NORTHWEST

FOREST PLAN

The First 15 Years (1994–2008)

Pacific Northwest

Research Station

Forest 

Service

United States 

Department of 

Agriculture

General Technical Report

PNW-GTR-850

October 2011

Status and Trends of Northern Spotted 

Owl Populations and Habitats 

Raymond J. Davis, Katie M. Dugger, Shawne Mohoric, Louisa Evers, 

and William C. Aney
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

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

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the national forests and national 

grasslands, it strives—as directed by Congress—to provide increasingly greater service to 

a growing Nation.

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Authors

Raymond J. Davis, Northern Spotted Owl Monitoring Module Leader, Northwest Forest 

Plan Interagency Monitoring Program, U.S. Department of Agriculture, Forest Service, 

Pacific Northwest Region, 2900 NW Stewart Parkway, Roseburg, OR 97471. Katie M. 

Dugger, associate professor, Senior Research, Department of Fisheries and Wildlife, 

Oregon State University, 104 Nash Hall, Corvallis, OR 97331-3803. Shawne Mohoric,

program manager, NWFP Interagency Monitoring Program, U.S. Department of Agriculture, Forest Service, Pacific Northwest Region, 333 SW First Avenue, Portland, OR 97204. 

Louisa Evers, fire ecologist, U.S. Department of the Interior, Bureau of Land Management, 

Oregon State Office, 333 SW First Avenue, Portland, OR 97204. William C. Aney, regional 

fuels specialist, U.S. Department of Agriculture, Forest Service, Pacific Northwest Region, 

2517 SW Hailey Avenue, Pendleton, OR 97801.

Cover: Photo by Peter Carlson.
i

Preface

This report is one of a set of periodic reports produced by the Northwest Forest Plan (the 

Plan) interagency monitoring program. These reports attempt to answer questions about 

the effectiveness of the Plan using the latest monitoring methods and research results. The 

reports focus on establishing baseline information from 1994, when the Plan was approved, 

and reporting changes that have occurred since then. The series includes late-successional 

and old-growth forests, northern spotted owl (Strix occidentalis caurina) population and 

habitat, marbled murrelet (Brachyramphus marmoratus) population and habitat, watershed 

condition, government-to-government tribal relationships, socioeconomic conditions, and 

project implementation. These monitoring reports are also intended to identify potential issues and to recommend solutions for future adaptive management changes and, as noted in 

the first reporting cycle, to resolve information management issues that inevitably surface 

during these analyses. 
ii

Abstract

Davis, Raymond J.; Dugger, Katie M.; Mohoric, Shawne; Evers, Louisa; Aney, 

William C. 2011. Northwest Forest Plan—the first 15 years (1994–2008): status 

and trends of northern spotted owl populations and habitats. Gen. Tech. Rep. PNWGTR-850. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station. 147 p.

This is the second in a series of periodic monitoring reports on northern spotted owl (Strix 

occidentalis caurina) population and habitat trends on federally administered lands since 

implementation of the Northwest Forest Plan in 1994. 

Here we summarize results from a population analysis that included data from longterm demographic studies during 1985–2008. This data was analyzed separately by study 

area, and also in a meta-analysis across all study areas to assess temporal and spatial patterns in fecundity, apparent survival, recruitment, and annual rates of population change. 

Estimated rates of annual population decline ranged from 0.4 to 7.1 percent across federal 

study areas (weighted average of 2.8 percent). Covariates for barred owls (Strix varia), 

weather, climate, habitat, and reproductive success were analyzed and had varying degrees 

of association with owl demographic parameters. We now have more evidence that increasing numbers of barred owls and loss of nesting/roosting habitat contributed to demographic 

declines in some study areas.

We also summarize results from a habitat analysis that used the above data in conjunction with remotely sensed data from 1994 to 2007 to develop “habitat suitability” models 

and habitat maps. These maps were used to quantify the amount and distribution of owl 

habitats. We also report on causes of habitat change during this period. On federal lands, 

nesting/roosting habitat declined by 3.4 percent rangewide, with some physiographic 

provinces experiencing losses of 10 percent. Dispersal habitat increased by 5.2 percent, but 

dispersal-capable landscapes declined by 1 percent.

Wildfire remains the leading cause of habitat loss. We developed a rangewide “wildfire 

suitability” model and map to illuminate the portions of the owl’s range where suitable nesting/roosting habitat overlaps with landscapes suitable for the occurrence of large wildfires. 

Barred owls and management of owl habitat in fire-prone areas continue to be topics 

for future monitoring, research, and management consideration. 

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

geographic information system, owl habitat, habitat suitability, wildfire suitability, demographic study, remote sensing, predictive model, habitat model.
iii

Summary

For the eight federal study areas associated with the Northwest Forest Plan (the Plan) 

effectiveness monitoring program, the average rate of population decline was 2.8 percent 

per year. Strong evidence of declines in annual rates of population change were reported 

for five of the eight individual effectiveness monitoring area study sites, but confidence 

limits on point estimates for three areas in the center of the northern spotted owl (Strix 

occidentalis caurina) range (southwest Oregon) overlapped lambda = 1.0, suggesting these 

three populations may not be declining. Rates of population decline were highest in the 

northern portions of the owl’s range (Washington and northern Oregon) where populations 

are estimated to have declined 40 to 60 percent since the Plan’s implementation. 

A variety of covariates including presence of barred owls (Strix varia), weather and 

long-term climate cycles, the amount of suitable nesting/roosting habitat on and adjacent to 

each study area, and the previous year’s reproductive success, were included in the analysis 

of demographic data to explore associations between them and observed population trends. 

These covariates had varying degrees of association with owl demographic parameters, but 

at least one vital rate (i.e., fecundity, apparent survival, or population) was declining on all 

study areas. 

The long-term demographic data we continue to collect are the key to understanding 

the range of factors that are affecting the recovery of spotted owl populations. At present, 

the invasion of the competitive barred owl and the amount of suitable nesting/roosting 

habitat are the factors most associated with spotted owl vital rates. Directly managing 

barred owl encroachment into spotted owl habitats may be beyond the scope of the Plan, 

but maintaining large blocks of suitable spotted owl habitat will likely play a key role in 

decreasing negative interactions between the two species and increasing the likelihood of 

the persistence of spotted owl populations. 

On federal lands, we estimated nesting/roosting habitat losses for 1994 through 2007 

in California, and 1996 through 2006 in Oregon and Washington at 3.4 percent rangewide. 

Although rangewide losses have not yet exceeded what was anticipated under the Plan, 

some physiographic provinces have incurred losses up to 10 percent. This and the fact that 

most of the nesting/roosting habitat loss occurred within reserved land use allocations, and 

not within the federal matrix outside of these reserves, raises some concern. But in spite 

of this paradox, the large, repetitive design of reserves appears to still be functioning as 

intended. Of the 12 million ac of nesting/roosting habitat remaining, 71 percent occurs on 

federally administered lands, and approximately 70 percent of this is in reserved land use 

allocations (not including riparian reserves). Over half of the nesting/roosting habitat occurs 

in the central (core) portions of the owl’s range, within the Klamath Mountain provinces of 

Oregon and California (27 percent) and the western Cascades of Oregon (26 percent). Not 

enough time has yet elapsed for us to accurately detect or estimate any significant recruitment of nesting/roosting habitat; however, increases were observed in “marginal” (younger) 

forests indicating that future recruitment of nesting/roosting habitat is on track to occur, as 

anticipated, within the next few decades.
iv

In addition to providing potential future nesting/roosting habitat, some younger 

forests function as dispersal habitat. Forest succession accounted for some dispersal habitat 

recruitment, especially in the more productive tree-growing portions of the range (i.e., 

Oregon Coast Range). Partial disturbances of nesting/roosting habitat also accounted for 

some of this recruitment as well. Loss of dispersal habitat, primarily from wildfires, was 

observed, but recruitment rates exceeded losses, resulting in a net increase in dispersal 

habitat of 5.2 percent (rangewide). In spite of this net gain, dispersal-capable landscapes 

actually decreased by 1 percent within the owl’s range because of the spatial distribution 

of this habitat. Even with this small decrease, the network of large reserves remains fairly 

well connected, with the exception of the northern portion of the eastern Cascades of Washington and also within the southern tip of the range where some large reserves appear to be 

isolated (including the Marin County population). 

Recent improvements in remotely sensed vegetation and change-detection mapping 

has resulted in better habitat maps to replace the baseline versions produced for the first 

monitoring report. Progress in habitat “niche” modeling methods and software has improved our ability to map not only habitat for spotted owls, but also “suitable habitat” for 

large wildfires. Wildfire remains the leading cause of owl habitat loss. About 3.6 million ac 

of nesting/roosting habitat remain in landscapes that are naturally prone to large wildfires. 

Most of this “fire-prone” habitat (85 percent) occurs within the “core” of the owl’s range 

(i.e., the Klamath Mountains and the western Cascades of Oregon). Not all habitat burned 

is lost to owls, as fire intensity and frequency play a role in the effect of fire on owl habitat 

use. Our monitoring showed that large wildfires resulted in 30 to 62 percent loss of the 

nesting/roosting owl habitat within their perimeters.

Wildfire is a natural ecological process under which northern spotted owls have 

evolved, but the landscapes in which this occurred were heavily altered during the 20th

century. Most remaining nesting/roosting habitat is now contained on federal land, and 

its fragmented condition makes it, and the populations that rely on it, more vulnerable to 

future large wildfires. Conservation management for northern spotted owls in relation to 

wildfire will involve understanding (1) where suitable owl habitats overlap suitable habitat 

for large wildfire; (2) the effect of fuel reduction treatments to reduce fire risk on owl habitat use and demographics; and (3) the relationships of fire frequency, severity, and extent 

with owl habitat use and demographics. 
v

Contents

1 Chapter 1: Introduction and Background

 Raymond J. Davis, Katie M. Dugger, and Shawne Mohoric

3 References

5 Chapter 2: Population Status and Trend

 Katie M. Dugger and Raymond J. Davis

5 Introduction

5 Data Sources and Methods

7 Field Data Collection

7 Data Analysis

7 Error Checking

9 Estimating Survival

9 Estimating Fecundity

10 Estimating Annual Rate of Population Change and Realized Population Change

10 Results

11 Survival

12 Fecundity

14 Annual Rate of Population Change

15 Realized Population Change

15 Discussion

17 Summary

18 References

21 Chapter 3: Habitat Status and Trend

 Raymond J. Davis and Katie M. Dugger

21 Introduction

23 Habitat Monitoring Under the Plan

24 Methods and Data Sources

24 Land Use Allocation Data

27 Vegetation Data

28 Change-Detection Data

30 Spotted Owl Presence Data

30 Habitats, the Niche Concept, and Habitat Modeling

32 Habitat Modeling Process

33 Environmental Variables

34 Modeling Regions

36 Habitat Map Development and Evaluation

38 Nesting/Roosting Habitat

40 Dispersal Habitat
vi

41 Habitat Fragmentation

43 Results

43 Habitat Suitability Modeling

43 Nesting/Roosting Habitat

49 Dispersal Habitat

52 Habitat Fragmentation

53 Discussion

54 Summary

55 References

63 Chapter 4: Large Wildfires Within the Owl’s Range

 Raymond J. Davis, William C. Aney, Louisa Evers, and Katie M. Dugger

63 Introduction

65 Methods and Data Sources

67 Environmental Data

69 Large Wildfire Data

70 Wildfire Suitability Modeling

71 Results

73 Discussion

78 Summary

80 References

87 Chapter 5: Emerging Issues, Related Research, and Research Needs

 Katie M. Dugger and Raymond J. Davis

87 Emerging Issues

89 Related Research and Research Needs

92 Summary

92 References

96 Acknowledgments

97 Metric Equivalents

99 Appendix A: Environmental Variables Used for Habitat Suitability Modeling

103 Appendix B: Nearest Neighbor Distance Analysis of Demographic Study

Area Data

107 Appendix C: Habitat Suitability Modeling Replicate Data

121 Appendix D: Nesting/Roosting Habitat Status and Trend Tables Based

on LandTrendr Analysis

125 Appendix E: Dispersal Habitat Status and Trend Tables Based on

LandTrendr Analysis

129 Appendix F: Crosswalk for Modifying Bookend 2 (2006/07) Map for Making

Habitat Suitability Histograms

135 Appendix G: Wildfire Suitability Modeling, MaxEnt Replicate Data

141 Appendix H: Regional Inventory Plot Analysis
1

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Chapter 1: Introduction and Background

In 1994, the Northwest Forest Plan (referred to hereafter as 

the Plan) amended 19 existing Forest Service and 7 Bureau 

of Land Management resource management plans within 

the range of the northern spotted owl (Strix occidentalis 

caurina). An interagency effectiveness monitoring framework was implemented to meet requirements for tracking 

the status and trends for late-successional and old-growth 

forests, northern spotted owl populations and habitat, 

marbled murrelet (Brachyramphus marmoratus) populations and habitat, watershed condition, social and economic 

conditions, and tribal relationships. Monitoring results 

are reported at 1-year intervals and evaluated at 5-year 

intervals. The first regional monitoring reports roughly 

covered the first 10 years of Plan implementation and were 

documented in a series of General Technical Reports posted 

at http://www.fs.fed.us/pnw/publications/gtrs.shtml. The 

first northern spotted owl population and habitat monitoring 

report was produced in 2005 covering status and trends of 

populations up to 2003 and habitat up to 2002 (Lint 2005). 

