: 

survival estimation from populations of marked animals. 

Bird Study. 46: 120–138.

White, G.C.; Burnham, K.P.; Anderson, D.R. 2001.

Advanced features of Program Mark. In: Field, R.; 

Warren, R.J.; Okarma, H.; Sievert, P.R., eds. Wildlife, 

land, and people: priorities for the 21st century. 

Proceedings of the Second International Wildlife 

Management Congress. Bethesda, MD: The Wildlife 

Society: 368–377. 
20

GENERAL TECHNICAL REPORT PNW-GTR-648

Frank Oliver
21

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Raymond Davis1 and Joseph Lint 2

Introduction

The primary contribution of the Northwest Forest Plan 

(the Plan) to conserving the northern spotted owl (the 

owl) was the federal network of reserved land use allocations designed to support clusters of reproducing owl 

pairs across the species’ range. These “reserves” include 

late-successional reserves, adaptive management reserves, 

congressionally reserved lands, managed late-successional 

areas, and larger blocks of administratively withdrawn 

lands (fig. 3-1). Federal lands between these reserves were 

designed to provide habitat to allow movement, or dispersal, 

of owls from one reserve to another. The “between” lands 

are a combination of matrix, riparian reserves, smaller 

tracts of administratively withdrawn lands and other smaller 

reserved areas such as 100-acre owl core areas. Individually, these areas may not support clusters of reproducing 

owls, but in combination provide population connectivity 

between the clusters. Monitoring the condition and trend of 

owl habitat on federal land—larger, reserved blocks as well 

as the land between them—are keys to assessing the success 

of maintaining and restoring owl habitat under the Plan.

Owl habitat is the term we use for forest stands used by 

territorial owls. It is commonly described in discrete terms 

as nesting, roosting, and foraging habitat (Lint et al. 1999), 

but it is more ambiguous. Thomas et al. (1990: 164) stated:

Structural components that distinguish superior owl 

habitat from less suitable habitat in Washington, 

Oregon and northwestern California include a multilayered, multispecies canopy dominated by large 

(>30 inches d.b.h.) conifer overstory trees, and an 

understory of shade-tolerant conifers or hardwoods; 

a moderate to high (60 to 80%) canopy closure; 

substantial decadence in the form of large, live 

Chapter 3: Habitat Status and Trend

1 Raymond Davis is a wildlife biologist, U.S. Department of 

Agriculture, Forest Service, Umpqua National Forest, 2900 

Stewart Parkway, Roseburg, OR 97470. 

2 Joseph Lint is a wildlife biologist, U.S. Department of the 

Interior, Bureau of Land Management, Oregon State Office, 777 

Garden Valley Blvd., NW, Roseburg, OR 97470. He is the module 

leader for northern spotted owl effectiveness monitoring under 

the Northwest Forest Plan.

coniferous trees with deformities—such as cavities, 

broken tops, and dwarf mistletoe infections; numerous large snags; ground-cover characterized by large 

accumulations of logs and other woody debris; and 

a canopy that is open enough to allow owls to fly 

within and beneath it.

The term dispersal habitat is commonly used to 

describe forest stands used by juvenile owls during movement away from natal areas or by subadult and adult owls 

moving from one territory to another (Forsman et al. 2002). 

Forest stands with average tree diameters >11 in and conifer 

overstory trees with closed canopies (>40 percent canopy 

closure) with open space beneath the canopy to allow for 

the owls to fly are considered owl dispersal habitat (Thomas 

et al. 1990). As such, all owl habitat meets the definition 

of dispersal habitat, but not all dispersal habitat meets the 

definition of owl habitat as it may be lacking the necessary 

structure for nesting or roosting.

Owl habitat monitoring addresses the central question 

about both owl habitat and dispersal habitat: Is owl habitat 

and dispersal habitat being maintained and restored as 

prescribed under the Plan? Additional questions were aimed 

at providing information to address the central question:

• What proportions of the total landscape on federal 

lands are capable of developing into owl habitat?

• What proportions of the total landscape on federal 

lands are owl habitat and dispersal habitat?

• What are the trends in amount and changes in distribution of owl habitat, particularly in the large, 

reserved blocks?

• What are the trends in amount and distribution 

of dispersal habitat outside of the large, reserved 

blocks?

• What are the sizes and distribution of owl habitat 

patches?

• What primary factors are leading to loss and fragmentation of both owl habitat and dispersal habitat, 

that is, the change agents and amount of loss, and 

what are the trends associated with these changes?
22

GENERAL TECHNICAL REPORT PNW-GTR-648

Land use allocations of

the Northwest Forest Plan

Reserved land use allocations

Congressionally reserved areas (CR)

Late-successional reserves (LSR)

Marbled murrelet sites (LSR-3)

Spotted owl cores (LSR-4)

Managed late-successional areas (MLSA)

Adaptive management areas (AMA)

Administratively withdrawn (AW)

Nonreserved land use allocations

Matrix/riparian reserves (Matrix/RR)

Adaptive management areas (AMA)

Other

Department of Defense and 

 National Wildlife Refuges

Not designated

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

Lakes and rivers

Urban areas

Interstate highway o

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers

Figure 3-1—Northwest Forest Plan land use allocations.
23

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

To address these questions, we used both spatial and 

nonspatial analysis methods. Lint et al. (1999) explained the 

basis for spatial monitoring of habitat under the spotted owl 

effectiveness monitoring plan, as follows:

The basic information needed for range-wide monitoring of spotted owl habitat is a set of map layers 

that collectively characterize spotted owl habitat 

and dispersal habitat. An overlay of map layers will 

allow the development of a GIS-compatible database 

used to describe amount and distribution of habitat 

in relation to land allocations or other geographic 

areas of interest. Once developed, the map would 

be updated periodically to track habitat change. 

Periodic updates of the map layers in the near term 

will allow the estimation of changes in amount and 

distribution of habitat over time resulting primarily from timber harvest and wildfires. Changes in 

vegetation due to forest succession are not expected 

to provide any significant changes in habitat condition for several decades.

Nonspatial habitat monitoring was based on the same 

premises as the spatial monitoring, but it assessed habitat 

conditions independent of the spatial analysis. This approach estimated forest attributes important to owl habitat 

through plot measurement data. The nonspatial assessment 

used grid-plot data from the current vegetation survey 

inventory program on USDA Forest Service (FS) and USDI 

Bureau of Land Management (BLM) lands in Oregon and 

Washington. Forest Inventory Analysis Program data were 

used for FS lands in California. Plot data were not available 

for the USDI National Park Service (NPS) lands or for BLM 

lands in Washington or California. 

The condition of owl habitat was reported at three 

broad geographical scales: (1) the physiographic province, 

(2) the state, and (3) the range of the owl. For the spatial 

analysis only, owl habitat conditions were also reported by 

land use allocation. An additional province-scale assessment examined owl habitat condition, both spatially and 

nonspatially, inside and outside of the large, reserved 

habitat blocks (reserved blocks). 

We used a step-wise approach to analyzing and 

reporting habitat conditions. First we estimated the federal 

land area covered by the Plan. We further refined this area 

by estimating the percentage capable of growing forests 

and the subset of the forest-capable area that was capable 

of producing owl habitat. The habitat-capable area was the 

basis for model application and analyses of model outputs.

Habitat, Habitat Suitability, and 

Habitat Modeling

Habitat models frequently address the potential of a given 

location to serve as habitat (Hill and Binford 2002). Habitat, 

in the strictest sense, implies not only species occupancy, 

but also incorporates elements of survival and reproduction 

(Block and Brennan 1993, Hall et al. 1997, Morrison et al. 

1992). Hall et al. (1997) provided the following definition 

and discussion of habitat:

We therefore define ”habitat” as the resources and 

conditions present in an area that produce occupancy—including survival and reproduction—by 

a given organism. Habitat is organism-specific; 

it relates the presence of a species, population, or 

individual (animal or plant) to an area’s physical and 

biological characteristics. Habitat implies more than 

vegetation or vegetation structure; it is the sum of 

the specific resources that are needed by organisms. 

Wherever an organism is provided with resources 

that allow it to survive, that is habitat.

Hall et al. (1997) also noted that it is possible to have 

“used” and “unused” habitats (occupied and unoccupied). 

They suggested that unoccupied habitat can occur for species that use patchy habitats where some of the patches are 

unused, at least temporarily. In the case of the owl, habitat 

patches may be unused (temporarily) perhaps owing to the 

patch size or distance from other used habitat. It was not the 

purpose of our analysis to identify occupied versus unoccupied habitat or predict the likelihood of owl occupancy 

on the landscape. That prediction is part of another element 

of the monitoring program that is attempting to develop 

models to predict the likelihood of occupancy for a given 

landscape habitat mosaic (refer to the “Predictive Model 
24

GENERAL TECHNICAL REPORT PNW-GTR-648

Development” section in chapter 5 of this report). Our 

objective was to produce maps of forest stands (regardless 

of patch size and spatial configuration) that showed their 

level of similarity to stand conditions known to be used by 

spotted owls. 

Habitat suitability indices are often used for assessing 

the suitability of an area as a function of several environmental variables that most affect species presence, abundance, and distribution (Morrison et al. 1992). However, 

habitat suitability is a term that Hall et al. (1997) advised 

against using because (by definition) all habitat is suitable 

and there is no such thing as unsuitable habitat. Instead, 

they suggested using the term “habitat quality,” which 

they defined as a continuous variable, ranging from low to 

medium to high, based on the ability of the environment to 

provide conditions appropriate for survival and reproduction (including demographic features). Thomas et al. (1990) 

used the terms “habitat suitability” and “habitat quality” 

interchangeably (Thomas et al. 1990: app. F):

Discussions of an organism’s habitat usually include 

assessments of its relative “value” or “suitability.” 

For any species, habitat suitability for various life 

functions—breeding, feeding, and cover—is not 

identical in all possible habitats. Suitability of 

different types of habitat can be graded from excellent to poor, which means that habitat suitability 

values tend to be continuous as opposed to discrete. 

Partitioning of habitats into categories, however, 

facilitates discussion. 

We used the term “habitat suitability” in our report 

because it is the descriptor used by Hirzel et al. (2002) for 

the output of their habitat model, which we used in our 

analyses. We did not consider it appropriate to change the 

nomenclature of the model output, but thought it was important to briefly discuss “habitat” and habitat-related terms to 

avoid confusion or misunderstanding of the use of the term 

“habitat suitability” in this report. It might be more helpful 

to view “habitat suitability” in terms of “habitat similarity.” 

The “suitability” statistic calculated by the model we used 

is based on the similarity of the biotic and abiotic characteristics of a habitat-capable map unit to the characteristics 

of sites inhabited by territorial owls. Habitat “similarity” 

ranges on a continuous scale from 0 to 100, where a value 

close to zero signifies that an individual map unit (pixel) has 

little in common with the conditions found where territorial 

owls are present, and those with values close to 100 have 

much in common with sites having territorial owl presence. 

Although use of habitat suitability indices prohibits 

exact population estimates, they can be used to compare 

one area to another (Dettki et al. 2003), or (as in the case 

of monitoring) the same area at different times. We used a 

set of histograms to profile habitat suitability for the three 

spatial scales previously described, and for individual land 

use allocations. Finally, we examined changes to the habitat 

suitability profiles caused by stand-replacing timber harvest 

and wildfire during the monitoring period (1994–2003).

Data Sources 

Vegetation Map Data 

The sources of vegetation data for habitat monitoring were 

the database from the classification and assessment via 

Landsat Thematic Mapper (TM) of visible ecological groupings (CALVEG) in California and the interagency vegetation mapping project (IVMP) in Oregon and Washington. 

Data from both sources were from the 1992–96 period, 

about the time of Plan implementation. We did not have 

spatial vegetation data from 2002 or 2003 for comparison of 

vegetation conditions (growth or loss) after 10 years of Plan 

implementation. The only vegetation change agents we were 

able to analyze were stand-replacing timber harvest and 

wildfire that occurred during the monitoring period. This 

was done by using the change-detection layer described 

later in this section.

The CALVEG data set comprises polygon data derived 

from 1994 satellite images. The minimum mapping unit 

was 2.5 ac. Qualitative categories or classes of vegetation 

type, tree cover, and overstory tree size were the polygon 

attributes we used. The average polygon was about 10 to 15 

ac, but some were hundreds to thousands of acres. Tree-size 

maps were slightly less accurate than tree-cover maps. 

Traditional (right or wrong) accuracy for size was about 42 

to 60 percent, but it improved to 68 to 78 percent with fuzzy 

(see “Glossary”) accuracy standards (± one class). User’s 
25

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

 3 User’s accuracy is associated with errors of inclusion that include 

an element in one class when it belongs in another. Producer’s 

accuracy is associated with errors of exclusion that exclude an 

element when it does belong in a class or category.

and producer’s accuracy3 for specific tree-size classes 

ranged from about 10 to 40 percent (traditional), improving to 80-plus percent for some map classes under fuzzy 

accuracy standards. Cover accuracies were 49 to 68 percent 

(traditional) and increased to 75 to 83 percent based on 

fuzzy accuracy assessments (http://www.fs.fed.us/r5/rsl/

projects/mapping/accuracy.shtml). Refer to Moeur et al. 

(2005) for additional discussion of accuracy assessments.

Satellite imagery information from the 1992–96 period 

was used to map vegetation conditions in Oregon and Washington. The IVMP products were 25-m-resolution (82-ft), 

raster-based data (0.15-ac pixel) of continuous (a numerical 

scale that can be subdivided into an infinite number of 

intervals—Schreuder et al. 2004), quantitative, or qualitative classes of vegetation cover, conifer canopy cover, tree 

size, and cover type. Traditional (right or wrong) accuracy 

for size was about 33 to 44 percent, which improved to 61 

to 87 percent with collapsed classes. User’s and producer’s 

accuracy for specific tree-size classes ranged from about 13 

to 71 percent (traditional). Traditional cover-class accuracies ranged from 10 to 86 percent, and map accuracies for 

cover classes ranged from 44 to 68 percent (traditional) 

and increased to 57 to 80 percent based on fuzzy accuracy 

assessments (http://www.or.blm.gov/gis/projects/ivmp.asp). 

Refer to Moeur et al. (2005) for additional discussion on 

IVMP and CALVEG.

These two vegetation data sources required slightly 

different habitat-map modeling strategies. To the extent 

possible, vegetation map attributes for modeling were made 

as consistent as possible between the two data sources. The 

final map products from California, however are not directly 

comparable to the maps from Oregon and Washington. In 

general, the finer resolution of the Oregon-Washington data 

and its division into specific vegetation attributes with continuous values was better suited for habitat modeling. The 

coarser nature of the California polygons may have affected 

model output. Engler et al. (2004) found that fine-resolution 

habitat predictors were superior to coarse-resolution data 

(25-m [82-ft] versus 500-m [1,640-ft] raster data). The lower 

model performances they observed at the 500-m resolution 

(roughly 62-ac pixel size) were probably caused by the 

unavoidable loss of information when environmental map 

units are aggregated. This aggregation of information may 

sometimes hide important combinations of habitat predictors that would otherwise be detectable with finer resolution 

data. This fine- versus coarse-scale dilemma might also apply to the “resolution” of habitat predictor attributes. When 

map pixels are assigned a unique continuous value from 

0 to some maximum (for example, 0 to 75-in diameter), a 

finer resolution of measure is obtained than with coarse 

categorical “class” attributes (for example, 0 to 4.9 in, 5 to 

9.9 in, ...etc.). Although nonspatial in nature, attribute classification aggregates information and may also cause loss of 

important data. This attributing problem (lack of continuous 

data) occurred in all attributes of the CALVEG data and 

may have reduced model performance in the eastern Cascades physiographic provinces of Oregon and Washington, 

because of a lack of continuous tree-size data.

Land Use Allocation Data 

An updated map of the Plan’s land use allocations was 

produced under the direction of the Regional Interagency 

Executive Committee in 2004. The new version of the map 

updated the original 1994 coarse-scale land use map (40-ac 

resolution) to correct inconsistencies for certain land use 

allocations among administrative units and to incorporate 

land use allocation changes since the Plan was implemented. Although improvements were made in the accuracy of 

land use allocation mapping, including representing the 

allocations at a finer resolution, the map product still had 

some limitations (fig. 3-1). The most significant limitation 

was its inability to show the riparian reserves. Lands 

outside of reserves, that might be mapped as riparian 

reserve, were combined with lands in matrix so that neither 

allocation was individually identifiable. Also, the variation 

in the size and classification criteria for administratively 

withdrawn areas resulted in inconsistent mapping across the 

range of the owl. Other mapping-related problems included 

inconsistent edge matching of allocations between some 
26

GENERAL TECHNICAL REPORT PNW-GTR-648

adjacent administrative units (for example, national parks4

and national forests) creating “sliver-sized gaps” in the map, 

and inconsistent attributing of large bodies of water. These 

problems, with the exception of the riparian reserve 

limitation, were dealt with case by case and either corrected 

or considered minor, in light of the analysis scale, and let 

stand. The allocation classes in the map were as follows:

• CR, congressionally reserved

• LSR, late-successional reserves

• AMR, adaptive management areas in reserves 

(an allocation designed to display the areas’ acres 

in late-successional reserves)

• MLSA, managed late-successional areas

• AW, administratively withdrawn

• LSR-3, marbled murrelet reserved areas

• LSR-4, 100-ac spotted owl cores

• AMA, adaptive management areas

• MATRIX/RR, matrix (which contains riparian 

reserves that were not mapped)

• ND, not designated (lands with no assigned land 

use allocation)

Spotted Owl Presence Data 

Owl presence data were compiled from various FS, BLM, 

NPS and nonfederal cooperators’ databases. These data 

were collected by wildlife biologists using standardized 

survey protocols to determine spotted owl occupancy 

and reproductive status in defined geographical locations 

(USDA FS 1988) for project clearance and demographic 

monitoring (Forsman 1995, USDI FWS 1992). Results 

of these surveys were recorded in [digital] databases and 

displayed in a geographic information system (GIS) format. 

The GIS data were linked to vegetation data to identify 

forest stands where owls were repeatedly detected, many of 

which were confirmed nest groves and, sometimes, the nest 

trees. From this rangewide (range of the owl) set of data, 

only points representing the presence of owl pairs (male 

and female) were used (n = 8,967). Each point represented 

an actual historical location of concentrated territorial owl 

activity from the late 1980s through the 1990s (fig. 3-2). 

Sometimes, especially for demographic study areas, more 

than one point signified the same owl pair. The separate 

locations denoted distinct nesting or roosting locations pairs 

used in different years. The locations were usually within 

the same general geographic location or forest stand.

An assessment of the spatial accuracy of each owl 

location was performed by overlaying the GIS points on 

digital orthophotos (1- to 3-m [3- to 9-ft] resolution) or 

satellite images (10-m [33-ft] resolution) in ArcView GIS. 

Sometimes, wildlife biologists familiar with the data were 

consulted and asked to conduct an independent quality 

control of a location’s spatial accuracy by using original 

data sources, maps, or orthographic photos. Many points 

were dropped from the original data set because they were 

in nonforested areas, (for example, clearcuts), which may 

have been owl habitat when the surveys were done, but 

subsequently harvested and thus no longer owl habitat and 

without value as training information for the model. Owl 

locations used for habitat modeling were always accurate 

to the forest stand in which the owl pair was recorded, and 

usually accurate to within 100 m (328 ft) of the actual location where the owls were observed in that stand. Prior to 

habitat modeling, the owl location data were converted into 

Boolean (values of 0 or 1) species maps matching the pixel 

resolution of the underlying vegetation data. Map values of 

“1” represented proof of territorial owl presence, and values 

of “0” represented a lack of proof of presence.

Change Detection Data 

Three data sets were used to estimate the amount of habitat 

change during the monitoring period (1994 to 2002–03). For 

Oregon and Washington, a change-detection grid (officially 

titled Stand-Replacing Harvests and Fires in Oregon, 1972 

through 2002), was developed by using multiple Landsat 

TM images dating from 1972 through 2002. Data for 

2003 were not available. Change pixels were smoothed, 

and filtered to a minimum mapping unit of 2 ha (5 ac). 

Disturbance by fire was separated from disturbance owing 

to harvest by visual inspection and expert knowledge. Refer 

to Cohen et al. (1998) for a description of their analysis 

methods. 

 4 National Park Service lands in the redwood region of California 

also included acres of state park lands that were not split out from 

the federal lands in the map update.
27

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure 3-2—Northern spotted owl range and owl pair-presence point locations.

Northern spotted owl

presence location data

 Territorial owl locations

 Demographic study areas

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers

Lakes and rivers

Urban areas

Interstate highway o
28

GENERAL TECHNICAL REPORT PNW-GTR-648

For California, change-detection data were generated 

in two parts by the USDA Forest Service and California 

Department of Forestry and Fire Protection. The first 

part was developed in 1998 (Levien et al. 1998) by using 

Landsat TM data from 1990 through 1998 to identify and 

map areas of vegetation change and to determine the cause 

of the change by using ancillary data and fieldwork. This 

part was then supplemented by using six additional Landsat 

TM scenes to create a map of stand-replacing disturbance 

during 1998–2003. Processing decisions about the degree 

of change were conservative so that possible change events 

meeting the “stand-replacing” criterion (Maurizi 2004) 

would not be excluded. Figure 3-3 displays the stand-replacing timber harvest and wildfire events that compose the 

change-detection layer. Refer to Moeur et al. (2005) for 

additional discussion of the change-detection map.

Wildfire Occurrence Data 

Spatial wildfire occurrence data were used to analyze 

the densities of wildfire across the owl’s range during the 

monitoring period as a complement to the change-detection 

data. The data set was developed by the Climate, Ecosystem, and Fire Applications (CEFA) Program of the Desert 

Research Institute (Brown et al. 2002). These data are a 

coarse assessment of historical federal wildfire occurrence 

records from the National Interagency Fire Management 

Integrated Database (NIFMID). In it, wildfire occurrence 

points were “flagged” as to their suitability for subsequent 

analyses (Brown et al. 2002). We 

used the portion of this data set 

from 1994 to 2002 for the FS-, 

BLM-, and NPS-managed lands 

within the range of the spotted 

owl (n = 13,159). Only points with 

a “flagged” value of “0” (acceptable for reporting) were used. 

A few minor corrections were 

made for large fires >10,000 ac in 

Oregon and Washington, based 

on cross-referencing this data 

set with an updated (1994–2003) 

NIFMID query.

Historical Forest Maps 

Maps created from the first large-scale forest survey of the 

Pacific Northwest in the early 1930s (Andrews and Cowlin 

1940, Harrington 2003) and from 1945 forest surveys 

by the California Forest and Range Experiment Station 

(Wieslander and Jensen 1946) were used to compare baseline (Plan implementation, 1994) and current owl habitat 

conditions with historical conditions. These historical 

forest maps were created by using intensive field reconnaissance methods and aerial photographs (Harrington 

2003, Wieslander and Jensen 1946). Timberland types 

from the Pacific Northwest map and age classes from 

the California map were used to create a proxy map of 

historical owl habitat (table 3-1). We also assumed that all 

cutover areas represented harvest of large timber, and they 

were included in our estimate of historical owl habitat.

Current Vegetation Survey Data 

The current vegetation survey (CVS) inventory is comprehensive information on vegetative resources on FS lands in 

Oregon and Washington and BLM lands in the Plan area 

in Oregon. There are no data for NPS lands or BLM lands 

in California. The CVS consists of four, 3.4-mi grids of 

field plots that are offset from one another to produce one 

1.7-mi grid across BLM lands and all FS lands except in 

wilderness areas where the grid intensity is 3.4-mi panels. 

The forest inventory analysis (FIA) provided data for FS 

lands in California. The FIA plots are on a 3.4 mi grid.

Table 3-1—Timberland types from the 1930s Pacific Northwest forest 

resources inventory mapping used to estimate historical owl habitat 

across the owl’s range

Timberland types and age classes Diameter at breast height

Douglas-fir old growth 22–40, 40+ inches

Douglas-fir large second growth 22–40 inches

Spruce hemlock, large 20+ inches

Cedar redwood, large 24+ inches

Balsam firs, mountain hemlock upper-slope types, large 16+ inches

Pine mixture, large 22+ inches

Recent cutovers Clearcut since the 1920s

Nonstocked cutovers Clearcut before 1920s

Old growth (California map) Not defined

Young growth–old growth (California map) Not defined 
29

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Stand-replacing disturbance

1994–2003

Change agent 

Timber harvesting

Wildfire

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

Lakes and rivers

Urban areas

Interstate highway o

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers

Figure 3-3—Stand-replacing timber harvest and wildfires during the first 10 years of Plan implementation. 
30

GENERAL TECHNICAL REPORT PNW-GTR-648

The primary sampling unit is 1 ha (2.47 ac) with five 

fixed-radius subplots in a nested design. There is one 

subplot located at the plot center and four subplots each 

in a cardinal direction and 133.9 ft from the center of the 

plot (Max et al. 1996). For specific information on the 

attributes that are collected on FS lands, refer to the Web 

site: http://www.fs.fed.us/r6/survey/. Refer to Moeur et al. 

(2005) for additional discussion of the current vegetation 

survey and forest inventory analysis.

Other Ancillary Data Sources 

We used several other data sources to support our habitat 

modeling effort. These included USDI Geological Survey 

(USGS) digital elevation models (DEMs) at 10-m (33-ft) 

resolution for Oregon and Washington and resampled to 

match IVMP data resolution of 25 m (82 ft). In California, USGS 30-m (100-ft) elevation models were used in 

California, and CALVEG data were resampled to match 

its resolution. Other sources were the Environmental 

Protection Agency’s (EPA) level III ecoregion map units 

(Omernik 1987), EPA level IV ecoregion subdivisions 

(Woods et al. 2003), forest-level soils maps, potential 

natural vegetation maps (Eyre 1980), and the geologic 

map of California (Jennings 1977).

Analysis Methods

Estimating Federal Land Area 

Our analysis of spotted owl habitat focused only on federal 

(public-owned) land in each physiographic province; 

privately owned land was not included. National wildlife 

refuges (Fish and Wildlife Service) and military reservations (Department of Defense) were also omitted from the 

analyses because they make up a very small proportion 

(<1 percent) of federal land in the owl’s range (Refer to 

Moeur et al. (2005) for data on these lands). 

The federal land in our analysis is land administered 

by the FS, BLM, and NPS. In the analysis, the combined 

lands for all three agencies are referred to as “federal 

land,” unless otherwise specified.

A GIS layer of federal land was created by using the 

land use allocation map layer and GIS techniques (fig. 

3-1). Large water bodies, (lakes and reservoirs), wildlife 

refuges, and military reservations were omitted. The 

federal land layer was queried by physiographic province 

to produce estimates of federal land area in each province. 

Province estimates were summed to obtain state and 

rangewide estimates.

Estimating “Forest-Capable” Federal Area 

Not all federal land in the range of the owl can grow forests. 

Unproductive areas include rock outcrops, barren lands, 

alpine meadows, and snow-covered mountain peaks. GIS 

methods were used to subtract nonforested areas delineated on the IVMP and CALVEG layers to estimate the 

federal forest-capable area. Forest-capable federal land was 

estimated for each physiographic province and summed to 

obtain estimates for each state and the range.

Estimating “Habitat-Capable” Federal Area 

Not all of the forest-capable federal land can develop owl 

habitat. Examples of nonhabitat are forested land higher in 

elevation than where territorial owls are found and forests 

on serpentine soils, which do not attain the necessary tree 

size and canopy closure to provide habitat for territorial 

owls. The federal forest land omitted was identified by 

using a rangewide elevation isopleth and geologic maps 

identifying serpentine soils. 

The elevation isopleth was developed by using a linear 

regression analysis on the relation between the elevation 

of the owl-pair activity center and latitude as defined by 

Universal Transverse Mercator (UTM) northings. Because 

of the extreme north-to-south extent of the owl range (about 

770 mi), it was subdivided into 10 latitudinal bands of equal 

width (77 mi north to south). The range was further divided 

into ecologically similar areas by using the EPA level 

III ecoregion map units (Omernik 1987). Physiographic 

province boundaries were not used because they contain 

nonecological divisions based on state boundaries (fig. 1-1). 

Because of the extreme north-to-south range of the Coast 

and Cascades ecoregions, these two level III ecoregions 

were split into northern and southern portions based on EPA 

level IV ecoregion subdivisions (Woods et al. 2003). The 

Washington Coast ecoregion was further subdivided for the 

Olympic Peninsula, based on an existing elevation isopleth 
31

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

used by Holthausen et al. (1995). An isopleth of 1219 m 

(4,000 ft) was used in the northeastern portion of the Olympic Peninsula, and an elevation limit of 914 m (3,000 ft) 

(Holthausen et al. 1995) was used for the remaining coastal 

areas of Washington and Oregon.

No elevation limit was used in four of the ecoregions 

or subdivisions (fig. 3-4) that were primarily low-elevation 

valleys and foothills (for example, the Willamette Valley), 

especially in the southern coastal areas where marine influences override latitudinal and altitudinal influences. The 

linear regression was applied in the remaining ecoregions 

by using the five owl-pair activity centers at the highest 

elevations in each latitudinal band. The derived equation 

from this linear relation (R2 = 0.73 to 0.88) was used to 

develop the isopleth for that ecoregion, and a rangewide 

isopleth map was created by aggregating the regional 

isopleths (fig. 3-5).

Almost 99 percent of the 8,967 owl-pair-presence 

points used in our analysis are below the rangewide 

elevation isopleth. Given the variation of conditions across 

the range and the need to create a consistent, repeatable 

process for habitat monitoring purposes, the small number 

of owl- pair points above the isopleth was considered 

acceptable. 

Areas of serpentine soils are primarily within the 

Klamath Mountains provinces in Oregon and California 

and to a lesser extent in the Coast and Cascade provinces 

of California (fig. 3-6). These soils are characterized by 

low available soil moisture and nutrients, which limit 

tree growth and produce forests with low canopy closure 

because of the scattered (parklike) distribution of the 

trees. These soils cannot grow forests suited for territorial 

owl nesting and roosting. Maps of these soil areas were 

assembled from soil maps for the Siskiyou National Forest 

Figure 3-4—Step-wise progression of delineations used to develop a rangewide elevation isopleth denoting areas above which owls 

commonly do not nest (shown as black areas in the “results” map). 
32

GENERAL TECHNICAL REPORT PNW-GTR-648

and Roseburg BLM District, the geologic map of California 

(Jennings 1977), and a California potential natural vegetation map (Eyre 1980). They were used to modify the federal 

land GIS layer to exclude areas incapable of developing owl 

habitat. 

The map of habitat-capable federal area was created by 

omitting land above the elevation isopleth and in the serpentine soil areas from the forest-capable federal land map. 

It delineates the federal land in the range of the owl that 

could produce owl habitat (fig. 3-7). This map was queried 

o

Isopleth = 1219 m

No isopleth

Isopleth = 914 m

No isopleth

No 

isopleth

Isopleth = 8922.9 m – (0.0015 × UTM north)

Isopleth = 7606.4 m – (0.0012 × UTM north)

0 100 200 Miles

No isopleth

Isopleth = 11 322 m – (0.0021 × UTM north)

Above elevation isopleth

Modified ecoregions

Northern Cascades

Puget Lowlands

Olympic Peninsula East

Northern Coastal Range

Willamette Valley

Southern Cascades

Klamath Mountains

Southern Coastal Range

California Chaparral/Oak Woodlands

Figure 3-5—Elevation isopleths by modified ecoregions. By using the regression equations that define the linear relation between 

owl-pair location elevations and latitude (UTM northings), the isopleth increases in elevation from north to south (R2 values ranged 

from 0.73 to 0.99).

by using GIS techniques to estimate habitat-capable federal 

area in each physiographic province. The estimates were 

summed to obtain estimates for each state and the range.

Estimating Habitat Suitability 

The owl monitoring plan stated that the habitat maps should 

be developed by using the best-suited, current technology 

(Lint et al. 1999). The maps produced should also be of 

known accuracy and the mapping process repeatable to 

allow for future iterations and revisions (Lint et al. 1999). 
33

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

o

Physiographic provinces

Serpentine soils

0 20 40 60 80 100 Miles

Figure 3-6–Serpentine soil areas associated with federal area not habitat-capable in the range of the northern spotted owl.
34

GENERAL TECHNICAL REPORT PNW-GTR-648

Habitat-capable federal land

and large reserve blocks

Federal lands

Habitat capable

Not habitat capable

Large reserve blocks

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers

Lakes and rivers

Urban areas

Interstate highway o

Figure 3-7—Northern spotted owl habitat-capable areas inside and outside of large, reserve blocks in Washington, Oregon, and California.
35

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

A commonly used and cost-effective method to produce 

habitat maps is through habitat modeling (Williams 2003). 

Although habitat models are often used by scientists and 

managers in support of species conservation, the maps they 

produce are never 100-percent correct. As Box (1979) noted, 

“All models are wrong, but some are useful.” Selecting an 

appropriate habitat modeling method should consider the 

goals of the project and the usability of the model, and not 

depend solely on statistical considerations (Guisan and 

Zimmermann 2000). Furthermore, the data available often 

influence the model selection (McComb et al. 2002). 

BioMapper v3.0 (Hirzel et al. 2004a) was chosen as the 

tool to model owl habitat for the monitoring program. One 

of the main reasons it was chosen was because it operates 

on species-presence-only data, which was the type of owl 

location data available—as is often the case with rare, 

threatened, or endangered species (Engler et al. 2004). Owl 

absence data are often difficult to obtain and frequently not 

reliable. Reasons for its unreliability may include local species extirpation (for example, barred owl displacement of a 

spotted owl), species’ daily activity patterns, or territoriality 

of adjacent owls. Any of these reasons might explain why a 

spotted owl may not have been detected in a particular stand 

of habitat where a survey was conducted. It does not mean 

the forest stand surveyed was not habitat, but only that a 

spotted owl was not detected. Lack of absence data results 

in severe limitations for many statistical models that require 

it, such as linear regression models (Engler et al. 2004). 

Where absence data are not available or not reliable, models 

based on presence-only data are appropriate (Engler et al. 

2004, Hirzel et al. 2002). BioMapper has been demonstrated 

to successfully perform multivariate habitat modeling with 

only presence data (Brotons et al. 2004; Chefaoui et al. 

2005; Dettki et al. 2003; Freer 2004; Gallego et al. 2004; 

Hirzel et al. 2002, 2004b; Leverette 2004; Mandleberg 

2004; Patthey 2003; Reutter et al. 2003; Sachot 2002; Williams 2003; Zaniewski et al. 2002; Zimmermann 2004). 

Another reason BioMapper was chosen was because 

its underlying assumptions are founded on peer-reviewed 

theories and equations well documented in the scientific 

literature. In addition, Hirzel et al. (2001) compared it with 

the more commonly used generalized linear model (GLM) 

and showed it to be better at predicting habitat suitability 

for species with expanding distributions, equally effective 

for species with equilibrium distributions (for example, the 

species uniformly occupies all suitable habitat), but poorer 

for predicting suitability when the species was over-abundant (for example, high densities force the species to occupy 

less-favorable habitat). In instances where the sample size 

of presence data were large (n > 300), BioMapper and GLM 

performed equally well for all three species-distribution 

scenarios (Hirzel et al. 2001). 

BioMapper is a recently developed software package 

that contains GIS and statistical tools designed to build habitat suitability models and maps. It is based on the theory 

of the ecological niche (Hutchinson 1957). There are many 

definitions of ecological niche dating back almost a century. 

Grinnell (1917) defined it as the physical area in which 

a species exists (owing to the combination of ecological 

conditions that allow it to live). Another definition describes 

the niche as a function (not a physical property) performed 

by a species in the area in which it lives with other species 

(Elton 1927). Currently, the generally accepted definition is 

that of Hutchinson (1957), where the niche is described as 

a “space” that has many dimensions equal to the number of 

environmental variables that limit the survival and reproduction of the species. This “multidimensional volume of 

space” defines the environmental conditions that allow the 

species to exist and is synonymous with the “fundamental 

niche.” The “realized niche” is a subset of the fundamental 

niche where, owing to competition and other processes, the 

species actually occurs. 

The modeling conducted for this report used only 

physical environmental variables found in the owl’s environment and did not consider other factors such as inter- or 

intraspecific competition that influence the owl’s occurrence 

and persistence. Thus, important niche elements may have 

been omitted, and the results should be viewed as a first 

iteration but not a complete representation of the owl’s 

realized niche. 

BioMapper performs an ecological-niche factor analysis (ENFA) that summarizes multiple habitat variables into 

a few uncorrelated ecological factors in a process similar 

to principal component analysis (PCA). For a thorough 
36

GENERAL TECHNICAL REPORT PNW-GTR-648

explanation of the differences between ENFA and PCA, 

the reader is referred to Hirzel et al. (2004b). In factor 

analysis, the use of a covariance matrix implies that all of 

the original habitat variables are in the same units. If not, 

(for example, one variable is quadratic mean diameter in 

inches and another variable is elevation in meters above 

sea level) then the variable with the largest variability 

(in this case elevation) would have more influence in the 

analysis. This problem can be eliminated by using a correlation matrix, which converts all of the original habitat 

variables into the same units. This process also gives each 

variable equal weight in the analysis. BioMapper performs 

this variable standardization by taking the mean of each 

habitat variable within the global area and dividing by 

its standard deviation (Hirzel et al. 2002). Therefore, 

the global covariance matrix is, in fact, a correlation 

matrix. These same standardized variables are then used 

to compute a species covariance matrix by taking into 

account only presence points. Therefore, strictly speaking, 

the species matrix is a covariance and not a correlation 

matrix; however, the variables are still without units 

(Hirzel 2005).