This report is the second in the series of northern spotted 

owl effectiveness monitoring reports (Lint et al. 1999) and 

covers population status and trend up to 2008 and habitat 

status and trend up to 2007. 

The goal of the northern spotted owl monitoring 

program is to evaluate the success of the Plan in arresting 

the downward trends in populations and habitats that were 

largely responsible for the establishment of the Plan. In part, 

the Plan was designed to maintain and restore habitat conditions necessary to support viable populations of the northern spotted owl on federally administered lands throughout 

the owl’s range (fig. 1-1). The objectives for northern spotted 

owl effectiveness monitoring are as follows:

1. Assess changes in population trends and demographic 

rates of spotted owls on federal lands within the owl’s 

range.

2. Assess changes in the amount and distribution of 

nesting, roosting, foraging and dispersal habitat for 

spotted owls on federal lands.

Raymond J. Davis, Katie M. Dugger, and Shawne Mohoric

The first monitoring effort reporting on status and 

trends of northern spotted owl populations and habitat (Lint 

2005) included a summary of the fourth northern spotted 

owl meta-analysis (Anthony et al. 2006) and produced a 

habitat baseline map using the latest technology and best 

available data at the time. This report covers the first 15 

years of implementation under the Plan, including a summary of the fifth northern spotted owl population meta-analysis 

(Forsman et al. 2011) and the development of new habitat 

maps based on new vegetation data, analytical methods, and 

habitat modeling technologies. 

Lint (2005) realized that as technology advances, there 

will be a need to refine or adapt old monitoring methods for 

new analytical approaches. With the help of leaders in the 

fields of statistics and wildlife demographics, the analytical methods for conducting the population meta-analysis 

continue to advance. Barred owl (Strix varia), climate, and 

habitat covariates were included in the latest analysis for the 

first time in 2009 (Forsman et al. 2011). The habitat covariates used were products from the 10-year report (Davis and 

Lint 2005). The inclusion of these new modeling techniques 

and covariates allowed us to investigate relationships between them and owl demographics for the very first time.

Likewise, the habitat analysis has evolved to incorporate new habitat modeling and forest pattern analysis 

software that can be used for identifying habitat conditions, 

characterization of change to those conditions, and the 

recruitment of those conditions through forest succession. 

Improvements were made to the vegetation data used to 

characterize owl habitat, including the addition of more 

variables for habitat modeling and analysis. Most notable, 

a consistent vegetation data set was produced for the entire 

range of the northern spotted owl, which has never been 

available before. This new vegetation data set replaces the 

two previously used data sets (IVMP and CALVEG) and, 

along with new modeling software, allowed us to refine 

the previous baseline habitat map. Therefore the baseline 

amounts and distribution of owl habitat reported in the 10-

year report are replaced by results presented in this report. 
2

GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 1-1—The range of the northern spotted owl. NASA = National Aeronautics Space Administration.
3

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Improvements were also made to the remotely sensed 

data used for estimating habitat changes. These improvements include a finer time sequence of change-detection 

(annual versus 4- to 5-year intervals) and an improved 

ability to detect lower intensity disturbances (i.e., thinning, 

insects, and disease). Another improvement in our ability to 

detect habitat changes came from the creation of a vegetation data set that contains the same variables as the baseline 

data set, but for a later period. We called these vegetation 

data sets “bookends.” Our first bookend is from 1994 in 

California and from 1996 in Oregon and Washington. The 

other bookend is from 2007 in California and from 2006 in 

Oregon and Washington. Therefore our habitat maps and 

our analysis of habitat status and trends cover the period 

from 1994/96 to 2006/07.

The spotted owl monitoring plan includes two phases 

of monitoring (Lint et al. 1999). Phase I entails demographic 

monitoring of individual territorial owls on eight federal 

study areas to estimate population demographics including 

survival, fecundity, and rate of population change while 

also tracking habitat conditions rangewide. The eight 

federal study areas that are part of phase I occur on federal 

lands administered by the U.S. Forest Service, Bureau of 

Land Management, and the National Park Service. They 

provide population trend data for a representative mix of 

areas considered key to the success of owl management 

under the Plan. The scientists who developed the monitoring plan determined that these eight study areas were the 

minimum number needed to be able to make scientifically 

credible and defensible inferences of population trends to 

the broader federal landscape within the owl’s range (Lint 

et al. 1999, Mulder 1997). It is hoped that eventually the 

demographic monitoring data can be combined with the 

habitat monitoring data to develop predictive models of 

owl occurrence and demographic performance based on 

observed habitat conditions. This would allow for implementation of phase II, which increases emphasis on habitat 

monitoring and decreases the population monitoring to a 

minimum of four study areas, which would provide a means 

to validate the population predictions of the habitat models. 

Implementation of phase II depends on our ability to relate 

owl demography to habitat conditions such that we can 

relate habitat status and trends directly to population status 

and trends with acceptable confidence. To date, attempts to 

develop predictive models have had mixed results (Dugger 

et al. 2005, Franklin et al. 2000, Olson et al. 2004) and have 

generally been unsuccessful across the range of the owl; 

however, some progress has been made as noted above and, 

as technology continues to advance, this remains our goal. 

After 15 years, agency managers continue to be 

proactive and supportive of the monitoring program. As 

Lint (2005) stated, this support is, “of utmost importance to 

the future of the effectiveness monitoring program.” The 

Northwest Forest Plan’s effectiveness monitoring program 

(Mulder et al. 1999) has received national and international 

attention (Gosselin 2009) and has been noted as the largest 

and most comprehensive regional forest plan monitoring 

ever conducted (McAlpine et al. 2007). The monitoring data 

created and the analysis results presented in the 10-year 

monitoring report have provided valuable information for 

managers and policymakers in making informed decisions. 

Examples include northern spotted owl recovery planning 

(USDI 2008b) and designation of critical habitat (USDI 

2008a) and increased emphasis by regulatory and management agencies to reduce risk of owl habitat and old forests 

from high-severity fire in dry provinces (Spies et al. 2006).

References

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

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

Schwarz, C.J.; Nichols, J.D.; Hines, J.E.; Olson, 

G.S.; Ackers, S.H.; Andrews, L.W.; Biswell, B.L.; 

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

K.E.; Fleming, T.L.; Gearhardt, R.P.; Gremel, 

S.A.; Gutiérrez, 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. 2006. Status and trends in 

demography of northern spotted owls, 1985–2003. 

Wildlife Monographs No. 163. 48 p.
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GENERAL TECHNICAL REPORT PNW-GTR-850

Davis, R.; Lint, J. 2005. Habitat status and trend. In: 

Lint, J., tech. coord. 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: 21–82.

Dugger, K.M.; Wagner, F.; Anthony, R.G.; Olson, G.S. 

2005. The relationship between habitat characteristics 

and demographic performance of northern spotted owls 

in southern Oregon. Condor. 107: 863–878.

Forsman, E.D.; Anthony, R.G.; Dugger, K.M.; Glenn, 

E.M.; Franklin, A.B.; White, G.C.; Schwarz, C.J.; 

Burnham, K.P.; Anderson, D.R.; Nichols, J.D.; Hines, 

J.E.; Lint, J.B.; Davis, R.J.; Ackers, S.H.; Andrews, 

L.S.; Biswell, B.L.; Carlson, P.C.; Diller, L.V.; 

Gremel, S.A.; Herter, D.R.; Higley, J.M.; Horn, R.B.; 

Reid, J.A.; Rockweit, J.; Schaberl, J.; Snetsinger, T.J.; 

Sovern, S.G. 2011. Population demography of northern 

spotted owls. Studies in Avian Biology 40. Berkeley, CA: 

University of California Press. 106 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(4): 539–590.

Gosselin, F. 2009. Management on the basis of the best 

scientific data or integration of ecological research 

within management? Lessons learned from the northern 

spotted owl saga on the connection between research and 

management in conservation biology. Biodiversity and 

Conservation. 18: 777–793.

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. 

Lint, J.B., 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.

McAlpine, C.A.; Spies, T.A.; Norman, P.; Peterson, A. 

2007. Conserving forest biodiversity across multiple land 

ownerships: lessons from the Northwest Forest Plan and 

the Southeast Queensland regional forests agreement 

(Australia). Biological Conservation. 134: 580–592.

Mulder, B. 1997. Supplemental information for the 

policy and management review: general monitoring 

program, late-succession old-growth, northern spotted 

owl and marbled murrelet. Report by the Effectiveness 

Monitoring Team. Unpublished report. On file with: U.S. 

Department of Agriculture, Forest Service, 333 SW 1st

Avenue, Portland, OR 97208-3623.

Mulder, B.S.; Noon, B.R.; Spies, T.A.; Raphael, M.G.; 

Palmer, C.J.; Olsen, A.R.; Reeves, G.H.; Welsh, 

H.H., tech. coords. 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. 

Olson, G.S.; Glenn, E.M.; 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: 1039–1053. 

Spies, T.A.; Hemstrom, M.A.; Youngblood, A.; Hummel, 

S. 2006. Conserving old-growth forest diversity in 

disturbance-prone landscapes. Conservation Biology. 20: 

351–362.

U.S. Department of the Interior [USDI]. 2008a.

Endangered and threatened wildlife and plants; revised 

designation of critical habitat for the northern spotted 

owl; final rule. Federal Register. 73(157): 47326–47522.

U.S. Department of the Interior [USDI]. 2008b.

Final recovery plan for the northern spotted owl 

(Strix occidentalis caurina). 142 p. On file with: U.S. 

Department of the Interior, Fish and Wildlife Service, 

2600 SE 98th Ave., Suite 100, Portland, OR 97266.
5

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Introduction

The collection of demography data is the foundation of the 

effectiveness monitoring program for northern spotted owls 

(Strix occidentalis caurina) (Lint et al. 1999), designed to 

monitor the effect of the Northwest Forest Plan (the Plan) on 

populations. Demographic surveys for spotted owls following standardized data collection protocols began on some 

study areas as early as 1985 (northwest California: Franklin 

et al. 1996a, 1996b) even before the monitoring plan was 

actually finalized. The first rangewide meta-analysis was 

conducted in 1991 (Anderson and Burnham 1992), then 

again in 1993 (Burnham et al. 1996), and every 5 years 

thereafter (1998: Franklin et al. 1999; 2004: Anthony et 

al. 2006; 2009: Forsman et al. 2011). This long history of 

owl surveys and demographic data collection represents 

the single largest, long-term mark-recapture data set in the 

world for a threatened species (Courtney et al. 2004), and 

these data are invaluable for monitoring spotted owls under 

the Plan.

The goal of the population component of the monitoring 

program is to determine if the Plan is arresting or slowing 

the declining trend in northern spotted owl populations 

on federally administered lands throughout the owl’s 

range. This is accomplished with annual data collection on 

eight federal study areas associated with the effectiveness 

monitoring plan (Lint et al. 1999). For the 10-year report 

(Lint 2005), these eight areas and data from three other 

independent study areas provided relevant data to address 

this question on federal lands managed under the Plan (Anthony et al. 2006). After 15 years, we report results from the 

eight federal demographic study areas and one independent 

study area. These nine areas are spread throughout the owl’s 

range (fig. 2-1) and data on owl occupancy, survival, and 

productivity were gathered annually from each to estimate 

apparent adult survival, reproduction, and annual rate of 

change of owl populations. Detailed results of the analyses 

of these data and data from two other, independent study 

areas within the range of the owl are reported by Forsman 

Chapter 2: Population Status and Trend

Katie M. Dugger and Raymond J. Davis

et al. (2011). The objectives of the most recent population 

status and trend meta-analysis were as follows:

• Estimate age-specific survival 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 (λ) and 

their sampling variances for individual study areas.

• Determine if the declines in apparent survival and 

populations, which were documented previously 

(Anthony et al. 2006), have continued or stabilized.

• Determine whether changes in the amount of suitable habitat, the presence of barred owls (Strix 

varia), or climate explain the observed annual variability in owl vital rates.

• Estimate components of the rate of population 

change, including apparent survival and recruitment rates that were not done in previous analyses 

(Anthony et al. 2006, Burnham et al. 1996, Franklin 

et al. 1999).

Data Sources and Methods

Data from eight demographic study areas in Washington, 

Oregon, and California were used to estimate status 

and trends of owl populations on federal lands (fig. 2-1). 

Although it is not part of the monitoring plan, data from 

the Rainier study area in Washington were also included because the study area occurs primarily on federal land. The 

two additional study areas in the latest meta-analysis are 

the Hoopa on tribal lands and the Green Diamond Resource 

study area on private timber company lands (Forsman et al. 

2011). Because Hoopa and Green Diamond Resources did 

not include any lands managed under the Plan, they were 

excluded from this monitoring report, except when metaanalysis results including all 11 study areas are presented.

This monitoring report is based on nine study areas 

managed under the Plan that include variation in climate, 

vegetation, and topography and encompass most of the 
6

GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 2-1—Location of nine demography study areas comprising primarily federal lands administered under the 

Northwest Forest Plan and included in the 2009 northern spotted owl meta-analysis. Source: Forsman et al. (2011). 
7

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

northern spotted owl’s geographic distribution. The forests 

on all study areas are dominated by conifers or mixtures 

of conifers and hardwoods, although there are regional 

differences in species composition (for more details, see 

Forsman et al. 2011). The nine study areas range from 396 

to 1514 mi2; the median study area size was 691 mi2, and 

the mean was 829 mi2 (table 2-1). These nine study areas 

encompassed 7460 mi2 or approximately 8 percent of the 

owl’s range, and the numbers of years included in these data 

sets ranged from 17 (Rainier) to 24 (Northwest California). 

Four of these study areas (Olympic, H.J. Andrews, South 

Cascades, and Northwest California) primarily comprised 

federal lands administered by USDA Forest Service, the 

USDI Bureau of Land Management, and the USDI National 

Park Service (table 2-1). The other five (Cle Elum, Rainier, 

Coast Ranges, Tyee, and Klamath) included a mixture of 

federal, private, and state lands intermixed in a checkerboard pattern of ownership (table 2-1).

Field Data Collection

Data on individually identifiable (i.e., banded) owls were 

collected from the nine demographic study areas annually. 

During each breeding season (March through August), 

multiple visits (usually > three per season) were made to 

owl territories to locate banded owls; confirm band numbers, sex, and age; and band any unmarked owls. In addition, the number of young produced was documented for 

each territorial owl, and fledglings were banded resulting 

in a known-age population of spotted owls on each study 

area. For details on the standardized field methods used 

to capture, mark, age, sex, and estimate productivity, see 

Franklin et al. (1996a). These methods resulted in complete 

capture histories over time of every owl banded during this 

study and the number of young fledged per territorial female 

(NYF) located each year. From these data, annual apparent 

survival (φ) by sex and age, annual productivity (NYF) 

by age, and the annual rate of population change (λ) were 

estimated (Forsman et al. 2011).

Data Analysis 

During a 9-day period in January 2009 (9th through 17th),

a workshop was held at Oregon State University in

Corvallis, Oregon, to analyze the data from 11 study areas. 

This workshop was led by research scientists with internationally recognized expertise in population dynamics, 

statistics, and the analysis of capture-recapture data. The 

analyses were conducted under the direct guidance of these 

scientists. Consistent with the previous four workshops convened since 1991 to analyze spotted owl demographic data, 

all participants adopted formal protocols for error-checking 

data sets and for the development of a priori model sets for 

each parameter of interest (Anderson et al. 1999). Thus, the 

data were collected and prepared in a consistent manner 

among study areas, and there were no analyses of additional 

models after post hoc examination of initial results (i.e., 

all data sets were analyzed the same way). Detailed results 

from this workshop (summary presented here) are reported 

in Forsman et al. (2011), and these analyses represent a 

retrospective, observational study, which assesses the 

strength of association between owl vital rates and a variety 

of explanatory covariates rather than addressing direct 

cause-effect relationships.

Error Checking

Crew leaders from each study area compiled survival, 

fecundity, and rate of population change data sets in a 

consistent manner, following specific instructions provided 

by workshop organizers. When digital files were completed, 

data entry was error checked by independent members of 

the workshop organizing team. The capture-history files 

for estimation of survival and annual rate of population 

change were error checked by randomly drawing 10 capture 

histories from each study area file and comparing them to 

paper copies of the field data that supported each of these 

capture histories. Fecundity data entry was error checked in 

a similar way, with 10 records of reproductive success for a 

specified female in a given year compared to paper copies of 

the field data forms. If errors were found in the first round 

of checking, the errors were corrected and the process was 

repeated with another sample of 10 records. If errors were 

found in the second round of data checking, the entire file 

was returned to the crew leader and principal investigator 

for review and correction. This sequence of error checking 

and correction was continued until no errors were found 
8

GENERAL TECHNICAL REPORT PNW-GTR-850 Table 2-1−Descriptions of nine demographic study areas associated with land managed under the Northwest Forest Plan Land- Number of banded owls Physiographic owner Study

Total

Study area province

Years class Ecological region size S1a S2a Adultsa Total encountersb

mi2

Washington: Cle Elumc Eastern Cascades 1989–2008 Mixed

Washington mixed conifer 689 31 32 148 211 1,170

 Rainier

Western Cascades 1992–2008 Mixed

Washington Douglas-fir 837 8 12 133 153 583

 Olympicc Olympic Peninsula 1990–2008 Federal

Washington Douglas-fir 861 19 32 337 388 1,510

Oregon: Coast Rangesc Coast Ranges 1990–2008 Mixed Oregon coastal Douglas-fir 1,514 66 97 486 649 3,306 H.J. Andrewsc Western Cascades 1988–2008 Federal Oregon Cascades Douglas-fir 619 28 91 457 576 3,082 Tyeec Coast Range 1990–2008 Mixed Oregon coastal Douglas-fir 396 137 110 243 490 2,315 Klamathc Klamath 1990–2008 Mixed Oregon/California mixed conifer 549 169 134 347 650 2,800 South Cascadesc Western and 1991–2008 Federal Oregon Cascades Douglas-fir 1,304 43 80 479 602 2,364

Eastern Cascades

California: Northwest Klamath 1985–2008 Federal Oregon/California mixed conifer 691 114 80 280 474 2,550

Californiac

Totals 7,460 615 668 2,910 4,193 19,680

a Age class codes indicate age at which owls were banded and became part of the mark-recapture data set: S1 = 1 year old, S2 = 2 years old, and adults ≥ 3 years old. b All captures, recaptures, and resightings, excluding multiple encounters of individuals in the same year.c One of eight study areas monitored under the northern spotted owl effectiveness monitoring program for the Northwest Forest Plan.

Source: adapted from Forsman et al. (2011).
9

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

in 10 randomly drawn records, although it is possible that 

a low level of data entry error might still persist. Copies 

of error-checked records and field data forms submitted to 

confirm these records were archived, and all crew leaders signed statements before submitting data for analysis 

certifying the accuracy of their data.

Estimating Survival

Cormack-Jolly-Seber open population models (CJS)

(Franklin et al. 1996a, Lebreton et al. 1992) in Program 

MARK (White and Burnham 1999) were used to estimate 

apparent survival of owls each year. Because survival estimates from CJS models cannot separate losses of individuals who died from losses owing to permanent emigration, 

these models estimate apparent survival, which incorporates 

the annual site fidelity of individuals (true survival x site 

fidelity = apparent survival). Spotted owls show high annual 

site fidelity (Forsman et al. 2002), so permanent emigration 

does not seriously bias model estimates, and apparent survival is believed to be very close to true survival (Anthony 

et al. 2006, Forsman et al. 2011). The general approach used 

to generate survival estimates from capture-recapture data 

on individual study areas was as follows:

• Decide on a set of a priori models for analysis and 

the order in which models will be run.

• Evaluate goodness-of-fit of the data to the general 

CJS model and estimate an over-dispersion parameter (c-hat = ĉ) using the median c-hat approach in 

Program MARK.

• Use the estimated c-hat to adjust covariance matrices for over-dispersion and to obtain quasi-Akaike’s 

information criteria (QAICc) for model selection.

• Run all models for capture probability and apparent 

survival developed in the pre-analysis a priori model 

set.

• Select appropriate models for inference based 

on QAICc model selection results (Burnham and 

Anderson 2002).

Several covariates expected to affect survival, including age, sex, the cost of reproduction, the proportion of 

territories where barred owls were detected each year, 

and climate covariates, were also included in the analysis. 

The nature (positive or negative) of these effects was 

hypothesized a priori, and the appropriate models reflecting 

these effects were included in the initial model sets prior to 

analysis. 

The meta-analysis of all 11 study areas combined was 

conducted in a similar fashion, but in addition to study area, 

time trends, the cost of reproduction, and the barred owl 

covariate, models also included land ownership, ecological 

region, latitude, climate, and habitat change.

Estimating Fecundity

All analyses of reproductive rate were based on the annual 

number of young produced per territorial female (NYF), but 

to be consistent with previous reports (Anthony et al. 2006, 

Forsman et al. 1996, Franklin et al. 1999), estimates from 

these models were presented as “fecundity,” where fecundity is the average annual number of female young produced 

per female owl (NYF/2). This adjustment assumes a 1:1 sex 

ratio at birth, which has been supported by previous genetic 

analyses of blood collected from juveniles (Fleming et al. 

1996). Models were developed a priori to investigate the effects of age, general time variation, a variety of time trends, 

the proportion of owl territories where barred owls were 

detected each year, and an even-odd year effect, which has 

previously been shown to reflect a temporal cycle in spotted 

owl reproduction (i.e., Anthony et al. 2006). In addition, 

climate and habitat covariates were included in the analysis. 

The general approach used to generate fecundity estimates 

was as follows:

• Decide on a set of a priori models for analysis and 

the order in which models will be run.

• Determine whether spatial variance (the random effect of territory) should be included in the modeling 

process.

• Use Proc Mixed in SAS (SAS Institute Inc. 2008) to 

fit all a priori models to the annual averages of NYF 

using a regression model based on a normal distribution.

• Select appropriate models for inference based 

on QAICc model selection results (Burnham and 

Anderson 2002).
10

GENERAL TECHNICAL REPORT PNW-GTR-850

In addition to an analysis of annual population change 

for each individual study area, a meta-analysis was conducted with all 11 study areas combined, where landownership, 

latitude, climate and weather, and ecological region were 

also included. Analysis details and meta-analysis results are 

reported in Forsman et al. (2011).

Estimates of realized population change (Dt) were also 

computed and reflect the proportional change in estimated 

population size relative to population size in the initial year 

of analysis, and were computed following the methods of 

Franklin et al. (2004). On each study area, annual estimate 

of realized population change was calculated as:

where x was the year of the first estimated λt. For example, 

given three, year-specific lambdas of say 0.9 in 1993, 1.2 in 

1994, and 0.7 in 1995, the realized population change would 

be 0.9 x 1.2 x 0.7 = 0.756. This value means that at the end 

of 1995, the population was 75.6 percent of the starting 

population in 1993. Thus, estimates of realized population 

change clearly illustrate the long-term, cumulative trends in 

annual population changes. 

Results

The following is a summary of the demographic analysis 

of apparent survival, fecundity, annual rate of population 

change, and realized population change for the northern 

spotted owl reported by Forsman et al. (2011). These 

analyses are the most long-term and comprehensive to date 

across the range of the owl; however, although the 11 study 

areas included in this analysis covered a large portion of the 

owl’s geographic range, they were not randomly selected. 

Thus, results cannot be considered representative of owl 

populations throughout its entire range and cannot be used 

to assess demographic trends on nonfederal lands because 

only two study areas on nonfederal lands were included 

in the analysis. However, Forsman et al. (2011) believed 

their results to be representative of most owl populations 

There was no consistent pattern regarding the best 

model for fecundity among study areas, so a nonparametric 

approach was used to estimate mean NYF by age class. The 

mean NYF was computed for each year and age class. Then 

these means were averaged across years within each age 

class. The estimated standard error was computed as the 

standard error of the average of the averages among years. 

This method gave equal weight to all years, regardless of 

the number of birds actually observed, and it did not force a 

model for changes over time. 