The ENFA compares ecological conditions that 

correspond with species presence to conditions across 

the entire area being analyzed (Hirzel et al. 2002). The 

marginality factor explains most of the difference between 

the “species-presence” conditions and the conditions of 

the whole landscape. It is a linear combination of habitat 

variables where the average species presence values differ 

most from the average values of the available habitat. The 

larger the absolute value of a habitat variable coefficient, 

the more important that habitat variable is for defining the 

areas that have species presence. Negative habitat variable 

coefficients indicate that the species prefers lower values 

of that variable, and positive coefficients indicate species 

preference for higher values (Hirzel et al. 2002). Specialization factors are linear combinations of the original 

habitat variables where the distribution of the speciespresence conditions shows the lowest variance relative to 

that of the province (fig. 3-8). They help explain how 

selective the species is compared to the available environmental conditions (Hirzel and Arlettaz 2003). For these 

factors, only the absolute value is informative, and their 

sign (+ or -) does not matter. Most of the relevant ecological 

information is normally accounted for by the first few 

factors (marginality factor, plus the first few specialization 

factors). Together, the marginality and specialization factors 

define the multidimensional space that approximates the 

realized niche of the species. 

To create a two-dimensional representation of this 

“niche” (in other words, a map) the “habitat suitability” 

value of every map unit (pixel) in the area being modeled is 

calculated by comparing its “ecological position” within the 

multidimensional space described by ENFA. To do this, the 

frequency distribution and the median of the presence-data 

pixels are computed along each ecological factor. Then, 

every map pixel is compared to these distributions, and the 

farther that cell’s value is from the presence-data median, 

the less suitable it is. The species distribution along the 

factors is assumed to be normal, and the tails of the distribution have the lowest habitat suitability. The final habitat 

suitability index of each grid cell is based on the weighted 

average of these distances to the medians of each factor. 

For a more detailed description of ecological-niche factor 

analysis and the mathematical methods used in BioMapper, 

see Hirzel et al. (2002).

σ

μ

Frequency

Owl habitat variable 

(e.g., percentage of conifer cover)

owl

σprovince

owl

μprovince Global 

 distribution

Species 

 distribution

Figure 3-8—Distribution of a habitat variable for both the global 

area (the province) and the area within the province where owl 

presence is known (species distribution). The difference between 

the global and species means (μ) defines the marginality factor 

and the ratio of the standard deviations (σ) of the global and species distributions defines the specialization factors.
37

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Calibrating the Model 

BioMapper is a raster-based analysis tool, where the 

individual pixel is the unit of analysis. It has been shown 

to perform better when using larger presence-data samples 

(Hirzel et al. 2001, Williams 2003). We used a range of 

1,890 to 49,100 (average = 20,120) pixels of owl presence 

data. Owl presence pixels were selected by buffering each 

owl-pair-location point by two pixels to create a 5- by 

5-pixel (5×5) “window” of owl presence data. These 5×5 

windows represent an area (a square approximately 4 ac) of 

concentrated owl pair activity (fig. 3-9). In other words, they 

are locations within a forest stand with recorded evidence of 

use by territorial spotted owls.

Northern spotted owls are only known to nest and roost 

and predominantly forage in forested areas (Thomas et al. 

1990). A small subset of owl-pair-presence points occurred 

along forest/nonforested edges (such as meadows and recent 

clearcuts). These points were manually shifted away from 

the edges but kept within the forest stand in which they occurred. This was done prior to buffering the points to create 

the 5×5 window. The purpose of these slight spatial shifts 

was to avoid creating a buffer that would include obviously 

faulty presence data (for example, owls do not nest or roost 

in clearcuts) into the model training data set (fig. 3-9).

Spatial autocorrelation is commonly present in ecological data sets (Legendre 1993); however, it does not always 

generate bias (Dinzi-Filho et al. 2003). The similarity of 

adjacent pixels caused by environmental factors that influence species presence may produce positive autocorrelation 

when modeling with grid data (Diniz-Filho et al. 2003). 

Therefore, we conducted a sensitivity analysis to test for 

spatial autocorrelation that may have resulted from using 

the 5×5 windows of owl-pair-presence data. We did this 

by comparing outputs from model runs using only single 

pixels (the central pixel) from the grid to those where all 

pixels in the 5×5-grid data set were used. Outputs from both 

the single-pixel and 5×5 data showed almost no change in 

percentage of habitat suitability between the 0 to 40 and 41 

to 100 habitat suitability categories (0.01 to 0.70 percent). 

Slightly greater changes (0.31 to 4.48 percent) were seen 

within the narrower habitat suitability intervals (0 to 20, 21 

to 40, ...81 to 100). The results also showed that marginality 

and specialization factors differed by only small amounts 

(1 to 4 percent) between the two data sets, and consisted 

of the same combination of habitat variable coefficients 

with similar real and absolute values. The mean variance 

between replicates (using cross-validation) increased by 17 

to 29 times for models using single-pixel data indicating 

poorer model repeatability when using smaller presence 

samples rather than larger ones (5×5 window). Because both 

model outputs were nearly identical, spatial autocorrelation was not considered to be a problem. These results are 

Figure 3-9—Owl-pair location points were converted into individual pixels to match the resolution of the underlying habitat variable 

data (for example, IVMP 25-m pixels) and then “buffered” by a 2-pixel width to create a 1.6 to 2.25 hectare “5×5 window” of owl 

presence data that BioMapper used to describe the species niche (n = number of pixels). 
38

GENERAL TECHNICAL REPORT PNW-GTR-648

similar to those found by Leverette (2004) in a sensitivity 

analysis on presence-data sample sizes. According to 

Hirzel (2005), 50 points should be enough to adequately 

model habitat, provided they come from as many different 

individual owls as possible and are not too clumped within 

the area being modeled. In fact, given the choice, it would 

be preferable to use fewer points with good distribution 

than many points that are only clumped in one or two 

places within the modeled area. In summary, it appears 

that larger samples (5×5 window) improve model repeatability and accuracy (based on the k-fold cross-validation 

technique).

Validating the Model 

When modeling habitat, it is important to determine if the 

model is based on “discrete” or “ambiguous” concepts; 

otherwise the model may be misinterpreted and not be 

defendable (Hill and Binford 2002). Models such as BioMapper, which are founded on theories of the niche and 

use terms like habitat suitability, are based on the fuzzyset theory and should be tested in a manner consistent with 

ambiguous categories (Hill and Binford 2002). Correlation 

tests are well-suited for testing the relative accuracy of 

these types of models. Boyce et al. (2002) recommended 

that such models be evaluated for their predictive capabilities with a k-fold cross-validation technique (Fielding and 

Bell 1997) and Spearman rank correlation statistic. Hirzel 

et al. (2004a) incorporated this model validation procedure 

into version 3.0 of the BioMapper software. It produces 

an area-adjusted frequency graph (fig. 3-10) where the 

x-axis is the continuum of habitat suitability categorized 

into bins (categories, for example 0 to 20, 21 to 40,...81 to 

100 habitat suitability) of equal sizes, and the y-axis is the 

area-adjusted frequency (AAF), which is the percentage 

of total owl points in the bin divided by the percentage of 

the global area (here, the forested portion of the province) 

in the same bin. The smaller the percentage of owl points 

and the larger the percentage of the global area in the bin, 

the smaller the AAF. For example, if 50 percent of both 

owl points and the global area are in the bin, the AAF for 

that bin would equal 1. This point (AAF = 1) separates the 

model predictions into two groups. One group contains 

bins with more validation points than expected by chance 

and the other group contains bins with fewer validation 

points than expected by chance. 

The lower the variance between the replicated crossvalidation curves, the better the predictive power. If all of 

the curves are close together, the model is highly reproducible. A linear curve with a positive slope is an indicator of 

a good model. The higher the Spearman rank correlation 

coefficient, the stronger the relation between predicted habitat suitability and owl presence. If the curve is a relatively 

flat line that hovers around the random frequency line (AAF 

= 1), the model is not much better than just giving random 

habitat suitability values to the cells in the map (Hirzel et al. 

2004a).

Model Limitations 

As with all models, there are limitations that should be 

recognized and addressed. One such limitation is in the 

use of the median algorithm in BioMapper. This algorithm 

assumes a normal and roughly symmetrical distribution 

in each environmental variable Hirzel et al. 2002). This is 

not always the case, and although Box-Cox transformations 

help to improve normality, they are not always successful. 

In addition, species distributions along a variable may not 

always be normal either, as some species may have positive linear correlations with the variable, and the species’ 

preferred habitat may occur at an extreme of the gradient. 

Asymmetry may also occur if the global area is situated in a 

marginal or highly fragmented portion of the species range, 

where the most suitable environmental conditions are scarce 

and the species is forced to use less suitable areas (Hirzel 

and Arlettaz 2003). This asymmetry will cause the median 

algorithm to overrate suboptimal areas and underrate the 

most suitable ones.

BioMapper now incorporates other algorithms based on 

distance metrics that compute habitat suitability in the environmental space without assuming any distribution of the 

species points (Hirzel and Arlettaz 2003), therefore solving 

the problems potentially encountered by the “median” algorithms. Use of these distance algorithms (or a combination 

of the median and a distance algorithm) in future iterations 

of modeling may improve the quality of the model.
39

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Profile models like BioMapper are also known to sometimes overpredict species presence (Engler et al. 2004). 

However, this occurs mainly when arbitrary decisions are 

made to categorize the continuum of habitat suitability (0 

to 100) into terms such as “habitat” and “nonhabitat.” This 

arbitrary categorization was avoided in our analysis.

In a “perfect world,” the presence data should come 

from an unbiased sample of the species distribution within 

each physiographic province. However, this is not the 

case with the presence data currently available. Because 

these presence data are compared with the environmental 

background in which they occur, their distribution within 

that area can influence the outcome of the model. For this 

reason, we analyzed the distribution of our presence data 

within each physiographic province and the habitat-capable 

land within it. Nearest neighbor analysis of presence points 

(Clark and Evans 1954) indicates that the presence data in 

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100

Habitat suitability

Replicate 1

Replicate 2

Replicate 3

Replicate 4

Replicate 5

Area-adjusted frequency

Random frequency line

Figure 3-10—Area-adjusted frequency of habitat-suitability categories (bins) for locations of spotted owl presence data 

used to validate the model in the k-fold cross-validation process. 

most provinces is fairly well distributed, although there is 

some tendency toward clumping (app. B). Presence data in 

Oregon Western Cascades and California Klamath provinces were not clumped and had good spatial distributions.

An Example of Model Application 

Owl habitat suitability was mapped for all physiographic 

provinces by using physical, biotic, and abiotic habitat 

variables such as quadratic mean diameter of trees, canopy 

cover of coniferous trees, and elevation. Where possible, 

variables were kept in a continuous format. A description 

of the variables is summarized in appendix C. 

The results from modeling owl habitat within the 

Oregon Coast Range are provided here to illustrate the modeling process and specific model outputs. A total of 1,560 

owl-pair-location points were used to identify forest stands 

used by owls in this province (fig. 3-2). The application of 
40

GENERAL TECHNICAL REPORT PNW-GTR-648

the 5×5 window to these locations identified 34,073 pixels 

with owl presence. Six habitat variables, normalized by using 

Box-Cox transformations, were used and converted into five 

ecological factor maps (table 3-2) based on comparing their 

eigenvalues to MacArthur’s broken-stick advice (Hirzel 

et al. 2002). All factors used had eigenvalues greater than 

one. These five ecological factors explained 97.5 percent of 

the difference between species occurrence and the habitatcapable portions of the Oregon Coast Range province. The 

median algorithm was used to calculate habitat suitability.

For the Oregon Coast Range province, the ecologicalniche factor analysis resulted in a marginality of 0.916 and 

specialization of 2.339. A high marginality factor score 

(close to 1.0) indicates that the species occupies a very particular habitat compared to the global set of conditions. The 

marginality factor is also directly correlated to the predictive 

accuracy of the model (Brotons et al. 2004). The absolute 

value of the specialization factor (range from 1.0 to infinity) 

indicates how selective the species is in its use of available 

habitat. This sensitivity is explained, more simply, in terms 

of a tolerance index, which is the inverse of specialization 

(1/Specialization Factor). A species showing a tolerance 

value close to 1.0 is more of a habitat generalist and inhabits 

a wider niche than a species with a tolerance close to zero. 

The tolerance value for the owl in the Coast Range province 

was 0.43.

The first two ecological factors (the marginality factor 

and the first specialization factor) explain more than 70 

percent of the owl’s ecological niche space. The habitat 

variable coefficients that make up these two factors correlate with elements often cited as particularly relevant for 

spotted owl habitat selection. For instance, the strongest 

habitat variable was the product of tree diameter and conifer 

cover. The next strongest variable was a positive association 

with the quadratic mean tree diameter (QMD) followed by 

conifer cover (CC). The structure index “variety,” which is 

the number of tree-size classes within the 5×5 window, also 

showed a positive relation. In this coastal Oregon province, 

elevation played a minor role in describing the niche space, 

but in some provinces it played a more important role (for 

example, the Washington Eastern Cascades province). 

Model results were tested by using the k-fold crossvalidation technique with five replicates. For each replicate, 

80 percent of the presence data was randomly selected to 

Table 3-2—Ecological factors and the habitat variables they were computed from in the 

ecological niche factor analysis for the Oregon Coast Ranges 

 Marginality factor Specialization factors

 Factor 1 (35.0%) Factor 2 (36.3%) Factor 3 (14.5%) Factor 4 (8.5%) Factor 5 (3.2%)

qmdcc (0.57) qmdcc (-0.77) cc (-0.67) elev (-0.78) bdlf (0.86)

 qmd (0.56) qmd (0.55) qmdcc (0.62) cc (0.47) qmdcc (0.32)

 cc (0.40) cc (0.33) qmd (-0.33) qmdcc (-0.36) cc (-0.28)

 variety (0.36) bdlf (0.01) bdlf (-0.23) qmd (0.14) qmd (0.25)

 bdlf (-0.27) variety (0.01) variety (0.09) bdlf (0.12) elev (0.13)

 elev (0.01) elev (0.00) elev (-0.03) variety (-0.07) variety (0.06)

Note: Variance and specialization accounted for by each factor is given in the parentheses in the column headings. Habitat 

variables with the largest absolute coefficient values have the most ecological meaning for describing the niche space. For 

the marginality factor only, positive values indicate the owl’s preference for locations with higher values of that variable; 

negative signs indicate owl preference for lower values of that variable. The sign of the specialization factor has no ecological 

significance, but its absolute value relates to the niche space width along that factor’s axis.

qmd = quadratic mean diameter.

cc = canopy cover of coniferous trees.

qmdcc = product of conifer tree size and canopy cover [(qmd × cc) / 10].

bdlf = canopy cover of deciduous trees.

variety = index of stand structure based on the number of tree diameter size classes within the 5×5 window.

elev = elevation from USGS digital elevation models.

Habitat variables
41

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

model habitat, and the remaining 20 percent was used for 

validation. The area-adjusted frequencies for each replicate 

were averaged to produce an average Spearman rank correlation coefficient for the model’s performance (fig. 3-10). 

The average Spearman rank correlation for the Oregon 

Coast Range was 0.988 (P<0.001) indicating the model 

predicted owl use locations well.

Simple statistics describing general habitat structure 

for each of the five habitat suitability bins are shown in 

table 3-3. The mean conifer size and cover measures in the 

last three bins (41 to 100) indicated that average QMD was 

at least 18 in and that conifer cover was greater than 76 

percent. These values are consistent with our knowledge of 

spotted owl habitat relations. Standard deviations indicated 

overlap between the bins and emphasize the continuous 

nature of habitat conditions.

vs. 82 ft). Therefore, model outputs were smoothed by using 

a raster algorithm that matched the 5×5 window resolution 

(125 × 125 m or 410 × 410 ft) representing owl presence and 

assigning to the center pixel the mean habitat suitability 

value of the 5×5 window. Smoothing is a commonly used 

map simplification practice that eliminates unwanted details 

while maintaining the level of information desired for 

further analysis or use of the map. 

When the map is “smoothed” by calculating the mean 

habitat suitability in the 5×5 window, 90 percent of owl pair 

sites were in areas with average habitat suitability of more 

than 52, as shown by the bottom arrow in figure 3-11. By 

displaying the continuous (0 to 100) range of conditions, we 

avoided the arbitrary categorizing of the range of conditions 

into discrete categories of habitat (Hill and Binford 2002, 

Schreuder et al. 2004), while still describing, in general 

terms, the likely use of this range of conditions by the owl 

for nesting, roosting, and foraging (fig. 3-11). This strategy 

also supports the use of fuzzy accuracies for the vegetation 

variables used in the modeling. The method of display also 

allows the continuum of habitat-capable lands to be tracked 

over time as habitat conditions change.

Estimating Habitat Conditions in the 

Spotted Owl Reserve Network 

A premise of the Plan was that a network of designated 

areas would be managed primarily to protect and enhance 

habitat for the spotted owl and other late-successional 

and old-growth forest related species. These designated 

areas are the reserved lands in the land use allocation map 

(fig. 3-1). They include late-successional reserves, congressionally reserved areas, administratively withdrawn 

areas, adaptive management reserves, managed latesuccessional areas, 100-ac owl core areas, and occupied 

marbled murrelet (Brachyramphus marmoratus) reserves. 

These areas, individually and in aggregate, form a network 

of small to large block- and linear-shaped patches of federal 

land in reserve status across the range of the owl. They 

range in size from tens of acres to tens of thousands of 

acres. However, not all reserves are of sufficient size to 

support reproductive pairs of owls (USDA USDI 1994). 

Table 3-3—A general description of owl-presence 

point distribution and forest structure attributes 

in the habitat suitability bins from raw BioMapper 

model output

Habitat Percentage of Mean quadratic Mean conifer

suitability owl-presence mean diameter cover

scores point locations (±1 SD) (±1 SD)

Inches Percent 

 0–20 2.2 4 (0–7) 16 (0–37)

 21–40 15.1 9 (0–22) 53 (26–80)

 41–60 23.7 18 (3–33) 77 (57–97)

 61–80 21.3 23 (9–37) 84 (68–100)

 81–100 37.7 29 (17–41) 88 (75–100)

The raw (or pure) model output is a pixel map of owl 

habitat suitability from 0 to 100 (app. D fig. D-6). For the 

Coast Range, most of the owl pairs (90 percent) coincide 

with a raw model output value for habitat suitability greater 

than 37 as shown by the top arrow in figure 3-11. Raw 

model outputs have high heterogeneity (fig. 3-12) because 

of the underlying satellite imagery vegetation data where 

each individual pixel was classified separately. Although 

the heterogeneity captured on the satellite maps might be 

important to spotted owls, land managers identify more 

commonly with patches (Glenn and Ripple 2004). In addition, the spatial accuracy of owl location points is coarser 

than the raw model pixel resolution (100 m vs. 25 m or 328 
42

GENERAL TECHNICAL REPORT PNW-GTR-648

Oregon Coast Range Province

1

13

28

21

15

22

0

10

20

30

40

50

60

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

90% of owl pairs

Raw model output

Smoothed model output

73>S

H

25>S

H

Habitat-capable federal area (percent)

Figure 3-11—Owl habitat conditions expressed in terms of a habitat suitability histogram, shows the continuum of habitat 

conditions in the area being analyzed. The arrows above the graph show the range of conditions under which 90 percent of 

owl-pair activity occurs for both the raw and “smoothed” (5×5 window) model outputs. The owls occur mainly in the top 

three habitat suitability bins. In our analysis, portions of the raw data in each province were not able to be classified, and 

we were not able to estimate its habitat suitability. This proportion is shown by the”Unknown” bar in the graph.

Figure 3-12—An example of the heterogenous raw model output and the more generalized smooth model output (5×5 window) from the 

Oregon Coast Range. Red points represent owl locations. 
43

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Prior to determining habitat conditions within the 

reserved network, we used the PATCH program from the 

HABSCAPES software package (Mellen et al. 1995) to 

aggregate the various sizes and shapes of reserved patches 

into large, reserved blocks to represent areas capable of supporting multiple pairs of owls. The PATCH program applies 

an algorithm that slowly “grows” patches of like values (in 

this case reserved land) by first identifying the patch core 

areas and then incrementally “growing” these cores by 

adding adjacent [reserved] pixels in accordance to specific 

rules set forth in the program’s parameter file (Mellen et al. 

1995).

Recognized differences in the owl’s home range size 

from north (larger) to south (smaller) were represented by 

varying the minimum size of patches by state (Thomas et 

al. 1990). We modified the PATCH algorithm to aggregate 

adjacent reserved acres that met or exceeded 15,000 in 

Washington, 10,000 in Oregon, and 5,000 in California. 

The PATCH program provided a consistent, repeatable 

procedure to delineate the large, reserved blocks across 

most of the owl’s range (fig. 3-13). However, because of 

discontinuity created by intervening nonfederal land—

primarily associated with BLM lands in western Oregon—

the program was not able to satisfactorily aggregate 

reserved patches by using the alternating sections on 

“checkerboard” lands. Because the Plan identified these 

large, checkerboard, late-successional reserves as areas 

capable of supporting multiple pairs of owls, they were 

added back into the map of large reserved blocks manually, 

based on the land use allocation layer.

The condition of owl habitat in the large, reserved 

blocks was estimated by querying the “raw” model (singlepixel) output for area in five equal-interval habitat suitability categories (0 to 20, 21 to 40...81 to 100). The resulting 

habitat suitability conditions inside the reserved blocks 

were compared to the habitat suitability conditions outside 

of the reserved blocks and portrayed in habitat suitability 

histograms for each province. They were then summed for 

state and rangewide estimates. In addition, the mean habitat 

suitability from the 5x5 grid was compared against historical conditions of owl habitat by using the 1930 PNW inventory map (Harrington 2003) and the 1945 map of California 

forests (Wieslander and Jensen 1946) for both inside and 

outside of the reserved blocks. These estimates indicate how 

vegetation conditions may have deviated from premanagement conditions and is portrayed in tabular form.

Estimating Habitat Change 

To measure habitat change, the preferred approach would 

be to run the models by using the same type of habitat variable data from the time periods representing “before” and 

“after” (in our case, 1994 and 2003). The 2003 data did not 

exist, so we used the “change detection” data set to quantify the changes in habitat conditions from the baseline. 

Habitat change was estimated by using GIS and overlaying 

the change data (fire and harvest) on the habitat condition 

maps and subtracting the areas of overlap. The results were 

portrayed similarly to the baseline habitat condition profiles 

by using histograms to show the amount of change in each 

habitat condition category (fig. 3-14). 

Changes were estimated for each physiographic province and the land use allocations in them. These changes 

were summed to get state and range estimates. Changes 

in habitat conditions inside and outside the large, reserved 

blocks in the spotted owl habitat management network 

were also calculated by using the same method. 

Estimating Habitat Fragmentation and 

Patch Metrics

Both patch and fragmentation metrics were used to quantify 

landscape changes in owl habitat over time. Fragmentation 

occurs when a contiguous patch of habitat is perforated or 

incised, and the remaining habitat is separated into smaller, 

isolated patches (Jaeger 2000, Neel et al. 2004). 

The FRAGSTATS program (McGarigal and Marks 

1995) was used to analyze patch metrics on federal lands. 

However, masking out nonfederal lands created some 

artificial patch edges; therefore, results should only be used 

to describe patch changes and trends between periods. We 

used the class area, number of patches, mean patch size, 

total edge, total core area (habitat more than 100 m from 

edge), and mean core area patch indices (McGarigal and 

Marks 1995, Rempel and Carr 2003) to compare patch 

metrics for the 1994 and 2003 maps. We also compared 
44

GENERAL TECHNICAL REPORT PNW-GTR-648

Reserved federal land

and large reserve blocks

Reserved land use allocations

Large block reserves

Other reserves

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

Lakes and rivers

Urban areas

Interstate highway o

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers

Figure 3-13—Large, reserve blocks intended to support clusters of reproducing spotted owls under the Northwest Forest Plan.
45

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

these maps with the historical map (1930–40s). Because 

both the 1994 and 2003 maps were derived from satellite 

imagery, they had smaller minimum mapping units and 

more patch heterogeneity than the historical maps, which 

were hand drawn from field inventories and photointerpretation. This produced a problem that commonly arises 

when conducting spatial comparisons between maps 

of different origins (Glenn and Ripple 2004). Indices 

from FRAGSTATS have been shown to be sensitive to 

minimum mapping units and are considered poor indicators of fragmentation when comparing between maps of 

different origins (Saura 2002). To address this problem, 

we “smoothed” habitat suitability maps derived from the 

satellite imagery to reduce the amount of pixel noise. Each 

map was resampled to a 1-ha (2.47-ac) pixel size and finally, 

isolated pixels or clusters of pixels smaller than 10 ha (24.7 

ac) were removed by using an eight-neighbor pixel-filtering procedure in the grid generalization tools extension of 

ArcView.5 Habitat suitability values that corresponded with 

Table 3-4—Mean habitat suitability range that 

accounted for 90 percent of the owl-presence 

points based on the average habitat suitability 

of the 5×5-pixel grid for each of the owl points

State/ Mean habitat Habitat-capable

physiographic suitability area in mean habitat

province range suitability range

Acres

Washington

 Eastern Cascades ≥44 577,600

 Western Cascades ≥45 1,204,900

 Olympic Peninsula ≥56 549,100

Oregon

 Klamath ≥51 768,300

 Eastern Cascades ≥50 442,300

 Western Cascades ≥56 2,007,100

 Coast Ranges ≥52 628,000

California

 Coast Range ≥33 215,500

 Klamath ≥36 2,522,800

 Cascades ≥36 387,800

 Total 9,303,400

90 percent of owl-pair occurrence represented the proxy for 

owl habitat for 1994 (table 3-4). The 2003 map was created 

by using the stand-replacing disturbance map as an overlay 

to identify habitat to be removed from the 1994 map.

5 The use of trade or firm names in this publication is for 

reader information and does not imply endorsement by the 

U.S. Department of Agriculture of any product or service.

Metrics introduced by Jaeger (2000) were used to 

analyze the degree of habitat fragmentation across all land 

ownerships within each physiographic province. Jaeger 

indices—landscape division, splitting index, and effective 

mesh—were useful because they allowed us to compare 

landscapes of different size and were not sensitive to the 

omission or addition of very small patches. The landscape 

division metric is based on a degree of coherence. Coherence is defined as the probability that two animals randomly 

placed in different areas within the landscape might find 

each other (Jaeger 2000). Landscape division is the complement of coherence and is defined as the probability that two 

randomly chosen places in the landscape do not occur in 

the same patch. The lower the coherence index, the higher 

the landscape division index and the more fragmented the 

landscape. The splitting index is the number of patches 

that result when the landscape is divided into patches of 

equal size such that the new configuration leads to the 

0–20

21–40

41–60

61–80

81–100

0

0.5

1.0

1.5

2.0 Wildfire

Timber 

harvest

Habitat-capable federal area (percent)

Unknown 

 

Habitat suitability

Figure 3-14—Change across the continuum of habitat conditions 

(before disturbance) in the area being analyzed. In this example, 

the majority of the change caused by fire was in habitat of lower 

suitability for owl nesting, roosting, and foraging.
46

GENERAL TECHNICAL REPORT PNW-GTR-648

same degree of landscape division. The effective mesh is 

the size of those patches and its lowest value is constrained 

by the map’s pixel size (in our case it was resampled to 

1-ha [2.47 ac] pixels) and is achieved when the landscape is 

maximally subdivided, or in other words, when every pixel 

is a separate patch. The mesh is at its maximum when the 

landscape consists of one single patch. Therefore, the higher 

the splitting index value, the smaller the effective mesh size 

and the more fragmented the landscape. 

The Subdivision Analysis extension for ArcView 

(Lang 2004) and FRAGSTATS (McGarigal and Marks 

1995) as part of the Patch Analyst extension for ArcView 

GIS (Rempel and Carr 2003) were the tools used to analyze 

the spatial arrangement of owl habitat for each physiographic province. Fragmentation and habitat patch indices were 

compared for three periods: (1) 1930–40s, representing 

historical conditions before widespread timber harvesting 

on federal lands; (2) 1994, representing conditions at the 

initiation of the Plan, and (3) 2003, representing conditions 

at the end of the first monitoring period. 

Estimating Dispersal-Capable Federal Area 

The condition of forest land between the large, reserved 

blocks is an important component of spotted owl habitat 

management because it allows for owl movement, or dispersal, from one large block to another. Thomas et al. (1990) 

explained that habitat between blocks functions better to 

allow owls to move the more nearly it resembles suitable 

habitat for the species in question. Dispersal movements are 

made primarily by nonterritorial spotted owls (juveniles 

and subadults) and, to a lesser degree, by adult spotted owls 

(Forsman et al. 2002). Assuring conditions to allow owls to 

move between the reserved blocks was an important facet 

of owl management under the Plan. 

The federal forest-capable layer also represented the 

dispersal-capable layer. It shows the federal land that can 

develop and maintain forested conditions suitable for 

dispersing owls. Serpentine soils were not excluded because 

they are usually capable of growing vegetation that is at 

least minimally suitable for dispersal. No elevation limit 

was imposed on forests for calculating dispersal habitat.

Estimating Dispersal Habitat Conditions 

We lacked sufficient owl dispersal “presence” data to 

develop a BioMapper model for predicting the full range 

of dispersal habitat conditions by using the same methods 

we used for owl habitat. Although the owl habitat maps did 

provide habitat conditions equally suitable for dispersal, 

the owl-presence points used to develop those models came 

from territorial owls, not juvenile owls dispersing from their 

natal areas. To do a credible job of modeling dispersal habitat, the presence points would have had to represent known 

locations of dispersing juveniles and subadults. These data 

were not available across the range of the owl because 

usable data points were only those from radio-marked birds, 

and that data set was limited. 

Instead, we mapped dispersal habitat conditions based 

on queries of IVMP and CALVEG data for conifer size and 

cover to represent the forest conditions commonly used 

to describe dispersal habitat throughout the range of the 

owl (Thomas et al. 1990). This universal query selected all 

forest stands ≥11 in QMD and ≥40 percent canopy cover. 

Because of the lack of continuous size-class data in the 

Eastern Cascades provinces of Oregon and Washington and 

in the CALVEG data, the query was slightly different for 

those provinces. Size cutoffs ranged from 10 in (IVMP) to 

12 in (CALVEG) rather than 11 in. Estimates of dispersal 

habitat conditions were summarized by physiographic province by using the dispersal-capable federal area layer as the 

basis for the analysis and summed for the states and range.

Estimating Owl Habitat Quality From Plot Data 

A data query was developed for the current vegetation 

survey data by using expert knowledge to create a spectrum 

of conditions from low to high similarity with areas known 

to be used by spotted owls (table 3-5). Quadratic mean 

diameter (<10.5 in, 10.5 to 20.5 in and >20.5 in), canopy 

cover (≤40 percent and >40 percent) and canopy structure 

(single layer and multilayer canopy) data attributes were 

matched in all possible combinations and placed in six 

query groups (A through F). Group A includes federal area 

with <10.5 in QMD, 0- to 100-percent canopy closure, and 

both single- and multicanopy structure. Areas in this group 

were on the low end of the owl-habitat-similarity spectrum. 
47

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Table 3-5—Forest stand condition query for current vegetation survey plot data

Low -------------------- Spotted owl habitat similarity -------------------- High

Query group

Query attributes A B C D E F

Query part 1:

 Quadratic mean diameter (inches) <10.5 10.5+ 10.5–20.5 10.5–20.5 20.5+ 20.5+

 Canopy cover (percent) 0–100 0–40 41–100 41–100 41–70 >70 

 Strata ALL ALL 1 (single) 2+ (multi) 2+ 2+

Query part 2:

 Quadratic mean diameter (inches) NA NA NA 20.5+ NA NA

 Canopy cover (percent) 41–100

 Strata 1

NA = Not applicable.

Groups B through F represented a progression of stand 

conditions increasing in tree diameter, canopy cover, and 

stand structure complexity as well as similarity to conditions used by spotted owls. Group F, for example, includes 

area with >20.5 in QMD, >70-percent canopy cover, and 

two or more canopy layers. Group F represents area on the 

high end of the conditions similar to those used by owls for 

nesting, roosting, and foraging. The data query was applied 

to current vegetation-survey-plot data in each physiographic 

province at elevations territorial owls are known to occupy 

delineated by the elevation isopleth. The query produces a 

numeric baseline of acres with confidence limits for each of 

the six query groups. 

The query results are reported for the six data query 

groups by physiographic province and also by inside 

and outside of the large, reserved blocks in the province. 

Confidence intervals are provided for the 68- and 90-percent intervals for all area estimates by group The use of two 

common confidence intervals that span the range of reasonable intervals is an attempt to address the needs (or curiosities) of the audience. We thought this would be helpful as 

the confidence intervals were derived by bootstrapping, not 

by using the standard deviation, thus precluding the creation 

of other confidence intervals by the reader. 

Confidence intervals for estimates were constructed 

by using a stratified two-stage bootstrapping routine. Each 

first-stage bootstrap sample consisted of nh randomly selected samples (with replacement) from each of the h strata 

to produce one bootstrap estimate. This was repeated 500 

times. From each first-stage plot, five subplots were randomly 

selected with replacement. The second-stage resample was 

conducted independently for each first-stage plot. The results 

were adjusted by using the Bias-Corrected and accelerated 

(Bcα) method as detailed in Efron and Tibshirani (1993). 

Because the current vegetation survey plot data includes 

only FS lands in the Pacific Southwest and Pacific Northwest 

Regions (Regions 5 and 6, respectively), and BLM lands in 

Oregon, the results are restricted to that inference area. 

Results

Spatial Analyses

Estimated federal area—

Estimated acres of federal land administered by the 

FS, BLM, and NPS within the boundaries of the Plan 

is 24,444,100 (table 3-6 and fig. 3-1), which is about 44 

percent of the total land in the Plan area. The federal 

acres were distributed across the three states under the 

Plan in the following proportions: Washington, 36 percent; Oregon, 39 percent; and California, 25 percent.

The distribution of federal land among the 12 physiographic provinces in the owl’s range is shown in table 3-6. 

Of the total federal land area, Oregon’s Willamette province 

had only 0.08 percent and Washington’s Western Lowlands 

province had only 0.009 percent. No further analysis was 

conducted for these provinces because of their few federal 

acres. Five of the remaining provinces—the Western 

Cascades provinces in Oregon and Washington, the 

Eastern Cascades province in Washington, and the 
48

GENERAL TECHNICAL REPORT PNW-GTR-648

Klamath provinces in Oregon and California—contained 

75 percent of the federal land in the Plan area. The remaining 25 percent of federal land was spread among the other 

five provinces.

The congressionally reserved land use allocation contained 29 percent of the federal land, rangewide. Another 

30 percent is in the late-successional reserves allocation 

and adaptive management reserves (late-successional 

reserves) (table 3-6). These reserve allocations, along 

with the riparian reserves, provide the foundation for 

conserving late-successional forest under the Plan. The 

matrix and riparian reserve allocations6 (combined in 

Table 3-6—Federal land by province, state, and the range of the spotted owl by land use allocation

 Matrix/

Province Area CR LSR AMR MLSA AW LSR3 LSR4 AMA RR ND

Acres - - - - - - - - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - - - - - - - -

Washington:

 Eastern Cascades 3,502,400 42 25 0 3 7 0 <0.5 4 20 <0.5

 Olympic Peninsula 1,522,300 64 27 0 0 0 <.5 <.5 8 0 <.5

 Western Cascades 3,721,000 47 27 2 0 5 <.5 <.5 4 13 <.5

 Western Lowlands 2,300 76 0 0 0 0 0 0 0 24 0

 Total 8,748,000 48 26 1 1 5 <.5 <.5 5 13 <.5

Oregon:

 Coast Range 1,413,300 2 54 12 0 3 3 <0.5 6 22 <0.5

 Eastern Cascades 1,551,800 28 24 0 0 11 0 1 0 36 0

 Klamath Mountains 2,118,200 12 39 2 0 2 1 2 11 32 <.5

 Western Cascades 4,476,700 17 28 <.5 0 8 0 2 5 40 <.5

 Willamette Valley 21,000 0 6 0 0 15 0 1 2 71 5

 Total 9,581,000 15 34 2 0 6 1 1 6 35 <.5

California:

 Cascades 1,091,300 4 22 0 1 9 0 <0.5 15 49 0

 Coast 503,600 38 25 0 0 10 <.5 <.5 0 27 1

 Klamath 4,520,200 26 27 <.5 0 7 0 1 8 30 0

 Total 6,115,100 23 26 <.5 <.5 8 <.5 <.5 9 33 <.5

Total 29 29 1 <.5 6 <.5 1 6 27 <.5

Reserve total 16,347,900 (67 percent)

Nonreserve total 8,096,200 (33 percent)

Plan total 24,444,100

Note: See figure 3-1 for land use codes.

land use allocation map, fig. 3-1) contained 27 percent of 

the federal land, and the remaining 14 percent was spread 

among the other land allocations shown in table 3-6. 

Estimated forest-capable federal area—

About 95 percent of total federal area in the range of the 

spotted owl can grow forests (Moeur et al. 2005). With 

the exception of the Coast province in California (70 

percent), more than 90 percent of federal land in all provinces is considered forest-capable. Although a portion 

of the forest-capable federal land was assumed able to 

provide dispersal habitat for the northern spotted owl, not 

all of it could provide habitat for territorial spotted owls. 

Estimated habitat-capable federal area—

About 74 percent of federal land has the capability to 

develop habitat for territorial spotted owls (table 3-7). 