As was done for survival, a meta-analysis of fecundity 

with all 11 study areas combined was conducted, and in 

addition to the covariates included in the individual study 

area analysis, land ownership, latitude, climate, and ecological region were also included. Analysis details and metaanalysis results are reported in Forsman et al. (2011).

Estimating Annual Rate of Population Change and 

Realized Population Change

The reparameterized Jolly-Seber method (Pradel 1996) was

used to estimate annual rates of population change (λRJS)

in Program MARK using capture-recapture data. A parameterization was used to generate annual estimates of λ (λt

) 

for each study area, which allowed for decomposition of λ

into two components, apparent survival (φ) and recruitment 

(f), where:

λt = φt+ft

Apparent survival (φt) reflects both survival of territory 

holders within study areas and site fidelity at time t (year), 

so both death and permanent emigration are included in this 

parameter. Recruitment (f

t

) is the number of new owls in 

the population at time t+1 per animal in the population at 

time t and reflects both individuals born on the study area 

that become established territory holders, and immigration 

of recruits from outside the study area. Thus, the estimate 

of λt accounts for all of the losses and gains in the study 

area populations during each year and results in minimum 

bias in estimation of the annual rate of population change 

(Anthony et al. 2006).
11

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

on federal lands as they include nine large study areas, 

with comprehensive geographic coverage and a variety of 

landownership and management strategies. Thus, the results 

from the nine study areas associated with federal land 

managed under the Plan can be used to make inferences to 

populations on those lands. 

Survival

For the nine individual study areas, the number of banded 

owls included in the survival analysis were 615 1-year-olds, 

668 2-year-olds, and 2,910 adults (>3 years old) with 19,680 

total encounters across all individuals and age classes (table 

2-1). The number of recaptures in this data set was 4.5 times 

the number of initial captures.

In general, survival was similar between sexes (except 

for Olympic where survival was higher for males) and 

higher for adults compared to subadults (table 2-2). Factors 

including time and time trends, the proportion of territories 

where barred owls were detected each year, reproductive 

rate (fledglings per pair) in the previous year, and weather 

had varying effects on survival depending on the study 

area. Mean annual estimates of model-averaged apparent 

survival of female owls ranged from 0.529 to 0.794 for 

1-year-olds, 0.674 to 0.864 for 2-year-olds, and 0.819 to 

0.865 for adults (≥3 years old) (table 2-2). Most notably, survival was declining on all but the Klamath study area, and 

in some cases, the declines occurred primarily in the last 

10 years or so (Coast Ranges, H.J. Andrews, Tyee, South 

Cascades). Declines were most evident in Washington and 

strongest in the last 5 years for the Cle Elum and Rainier 

study areas. The Klamath study area was the only one for 

which no trend in survival was observed, although large 

amounts of annual variation in adult survival were observed 

(see fig. 5b in Forsman et al. 2011).

For the Rainier and Olympic study areas in Washington, survival was negatively associated with high rates of 

reproduction in the previous year, but this effect was not 

evident on any of the other study areas. In the meta-analysis 

Table 2-2—Average survival rates with standard errors (SE) for female northern spotted owls by age class in 

the nine demographic study areas associated with land managed under the Northwest Forest Plan

Age class

Landowner 1 year old 2 years old ≥3 years old

Study area class Survivala SE Survivala SE Survivala SE

Washington:

Cle Elumb Mixed 0.794 0.051 0.820 0.023 0.819 0.013

Rainier Mixed 0.541 0.181 0.674 0.156 0.841 0.019

Olympicb Federal 0.529 0.148 0.786 0.081 0.828 0.016

Oregon:

Coast Rangesb Mixed 0.742 0.072 0.864 0.031 0.859 0.009

H.J. Andrewsb Federal 0.717 0.084 0.830 0.042 0.865 0.010

Tyeeb Mixed 0.761 0.043 0.864 0.020 0.856 0.008

Klamathb Mixed 0.788 0.040 0.858 0.020 0.848 0.008

South Cascadesb Federal 0.692 0.069 0.733 0.053 0.851 0.010

California:

Northwest 

Californiab Mixed 0.774 0.031 0.784 0.031 0.844 0.009

Note: See table 2-1 for data years.

a Average survival is the arithmetic mean of model-averaged annual survival estimates for females. Standard errors were calculated using the 

delta method. 

b One of eight study areas monitored under the northern spotted owl effectiveness monitoring program for the Northwest Forest Plan.

Source: adapted from Forsman et al. (2011).
12

GENERAL TECHNICAL REPORT PNW-GTR-850

of all 11 study areas, the negative cost of reproduction 

on survival was an important covariate and a consistent 

effect across all study areas. The analyses of individual 

study areas supported the negative effect of barred owls 

on survival, but the effect was variable among study areas: 

decreased survival was associated with higher proportions 

of territories where barred owls were detected for Rainier, 

Coast Ranges, and H.J. Andrews, with weaker evidence 

found for the Olympic and Northwest California, and negligible evidence of a barred owl effect for Cle Elum, Tyee, 

and Klamath study areas. The results of the meta-analysis 

support much stronger negative effects of barred owl presence on spotted owl survival. The model with an additive 

barred owl effect ranked higher compared to the model with 

an interaction between barred owl presence and study area, 

supporting the importance of a consistent barred owl effect 

across all study areas, rather than an effect that varies in 

magnitude among areas. 

The effects of climate, weather, and the amount of

suitable owl habitat on survival were only investigated 

during the meta-analysis. There was some support for decreasing time trends in survival and a negative relationship 

between early nesting season precipitation and survival, 

but the amount of suitable habitat had no effect (Forsman 

et al. 2011). In addition, there was also some support for 

differences in survival among ecological regions, with the 

lowest survival rates reported for study areas in Washington 

mixed-conifer regions and highest survival for the Coast 

Ranges. The meta-analysis suggested several factors 

affected survival, but none of the covariates explored in 

this analysis explained a substantial portion of the variation 

among years and study areas (between 0.0 and 5.7 percent 

only).

Fecundity

The analysis across all 11 study areas by Forsman et al. 

(2011) included 11,450 observations of the number of young 

produced by territorial females, and 90 percent of those 

observations were from adult females ( >3 years old). The 

younger age classes were observed breeding much less frequently (3.8 percent for 1-year-olds, 6.1 percent for 2-yearolds), and age had a strong effect on productivity (Forsman 

et al. 2011). Mean fecundity was highest for adults (0.330, 

SE = 0.025), lower for 2-year-olds (0.202, SE = 0.042), and 

nearly negligible (0.07, SE = 0.015) for 1-year-olds (Forsman 

et al. 2011). 

Fecundity differed greatly by study area, and adult 

fecundity was highest on Cle Elum (0.553, SE = 0.052) and 

lowest in the Coast Ranges (0.263, SE = 0.04) (table 2-3). 

There was considerable annual variation in fecundity, but 

the patterns in variation were not consistent among study 

areas. A cyclic, even-odd-year effect where fecundity was 

high in even years and low in odd years was still important 

for some study areas (Forsman et al. 2011), but has generally 

become less evident since the last analysis (Anthony et al. 

2006). Overall, fecundity was declining in four areas (Cle 

Elum, Klamath, South Cascades, Northwest California), 

stable in two areas (Olympic, Tyee), and increasing in three 

areas (Rainier, Coast Ranges, H.J. Andrews) (table 2-4). 

The effects of several covariates on owl fecundity were 

also reported by Forsman et al. (2011). The proportion of 

owl territories on each study area where barred owls were 

detected at least once during a breeding season had a negative effect on fecundity for three study areas (Coast Ranges, 

Klamath, South Cascades), a positive effect on fecundity in 

one study area (H.J. Andrews), and no effect on the other 

five areas. There was also evidence that low temperatures 

during the early nesting season had negative effects on fecundity in three study areas (Rainier, Coast Ranges, South 

Cascades); late nesting season temperatures had a negative 

effect on fecundity on one study area (Tyee); and high 

precipitation during the early nesting season had negative 

effects on fecundity in three study areas (Cle Elum, Coast 

Ranges, Northwest California). Support for a negative effect 

of barred owls and effects of climate and weather on fecundity was generally weak. In Oregon, increased fecundity 

on four of five study areas (Coast Ranges, H.J. Andrews, 

Tyee, South Cascades) were associated with higher annual 

estimates of the amount of suitable habitat associated with 

each study area; however, more suitable habitat resulted in 

decreased productivity on the Klamath study area (Forsman 

et al. 2011). There was little indication of any association 

between the amount of suitable habitat and fecundity on 

the Washington study areas, and this association was not 
13

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Table 2-4—Trends in fecundity and survival, and mean rate of population change (x) with standard errors (SE) and 

95-percent confidence limits (95% CI) for northern spotted owls from nine demographic study areas associated with 

land managed under the Northwest Forest Plan

Estimated annual rate

of population change (λRJS)

a

Landowner Population

Study area class Fecundity Survival x SE 95% CI trendb

Washington:

Cle Elumc Mixed Declining Declining 0.937 0.014 0.910–0.964 Declining

Rainier Mixed Increasing Declining 0.929 0.026 0.877–0.977 Declining

Olympicc Federal Stable Declining 0.957 0.020 0.918 – 0.997 Declining

Oregon:

Coast Rangesc Mixed Increasing Declining since 1998 0.966 0.011 0.943–0.985 Declining

H.J. Andrewsc Federal Increasing Declining since 1997 0.977 0.010 0.957–0.996 Declining

Tyeec Mixed Stable Declining since 2000 0.996 0.020 0.957–1.035 Stationary

Klamathc Mixed Declining Stable 0.990 0.014 0.962–1.017 Stationary

South Cascadesc Federal Declining Declining since 2000 0.982 0.030 0.923–1.040 Stationary

California:

Northwest 

Californiac Federal Declining Declining 0.983 0.008 0.968–0.998 Declining

a λRJS = reparameterized Jolly-Seber estimate of population change (Pradel 1996). b

 Population trends based on estimates of realized population change.

c

 One of eight study areas monitored under the northern spotted owl effectiveness monitoring program for the Northwest Forest Plan.

Source: adapted from Forsman et al. (2011).

Table 2-3—Mean (x), age-specific fecundity (number of female young produced per female) with standard errors 

(SE) for northern spotted owls in the nine demographic study areas associated with land managed under the 

Northwest Forest Plan 

Age class

Landowner 1 year old 2 years old ≥3 years old

Study area class x SE x SE x SE

Washington:

Cle Eluma Mixed 0.115 0.083 0.517 0.109 0.553 0.052

Rainier Mixed 0.100 0.100 0.111 0.111 0.302 0.065

Olympica Federal 0.150 0.100 0.361 0.162 0.300 0.060

Oregon:

Coast Rangesa Mixed 0.000 0.000 0.094 0.039 0.263 0.040

H.J. Andrewsa Federal 0.083 0.083 0.110 0.043 0.323 0.041

Tyeea Mixed 0.018 0.013 0.218 0.065 0.305 0.034

Klamatha Mixed 0.056 0.024 0.289 0.045 0.377 0.033

South Cascadesa Federal 0.060 0.038 0.210 0.064 0.347 0.052

California:

Northwest 

Californiaa Mixed 0.088 0.054 0.152 0.038 0.324 0.027

Note: See table 2-1 for data years.

a One of eight study areas monitored under the northern spotted owl effectiveness monitoring program for the Northwest Forest Plan.

Source: adapted from Forsman et al. (2011).
14

GENERAL TECHNICAL REPORT PNW-GTR-850

investigated for California study areas because comparable 

maps to develop the covariate were not available (Forsman 

et al. 2011).

Annual Rate of Population Change

Estimates of the annual rate of population change (λ) on 

the nine study areas ranged from 0.929 to 0.996 (table 2-4). 

There was strong evidence that populations on the Cle 

Elum, Rainier, Olympic, Coast Ranges, H.J. Andrews, and 

Northwest California study areas declined during the study 

(table 2-4, fig. 2-2), with particularly low estimates of λ for 

Cle Elum and Rainier, which suggested population declines 

of 6.3 and 7.1 percent per year, respectively (table 2-4). 

Point estimates of λ for the Tyee, Klamath, and South Cascades study areas were all <1.0, but 95-percent confidence 

intervals (CIs) included 1.0 (table 2-4), suggesting populations may be stationary. The weighted mean estimate of λ

for all the study areas included in the analysis by Forsman 

et al. (2011) was 0.971 (SE = 0.007, 95-percent CI = 0.960 

to 0.983), which indicated that the average rate of population decline was 2.9 percent per year during the study. The 

weighted mean estimate of λ for the eight federal effectiveness monitoring areas (excluding Rainier) was 0.972 (SE = 

0.006, 95-percent CI = 0.958 to 0.985), which indicated an 

estimated decline of 2.8 percent per year. 

Results from the meta-analysis on the annual rate of 

population change indicated that both survival and recruitment differed by ecological region, with the highest survival 

in the Oregon Coast Douglas-fir region and lowest survival 

in Washington mixed-conifer region (Forsman et al. 2011). 

Recruitment was highest in the Oregon/California mixedconifer region and lower elsewhere (Forsman et al. 2011). 

A negative association between barred owl detections and 

survival in the rate of population change analysis was also 

evident and consistent with results from the meta-analysis 

of survival (see above). A weak association between surFigure 2-2−Estimates of mean annual rate of population change (λ), with 95-percent confidence intervals for northern

spotted owls in nine study areas associated with lands managed under the Northwest Forest Plan in Washington, Oregon,

and California. Source: Forsman et al. (2011). 
15

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

vival and the Pacific Decadal Oscillation was also evident, 

with higher survival observed during warmer phases of this 

regional climate cycle. No other climate or weather covariates were important. Estimates of recruitment were higher 

on study areas comprising primarily federal lands (Olympic, 

H.J. Andrews, South Cascades, Northwest California) 

compared to mixed or private ownerships. Recruitment was 

also higher when the proportion of suitable owl habitat was 

higher within study areas, but was lower in association with 

higher proportions of suitable habitat outside study area 

boundaries. 

Realized Population Change

Estimates of realized population change reflected the trend 

in the proportion of the population remaining each year, 

based on annual changes in λ in relation to the population at the beginning of the study (Forsman et al. 2011). 

Populations in Washington and northern Oregon (Olympic, 

Rainier, Cle Elum, Coast Ranges) declined by 40 to 60 

percent during this study (fig. 2-3), and there is some evidence that populations on the H.J. Andrews and Northwest 

California study areas were also declining (20 to 30 percent) 

although 95-percent CIs around estimates of realized 

population change overlapped 1.0 slightly (fig. 2-3). There 

was less evidence that populations on South Cascades, 

Tyee, and Klamath areas were in decline (5 to 15 percent), 

but many point estimates of realized population change for 

these areas were less than 1.0 even though 95-percent CIs 

broadly overlapped 1.0 (fig. 2-3).

Discussion

These demographic results are a summary of Forsman 

et al. (2011) and they represent the fifth meta-analysis of 

demographic data from northern spotted owls (Anderson 

and Burnham 1992, Anthony et al. 2006, Burnham et al. 

1996, Franklin et al. 1999). The second meta-analysis of 

demographic rates of northern spotted owls was conducted 

in 1993 and included 11 study areas (Burnham et al. 1996, 

Forsman et al. 1996). At that time, owl fecundity rates 

varied among years and with owl age, and exhibited no 

increasing or decreasing trend over time (Burnham et al. 

1996). Survival rates were dependent on age, and there was 

a decreasing trend in adult female survival. The annual rate 

of population change was <1.0 for 10 of 11 areas examined, 

with an estimated average rate of population decline of 

4.5 percent per year (Burnham et al. 1996). By 2004, owl 

fecundity was relatively stable among the 14 study areas 

examined, survival rates were declining on 5 of the 14 

areas, and populations were declining on 9 of 13 study areas 

for which there were adequate data to estimate λ (Anthony 

et al. 2006). However, the annual rate of decline was less, 

as mean λ for the 13 areas was 0.963, indicating populations were declining 3.7 percent annually during the study 

(Anthony et al. 2006). 

Declines in fecundity, survival, and rate of population 

change were observed across most study areas in this most 

recent analysis by Forsman et al. (2011). Over the last 15 

years, populations on all 11 areas included in the recent 

meta-analysis declined on average 2.9 percent per year 

(Forsman et al. 2011). This is a lower rate of decline than 

the 3.7 percent reported in the last meta-analysis (Anthony 

et al. 2006), but the rates of decline are not directly comparable between analyses. The current analysis represents 

a different time series than past efforts, and data collection 

on two of the study areas included in past analyses was 

discontinued (Wenatchee, Warm Springs Reservoir), so 

these areas could not be included in the most recent analysis 

(Forsman et al. 2011). In addition to the Rainier study area, 

apparent survival rates of owls were declining on seven 

(Cle Elum, Olympic, Coast Ranges, H.J. Andrews, Tyee, 

South Cascades, Northwest California) of the eight study 

areas associated with the Plan (table 2-4) and fecundity 

was also declining in four of these populations (table 2-4) 

(Forsman et al. 2011). In Washington and northern Oregon, 

the number of declining populations and the rate of decline 

raises concern about the long-term sustainability of the owl 

throughout its range (Forsman et al. 2011).

The reasons for declines in spotted owl populations 

were not readily apparent in any of the previous metaanalyses (Anthony et al. 2006, Burnham et al. 1996, 

Franklin et al. 1999). The analysis done by Forsman et al. 

(2011) incorporated covariates to investigate the influence of 

barred owls, weather and climate, and habitat on fecundity, 

survival, and rate of population change. As a result, we now 
16

GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 2-3−Estimates of realized population change, Δt, with 95-percent confidence intervals for northern spotted owls on nine 

study areas associated with lands managed under the Northwest Forest Plan in Washington, Oregon, and California, 1990–2006.
17

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

have some evidence that increasing numbers of barred owls 

and loss of habitat contributed to demographic declines 

reported in some study areas (Forsman et al. 2011). The 

presence of barred owls appeared to be the strongest and 

most consistent negative factor relating to spotted owl survival, but the strength of the response was variable among 

study areas. Forsman et al. (2011) concluded that although 

their results do not represent cause-effect relationships, they 

certainly suggest that barred owl invasion into the range of 

the spotted owl is at least partly to blame for the continued 

decline of the owl on federal lands. However, recovery of 

habitat lost over the last century is a slow process and likely 

continues to negatively impact owl populations. 

From the perspective of evaluating the effectiveness 

of the Plan on the conservation and recovery of the owl, 

the relationship between demographic rates and habitat 

are of particular importance. Because of the differences in 

the vegetation data used to develop habitat models for the 

10-year report, as discussed in chapter 3 of this report, the 

development of the habitat covariate in California was not 

possible, and its effect on demographic rates could only be 

investigated for Washington and Oregon (see Forsman et al. 

2011 for details). From this analysis, there was evidence that 

the percentage cover of suitable owl habitat had a positive 

influence on recruitment of owls in the meta-analysis of λ

(Forsman et al. 2011); however, this relationship was not 

strong or prevalent for all demographic parameters or 

among all study areas. 

Based on the meta-analysis of λ, there was some 

evidence that apparent survival was related positively to the 

percentage cover of suitable habitat in the Cle Elum, Coast 

Ranges, H.J. Andrews, and Tyee study areas in Washington 

and Oregon (Forsman et al. 2011). Also, a positive relationship between recruitment and the percentage cover of suitable owl habitat within the study area in the meta-analysis 

of λ was also found (Forsman et al. 2011). Recruitment was 

also highest on federally owned lands where the amount 

of suitable habitat was highest compared to private lands 

(Davis and Lint 2005). One possible explanation for this 

result is that more suitable habitat within the study areas 

provided areas where nonterritorial owls could survive until 

they were able to recruit into the territorial population.

Summary

After 15 years of population monitoring, we continue to observe significant annual declines in spotted owl populations 

(2.9 percent all ownerships, 2.8 percent federal ownership) 

(Forsman et al. 2011). Our ability to monitor the trend in 

owl populations is improving with newer technologies, the 

inclusion of explanatory covariates, and more years of data. 

We now have some evidence to support the suggestions of 

Anthony et al. (2006) that possible causes for declines in 

owl survival and populations may include high densities 

of barred owls and loss of habitat. However, a lot of uncertainty remains, and we are just beginning to understand the 

effects of these two factors on owl demography. We also 

must continue to stress the caution put forth in the Plan for 

projecting current estimates of population decline into the 

future. 

At its implementation, the Plan’s assumption was that 

owl populations across the range would continue to decline 

for the first three to five decades, eventually stabilizing at 

lower levels as losses of habitat lessen and habitat is restored 

in the network of large reserves scattered throughout its 

range. Since the Plan’s inception, the rate of habitat loss has 

certainly lessened, and here we report an overall habitat decline of 3.4 percent on federal lands in the last 15 years (see 

chapter 3 in this report), which is less than the anticipated 

rate of habitat loss of 5 percent per decade. We also report 

an overall 2.8 percent annual population decline on federal 

lands, with higher declines in the northern portions of the 

range and stationary populations in the central portion of 

the range as first noted by Anthony et al. (2006). These 

stationary populations were also not expected at the Plan’s 

implementation (Lint 2005). Although habitat is being 

maintained, the restoration of habitat under the Plan is still 

a few decades away. Forest succession is a slow process, 

but there are suggestions that it can be accelerated through 

well-designed silviculture (Garman et al. 2003, Muir et al. 

2002). We were not yet able to accurately measure recruitment of nesting/roosting habitat with current technologies; 

however, we were able to detect recruitment of the younger 

forests that serve as dispersal habitat (see chapter 3 in this 

report). We speculate that declining spotted owl populations 
18

GENERAL TECHNICAL REPORT PNW-GTR-850

will not begin to stabilize across the range at least until 

nesting/roosting habitat begins to increase significantly. 

And although habitat is a key element in the conservation 

of spotted owls (Lint 2005), it may no longer be the primary 

factor affecting population stability in either the short 

or long term. The rapidly increasing trend in barred owl 

populations has produced an unanticipated and confounding 

influence, as these species may compete for resources.

The answer to the question, “Will the Plan reverse 

the declining population trend and maintain the historical 

geographic range of the northern spotted owl?” still eludes 

us. Five more years of monitoring has shed more light 

on the subject, but a definitive answer will require more 

long-term monitoring to better understand the temporal 

and spatial variability in owl demographics and the factors 

that affect owl vital rates. Until then, we believe that habitat 

maintenance and restoration, as currently envisioned under 

the Plan, remains essential to the owl’s recovery. However, 

additional conservation measures (i.e., barred owl control) 

that were not envisioned under the Plan may ultimately be 

needed to recover the species in the face of the barred owl 

expansion into the Pacific Northwest.

References

Anderson, D.R.; Burnham, K.P. 1992. Demographic 

analysis of northern spotted owl populations. In: Final 

draft recovery plan for the northern spotted owl. Volume 

2: 66–75. Unpublished report. On file with: U.S. Fish and 

Wildlife Service, 2600 SE 98th Ave., Suite 100, Portland, 

OR 97266. 

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.D.; Hines, J.E.; Olson, 

G.S.; Ackers, S.H.; Andrews, L.W.; Biswell, B.L.; 

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

K.E.; Fleming, T.L.; Gearhardt, R.P.; Gremel, 

S.A.; Gutiérrez, 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. 2006. Status and trends in 

demography of northern spotted owls, 1985–2003. 

Wildlife Monographs No. 163. Washington, DC: The 

Wildlife Society. 48 p.

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

and multimodel inference: a practical information 

theoretical approach. 2nd ed. New York, NY: SpringerVerlag. 488 p. 

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.

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.

Davis, R.; Lint, J. 2005. Habitat status and trend. In: 

Lint, J., tech. coord. 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: 21–82.

Fleming, T.L.; Halverson, J.L.; Buchanan, J.B. 1996.

Use of DNA analysis to identify sex of northern spotted 

owls (Strix occidentalis caurina). Journal of Raptor 

Research. 30: 18–122.
19

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Forsman, E.D.; Anthony, R.G.; Dugger, K.M.; Glenn, 

E.M.; Franklin, A.B.; White, G.C.; Schwarz, C.J.; 

Burnham, K.P.; Anderson, D.R.; Nichols, J.D.; Hines, 

J.E.; Lint, J.B.; Davis, R.J.; Ackers, S.H.; Andrews, 

L.S.; Biswell, B.L.; Carlson, P.C.; Diller, L.V.; 

Gremel, S.A.; Herter, D.R.; Higley, J.M.; Horn, R.B.; 

Reid, J.A.; Rockweit, J.; Schaberl, J.; Snetsinger, T.J.; 

Sovern, S.G. 2011. Population demography of northern 

spotted owls. Studies in Avian Biology 40. Berkeley, CA: 

University of California Press. 106 p.

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.