Habitat-capable federal area (habitat-capable area) 

6 In this report, the matrix and riparian reserve allocations refers 

to the matrix land use allocation and that portion of the riparian 

reserve allocation that is intermingled with the matrix. This is 

necessary because the riparian reserves were not mapped; thus we 

were unable to discern what portion of the lands outside reserves 

were matrix and which were riparian reserves, so they are reported 

as combined allocations.
49

Table 3-7–Habitat-capable federal land by land use allocation in the range of the spotted owlState/ Estimated Habitat- Reserve Nonreservephysiographic habitat- capable land

province capable area in range CR LSR AMR MLSA AW LSR3 LSR4 AMA MATRR 

Acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - - 

Washington: Eastern Cascades 1,360,800 7.5 18.2 27.5 0 6.3 5.0 0 0.5 5.7 36.2 

 Western Cascades 2,363,300 13.1 28.4 36.0 3.1 0 5.7 <.5 <.5 6.1 19.8  Western Lowlands NC Olympic Peninsula 1,067,000 5.9 51.7 36.3 0 0 0 .5 <.5 11.4 <.5  Total 4,791,100 26.5 30.7 33.6 1.5 1.8 4.2 <.5 <.5 7.1 20.0 

Oregon: Klamath 1,818,700 10.1 11.2 39.0 1.6 0 1.3 0.9 1.7 11.4 32.6 

 Eastern Cascades 1,052,800 5.8 14.7 30.7 0 0 7.8 0 .7 0 46.1  Western Cascades 4,084,500 22.6 12.5 30.0 <.5 0 6.5 0 2.1 5.6 42.8  Coast Range 1,391,100 7.7 1.6 53.7 11.8 0 1.5 2.6 <.5 5.5 21.7  Willamette Valley NC Total 8,347,100 46.2 10.7 36.0 2.4 0 4.7 0.6 1.6 6.1 37.5 

California: Coast Range 346,700 1.9 40.0 31.7 0 0 9.2 <.5 <.5 0 17.3  Klamath 3,703,500 20.5 22.6 30.1 <.5 0 5.3 0 .7 9.5 31.3  Cascades 876,200 4.8 1.6 24.0 0 .8 8.0 0 <.5 15.0 50.4  Total 4,926,400 27.3 20.0 29.1 <.5 <.5 6.0 <.5 .6 9.8 33.7 

Plan total 18,064,600 100 18.5 33.5 1.6 0.5 4.9 <.5 1.0 7.4 31.8 

Note: NC = not calculated because of too little federal area in the province.Note: see figure 3-1 for land use codes.
50

GENERAL TECHNICAL REPORT PNW-GTR-648

includes that below the elevation limits of occupancy by 

territorial owls (fig. 3-5) and not on serpentine soil areas 

(fig. 3-6). 

Rangewide, over half (52 percent) of the habitatcapable area occurred in the combination of congressionally reserved land (18.5 percent), and late-successional 

reserves (33.5 percent). Another 32 percent occurred in 

the combination of matrix and riparian reserve allocations 

(table 3-7). Administratively withdrawn areas accounted 

for 5 percent, and the adaptive management areas contained 

7 percent. The remaining allocations each contained less 

than 1 percent of the total. 

In Oregon, 87 percent of federal land was habitatcapable, in Washington 55 percent, and in California 81 

percent (tables 3-7 and 3-8). The elevation limit for occupation of habitat by territorial owls was primarily responsible 

for the pattern. Habitat-capable area was less in Washington 

compared to the other two states, where the elevation limit 

was at higher elevations (fig. 3-5). 

The proportion of total habitat-capable area in the 10 

provinces modeled was varied (table 3-8). The proportion in 

the Olympic Peninsula and Western Cascades provinces of 

Washington and the Eastern Cascades province in Oregon 

ranged between 60 and 70 percent (table 3-8). The proportion of the other three Oregon provinces was >85 percent. 

The Eastern Cascades province in Washington had the 

lowest proportion, only 39 percent. In California, more than 

80 percent of the federal land area was habitat-capable in the 

Klamath and Cascades provinces. In the Coast province, the 

proportion was about 70 percent. Combined, the Western 

Cascades provinces in both Oregon and Washington and 

the Klamath provinces in Oregon and California contain 

two-thirds of the habitat-capable land in the Plan area (table 

3-7). The higher the percentage of habitat-capable area, 

Table 3-8—Habitat-capable federal land inside and outside of large, reserved habitat blocks in the range of 

the spotted owl

Reserved block land

State/ Total Habitat- Habitat-capable Habitat-capable land Not

physiographic federal capable land outside large land inside large Habitat- habitatprovince land federal land reserved blocks reserved blocks capable capable

Acres - - - - - - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - - - - - -

Washington:

 Eastern Cascades 3,502,400 39 53 47 26 74

 Western Cascades 3,721,000 64 35 65 55 45

 Western Lowlands NC NC NC NC NC

 Olympic Peninsula 1,522,300 70 24 76 65 35

 Total 8,745,700 55 38 62 46 54

Oregon:

 Klamath 2,118,200 86 52 48 84 16

 Eastern Cascades 1,551,800 68 55 45 54 46

 Western Cascades 4,476,700 91 58 42 84 16

 Coast Range 1,413,300 98 42 58 99+ <1

 Willamette Valley NC NC NC NC NC

 Total 9,560,000 87 54 46 81 19

California:

 Coast Range 503,600 69 46 54 77 23

 Klamath 4,520,200 82 48 52 79 21

 Cascades 1,091,300 80 76 24 77 23

 Total 6,115,100 81 53 47 78 22

Plan total 24,420,800 74 49 51 65 35

Note: NC = not calculated because of too little federal area in the province.
51

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

the greater the capacity of the land to produce owl habitat 

as long as the area fell within those land use allocations 

intended to support territorial owls (for example, congressionally reserved lands and larger blocks of latesuccessional reserves).

At the province scale, the distribution of habitatcapable area among the land use allocations intended to 

support territorial owls greatly depended on the presence of 

the allocations in the province (fig. 3-1). The congressionalreserve allocation was an important source of base habitatcapable area in some provinces, but not all. The proportion 

of habitat-capable area in the congressionally reserved 

allocation ranged from only 1.6 percent in the Coast Range 

province of Oregon and the Cascades province of California 

to 52 percent in the Olympic Peninsula province (table 3-7). 

All other provinces, except the Western Cascades province 

in Washington (28 percent) and the Coast province in California (40 percent), had less than 25 percent of their habitatcapable area in the congressionally reserved allocation.

The percentage of habitat-capable area in the latesuccessional reserves and adaptive management reserves 

ranged from 24 percent in the Cascades province of 

California to 66 percent in the Coast Range province of 

Oregon. The proportion of habitat-capable area in the latesuccessional reserves and adaptive management reserves in 

the remaining provinces was from 27 to 40 percent (table 

3-7). The late-successional reserves were important sources 

of habitat-capable area for maintaining and restoring owl 

habitat. 

The percentage of habitat-capable area in the matrix/

riparian reserves (combined) ranged from 0 (the Olympic 

Peninsula has no matrix) to 50 percent (Cascade province 

of California). Percentages of habitat-capable area in the remaining provinces ranged from 17 to 46 percent. A portion 

of the habitat-capable area in these allocations contributes 

habitat for territorial owls in the short term and for dispersal 

habitat both short and long term.

The pattern of federal lands inside and outside the 

large, reserved blocks (reserved blocks) is depicted in figure 

3-7. Rangewide, about 58 percent of federal land area was in 

the reserved blocks intended to provide habitat for clusters 

of owls under the Plan. However, about 35 percent of the 

land in the reserved blocks was not expected to provide 

habitat for territorial spotted owls because it is not habitatcapable. Rangewide, 51 percent of the habitat-capable 

federal area was in the reserved blocks and 49 percent 

was outside the blocks (table 3-8).

The federal land outside the reserved blocks was in 

smaller tracts of late-successional reserves, administratively withdrawn, matrix and riparian reserves. Under the 

Plan, all federal land area outside of the large, reserved 

blocks, regardless of allocation, contributes to dispersal 

habitat for movement of owls between the blocks and is not 

expected to provide habitat for territorial owls in the longer 

term, although some owls may be found there. 

At the province scale, the proportion of habitat-capable 

area inside the large, reserved blocks varied (fig. 3-7 and 

table 3-8). With the exception of the Cascades province 

in California (24 percent), all provinces had at least 40 

percent of their habitat-capable area in reserved blocks. 

The Olympic Peninsula and Western Cascades provinces 

in Washington and the Coast Range province in Oregon 

had the highest proportion of habitat-capable area inside 

the large reserved blocks with 76, 65, and 58 percent, 

respectively. Values in the other provinces ranged between 

42 and 54 percent (table 3-8).

Within the reserved blocks, the proportion of habitatcapable area was lowest in the Eastern Cascades provinces 

of Washington (26 percent) and Oregon (54 percent). In 

the Olympic Peninsula province, 65 percent of the land in 

the blocks was habitat-capable (table 3-8). More than 75 

percent of land in the reserved blocks was habitat-capable 

in the remaining provinces in Oregon and all provinces in 

California. In the Coast Range province in Oregon, almost 

all of the federal land in the reserved blocks was habitatcapable.

Estimated habitat suitability—

Maps depicting habitat suitability for territorial owls on 

federal lands were derived for each physiographic province 

from the IVMP and CALVEG vegetation data by using the 

BioMapper software program (app. D, figs. D-1 through 

D-10). 
52

GENERAL TECHNICAL REPORT PNW-GTR-648

Our assessment of the model output relied on current scientific knowledge of habitat use by the owl in the 

activity centers around nest trees. These areas are used for 

nesting, roosting, and foraging by the owls. Marginality 

factor scores for the model output ranged between 0.72 

and 0.96 for all provinces except the Klamath province in 

California where the score was 0.41 (app. E). This range 

of marginality scores is similar to those noted by Brotons 

et al. (2004) where BioMapper marginality scores ranged 

from 0.41 to 0.99, with model accuracies from 60 to 95 

percent (mean accuracy of 74 percent) for 30 species of 

forest-dwelling birds. Specialization factor scores from 

our models ranged between 2.0 and 2.9 for all provinces in 

Washington and Oregon (app. E). In California, the scores 

were between 1.3 and 1.8. The tolerance indices for all 

provinces in Washington and Oregon ranged from 0.35 to 

0.49. In California, the tolerance index values ranged from 

0.56 in the Cascades province to 0.76 in the Coast province 

and 0.85 in the Klamath province. The higher index values 

in California suggest that the owl may use a wider range of 

habitat conditions compared to Washington and Oregon. 

This apparent use of a broader variety of habitats in the 

southern portion of the owl’s range is consistent with 

findings by others (Zabel et al. 2003). However, the higher 

tolerance values may also be due to differences in vegetation data sources (IVMP vs. CALVEG).

Cross-validation of the Spearman rank correlations 

(rs) indicated that our models predicted owl use locations 

well (table 3-9). On a scale of 0 to 1.0, the average rs values 

ranged from 0.83 to 0.99 (P<0.001). In addition, we used 

owl telemetry data (independent of model training data) 

from 19 owl home ranges to test the models in three of the 

physiographic provinces in Oregon (app. F). The results 

from these independent data tests also indicated that the 

models predicted owl-use locations well (in those provinces) with average Spearman rank correlations greater 

than 0.93 (P<0.001).

Estimated Habitat Suitability for 

Habitat-Capable Area—

For each analysis scale, a portion of the total habitatcapable area had insufficient data to classify the vegetation attributes of the pixels, so this area is reported in the 

histograms as “unknown” (app. G). The classified portion, 

rangewide, (94 percent) was analyzed by using the BioMapper software.

Table 3-9—Cross-validated Spearman’s rank correlations (rs) between habitat suitability 

classes of equal intervals and area-adjusted frequencies for individual model replicates for 

each physiographic province modeled

Olympic Western Eastern Coast Klamath

Peninsula Cascades (WA) Cascades (WA) Range (OR) Mountains (OR)

Replicates rs P rs P rs P rs P rs P

1 0.84 <0.01 0.88 <0.001 0.82 <0.01 0.99 <0.001 0.96 <0.001

2 .84 <.01 .88 <.001 .83 <.01 .98 <.001 .98 <.001

3 .84 <.01 .89 <.001 .87 <.01 .99 <.001 .99 <.001

4 .84 <.01 .85 <.01 .82 <.01 .99 <.001 .99 <.001

5 .84 <.01 .89 <.001 .88 <.001 1.00 <.001 .95 <.001

Average .84 <.001 .88 <.001 .83 <.001 .99 <.001 .96 <.001

Western Eastern California Klamath Cascade

 Cascades (OR) Cascades (OR) Coast Mountains (CA) Mountains (CA)

Replicates rs P rs P rs P rs P rs P

1 0.94 <0.001 0.96 <0.001 0.99 <0.001 0.92 <0.001 0.96 <0.001

2 .94 <.001 .94 <.001 1.00 <.001 .99 <.001 .94 <.001

3 .84 <.01 .96 <.001 .99 <.001 .98 <.001 .98 <.001

4 .90 <.001 .94 <.001 .99 <.001 .94 <.001 .98 <.001

5 .88 <.001 .96 <.001 .98 <.001 .98 <.001 .96 <.001

Average .87 <.001 .96 <.001 .99 <.001 .98 <.001 .99 <.001
53

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Habitat suitability of habitat-capable area is displayed 

in histograms by five equal categories ranging from 0 to 100 

(that is, 0 to 20, 21 to 40, and so on) for the range, by state 

and by province (app. G). Habitat suitability is also reported 

for the individual land-use allocations for each of the scales 

above. These histograms provide a habitat condition profile 

of the habitat-capable area. Habitat-capable area that falls 

on the right side of the profile (habitat suitability of 41 to 

100) has characteristics similar to the characteristics of 

areas where territorial owls have been found. The closer to 

100, the more similar they are. The habitat-capable area on 

the left side of the histogram (habitat suitability of 0 to 40) 

is less similar to the owl-presence locations and that area 

approaching 0 is the most dissimilar. 

Tracking the change in the respective profiles over time 

is expected to provide useful information for judging the 

success of the Plan in maintaining and restoring habitat in 

the reserves, in general, and the reserved blocks, in particular. In time, the percentage of habitat-capable area in the 

41 to 60, 61 to 80, and 81 to 100 categories should increase 

as habitat conditions improve in the reserved blocks. 

Conversely, the expected decline in habitat conditions in 

the matrix allocation can be monitored. Monitoring the 

proportion of the habitat-capable area in the 0 to 40 range 

will track the recruitment of area to the right side of the 

profile, especially from area in the 21 to 40 category, a large 

portion of which should move, by forest succession, from 

the left to right side of the profile in the next few decades. 

Thomas et al. (1990) noted that considerable owl use of 

midaged and young stands also occurred, suggesting that 

as forests develop along the continuum from young to old, 

they gradually become more suitable for spotted owls. The 

habitat condition profiles provide a useful tool for tracking 

habitat condition over time at various scales.

Across the owl range, about 57 percent of the habitatcapable area had a habitat suitability of ≥41. About 36 

percent had a score in the range of 0 to 40, and the remaining 7 percent was in the unknown class (app. G, fig. G-1). 

The histogram of habitat suitability for the land use 

allocations at the rangewide scale is shown in app. G, fig. 

G-1. The congressional reserves, late-succession reserves, 

and the combined matrix and riparian reserve allocations 

were the major contributors of habitat-capable area in the 

41 to 100 range at the beginning of the monitoring period. 

More than 67 percent of the habitat-capable area in these 

combined allocations were in the habitat suitability range 

of 41 to 100. The congressionally reserved land had the 

highest percentage (67 percent) with a habitat suitability 

score of ≥41. The late-successional reserves were a close 

second with 60 percent of habitat-capable area ≥41 percent. 

We assumed that at least 50 percent of the habitatcapable land in the adaptive management areas and the 

combined matrix/riparian reserve land use allocations 

would fall into the riparian reserves. Under that assumption, 

we estimated that, rangewide, over 80 percent of the habitatcapable area in the 41 to 100 range of habitat suitability 

occurs in a reserved land use allocation. 

About 36 percent of habitat-capable area at the rangewide scale had habitat suitability scores of <41, indicating 

the presence of habitat-capable area that could grow into 

habitat over time depending on the allocations they were in. 

Almost half of the habitat-capable area in the lower suitability categories was in the congressionally reserved and 

late-successional reserve allocations, where the management focus is maintenance and restoration of late-successional forest and habitat.

At the state scale, the unknown category included 9 

percent of habitat-capable area in Washington, 4 percent 

in Oregon, and 9 percent in California. In Washington, 52 

percent had a habitat suitability score in the 41 to 100 range 

and another 39 percent was in 0 to 40 range (app. G, fig. 

G-2). Corresponding values for Oregon were 60 percent, 

≥41, and 37 percent, <41 (app. G, fig. G-3). In California, 57 

percent was ≥41 and 33 percent was <41 (app. G, fig. G-4). 

In Washington, 63 percent of habitat-capable area in the 

congressional reserves had a habitat suitability score in the 

range of 41 to 100 (app. G, fig. G-2). In Oregon, although 

the percentage of habitat-capable area in congressional 

reserves was only 11, the percentage of habitat-capable area 

with a habitat suitability score ≥41 was 74 percent (app. G, 

fig. G-3). This land use allocation in California contained 

20 percent of the habitat-capable area and 66 percent of that 

had habitat suitability scores of ≥41 (app. G, fig. G-4).
54

GENERAL TECHNICAL REPORT PNW-GTR-648

The late-successional reserves and adaptive management 

reserves contained 35 percent of the habitat-capable area 

in Washington compared to 38 percent in Oregon and 29 

percent in California. The percentage in the 41 to 100 range 

of habitat suitability scores was 53 for Washington, 61 for 

Oregon, and 64 percent for California.

 The managed late-successional areas were in Washington and California. In Washington, only about 2 percent of 

the habitat-capable area in the state was in this allocation and 

in California, less than 1 percent; more than 50 percent of 

habitat-capable area in the managed late-successional areas 

in Washington and 68 percent in California had a habitat 

suitability score of ≥41.

At the province scale, the proportion of habitat-capable 

area that could not be classified because data were lacking 

(reported as “unknown”) ranged from 1.0 to 2.0 percent in 

the coast provinces in Oregon and California and the Eastern 

Cascades province in Oregon to 10 percent in the Cascades 

province in California and 15 percent in the Eastern Cascades province of Washington. The unknown category 

ranged between 5 and 9 percent in the five other provinces.

Habitat suitability for each province is displayed in the 

histograms in appendix G, (figs. G-5 through G-14). All 

provinces, except the Eastern Cascades in Washington (44 

percent) and the Cascades in California (41 percent), had 

50 to 63 percent of the habitat-capable area in the habitat 

suitability range of 41 to 100. Seven of ten provinces had 

between 30 and 40 percent of the habitat-capable area in the 

<41 category.

Histograms of habitat suitability for land use allocations 

by province are also shown in figures G-5 through G-14 in 

appendix G. Habitat suitability is provided by individual 

allocations and by province. For the congressionally reserved 

allocations, 36 to 83 percent of the habitat-capable area had 

a habitat suitability of ≥41 across the 10 provinces. For those 

four provinces where the congressionally reserved allocation contained >20 percent of the habitat-capable area in 

the province, 47 to 73 percent of such reserves had habitat 

suitability ≥41. 

The late-successional reserves and adaptive management 

reserves were combined for analysis. Among the provinces, 

the proportion of habitat-capable area with habitat suitability 

of 41 to 100 in these reserves ranged from 53 to 69 percent. 

All of the provinces in Oregon and California had more 

than 57 percent of their habitat-capable area in the ≥41 

habitat suitability range.

Habitat suitability was also analyzed for habitat-capable 

area inside and outside of the large, reserved blocks (fig. 16, 

app. D, and table 3-10). Across the range, about 62 percent 

(19.7 + 16.3 + 26.4) of habitat-capable area inside the reserved blocks had habitat suitability of ≥41, with two-thirds 

of that in the 61 to 100 range (table 3-10). Of the 32 percent 

of the habitat-capable area with <41 habitat suitability, 

slightly less than half was in the 0 to 20 category. 

Habitat suitability conditions outside of the reserved 

blocks were different than inside. About 52 percent of the 

habitat-capable area was in the 41 to 100 category, and 

41 percent was in the ≤41 category. The 0 to 20 category 

contained 21 percent of the habitat-capable area outside the 

reserved blocks.

The percentages of habitat-capable area in the ≥41 and 

<41 categories inside the reserved blocks in each state were 

relatively consistent and tracked proportionally with values 

at the rangewide scale. Outside of the blocks, California and 

Oregon mirrored the rangewide percentage for the ≥41 habitat suitability (52 percent), but Washington’s (42 percent) 

was lower. All of the states had more habitat-capable area 

with habitat suitability of 0 to 20 outside of the reserved 

blocks compared to inside. Washington, in particular, had 

about 20 percent of its habitat-capable area in the 0 to 

20 category inside the reserved blocks, and a notable 31 

percent in the category outside the blocks. 

At the province scale, the proportion of area inside the 

reserved blocks with a habitat suitability ≥41 was highest 

in the Olympic Peninsula (67 percent) of Washington, the 

Eastern Cascades (71 percent) and Western Cascades (68 

percent) provinces in Oregon, and the Klamath province (66 

percent) in California. In the other provinces, the percentage of habitat-capable area with a ≥41 habitat suitability 

ranged from 54 to 60 percent (table 3-10). Habitat suitability 

<41 ranged from 25 percent to 41 percent. None of the provinces had more than 25 percent of their habitat-capable area 

in the 0 to 20 habitat suitability category inside the reserved 

blocks, and 7 of 10 had less than 15 percent.
55

Table 3-10—Habitat-capable federal land inside and outside of large reserved habitat blocks, by habitat suitability categorof the spotted owl

Area inside habitat blocks, Area outside habitat bloc

by habitat suitability category by habitat suitability cate

Physiographic province by state Unknown 0–20 21–40 41–60 61–80 80–100 Unknown 0–20 21–40 41–60 

Percent

Washington: Eastern Cascades 10.4 22.3 13.4 8.4 27.0 18.4 19.5 33.1 12.0 5.7  Western Cascades 6.2 22.0 16.5 16.6 12.3 26.4 5.8 29.8 16.8 13.2  Western Lowlands NC  Olympic Peninsula 5.9 13.6 13.4 20.1 9.2 37.8 8.0 30.6 18.1 17.7  Total 7.0 19.8 15.0 15.8 14.7 27.7 11.5 31.3 15.1 10.9 Oregon: Klamath 4.2 14.9 23.9 20.3 14.2 22.5 5.5 18.8 22.1 18.6  Eastern Cascades 0.7 19.5 9.0 35.3 16.5 19.0 0.8 31.4 12.3 29.5  Western Cascades 4.5 13.6 13.3 21.3 12.4 34.8 5.7 20.0 18.9 19.3  Coast Range 1.5 11.2 26.9 21.8 15.5 23.1 1.5 14.3 28.7 21.0  Willamette Valley NC Total 3.3 14.2 18.0 22.8 14.0 27.7 4.5 20.5 20.0 20.7 California: Coast Range 1.8 8.4 32.3 21.9 19.3 16.3 2.6 30.6 24.8 18.7  Klamath 8.5 4.2 21.0 20.2 22.9 23.2 9.4 7.0 24.9 18.1  Cascades 8.9 13.6 23.2 13.0 20.8 20.5 11.1 30.6 21.0 13.5  Total 8.0 5.4 22.1 19.6 22.5 22.4 9.4 14.4 23.9 17.0 Plan total 5.7 13.8 18.1 19.7 16.3 26.4 7.4 20.9 20.1 17.6 

Note: NC = not calculated because of too little federal area in the province.
56

GENERAL TECHNICAL REPORT PNW-GTR-648

Outside of reserved blocks, the proportion of habitatcapable area with a score ≥41 differed among provinces 

(table 3-10). All of the provinces in Oregon, along with the 

Klamath province in California, had 54 to 59 percent of 

the habitat-capable area in the ≥41 category. All of the other 

provinces had between 35 and 48 percent of the habitatcapable area in the 41-100 range. Percentage of area in the 

<41 habitat suitability category was much higher outside 

than inside the reserved blocks for most of the provinces. 

The Western Cascades province in Oregon and the Klamath 

provinces in Oregon and California all had 31 to 41 percent 

of the habitat-capable area in the <41 category. All the 

rest of the provinces had between 45 and 55 percent of the 

capable area in the <41 habitat suitability category.

Estimated change of condition for habitat-capable 

federal area—

Decreases in forest condition from stand-replacing wildfire 

and timber harvest were assessed for the habitat-capable 

area. Rangewide, 1.5 percent of the habitat-capable area was 

changed by stand-replacing events based on information 

from the regional change-detection data (table 3-11). Losses 

to harvest and wildfire are shown by province and land use 

allocation within province in table 3-11 and appendix H, 

figures H-5 through H-14. Total stand-replacing timber 

harvest affected about 0.26 percent of the habitat-capable 

area rangewide. Most of this change (35 percent of total 

change) was in the Western Cascades province in Oregon. 

About 90 percent of the stand-replacing timber harvest 

occurred in the matrix and riparian reserve (combined) 

allocation and adaptive-management area allocation. 

Loss of forest in habitat-capable areas was less than 

2.5 percent in each of the three states (app. H, figs. H-2 

through H-4 and table 3-11). Washington had only a 0.44-

percent loss, Oregon lost 2.38 percent, and California lost 

1.32 percent. Loss of habitat-capable forest in the individual 

provinces was less than 1 percent in five provinces and 

less than 2 percent in four other provinces. The Klamath 

province in Oregon (7.5 percent) was the only province that 

lost forest on more than 5 percent of the habitat-capable area 

to stand-replacing harvest or wildfire. 

The percentages of habitat-capable area lost in 

the habitat suitability categories ≥41 by timber harvest 

and wildfire are shown in table 3-12. Rangewide, standreplacing timber harvest affected only 0.26 percent. No 

province lost more than 0.90 percent of the habitat-capable 

area to stand-replacing timber harvest, and all but one 

were under 0.5 percent.

The effect of stand-replacing wildfire events on 

habitat-capable area was less widespread, but it had more 

locally negative effects than did timber harvest. Only 1.3 

percent of the habitat-capable area was affected by wildfire 

(table 3-11). The loss of habitat-capable area to wildfire as a 

proportion of province habitat-capable area, was greatest in 

the Eastern Cascades provinces of Washington (1.2 percent) 

and Oregon (0.85 percent), the Western Cascades province 

(0.68 percent) in Oregon, and the Klamath provinces in 

California (1.5 percent) and Oregon (7.1 percent). 

The congressionally reserved and late-successional 

reserve allocations had the greatest proportional change of 

forest condition in habitat-capable area from wildfire (table 

3-11 and app. H, figs. H-1 through H-14). Rangewide, 2.7 

percent of the habitat-capable area in the congressionally 

reserved areas was affected, and about 1.6 percent in the 

late-successional reserves was affected. The highest percentage loss of habitat-capable area in all of these reserve 

allocations was in the Klamath province where about 24 

percent of the habitat-capable area in the congressional reserves was lost and about 9 percent in the late-successional 

reserves. Losses in reserve allocations in other provinces 

were much lower.

Wildfire, unlike timber harvest, affected all categories 

of habitat suitability. Rangewide, 1.3 percent of the habitatcapable area in the 41 to 100 category was lost to wildfire 

(table 3-12). The loss of habitat-capable area with ≥41 

habitat suitability was greater in provinces with more 

wildfires. In the Klamath province of Oregon, about 6.6 

percent of the habitat-capable area in the 41 to 100 category 

was lost to wildfire, and nearly all (86 percent) of the loss 

was in the congressional and late-successional reserves. 

Loss of habitat-capable area in the ≥41 habitat suitability 

range was less than 1.6 percent in all other provinces 

(table 3-12). 
57

Table 3-11—Habitat-capable federal land where stands were lost to stand-replacing timber harvest (H) and wildfire (F) in th

 Area lost by land-use allocation

State and

physiographic Total habitat- Total CR LSR AMR AW MLSA LSR3 L

province capable land H F H F H F H F H F H F H F H 

Acres - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - - - - - - - - 

Washington: Eastern Cascades 1,360,800 0.26 1.17 0 2.20 0.07 0.46 0 0 <0.01 2.4 0.04 0.60 0 0 0.25  Western Cascades 2,363,300 .06 <.01 .02 <.01 <.01 0 0 0 0 0 0 0 0 0 .01  Western Lowlands NC Olympic Peninsula 1,067,000 .02 <.01 <.01 <.01 .02 0 0 0 0 0 0 0 <.09 0 0  Total 4,791,100 .11 .33 <.01 .38 .02 .11 0 0 <.01 0.8 .04 0.6 .05 0 .11 Oregon: Klamath 1,818,700 .44 7.10 <.01 24.17 .16 8.90 0 0 <.01 11.1 0 0 .53 .90 .05  Eastern Cascades 1,052,800 .46 .85 <.01 .20 .52 1.12 0 0 .02 2.70 0 0 0 0 .12  Western Cascades 4,084,500 .39 .68 <.01 1.92 .07 1.14 0 0 <.01 .07 0 0 0 0 .06  Coast Range 1,391,100 .23 0 0 0 .08 0 <.01 0 <.01 0 0 0 <.01 0 .03  Willamette Valley NC Total 8,347,100 .38 2.0 <.01 6.70 .09 2.7 <.01 0 <.01 1.30 0 0 .16 .28 .60 California: Coast Range 346,700 .04 <.01 .09 0 .01 0 0 0 .02 .01 0 0 0 0 0  Klamath 3,703,500 .10 1.47 .05 3.17 .03 1.40 0 0 .09 1.71 0 0 0 0 .04  Cascades 876,200 .66 .13 0 0 .13 .14 0 0 .16 .03 .01 0 0 0 0  Total 4,926,400 .19 1.13 .05 2.70 .04 1.14 0 0 .10 1.13 0 0 0 0 .03 Plan Total 18,064,600 .26 1.30 .02 2.70 .057 1.63 <.01 0 .04 1.13 .04 .57 .14 0.23 .06 

Note: NC = not calculated because of too little federal area in the province.Note: See figure 3-1 for land use codes.
58

GENERAL TECHNICAL REPORT PNW-GTR-648

Loss of habitat-capable area to timber harvest and 

wildfire amounted to 2.1 percent of the habitat-capable area 

inside large, reserved blocks (table 3-13). Loss in the reserved blocks was greatest in Oregon (3.7 percent) followed 

by California (1.9 percent) and Washington (0.3 percent). 

The provinces with the greatest losses inside the reserved 

blocks were the Klamath provinces in Oregon (13.0 percent) 

and California (2.3 percent). Nearly all of the loss inside the 

reserved blocks was caused by wildfire and concentrated in 

the Klamath province in Oregon. Losses inside the reserved 

blocks in other provinces ranged from 0 percent to about 1.4 

percent.

Loss of habitat-capable area outside of the reserved 

blocks was less than 1 percent (0.48 + 0.47) at the rangewide scale and 1.2 percent at the state scale (table 3-14). 

The largest percentage loss of forest on habitat-capable area 

outside the reserved blocks was 2.42 percent in the Klamath 

province of Oregon. With the exception of the Eastern Cascades provinces in Oregon (1.27 percent) and Washington 

(1.46 percent), all other provinces lost less than 1 percent.

Loss of habitat-capable area with a habitat suitability of 

≥41 because of stand-replacing timber harvest was assessed 

inside and outside of the large, reserved habitat blocks (table 

3-15). Rangewide, the loss inside the reserved blocks from 

stand-replacing timber harvest was 0.04 percent and outside 

the blocks it was only 0.54 percent. Losses at the state scale 

paralleled the rangewide values both inside and outside 

the reserved blocks. In the provinces, losses ranged from 

0 to 0.22 percent inside the blocks and 0.03 to 1.2 percent 

outside the blocks.

Losses of habitat-capable area in the ≥41 habitat suitability range because of wildfire were greater than those 

from timber harvest, but as noted earlier were limited to 

specific provinces (table 3-11). About 2.0 percent of the ≥41 

habitat-capable area was lost, rangewide, inside the reserved 

blocks. Less than 0.5 percent of the area in this range of 

habitat suitability was lost outside the blocks, rangewide. 

Washington lost only 0.23 percent of the habitat-capable 

area ≥41 habitat suitability (table 3-15). Oregon lost about 

3.2 percent and California about 2.0 percent. The Klamath 

provinces in Oregon and California had the highest percentage losses, 11.5 and 2.30 percent, respectively. They were 

followed by the Western Cascades province in Oregon with 

a 1.6 percent loss and the Eastern Cascades provinces in 

Washington and Oregon where each lost about 1 percent of 

the habitat-capable area inside the reserved blocks in this 

range of habitat suitability. All other provinces had less 

than 1 percent loss. 

Losses of habitat-capable area in the ≥41 habitat 

suitability range outside the reserved blocks to standreplacing fire was less than 0.6 percent rangewide. Only 

two provinces lost more than 1 percent of this habitatcapable area outside the reserved blocks. These provinces 

were the Klamath in Oregon (1.7 percent) and the Eastern 

Cascades in Washington (1.1 percent).

Table 3-12—Habitat-capable federal land with a 

habitat suitability ≥41 where stands were lost to 

timber harvest and wildfire in the range of the 

spotted owl 

Loss of habitat-capable area 

with a habitat suitability ≥41

State/physiographic Stand-replacing Stand-replacing

province timber harvest wildfire

Percent

Washington:

 Eastern Cascades 0.38 1.10

 Western Cascades .10 <.01

 Western Lowlands NC NC

 Olympic Peninsula .02 <.01

 Total .11 .32

Oregon:

 Klamath .44 6.6

 Eastern Cascades .26 .74

 Western Cascades .36 .84

 Coast Range .23 0

 Willamette Valley NC NC

 Total .35 1.87

California:

 Coast Range .06 <.01

 Klamath .10 1.56

 Cascades .88 .14

 Total .19 1.28

Plan total .26 1.30

Note: NC = not calculated because of too little federal area in the 

province.
59

Table 3-13—Habitat-capable federal land inside of large, reserved habitat blocks where stands were lost to timber harvest by habitat suitability category, in the range of the spotted owl

Area inside large reserved blocks lost to stand-replacing Area inside large reserved blocks los

Physiographic timber harvest, by habitat suitability category replacing wildfire, by habitat suitabili

province by state Unknown 0–20 21–40 41–60 61–80 81–100 Total Unknown 0–20 21–40 41–60 61–80 

Percent

Washington: Eastern Cascades 0.01 0.01 0.04 0.07 0.06 0.06 0.04 2.64 1.19 1.51 1.30 1.06  Western Cascades 0 0 0 0 0 0 0 0 0 0 0 0  Western Lowlands NC Olympic Peninsula 0 0 0 0 .01 .01 .01 .01 .02 .01 0 .01  Total 0 0 .01 .01 .02 .01 .01 .85 .29 .29 .15 .42 Oregon: Klamath .10 .13 .08 .09 .14 .14 .11 16.24 14.88 14.77 13.12 11.51  Eastern Cascades 0 .09 .10 .03 .01 .01 .04 2.01 1.93 .42 .96 1.35  Western Cascades .11 .13 .04 .02 .01 .03 .05 .48 .97 .95 1.34 1.65  Coast Range .44 .12 .05 .05 .13 .02 .07 0 0 0 0 0  Willamette Valley NC Total .14 .12 .06 .04 .07 .05 .06 4.91 4.3 4.77 3.37 3.5 California: Coast Range 0 .04 .01 .10 .06 .09 .05 0 0 0 0 0  Klamath 0 .06 .03 .02 .05 .05 .04 2.23 1.79 2.15 2.41 2.23  Cascades .09 .13 .08 .21 .27 .18 .16 .07 .06 .12 .08 .33  Total .01 .07 .04 .04 .07 .06 .05 1.97 1.17 1.70 2.05 1.91 Plan total .04 .06 .04 .03 .06 .04 .04 2.25 2.12 2.61 2.20 2.05 

Note: NC = not calculated because of too little federal area in the province.
60

Table 3-14—Habitat-capable federal land outside of large, reserved habitat blocks where stands were lost to timber harvesby habitat suitability category, in the range of the spotted owl Area outside large reserved blocks lost to stand-replacing Area outside large reserved blocks lo

Physiographic timber harvest, by habitat suitability category replacing wildfire, by habitat suitabili

province by state Unknown 0–20 21–40 41–60 61–80 81–100 Total Unknown 0–20 21–40 41–60 61–80 

Percent Washington: Eastern Cascades 0.13 0.19 0.60 0.75 0.78 0.91 0.45 1.52 0.58 1.21 1.96 0.90 

 Western Cascades .02 .06 .12 .21 .20 .41 .18 0 0 0 0 0  Western Lowlands NC Olympic Peninsula .01 .06 .09 .07 .15 .07 .07 0 0 0 0 0  Total .09 .12 .27 .29 .50 .49 .27 1.01 .25 .38 .41 .46 Oregon: Klamath .64 .76 .68 .70 .81 .79 .74 1.59 1.26 1.92 1.91 1.84  Eastern Cascades .10 .52 .91 .82 .78 .79 .72 .01 .61 .18 .59 .79  Western Cascades .81 .70 .47 .66 .68 .68 .65 .14 .14 .14 .18 .19  Coast Range 2.14 .58 .39 .43 1.12 .07 .50 0 0 0 0 0  Willamette Valley NC Total .81 .66 .54 .67 .80 .64 .66 .51 .44 .53 .56 .65 California: Coast Range 0 .02 .05 .03 .04 .02 .03 0 0 0 0 0  Klamath .26 .15 .15 .11 .19 .19 .17 .90 .68 .50 .63 .65  Cascades .74 .63 .50 .99 1.21 1.45 .82 .02 .14 .16 .13 .11  Total .4 .39 .22 .29 .36 .38 .33 .62 .30 .39 .49 .52 Plan total .42 .44 .39 .51 .57 .55 .48 .71 .35 .46 .52 .56 

Note: NC = not calculated because of too little federal area in the province.
61

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Table 3-15—Habitat-capable federal land inside and outside of large reserved habitat 

blocks with a habitat suitability ≥41 in the range of the spotted owl

Area with a habitat suitability ≥41 Area with a habitat suitability ≥41

lost inside habitat blocks lost outside habitat blocks

State/physiographic Stand-replacing Stand-replacing Stand-replacing Stand-replacing

province timber harvest wildfire timber harvest wildfire

Percent

Washington:

 Eastern Cascades 0.06 1.13 0.82 1.06

 Western Cascades 0 <.01 .30 0

 Western Lowlands NC

 Olympic Peninsula .01 <.01 .08 0

 Total .01 .23 .44 .35

Oregon:

 Klamath .12 11.5 .76 1.74

 Eastern Cascades .02 1.06 .80 .60

 Western Cascades .03 1.6 .67 .20

 Coast Range .06 0 .48 0

 Willamette Valley NC

 Total .05 3.18 .68 .54

California:

 Coast Range .08 0 .03 <.01

 Klamath .04 2.35 .16 .58

 Cascades .22 .20 1.20 .12

 Total .06 2.01 .35 .47

Plan total .04 1.98 .54 .49

Note: NC = not calculated because of too little federal area in the province.