Forsman, E.D.; DeStefano, S.; Raphael, M.G.; 

Gutíerrez, R.J., eds. 1996. Demography of the northern 

spotted owl. Studies in Avian Biology No. 17. Camarillo, 

CA: Cooper Ornithological Society. 122 p.

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

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

collecting and analyzing demographic data on northern 

spotted owl. Studies in Avian Biology. 17: 12–20.

Franklin, A.B.; Burnham, K.P.; White, G.C.; Anthony, 

R.G.; Forsman, E.D.; Schwarz, C.; Nichols, J.D.; 

Hines, J. 1999. Range-wide status and trends in northern spotted owl populations. Colorado Cooperative Fish 

and Wildlife Research Unit, Colorado State University, 

Fort Collins, Colorado, USA. 72 p. http://warnercnr.

colostate.edu/~alanf/reprints/abf_ea99nsometa.pdf.

(18 December 2010).

Franklin, A.B.; Gutíerrez, R.J.; Nichols, J.D.; Seamans, 

M.E.; White, G.C.; Zimmerman, G.S.; Hines, 

J.E.; Munton, T.E.; LaHaye, W.S.; Blakesley, 

J.A.; Steger, G.N.; Noon, B.R.; Shaw, D.W.H.; 

Keane, J.J.; McDonald, T.L.; Britting, S. 2004. 

Population dynamics of the California spotted owl 

(Strix occidentalis occidentalis): a meta-analysis. 

Ornithological Monographs. 54 p.

Franklin, A.B.; Gutiérrez, R.J.; Noon, B.R.; Ward, 

J.P. 1996b. Demographic characteristics and trends 

of northern spotted owl populations in northwestern 

California. Studies in Avian Biology. 17: 83–91. 

Garman, S.L.; Cissel, J.H.; Mayo, J.H. 2003.

Accelerating development of late-successional conditions 

in young managed Douglas-fir stands: a simulation 

study. Gen. Tech. Rep. PNW-GTR-557. Portland, OR: 

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

Northwest Research Station. 119 p.

Lebreton, J-D.; Burnham, K.P.; Clobert, J.; Anderson, 

D.R. 1992. Modeling survival and testing biological 

hypotheses using marked animals: 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. 

Lint, J.B., 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.
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Muir, P.S.; Mattingly, R.L.; Tappeiner, J.C., II; Bailey, 

J.D.; Elliott, W.E.; Hagar, J.C.; Miller, J.C.; Peterson, 

E.B.; Starkey, E.E. 2002. Managing for biodiversity 

in young Douglas-fir forests of western Oregon. U.S. 

Geological Survey, Biological Resources Division, 

Biological Science Report USGS/BRD/BSR–2002-0006. 

76 p. http://fresc.usgs.gov/products/papers/mang_bio.pdf. 

(18 December 2010).

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

the study of recruitment and population growth rate. 

Biometrics. 52: 703–709. 

SAS Institute, Inc. 2008. SAS/STAT® 9.2 users guide. 

SAS Institute, Inc., Cary, NC.

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

survival estimation from populations of marked animals. 

Bird Study. 46: 120–138.

Participants in the 2009 META-Analysis. 

Peter Carlson
21

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Chapter 3: Habitat Status and Trend

Raymond J. Davis and Katie M. Dugger

Introduction

The first rangewide northern spotted owl (Strix occidentalis 

caurina) habitat map was developed for the Forest Ecosystem Management Assessment Team (FEMAT) in 1993. 

It was constructed through a combination of digital maps 

derived from satellite imagery and maps derived from aerial 

photo interpretation. The team used the best available data 

and geographical information system (GIS) technologies 

at that time to represent owl habitat conditions at the start 

of the Northwest Forest Plan (the Plan), which we call the 

“baseline.” However, the authors acknowledged that the 

map was an estimate and had not been assessed for accuracy (FEMAT 1993). Six years later, the northern spotted 

owl effectiveness monitoring plan concluded that this map 

lacked the spatial resolution and accuracy needed for a 

baseline spotted owl habitat map for monitoring purposes 

(Lint et al. 1999). They proposed the development of a new 

rangewide baseline habitat map to “provide the landscapescale view of habitat conditions at different resolutions.” 

Having a good baseline habitat map is essential to the 

effectiveness monitoring program because it provides a 

snapshot in time of what conditions were like when the

Plan was implemented. Without an understanding of baseline conditions, we would not be able to answer the primary 

question of whether owl habitat and dispersal habitat are 

being maintained and restored under the Plan. The first 

rangewide baseline habitat monitoring map was developed 

by Davis and Lint (2005) for the 10-year monitoring report 

(Lint 2005). The data sources and methods used to develop 

that map are fully described in Davis and Lint (2005) and 

are not repeated in this report. Limitations in the first baseline map were noted by Davis and Lint (2005) and Raphael 

(2006) and are reviewed in the following discussion. 

The Northwest Forest Plan effectiveness monitoring 

program was in its early stages of development at the time 

of the 10-year reporting analysis. A consistent rangewide 

vegetation data set as described in Lint et al. (1999) did 

not exist. Instead, two distinctly different vegetation data 

sources covered the owl’s range: Interagency Vegetation 

Mapping Project (IVMP) data (Oregon and Washington) 

and Classification and Assessment with Landsat of Visible 

Ecological Groupings (CALVEG) data (California) (Davis 

and Lint 2005, Moeur et al. 2005). The choice of vegetation 

variables provided by these two sources was limited and 

included only tree size class and cover attributes, which 

were not mapped consistently between the two products. 

Other habitat mapping “core elements” discussed by Lint 

et al. (1999), such as stand age and tree species data, were 

not available, resulting in omission of important habitat 

relationship variables in the models used to create the first 

baseline map. To compensate for lack of tree species data, 

Davis and Lint (2005) used elevation as a variable in their 

habitat modeling and also built a “habitat-capable” GIS 

layer, largely based on a rangewide elevation isopleth that 

would “mask out” subalpine forests, in which spotted owls 

avoid nesting. There was no way to “mask” pine-dominated 

forests or to include evergreen hardwoods, which are 

important components of owl habitat in the southern physiographic provinces. As a result, where tree size and cover 

conditions were otherwise similar to those used by nesting 

and roosting territorial owls, the models classified them as 

suitable, even when they probably were not because of tree 

species composition. 

Another problem was the coarse spatial resolution and 

lack of continuous attribution in the CALVEG data (Davis 

and Lint 2005). This resulted in poorer estimates of habitat 

in the California physiographic provinces and habitat maps 

that were not directly comparable to the Oregon and Washington maps. The lack of a consistent rangewide habitat map 

resulted in our inability to fully model associations between 

spotted owl demography rates and habitat during the 2009 

population meta-analysis (Forsman et al. 2011). 

Additional limitations of the 10-year report’s baseline 

owl habitat map (Davis and Lint 2005) included the use of 

the median algorithm in the BioMapper habitat modeling 

software (Hirzel et al. 2002), which was the only algorithm 

available at the time (Davis and Lint 2005). This algorithm 

assumed species distribution along the environmental 

factors was normal (see fig. 3-8 on page 36 in the 10-year 

report); however, in reality, this is not always the case, and 

nonnormal relationships resulted in the overestimations of 
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GENERAL TECHNICAL REPORT PNW-GTR-850

habitat suitability. In general, profile models like BioMapper are known to sometimes overpredict habitat suitability 

(Engler et al. 2004). To compensate for this, Davis and Lint 

(2005) provided a habitat map with a continuous scale from 

0 to 100, where a value close to zero signified that an individual map unit (pixel) had little in common with the conditions found where territorial owls are present, and those 

with values close to 100 had much in common with sites 

having territorial owl presence. During this initial effort, a 

threshold value that designated a cutoff between “suitable” 

and “not suitable” habitat was not chosen. Instead, Davis 

and Lint (2005) reported on status and trend of the spectrum 

of habitat suitability (HS) divided into equal-interval bins, 

and areas with HS >40, which “had characteristics similar 

to areas where territorial owls have been found.” 

Based on our latest work (presented here), we now 

conclude that the baseline habitat map developed for the 

10-year report did overestimate owl habitat suitability in 

portions of the range. Overestimations occurred within 

pine-dominated forests of the eastern Cascades for reasons 

discussed above, and, as noted by Raphael (2006), habitat 

suitability scores greater than 40 were achieved in stands 

as young as 30 years in the Coast Range of Oregon and 

50 years in Oregon western Cascades, providing further 

evidence of profile model overpredictions. Based on visual 

comparisons of the former baseline maps and the new one, 

we also believe that the use of the coarser scale CALVEG 

data in the 10-year habitat modeling resulted in considerably more habitat suitability >40 estimated for California. 

Since the 10-year report, much progress has been made 

in developing a consistent rangewide vegetation data layer, 

with a larger suite of vegetation attributes to be used as 

“core elements” for habitat mapping, including tree species information (Ohmann and Gregory 2002). These new 

rangewide vegetation data are produced by the Landscape 

Ecology, Modeling, Mapping, and Assessment group 

(LEMMA) based at the Pacific Northwest Research Station 

in Corvallis, Oregon (link to Web page: http://www.fsl.orst.

edu/lemma/). Detailed attributes of forest composition and 

structure were mapped for all forests in the Plan area for 

two “bookend” dates. The bookend dates were 1996 and 

2006 in Washington and Oregon, and 1994 and 2007 in 

California. This marks the first application of using multiple 

satellite imagery dates to create “bookend” vegetation maps 

for habitat monitoring purposes (Ohmann et al. 2010).

In addition to improved vegetation map products, the 

science of habitat modeling has evolved since the 10-year 

report. Species distribution and habitat suitability modeling 

has been the subject of much current research and discussion in ecology (Elith et al. 2006, Guisan and Zimmermann 

2000, Hirzel and Le Lay 2008), so we spent a substantial 

amount of time reviewing modeling options and testing 

several types of software used for habitat modeling before 

deciding on the approach presented here.

One thing we have observed through these efforts is 

that regardless of the methods used, the map products are 

visually similar at the rangewide scale (fig. 3-1). Therefore, 

it is important to test the map’s accuracy with actual spotted 

owl nesting and roosting location data. This is one area 

where the population monitoring and habitat monitoring 

efforts connect, as we used different subsets of the demographic data to first train and then test the accuracy of our 

habitat model mapped predictions.

The use of the new rangewide vegetation data set and 

the latest habitat modeling software has resulted in an 

improved baseline habitat map that has tested well with 

actual owl pair location data (including independent data 

sets). These improvements included better discrimination 

of habitat in the eastern Cascades, where pine-dominated 

forests mostly occur, and the use of the “habitat-capable” 

layer from Davis and Lint (2005) was no longer required 

for habitat modeling with the inclusion of a subalpine forest 

type variable. We use this new baseline map (1994/96) and 

the other bookend map (2006/07) for conducting our habitat 

status and trend analysis.

The development of bookend maps was an innovative 

advancement in our monitoring methods, but aspects of 

it remain to be tested. Given its novelty, we restricted our 

use of the 2006/07 bookend to only inform us on habitat 

changes within areas that were identified as having experienced a disturbance by the LandTrendr data. It is important 

to make sure that the bookend maps used for later analyses 

are generated with the same data sets and methods, and 

tested so that the detection of change from one to the other 
23

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

is a fair comparison of “real” change and not one caused 

by analytical or data differences. In future monitoring 

cycles, we anticipate more advancements in both vegetation 

data mapping and habitat modeling science; therefore, we 

anticipate that future modifications will be made to the 

baseline map, including the use of 1994 satellite imagery for 

the entire range. This is appropriate as the status and trend 

analysis is based on the use of the best available vegetation 

and change-detection data and technologies. 

Habitat Monitoring Under the Plan

Under the monitoring plan, habitat status and trends are to 

be estimated approximately every 5 years after the baseline 

map was developed because it was believed that changes 

in forest vegetation conditions would not be discernable 

from the remote sensed vegetation data on more frequent 

intervals (Lint et al. 1999). The intent of habitat monitoring 

is to determine if assumptions made during the development of the Plan are holding true. Testing the assumption 

that habitat will not decline faster than predicted in the final 

environmental impact statement (USDA and USDI 1994) 

is of particular interest. The initial list of assumptions is as 

follows (Lint et al. 1999):

1. Habitat conditions within late-successional reserves 

(LSRs) will improve over time at a rate controlled by 

successional processes in stands that currently are not 

habitat. However, this is not expected to produce any 

significant changes in habitat conditions for several 

decades.

2. Habitat conditions outside of reserved allocations 

will generally decline because of timber harvest and 

other habitat-altering disturbances, but the vegetation 

structure across the landscape will continue to facilitate 

owl movements.

Figure 3-1—Baseline maps are representative of habitat at the implementation of the Northwest Forest Plan. 

They evolve with new mapping technology. Even so, at the range scale, the general spatial patterns of habitat 

between them are similar. FEMAT = Forest Ecosystem Management Assessment Team.
24

GENERAL TECHNICAL REPORT PNW-GTR-850

3. Catastrophic events are expected to halt or reverse 

the trend of habitat improvement in some reserves; 

however, the repetitive design of reserves should 

provide resiliency, and not result in isolation of 

population segments.

Central to these questions is the federal network of 

reserved land use allocations designed to support groups 

of reproducing owl pairs across the species’ range. These 

reserves include LSRS, adaptive management reserves, 

congressionally reserved lands, managed late-successional 

areas, and larger blocks of administratively withdrawn 

lands. It is also important to monitor the lands between 

these reserves because they provide for recruitment of new 

owls into the territorial populations (see chapter 2, this report) and are important for dispersal and movement of owls 

between larger reserves. These dispersal habitats occur in a 

combination of matrix, adaptive management areas, riparian 

reserves, small tracts of administratively withdrawn lands, 

and other small reserved areas such as 100-ac owl core 

areas. To understand whether the Plan is contributing to the 

conservation and restoration of owl habitat, the condition 

and trends of owl habitat must be regularly assessed. The 

specific questions that were addressed in the 10-year report 

and that will be addressed here as well include:

1. What proportion of the total landscape on federal lands 

are owl habitat and dispersal habitat?

2. What are the trends in amount and changes in 

distribution of owl habitat, particularly in large, 

reserved blocks?

3. What are the trends in amount and distribution of 

dispersal habitat outside of the large, reserved blocks?

4. What are the primary factors leading to loss and 

fragmentation of both owl habitat and dispersal habitat?

Following the approach of Davis and Lint (2005), the 

condition of owl habitat will be reported at three broad geographic scales: (1) the physiographic province, (2) the state, 

and (3) the geographic range of the owl. However, because 

of changes that have occurred in federal land use allocations since the 10-year report (fig. 3-2), we will no longer 

report status and trends within every land use allocation. 

Instead, we will report by broad federal land use allocations 

representing “reserved” and “nonreserved” landscapes (fig. 

3-3), which we feel is a more consistent and appropriate 

scale for monitoring. Because the “large block” reserves 

(see fig. 3-13, page 44 in the 10-year report) make up about 

90 percent of the reserved landscape, we now consider our 

reporting of status and trend in the reserved landscape as 

one entity, whereas in the 10-year report we separated them. 

Although the effectiveness monitoring is focused to address 

questions about the Plan, its developers realized that the 

status and trends of the subjects being monitored are often 

influenced by conditions on the surrounding nonfederal 

lands. Therefore, we will report on habitat conditions on 

nonfederal lands at the state and range scales because these 

were included in the 10-year monitoring synthesis report by 

Raphael (2006). 

As stated in the 10-year report, our objective was to 

produce maps of forest stands (regardless of patch size and 

spatial configuration) that showed the level of similarity to 

stand conditions known to be used for nesting and roosting 

by spotted owls. Forest stands with conditions most similar 

to what is used by nesting and roosting owl pairs are what 

we will refer to as “nesting/roosting habitat” throughout this 

document. We will also report on forest stand conditions 

that are known to be used by dispersing owls, which we 

refer to as “dispersal habitat.”

Methods and Data Sources

Land Use Allocation Data

An updated map of the Plan’s land use allocations (LUA) 

was produced in 2002 for the 10-year monitoring reports 

(Huff et al. 2005, Lint 2005, Moeur et al. 2005). It updated 

the original 1994 version, which was mapped with older 

GIS technology and had a 40-ac resolution. This first 

update corrected some mapping inconsistencies, but more 

importantly, incorporated allocation changes that occurred 

between 1994 and 2002. Although this map was considered 

an improvement from the earlier version, some limitations 

still remained (Davis and Lint 2005, Huff et al. 2005). The 

major limitations were the inability to map riparian reserves 
25

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Figure 3.2—Changes made to the land use allocations since the 10-year report (Lint et al. 2005).
26

GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 3-3—Federally administered lands within the range of the northern spotted owl.
27

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

(which can cover significant amounts of land where stream 

densities are high) and inconsistencies in how administratively withdrawn areas were delineated. Errors that remained after the 2002 update included the misidentification 

of a state-owned park in the redwood region of California as 

federally owned National Park Service land and inaccurate 

or missing boundaries of national wildlife refuges, mainly 

in Washington and Oregon. Other minor mapping issues 

included edge matching inconsistencies that caused “sliver 

gaps” and inconsistent attribution of large water bodies.

A second update of LUAs performed in 2009 (fig. 3-2) 

produced a new version that is used for this 15-year report. 

The new version incorporates major LUA changes that 

occurred between 2002 and 2009, and it also corrects the 

errors identified above. Minor issues with inconsistent mapping of administratively withdrawn areas still remain, and 

a small amount (<1 percent) of federally administered lands 

are awaiting official LUA designations and are identified as 

“not designated” in the 2009 map. Riparian reserves still 

remain unmapped because, as Moeur et al. (2005) noted, 

“...at the Plan scale, they cannot be reliably distinguished 

from matrix because of a lack of consistency in defining 

intermittent stream corridors and varying definitions for 

riparian buffers.”

The Plan allowed for land exchanges involving LSRs 

if they provide benefits equal to or better than current 

conditions, such as to improve area, distribution, and 

connectivity of the LSR system (USDA and USDI 1994). 

It also acknowledges that future changes would occur for 

the administratively withdrawn allocation. At the end of 

the 15-year monitoring period, we note a net increase of 

about 25,000 ac in reserved allocations, and a net decrease 

of about 17,000 ac in nonreserved allocations. Most of 

the changes that occurred were designations of otherwise 

reserved allocations into 237,000 ac of congressionally designated reserves, and most of this (83 percent) occurred in 

northern California. Because some of the changes included 

land exchanges or acquisitions, the increase in reserved 

allocations and decrease in nonreserved allocations are not 

equal.

Land use allocations will continue to change, and 

we will continue to update this map with the intent of 

improving it for each monitoring effort. For monitoring 

purposes, we archive the previous versions and report 

vegetation and habitat changes for all monitoring modules 

within the reference frame of the most up-to-date allocation map. Major LUA changes that are important for us 

to note include changes that cover thousands of acres and 

involve gains or losses of reserved allocations. We will 

discuss these changes in relationship to the standard and 

guidelines within the record of decision (USDA and USDI 

1994). Given the most recent information, the latest changes 

in reserved allocations (fig. 3-2) have resulted in a slightly 

increased area and improved distribution and connectivity 

of the reserved allocation system.

Vegetation Data

The vegetation data used for habitat modeling and mapping 

were developed through a method for predictive vegetation 

mapping using direct gradient analysis (Gauch 1982, ter 

Braak 1986) and nearest-neighbor imputation (Moeur and 

Stage 1995) to assign detailed forest vegetation plot information to every pixel in a GIS raster map. The combining 

of these methods to develop vegetation maps was termed 

“gradient nearest neighbor” (GNN) and is thoroughly 

described in Ohmann and Gregory (2002). The GNN maps 

developed in the Pacific Northwest have previously been 

applied to broad-scale vegetation mapping efforts across a 

wide range of forest ecosystems (Ohmann et al. 2007, Pierce 

et al. 2009). Forest attributes from regional inventory plots 

are assigned to map pixels where data are missing, on the 

basis of a modeled relationship between the detailed forest 

attributes from plots and a combination of spatial predictor 

variables derived from Landsat satellite imagery, climate 

variables, topographic variables, and soil parent materials. 

The assumption behind GNN methods is that two locations 

with similar combined spatial “signatures” should also have 

similar forest structure and composition. Plot data are from 

regional forest inventory plots: Forest Inventory and Analysis (FIA) periodic inventories on nonfederal lands, FIA 

annual inventory on all ownerships, and Current Vegetation 

Survey inventories. The GNN data used for habitat modeling and mapping covers the entire breadth of the owl’s range 

from Washington to northern California for two points in 
28

GENERAL TECHNICAL REPORT PNW-GTR-850

time. We call these two data sets “bookends” because the 

changes in habitat that we analyzed and report on occurred 

between them. The satellite imagery from which GNN was 

created covers the period from 1994 to 2007 in California 

and 1996 to 2006 in Oregon and Washington. The on-theground plot data used to create the vegetation maps covers 

the period 1991 to 2000 for bookend 1, and 2001 to 2007 for 

bookend 2. The GNN products are 30-m (98.4-ft) grids that 

were specifically developed for mid- to large-scale spatial 

analysis (Ohmann and Gregory 2002). 

The primary challenge was to develop GNN modelbased maps for the two bookend dates that minimized 

spectral differences owing to different image dates that 

might produce false vegetation changes. To achieve this, the 

GNN models used Landsat imagery that was geometrically 

rectified and radiometrically normalized through time 

using the LandTrendr algorithms (Kennedy et al. 2007). A 

full description of the GNN bookends methodology can be 

found in Moeur et al. (2011).

The accuracy assessment for GNN continuous variables was based on the correlation of observed plot values 

against predicted (modeled) values. Ohmann et al. (2010) 

used a modified leave-one-out cross-validation approach 

that yields results similar to those of a true cross-validation 

approach, but probably slightly underestimates the true 

accuracy. The accuracy assessments are based on pooled 

plots for each modeling region. Canopy characteristics are 

usually the most easily determined via space-borne remote 

sensing instruments, and the most accurate GNN variable 

was conifer canopy cover, with an average plot correlation of 0.74 (±1 standard deviation [SD] = 0.07). Inferring 

vegetation characteristics underneath the canopy is more 

difficult, and the correlation coefficients for the structural 

and age vegetation variables we chose to use ranged from 

0.38 to 0.82, with an average plot correlation of 0.63 (±1 SD 

= 0.12). The accuracy assessment for the species composition variables is based on Cohen’s kappa coefficient, which 

is a measure of agreement between predicted and actual 

conditions (in this case dominant tree species), taking into 

consideration agreement occurring by chance (Cohen 1960). 

We combined several species to produce “forest type” basal 

area variables as shown in appendix A. The average kappas 

for these species groups, or forest-type, variables ranged 

from 0.30 to 0.46, with an average kappa of 0.40 (±1 SD = 

0.07). Oak woodland was the most accurate species group, 

followed by subalpine, evergreen hardwoods, and pine.

Change-Detection Data

A new approach to monitoring landscape vegetation change 

was implemented to map forest disturbances in the owl’s 

range. Landsat-based detection of trends in disturbance 

and recovery (LandTrendr) produces yearly maps of 

forest disturbance using a new analysis of annual Landsat 

Thematic Mapper satellite imagery (Kennedy et al. 2010). 

In general, LandTrendr detects spectral trajectories from 

Landsat time-series stacks and correlates them to land 

surface changes. The time series of Landsat imagery that 

was assembled for the Plan area was processed using basic 

atmospheric correction, cloud screening, and radiometric 

normalization to separate imagery noise (i.e., cloud cover, 

smoke, snow, or shadows) from actual vegetation change. 

Predictions of vegetation cover change were then evaluated 

using a statistical model of vegetation cover developed from 

photointerpreted plots (Cohen et al. 2010). The results of 

this evaluation found that LandTrendr detected vegetation 

disturbances as well as or better than two-date changedetection methods, and that it detects with reasonable 

robustness a range of other dynamics such as insect-related 

disturbance and growth (Kennedy et al. 2010). Errors in 

LandTrendr predictions were generally confined to very 

subtle change phenomena (Kennedy et al. 2010). In summary, LandTrendr improved the temporal frequency of 

disturbance maps used for monitoring, better separates 

subtle changes from background noise, and detects a wider 

range of vegetation change phenomena than was possible 

with previous technologies (Kennedy et al. 2010, Moeur et 

al. 2011). 

We used the LandTrendr data to verify habitat losses 

between our bookend maps and to attribute the most likely 

cause of habitat loss (fig. 3-4). The data covered the entire 

analysis area and period (1994–2007) and provided information by 30- by 30-m pixels on initial year of disturbance. 