Estimated total wildfire occurrence—

Approximately 13,200 wildfires were recorded on federal 

lands from 1994 to 2002 in the 10 provinces where we 

mapped owl habitat (Brown et al. 2002). About half were 

caused by lightning and half were human caused. However, 

about 75 percent of the total area burned was the result of 

a lightning ignition.

Using these data, about 1.7 million ac of federal land 

(USFS, NPS, and BLM) burned within the range of the 

northern spotted owl. Wildfire data (Healey et al. n.d.) 

show that about 230,000 ac were affected by standreplacement fires—equating to about 14 percent of the 

total area burned. We assumed the remaining 86 percent 

burned at lower intensities and severities as they were not 

mapped as stand-replacing fires by Healey et al. (n.d.). 

We were unable to describe the effect this may have had 

on owl habitat conditions.

Because lightning-ignited wildfire accounts for the 

most area of habitat burned, the density of lightning fires 

was calculated in ArcView Spatial Analysis (by using a 

10-mi search radius) and is shown in figure 3-15. Density 

of lightning-ignited wildfire occurrence is highest (>30 

wildfires/100 mi2

) in the Cascade province of Oregon and 

Klamath province of California. It is moderate (10 to 30 

wildfires/100 mi2 in the higher Cascades of Washington, 

the northeastern portion of the Olympics, and the Klamath 

provinces. Of the over 6,000 lightning fires that have 

occurred from 1994 to 2002, we considered 17 as notable, 

based on their large sizes in relation to the minimum 

reserved block criteria used (for example, fires ≥15,000 

acres in Washington, ≥10,000 acres in Oregon, and ≥5,000 

acres in California are included). These larger fires were 

concentrated in areas of moderate to high lightning-ignited 

wildfire density (fig. 3-15), suggesting that those portions 
62

GENERAL TECHNICAL REPORT PNW-GTR-648

Density of lightning-ignited

wildfires (1994–2003)

 Large lightning-ignited fires

>15,000 ac — Washington

>10,000 ac — Oregon

>5,000 ac — California

Wildfire density (fires/100 mi

2

)

0–9

10–30

31–98

Physiographic provinces

1. Washington Olympic Peninsula

2. Washington Western Lowlands

3. Washington Western Cascades

4. Washington Eastern Cascades

5. Oregon Western Cascades

6. Oregon Eastern Cascades

7. Oregon Coast Range

8. Oregon Willamette Valley

9. Oregon Klamath

10. California Klamath

11. California Coast Range

12. California Cascades

Lakes and rivers

Urban areas

Interstate highway o

0 50 100 150 200 Miles

0 80 160 240 320 Kilometers

Figure 3-15—Spatial density of lightning fires in the range of the northern spotted owl.
63

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

of the owl’s range may be more likely to experience large, 

habitat altering, lightning-ignited wildfires.

Table 3-16 shows the lightning fire density (number 

of fires divided by federal land area) in each province. It 

also shows the percentage of that federal land burned by 

these fires. Although the highest density of fires was in the 

Cascades of Oregon, the fires burned only 1 to 3 percent 

of the federal land. Although, in the eastern Cascades of 

Washington the fire density was only about one-third that 

of the Oregon Cascades, the fires burned a larger percentage (7.6 percent) of the federal land. The province with 

the highest amount of burned federal land is the Klamath 

province in Oregon, which has moderate lightning fire 

densities. Lightning fires did not appear to be significant 

in the coastal areas of the range.

(based on division and splitting indices) because of elevation. This province has had the least amount of fragmentation from stand-replacing timber harvest and wildfires. 

However, it still has one of the highest levels of habitat 

fragmentation exceeded only by the Coast and Klamath 

provinces of Oregon followed closely by the Eastern Cascades province of Oregon. Trends in most Washington and 

Oregon provinces, since 1994, indicate slight increases in 

habitat fragmentation based on landscape division indices. 

The Oregon Coast Range province shows the most increase 

in fragmentation since 1994, based on the splitting index.

In California, despite an increase in the number of 

habitat patches, fragmentation (based on all indices) appears to be less today than it was in the 1940s. This condition is most apparent in the Klamath province of California 

where the effective mesh size has quadrupled since the 

1940s. We speculate that this may be due to differences in 

the historical map sources for the historical and contemporary periods. Or it may be that decades of fire suppression 

has resulted in decreased habitat fragmentation. 

FRAGSTATS metrics describing the patches of habitat 

at the province scale at the beginning (1994) and the end 

of the monitoring period (2003), independent of land use 

allocations, are shown in table 3-18. There was very little 

change in any of the patch metrics during the Plan monitoring period (1994 to 2003). The changes that did occur were 

greatest in the Western Cascades and Klamath provinces 

in Oregon and the Cascades and Klamath provinces in 

California.

The patch metric results inside the reserved blocks 

were similar to those for the whole landscape, although they 

varied in magnitude for some (table 3-19). For example, the 

ratio of number of patches in the 1930s and 1940s to number 

of patches in 2003 was lower inside reserves indicating a 

smaller increase in the number of patches for lands inside 

the blocks over time. The average size of core areas was 

also larger inside the blocks than the landscape, in general. 

Estimated dispersal-capable federal area—

Rangewide, 95 percent of federal land was estimated to 

have potential to produce forest conditions to support 

dispersal habitat for spotted owls. Dispersal-capable federal 

Table 3-16—Lightning-fire density (number of fires 

divided by federal land area) in each province and 

the percentage of that federal land burned

Fires per Percentage

Province square mile burned

Oregon Eastern Cascades 0.320 0.726

Oregon Western Cascades .279 2.966

California Cascades .219 3.454

California Klamath .195 4.431

Oregon Klamath .166 24.766

Washington Eastern Cascades .131 7.624

Washington Olympic Peninsula .063 .038

Washington Western Cascades .029 .022

Oregon Coast Range .029 .009

California Coast Range .009 .001

Estimated habitat fragmentation and patch metrics— 

Jaeger indices describing fragmentation of habitat are 

shown in table 3-17. Based on all four of these indices, all 

provinces in Washington and Oregon appear more fragmented today than in the 1930s, especially in the provinces 

west of the crest of the Cascade Mountains. Provinces east 

of the Cascade Mountains show only slightly higher levels 

of fragmentation since the 1930s. The Olympic Peninsula 

of Washington was one of the least fragmented provinces 

historically (1930s), but has undergone the highest amount 

of landscape division change, a 53 percent increase (64.9 

to 99.5). The habitat-capable lands in the Eastern Cascades 

province of Washington were naturally, highly subdivided 
64

GENERAL TECHNICAL REPORT PNW-GTR-648

Table 3-17—Jaeger indices for habitat patches from 1930s and 1940s, 1994, and 2002 for the range of 

the spotted owl

State/physiographic Number Jaeger indices

province Year of patches Coherence Division Splitting index Effective mesh

- - - - Percent - - - - Hectares

Washington:

 Eastern Cascades 1930 767 0.38 99.62 266.04 4,769

 1994 12,053 .32 99.68 315.40 4,020

 2002 12,532 .24 99.76 420.30 3,016

 Western Cascades 1930 1,114 5.09 94.91 19.64 94,464

 1994 11,456 .80 99.20 124.66 14,878

 2002 11,384 .79 99.21 125.86 14,735

 Olympic Peninsula 1930 537 35.14 64.86 2.85 350,088

 1994 4,838 .51 99.49 195.71 5,089

 2002 4,705 .51 99.49 196.54 5,068

Oregon:

 Klamath 1930 648 13.94 86.06 7.17 188,372

 1994 9,263 .17 99.83 586.86 2,301

 2002 9,642 .13 99.87 793.50 1,702

 Eastern Cascades 1930 381 .71 99.29 140.30 4,839

 1994 4,066 .40 99.60 248.31 2,732

 2002 4,209 .37 99.63 273.80 2,478

 Western Cascades 1930 578 36.18 63.82 2.76 887,536

 1994 12,525 18.65 81.35 5.36 457,132

 2002 13,337 17.57 82.43 5.69 430,609

 Coast Range 1930 996 10.95 89.05 9.13 237,703

 1994 13,974 .15 99.85 646.08 3,359

 2002 14,416 .05 99.95 2,113.12 1,027

California:

 Klamath 1930 948 8.65 91.35 11.56 174,051

 1994 5,150 41.95 58.05 2.38 842,852

 2002 5,905 40.29 59.71 2.48 809,479

 Cascades 1930 274 4.19 95.81 23.84 31,234

 1994 1,991 5.42 94.58 18.45 40,325

 2002 2,147 5.26 94.74 19.02 39,105

 Coast 1930 1,247 4.34 95.66 23.04 70,145

 1994 5,573 10.49 89.51 9.54 168,965

 2002 5,881 10.18 89.82 9.82 164,005
65

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Table 3-18—FRAGSTATS metrics for habitat patches on federal lands from 1930s and 1940s, 1994, and 2002 in 

the range of the spotted owl

State/physiographic Number Total Average Habitat Total Average core

province Year of patches patch area patch size edge core area area size

- - - - - - - - - - - Acres - - - - - - - - - - - Miles - - - - - - Acres - - - - - -

Washington:

 Eastern Cascades 1930 495 437,523 884 3,876 329,432 568

 1994 2,044 678,615 332 12,254 386,639 119

 2002 2,016 668,237 331 12,074 380,637 119

 Western Cascades 1930 515 1,432,652 2,782 7,419 1,221,803 2,190

 1994 2,467 1,313,521 532 20,087 815,225 162

 2002 2,454 1,312,250 535 20,093 813,863 162

 Olympic Peninsula 1930 234 786,795 3,362 3,307 690,061 3,366

 1994 933 615,431 660 8,036 421,019 221

 2002 929 615,228 662 8,029 420,974 221

Oregon:

 Klamath 1930 863 1,001,418 1,160 7,573 774,289 621

 1994 3,405 899,845 264 18,001 459,234 78

 2002 3,426 837,289 244 17,435 413,870 71

 Eastern Cascades 1930 274 308,337 1,125 2,320 242,639 820

 1994 1,067 462,367 433 8,190 259,516 106

 2002 1,079 456,901 423 8,119 256,111 106

 Western Cascades 1930 851 2,812,209 8,166 10,364 2,495,754 1,971

 1994 2,693 2,867,721 2,631 32,081 2,027,954 307

 2002 2,674 2,834,176 2,619 32,461 1,987,537 295

 Coast Range 1930 659 941,233 1,428 5,930 748,136 670

 1994 3,074 704,962 229 13,774 359,443 75

 2002 3,019 701,769 232 13,731 357,582 75

California:

 Klamath 1930 714 2,238,585 3,135 9,276 1,964,117 2,067

 1994 2,948 2,925,126 992 25,163 2,231,696 568

 2002 3,060 2,886,142 943 25,753 2,181,835 532

 Cascades 1930 221 294,306 1,332 1,739 242,239 859

 1994 1,220 427,567 350 5,650 276,903 171

 2002 1,223 424,108 347 5,705 272,482 167

 Coast 1930 225 159,849 710 970 131,544 678

 1994 891 222,740 250 2,765 150,714 200

 2002 893 222,671 249 2,764 150,667 200
66

GENERAL TECHNICAL REPORT PNW-GTR-648

Table 3-19—FRAGSTATS metrics for habitat patches inside the large reserved habitat blocks from 1930s and 

1940s, 1994, and 2002 in the range of the spotted owl

State/physiographic Number Total Average Habitat Total Average core

province Year of patches patch area patch size edge core area area size

- - - - - - - - - - - Acres - - - - - - - - - - - Miles - - - - - - Acres - - - - - -

Washington:

 Eastern Cascades 1930 359 247,670 690 2,541 178,644 442

 1994 973 398,700 410 6,436 243,489 165

 2002 950 393,719 414 6,317 241,115 167

 Western Cascades 1930 407 960,680 2,360 5,556 807,522 1,767

 1994 1,764 912,942 518 13,710 574,360 177

 2002 1,760 912,907 519 13,707 574,360 177

 Olympic Peninsula 1930 107 588,531 5,500 2,507 517,738 4,502

 1994 605 550,423 910 6,427 392,856 295

 2002 605 550,410 910 6,427 392,849 295

Oregon: 

 Klamath 1930 321 510,510 1,590 3,478 409,459 1,042

 1994 1,224 448,581 366 8,412 243,091 94

 2002 1,254 398,478 318 7,951 206,631 81

 Eastern Cascades 1930 192 149,884 781 1,369 112,766 564

 1994 512 254,382 497 3,939 156,486 154

 2002 511 251,812 493 3,896 155,083 154

 Western Cascades 1930 348 1,144,775 3,290 4,478 1,016,454 2,696

 1994 695 1,342,244 1,931 11,998 1,026,815 532

 2002 715 1,322,258 1,849 12,276 1,001,223 497

 Coast Range 1930 327 546,348 1,671 3,047 448,855 1,097

 1994 1,384 439,641 318 7,956 239,029 96

 2002 1,360 439,006 323 7,942 238,705 96

California: 

 Klamath 1930 290 1,209,984 4,172 4,548 1,079,802 3,292

 1994 1,181 1,600,347 1,355 12,550 1,257,322 848

 2002 1,272 1,568,236 1,233 13,043 1,215,806 745

 Cascades 1930 63 112,079 1,779 580 94,693 1,297

 1994 260 143,869 553 1,649 99,002 275

 2002 260 143,494 552 1,659 98,437 273

 Coast 1930 46 106,717 2,320 405 95,540 2,810

 1994 148 139,421 942 1,161 108,479 650

 2002 148 139,421 942 1,161 108,479 650
67

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

area was mapped for the physiographic provinces (app. I, 

and table 3-20); in all of them, except the Coast province 

in California (70 percent), more than 90 percent of federal 

land area could support forest with the potential to provide 

dispersal habitat for northern spotted owls (table 3-20). Not 

all land with potential was expected to support dispersal 

habitat at any one point in time. Riparian reserves, outside 

of the large reserved blocks, are counted on as major 

contributors to dispersal habitat under the Plan. Additional 

contributions come from smaller reserves (100-ac owl 

activity centers) and to a lesser extent from the matrix. 

The assessment of dispersal habitat focused on land 

outside of the reserved blocks (app. I) because this land was 

identified in the Plan as key to movement of owls between 

the reserved blocks. The percentage of dispersal-capable 

area outside the large reserved blocks ranged from 18 to 

30 percent in the Washington provinces and from 42 to 55 

percent in the provinces in Oregon and California. The one 

exception was the Cascades province in California where 

about 75 percent of the dispersal-capable land fell outside 

of the large reserved blocks. These percentages indicate 

that the reserved blocks make significant contributions to 

dispersal conditions at the landscape scale in Washington 

and to a lesser degree in the other two states. 

Estimated dispersal habitat condition—

At the range scale, 55 percent of the dispersal-capable area 

was in dispersal habitat. In 8 of 10 provinces, more than 50 

percent was currently in dispersal habitat (table 3-20). The 

Eastern Cascades province of Washington had 35 percent in 

dispersal habitat and the Klamath province, 48 percent. 

Table 3-20—Dispersal-capable federal land and current condition on the entire federal 

landscape and for that portion outside the large reserved blocks in the range of the spotted 

owl

Federal land outside

Total federal land large, reserved blocks

State/physiographic Dispersal Currently in Dispersal Currently in

province All land capable dispersal habitat capable dispersal habitat

Acres - - - - - - - - - - - - - - - - - Percent - - - - - - - - - - - - - - - - -

Washington:

 Eastern Cascades 3,502,400 96 35 30 34

 Western Cascades 3,721,000 95 56 26 55

 Western Lowlands NC

 Olympic Peninsula 1,522,300 93 60 18 49

 Total 8,745,700 95 48 26 45

Oregon:

 Klamath 2,118,200 99 48 51 47

 Eastern Cascades 1,551,800 95 61 45 57

 Western Cascades 4,476,700 98 65 54 59

 Coast Range 1,413,300 99 51 42 47

 Willamette Valley NC

 Total 9,560,000 98 59 50 54

California:

 Coast Range 503,600 70 68 47 61

 Klamath 4,520,200 93 60 45 54

 Cascades 1,091,300 91 57 75 53

 Total 6,115,100 91 60 51 54

Plan total 24,420,800 95 55 42 52

Note: NC = not calculated because of too little federal area in the province.
68

GENERAL TECHNICAL REPORT PNW-GTR-648

Dispersal habitat conditions on the dispersal-capable 

area outside the large blocks for those provinces (mostly in 

Oregon and California) where more than 30 percent of the 

area was outside the reserved blocks, ranged from 47 to 61 

percent. The variation in the pattern and amount of dispersal habitat present on the landscape at the beginning of the 

monitoring period is apparent (app. I). The dispersal habitat 

maps show the mosaic of dispersal-capable area currently in 

dispersal habitat condition. 

Nonspatial Analyses

Estimated federal area in forest structure groups—

The FS and BLM land in the six data query groups (table 

3-5) is reported by physiographic province (table 3-21). A 

portion of the plots in each province did not have any data 

for the attributes of interest so they were omitted from 

the analysis and reported in the summary tables under the 

“unknown” category. The unknown plot acreage ranged 

from 5 to 11 percent of the total acres in a province. 

Table 3-21 shows the baseline acres in each of the six 

groups for the described FS and BLM land. At the beginning of the monitoring, groups D, E, and F, combined, 

accounted for about 28 percent of the FS and BLM land in 

the Eastern Cascades provinces of both Oregon and Washington and 34 percent of the FS land in the Coast Range of 

California. In the other provinces, about 40 to 50 percent 

of the FS and BLM land was in these three groups. On the 

other end of the habitat condition spectrum, the percentage 

of the FS and BLM land in category A, the poorest habitat 

condition, is fairly consistent among the provinces ranging 

between 20 and 30 percent. The exceptions are the FS land 

in Olympic Peninsula province in Washington (35 percent) 

and FS and BLM land in Oregon Klamath province (37 

percent). Over time, monitoring the area change in the 

groups and the percentage of area in the groups will provide 

information on the maintenance and restoration habitat for 

spotted owls. 

The area in the six data query groups inside and outside 

of the reserved blocks was also summarized. About 48 percent of the FS and BLM land fell inside the reserved blocks, 

and the remaining 52 percent was outside the blocks (tables 

3-22 and 3-23). Taking into account the absence of all NPS 

land in the range and BLM land in California (about 2.5 

million additional acres, most of which would be inside 

the blocks) in the plot data, we find that these percentages 

are similar to the results of the spatial analysis (51 percent 

inside and 49 percent outside). The area in groups D, E and 

F, combined, makes up 45 to 60 percent of the total in all 

provinces except the Eastern Cascades provinces of Washington and Oregon. In these provinces, about 35 percent 

of the area is in groups D, E, and F. Outside of the blocks, 

the area in groups D, E, and F accounts for a much smaller 

proportion of the total area. Between 22 and 50 percent of 

the area outside the blocks are in groups D, E, and F. Only 

one province, the Western Cascades province in Oregon 

(46 percent) has more than 45 percent of the area outside 

the blocks in groups D, E, and F. The area in groups A, B, 

and C, combined, is much higher outside of the reserved 

blocks than inside, as expected from the D, E, and F values 

reported above. 

The plot data would be the most logical source to 

use in creating an estimate of owl habitat, but it would 

be incomplete because of the absence of data from NPS 

land in the range and BLM land in California. We did not 

create queries to estimate owl habitat, but instead chose to 

estimate habitat conditions across the full spectrum from 

low to high quality as we did with the spatial analysis.

Estimated change of federal area in 

forest structure groups—

Although some remeasured plot data are available, the 

processing of the canopy strata and cover attributes was not 

complete, thus we could not run the data query to assess the 

changes in the six data query groups from the first to second 

measurement occasion. The assessment of the change in the 

six groups will be completed during the next monitoring 

period.

Limitations of Analysis

Although we were able to quantify changes in habitat 

profiles from stand-replacing disturbances, we were unable 

to determine the effects of partial-cut harvest actions or 

lower intensity wildfires. We do know, from other sources, 

that there were about 287,000 ac of partial-cut harvest 
69

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Table 3-21—Vegetation survey plot data for federal land by data query group in the range of the spotted owl

State/ Forest Estimated area Province area Confidence intervals for estimated area

physiographic structure in forest in forest

province group structure group structure group 68% 90%

Acres Percent - - - - - - - - - - - - - Acres - - - - - - - - - - - - 

Washington:

 Eastern Cascades A 374,467 28 354,027– 391,302 343,705– 404,009

 B 307,147 23 288,570– 329,244 277,378– 344,661

 C 140,904 11 129,209– 153,419 121,922– 160,411

 D 245,672 18 232,129– 264,446 221,190– 275,031

 E 86,447 7 74,892– 99,668 67,440– 109,103

 F 26,468 2 22,073– 34,663 19,036– 40,018

 Unknown 147,868 11

 Total 1,328,973 100

 Western Cascades A 591,414 28 567,744– 621,674 554,122– 637,512

 B 131,884 6 119,243– 148,675 111,575– 157,327

 C 230,880 11 215,310– 248,731 202,626– 261,663

 D 455,601 21 430,207– 477,251 417,818– 491,014

 E 274,663 13 253,668– 294,033 239,671– 311,733

 F 295,554 14 272,796– 318,176 259,372– 332,585

 Unknown 156,552 7

 Total 2,136,548 100

 Olympic Peninsula A 195,995 35 180,028– 211,276 170,172– 222,040

 B 23,342 4 19,201– 27,860 15,812– 30,872

 C 76,755 14 67,768– 88,648 62,873– 96,767

 D 110,320 20 101,169– 122,990 93,127– 130,814

 E 51,955 9 44,049– 57,979 40,284– 62,873

 F 71,754 13 61,802– 82,190 55,778– 88,821

 Unknown 28,188 5

 Total 558,309 100

Washington total 4,023,832

Oregon:

 Klamath A 786,737 37 760,931– 823,687 742,324– 846,247

 B 173,190 8 160,584– 186,342 152,690– 193,752

 C 173,049 8 159,888– 186,917 151,997– 196,546

 D 430,214 20 412,307– 450,377 401,504– 465,558

 E 251,278 12 231,709– 266,887 219,435– 278,509

 F 221,992 11 206,373– 238,755 198,162– 251,734

 Unknown 90,511 4

 Total 2,126,971 100

 Eastern Cascades A 355,018 31 331,007– 375,580 320,641– 389,867

 B 279,509 24 263,280– 294,148 253,932– 305,024

 C 145,798 13 131,320– 157,248 123,076– 165,417

 D 207,090 18 190,292– 227,648 179,136– 239,679

 E 88,144 8 73,048– 102,039 65,586– 111,110

 F 24,891 2 21,114– 31,535 16,442– 34,066

 Unknown 47,442 4

 Total 1,147,892 100
70

GENERAL TECHNICAL REPORT PNW-GTR-648

Table 3-21—Vegetation survey plot data for federal land by data query group in the range of the spotted owl 

(continued)

State/ Forest Estimated area Province area Confidence intervals for estimated area

physiographic structure in forest in forest

province group structure group structure group 68% 90%

Acres Percent - - - - - - - - - - - - - Acres - - - - - - - - - - - - 

 Western Cascades A 1,035,863 25 1,004,524– 1,073,253 982,719– 1,092,721

 B 418,893 10 398,511– 439,364 386,101– 452,866

 C 395,557 9 369,110– 418,515 359,998– 435,802

 D 900,299 22 868,464– 933,570 847,498– 954,090

 E 763,403 18 736,412– 791,888 718,769– 812,243

 F 454,054 11 435,191– 479,298 418,643– 496,922

 Unknown 198,456 5

 Total 4,166,525 100

Coast Range A 367,758 25 348,755– 387,550 335,222– 401,505

 B 124,234 8 118,735– 139,547 108,963– 149,693

 C 195,959 13 181,036– 208,914 172,714– 217,231

 D 329,327 22 312,684– 346,583 303,048– 359,872

 E 263,874 18 249,604– 279,972 240,544– 290,605

 F 116,122 9 105,858– 127,832 98,736– 134,514

 Unknown 84,248 5

 Total 1,481,522 100

Oregon total 8,922,911

California:

 Klamath A 994,853 25 949,486– 1,066,159 902,165– 1,104,010

 B 569,384 15 527,782– 618,418 502,136– 647,109

 C 160,185 4 138,412– 181,181 125,407– 197,545

 D 786,214 20 743,889– 840,217 713,405– 863,548

 E 511,017 13 475,972– 551,006 452,518– 575,995

 F 473,055 12 430,323– 512,071 402,510– 536,987

 Unknown 407,339 10

 Total 3,902,047 100

 Cascades A 206,450 21 181,854– 234,495 162,406– 256,289

 B 217,509 22 193,505– 240,643 173,312– 255,449

 C 66,530 7 53,740– 80,302 45,585– 89,407

 D 255,167 26 233,549– 282,675 213,225– 301,285

 E 92,625 9 76,779– 110,284 69,511– 123,336

 F 55,637 6 43,753– 69,950 36,225– 80,330

 Unknown 83,109 9

 Total 977,024 100

 Coast A 39,931 50 27,156– 49,967 18,872– 56,257

 B 4,329 5 1,378– 8,852 0– 11,068

 C 1,573 2 0– 3,145 0– 4,718

 D 10,814 13 5,901– 15,726 2,756– 20,250

 E 10,035 12 4,718– 16,521 1,378– 21,449

 F 7,085 9 2,756– 14,170 0– 19,293

 Unknown 9

 Total 80,657 100

California total 4,959,728

Plan total 17,906,471
71

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Table 3-22—Vegetation survey plot data for federal land by data query group INSIDE large, reserved blocks in 

the range of the spotted owl

 Estimated area in Province area in

State/ Forest forest structure forest structure Confidence intervals for estimated area

physiographic structure group inside group inside

province group reserved blocks reserved blocks 68% 90%

Acres Percent - - - - - - - - - - - - - Acres - - - - - - - - - - - - 

Washington:

 Eastern Cascades A 100,982 16.6 89,126– 110,202 81,643– 117,981

 B 120,356 19.8 106,899– 137,643 98,502– 148,179

 C 78,028 12.9 68,270– 88,824 63,869– 95,542

 D 134,313 22.1 122,337– 151,045 115,476– 159,718

 E 54,807 9.0 47,694– 67,675 42,429– 74,228

 F 12,215 2.0 7,858– 16,571 5,661– 20,793

 Unknown 105,968 17.5

 Total 606,670 99.9

 Western Cascades A 342,323 25.4 321,565– 365,946 305,346– 380,697

 B 91,324 6.8 79,934– 107,154 72,497– 113,808

 C 129,445 9.6 113,303– 144,346 104,010– 155,212

 D 269,098 20.0 249,620– 286,678 238,643– 302,514

 E 198,971 14.8 184,949– 221,814 172,599– 236,911

 F 207,714 15.4 190,268– 230,289 178,530– 243,463

 Unknown 108,491 8.0

 Total 1,347,366 100

 Olympic Peninsula A 109,779 31.8 98,485– 122,957 90,955– 132,214

 B 15,812 4.6 12,048– 18,824 9,036– 21,460

 C 34,588 10.0 28,613– 46,858 24,472– 58,587

 D 68,154 19.7 59,224– 78,753 53,461– 85,530

 E 39,908 11.5 33,507– 45,931 30,119– 50,449

 F 54,436 15.8 45,931– 64,365 40,718– 69,824

 Unknown 22,917 6.6

 Total 345,594 100

Washington total: 2,299,630

Oregon

 Klamath A 379,036 37.4 356,584–4 07,034 342,709– 425,226

 B 70,451 6.9 60,969– 81,882 54,778– 89,598

 C 76,925 7.6 69,180– 85,863 63,778– 93,191

 D 206,864 20.4 190,306– 220,762 181,583– 230,463

 E 121,499 12.0 108,126– 133,914 98,416– 142,504

 F 138,414 13.7 125,566– 156,349 117,516– 166,946

 Unknown 19,889 2.0

 Total 1,013,078 100

 Eastern Cascades A 129,974 27.6 116,322– 147,724 108,547– 156,951

 B 94,471 20.0 85,286– 106,918 78,274– 115,177

 C 68,668 14.6 58,812– 80,710 53,237– 90,164

 D 99,786 21.2 86,153– 119,694 76,975– 133,510

 E 49,803 10.6 38,583– 65,444 32,882– 74,480

 F 12,577 2.8 9,127– 19,231 6,844– 24,351

 Unknown 15,280 3.2

 Total 470,559 100
72

GENERAL TECHNICAL REPORT PNW-GTR-648

Table 3-22—Vegetation survey plot data for federal land by data query group INSIDE large, reserved blocks in 

the range of the spotted owl (continued)

 Estimated area in Province area in

State/ Forest forest structure forest structure Confidence intervals for estimated area

physiographic structure group inside group inside

province group reserved blocks reserved blocks 68% 90%

Acres Percent - - - - - - - - - - - - - Acres - - - - - - - - - - - -

 Western Cascades A 314,038 18.5 297,090– 336,444 284,773– 354,799

 B 166,398 9.8 151,180– 179,854 142,209– 192,550

 C 174,441 10.3 156,983– 190,464 145,168– 203,797

 D 404,458 23.9 382,530– 429,494 364,281– 447,396

 E 365,841 21.6 339,740– 380,354 328,111– 393,311

 F 210,187 12.4 195,213– 228,283 185,824– 239,261

 Unknown 59,754 3.5

 Total 1,695,117 100

 Coast Range A 198,823 24.1 184,007– 214,969 169,959– 224,264

 B 73,531 8.9 66,539– 82,397 62,126– 86,724

 C 88,453 10.7 80,555– 97,856 75,419– 105,459

 D 183,052 22.2 171,120– 198,267 162,807– 205,697

 E 184,218 22.3 171,141– 195,181 163,684– 204,075

 F 82,244 10.0 74,014– 91,455 69,620– 96,904

 Unknown 15,377 1.8

 Total 825,698 100

Oregon total 4,004,452

California:

 Klamath A 431,457 21.5 397,472– 475,217 373,762– 499,105

 B 283,449 14.1 253,395– 314,776 237,676– 336,624

 C 68,610 3.4 57,172– 85,489 49,953– 95,794

 D 376,738 18.8 343,008– 408,179 323,985– 429,179

 E 333,988 16.7 301,652– 363,875 280,135– 386,752

 F 261,442 13.0 228,952– 285,735 212,736– 302,506

 Unknown 247,560 12.5

 Total 2,003,244 100

 Cascades A 29,165 12.4 20,261– 40,625 15,757– 49,117

 B 44,923 18.8 34,586– 59,042 27,630– 66,616

 C 14,634 6.1 7,575– 21,797 5,730– 27,217

 D 66,616 27.9 55,466– 79,612 49,323– 88,104

 E 41,542 17.4 32,947– 54,435 25,785– 60,165

 F 24,352 10.2 15,757– 32,947 11,460– 40,110

 Unknown 17,190 7.2

 Total 238,422 100

 Coast A 32,068 74.0 24,983– 37,191 21,044– 40,531

 B 2,756 6.4 0– 4,134 0– 5,512

 C 0 0.0 0– 0 0– 0

 D 1,573 3.6 0– 3,145 0– 4,718

 E 1,378 3.2 0– 2,756 0– 4,134

 F 5,512 12.7 0– 8,268 0– 12,402

 Unknown 0 0.0

 Total 43,287 99.9

California total 2,284,953

Plan total 8,589,035
73

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Table 3-23—Vegetation survey plot data for federal land by data query group OUTSIDE large, reserved blocks 

in the range of the spotted owl

 Estimated area in Province area in

State/ Forest forest structure forest structure Confidence intervals for estimated area

physiographic structure group outside group outside

province group reserved blocks reserved blocks 68% 90%

Acres Percent - - - - - - - - - - - - - Acres - - - - - - - - - - - - 

Washington:

Eastern Cascades A 273,486 37.8 259,346– 286,769 251,184– 294,322

 B 186,791 25.8 174,496– 200,541 166,108– 211,830

 C 62,876 8.7 56,848– 71,799 52,990– 76,364

 D 111,358 15.4 102,161– 120,955 97,486– 127,342

 E 31,640 4.4 25,584– 37,281 22,582– 39,920

 F 14,253 2.0 11,221– 17,963 9,376– 19,865

 Unknown 41,900 5.8

 Total 722,304 99.9

 Western Cascades A 249,091 31.6 233,501– 264,909 224,677– 276,713

 B 40,561 5.1 34,749– 45,874 31,733– 51,400

 C 101,435 12.8 91,756– 110,825 85,781– 117,185

 D 186,503 23.6 173,079– 200,468 166,599– 208,811

 E 75,692 9.6 67,681– 84,350 62,589– 91,279

 F 87,840 11.1 77,768– 95,623 72,012– 101,304

 Unknown 48,061 6.1

 Total 789,183 99.9

 Olympic Peninsula A 86,215 40.5 77,933– 93,745 73,791– 99,392

 B 7,530 3.5 4,894– 9,412 3,388– 10,918

 C 42,166 19.8 36,143– 47,814 32,378– 51,955

 D 42,166 19.8 36,519– 46,308 33,131– 49,696

 E 12,048 5.7 8,659– 15,436 6,777– 17,695

 F 17,318 8.1 13,554– 21,083 11,671– 23,342

 Unknown 5,271 2.5

 Total 212,714 99.9

Washington total: 1,724,201

Oregon:

 Klamath A 407,701 36.6 389,938– 427,501 378,969– 439,878

 B 102,739 9.2 93,615– 111,586 87,715– 117,324

 C 96,124 8.6 87,797– 105,040 80,554– 109,819

 D 223,350 20.0 210,765– 235,872 203,608– 243,556

 E 129,780 11.6 121,510– 140,541 115,812– 146,891

 F 83,578 7.5 75,530– 92,224 70,433– 97,068

 Unknown 70,623 6.3

 Total 1,113,895 99.8

 Eastern Cascades A 225,044 33.2 213,211– 243,779 202,860– 254,045

 B 185,037 27.3 174,351– 196,813 168,021– 205,269

 C 77,129 11.4 69,857– 84,535 64,371– 89,683

 D 107,304 15.8 98,973– 117,284 93,973– 122,050

 E 38,342 5.7 33,343– 44,698 30,159– 48,396

 F 12,314 1.8 9,606– 15,678 7,742– 17,952

 Unknown 32,162 4.7

 Total 677,332 99.9
74

GENERAL TECHNICAL REPORT PNW-GTR-648

Table 3-23—Vegetation survey plot data for federal land by data query group OUTSIDE large, reserved blocks 

in the range of the spotted owl (continued)

 Estimated area in Province area in

State/ Forest forest structure forest structure Confidence intervals for estimated area

physiographic structure group outside group outside

province group reserved blocks reserved blocks 68% 90%

Acres Percent - - - - - - - - - - - - - Acres - - - - - - - - - - - - 

 Western Cascades A 721,825 29.2 698,524– 751,820 682,981– 765,131

 B 252,496 10.2 240,500– 268,264 230,754– 278,086

 C 221,116 8.9 207,988– 233,860 197,243– 243,312

 D 495,841 20.1 476,076– 513,790 464,699– 525,519

 E 397,562 16.1 384,803– 418,694 374,317– 433,449

 F 243,866 9.5 229,012– 256,578 219,844– 276,707

 Unknown 138,702 5.7

 Total 2,471,408 99.7

 Coast Range A 168,935 25.8 154,762– 180,754 146,309– 188,999

 B 50,702 7.7 44,787– 57,094 41,298– 61,977

 C 107,506 16.4 100,484– 118,753 95,031– 126,232

 D 146,275 22.3 135,349– 155,899 126,983– 162,301

 E 79,655 12.1 71,692– 87,837 66,279– 93,234

 F 33,878 5.2 28,826– 39,058 25,330– 42,656

 Unknown 68,871 10.5

 Total 655,822 100

Oregon total 4,918,457

California:

 Klamath A 563,396 29.7 516,193– 604,552 487,910– 638,165

 B 285,936 15.1 254,834– 314,284 238,418– 333,000

 C 91,575 4.8 79,698– 109,939 70,378– 123,873

 D 409,476 21.6 378,405– 445,128 361,874– 479,148

 E 177,030 9.3 154,434– 201,235 141,950– 216,616

 F 211,613 11.1 185,347– 238,070 165,059– 254,931

 Unknown 159,779 8.4

 Total 1,898,805 100

 Cascades A 177,284 24.0 155,999– 203,047 144,433– 223,614

 B 172,584 23.4 151,839– 193,284 139,002– 209,849

 C 51,896 7.0 40,775– 63,865 35,284– 73,497

 D 188,551 25.5 166,397– 213,886 149,321– 227,980

 E 51,082 6.9 38,641– 62,996 31,788– 70,274

 F 31,285 4.2 21,269– 42,102 15,848– 48,462

 Unknown 65,919 8.9

 Total 738,601 99.9

 Coast A 7,863 21.0 0– 7,863 0– 7,863

 B 1,573 4.2 0– 3,145 0– 4,718

 C 1,573 4.2 0– 3,145 0– 4,718

 D 9,241 24.7 4,523– 13,764 1,573– 16,910

 E 8,657 23.2 3,145– 13,975 0– 17,315

 F 1,573 4.2 0– 3,145 0– 4,718

 Unknown 6,890 18.4

 Total 37,370 99.9

California total 2,674,776

Plan total 9,317,776
75

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

on federal land during the monitoring period (Baker et al., 

in press) and about 1.7 million ac of wildfire (Brown et al. 

2002). 

The Plan’s record of decision estimated that about 2.5 

percent of the existing owl habitat would be removed by 

harvest actions in the first decade. The changes we reported 

for loss of forest on habitat-capable federal area as a result 

of stand-replacing timber harvest represent the minimum 

area removed. Likewise, we were not able to account for 

loss of forest due to insects, disease, or windstorms. Bigley 

and Franklin (2004) relied on information assembled by the 

USDI Fish and Wildlife Service that estimated the loss of 

about 380,000 ac of owl habitat from 1994 to 2002. Losses 

from management (including partial harvest) were estimated to be 156,000 ac; wildfire, 168,300; windthrow, 100; 

and insects and disease, 55,640. Using 380,000 ac as the 

likely maximum removed, our analysis would show about a 

3.6 percent removal of habitat-capable acres in the 41 to 100 

range of habitat suitability compared to 1.5 percent from our 

change-detection analysis that used only data from standreplacing timber harvest and wildfire and assumed all acres 

affected were habitat-capable. Based on the estimated loss 

from management (156,000 ac), it is reasonable to conclude that less than 2.5 percent of the existing habitat was 

removed by timber harvest during the monitoring period.

It is also likely that the coarse resolution of the polygon 

vegetation map in California led to an overestimation of 

area with 41 to 100 habitat suitability. Finer resolution 

input data are better suited for habitat modeling and would 

have given us better resolution and a consistent baseline 

across the range. This inconsistency between the OregonWashington and California maps introduces a measure 

of uncertainty to the results, particularly in comparing 

Oregon-Washington findings to those in California. In 

addition, analyzing and reporting results by physiographic 

province created an administrative division (Oregon and 

California border) in a biological analysis. However, the results were reported this way to be consistent with the FSEIS 

results. The use of ecological unit boundaries that ignore 

administrative boundaries may be more appropriate (for 

example, EPA ecoregions) for future monitoring analyses.

Lastly, we were not able to measure the change in 

habitat suitability over the monitoring period resulting from 

forest succession because we did not have remotely-sensed 

vegetation data that could have captured the ingrowth 

during the monitoring period. If we had had this data from 

2002 or 2003, we could have compared the habitat condition at the beginning and end of the monitoring period and 

estimated changes in the habitat suitability profile from 

forest growth.

Discussion

One of the central purposes of the network of large reserved 

blocks was to maintain and restore habitat to support 

territorial owls under the spotted owl management framework of the Plan. Our analyses focused on understanding 

the capability of the reserve network to provide the large, 

reserved blocks of habitat and to determine the suitability of 

the habitat-capable lands and the loss of habitat suitability. 

The Plan’s long-term strategy relies on the network of 

reserved blocks to support the territorial owl population, but 

in the first several decades there is a significant contribution 

of habitat-capable area with habitat suitability of 41 to 100 

outside the reserved blocks. Almost as much (49 percent) of 

the habitat-capable area is outside of the reserved blocks as 

inside (51 percent). Forty-four percent of the habitat-capable 

land with 41 to 100 habitat suitability occurs outside the 

reserved blocks. The land outside the reserved blocks makes 

a valuable short-term contribution to the Plan’s habitat management strategy for territorial owl pairs and is important 

in the long term by providing dispersal habitat. 

At the beginning of the monitoring period, we estimated that 57 percent of habitat-capable federal area was 

in the range of 41 to 100 habitat suitability and 36 percent 

was in the 0 to 40 range. At the end of the monitoring 

period, 56.2 percent of the habitat-capable land was in the 

41 to 100 range, with no recruitment accounted for. Loss of 

vegetation present at the beginning of the monitoring period 

on habitat-capable land from stand-replacing harvest and 

wildfire in the 41 to 100 range was greater (2.0 percent) 

inside the habitat blocks than outside (1.0 percent), but 

overall affected only about 0.8 percent of the habitatcapable area in the 41 to 100 range of habitat suitability. 
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GENERAL TECHNICAL REPORT PNW-GTR-648

In some provinces, the loss of habitat-capable land in 

the 41 to 100 range of habitat suitability was proportionally 

greater than the rangewide loss. At the beginning of the 

monitoring period, about 55 percent of the habitat-capable 

area in the Klamath province of Oregon was in the 41 to 

100 range of habitat suitability. At the end of the monitoring period, 51 percent of the habitat-capable area was in 

the 41 to 100 range. Again, no recruitment of habitat was 

accounted for. Loss to stand-replacing events inside the 

habitat blocks was greater than outside in the Klamath 

province. About 11.5 percent of the habitat-capable area in 

the 41 to 100 range of habitat suitability inside the blocks 

was lost in contrast to 2.5 percent outside. In either case, a 

high percentage of the habitat-capable area most similar to 

that used by owl pairs was maintained even in the province 

where the loss to wildfire was greatest.

Initially, the Klamath province in California had about 

62 percent of the habitat-capable area in the 41 to 100 range 

of habitat suitability. Over 61 percent of the area was in 

that suitability range at the end of the monitoring period. 

Again, loss of habitat in the 41 to 100 range inside the 

blocks (2.0 percent) was greater than outside the blocks (1.0 

percent), but maintenance of existing habitat was still high. 

Maintaining 60 to 80 percent of the habitat-capable area in 

the 41 to 100 range of habitat suitability in reserved blocks 

that occur in the drier southern and eastern provinces, or 

areas of moderate to high risk of lightning-ignited wildfire 

may be a challenge without stand treatment to reduce the 

risk of loss to wildfire. However, understanding the effects 

on owl habitat and owls of stand treatments to reduce fire 

risk before they are applied is an important step so that the 

treatments do not have a greater effect than the wildfires.

During the next 50 years, owl populations are expected 

to decline to a lower (compared to 1994), but stable population level (USDA USDI 1994: 3&4-228). From the habitat 

perspective, the transition is less about loss of area or 

amount of habitat and more about its occurrence inside and 

outside of large, reserved blocks. Currently, there are about 

10,300,000 ac of habitat-capable land in the 41 to 100 range 

of habitat suitability in the owl’s range. Over time, that 

number is expected to decline, as management of the matrix 

lands removes forest stands that are owl habitat. Outside 

of the large, reserved blocks, habitat will be maintained 

and accrue in the smaller reserves (for example, riparian 

reserves and owl core areas) that are interspersed with the 

matrix allocation. At the same time, habitat is expected 

to increase on the 9,200,000 ac of habitat-capable land 

inside the large reserved blocks, of which about 62 percent 

(5,700,000 ac) is currently in the 41 to 100 range of habitat 

suitability. That means there is another 35-plus percent of 

the habitat-capable lands in the blocks that will continue to 

grow and, depending on the occurrence of natural events, 

develop into owl habitat. We know from experience that 

natural disturbances will keep a portion of the area from 

being in the 41 to 100 habitat suitability range. A snapshot 

of forest conditions present in the 1930s showed the amount 

of owl habitat (based on our interpretation of the data) for 

lands inside and outside of the reserved blocks. With the 

exception of the Eastern Cascades provinces in Washington 

and Oregon, all provinces had between 50 and 75 percent 

of the forest land in owl habitat. Today’s conditions, some 

70 years later, are not that different. The percentage of 

the habitat-capable area inside the reserved blocks in the 

provinces today ranges from 50 to 81 percent. However, 

we noted that it is more fragmented; there are three to five 

times the number of patches with about the same amount of 

habitat.

Lint et al. (1999) stated the expectation that owl 

populations would be self-sustaining where the land area 

(assumed to be habitat-capable land area) in individual 

late-successional reserves was at least 60 percent owl 

habitat. In the future, the 9,200,000 ac inside the habitat 

blocks will likely yield between 5,500,000 (60 percent) 

and 7,400,000 (80 percent) ac in the 41 to 100 range of 

habitat suitability taking into account losses to natural 

events. Although we did not evaluate individual habitat 

blocks, the province values for habitat-capable land inside 

the blocks indicate that conditions are not far from expected 

with the exception of the few reserved blocks where wildfire 

was a significant influence. Rangewide the habitat blocks 

are already in the 60- to 80-percent range.
77

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Increases in the percentage of habitat-capable area in 

the 41 to 100 range of habitat suitability will accrue in the 

short term from the transition of area currently in the 21 to 

40 range. Based upon the percentage of total habitat-capable 

area in the 21 to 40 range, the greatest increases in habitat 

suitability will likely be in the Western Cascades provinces 

of Washington and Oregon, the Klamath provinces of 

Oregon and California, and the Coast Range province of 

Oregon. This will result in significant increases in habitat in 

the 41 to 100 habitat suitability range in the coming decades 

as these provinces contain over two-thirds of the habitatcapable Plan area. One of the primary reasons for using the 

histogram format to track the BioMapper outputs and the 

query groups to track the plot data was to show the changes 

over time in terms of the habitat suitability scale (0 to 100). 

We expect to see increases in the bars on the right side of 

the histograms for habitat-capable land inside the reserved 

blocks. By tracking all levels of habitat suitability, we will 

also see decreases on the left side of the histograms in the 0 

to 40 range of suitability as area is recruited into the 41 to 

100 range.

Over time we should expect to see cumulative increases 

in the percentages of habitat-capable area in the 41 to 100 

range of habitat suitability.

Conclusions

Vegetation conditions inside the reserved blocks in all habitat suitability categories (0 to 20, 21 to 40, etc.) improved 

during the decade because only a small percentage of the 

blocks’ areas were set back by harvest or wildfire. The loss 

of owl habitat did not exceed the rate expected under the 

Plan. Habitat outside the reserved blocks provided habitat 

in the short term for territorial owls and also for dispersing 

owls. There were catastrophic wildfire events that changed 

the owl habitat in local areas in several of the provinces, but 

the analysis of the loss at the province and rangewide scales 

showed the strength of the Plan’s repetitive, reserved-block 

design to absorb these losses. In the short term, the losses 

were also buffered by the better quality habitat outside the 

habitat blocks. It would have been better if the larger fires 

had not occurred inside the large reserved blocks, but it is 

better they occurred early in the Plan when more habitat 

remains outside the reserved blocks. The Plan has shown its 

strength in the short term for maintaining habitat and is expected to do equally well in restoring habitat over time. At 

the end of the first 10 years, habitat conditions are no worse, 

and perhaps better than expected. Refer also to discussions 

on wildfire in Moeur et al. (2005).

Our analyses, both spatial and nonspatial, indicate that 

there is considerably more habitat in California than was 

accounted for in the Plan FSEIS. The magnitude of this 

increase will depend on the owl habitat definition applied to 

the plot or spatial data. Assessments using a finer resolution, 

pixel-based map for the California provinces will provide 

a more consistent baseline of habitat-capable area across 

the range and may improve the estimates of owl habitat, 

whatever definition is used.

 The only habitat warning light we noted is not new. 

It was recognized in the Plan FSEIS. Wildfire is an inherent part of managing owl habitat in certain portions of the 

range. We should not depend solely on the repetitive design 

of the Plan to mitigate the effects of catastrophic events. 

We may be able to influence, through management, how 

owl habitat will burn and the extent of the burn when the 

inevitable happens. 

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Ecology. 19: 435–455.

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of the Association of American Geographers. 77(1): 

118–125.

Patthey, P. 2003. Habitat and corridor selection of an 

expanding red deer (Cervus elaphus) population. 

Lausanne, Switzerland: Institute of Ecology, University 

of Lausanne. 152 p. Ph.D. dissertation.

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ArcView: version 3. http://flash.lakeheadu.ca/~rrempel/

patch/index.html. (November 2004).

Reutter, B.A.; Helfer, V.; Hirzel, A.H.; Vogel, P. 2003. 

Modelling habitat-suitability using museum collections: 

an example with three sympatric Apodemus species from 

the Alps. Journal of Biogeography. 30: 581–590.

Sachot, S. 2002. Viability and management of 

an endangered capercaillie (Tetrao urogallus) 

metapopulation. Lausanne, Switzerland: Institute 

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land cover data spatial configuration and composition. 

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4853–4880.

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natural resources. Gen. Tech. Rep. RMRS-GTR-126. 

Fort Collins, CO: U.S. Department of Agriculture, Forest 

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Noon, B.R.; Verner, J. 1990. A conservation strategy 

for the northern spotted owl: a report of the Interagency 

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of Agriculture, Forest Service; U.S. Department of the 

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U.S. Department of the Interior, Bureau of Land 

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GENERAL TECHNICAL REPORT PNW-GTR-648

Wieslander, A.E.; Jensen, H.A. 1946. Forest areas, timber 

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dissertation.

Jason Mowdy
83

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Joseph Lint1, Jason Mowdy2, Janice Reid 3, 

Eric Forsman4, and Robert Anthony5

Introduction

The Northwest Forest Plan’s (the Plan) network of reserve 

land use allocations provides blocks of habitat for late-successional forest species such as the northern spotted owl 

(Strix occidentalis caurina) (USDA USDI 1994). The role 

of the reserved blocks is to support clusters of reproducing 

spotted owls. Within the reserves, existing owl habitat 

will be maintained, and other forest will be managed 

actively (tree density management) and passively (forest 

succession) to restore habitat. In addition, the federal land 

between the reserved blocks, primarily that in riparian 

reserves, is expected to contribute dispersal habitat for owl 

movement between the blocks. Movement is important for 

recruitment of owls in the reserved blocks and for genetic 

interchange. The 6- to 12-mi spacing of the blocks was 

based on the known dispersal capabilities of spotted owls 

from earlier data (Thomas et al. 1990). 

The status of dispersal habitat was reported in chapter 

3, “Habitat Status and Trends.” This chapter provides information about owl movement across the landscape. This 

analysis does not address whether the recorded movements 

were adequate to assure continued population distribution 

and genetic interchange, but does show whether movement 

occurred across the allocation landscape created by the 

Plan. 

Data Sources and Methods

Banding and reobservation of northern spotted owls began 

in the mid-1980s and has continued to the present (Anthony 

et al. 2004). Forsman et al. (2002) published a comprehensive assessment of natal and breeding dispersal of spotted 

owls from records of banded dispersers (1,151) and radiomarked (324) owls studied between 1985 and 1996. 

Forsman et al. (2002) presented the following findings 

on northern spotted owl movements:

• The straight-line distance from the natal site to 

the final location where the owl settled, died, or 

disappeared for banded juvenile owls ranged from 

0.37 to 69.1 mi.

• The median, straight-line distance from the natal 

site to the final location where the owl settled, died, 

or disappeared for banded juveniles was 15.2 mi for 

females and 9.1 mi for males.

• Only about 6 percent of nonjuveniles changed 

territories each year. Median dispersal distance of 

adult owls (>3 years old) was 2.2 miles. 

• Only 8.7 percent of dispersing individuals moved 

more than 31 mi.

• Dispersal of adults was often associated with the 

death or disappearance of a mate or with pair bond 

dissolution.

• Probability of movement was greatest for 1-yearold owls on territories, and female owls were more 

likely to disperse than males.

• Owls regularly dispersed across fragmented landscapes, but large valleys (Willamette, Umpqua, and 

Rogue valleys) were rarely crossed. However, owls 

did disperse between the Cascades and Coastal 

provinces via connecting forested lands in areas 

between the large valleys. They also occasionally 

dispersed both directions across the crest of the 

Cascade Range. 

Forsman et al. (2002) did not analyze owl movements 

in relation to the reserve network of the Plan. Our analysis 

used an expanded version of the data used by Forsman et al. 

(2002) to assess owl movements in the reserve network. The 

additional records were from owls banded and observed in 

Chapter 4: Owl Movement

1 Joseph Lint is a wildlife biologist, U.S. Department of the 

Interior, Bureau of Land Management, Oregon State Office, 777 

Garden Valley Blvd., NW, Roseburg, OR 97470. He is the module 

leader for northern spotted owl effectiveness monitoring under the 

Northwest Forest Plan.

2 Jason Mowdy is a faculty research assistant, Oregon State 

University, 777 Garden Valley Blvd., Roseburg, OR 94770. 

3 Janice Reid is a wildlife biologist, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 777 

Garden Valley Blvd., Roseburg, OR 97470. 

4 Eric Forsman is a research wildlife biologist, U.S. Department 

of Agriculture, Forest Service, Pacific Northwest Research Station, 

3200 Jefferson Way, Corvallis, OR 97470.

5 Robert Anthony is a research wildlife biologist, U.S. Department of the Interior, Geological Survey, 104 Nash Hall, Oregon 

State University, Corvallis, OR 97331.
84

GENERAL TECHNICAL REPORT PNW-GTR-648

Oregon and Washington from 1997 through 2003. No data 

were available for California. The full data set comprised 

movement records for 1,210 juvenile and 1,388 subadult and 

adult (nonjuvenile) spotted owls. Figure 4-1 shows the locations of the movement lines for both juvenile and nonjuvenile owls in relation to the large, reserved blocks. The data 

were analyzed according to their movement path into, out 

of, and between the reserved blocks. The movement paths 

we analyzed were as follows: 

• From a large, reserved block to another 

reserved block.

• From a reserved block to outside of the reserved 

block.

• From outside of a reserved block to inside a 

reserved block.

• From outside of a reserved block to another 

point outside of a reserved block.

• Within a single reserved block.

The mapped points of the original bandings and the 

resighting points for all movement records were classified as 

either “inside” or “outside” of reserved blocks. The reserved 

block network used in the analysis was the same one 

described in chapter 3 of this report (fig. 3-13). A unique 

number was assigned to each reserved block. Individual 

owl movement records with origin or resighting points in 

specific blocks were coded with the block number to track 

movements within and between reserved blocks.

Movement distances were calculated as straight-line 

distances between the original banding locations and the 

resighting point. The minimum, maximum, median, and 

average movement distances were calculated for 1,210 

juvenile movement records and the 1,388 nonjuvenile 

records. The movement distance statistics were reported by 

movement path for all records combined, and by selected 

physiographic provinces. Movements were assigned to a 

given province based on the resighting point regardless of 

the point of the original banding.

Figure 4-1—Straight-line paths from origin to resighting point of juvenile and nonjuvenile spotted owls 

from 1985 through 2003 in relation to large reserved habitat blocks and physiographic provinces. 
85

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Results

Results for analyses of movements, independent of physiographic province, are shown in figure 4-2. Juvenile and 

nonjuvenile movements were recorded for all movement 

paths. Juvenile movements occurred from one reserved 

block to another (142), from outside a reserve block to inside 

a reserved block (247), and within a single reserved block 

(232). All these movements had resighting points inside the 

reserved blocks and accounted for 51 percent of juvenile 

movement records. Fifty-eight percent of the juvenile owls 

that were fledged inside reserves (142 + 232) were resighted 

inside reserved blocks.

Juvenile movements with resighting points outside 

the reserved blocks included 268 juveniles moving from 

a reserved block to outside of the block and another 321 

that moved from outside a reserved block to another point 

outside a reserved block. These movements made up about 

49 percent of the juvenile movement records.

Compared to juvenile movements, the nonjuvenile 

movements between reserved blocks was markedly lower; 

accounting for only 2.9 percent of all nonjuvenile records. 

When nonjuvenile owls became territorial, their movement 

frequency and distances moved decreased. The 576 nonjuvenile movements within a given single, reserved block 

accounted for 41 percent of all the nonjuvenile movements 

and averaged only 2.7 mi. The median distance for these 

movements was 1.9 mi (table 4-1). 

As expected (based on Forsman et al. 2002), the maximum, median, and average movement distances of nonjuveniles were less than for juveniles on all movement paths 

(table 4-1). For juvenile spotted owls, the median distance 

traveled from one reserved block to another was 17.9 mi and 

the average distance moved was 21.8 mi. For those juvenile 

owls that moved from outside a reserved block to inside a 

block, the median distance was 12.7 mi, and the average 

distance was 16.9 mi. The median movement distances 

of juvenile and nonjuvenile spotted owls within a given 

reserved block were 7.3 and 1.9 mi, respectively.

Movement data were also analyzed for selected physiographic provinces (table 4-2). Movement records were 

available for all nine Oregon and Washington provinces, 

but three provinces had only a few movement records and 

were not included in the province analysis. The number of 

Juvenile

0

100

200

300

400

500

600

700

800

900

Nonjuvenile

Juvenile = 1,210

Nonjuvenile = 1,388

Total = 2,598

Number of movement records

Block to block

Inside block 

to outside

Outside block 

to inside

Outside block 

to outside

Within block

Movement path

Figure 4-2—Movement paths for juvenile and nonjuvenile spotted 

owls between 1985 and 2003 in Oregon and Washington. (Refer to 

figure 4-1 for geographic location of movement records.)

Table 4-1—Movement statistics for juvenile and nonjuvenile spotted owls in Oregon and Washington from 

1985 to 2003 by movement path 

Inside habitat Outside habitat Outside habitat

 Habitat block to block to outside block to inside block to outside Within a single

Movement habitat block habitat block habitat block habitat block habitat block

distance Juvenile Nonjuvenile Juvenile Nonjuvenile Juvenile Nonjuvenile Juvenile Nonjuvenile Juvenile Nonjuvenile

measures n = 142 n = 40 n = 268 n = 110 n = 247 n = 140 n = 321 n = 522 n = 232 n = 576

Miles

Minimum 5.39 1.57 1.86 0.93 0.65 0.76 0.16 0.16 0.54 0.16

Maximum 71.85 51.89 58.41 52.93 74.02 39.56 53.82 35.68 45.40 35.74

Median 17.90 12.04 12.40 3.91 12.72 4.87 9.52 1.94 7.32 1.91

Average 21.86 13.22 15.02 6.83 16.95 6.81 12.43 3.08 8.79 2.73
86

Table 4-2—Movement statistics for juvenile and nonjuvenile spotted owls in selected physiographic provinces in Oregon afrom 1985 to 2003 by movement path Inside habitat Outside habitat Outside habitat Sample size Habitat block to block to outside block to inside block to outside 

Physiographic (n) and habitat block habitat block habitat block habitat block 

province distance measures Juvenile Nonjuvenile Juvenile Nonjuvenile Juvenile Nonjuvenile Juvenile Nonjuvenile JuveOregon Eastern n 0 0 2 4 10 4 8 7 1

 Cascades Minimum (miles) 12.89 1.42 7.61 5.33 5.60 1.39 .

Maximum (miles) 20.83 52.93 74.02 23.09 40.22 32.41 37.Median (miles) 16.86 3.14 22.47 18.77 12.22 9.03 10.Average (miles) 16.86 15.16 25.55 16.49 18.79 11.78 14.

Oregon Western Cascades n 8 1 58 18 51 36 117 231 3

Minimum (miles) 5.39 20.05 4.19 1.13 3.01 1.19 .16 .16 .Maximum (miles) 43.38 20.05 57.90 46 59.70 29.80 46.10 35.68 26.Median (miles) 13.99 20.05 16.97 3.50 12.94 5.56 11.06 1.93 9.Average (miles) 20.12 20.05 18.59 8.64 16.12 8.06 12.60 3.15 10.

Oregon Coast Range n 116 37 101 61 114 67 64 161 9

Minimum (miles) 6.06 1.57 1.86 .93 .65 .99 1.25 .49 1.Maximum (miles) 71.85 51.89 56.03 31.25 54.61 39.56 53.82 22.92 20.Median (miles) 17.97 11.92 12.04 4.23 12.72 4.81 10.84 1.96 6.Average (miles) 21.38 12.85 14.97 6.77 17.05 6.75 15.12 2.90 6.

Washington Eastern n 3 1 7 5 8 5 8 14 1 Cascades Minimum (miles) 12.54 25.81 4.35 4.86 5.79 3.32 2.99 .73 2.

Maximum (miles) 20.52 25.81 18.21 11.07 49.99 12.31 26 7.15 44.Median (miles) 19.58 25.81 10.58 9.70 13.05 5.32 7.03 1.57 11.Average (miles) 17.55 25.81 10.83 8.26 19.96 6.28 12.37 2.44 14.

Oregon Klamath n 14 1 93 20 57 26 121 107 5 Mountains Minimum (miles) 12.64 7.38 2.33 1.11 2.09 .76 1 .59 .

Maximum (miles) 69.06 7.38 58.41 9.26 48.57 12.22 41.76 15.42 12.Median (miles) 17.73 7.38 10.38 2.48 12.13 3.33 7.84 1.93 5.Average (miles) 28.18 7.38 13.24 3.51 14.67 3.97 10.54 2.54 6.

Washington Olympic n 0 0 4 1 7 2 2 1 2 Peninsula Minimum (miles) 4.72 8.94 11.66 2.44 6.10 22.69 .

Maximum (miles) 22.24 8.94 31.87 7.62 13.84 22.69 45.Median (miles) 7.01 8.94 22.78 5.03 9.97 22.69 7.Average (miles) 10.24 8.94 24.11 5.03 9.97 22.69 11.
87

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

movement records per province ranged from less than 50 

in three provinces to over 480 records in the Oregon Coast 

Range province. This variation was the result of study area 

size, years of banding effort, and owl population size. 

The median distance moved by juvenile owls from 

one reserved block to another ranged from about 14 mi 

in the Oregon Western Cascades province to 19.6 mi in 

the Eastern Cascades province in Washington. Similar 

median distances were observed for juvenile movements 

from outside a reserved block to inside a block (12.1 to 22.8 

mi) across the provinces. Median movement distances of 

juvenile owls that moved within the same block ranged from 

5.9 mi in the Klamath province of Oregon to 11.2 mi in the 

Eastern Cascades province of Washington. In four of six 

provinces, movements with resighting points in reserved 

blocks accounted for about two-thirds of juvenile movement 

records. In the other two provinces, about one-third of the 

records involved juvenile movement with resighting points 

inside reserved blocks.

Discussion and Conclusions

Dispersal of juvenile and subadult owls is an important 

mechanism for the recruitment of replacement owls into the 

territorial population. Forsman et al. (2002) observed that 

juvenile owls move rapidly away from their natal site in the 

fall, settling in one or more temporary home range areas 

before acquiring a territory. The majority of young spotted 

owls are integrated into the territorial population within 

the first 2 years, but 32 percent of males and 23 percent of 

females from a sample of radio-marked owls did not acquire 

territories until they were >3 years old (Forsman et al. 

2002). 

The spatial assessment of dispersal habitat in this report 

indicated both numerically and visually that nearly half of 

the federal forest acres are providing dispersal habitat for 

spotted owls. These acres are dispersed across the landscape providing a mosaic of dispersal habitat. The results 

from our analysis of owl movement showed that movements 

with resighting points inside reserved blocks accounted for 

51 percent of juvenile movement records. Over 30 percent 

of the juvenile movements were into reserved blocks from 

outside points.

The movement records provide tangible evidence that 

spotted owls are dispersing across the landscape under 

the Plan, and supports the conclusion by Forsman et al. 

(2002) that a conservation strategy that consists of numerous, closely spaced reserves of old forest (for example, the 

Northwest Forest Plan) would not likely result in genetic or 

demographic isolation of local populations because dispersal between reserves will be a common occurrence even 

if the landscapes between the reserves consist of highly 

fragmented forests.

References

Anthony, R.G.; Forsman, E.D.; Franklin, A.B.; 

Anderson, D.R.; Burnham, K.P.; White, G.C.; 

Schwarz, C.J.; Nichols, J.; Hines, J.; Olson, G.S.; 

Ackers, S.H.; Andrews, S.; Biswell, B.L.; Carlson, 

P.C.; Diller, L.V.; Dugger, K.M.; Fehring, K.E.; 

Fleming, T.L.; Gerhardt, R.P.; Gremel, S.A.; 

Gutierrez, R.J.; Happe, P.J.; Herter, D.R.; Higley, 

J.M.: Horn, R.B.; Irwin, L.L.; Loschl, P.J.; 

Reid, J.A.; Sovern, S.G. 2004. Status and trends in 

demography of northern spotted owls, 1985-2003. Final 

report to the Regional Interagency Executive Committee. 

On file with: Regional Ecosystem Office, 333 SW First 

Avenue, Portland, OR 97204. 

Forsman. E.D.; Anthony, R.G.; Reid, J.A.; Loschl, P.J.; 

Sovern, S.G.; Taylor, M.; Biswell, B.L.; Ellingson, 

A.; Meslow, E. C.; Miller, G.S.; Swindle, K.A.; 

Thrailkill, J.A.; Wagner, F.F.; Seaman, D.E. 2002.

Natal and breeding dispersal of northern spotted owls. 

Wildlife Monograph No. 149. Washington, DC: The 

Wildlife Society. 35 p.

Thomas, J.W.; Forsman, E.D.; Lint, J.B.; Meslow, E.C.; 

Noon, B.R.; Verner, J. 1990. A conservation strategy 

for the northern spotted owl: a report of the Interagency 

Scientific Committee to address the conservation of the 

northern spotted owl. Portland, OR: U.S. Department 

of Agriculture, Forest Service; U.S. Department of the 

Interior, Bureau of Land Management, Fish and Wildlife 

Service, National Park Service. 427 p. 
88

GENERAL TECHNICAL REPORT PNW-GTR-648

U.S Department of Agriculture, Forest Service; 

U.S. Department of the Interior, Bureau of Land 

Management [USDA USDI]. 1994. Final supplemental 

environmental impact statement on management of 

habitat for late-successional and old-growth forest related 

species within the range of the northern spotted owl. 

Volumes 1-2 + Record of Decision. 

Jason Mowdy
89

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Chapter 5: Related Research

1Joseph Lint is a wildlife biologist, U.S. Department of the 

Interior, Bureau of Land Management, Oregon State Office, 777 

Garden Valley Blvd., NW, Roseburg, OR 97470. He is the module 

leader for northern spotted owl effectiveness monitoring under the 

Northwest Forest Plan.

Joseph Lint1

Introduction

The effectiveness portion of the northern spotted owl (Strix 

occidentalis caurina) monitoring plan focused on estimating population demographic rates and assessing habitat 

conditions. Equally important, however, was the related 

research on predictive models funded through the monitoring program. The aim of the model research was to provide 

a monitoring tool for predicting occupancy, distribution, 

and demographic performance of spotted owls based on a 

map of habitat conditions.

On another related research front, data on barred owls 

collected incidental to the survey of the demographic study 

areas, along with data from other spotted owl surveys, 

spawned research papers that provided insight on the occurrence and distribution of the barred owl (Strix varia) in the 

range of the spotted owl.

This chapter summarizes information on barred owls 

from selected research papers to provide managers with a 

perspective on the arrival and spread of the barred owl in 

the range of the spotted owl. We also review research on 

the relations of climate and habitat quality to spotted owl 

populations and report on the progress of development of 

models to predict demographic rates and occupancy. 

Barred Owl

The monitoring plan did not contain any provisions for 

the monitoring of barred owls (Strix varia). However, in 

the course of collecting demography data on spotted owls, 

ancillary information on the occurrence and distribution of 

barred owls was recorded. Gutierrez et al. (2004) pointed 

out there is uncertainty about the barred owl’s pattern of 

range expansion, interactions with spotted owls (Strix occidentalis caurina), and the contribution of barred owls to the 

decline of spotted owls because the information on barred 

owls was collected incidental to spotted owl surveys, and 

neither consistently collected nor consistently reported. In 

this chapter, we review information on barred owl distribution and general population trend from selected publications. Five peer-reviewed, published papers on barred owls 

were selected to provide insight on barred owl distribution 

and population trend for our report. Kelly et al. (2003) and 

Anthony et al. (2004) used barred owl data gathered in 

the demography study areas in their analyses. Some key 

findings of these five papers are summarized below. A more 

complete review of the barred owl-spotted owl issue is 

found in Gutierrez et al. (2004). 

Data Sources and Methods

Refer to the cited, peer-reviewed papers for information on 

the sources of data and the analyses methods used.

Results

The barred owl’s gradual movement across the Canadian 

provinces and northern Rocky Mountains from the Eastern 

United States in the past 50 years resulted in its eventual 

entry into the Pacific Northwest (Gutierrez et al. 2004). 

The barred owl was not prevalent in the demographic study 

areas at the beginning of the monitoring period (1985), 

it had only recently been detected in west-side forests of 

Washington (1965), Oregon (1979), and California (1981). 

The barred owl range now overlaps most of the range of the 

northern spotted owl (Kelly et al. 2003). 

Over the past two decades, the barred owl has increased 

in numbers and extended its distribution in the range of 

the northern spotted owl (Dark et al. 1998, Herter and 

Hicks 2000, Kelly et al. 2003, Pearson and Livezey 2003). 

Anthony et al. (2004) presented information on the proportion of spotted owl territories that were occupied by barred 

owls each year. This coarse-grained estimate of barred owl 

occurrence, by year, provided a depiction of the barred owl 

increase across the range of the spotted owl (fig. 5-1). The 

relative differences in barred owl occurrence between the 

three states, the north-to-south gradient in occurrence, and 

the increasing occurrence of barred owls are evident from 

figure 5-1.

The first pair of barred owls was recorded in Washington in the northern portion of the Cascade Mountains 

in 1974 (Taylor and Forsman 1976). Pearson and Livezey 
90

GENERAL TECHNICAL REPORT PNW-GTR-648

Washington

0

0.1

0.2

0.3

0.4

0.5

0.6

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Year

Proportion of territories 

occupied by barred owls

Cle Elum

Rainier

Wenatchee

Olympic Peninsula

Oregon

0

0.1

0.2

0.3

0.4

0.5

0.6

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Year

Proportion of territories 

occupied by barred owls

Tyee

Southern Cascades

Coast Range

Warm Springs Reservation

H.J. Andrews

Klamath

California

0

0.1

0.2

0.3

0.4

0.5

0.6

1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

Year

Proportion of territories 

occupied by barred owls

Simpson Timber

Northwest California

Hoopa Reservation

Marin

Figure 5-1—Proportion of spotted owl territories occupied by barred owls (BO) each year in study areas within 

Washington, Oregon, and California (Anthony et al. 2004).
91

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

(2003) reported that barred owl detections increased 8.6 percent annually from 1982 to 2000 in their southwest Washington study area, with no indication of any leveling-off of 

this increase. Herter and Hicks (2000) stated that barred 

owls have completely overlapped the known geographic 

range of the spotted owl in central Washington. 

Pearson and Livezey (2003) determined that barred owl 

sites surpassed the number of occupied spotted owl sites in 

late-successional reserves in southwest Washington. Barred 

owls were at least as numerous as spotted owls in their study 

area. They also noted there were significantly more barred 

owl sites within 0.8-km-, 1.6-km- and 2.9-km-radius circles 

centered on unoccupied spotted owl sites than occupied 

spotted owl sites. Pearson and Livezey (2003) suggested 

that spotted owls are more likely to abandon a site if barred 

owls take up residence close to that site.

Barred owls were first recorded in Oregon in 1974 

in the eastern part of the state in the Blue Mountains. 

Barred owls were noted in the range of the spotted owl in 

the northern part of the Western Cascades Mountains of 

Oregon around Mount Hood in 1979, and were confirmed 

in the southern portion of the Western Cascades Mountains 

and on the west flank in Lane County by 1981.

By 1998, Kelly et al. (2003) estimated that there were 

706 territories in Oregon where barred owls had been observed one or more years since 1974 (fig. 5-2). Most of these 

were in western Oregon forests, owing, in part, to the number and extent of spotted owl surveys conducted in western 

Figure 5-2—Barred owl locations in Oregon through 1998 (Kelly et al. 2003).

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LAKE

N

LINCOLN POLK

CURRY JACKSON

KLAMATH

DOUGLAS

JEFFERSON

HARNEY

GRANT

WHEELER

CLATSOP

COLUMBIA

TILLAMOOK

WASCO

MARION

YAMHILL

BENTON

LINN

CLACKAMAS

MULTNOMAH

WASHINGTON

LANE

DESCHUTES

CROOK

SHERMAN

HOOD RIVER

GILLIAM

MORROW

UMATILLA

COOS

JOSEPHINE

MALHEUR

BAKER

UNION

WALLOWA

0 50 100 200 Kilometers
92

GENERAL TECHNICAL REPORT PNW-GTR-648

Oregon as opposed to none in eastern Oregon. A steady, 

linear increase in new barred owl territories occurred in 

Oregon between 1974 and 1998 with approximately 60 new 

barred owl territories reported annually in Oregon between 

1989 and 1998 (Kelly et al. 2003). Kelly et al. (2003) stated 

it was obvious from their analyses that the barred owl population was increasing rapidly in Oregon. This trend appears 

to have continued between 1998 and 2003 based upon the 

data presented by Anthony et al. (2004) (fig. 5-1). 

Kelly et al. (2003) examined data on five of the demographic study areas under the monitoring plan in Oregon 

and Washington and found steady increases in the percentage of spotted owl territories with barred owl detections 

between 1987 and 1999. This was validated by data presented in Anthony et al. (2004).

Kelly et al. (2003) assessed the mean occupancy by 

spotted owls for spotted owl territories where barred owls 

were detected. There was a decline when barred owls were 

detected within 0.80 km of the territory center, but when 

the barred owl detection occurred 0.81 to 2.4 km from the 

center, occupancy was only marginally less.

Dark et al. (1998) presented information on the range 

expansion of barred owls in California since they were 

first documented in 1981 (Evens and LeValley 1982). Dark 

et al. (1998) identified 61 barred owl sites in 12 counties 

in California since 1980. The first documented sightings 

(Evens and LeValley 1982) occurred in Del Norte and 

Trinity Counties in northern California in 1981 followed 

by observations in Humboldt County in 1983. From 1986 

to 1996, barred owl detections appeared to expand to the 

south and east from the initial northern locations (Dark 

et al. 1998). Their information indicated an increase in 

barred owl detections in the early 1980s with an accelerated 

increase in the mid 1990s. Dark et al. (1998) characterized 

the expansion of barred owls in California as rapid and 

widespread. Information provided in Anthony et al. (2004) 

indicates that barred owls were present in the demographic 

study areas in California, but were not nearly as prevalent 

there as in study areas in Oregon and Washington.

Effect of the Barred Owl

The papers reviewed in this chapter revealed the continued 

range expansion and increase in numbers of barred owls 

in the range of the northern spotted owl along with an 

indication of likely competitive interaction between the 

two species. 

The concern is whether the barred owl is a threat to

 the conservation and eventual recovery of the spotted 

owl. This concern was evaluated by Gutierrez et al. (2004). 

Their chapter in the Scientific Evaluation of the Status 

and Trend of the Northern Spotted Owl provides an informative examination of the barred owl-spotted owl issue. 

They pointed out:

The greatest uncertainties associated with the 

actual and potential effects of the Barred Owl on 

the Northern Spotted Owl is that we lack accurate 

information on Barred Owl density, numbers, and 

population trends, and that we are unable to resolve 

with certainty whether the observed changes of 

Barred Owls and Spotted Owls are causal or merely 

correlated in opposite ways with some other, 

unknown, factor(s). While the evidence for a negative effect of Barred Owls is clearly correlational, 

we believe the Barred Owl is having a substantial 

effect on the Spotted Owl in some areas because of 

the preponderance of circumstantial and anecdotal 

information. In our evaluation, the Barred Owl 

currently constitutes a significantly greater threat to 

the Northern Spotted Owl than originally envisaged 

at the time of listing.

Gutierrez et al. (2004) offered a nonexhaustive set of 

nine alternative hypotheses on the potential outcome of 

barred owl-spotted owl interactions. They categorized them 

as either clearly plausible, plausible, and not plausible or not 

clear. Those listed in the clearly plausible category were:

• Barred owls will replace the northern spotted owl 

throughout its range (behavioral and competitive 

dominance hypothesis).

• Barred owls will replace the northern spotted 

owl in the northern, more mesic areas of its 

range (moisture-dependent hypothesis). 
93

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

• Barred owls and northern spotted owls will compete, with the outcome being an equilibrium favoring barred owls over spotted owls in most but not 

all of the present northern spotted owl habitat range 

(quasi-balanced competition hypothesis).

In light of these potential outcomes, the barred owl 

may present a concern for the conservation and recovery of 

the spotted owl. It is not an issue limited to the confines of 

federal land under the habitat-based Northwest Forest Plan. 

Because it is likely a species interaction-competition issue, 

it deserves broader consideration at the species-status 

scale. A status review of the spotted owl by the USDI Fish 

and Wildlife Service (FWS) was completed in November 

of 2004. The FWS considered information on the barred 

owl in the status review. The FWS recommended that the 

northern spotted owl remain listed as a federal threatened 

species. The status review is available online at: http://

pacific.fws.gov/ecoservices/endangered/recovery/pdf/

NSO_5-yr_Summary.pdf. 

Predictive Model Development

Lint et al. (1999) recommended the development of models 

for predicting occupancy, distribution, and demographic 

performance (survival and reproductive success) of spotted 

owls based on vegetative characteristics assessed at a variety of spatial scales. They envisioned a shift from markrecapture studies to increased reliance on habitat monitoring by using predictive models to indirectly estimate the 

occurrence and demographic performance of spotted owls 

(Lint et al. 1999).

Lint et al. (1999) provided the following explanation 

of the task of building predictive models.

To accomplish this transition, [from mark-recapture 

to habitat-based monitoring] we must identify 

those aspects of vegetation structure and composition that have the greatest power and precision to 

predict the number, distribution, and demographic 

performance of owls at the landscape scale, as well 

as to explain the observed variation in demographic 

rates (owl birth and death rates) at a local, home 

range-level scale. Accomplishing this task will 

require characterizing the vegetation at a variety 

of spatial scales in the existing demographic study 

areas. The combination of spatially referenced data 

from both the owl demographic studies and mapped 

vegetation attributes provides the fundamental data 

for the model-building phase. The degree to which 

these models explain the observed variation in owl 

distribution and demographic performance will 

estimate the certainty with which habitat variation 

predicts population performance and stability. 

Explained variation is thus a direct measure of the 

confidence we have in habitat as an appropriate 

monitoring surrogate for population performance.

Questions from Lint et al (1999) provided the impetus 

for exploring the efficacy of predictive models. They were:

1. Can the status and trends in spotted owl abundance 

and demographic performance be inferred from the 

distribution and abundance of habitat?

2. Can the relation between owl occurrence and demographic performance be reliably predicted given a 

set of habitat characteristics at the landscape scale?

3. How well do habitat-based models predict occurrence and demographic performance in different 

land allocations? 

The initial research on the relations between landscape habitat configuration and spotted owl survival and 

reproduction was completed by Franklin et al. (2000) in the 

north Coast Range and Klamath Mountains in northwestern 

California. They explored the variation in survival and 

reproduction of spotted owls relative to landscape habitat 

characteristics at the individual owl-territory scale. Franklin 

et al. (2000) were particularly interested in the effects of 

fragmentation of mature and old-growth forests on life history traits and fitness of spotted owls. In addition, Franklin’s 

work examined the influence of climate on survival and 

reproduction. 

In 1999, a project proposal by Anthony et al. (1998) 

for a 5-year study to explore predicting abundance and 

demographic performance of northern spotted owls from 

vegetative characteristics was funded by the spotted 

owl monitoring program. The study addressed the three 
94

GENERAL TECHNICAL REPORT PNW-GTR-648

questions posed above to determine if habitat quality and 

quantity could be used to provide reliable predictions of 

abundance and demographic performance of northern 

spotted owls. The stated objectives of the study were: 

1. Summarize abundance and demographic performance of spotted owls at the home range and 

landscape scales. 

2. Characterize landscape composition and patterns 

for home ranges and landscapes. 

3. Develop statistical models that relate abundance 

and demographic performance of owls to landscape 

characteristics for a subset of home ranges in the 

demographic study areas.

4. Validate the statistical models by testing them on 

the remaining home ranges. 

5. Use the statistical models to develop or refine existing spatially explicit models for spotted owls. 

Another, separate study by Anthony et al. (2002b), 

not funded by the monitoring program, also explored the 

development of models to relate demographic performance 

of spotted owls to habitat characteristics at individual owl 

territories. In this study, they attempted to use models to 

evaluate the potential importance of specific habitat types 

to owl populations. This information will eventually be 

fed back into a larger project examining the development 

of silviculture systems for managing habitat important 

for sustaining spotted owl populations. It also has direct 

application to the owl abundance and demographic performance questions posed in the monitoring plan. By design, 

the model-building process employed in this work closely 

paralleled the work funded by the monitoring program, in 

part, because the projects are exploring similar questions 

and because the studies have many of the same principal 

investigators in common.

These three complementary studies form the nucleus 

of effort to develop models to predict spotted owl demographic parameters from habitat characteristics. Peerreviewed results on the modeling efforts are reported by 

Franklin et al. (2000) and Olson et al. (2004). A summary 

of the findings in these two papers is provided in the 

“Results” section below. 

Data Sources and Methods 

Survival and productivity modeling—

Development of predictive models by using demographic 

information was initiated in four study areas under three 

separate studies in the range of the spotted owl. Owl 

population data from the northern California, Tyee 

(Roseburg), and H.J. Andrews demographic study areas 

and demographic data from the Umpqua and Rogue River 

National Forests and the Medford District of the Bureau of 

Land Management were used in the model-building work. 

Each of the modeling studies (Anthony et al. 2002a, 2002b; 

Franklin et al. 2000; Olson et al. 2004), followed the same 

general methods. 

Franklin et al. (2000) formulated the initial analyses 

methods and the other investigators applied and expanded 

upon this pioneer work. The decision to pursue the same 

methods for analyses and model construction was beneficial 

because it allowed comparison of the results between study 

areas. The detailed methodologies of the respective studies 

can be reviewed in the individual reports (Anthony et al. 

2002a, 2002b; Franklin et al. 2000; Olson et al. 2004). A 

general discussion of the methods used by the investigators 

is provided below.

General discussion of survival and reproductive output 

analytical approach—

The use of a priori hypotheses to describe how climate 

and habitat might affect owls was central to each of the 

predictive model development studies. These hypotheses 

were then expressed as models. The models used survival 

and reproduction as response variables, climate covariates 

as explanatory variables, and age and sex of the owls as 

individual covariates. An example hypothesis from Franklin 

et al. (2000) was that owl survival would be negatively 

affected by high precipitation and cold temperatures in 

both the winter and early nesting seasons. Franklin et al. 

(2000) pointed out the importance of the a priori model 

development approach in contrast to analyzing the data by 

iteratively searching the data for relations.

Capture-recapture data and Cormack-Jolly-Seber 

(Lebreton et al. 1992, Pollack et al. 1990) models were 
95

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

used to estimate survival rates. Owl reproductive output 

was estimated for each female from field counts of young 

fledged or by confirming via established survey protocols 

that no young were produced (Lint et al. 1999).

Climate was used as a time-varying covariate in the 

reproductive output and survival models. Three types of 

climate effects–seasonal, cumulative, and episodic–were 

considered. Franklin et al. (2000) defined five seasonal 

periods, which were also used, with minor variation of date 

sequences, by the other investigators. These periods were 

linked to annual life cycle of the spotted owl. The periods, 

as defined by Franklin et al. (2000) were:

• Winter stress period—November 15 through 

February 14

• Early nesting period—February 15 through May 14

• Late nesting period—May 15 through July 14

• Heat stress period—July 15 through September 14

• Dispersal period—September 15 through November 14

Daily measurements of amount of precipitation and 

minimum and maximum temperatures for each of the 

seasonal periods were obtained from National Oceanic 

and Atmospheric Administration National Weather Service 

stations in the vicinities of the respective study areas. 

Covariates for precipitation and temperature were derived 

from the weather station data.

Landscape habitat information was derived from 

available habitat map products ranging from aerial photointerpreted products to satellite imagery-derived maps. 

Olson et al. (2004) used nested circles of varying radii 

(600-, 1500- and 2400-m radius; or 1,920-, 4,800- and 

7,680-ft radius, respectively) around the owl nest site or 

primary roost area to quantify the habitat data for later 

analyses. Franklin et al. (2000) used a 0.71-km-radius 

(2,272-ft) circle around the territory centers to represent 

the spotted owl territory.

The a priori models were ranked by using model 

selection criteria following Akaike (1973) to determine 

which models best explained the empirical data. A multistep 

approach was used to model survival and productivity. The 

information theoretic approach (Burnham and Anderson 

2002) was used to select the best model at each step in the 

model process. Refer to Franklin et al. (2000) and Olson et 

al. (2004) for details on model development and covariate 

analyses.

Occupancy modeling—

The development of models to predict likelihood of owl 

occupancy for a given habitat mosaic relied heavily upon 

the methodology developed by MacKenzie et al. (2002). 

This method estimates the proportion of sites occupied by 

a species at a single point in time, such as a given breeding 

season, when the probability of detection is less than 1. At 

this writing, the occupancy modeling effort is a work in 

progress and no peer-reviewed results are available.

Modeling was initiated in three of the demography 

study areas—Tyee, Oregon North Coast Range, and H.J 

Andrews. The interagency vegetation mapping project 

province maps were used for modeling in each of the study 

areas. In the Roseburg study area, two additional map 

sources were used. One was a map developed from aerial 

photointerpretation and the other was a satellite imagerybased map from the coastal landscape analysis and modeling study.

Habitat models were developed for both simple occupancy (occupied by any spotted owl) and pair occupancy 

(occupied by paired male and female spotted owl) using 

the parameters of initial occupancy, extinction probability, 

and colonization probability after MacKenzie et al. (2003). 

Additional models were developed in a post hoc manner by 

using combinations of these three parameters. The habitat 

variables and the radius distances around the owl activity 

centers were the same as those in the survival and reproduction model work explained above and detailed in Olson et al. 

(2004). Other covariates being explored include year, evenodd year effect, precipitation, and barred owl effect. The 

barred owl data are specific to the spotted owl territory, as 

opposed to the more general nature of the barred owl 

occurrence data used by Anthony et al. (2004).

Results

The survival and productivity of northern spotted owls is 

governed by biological and physical factors in their environment. Investigators (Franklin et al. 2000, Olson et al. 2004) 
96

GENERAL TECHNICAL REPORT PNW-GTR-648

examined the influence of climate, age and sex of the owls, 

amount of suitable habitat, and amount of unsuitable habitat 

to understand how these variables may affect spotted owl 

survival and productivity. 

Survival modeling—

Franklin et al. (2000) found that annual survival was 

negatively affected by increased precipitation and positively 

affected by increased temperature during the early nesting 

period. Cold, wet springs had a negative effect on spotted 

owl survival, whereas warm, dry springs had a positive effect. Olson et al. (2004) noted that the relationship between 

early nesting season precipitation and survival was negative, but found that in their Oregon Coast Range province 

study area late nesting season precipitation had a positive 

effect on survival. 

Franklin et al. (2000) discovered that apparent survival 

increased with increasing amounts of spotted owl habitat, 

increasing edge between spotted owl habitat and other 

habitats, and increasing mean nearest neighbor distance 

between patches of spotted owl habitat up to about 400 m 

(1,280 ft). As distance between patches increased above 

400 m, survival declined with increasing distance. Similarly, Olson et al. (2004) stated that, in general, increases in 

late-seral forest had a positive effect on survival, whereas 

increases in early-seral and nonforest area had a negative 

effect. Their best model indicated a nonlinear relationship 

between late- and midseral forest and survival, with a slight 

decrease in survival when proportions of these cover types 

in the 1500-m-radius (4,800-ft) circle around the owl activity center were high. 

Reproductive output modeling—

Franklin et al. (2000) pointed out that reproductive 

output was:

• Negatively associated with female age class 

(1- and 2-year-old owls fledged fewer young than

 >2-year-old owls).

• Negatively associated with the amount of core 

spotted owl habitat (nonlinearly).

• Positively associated with the amount of edge 

between spotted owl habitat and other habitats 

(nonlinearly).

• Associated with the number of patches of spotted 

owl habitat by an inverse quadratic relationship in 

which reproductive output was highest when the 

number of patches was either few or many and 

lowest when the number was intermediate. 

• The changes in reproductive output were most 

sensitive to changes in edge between spotted owl 

habitat and other habitats.

They also determined that late-season precipitation was 

an important covariate in explaining reproductive output 

and provided support to the a priori hypothesis that predicted a negative relationship between reproductive output 

and precipitation during the late nesting period.

Olson et al. (2004) found the following relationships in 

the Oregon Coast Range study area:

• Age, in general, had a positive effect on productivity, 

and productivity of all owls was greater in even than 

odd years.

• There was a positive relation between precipitation 

in late nesting season and reproduction—opposite of 

Franklin et al. (2000).

• The presence of barred owls had a negative effect on 

productivity.

• Although there was a significant relation noted between habitat and productivity, the nature of the relation was unexpected. Productivity was apparently 

declining with increases in mid- and late-seral forest 

and was increasing with increases in early seral and 

nonforest area.

• The model with the greatest Akaike weight had a 

positive linear relationship between productivity and 

the amount of edge between early seral and nonforest and other classes combined.

Olson et al. (2004) offered the following summary 

findings on the relationship of habitat to spotted owl 

survival and productivity.

• For both survival and productivity, a mixture of 

early seral and nonforest with mid- and late-seral 

forest seemed to provide better habitat conditions 

for spotted owls.
97

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

• Owl survival was highest at about 70 percent of the 

forest in mid- and late-seral condition with productivity increasing linearly as the amount of edge between these forest types and other habitats increased 

(within 1500-m-radius [4,800-ft] circles).

• Maximum fitness potential was realized in territories where both factors were optimal; simply 

increasing the amount of mid- and late-seral forest 

could not achieve the goal. The pattern of forest type 

within territories is important.

• Large expanses of mid- and late-seral forests may 

not be optimal for owls, but neither are forests with 

small amounts of these forest types.

• Although there was a positive relationship between 

the amount of edge and productivity of spotted owls, 

the importance of edge for spotted owls is not well 

understood.

Franklin et al. (2000) discussed several hypotheses, 

first advanced by Gutierrez (1985), as potential mechanisms 

for their results, many of which Olson et al. (2004) also 

observed. Under the predation hypothesis, owls use older 

forests as cover to avoid predation by avian predators 

such as the great horned owl (Bubo virginianus). They 

explained that great horned owls are not as well adapted 

to hunt in older forest, and thus spotted owls could use 

these mature and old-growth forests that were not useable 

by great horned owls. In reference to the thermoregulation 

hypothesis, they pointed out that mature and old-growth 

forests provide a more stable microclimate and protection 

from inclement weather, which would promote use by 

the spotted owls. And finally, they offered that under the 

prey hypothesis, ecotones between older forests and early 

seral stages, at least in California, may be areas within the 

owl’s home range where prey species such as the woodrat 

(Neotoma spp.) are both abundant and accessible to the owl 

as prey. They suggested that “sufficient core area” interspersed with other vegetation types may provide a source of 

large, accessible prey. Franklin et al. (2000) stated that there 

was a plausible link between the arrangement of habitat on 

individual owl territories, and survival and reproductive 

output. 

Both of the studies provided measures of habitat 

fitness potential for individual owl territories. Franklin et 

al. (2000) noted from their habitat fitness potential analysis 

that spotted owl territories where survival is maximized 

had relatively large core areas of habitat with some edge. 

They found that those territories where fecundity was 

maximized had minimized core area habitat and maximized 

edge between spotted owl habitat and other habitats either 

by minimizing or maximizing the discrete patches of 

spotted owl habitat. Franklin et al. (2000) determined that 

in territories where the habitat fitness potential was high, it 

appeared that both survival and fecundity were high, while 

in those areas with low or medium fitness potential, either 

survival or fecundity were high, but never both. See figure 

10, page 573 in Franklin et al. (2000) for examples of high, 

medium, and low fitness territories.

Occupancy modeling—

There were no peer-reviewed results available from the 

occupancy modeling efforts. Research work has focused 

on assessing the effects of barred owl presence on spotted 

owl occupancy and exploring the methods described by 

MacKenzie et al. (2002). Initial results should be available 

in late 2005. 

What Did We Learn from the Model Work?

The original reason for embarking on predictive modeling 

was to explore the possibility of being able to shift from 

mark-recapture studies to increased reliance on predictive 

models to indirectly estimate the occurrence and demographic performance of spotted owls (Lint et al. 1999). 

Phase I involved conducting the research and building 

the models. Before moving to phase II where the models 

would be put into practice, it was envisioned we would 

have answers to the following questions.

• Can the status and trends in spotted owl abundance 

and demographic performance be inferred from the 

distribution and abundance of habitat?

• Can the relation between owl occurrence and demographic performance be reliably predicted given a 

set of habitat characteristics at the landscape scale?
98

GENERAL TECHNICAL REPORT PNW-GTR-648

• How well do habitat-based models predict occurrence and demographic performance in different 

land allocations? 

Olson et al. (2004) concluded that although one study, 

such as theirs, could not definitively address the second 

question, the answer to the demographic performance 

portion of the question may be “no.” Work is ongoing on 

the prediction of occupancy (abundance), so no answers are 

available on that portion of the questions. From these results 

we are not in a position, now or in the foreseeable future, to 

move to Phase II where the models would be substituted for 

mark-recapture studies.

We learned from Franklin et al. (2000) and Olson et al. 

(2004) that numerous sources of variation affect survival 

and productivity. Climate, primarily precipitation, can have 

a noticeable influence on survival and productivity depending upon when it occurs. Olson et al. (2004) and Franklin 

et al. (2000) suggested that survival and productivity of 

resident owls are more affected by precipitation during 

spring than other times of the year. Franklin et al. (2000) 

pointed out that their climate models do not demonstrate 

cause and effect, and testing of the models or their described effects cannot be tested with experiments because 

we have no control over climatic variation. The climate 

models have made us aware that understanding the influence of more than the amount and arrangement of habitat is 

important in predictive modeling. 

We also learned that survival and productivity may 

be affected differently by habitat and climate. Franklin et 

al. (2000) stated that most of the variation in survival was 

based on habitat variation, and variation in reproductive 

output was equally based on climate and habitat variation. 

Olson et al. (2004) noted that climate variables accounted 

for less variability in productivity in their study than in 

Franklin et al. (2000) (38 percent vs. 55 percent). Also 

notable was the 43 percent of the variability explained by 

habitat in the Franklin et al. (2000) model compared to 

less than 3 percent in the best model of Olson et al. (2004). 

Olson et al. (2004) noted that it would require further study 

to determine whether these differences reflect different 

relative importance of habitat between the two study areas 

or are due to different methodologies and habitat classifications. Olson et al. (2004) noted that 59 percent of the model 

variability was explained by factors other than climate or 

habitat, which explained <2 percent.

Olson et al. (2004) stated, in summary, that their results 

indicate we have a lot to learn about how habitat affects 

spotted owl demography, but that they thought examination 

of habitat influences on fitness parameters represents the 

best way to learn. Franklin et al. (2000) offered “that understanding the magnitude, strength and relative importance of 

different factors under varying conditions provides a deeper 

understanding of population dynamics.” The results of 

these two studies, although they did not produce operational 

models, follow the rationale of needing to try and to learn 

expressed by Lint et al. (1999).

This learning process continues with the exploration 

of models to predict occupancy for a given habitat mosaic. 

Initial results from the occupancy modeling will be available in late 2005. 

References

Anthony, R.G.; Forsman, E.D.; Ripple, W.R. 1998. 

Research proposal: predicting abundance and 

demographic performance of northern spotted owls 

from vegetative characteristics. Unpublished research 

proposal. 20 p. On file with: Oregon Cooperative Fish 

and Wildlife Research Unit, Department of Fisheries and 

Wildlife, Oregon State University, Corvallis, OR. 97331.

Anthony, R.G.; Forsman, E.D.; Franklin, A.B.; 

Anderson, D.R.; Burnham, K.P.; White, G.C.; 

Schwarz, C.J.; Nichols, J.; Hines, J.; Olson, G.S.; 

Ackers, S.H.; Andrews, S.; Biswell, B.L.; Carlson, 

P.C.; Diller, L.V.; Dugger, K.M.; Fehring, K.E.; 

Fleming, T.L.; Gerhardt, R.P.; Gremel, S.A.; 

Gutierrez, R.J.; Happe, P.J.; Herter, D.R.; Higley, 

J.M.: Horn, R.B.; Irwin, L.L.; Loschl, P.J.; 

Reid, J.A.; Sovern, S.G. 2004. Status and trends in 

demography of northern spotted owls, 1985–2003. 

Final report to the Regional Interagency Executive 

Committee. On file with: Regional Ecosystem Office, 

333 SW First Avenue, Portland, OR 97204. 
99

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Anthony, R.G.; Forsman, E.D.; Ripple, W.R.; Glenn, E. 

2002a. Predicting abundance and demographic 

performance of northern spotted owls from vegetative 

characteristics. Report on results of Western Oregon 

Cascades—H.J. Andrews Experimental Forest. 57 p. 

Progress Report. On file with: Oregon Cooperative Fish 

and Wildlife Research Unit, Department of Fisheries and 

Wildlife, Oregon State University, Corvallis, OR 97331.

Anthony, R.; Wagner, F.; Dugger, K.; Olson, G. 2002b. 

Identification and evaluation of northern spotted owl 

habitat in managed forests of southwestern Oregon and 

the development of silvicultural systems for managing 

such habitat. Report on step 2: analysis of habitat 

characteristics and owl demography on three density 

study areas. 77 p. On file with: Oregon Cooperative Fish 

and Wildlife Research Unit, Department of Fisheries and 

Wildlife, Oregon State University, Corvallis, OR 97331.

Akaike, H. 1973. Information theory and an extension of 

the maximum likelihood principle. In: Petran, B.N.; 

Csake, F., eds. International symposium on information 

theory. 2nd ed. Budapest, Hungary: Akademiai Kiadi: 

267–281.

Burnham, K.P.; Anderson, D.R. 2002. Model selection 

and multimodel inference: a practical informationtheoretical approach. 2nd ed. New York, NY: SpringerVerlag. 488 p.

Dark, S.J.; Gutiérrez, R.J.; Gould. G.I. 1998. The 

barred owl (Strix varia) invasion in California. Auk. 

115: 50–56.

Evens, J.; LeValley, R. 1982. Middle coast region. 

American Birds. 36: 890.

Forsman, E.D.; Anthony, R.G.; Reid, J.A.; Loschl, P.J.; 

Sovern, S.G.; Taylor, M.; Biswell, B.L.; Ellingson, A.; 

Meslow, E.C.; Miller, G.S.; Swindle, K.A.; Thrailkill, 

J.A.; Wagner, F.F.; Seaman, D.E. 2002. Natal and 

breeding dispersal of northern spotted owls. Wildlife 

Monograph No. 149. Washington, DC: The Wildlife 

Society. 35 p.

Franklin, A.B.; Anderson, D.R.; Gutiérrez, R.J.; 

Burnham, K.P. 2000. Climate, habitat quality, and 

fitness in northern spotted owl populations in 

northwestern California. Ecological Monographs. 

70: 539–590.

Gutiérrez, R.J. 1985. An overview of recent research on 

the spotted owl. In: Gutiérrez, R.J.; Carey, A.B., tech. 

coords. Ecology and management of the spotted owl 

in the Pacific Northwest. Gen. Tech. Rep. PNW-185. 

Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 119 p.

Gutiérrez, R.; Cody, M.; Courtney, S.; Kennedy, D. 

2004. Assessment of the potential threat of the northern 

barred owl. In: Courtney, S.P.; Blakesley, J.A.; Bigley, 

R.E.; Cody, M.L.; Dumbacher, J.P.; Fleischer, R.C.; 

Franklin, A.B.; Franklin, J.F.; Gutiérrez, R.J.; Marzluff, 

J.M.; Sztukowski, L. Scientific evaluation of the status 

of the northern spotted owl. Portland, OR: Sustainable 

Ecosystem Institute: 7-1 to 7-51.

Herter, D.R.; Hicks, L.L. 2000. Barred owl and spotted 

owl populations and habitat in the central Cascade Range 

of Washington. Journal of Raptor Research. 34: 279–286.

Kelly, E.G.; Forsman, E.D.; Anthony, R.G. 2003. Are 

barred owls displacing spotted owls? Condor. 105: 45–53. 

Lebreton, J-D.; Burnham, K.P.; Clobert, J.; Anderson, 

D.R. 1992. Modeling survival and testing biological 

hypotheses using marked animal: a unified approach 

with case studies. Ecological Monographs. 62: 67–118.

Lint, J.; Noon, B.; Anthony, R.; Forsman, E.; Raphael, 

M.; Collopy, M.; Starkey E. 1999. Northern spotted 

owl effectiveness monitoring plan for the Northwest 

Forest Plan. Gen. Tech. Rep. PNW-GTR-440. Portland, 

OR: U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Research Station. 43 p.

MacKenzie, D.I.; Nichols, J.D.; Lachman, G.B.; Droege, 

S.; Royle, J.A.A.; Langtimm, C.A. 2002. Estimating 

site occupancy rates when detection probabilities are less 

than one. Ecology. 83: 2248–2255.
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MacKenzie, D.I.; Nichols, J.D.; Hines, J.E.; Knutson, 

M.G.; Franklin, A.B. 2003. Estimating site occupancy, 

colonization, and local extinction when a species is 

detected imperfectly. Ecology. 84: 2200–2207.

Olson, G.S.; Glenn, E.; Anthony, R.G.; Forsman, E.D.; 

Reid, J.A.; Loschl, P.J.; Ripple, W.J. 2004. Modeling 

demographic performance of northern spotted owls 

relative to forest habitat in Oregon. Journal of Wildlife 

Management. 68(4): 1039–1053.

Joe Lint

Pollack, K.H.; Nichols, J.D.; Brownie, C.; Hines, 

J.F. 1990. Statistical inference for capture-recapture 

experiments. Wildlife Monographs. 107: 1–97.

Pearson, R.R.; Livezey, K.B. 2003. Distribution, numbers, 

and site characteristics of spotted owls and barred owls 

in the Cascade Mountains of Washington. Journal of 

Raptor Research. 37: 265–276.

Taylor, A.L; Forsman, E.D. 1976. Recent range extensions 

of the barred owl in western North America, including 

the first records for Oregon. Condor. 78: 560–561.
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Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Chapter 6: Emerging Issues and Monitoring Program Needs 

1Joseph Lint is a wildlife biologist, U.S. Department of the 

Interior, Bureau of Land Management, Oregon State Office, 777 

Garden Valley Blvd., NW, Roseburg, OR 97470. He is the module 

leader for northern spotted owl effectiveness monitoring under the 

Northwest Forest Plan.

Joseph Lint1

Emerging Issues

There are two emerging issues with potential to negatively 

affect the conservation and recovery of the spotted owl. 

Neither is directly related to the management of habitat 

under the Plan, but both may affect the Plan. The two issues 

are the interspecific interaction and competition between 

the barred owl and the spotted owl, and the potential threat 

of West Nile virus (Flavivirus spp.) infections in the spotted 

owl population.

The barred owl currently constitutes a significantly 

greater threat to the northern spotted owl than originally 

thought at the time of listing (Courtney et al. 2004). There 

are many ideas as to the cause of the barred owl invasion 

ranging from climate change to forest management practices. However, there are no data to support or refute any of 

these hypotheses (Kelly et al. 2003) and it is unclear if forest management has an effect on the outcome of interactions 

between barred and spotted owls (Courtney et al. 2004). It 

is known that barred owls appear to be habitat generalists 

that can occupy a broad range of forest conditions (Courtney et al. 2004). The continuing range expansion, increasing 

numbers, and possible negative influence on spotted owl 

occupancy make the barred owl a concern for the future. 

The West Nile virus is a threat to the spotted owl because it has the potential to reduce the population numbers 

beyond the projected decline under the Plan. The magnitude 

of the potential effect is unknown. So far, no mortality 

in wild, northern spotted owls has been recorded, but the 

first cases of the virus in other species were only recently 

recorded in the range of the owl.

The barred owl and West Nile virus are potential stressors to the spotted owl. These stressors may have a direct 

effect on the success of the Plan in arresting the downward 

trend in the spotted owl population. 

These two stressors clearly add a dimension to spotted 

owl management that is not a part of the habitat-based strategy of the Plan. The barred owl and West Nile virus are best 

addressed in the broader species conservation arena where 

all affected parties are involved. If and when strategies are 

developed to address these stressors, we will be better able 

to judge what role, new or continued, the Plan may have in 

managing the stressors.

For additional discussion of these two stressors, particularly barred owls, and other threats to the spotted owl 

refer to Gutierrez et al. (2004) and Courtney and Guiterrez 

(2004). 

Monitoring Program Needs

The conclusion of the first 10 years of monitoring of spotted 

owls and their habitat under the Plan affords the opportunity 

for partner agencies to examine the spotted owl monitoring 

program and make adjustments as needed. In recent years, 

program managers from the partner agencies have reviewed 

and discussed the monitoring program to gain understanding and to inform decisionmakers about the program. The 

chapter on “Information Needs” by Courtney and Franklin 

(2004) in the larger report prepared by Sustainable Ecosystems Institute (SEI) provides additional information for 

consideration during the continued review of spotted owl 

monitoring program. Topic areas in Courtney and Franklin 

(2004) that may be germane to the review of the monitoring 

program are:

• Interaction with prey and prey biology

• Habitat associations

• Habitat trends

• Barred owls

• West Nile virus

• Demography

Copies of the SEI report are available at www.sei.org.

As Plan monitoring enters its second decade, discussions on the future of monitoring need to address the 

continuation of existing monitoring and research efforts, 

to include modifications to the scope and effort of these 

activities and the initiation of new activities. Foremost 

among the activities in the continuation discussion are 
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GENERAL TECHNICAL REPORT PNW-GTR-648

the demographic studies. The data from these study areas 

have proven invaluable in assessing the status and trend of 

spotted owl populations. The data sets from years of demographic study are recognized as the best information on any 

raptor species anywhere in the world. Careful consideration 

of the consequences versus the benefits will need to be 

given to any proposal to continue, reduce, or expand the 

demographic study areas. 

Initial efforts into the realm of predictive modeling to 

link landscape habitat characteristics with owl survival, 

productivity, and occupancy will be concluding in the next 

year. This will present a decision point on the future of this 

research. It will be time for managers to assess the results 

of these efforts and determine what next steps in predictive 

modeling, if any, might warrant investment and exploration.

The habitat status and trend information assembled for 

this 10-year monitoring report provided a good foundation 

for continued monitoring of the maintenance and restoration of owl habitat under the Plan. The tangible measures 

of success of the habitat-based Plan lie in the maintenance 

and restoration of habitat. Tracking habitat conditions over 

time will require attention to all the components involved. 

This includes the refinement of methods to identify habitat 

conditions, the characterization of change to those conditions, and the recruitment of those conditions through forest 

succession. Attention should also be given to solving the 

information management problems encountered during the 

preparation of this report. 

There are several opportunities for initiating new 

monitoring and research. In the forefront is the need to 

explore cause-and-effect relations through formal experimentation for several of the correlative relations noted. Lack 

of information on several fronts precluded the authors of the 

10-year report from offering much in the way of cause-andeffect relations for the population status and trend information presented. We are getting a handle on what happened, 

and now we must press to understand why and how particular things happened, recognizing that we must be selective 

in what we pursue. Inherent in deciding what to study is 

some idea of what we might do in response to confirmation 

of specific cause-and-effect relations. Candidate topics for 

understanding cause and effect include, but are not limited 

to, studies on spotted owl and barred owl interactions, effect 

of small-mammal abundance on owl demography, impacts 

of West Nile virus on owls, and effects of forest thinning on 

owls and their prey. Specific examples include controlled 

experiments of the effects of barred owl removal on spotted 

owl occupancy and productivity and the study of prey ecology at forest ecotones. 

Of utmost importance to the future of the effectiveness 

monitoring program is for the agency managers to be proactive and decisive on both the continuation and initiation 

fronts. Courtney and Franklin (2004) pointed out that without continued monitoring and research efforts, subsequent 

reviews of the status of the owl will put us in the position of 

knowing less than we know now. Identifying the information needed for informed decisionmaking, and following up 

with direction and commitment to obtain that information 

are all essential steps to setting the course for spotted owl 

monitoring and research under the Plan in future decades.

References

Courtney, S.P.; Blakesley, J.A.; Bigley, R.E.; Cody, 

M.L.; Dumbacher, J.P.; Fleischer, R.C.; Franklin, 

A.B.; Franklin, J.F.; Gutiérrez, R.J.; Marzluff, J.M.; 

Sztukowski, L. 2004. Scientific evaluation of the status 

of the northern spotted owl. Portland, OR: Sustainable 

Ecosystem Institute. 348 p. + appendixes.

Courtney, S.; Franklin, A. 2004. Information needs. 

In: Courtney, S.P.; Blakesley, J.A.; Bigley, R.E.; Cody, 

M.L.; Dumbacher, J.P.; Fleischer, R.C.; Franklin, 

A.B.; Franklin, J.F.; Gutiérrez, R.J.; Marzluff, J.M.; 

Sztukowski, L. Scientific evaluation of the status of 

the northern spotted owl. Portland, OR: Sustainable 

Ecosystem Institute: 12-1 to 12-18.

Courtney, S.; Gutiérrez, R. 2004. Threats. In: 

Courtney, S.P.; Blakesley, J.A.; Bigley, R.E.; Cody, 

M.L.; Dumbacher, J.P.; Fleischer, R.C.; Franklin, 

A.B.; Franklin, J.F.; Gutiérrez, R.J.; Marzluff, J.M.; 

Sztukowski, L. Scientific evaluation of the status of 

the northern spotted owl. Portland, OR: Sustainable 

Ecosystem Institute: 11-1 to 11-17.
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Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Gutiérrez, R.; Cody, M.; Courtney, S.; Kennedy, D. 

2004. Assessment of the potential threat of the northern 

barred owl. In: Courtney, S.P.; Blakesley, J.A.; Bigley, 

R.E.; Cody, M.L.; Dumbacher, J.P.; Fleischer, R.C.; 

Franklin, A.B.; Franklin, J.F.; Gutiérrez, R.J.; Marzluff, 

J.M.; Sztukowski, L. Scientific evaluation of the status 

of the northern spotted owl. Portland, OR: Sustainable 

Ecosystem Institute: 7-1 to 7-51.

Kelly, E.G.; Forsman, E.D.; Anthony, R.G. 2003. Are 

barred owls displacing spotted owls? Condor. 105: 45–53.
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GENERAL TECHNICAL REPORT PNW-GTR-648

Acknowledgments

We are grateful to the hundreds of individuals who gathered 

or created the many pieces of natural resource information 

that we analyzed, categorized, summarized, and synthesized to produce this 10-year monitoring report on management of spotted owls under the Northwest Forest Plan. 

We thank the principal investigators of the demography 

study areas–Robert Anthony, USGS; Eric Forsman, USDA 

Forest Service; Alan Franklin, Colorado State University: 

Rocky Gutierrez, University of Minnesota; and their many 

research associates–for the decades of data collection and 

completion of the recent population status and trend analysis that was a cornerstone of our report. Appendix A in this 

report lists the senior analysts that have always answered 

the call for assistance with the population analysis work; 

they were integral to the success of the analysis–thank you. 

Thanks also to Sean Healey and Warren Cohen, USDA 

Forest Service, for their analysis of vegetation change that 

allowed us to look at the effects of stand-replacing wildfire 

and timber harvest. Special thanks to Joe Graham of the 

BLM who provided us with helpful insight during the application of the vegetation change information.

We are indebted to the reviewers who spent much time 

reading and commenting on various drafts of this report or 

sections of it. These individuals include the statistical review group comprising Tim Max, Jim Baldwin, and David 

Turner of the USDA Forest Service along with Jim Alegria 

of USDA Forest Service and BLM. Our appreciation to Jim 

Baldwin and Jim Alegria for pointing us down the right 

path and to Alexandre Hirzel, University of Lausanne, 

and his colleagues for their development of the BioMapper 

model that we discovered along that path. Dr. Hirzel was 

helpful in answering the myriad questions that came up and 

in providing a technical review of the habitat chapter. Bill 

Ripple, Oregon State University, provided excellent comments on the habitat chapter as well. 

Without the help of Jim Alegria and Carol Apple of the 

USDA Forest Service, who executed the queries of the vegetation plot data, we would not have been able to complete 

the nonspatial habitat analysis.

A special thank you to Kim Mellen and Rich Hagestedt 

of the Forest Service for their assistance with our application of the HabScapes program.

To Martha Brookes and Lynn Starr, a special thank 

you for their expertise in editing the written expression of 

our thoughts and adding the measure of clarity necessary 

to communicate effectively with our intended audiences. 

The comments from our agency peers during the management review kept us on track to producing a document 

they would find useful. And finally, the comments of three 

peer reviewers ensured we were attentive to explaining our 

monitoring methods and results in a scientifically credible 

manner.

Special thanks to Gail Olson, Oregon State University, 

and again to Alan Franklin and their research associates for 

producing the two excellent papers on the effects of climate 

and landscape characteristics on owl survival, reproductive 

output, and fitness. These two papers were the basis for our 

predictive model review.

A special thanks to all those involved in the Interagency Regional Monitoring Program—especially the other 

module leaders that demonstrated many heads are better 

than one. Especially to Jon Martin for steering us down 

the path of completion and to Bruce Bingham who early on 

showed us that organization would be key to our success. 

We greatly appreciate the budget and personnel support 

of the federal partnership that made this all happen—the 

Monitoring Program Managers Group and the Regional 

Interagency Executive Committee were unfailing in their 

commitment to owl monitoring. 

Metric Equivalents

When you know: Multiply by: To find:

Inches (in) 2.54 Centimeters (cm)

Feet (ft) .305 Meters (m)

Miles (mi) 1.609 Kilometers (km)

Square miles (mi2

) 2.59 Square kilometers (km2)

Acres (ac) .405 Hectares (ha)
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Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Glossary 

adaptive management areas—Landscape units designated 

for development and testing of technical and social approaches to achieving desired ecological, economic, and 

other social objectives.

adaptive management reserve—adaptive management 

areas that occur within the boundaries of late-successional 

reserves (see late-successional reserves).

algorithm—A procedure used to solve a mathematical or 

computational problem or to address a data processing issue. In the latter sense, an algorithm is a set of step-by-step 

commands or instructions designed to reach a particular 

goal.

associated species—A species found to be numerically 

more abundant in a particular forest successional stage or 

type compared to other areas.

attribute—Information about a geographic feature 

(grid-cell or polygon) in a geographic information system, 

usually stored in a table.

autocorrelation—The lack of independence between pairs 

of observations at given distances in time or space as is 

commonly found in ecological data.

baseline—The starting point for analysis of environmental 

consequences. This may be the conditions at a point in time 

(for example, when inventory data are collected) or may be 

the average of a set of data collected over a specified period.

blowdown—Trees felled by high winds.

correlation matrix—A table showing the intercorrelations 

among all variables.

covariance matrix—A square matrix that contains all of 

the variances and covariances among variables.

DEM—Digital elevation model. Grid-based geographic 

information system data that contains x, y, and z (east, 

north, and elevation) coordinates for each grid cell. Developed by the U.S. Geological Survey for geographical and 

topographical data.

demography—The quantitative analysis of population 

structure and trends; population dynamics.

diameter at breast height—The diameter of a tree 

measured 4.5 feet above the ground on the uphill side 

of the tree.

digital orthophoto—An aerial photo or satellite scene 

that has been transformed by the orthogonal projection, 

yielding an image that is free of most significant geometric 

distortions.

dispersal—The movement, usually one way and on any 

time scale, of plants or animals from their point of origin to 

another location where they subsequently produce offspring.

dispersal habitat—Forest stands with average tree diameters >11 inches and conifer overstory trees with closed 

canopies (>40 percent canopy closure) and with open space 

beneath the canopy to allow the owls to fly (Thomas et al. 

1990).

distribution (of a species)—The spatial arrangement of a 

species within its range.

edge—Where plant communities meet or where successional stages or vegetative conditions with plant communities come together.

Eigenvalue—Column sum of squared loadings for a factor; 

also referred to as the latent root. It conceptually represents 

that amount of variance accounted for by a factor.

factor—Linear combination (variate) of the original 

variables. Factors also represent the underlying dimensions 

(constructs) that summarize or account for the original set 

of observed variables.

fecundity—A measure of animal (in this case, spotted owl) 

productivity expressed as the number of female young per 

adult female.

final environmental impact statement (FEIS)—The final 

report of environmental effects of proposed action on an 

area of land. This is required for major federal actions under 

Section 102 of the National Environmental Policy Act. It is 

a revision of the draft environmental impact statement that 

includes public and agency responses to the draft.
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GENERAL TECHNICAL REPORT PNW-GTR-648

Forest Ecosystem Management Assessment Team 

(FEMAT)—As assigned by President Clinton, the team of 

scientists, researchers, and technicians from seven federal 

agencies who created the Forest Ecosystem Management 

Assessment Team report (1993).

fuzzy accuracy classification—A widely accepted technique in remote sensing that permits greater flexibility in 

interpretation of map predictions of what is actually on 

the ground. It works by degrees of membership, whereas 

traditional techniques are discrete (right or wrong). 

fuzzy-set—Using conventional logic, individual values or 

items either are, or are not, members of a set. In contrast, 

fuzzy sets attempt to model imprecision, approximation, 

or vagueness. In fuzzy logic, a fuzzy set is a set of values 

or items whose individual degree of membership in the set 

may range from 0 to 1.

geographic information system (GIS)—A computer 

system capable of storing, manipulating, and displaying 

spatial (that is, mapped) data.

grid cell—A single element in a grid or raster-based GIS, 

similar to the individual cell of a spreadsheet. The cell, 

also called a pixel, contains two types of information, its 

geographic (x,y) coordinates and a thematic attribute value. 

Grid size or scale can usually be defined by the user.

guideline—A policy statement that is not a mandatory requirement (as opposed to a standard, which is mandatory).

habitat— The resources and conditions present in an area 

that produce occupancy—including survival and reproduction—by a given organism.

habitat diversity—The number of different types of habitat 

within a given area.

habitat fragmentation—A landscape-scale process of 

breaking up habitat into smaller, and sometimes more isolated, patches through modification or conversion of habitat 

by natural processes or human land use activities.

histogram—A graphical way of showing the characteristics 

of the distribution of values in a given population or sample. 

A bar graph that shows the frequency of data in intervals.

Interagency Scientific Committee (ISC)—A committee 

of scientists that was established by the Forest Service, 

Bureau of Land Management, Fish and Wildlife Service, 

and National Park Service to develop a conservation 

strategy for northern spotted owls.

land use allocation—The specification in forest plans of 

where activities, including timber harvest, can occur on a 

national forest or Bureau of Land Management district.

landscape—A heterogeneous land area with interacting 

ecosystems that are repeated in similar form throughout.

late-successional old-growth habitat—A forest in its 

mature or old-growth stages.

late-successional reserve—A forest in its mature or oldgrowth stages that has been reserved under a management 

option (see “old-growth forest” and “succession”).

management activity—An activity undertaken for the 

purpose of harvesting, traversing, transporting, protecting, 

changing, replenishing, or otherwise using resources.

map unit—A discretely mapped entity, also called mapping 

unit (see “pixel”).

matrix—Federal lands outside of reserves, withdrawn 

areas, and managed late-successional areas.

model—An idealized representation of reality developed 

to describe, analyze, or understand the behavior of some 

aspect of it; a mathematical representation of the relations 

under study. The term “model” is applicable to a broad class 

of representations, ranging from a relatively simple qualitative description of a system or organization to a highly 

abstract set of mathematical equations.

monitoring—The process of collecting information to 

evaluate if objective and anticipated or assumed results of 

a management plan are being realized or if implementation 

is proceeding as planned.

monitoring program—The administrative program used 

for monitoring.
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Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

neighborhood functions—Geographic information system 

analytical functions (such as mean, maximum, or variety 

of values) that assign a value to each grid cell by taking its 

surrounding pixels into consideration.

niche—A region in a multidimensional space of 

environmental factors that affect the welfare of a 

species (Hutchinson 1957).

normal distribution—A symmetrical data distribution 

that can be expressed in terms of the mean and standard 

deviation of the data. The normal distribution is the most 

widely encountered model for probability and is characterized by the “bell curve.” Also called “Gaussian distribution.”

northern spotted owl—One (Strix occidentalis caurina) of 

three subspecies of the spotted owl that ranges from southern British Columbia, Canada, through western Washington 

and Oregon, and into northwestern California. Listed as a 

threatened species by the U.S. Fish and Wildlife Service.

old growth—This successional stage constitutes the potential plant community capable of existing on a site given the 

frequency of natural disturbance events. For forest communities, this stage exists from about age 200 until stand 

replacement occurs and secondary succession begins again. 

Depending on fire frequency and intensity, older forests 

may have different structures, species composition, and age 

distributions. In forests with longer periods between natural 

disturbance, the forest structure will be more even-aged at 

late mature or early old-growth stages.

old-growth forest—A forest stand usually at least 180 

to 220 years old with moderate to high canopy closure; 

a multilayered, multispecies canopy dominated by large 

overstory trees; high incidence of large trees, some with 

broken tops and other indications of old and decaying wood 

(decadence); many large snags; and heavy accumulations 

of wood, including large logs on the ground.

old-growth stand—A mappable area of old-growth forest.

overstory—Trees that provide the uppermost layer of 

foliage in a forest with more than one roughly horizontal 

layer of foliage.

physiographic province—A geographic area having a 

similar set of biophysical characteristics and processes 

because of the effects of climate and geology that result 

in patterns of soils and broad-scale plant communities. 

Habitat patterns, wildlife distributions, and historical land 

use patterns may differ significantly from those of adjacent 

provinces.

pixel—See grid cell.

pixel noise—The “speckled” or “salt and pepper” appearance in a grid map caused by pixels in close proximity to 

each other having a wide variation of values.

polygon—A graphic figure that represents an area in a 

geographic information system.

population—A collection of individual organisms of the 

same species that potentially interbreed and share a common gene pool. Population density refers to the number of 

individuals of a species per unit area, population persistence 

to the capacity of the population to maintain sufficient 

density to persist, well distributed, over time.

process—Change in state of an entity.

range (of a species)—The area or region over which an 

organism occurs.

record of decision—A document separate from but 

associated with an environmental impact statement that 

states the management decision, identifies all alternatives 

including both the environmentally preferable and preferred 

alternatives, states whether all practicable means to avoid 

environmental harm from the preferred alternative have 

been adopted, and if not, why not.

recovery—Action that is necessary to reduce or resolve the 

threats that caused a species to be listed as threatened or 

endangered.
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GENERAL TECHNICAL REPORT PNW-GTR-648

sensitivity analysis—An investigation of how the outcome 

might change with different input data.

species—(1) A group of individuals that have their major 

characteristics in common and are potentially interfertile. 

(2) The Endangered Species Act defines species as including any species or subspecies of plant or animal. Distinct 

populations of vertebrates also are considered to be species 

under the act.

stand (tree stand)—An aggregation of trees occupying a 

specific area and sufficiently uniform in composition, age, 

arrangement, and condition so that it is distinguishable 

from the forest in adjoining areas. 

stand condition—A description of the physical properties 

of a stand such as crown closure or diameters.

stand-replacing event—A disturbance that is severe 

enough over a large enough area (for example, 10 acres) to 

virtually eliminate an existing stand of trees and initiate a 

new stand.

standards and guidelines—The primary instructions 

for land managers. Standards address mandatory actions, 

whereas guidelines are recommended actions necessary to 

a land management decision.

stationary population—A population of owls where 

estimates of the annual rate of population change are not 

significantly different from 1.0 suggesting that the population is neither increasing or decreasing.

stochastic—Random, uncertain; involving a random 

variable.

successional stage—A stage or recognizable condition of 

a plant community that occurs during its development from 

bare ground to climax. For example, coniferous forests in 

the Blue Mountains progress through six recognized stages: 

grass-forb, shrub-seedling, pole-sapling, young, mature, 

and old growth.

structure—The various horizontal and vertical physical 

elements of the forest.

succession—A series of dynamic changes by which one 

group of organisms succeeds another through stages leading 

to potential natural community or climax. An example is 

the development of a series of plant communities (called 

seral stages) following a major disturbance.

threatened species—Those plant or animal species likely 

to become endangered species throughout all or a significant portion of their range within the foreseeable future. A 

plant or animal identified and defined in accordance with 

the 1973 Endangered Species Act and published in the 

Federal Register.

training data set—Data points used to construct a model.

wildfire—Any wildland fire that is not a prescribed fire.

windthrow—Synonymous with windfall, blowdown.

young stands—Forest stands not yet mature, generally 

less than 50 years old; typically 20 to 40 years old.
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Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Appendix A—Senior Analysts for 2004 Northern Spotted Owl Population 

Status and Trend Workshop 

Name Affiliation

Dr. David R. Anderson Applied Information Company, Fort Collins, CO, U.S. Geological Survey (retired)

Dr. Robert G. Anthony U.S. Geological Survey, Corvallis, OR

Dr. Kenneth P. Burnham U.S. Geological Survey, Fort Collins, CO

Dr. Eric Forsman USDA Forest Service, Pacific Northwest Research Station, Corvallis, OR

Dr. Alan B. Franklin Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO

Dr. James E. Hines U.S. Geological Survey, Laurel, MD

Dr. James Nichols U.S. Geological Survey, Laurel, MD

Dr. Gail Olson Oregon State University, Corvallis, OR

Dr. Carl Schwarz Simon Fraser University; Burnaby, BC, Canada

Dr. Gary White Colorado State University, Fort Collins, CO

Frank Oliver
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GENERAL TECHNICAL REPORT PNW-GTR-648

Appendix B—Nearest Neighbor Analysis of Owl Presence Data

A nearest neighbor analysis was performed on owl presence 

location data for each physiographic province to provide an 

index of spatial distribution for the point data. Preliminary 

steps included resampling the presence data grids (25-m 

pixel resolution) into 1-mi-square pixel resolution grid data 

sets. This was performed because we were interested in 

determining the overall distribution patterns of presence 

data across a large geographic area (the province), and 

situations where multiple points (separated by hundreds 

of feet) represented a single owl pair within an individual 

forest stand (a situation that sometimes occurred with 

demographic data) would provide too much detail resulting 

in erroneous distribution statistics. 

The analysis was conducted in ArcView Spatial 

Analyst, by using the Animal Movement extension (v2.0) 

by Hooge and Eichenlaub (2000). The nearest neighbor 

analysis calculates a series of descriptive statistics of the 

animal-location point patterns. It tests for complete spatial 

randomness by using a selected polygon shapefile. It implements the Clark and Evans (1954) algorithm. The R-value 

relates to how clustered or dispersed points are within the 

polygon specified (in our case the physiographic province 

boundary and the habitat-capable land within it) (table B-1). 

An R-value of less than 1 indicates that the points have a 

tendency toward a clumped (clustered) pattern, an R-value 

of 1 indicates a random distribution, and an R-value of 

greater than 1 indicates an organized (uniform) pattern. 

The possible range of R-values is 0 to 2.15, where a value 

of 0 represents maximum aggregation, and 2.15 represents 

perfect uniformity.

These statistics indicate slightly clumpy spatial patterns 

of owl presence location data in most physiographic provinces. The pattern is randomly distributed in the California 

Klamath province and slightly uniform in Oregon Western 

Cascades province. Level of “clumpiness” diminishes 

slightly when point patterns are analyzed within the context 

of habitat-capable land within the province. The following 

figures show graphic representations of the presence data 

distributions by state and province (figures B-1 through 

B-3).

References

Clark P.; Evans, F.C. 1954. Distance to the nearest 

neighbor as a measure of spatial relationship in 

populations. Ecology. 35: 445–453.

Hooge P.N.; Eichenlaub, B. 2000. Animal movement 

extension to ArcView, 2.0., Anchorage, AK: Alaska 

Science Center—Biological Science Office, U.S. 

Geological Survey. 

Table B-1—Spatial distribution indices for owl presence location data used for modeling habitat

R-values z-values

 Province Habitat-capable Province Habitat-capable

Physiographic province n boundary lands boundary lands

Washington Olympic Peninsula 642 0.72 0.76 -7.81 -9.59

Washington Western Cascades 405 .73 .81 -6.89 -10.16

Washington Eastern Cascades 712 .62 .78 -7.11 -12.98

Oregon Coast Range 1,564 .74 .77 -11.52 -13.15

Oregon Western Cascades 2,382 1.03 1.07 4.87 2.01

Oregon Eastern Cascades 549 .81 .86 -3.69 -5.09

Oregon Klamath 697 .90 .92 -3.84 -4.77

California Cascades 77 .80 .84 -2.55 -3.34

California Klamath 893 .99 1.02 1.25 -.75

California Coast Range 1,046 .88 .92 -4.42 -7.15
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Figure B-1—Owl presence for Washington physiographic provinces. The R-values for both the province and habitatcapable lands within it are shown above the province (province/habitat capable).
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GENERAL TECHNICAL REPORT PNW-GTR-648

Figure B-2—Owl presence data for Oregon physiographic provinces. The R-values for both the province and habitatcapable lands within it are shown above the province (province/habitat capable).
113

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure B-3—Owl presence data for California physiographic provinces. The R-values for both the province and habitatcapable lands within it are shown above the province (province/habitat capable).
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GENERAL TECHNICAL REPORT PNW-GTR-648

Appendix C—Habitat Variables Used in BioMapper Modeling

Variable Description Range of values

qmd Quadratic mean diameter: Diameter at breast Continuous integer values in 1-inch increments from 0 to

 height of dominant and codominant trees of 75 inches in Oregon and Washington with the exception 

 average basal area of the east Cascade and California provinces, where the 

 mean of vegetation-strike-team size classes were used 

 (e.g., 2, 7, 15...55 inches)

cc Canopy cover of coniferous trees Continuous integer values in 1-percent increments from 

 0 to 100% in Oregon and Washington and using the mean 

 of the 10% increments from 0 to 100% in California (e.g., 

 5, 15, 25...95%)

qmdcc Index of the product of conifer tree size and Continuous integer values from 0 to 750

 canopy cover [(qmd × cc)/10]: A small 

 value indicates small-diameter trees or 

 an open canopy and a large value indicates 

 closed canopy of large conifer trees

bdlf Canopy cover of deciduous trees Continuous integer values in 1-percent increments from 

 0 to 100% in Oregon and Washington and using the mean 

 of the 10% increments from 0 to 100% in California (e.g., 

 5, 15, 25....95%) 

variety An index of stand structure based on the Integer values from 1 to 6

 number of vegetation-strike-team size classes 

 within a 5x5 window (25 pixels = 3.9 ac 

 square): Used in Oregon and Washington 

 with IVMP QMD data

struct Focal mean of discrete-structure values (0 if Continuous values from 1 to 100 percent

 simple or 1 if complex stand structure) within 

 a 5×5 window (25 pixels = 5.5 ac square): 

 Used only in California because of polygon 

 data issue 

elev Elevation from USGS digital elevation models Continuous values in meters
115

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Appendix D—Spotted Owl Habitat Suitability Maps

Figure D-1—Spotted owl habitat suitability for habitat-capable lands in the Olympic Peninsula province in Washington.
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GENERAL TECHNICAL REPORT PNW-GTR-648

Figure D-2—Spotted owl habitat suitability for habitat-capable lands in the Western Cascades province in Washington.
117

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Figure D-3—Spotted owl habitat suitability for habitat-capable lands in the Eastern Cascades province in Washington.
118

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure D-4—Spotted owl habitat suitability for habitat-capable lands in the Eastern Cascades province in Oregon.
119

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure D-5—Spotted owl habitat suitability for habitat-capable lands in the Western Cascades province in Oregon.
120

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure D-6—Spotted owl habitat suitability for habitat-capable lands in the Coast Range province in Oregon.
121

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure D-7—Spotted owl habitat suitability for habitat-capable lands in the Klamath province in Oregon.
122

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure D-8—Spotted owl habitat suitability for habitat-capable lands in the Klamath province in California.
123

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Figure D-9—Spotted owl habitat suitability for habitat-capable lands in the Cascades province in California.
124

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure D-10—Spotted owl habitat suitability for habitat-capable lands in the Coast province in California.
125

00.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Random frequency line

Marginality: 

Global area* 16,631,457 2,568,566 84.75% of total province land area Total province acres = 3,030,862 Specialization: 

Owl presence 14,257 2,202 0.09% of modeled area Tolerance (1/S): 

*area that is being modeled

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.55) qmdcc(0.76) qmdcc(0.64) cc(-0.64) bdlf(-0.63) variety(-0.66)

qmd(0.54) qmd(-0.60) cc(-0.59) qmdcc(0.60) qmdcc(0.48) bdlf(0.57)

cc(0.38) cc(-0.23) qmd(-0.42) elev(-0.33) cc(-0.43) qmdcc(0.45)

variety(0.30) bdlf(0.02) elev(0.20) variety(0.27) variety(-0.32) qmd(0.15)

elev(0.29) variety(0.00) bdlf(-0.17) qmd(-0.18) elev(-0.21) elev(0.13)

bdlf(-0.29) elev(0.00) variety(-0.01) bdlf(-0.11) qmd(-0.21) cc(-0.01)

Factor Eigen values Explains variation

1 8.725 32.50% 2.10 0.81 0.422 10.525 39.30% 2.00 0.80 0.42

3 3.609 13.50% 1.90 0.80 0.41

4 1.895 7.10% 2.00 0.81 0.425 1.238 4.60% 2.20 0.81 0.42

Total variation explained = 97.0%

1 0.12 0.086 0.41 0.75 1.4 1.4 2.8 1.4 2.5 2.5 2 0.12 0.07 0.41 0.81 1.4 1.6 2.8 1.2 2.6 2.4 3 0.099 0.075 0.43 0.83 1.5 1.6 2.9 1.2 2.7 2.2 

4 0.1 0.09 0.42 0.68 1.5 1.7 2.8 1.1 2.7 2.3 5 0.13 0.09 0.39 0.69 1.5 1.5 3.2 1.1 2.5 2.6 

Mean 0.114 0.082 0.412 0.752 1.460 1.560 2.900 1.200 2.600 2.400

Rank 9 10 8 7 5 4 1 6 2 3

Pixels (n) Acres Percentage of area

Ecological niche factor analysis results

Factors used Model Indices

Model

quality

Absolute

validation

Contrast

validation

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5

Figure E-1—BioMapper habitat model output statistics summary for the Olympic Peninsula province of Washington.
126

Figure E-2—BioMapper habitat model output statistics summary for the Western Cascades province of Washington.

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Margin

34,568,980 5,338,840 86.81% of total province land area Total province acres = 6,149,917 Specializ

9,931 1,534 0.03% of modeled area Tolerance 

qmdcc(0.54) qmdcc(-0.77) cc(0.66) cc(0.59) bdlf(0.77) variety(0.82)

cc(0.50) qmd(0.56) qmdcc(-0.49) qmd(-0.58) qmd(0.50) qmdcc(-0.51)

qmd(0.49) cc(0.31) elev(-0.47) elev(0.38) cc(0.31) bdlf(-0.20)

bdlf(-0.45) bdlf(0.03) bdlf(0.27) bdlf(0.32) elev(0.22) cc(0.15)

variety(0.12) elev(0.01) qmd(0.15) qmdcc(0.24) qmdcc(-0.11) elev(-0.03)

elev(0.01) variety(0.00) variety(-0.06) variety(-0.03) variety(0.06) qmd(0.03)

1 17.319 38.10% 2.10 0.76 0.39

2 17.713 39.00% 2.00 0.78 0.413 5.195 11.40% 2.30 0.79 0.424 2.684 5.90% 2.30 0.78 0.41

5 1.31 2.90% 2.20 0.76 0.39

Total variation explained = 97.3%

1 0.055 0.25 0.67 1.2 0.82 2 1.5 1.1 2.6 2.4 2 0.029 0.2 0.56 1.1 0.9 2.1 1.7 0.94 2.6 2.3 3 0.038 0.2 0.53 1.2 0.75 1.9 1.9 0.82 2.6 2.6 4 0.043 0.26 0.58 1.2 0.51 1.8 1.6 0.78 2.7 2.7 5 0.058 0.2 0.66 1.2 0.85 2 1.5 1 2.5 2.5 

0.045 0.222 0.600 1.180 0.766 1.960 1.640 0.928 2.600 2.500

10 9 8 5 7 3 4 6 1 2

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

Replicate

Mean

Rank

Factors used Model Indices

Factor Eigen values Explains variation Model quality Absolute validation Contrast validation

Global area*

Owl presence

Ecological niche factor analysis results

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

*area that is being modeled

Pixels (n) Acres Percentage of area

0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 

Random frequency line

Replicate 1Replicate 2Replicate 3Replicate 4Replicate 5
127

Figure E-3—BioMapper habitat model output statistics summary for the Eastern Cascades province of Washington. 00.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Pixels (n) Acres Percentage of area Marginality: 

Global area* 26,846,530 4,146,183 72.97% of total province land area Total province acres = 5,682,385 Specialization: 

Owl presence 15,324 2,367 0.06% of modeled area Tolerance (1/S): 

*area that is being modeled

Ecological niche factor analysis results

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.52) qmdcc(0.78) qmdcc(-0.77) cc(0.62) qmdcc(0.80) qmdcc(0.81)

qmd(0.50) qmd(-0.60) qmd(0.56) qmdcc(-0.57) cc(-0.47) cc(-0.41)

cc-box(0.49) cc(-0.16) cc(0.28) qmd(0.32) qmd(-0.36) qmd(-0.31)

variety(0.30) elev(0.07) elev(0.05) variety(-0.32) bdlf(-0.10) bdlf(0.26)

elev(-0.30) bdlf(0.03) bdlf(0.02) bdlf(0.30) variety(-0.08) elev(0.08)

bdlf(-0.24) variety(0.00) variety(0.01) elev(0.01) elev(0.01) variety(0.07)

Factors used Model Indices

Factor Eigen values Explains variation Model quality Absolute validation Contrast validation

1 11.625 46.70% 2.80 0.74 0.37

2 6.136 24.70% 2.80 0.72 0.363 3.999 16.10% 2.80 0.74 0.374 1.333 5.40% 2.80 0.74 0.37

5 1.008 4.10% 2.80 0.73 0.36

Total variation explained = 97.0%

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

1 0.081 0.27 0.71 0.83 0.81 1.3 0.61 3 1.1 3.5 2 0.065 0.27 0.76 0.84 1 1.5 0.54 2.8 1.2 3.4 3 0.078 0.31 0.66 0.87 0.77 1.7 0.72 3.1 1.1 3.2 4 0.062 0.29 0.71 0.86 0.79 1.4 0.67 2.9 1.2 3.4 5 0.071 0.3 0.71 0.84 0.94 1.4 0.77 3 1.1 3.2 

Mean 0.071 0.288 0.710 0.848 0.862 1.460 0.662 2.960 1.140 3.340

Rank 10 9 7 6 5 3 8 2 4 1

Random frequency line

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 
128

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Pixels (n) Acres Percentage of area Marginality

Global area* 33,876,170 5,231,842 90.32% of total province land area Total province acres = 5,792,309 Specialization

Owl presence 34,073 5,262 0.10% of modeled area Tolerance (1/S

*area that is being modeled

Ecological niche factor analysis results

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.57) qmdcc(-0.77) cc(-0.67) elev(-0.78) bdlf(0.86) qmdcc(-0.70)

qmd(0.56) qmd(0.55) qmdcc(0.62) cc(0.47) qmdcc(0.32) variety(0.62)

cc(0.40) cc(0.33) qmd(-0.33) qmdcc(-0.36) cc(-0.28) cc(0.33)

variety(0.36) bdlf(0.01) bdlf(-0.23) qmd(0.14) qmd(0.25) qmd(0.08)

bdlf(-0.27) variety(0.01) variety(0.09) bdlf(0.12) elev(0.13) elev(0.00)

elev(0.01) elev(0.00) elev(-0.03) variety(-0.07) variety(0.06) bdlf(0.00)

Factors used Model Indices

Factor Eigen values Explains variation Model quality Absolute validation Contrast validation

1 11.482 34.97% 2.70 0.74 0.39

2 11.914 36.29% 2.70 0.75 0.413 4.748 14.46% 2.70 0.74 0.404 2.795 8.51% 2.80 0.76 0.41

5 1.056 3.22% 2.80 0.74 0.40

Total variation explained = 97.5%

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

1 0.038 0.18 0.27 0.57 0.88 1.7 1.6 2.4 2.5 2.7 2 0.064 0.13 0.29 0.51 0.87 1.7 1.7 2.5 2.4 2.8 3 0.049 0.15 0.3 0.53 0.87 1.8 1.7 2.4 2.4 2.7 4 0.041 0.15 0.28 0.54 0.79 1.8 1.7 2.3 2.5 2.8 5 0.048 0.15 0.31 0.55 0.84 1.7 1.8 2.3 2.4 2.8 

Mean 0.048 0.152 0.290 0.540 0.850 1.740 1.700 2.380 2.440 2.760

Rank 10 9 8 7 6 4 5 3 2 1

Random frequency line

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5

Figure E-4—BioMapper habitat model output statistics summary for the Coast Range province of Oregon.
129

Figure E-5—BioMapper habitat model output statistics summary for the Western Cascades province of Oregon. 00.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Pixels (n) Acres Percentage of area Marginality

Global area* 33,276,259 5,139,192 91.77% of total province land area Total province acres = 5,600,270 Specialization

Owl presence 49,106 7,584 0.15% of modeled area Tolerance (1/S

*area that is being modeled

Ecological niche factor analysis results

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.58) qmdcc(0.75) cc(-0.74) elev(-0.69) variety(0.71) bdlf(0.78)

qmd(0.54) qmd(-0.63) qmdcc(0.55) qmdcc(0.50) qmdcc(-0.56) cc(0.46)

cc(0.44) cc(-0.21) bdlf(-0.31) cc(-0.42) qmd(0.33) qmd(0.39)

bdlf(-0.40) bdlf(-0.01) qmd(-0.23) qmd(-0.23) cc(0.23) qmdcc(-0.17)

variety(0.12) elev(0.00) variety(0.08) bdlf(-0.20) bdlf(0.09) variety(-0.05)

elev(0.07) variety(0.00) elev(0.05) variety(-0.07) elev(-0.07) elev(0.03)

Factors used Model Indices

Factor Eigen values Explains variation Model quality Absolute validation Contrast validation

1 9.394 28.50% 2.10 0.82 0.41

2 16.64 50.50% 2.00 0.81 0.403 3.18 9.60% 1.80 0.81 0.414 1.671 5.10% 2.00 0.81 0.41

5 1.146 3.50% 1.90 0.81 0.41

Total variation explained = 97.2%

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

1 0.065 0.25 0.088 0.34 0.96 1.2 2.1 1.1 2.3 2.2 2 0.069 0.23 0.089 0.41 0.93 1.4 2.2 0.94 2.2 2.2 3 0.072 0.25 0.1 0.33 0.97 1.4 2.3 0.84 2.2 2.1 4 0.067 0.23 0.13 0.33 0.98 1.3 2.2 1.2 2.2 2.2 5 0.068 0.19 0.13 0.36 0.94 1.3 2.3 1.1 2.2 2.2 

Mean 0.068 0.230 0.107 0.354 0.956 1.320 2.220 1.036 2.220 2.180

Rank 10 8 9 7 6 4 1 5 1 3

Random frequency line

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5
130

Figure E-6—BioMapper habitat model output statistics summary for the Eastern Cascades province of Oregon.

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Pixels (n) Acres Percentage of area Marginality: 

Global area* 19,806,907 3,058,983 90.98% of total province land area Total province acres = 3,362,271 Specialization: 

Owl presence 12,955 2,001 0.07% of modeled area Tolerance (1/S): 

*area that is being modeled

Ecological niche factor analysis results

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.58) qmdcc(0.77) cc(-0.76) variety(0.92) bdlf(0.87) cc(-0.69)

qmd(0.52) qmd(-0.53) qmdcc(0.52) qmdcc(-0.24) qmdcc(0.43) qmdcc(0.64)

cc(0.45) cc(-0.37) bdlf(-0.29) bdlf(-0.19) cc(0.20) elev(0.24)

bdlf(-0.40) bdlf(0.02) qmd(-0.20) qmd(-0.18) variety(0.15) qmd(-0.17)

elev(-0.15) elev(0.00) elev(-0.17) cc-box(0.13) elev(-0.04) bdlf(-0.16)

variety(0.11) variety(0.00) variety(0.01) elev(0.04) qmd(-0.02) variety(0.01)

Factors used Model Indices

Factor Eigen values Explains variation Model quality Absolute validation Contrast validation

1 5.935 18.30% 2.30 0.84 0.39

2 20.189 62.40% 2.10 0.82 0.373 2.989 9.20% 2.30 0.84 0.384 1.241 3.80% 2.10 0.84 0.38

5 1.209 3.70% 2.20 0.82 0.37

Total variation explained = 97.4%

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

1 0.082 0.11 0.3 0.17 0.61 1 2.1 1.3 2.8 2.4 2 0.063 0.22 0.19 0.21 0.7 1.1 2.1 1.4 2.7 2.2 3 0.066 0.16 0.42 0.17 0.64 1 2 1.4 2.7 2.4 4 0.089 0.25 0.31 0.14 0.62 1.1 2.2 1.2 2.8 2.2 5 0.058 0.13 0.38 0.24 0.71 1.1 1.9 1.3 2.7 2.3 

Mean 0.072 0.174 0.320 0.186 0.656 1.060 2.060 1.320 2.740 2.300

Rank 10 9 7 8 6 5 3 4 1 2

Random frequency line

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5
131

Figure E-7—BioMapper habitat model output statistics summary for the Klamath province of Oregon. 00.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Pixels (n) Acres Percentage of area Marginality

Global area* 22,518,397 3,477,746 86.90% of total province land area Total province acres = 4,001,997 Specialization

Owl presence 16,572 2,559 0.07% of modeled area Tolerance (1/S)

*area that is being modeled

Ecological niche factor analysis results

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.54) qmdcc(-0.78) cc(0.73) elev(0.87) qmdcc(-0.66) qmdcc(0.72)

qmd(0.51) qmd(0.52) qmdcc(-0.57) bdlf(0.46) cc(0.53) variety(-0.60)

cc(0.46) cc(0.35) bdlf(0.27) variety(0.12) bdlf(-0.42) cc(-0.31)

bdlf(-0.39) bdlf(0.02) qmd(0.23) qmdcc(0.08) variety(-0.27) bdlf(0.12)

variety(0.26) variety(0.01) variety(-0.13) cc(-0.08) elev(0.20) qmd(-0.10)

elev(0.13) elev(0.00) elev(-0.03) qmd(0.05) qmd(-0.01) elev(0.02)

Factors used Model Indices

Factor Eigen values Explains variation Model quality Absolute validation Contrast validation

1 19.206 38.60% 3.00 0.80 0.44

2 23.356 47.00% 3.10 0.79 0.43

3 3.155 6.30% 3.10 0.79 0.434 1.654 3.30% 3.10 0.79 0.43

5 1.295 2.60% 3.00 0.81 0.44

Total variation explained = 97.8%

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

1 0.051 0.081 0.11 0.56 0.88 1.9 2 2.2 1.9 3.1 

2 0.061 0.087 0.16 0.6 0.87 1.8 1.8 2 2 3.2 3 0.053 0.056 0.14 0.63 0.82 1.8 1.9 2.2 2 3.2 

4 0.053 0.1 0.15 0.63 0.81 1.7 2 1.9 2 3.1 

5 0.046 0.13 0.12 0.54 0.85 1.6 2 2.4 1.9 3.2 

Mean 0.053 0.091 0.136 0.592 0.846 1.760 1.940 2.140 1.960 3.160

Rank 10 9 8 7 6 5 4 2 3 1

Random frequency line

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5
132

Figure E-8—BioMapper habitat model output statistics summary for the Cascades province of California.

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Random frequency line

Marginality

Global area* 8,331,740 1,852,929 74.06% of total province land area Total province acres = 2,502,094 Specialization

Owl presence 1,890 420 0.02% of modeled area Tolerance (1/S

*area that is being modeled

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.53) qmdcc(-0.77) qmdcc(0.79) bdlf(0.69) struct(-0.63) qmdcc(0.73)

cc(0.50) cc(0.47) qmd(-0.41) elev(0.53) cc(0.59) cc(-0.67)

qmd(0.43) qmd(0.42) cc(-0.37) cc(0.42) qmd(0.35) struct(-0.12)

struct(0.41) bdlf(0.02) bdlf(0.24) struct(-0.24) elev(-0.27) qmd(-0.04)

bdlf(-0.27) struct(0.01) elev(-0.12) qmdcc(-0.09) qmdcc(-0.22) elev(0.04)

elev(0.19) elev(-0.01) struct(0.06) qmd(0.07) bdlf(0.06) bdlf(0.00)

Factor Eigen values Explains variation

1 4.768 24.70% 2.70 0.76 0.38

2 8.66 44.80% 2.30 0.70 0.33

3 2.347 12.10% 2.50 0.78 0.404 1.494 7.70% 2.60 0.77 0.39

5 1.221 6.30% 2.40 0.74 0.37

Total variation explained = 95.6%

1 0 0.12 0.8 0.55 0.68 1 1.4 2 2.8 2.8 

2 0.03 0.16 0.68 0.6 1.4 1.1 1.2 2.1 2.1 2.4 

3 0 0.11 0.76 0.37 0.87 1 2.2 2.1 2.2 2.6 4 0 0.09 0.51 0.64 0.89 0.86 1.3 2.3 2.6 2.6 5 0 0.2 0.69 0.46 0.97 1.4 1.2 2.3 1.8 2.5 

Mean 0.006 0.136 0.688 0.524 0.962 1.072 1.460 2.160 2.300 2.580

Rank 10 9 7 8 6 5 4 3 2 1

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5

Pixels (n) Acres Percentage of area

Ecological niche factor analysis results

Factors used Model Indices

Model

quality

Absolute

validation

Contrast

validation

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 
133

Figure E-9—BioMapper habitat model output statistics summary for the Klamath province of California. 00.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Marginality

Global area* 23,788,141 5,290,340 87.01% of total province land area Total province acres = 6,080,289 Specialization

Owl presence 21,380 4,755 0.09% of modeled area Tolerance (1/S)

*area that is being modeled

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.52) qmdcc(-0.68) struct(-0.63) qmdcc(0.78) qmdcc(0.79) qmdcc(0.76)

qmd(0.51) cc(0.59) cc(0.57) qmd(-0.51) cc(-0.51) cc(-0.52)

struct(0.47) qmd(0.37) bdlf(0.38) cc(-0.34) bdlf(0.25) struct(-0.31)

cc(0.42) elev(0.15) elev(-0.35) bdlf(0.06) struct(-0.16) bdlf(-0.21)

elev(-0.26) bdlf(0.12) qmd(0.01) elev(0.04) qmd(-0.14) qmd(-0.11)

bdlf(-0.09) struct(-0.07) qmdcc(-0.01) struct(0.03) elev(0.11) elev(-0.02)

Factor Eigen values Explains variation

1 1.843 22.40% 1.60 0.72 0.17

2 1.711 20.80% 1.40 0.71 0.15

3 1.574 19.10% 1.50 0.73 0.174 1.09 13.20% 1.40 0.72 0.16

5 1.077 13.10% 1.50 0.71 0.15

Total variation explained = 88.6%

1 0.14 0.26 0.56 0.75 0.75 1.3 1.2 1.3 1.2 1.7 

2 0.091 0.33 0.6 0.71 0.83 1.2 1.2 1.4 1.2 1.5 

3 0.23 0.34 0.54 0.67 0.79 1.2 1.2 1.4 1.3 1.5 4 0.14 0.38 0.59 0.67 0.78 1.1 1.2 1.5 1.2 1.5 5 0.091 0.29 0.64 0.69 0.84 1.1 1.1 1.4 1.2 1.5 

Mean 0.138 0.320 0.586 0.698 0.798 1.180 1.180 1.400 1.220 1.540

Rank 10 9 8 7 6 4 4 2 3 1

Replicate 1Replicate 2

Replicate 3Replicate 4

Replicate 5

Pixels (n) Acres Percentage of area

Ecological niche factor analysis results

Factors used Model Indices

Model

quality

Absolute

validation

Contrast

validation

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

Random frequency line

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 
134

Figure E-10—BioMapper habitat model output statistics summary for the Coast province of California.

Pixels (n) Acres Percentage of area Marginality

Global area* 17,810,943 3,961,047 69.61% of total province land area Total province acres = 5,690,268 Specialization

Owl presence 25,731 5,722 0.14% of modeled area Tolerance (1/S)

*area that is being modeled

Ecological niche factor analysis results

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6

qmdcc(0.50) qmdcc(-0.75) cc(-0.82) struct(-0.69) qmdcc(0.77) cc(0.74)

cc(0.45) cc(0.62) qmdcc(0.48) qmdcc(0.47) cc(-0.59) qmdcc(-0.49)

qmd(0.40) qmd(0.25) bdlf(-0.19) cc(-0.46) bdlf(0.25) bdlf(0.35)

elev(-0.38) bdlf(0.02) elev(-0.18) bdlf(-0.23) struct(-0.07) elev(-0.26)

bdlf(-0.37) elev(-0.01) qmd(-0.11) elev(-0.18) qmd(-0.01) qmd(-0.14)

struct(0.33) struct(0.00) struct(0.10) qmd(0.11) elev(-0.01) struct(-0.01)

Factors used Model Indices

Factor Eigen values Explains variation Model quality Absolute validation Contrast validation

1 2.494 23.90% 2.00 0.72 0.28

2 3.347 32.10% 2.10 0.73 0.28

3 1.543 14.80% 2.00 0.71 0.274 1.181 11.30% 2.00 0.73 0.28

5 1.031 9.90% 1.90 0.73 0.28

Total variation explained = 92.0%

k-fold cross-validations of habitat suitability (Rs = Spearman rank correlation)

1 0.15 0.26 0.39 0.99 0.79 1.3 1.4 1.7 1.9 2 

2 0.049 0.27 0.36 0.85 0.87 1.3 1.5 1.7 1.9 2.1 

3 0.049 0.28 0.4 0.95 0.9 1.3 1.3 1.7 1.8 2 4 0.1 0.27 0.38 0.87 0.84 1.3 1.4 1.7 1.9 2 5 0.05 0.28 0.35 0.87 0.87 1.3 1.4 1.8 1.8 1.9 

Mean 0.080 0.272 0.376 0.906 0.854 1.300 1.400 1.720 1.860 2.000

Rank 10 9 8 6 7 5 4 3 2 1

Replicate 1

Replicate 2

Replicate 3Replicate 4Replicate 5

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Habitat suitability

Area-adjusted frequency

0–10 10–20 20–30 30–40 40–50 50–60 60–70 

Random frequency line

Replicate 0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100 
135

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Appendix F—Model Validation with Independent Data Sets

Twenty-three independent data sets were used to validate 

habitat suitability maps for three physiographic provinces. 

These data sets consisted of radio telemetry data (Rock 

2004) and were not used to train the habitat models. 

Telemetry locations were separated into data sets 

for each owl pair with a minimum of 100 recorded locations. One percent of owl telemetry location outliers were 

removed by using the harmonic mean methodology of 

Dixon and Chapman (1980). A minimum convex polygon 

(MCP) was created for the remaining 99 percent by using 

the Animal Movement (v2.0) extension for ArcView Spatial 

Analyst (Hooge and Eichenlaub 2000). Area-adjusted 

frequencies (AAF) were generated for each MCP by 

dividing the percentage of telemetry points within a habitat 

suitability category or bin (e.g., 0 to 20, 21 to 40, etc.) by the 

percentage of the MCP with habitat suitability values in that 

bin. A Spearman rank correlation (Boyce et al. 2002) was 

performed for the AAF within each MCP and then averaged 

for the province in which they occurred.

Area 1 is located west of Eugene, Oregon, within the 

Oregon Coast Range province. Data were collected from 

1999 to 2004. 

Table F-1–Correlation of owl telemetry locations (n) 

with habitat suitability for area 1

Validation sites n rs P

Cedar Creek 452 0.92 <0.001

Eames Creek 645 .85 <.001

Wolf Creek 325 .99 <.001

Salt Creek 497 .82 <.001

Pittenger Creek 463 .97 <.001

Luyne Creek 101 .93 <.001

Grenshaw Creek 413 .96 <.001

Average* .99 <.001

*Average Spearman rank correlations are based on the rank of the 

averaged area-adjusted frequencies for all sites and are not an average of 

the Spearman rankings for each site. 

Area 2 is located east of Eugene, Oregon, within the 

Oregon Western Cascades province. Data were collected 

from 1999 to 2004. 

Table F-2–Correlation of owl telemetry locations (n) 

with habitat suitability for area 2

Validation sites n rs P

Anthony Creek 405 0.48 <0.001

Boundary 421 .64 <.001

Drury Creek 289 .78 <.001

Brush Creek 402 .87 <.001

Eagles Rest 354 .76 <.001

Horne Butte 287 .75 <.001

Lost Creek 338 .77 <.001

Shotgun Creek 247 .67 <.001

East Brush Creek 101 .96 <.001

Average .93 <.001

Area 9 is located in the southern portion of the Oregon 

Eastern Cascades province. Data were collected from 1999 

to 2004.

Table F-3–Correlation of owl telemetry locations (n) 

with habitat suitability for area 9

Validation sites n rs P

Long Prairie 224 -0.15 <0.001

Topsy 217 .88 <.001

Miners Creek 223 .93 <.001

Edge Creek 133 .79 <.001

Buck Mountain 103 .72 <.001

Johnson Too 191 .78 <.001

Lower Horse 145 .27 <.02

Average .94 <.001

Overall, most correlations showed significant positive 

relationships between owl locations and habitat suitability. 

Two sites (one in area 2 and one in area 9) did not show significant positive correlations, with Spearman rank correlations of 0.48 and 0.27, respectively. One site in area 9 had a 

nonsignificant, negative correlation. However, when MCPs 

were pooled and averaged across the province, correlations 

improved significantly (fig. F-1).

Telemetry data used was collected during both day 

and night and throughout the entire year. Nesting season 

(March–July) data was not separated from nonnesting 

season data so the correlations represent year-round use by 

owl pairs in these three provinces.
136

GENERAL TECHNICAL REPORT PNW-GTR-648

Oregon Coast Range (Area 1)

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100

Habitat suitability

Oregon Western Cascades (Area 2)

Oregon Eastern Cascades (Area 9)

Area-adjusted frequency

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100

Habitat suitability

Area-adjusted frequency

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0–10 10–20 20–30 30–40 40–50 50–60 60–70 70–80 80–90 90–100

Habitat suitability

Area-adjusted frequency

Figure F-1—Spearman-rank correlations for mean (±SD) area-adjusted frequencies from independent 

owl use locations of three physiographic provinces indicate these three models predicted spotted owl 

use locations well.

References

Boyce M.S.; Vernier P.R.; Nielsen, S.E.; Schmiegelow, 

F.K.A. 2002. Evaluating resource selection functions. 

Ecological Modelling. 157: 281–300.

Dixon, K.R.; Chapman, J.A. 1980. Harmonic 

mean measure of animal activity areas. Ecology. 

61: 1040–1044.

Hooge P.N.; Eichenlaub, B. 2000. Animal movement 

extension to ArcView, 2.0., Anchorage, AK: Alaska 

Science Center—Biological Science Office, U.S. 

Geological Survey. 

Rock, D. 2004. Personal communication. Wildlife biologist. 

National Council for Air and Stream Improvement, 

43613 NE 309th Avenue, Amboy, WA 98601. 
137

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Appendix G—Spotted Owl Habitat Suitability Histograms 

Explanation of codes used in the tables:

• CR, congressionally-reserved

• LSR, late-successional reserves

• AMR, adaptive management areas in reserves (an allocation designed to display the 

areas’ acres in late-successional reserves)

• MLSA, managed late-successional areas

• AW, administratively withdrawn

• LSR-3, marbled murrelet reserved areas

• LSR-4, 100-acre spotted owl cores

• AMA, adaptive management areas

• MATRIX/RR, matrix (which contains riparian reserves that were not mapped)
138

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure G-1—Habitat suitability histograms for the range of the northern spotted owl. Top histogram shows 

percentage of habitat-capable area in the range by habitat suitability bin (category). The nine smaller 

histograms show the percentage of habitat-capable area in each land use allocation in the range by habitat 

suitability bin. Number in parentheses shows percentage of habitat-capable area in the range in that land use 

allocation.

Rangewide

7

17

19 19

15

23

0%

20%

40%

60%

CR (19%)

0%

20%

40%

60%

AW (5%)

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60%

MLSA (<1%)

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60%

LSR (33%)

0%

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40%

60%

LSR-3 (<1%)

0%

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40%

60%

LSR-4 (1%)

0%

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40%

60%

AMR (2%)

0%

20%

40%

60%

AMA (7%)

0%

20%

40%

60%

Matrix / RR (32%)

0

10

20

30

40

50

60

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
139

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure G-2—Habitat suitability histograms for Washington. Top histogram shows percentage of habitatcapable area in the state by habitat suitability bin (category). The nine smaller histograms show the percentage of habitat-capable area in each land use allocation in the state by habitat suitability bin. Number 

in parentheses shows percentage of habitat-capable area in the state in that land use allocation.

Washington

9

24

15 14 14

24

0%

20%

40%

60%

CR (31%)

0%

20%

40%

60%

AW (4%)

0%

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60%

MLSA (2%)

0%

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40%

60%

LSR (34%)

0%

20%

40%

60%

LSR-3 (<1%)

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60%

LSR-4 (<1%)

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40%

60%

AMR (1.5%)

0%

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40%

60%

AMA (7%)

0%

20%

40%

60%

Matrix / RR (20%)

0

10

20

30

40

50

60

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
140

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure G-3—Habitat suitability histograms for Oregon. Top histogram shows percentage of habitat-capable 

area in the state by habitat suitability bin (category). The nine smaller histograms show the percentage of 

habitat-capable area in each land use allocation in the state by habitat suitability bin. Number in parentheses 

shows percentage of habitat-capable area in the state in that land use allocation.

Oregon

4

18 19

22

13

25

0%

20%

40%

60%

CR (11%)

0%

20%

40%

60%

AW (5%)

0%

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60%

MLSA (0%)

0%

20%

40%

60%

LSR (36%)

0%

20%

40%

60%

LSR-3 (<1%)

0%

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40%

60%

LSR-4 (1.5%)

0%

20%

40%

60%

AMR (2%)

0%

20%

40%

60%

AMA (6%)

0%

20%

40%

60%

Matrix / RR (38%)

0

10

20

30

40

50

60

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
141

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure G-4—Habitat suitability histograms for California. Top histogram shows percentage of habitatcapable area in the state by habitat suitability bin (category). The nine smaller histograms show the 

percentage of habitat-capable area in each land use allocation in the state by habitat suitability bin. 

Number in parentheses shows percentage of habitat-capable area in the state in that land use allocation.

California

9 10

23

18

20 19

0

10

20

30

40

50

60

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

0%

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60%

CR (20%)

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60%

AW (6%)

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60%

MLSA (<1%)

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60%

LSR (29%)

0%

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40%

60%

LSR-3 (<1%)

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60%

LSR-4 (<1%)

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AMR (<1%)

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60%

AMA (10%)

0%

20%

40%

60%

Matrix/RR (34%)

Habitat-capable federal area (percent)
142

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure G-5—Habitat suitability histograms for the Olympic Peninsula province in Washington. Top histogram shows percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows 

show where 90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean 

habitat suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable area in each land use allocation in the province by habitat suitability bin. Number in 

parentheses shows percentage of habitat-capable area in the province in that land use allocation.

Washington Olympic Peninsula Province

6

18

15

19

9

33

90% of owl pairs

Raw model output

Smoothed model output

73>S

H

65>S

H

0%

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60%

CR (52%)

0%

20%

40%

60%

AW (0%)

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60%

MLSA (0%)

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40%

60%

LSR (36%)

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60%

LSR-3 (<1%)

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60%

LSR-4 <1%)

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AMA (11%)

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60%

Matrix / RR (0%)

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10

20

30

40

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60

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
143

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure G-6—Habitat suitability histograms for the Western Cascades province in Washington. Top histogram shows percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows 

show where 90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean 

habitat suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable area in each land use allocation in the province by habitat suitability bin. Number in 

parentheses shows percentage of habitat-capable area in the province in that land use allocation.

Washington Western Cascades Province

6

25

15

12

25

17

90% of owl pairs

Raw model output

Smoothed model output

0%

20%

40%

60%

CR (28%)

0%

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40%

60%

AW (6%)

0%

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60%

MLSA (0%)

0%

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40%

60%

LSR (36%)

0%

20%

40%

60%

LSR-3 (<1%)

0%

20%

40%

60%

LSR-4 (<1%)

0%

20%

40%

60%

AMR (3%)

0%

20%

40%

60%

AMA (6%)

0%

20%

40%

60%

Matrix / RR (20%)

0

10

20

30

40

50

60

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)

HS>32

HS>45
144

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure G-7—Habitat suitability histograms for the Eastern Cascades province in Washington. Top histogram 

shows percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows show 

where 90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean habitat 

suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable area in each land use allocation in the province by habitat suitability bin. Number in parentheses 

shows percentage of habitat-capable area in the province in that land use allocation.

Washington Eastern Cascades Province

15

28

13

7

22

15

90% of owl pairs

Raw model output

Smoothed model output

0%

20%

40%

60%

CR (18%)

0%

20%

40%

60%

AW (5%)

0%

20%

40%

60%

MLSA (6%)

0%

20%

40%

60%

LSR (27%)

0%

20%

40%

60%

LSR-3 (0%)

0%

20%

40%

60%

LSR-4 (<1%)

0%

20%

40%

60%

AMR (0%)

0%

20%

40%

60%

AMA (6%)

0%

20%

40%

60%

Matrix / RR (37%)

20

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)

0

10

30

40

50

60

HS>24

HS>44
145

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure G-8—Habitat suitability histograms for the Coast Range province in Oregon. Top histogram shows 

percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows show where 

90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean habitat 

suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage 

of habitat-capable area in each land use allocation in the province by habitat suitability bin. Number in 

parentheses shows percentage of habitat-capable area in the province in that land use allocation.

Oregon Coast Range Province

1

13

28

21

15

22

0

10

20

30

40

50

60

Habitat suitability

)t necr ep( aer al ar edef el bapac-t ati ba H

90% of owl pairs

Raw model output

Smoothed model output

0%

20%

40%

60%

CR (1.5%)

0%

20%

40%

60%

AW (1.5%)

0%

20%

40%

60%

MLSA (0%)

0%

20%

40%

60%

LSR (54%)

0%

20%

40%

60%

LSR-3 (2.5%)

0%

20%

40%

60%

LSR-4 (<1%)

0%

20%

40%

60%

AMR (12%)

0%

20%

40%

60%

AMA (5.5%)

0%

20%

40%

60%

Matrix / RR (22%)

HS>37

HS>52

Unknown 0–20 21–40 41–60 61–80 81–100
146

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure G-9—Habitat suitability histograms for the Klamath province in Oregon. Top histogram shows 

percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows show 

where 90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean 

habitat suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable area in each land use allocation in the province by habitat suitability bin. Number 

in parentheses shows percentage of habitat-capable area in the province in that land use allocation.

Oregon Klamath Province

5

17

23

19

14

22

0

10

20

30

40

50

60

Habitat suitability

90% of owl pairs

Raw model output

Smoothed model output

0%

20%

40%

60%

CR (11%)

0%

20%

40%

60%

AW (1%)

0%

20%

40%

60%

MLSA (0%)

0%

20%

40%

60%

LSR (39%)

0%

20%

40%

60%

LSR-3 (<1%)

0%

20%

40%

60%

LSR-4 (2%)

0%

20%

40%

60%

AMR (2%)

0%

20%

40%

60%

AMA (11%)

0%

20%

40%

60%

Matrix / RR (33%)

HS>37

HS>51

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat-capable federal area (percent)
147

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure G-10—Habitat suitability histograms for the Western Cascades province in Oregon. Top histogram 

shows percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows 

show where 90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean 

habitat suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable area in each land use allocation in the province by habitat suitability bin. Number in 

parentheses shows percentage of habitat-capable area in the province in that land use allocation. HS>39 HS>56

Oregon Western Cascades Province

5

17 17

20

11

30

0

10

20

30

40

50

60

90% of owl pairs

Raw model output

Smoothed model output

0%

20%

40%

60%

CR (12%)

0%

20%

40%

60%

AW (6%)

0%

20%

40%

60%

MLSA (0%)

0%

20%

40%

60%

LSR (30%)

0%

20%

40%

60%

LSR-3 (0%)

0%

20%

40%

60%

LSR-4 (2%)

0%

20%

40%

60%

AMR (<1%)

0%

20%

40%

60%

AMA (6%)

0%

20%

40%

60%

Matrix / RR (43%)

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
148

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure G-11—Habitat suitability histograms for the Eastern Cascades province in Oregon. Top histogram 

shows percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows show 

where 90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean habitat 

suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable area in each land use allocation in the province by habitat suitability bin. Number in parentheses 

shows percentage of habitat-capable area in the province in that land use allocation.

Oregon Eastern Cascades Province

1

26

11

14

16

32

0

10

20

30

40

50

60

0%

20%

40%

60%

CR (15%)

0%

20%

40%

60%

AW (8%)

0%

20%

40%

60%

MLSA (0%)

0%

20%

40%

60%

LSR (31%)

0%

20%

40%

60%

LSR-3 (0%)

0%

20%

40%

60%

LSR-4 (<1%)

0%

20%

40%

60%

AMR (0%)

0%

20%

40%

60%

AMA (0%)

0%

20%

40%

60%

Matrix / RR (46%)

HS>44

HS>50

90% of owl pairs

Raw model output

Smoothed model output

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
149

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure G-12—Habitat suitability histograms for the Cascades province in California. Top histogram shows 

percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows show where 

90 percent of the owl-pair location points occurred in relation to the raw and smoothed (mean habitat suitability within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitatcapable area in each land use allocation in the province by habitat suitability bin. Number in parentheses 

shows percentage of habitat-capable area in the province in that land use allocation.

California Cascades Province

11

26

13 15

13

22

0

10

20

30

40

50

60

0%

20%

40%

60%

CR (2%)

0%

20%

40%

60%

AW (8%)

0%

20%

40%

60%

MLSA (<1%)

0%

20%

40%

60%

LSR (24%)

0%

20%

40%

60%

LSR-3 (0%)

0%

20%

40%

60%

LSR-4 (<1%)

0%

20%

40%

60%

AMR (0%)

0%

20%

40%

60%

AMA (15%)

0%

20%

40%

60%

Matrix / RR (50%)

HS>31

HS>36

90% of owl pairs

Raw model output

Smoothed model output

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
150

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure G-13—Habitat suitability histograms for the Klamath province in California. Top histogram shows 

percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows show where 90 

percent of the owl-pair location points occurred in relation to the raw and smoothed (mean habitat suitability 

within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable 

area in each land use allocation in the province by habitat suitability bin. Number in parentheses shows 

percentage of habitat-capable area in the province in that land use allocation.

California Klamath Province

9

6

19

22 21 23

0

10

20

30

40

50

60

0%

20%

40%

60%

CR (23%)

0%

20%

40%

60%

AW (5%)

0%

20%

40%

60%

MLSA (0%)

0%

20%

40%

60%

LSR (30%)

0%

20%

40%

60%

LSR-3 (0%)

0%

20%

40%

60%

LSR-4 (<1%)

0%

20%

40%

60%

AMR (<1%)

0%

20%

40%

60%

AMA (10%)

0%

20%

40%

60%

Matrix / RR (31%)

HS>29HS>36

90% of owl pairs

Raw model output

Smoothed model output

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
151

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure G-14—Habitat suitability histograms for the Coast province in California. Top histogram shows percentage of habitat-capable area in the province by habitat suitability bin (category). Arrows show where 90 

percent of the owl-pair location points occurred in relation to the raw and smoothed (mean habitat suitability 

within the 5×5 window) model outputs. The nine smaller histograms show the percentage of habitat-capable 

area in each land use allocation in the province by habitat suitability bin. Number in parentheses shows 

percentage of habitat-capable area in the province in that land use allocation.

California Coast Province

2

19 20

17

13

29

0

10

20

30

40

50

60

0%

20%

40%

60%

CR (40%)

0%

20%

40%

60%

AW (9%)

0%

20%

40%

60%

MLSA (0%)

0%

20%

40%

60%

LSR (32%)

0%

20%

40%

60%

LSR-3 (<1%)

0%

20%

40%

60%

LSR-4 (<1%)

0%

20%

40%

60%

AMR (0%)

0%

20%

40%

60%

AMA (0%)

0%

20%

40%

60%

Matrix / RR (19%)

HS>29

HS>33

90% of owl pairs

Raw model output

Smoothed model output

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Habitat-capable federal area (percent)
152

GENERAL TECHNICAL REPORT PNW-GTR-648

Appendix H—Timber Harvest and Wildfire Change Histograms

Explanation of codes used in the tables:

• CR, congressionally-reserved

• LSR, late-successional reserves

• AMR, adaptive management areas in reserves (an allocation designed to display the areas’ 

acres in late-successional reserves)

• MLSA, managed late-successional areas

• AW, administratively withdrawn

• LSR-3, marbled murrelet reserved areas

• LSR-4, 100-acre spotted owl cores

• AMA, adaptive management areas

• MATRIX/RR, matrix (which contains riparian reserves that were not mapped)

• HS, habitat suitability
153

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure H-1—Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and 

wildfire in the range of the northern spotted owl during the first decade of the Plan implementation. The tables in the 

middle of the figure show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use 

allocation. The histogram at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Rangewide

0.121 0.061 0 0.082 0.073 0.011 0.003 0.010 0.062

.345 .231 0 .060 .194 .061 .008 .027 .080

.631 .350 0 .089 .206 .105 .067 .046 .120

.542 .335 0 .059 .260 .028 .092 .053 .093

.419 .256 0 .183 .186 .013 .080 .028 .080

.610 .354 0 .098 .173 .010 .157 .028 .092

Totals 2.668 1.586 0 .571 1.093 .229 .407 .193 .527

0 0.003 0 0 0 0.005 0.002 0.019 0.040

.002 .012 .001 .006 .005 .037 .005 .132 .093

.003 .009 .001 .010 .011 .021 .009 .081 .104

.003 .009 .002 .005 .005 .021 .011 .110 .093

.006 .010 .001 .011 .011 .019 .012 .085 .090

.007 .014 .001 .007 .006 .040 .021 .095 .149

Totals .020 .057 .005 .039 .038 .143 .059 .524 .569
154

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure H-2—Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and 

wildfire in Washington during the first decade of Plan implementation. The tables in the middle of the figure show the 

percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The histogram at 

the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Washington

0.088 0.007 0 0.089 0.245 0 0 0 0.195

.066 .023 0 .065 .199 0 0 0 .134

.056 .017 0 .097 .108 0 0 0 .097

.031 .009 0 .064 .054 0 0 0 .077

.078 .027 0 .199 .118 0 0 0 .103

.058 .024 0 .107 .072 0 0 0 .068

Totals .377 .108 0 .621 .796 0 0 0 .673

0 0.001 0 0 0 0 0 0.006 0.016

.001 .001 0 .006 .004 .010 .007 .047 .044

.002 .003 0 .011 .002 .015 .019 .049 .052

.001 .003 0 .006 0 .001 .009 .033 .043

.001 .007 0 .012 .001 .009 .045 .071 .097

.003 .006 0 .007 .001 .015 .031 .060 .131

Totals .008 .021 0 .042 .009 .050 .111 .266 .383
155

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure H-3–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire in Oregon during the first decade of Plan implementation. The tables in the middle of the figure show the percentage 

of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The histogram at the bottom 

shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Oregon

0.310 0.118 0 0 0.041 0.013 0.004 0.027 0.054

1.104 .433 0 0 .305 .075 .011 .054 .077

1.669 .603 0 0 .206 .129 .068 .078 .144

1.356 .571 0 0 .327 .035 .110 .074 .115

.829 .376 0 0 .187 .016 .067 .049 .076

1.429 .585 0 0 .236 .012 .195 .067 .118

Totals 6.697 2.687 0 0 1.301 .279 .455 .349 .584

0 0.005 0 0 0 0.006 0.002 0.047 0.069

.001 .023 .001 0 .002 .043 .005 .176 .140

0 .015 .001 0 .001 .023 .009 .134 .145

0 .014 .003 0 .002 .025 .013 .135 .144

0 .011 .001 0 .001 .021 .008 .098 .098

.001 .022 .001 0 .002 .046 .024 .131 .201

Totals .003 .092 .007 0 .008 .164 .062 .721 .797
156

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure H-4—Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire in California during the first decade of Plan implementation. The tables in the middle of the figure show the percentage 

of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The histogram at the bottom shows 

the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

California

0 0 0 0 0 0 0 0 0

.077 .042 0 0 .046 0 .000 .018 .052

.552 .191 0 0 .273 0 .101 .045 .088

.566 .207 0 0 .311 0 .064 .069 .062

.557 .260 0 0 .232 0 .180 .024 .074

.691 .240 0 0 .158 0 .076 .008 .058

Total 2.443 .940 0 0 1.020 0 .421 .165 .335

0 0 0 0 0 0 0 0 0

.003 .002 0 0 .008 0 .000 .146 .034

.007 .003 0 0 .030 0 .001 .049 .060

.009 .005 0 0 .012 0 .003 .138 .026

.018 .009 0 0 .031 0 .008 .082 .070

.017 .006 0 0 .015 0 .004 .082 .062

Totals .054 .025 0 0 .096 0 .016 .498 .252
157

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure H-5–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Olympic Peninsula province of Washington during the first decade of Plan implementation. The tables in the middle 

of the figure show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. 

The histogram at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Washington Olympic Peninsula Province

0.001 0 0 0 0 0 0 0 0

.003 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

Totals .008 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0.001 0

.001 .002 0 0 0 .019 0 .023 0

0 .002 0 0 0 .027 0 .022 0

0 .003 0 0 0 .003 0 .018 0

0 .003 0 0 0 .016 0 .019 0

0 .006 0 0 0 .027 0 .020 0

Totals .001 .015 0 0 0 .091 0 .102 0
158

GENERAL TECHNICAL REPORT PNW-GTR-648

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Washington Western Cascades Province

0 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

.001 0 0 0 0 0 0 0 0

Totals .005 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0.001

.002 0 0 0 .006 0 0 .023 .017

.003 0 0 0 .002 0 .008 .030 .021

.002 0 0 0 0 0 .002 .036 .033

.002 0 0 0 0 0 .002 .017 .033

.006 0 0 0 0 0 0 .025 .143

Totals .016 .001 0 0 .009 0 .012 .131 .247

Figure H-6–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Western Cascades province of Washington during the first decade of Plan implementation. The tables in the middle 

of the figure show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. 

The histogram at the bottom shows the loss from timber harvest and wildfire within each land use allocation.
159

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure H-7–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Eastern Cascades province of Washington during the first decade of Plan implementation. The tables in the middle of 

the figure show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The 

histogram at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Washington Eastern Cascades Province

0.518 0.029 0 0.089 0.730 0 0 0 0.381

.384 .099 0 .065 .592 0 0 0 .261

.326 .075 0 .097 .321 0 0 0 .188

.179 .038 0 .064 .162 0 0 0 .151

.457 .118 0 .199 .353 0 0 0 .201

.339 .105 0 .107 .215 0 0 0 .132

Totals 2.204 .464 0 .621 2.372 0 0 0 1.314

0 0.004 0 0 0 0 0 0.023 0.030

0 .002 0 .006 0 0 .018 .130 .071

0 .009 0 .011 .001 0 .034 .128 .081

0 .009 0 .006 .001 0 .020 .052 .052

0 .026 0 .012 .003 0 .106 .255 .158

0 .020 0 .007 .003 0 .075 .190 .120

Totals 0 .071 0 .042 .008 0 .254 .777 .512
160

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure H-8–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

the Coast Range province of Oregon during the first decade of Plan implementation. The tables in the middle of the figure 

show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The histogram 

at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Oregon Coast Range Province

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0

Totals 0 0 0 0 0 0 0 0 0

0 0.005 0 0 0 0 0 0.005 0.024

0 .008 .001 0 0 0 0 .062 .041

0 .016 .002 0 0 .001 .004 .109 .111

0 .017 .003 0 .001 0 0 .112 .159

0 .015 .001 0 0 0 .011 .079 .128

0 .024 .001 0 0 0 .015 .117 .254

Totals 0 .084 .009 0 .001 .001 .030 .485 .717
161

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure H-9–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Klamath province of Oregon during the first decade of Plan implementation. The tables in the middle of the figure 

show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The histogram 

at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Oregon Klamath Province

1.266 0.460 0 0 0.623 0.041 0.012 0.066 0.103

4.705 1.364 0 0 1.535 .242 .026 .131 .293

7.033 2.282 0 0 2.985 .415 .163 .191 .526

4.761 1.887 0 0 2.314 .112 .283 .181 .415

2.691 1.241 0 0 1.436 .053 .182 .121 .293

3.716 1.657 0 0 2.227 .039 .462 .165 .387

Totals 24.171 8.890 0 0 11.120 .902 1.126 .855 2.017

0 0.006 0 0 0.001 0.020 0.003 0.080 0.027

.001 .031 0 0 .001 .138 .003 .213 .141

.000 .028 0 0 .001 .072 .008 .193 .162

.000 .026 0 0 .002 .080 .013 .212 .124

.000 .026 0 0 .001 .068 .009 .180 .106

.000 .042 0 0 .002 .148 .018 .205 .189

Totals .001 .158 0 0 .008 .527 .054 1.083 .749
162

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure H-10–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Western Cascades province of Oregon during the first decade of Plan implementation. The tables in the middle of 

the figure show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The 

histogram at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Oregon Western Cascades Province

0.021 0.023 0 0 0.002 0 0.002 0 0.009

.033 .177 0 0 .013 0 .007 .003 .033

.085 .147 0 0 .006 0 .042 .001 .030

.435 .227 0 0 .016 0 .062 0 .038

.357 .142 0 0 .010 0 .035 0 .020

.990 .428 0 0 .022 0 .124 0 .067

Totals 1.920 1.143 0 0 .070 0 .271 .005 .198

0 0.007 0 0 0 0 0.002 0.030 0.057

.001 .027 0 0 .001 0 .006 .179 .158

0 .008 0 0 .001 0 .010 .088 .104

0 .007 0 0 .001 0 .013 .072 .157

0 .003 0 0 0 0 .005 .030 .081

0 .015 0 0 .002 0 .025 .069 .227

Totals .001 .068 0 0 .007 0 .062 .469 .784
163

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure H-11–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Eastern Cascades province of Oregon during the first decade of Plan implementation. The tables in the middle of 

the figure show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The 

histogram at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Oregon Eastern Cascades Province

0.040 0.001 0 0 0.002 0 0 0 0.191

.009 .360 0 0 .954 0 0 0 .024

.007 .039 0 0 .074 0 0 0 .174

.069 .306 0 0 .820 0 0 0 .093

.033 .232 0 0 .428 0 0 0 .057

.054 .181 0 0 .392 0 0 0 .046

Totals .212 1.118 0 0 2.670 0 0 0 .585

0 0 0 0 0 0 0 0 0.195

0 .025 0 0 .007 0 .006 0 .134

.001 .012 0 0 .001 0 .008 0 .291

.002 .012 0 0 .006 0 .026 0 .112

.002 .002 0 0 .002 0 .034 0 .131

.004 .000 0 0 0 0 .044 0 .090

Totals .009 .052 0 0 .015 0 .119 0 .953
164

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure H-12–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Cascades province of California during the first decade of Plan implementation. The tables in the middle of the figure 

show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The histogram 

at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

California Cascades Province

0 0 0 0 0 0 0 0 0

0 .008 0 0 .006 0 0 0 .064

0 .023 0 0 .032 0 0 0 .050

0 .014 0 0 0 0 0 .004 .025

0 .072 0 0 0 0 0 0 .019

0 .023 0 0 0 0 0 0 .018

Totals 0 .140 0 0 .038 0 0 .004 .175

0 0 0 0 0 0 0 0 0

0 .014 0 0 .010 0 0 .495 .106

0 .010 0 0 .029 0 0 .069 .124

0 .018 0 0 .017 0 0 .413 .055

0 .040 0 0 .061 0 0 .154 .171

0 .026 0 0 .028 0 0 .144 .155

Totals 0 .107 0 0 .145 0 0 1.274 .611
165

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure H-13—Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and 

wildfire in the Klamath province of California during the first decade of Plan implementation. The tables in the middle 

of the figure show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. 

The histogram at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

California Klamath Province

0 0 0 0 0 0 0 0 0

.091 .053 0 0 .067 0 0 .025 .050

.652 .241 0 0 .403 0 .107 .062 .108

.669 .264 0 0 .473 0 .067 .094 .080

.658 .321 0 0 .353 0 .190 .033 .099

.817 .304 0 0 .240 0 .080 .010 .077

Totals 2.887 1.184 0 0 1.536 0 .444 .225 .414

0 0 0 0 0 0 0 0 0

.003 0 0 0 .008 0 0 .015 .009

.006 .002 0 0 .033 0 .001 .042 .039

.005 .002 0 0 .012 0 .003 .034 .017

.018 .004 0 0 .026 0 .009 .055 .035

.017 .002 0 0 .013 0 .004 .059 .031

Totals .049 .010 0 0 .091 0 .017 .205 .130
166

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure H-14–Top histogram shows the percentage of habitat-capable area lost to stand-replacing timber harvest and wildfire 

in the Coast province of California during the first decade of Plan implementation. The tables in the middle of the figure 

show the percentage of habitat-capable area lost to timber harvest and wildfire within a land use allocation. The histogram 

at the bottom shows the loss from timber harvest and wildfire within each land use allocation.

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat-capable area (%)

0

1.0

1.5

2.0

Unknown 0–20 21–40 41–60 61–80 81–100

Habitat suitability

Wildfire

Timber harvest

0

1

2

3

4

5

6

7

8

CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix/RR

Land use allocations

Unknown

0–20

21–40

41–60

61–80

81–100

Habitat

suitability

Habitat-capable federal area (%)

Stand-replacing wildfire (summarized by land use allocation) as percentage of habitat-capable federal area

Stand-replacing timber harvest (summarized by land use allocation) as percentage of habitat-capable federal area

0.5

HS CR LSR AMR MLSA AW LSR-3 LSR-4 AMA Matrix / RR

Unknown

0–20

21–40

41–60

61–80

81–100

California Coast Province

0 0 0 0 0 0 0 0 0

0 0 0 0 .006 0 0 0 0

0 0 0 0 0 0 0 0 0

0 0 0 0 .003 0 0 0 0

0 0 0 0 .004 0 0 0 0

0 0 0 0 .000 0 0 0 0

Totals 0 0 0 0 .014 0 0 0 0

0 0 0 0 0 0 0 0 0

.006 0 0 0 0 0 0 0 0

.012 .004 0 0 .015 0 0 0 0

.034 .003 0 0 0 0 0 0 0

.017 .005 0 0 0 0 0 0 0

.020 .002 0 0 0 0 0 0 0

Totals .090 .014 0 0 .015 0 0 0 0
167

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Appendix I—Spotted Owl DIspersal Habitat Maps

Figure I-1—Spotted owl dispersal habitat for dispersal-capable federal land in the Olympic Peninsula 

province in Washington.
168

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure I-2—Spotted owl dispersal habitat for dispersal-capable federal land in the Western Cascades 

province in Washington.
169

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure I-3—Spotted owl dispersal habitat for dispersal-capable federal land in the Eastern Cascades 

province in Washington.
170

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure I-4—Spotted owl dispersal habitat for dispersal-capable federal land in the Eastern Cascades 

province in Oregon.
171

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure I-5—Spotted owl dispersal habitat for dispersal-capable federal land in the Western Cascades 

province in Oregon.
172

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure I-6—Spotted owl dispersal habitat for dispersal-capable federal land in the Coast Range 

province in Oregon.
173

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure I-7—Spotted owl dispersal habitat for dispersal-capable federal land in the Klamath province 

in Oregon.
174

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure I-8—Spotted owl dispersal habitat for dispersal-capable federal land in the Klamath province 

in California.
175

Northwest Forest Plan—the First 10 Years (1994–2003): Status and Trends of Northern Spotted Owl Populations and Habitat

Figure I-9—Spotted owl dispersal habitat for dispersal-capable federal land in the Cascades province 

in California.
176

GENERAL TECHNICAL REPORT PNW-GTR-648

Figure I-10—Spotted owl dispersal habitat for dispersal-capable federal land in the Coast province in 

California.
Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw

Telephone (503) 808-2592

Publication requests (503) 808-2138

FAX (503) 808-2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution

 Pacific Northwest Research Station

 P.O. Box 3890

 Portland, OR 97208-3890
U.S. Department of Agriculture

Pacific Northwest Research Station

333 SW First Avenue

P.O. Box 3890

Portland, OR 97208-3890

Official Business

Penalty for Private Use, $300