LandTrendr classified the cause of disturbance (vegetation 

cover loss) into three types: (1) timber harvest, (2) insect 
29

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Figure 3-4—LandTrendr change-detection data (Kennedy et al. 2010).
30

GENERAL TECHNICAL REPORT PNW-GTR-850

and disease (can also include pathogens and other nonabrupt 

processes), and (3) wildfire. Fire locations were identified 

based on fire perimeter GIS data from Monitoring Trends 

in Burn Severity (MTBS1) data, Geospatial Multi-Agency 

Coordination (GeoMAC2) data, and other sources (i.e., individual forest data). The remaining short-term disturbances 

were assigned “harvest” as the probable cause of disturbance, although wind may account for a small percentage.

Spotted Owl Presence Data

The owl survey data collected under the effectiveness 

monitoring program are important not only for population 

monitoring (chapter 2, this report), but also for monitoring 

suitable habitat. The owl pair location data (presence only 

spatial data) from demographic study areas are collected 

annually and are spatially very accurate. This made such 

data ideal for habitat suitability modeling; thus, we used 

them as the foundation for training our habitat models. 

However, the results of our preliminary model testing 

indicated that using only demographic study area data was 

problematic for modeling habitat in some modeling regions. 

Confining our model training data to the demography areas 

produced a “geographically clumped” distribution of model 

training points within the boundaries of our larger modeling 

regions. This clumping violated the basic assumption of 

habitat modeling methods that require independence and 

sampling without bias for presence data (training data) from 

the modeling region (Gillison and Brewer 1985, Phillips et 

al. 2009, Williams et al. 2002). We therefore matched our 

modeling regions to the boundaries of the demographic 

study areas, trained the habitat model to those areas, and 

then extrapolated the model results to the larger geographic 

regions. This produced mixed results, with some models 

testing well, while others could not be projected (extrapolated) successfully when the larger geographic area did not

1

 Data accessible thru the Forest Service’s Remote Sensing Applications Center (RSAC) Web site http://www.fs.fed.us/eng/rsac/.

2

 Geospatial Multi-Agency Coordination Group or GeoMAC, is 

an Internet-based mapping application originally designed for 

fire managers to access online maps of current fire locations and 

perimeters in the conterminous 48 states and Alaska. Data are 

available at http://www.geomac.gov/.

contain all the environmental variables that were used for 

habitat modeling. Our solution was to supplement the owl 

location data from demography study areas with the owl 

presence location from the broader geographic areas surrounding them to reduce sampling bias issues and produce 

a training data set that was better distributed within the 

modeling region. To do this we used the data set used for 

the 10-year report (Davis and Lint 2005). 

The first step in this process was to conduct a nearest 

neighbor distance analysis on owl pair site centers from 

study areas within each modeling region (app. B). We used 

the average nearest neighbor distances calculated from the 

50-percentile harmonic cores (to remove outlier sites) from 

each of the study areas as a minimum distance parameter 

for randomly selecting a number (equal in size to the demographic study area data) of northern spotted owl pair sites 

from the 10-year report training data set (Davis and Lint 

2005) that was outside of the study area boundaries. Both 

the demographic study area sites and the random selection 

of owl pair locations outside of them were combined to form 

the habitat suitability model training data set. This provided 

a well-distributed and nonclumped training data set for each 

modeling region. 

We also attempted to match the date of our training 

data to the date of the satellite imagery used to create 

the vegetation data set that provided habitat variables for 

modeling. And finally, because we suspected interspecific 

competition between spotted owls and barred owls (Strix 

varia) to potentially confound the spotted owl/habitat use 

relationship, we used activity centers from the study areas 

based on surveys done between 1994 and 1996 because 

barred owl densities were lower than in 2006 and 2007. 

Our training data outside of the study area cover a broader 

period that roughly frames that period as discussed in Davis 

and Lint (2005).

Habitats, the Niche Concept, and Habitat 

Modeling

Understanding where animals live and the myriad factors 

associated with how and why they make the choices as

to where to live has been the subject of extensive research 

(Stauffer 2002). As stated by Morrison et al. (1992), “an
31

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

animal’s habitat is, in the most general sense, the place 

where it lives.” This seems simple enough: an animal

can only live in an area that meets its basic needs for resources (food, water, nest sites, etc.), includes competitors 

and predators with which it can coexist, and in climatic 

extremes it can withstand (Morrison et al. 1992). This is 

maybe best articulated within the niche concept, which has 

a long evolution in the science literature (see Morrison et 

al. 1992 for review) and has become a useful construct for 

conceptualizing and quantifying wildlife-habitat relationships. The multivariate, or n-dimensional, niche as defined 

by Hutchinson (1957) lends itself well to current attempts 

to model wildlife-habitat interactions, as it allows us to 

conceptualize all the complexities associated with how and 

why animals choose where they live. A species potential or 

“fundamental” niche includes a subset of all the environmental conditions required for a species long-term survival; 

however, this “fundamental niche” can be further restricted 

by predators and competitors resulting in a “realized niche” 

(Hutchinson 1957). This realized niche reflects a subset of 

the conditions found in the fundamental niche and is the set 

of environmental conditions that characterize the space a 

species actually occupies (Hutchinson 1957) and is reflected 

in the observed distribution of a species.

Many types of species distribution models are available 

for estimating a species’ realized niche (and producing a 

geographic distribution map of it) using species presence 

data that are correlated to environmental data of relevance 

to the species occurrence. For the 10-year report (Davis and 

Lint 2005), we used modeling software called BioMapper 

(Hirzel et al. 2002). However, species distribution modeling 

is a rapidly evolving field of study, so before conducting the 

spotted owl habitat modeling, we conferred with some of 

the species distribution modeling software developers (A. 

Hirzel and S. Phillips) and evaluated various habitat modeling methods (i.e., BioMapper: Hirzel et al. 2002; MaxEnt: 

Phillips et al. 2006, Phillips and Dudík 2008; Mahalanobis 

distance method: Jenness 2003; resource selection functions: Manley et al. 1993). We also ran comparison tests 

between BioMapper (all algorithms) and MaxEnt using 

“virtual species” data sets provided by Dr. Alexandre 

Hirzel (the developer of BioMapper) with known species 

occurrence and distributions. The details of these tests are 

not provided in this report; however, our conclusions were 

similar to those of Braunisch and Suchant (2010) who found 

that BioMapper and MaxEnt produced models with similar 

accuracy, but that MaxEnt performed better when trained 

with systematically sampled data that were well-distributed 

within the modeling region. However, BioMapper outperformed MaxEnt when the model results were extrapolated 

to areas outside of the model training data area (Braunisch 

and Suchant 2010). In summary, our tests found that as 

long as species presence [training] data were fairly well 

distributed within a modeled region, MaxEnt outperformed 

the other modeling methods, and we selected it as the 

habitat modeling tool for this reporting cycle. Several other 

comparisons between MaxEnt and a number of other habitat 

modeling approaches are available in the scientific literature, and in most cases, distribution models generated by 

MaxEnt performed as well or better than the other methods 

(Baldwin 2009). 

Other notable factors associated with our selection of 

MaxEnt included its user-friendly interface, its ability to 

run replicated models for testing purposes and to provide 

information on the importance of the environmental variables used for modeling, and most importantly, its ability to 

“project” or “transfer” model results. Model transferability 

is the term given for applying the results of a model that 

is calibrated for specific location or period, to a different 

geographic location or period (Turner et al. 1989). The 

concept is based on the idea that calibrated model parameters from one area or time may provide useful information 

in estimating conditions in a different time or place. In our 

situation, we attempted to transfer our models, which were 

trained in 1994/96 to the same geographic location, but in a 

different period—2006/07. Model transferability is a fairly 

new concept, and one that is rarely assessed (Randin et 

al. 2006). Issues with MaxEnt projections documented by 

Braunisch and Suchant (2010), our model testing, and the 

current literature advise for caution in its use and interpretation (Jiménez-Valverde et al. 2009, Peterson et al. 2007, 

Phillips 2008).
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GENERAL TECHNICAL REPORT PNW-GTR-850

Habitat Modeling Process

MaxEnt uses a machine learning process and a suite of 

potential response functions to estimate the most uniform 

distribution (maximum entropy) of the “average” environmental conditions at known species locations compared 

to what is available across the modeled area (background) 

(Phillips et al. 2006). The modeling process does not 

require an a priori specification of a set of models, but 

instead fits training data (presence locations of owl pairs) 

to environmental covariates using various combinations 

of response functions (features) such as linear, quadratic, 

product, hinge, and threshold structures. However, the use 

of all feature types may lead to model overfitting depending 

on the sample size of the training data (Phillips et al. 2006); 

therefore, the “auto feature” (default) restricts the model to 

simpler features, such as linear, quadratic, and hinge, for 

smaller sample sizes (Elith et al. 2011). In our preliminary 

model tests, overfitting seemed to occur from the use of the 

threshold feature, which requires a minimum of 80 training 

samples and produced sharp jumps (both up and down) in 

the variable response curves. Modeling with just the hinge 

feature produces models with simpler or smoother functions 

and is generally a useful simplification that can reduce 

overfitting (Phillips 2010). Our final selection incorporated 

a combination of linear, product, and hinge features because 

most of our hypothesized variable responses fit those 

choices. We considered using the quadratic feature; however, during our model testing, MaxEnt applied this feature 

to variables in which the response function did not make 

ecological sense (i.e., tree diameter). This was most apparent in modeling regions where the variables had outlier 

values at the extreme high end of the distribution histogram. 

The inclusion of the hinge and product features compensated for the omission of the quadratic feature, because in 

combination, they can conform to a quadratic shape. We 

also selected the “auto features” option, which allows

MaxEnt to further limit the subset of response features 

from those we selected above by retaining only those with 

some effect.

Other techniques can be used to control overfitting 

the data, such as reducing the number of parameters in the 

model. To do this, MaxEnt provides a “regularization

feature” that performs a function similar to Akaike’s 

information criterion (Akaike 1974) by penalizing the complexity of the model. The regularization multiplier affects 

the fit of the model training data to the modeling variable 

empirical means. A smaller value results in a tighter fit but 

potentially leads to overfitting the model to the data. The 

default setting of 1.0 is believed to be an appropriate setting 

for most modeling efforts (Phillips and Dudík 2008). A 

higher regularization multiplier setting reduces the number 

of model parameters, allowing for a more spread out fit 

around the mean, and simplifies the model. 

Observing the statistical performance on test (versus 

training) data is the best approach to final model calibration 

(Phillips 2010). We therefore evaluated our model’s performance beginning with the model test gain, which indicates 

how different the testing data are from the background data.

It is similar to “deviance” as used in generalized linear 

modeling (Phillips et al. 2006) and higher gains indicate 

larger differences between occurrence location environmental conditions and average background environmental 

conditions. The exponent of gain produces the mean probability value of predicted species occurrence compared to 

a random location selected from the surrounding modeled 

landscape. Or in other words, an average testing gain of 

0.80 indicates that the model predicted owl occurrence 

2.2 times what would be expected by chance. In addition, 

observing the differences between model testing gain and 

regularized training gain can be used to control model overfitting, as a large difference between the two is an indication 

of model overfitting (Phillips 2010). 

Using more than just one evaluation statistic to evaluate 

habitat model performance is highly recommended (Liu et 

al. 2005), so in addition to gain, we evaluated the area under 

the receiver operating curve (AUC) statistic to determine 

model accuracy and fit to the testing data (Fielding and 

Bell 1997). The AUC statistic is a measure of the model’s 

predictive accuracy, and it was originally developed for 

evaluations using presence and absence data, producing an 

index value from 0.5 to 1 with values close to 0.5 indicating poor discrimination and a value of 1 indicating perfect 

predictions. The AUC values can be interpreted similarly to 

the traditional academic point system where values between 
33

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

0.9 and 1.0 indicate an excellent model (A), 0.8 to 0.9 is 

good (B), 0.7 to 0.8 is fair (C), 0.6 to 0.7 is poor (D), and 

AUC values between 0.5 and 0.6 represent failure (F), or 

models that don’t predict much better than a random guess. 

An example of this interpretation in the field of niche-based 

species distribution models can be found in Araújo et al. 

(2005) and Randin et al. (2006). In our situation, MaxEnt 

uses 10,000 randomly selected background locations (map 

pixels) instead of true absence data, so it is not possible to 

achieve an AUC value of 1.0 (Wiley et al. 2003). However, 

interpretation is similar, with higher AUCs indicating better 

model predictions (Phillips et al. 2006). Specific to our case, 

AUC values represent the percentage of times a spotted owl 

nest site location would have a higher habitat suitability 

value than a randomly selected location from the modeling 

region.

Our third measure of model performance was the 

continuous Boyce index (CBI) as described by Hirzel et

al. (2006). This index and methodology is designed specifically for testing habitat suitability models produced from 

presence-only data. The index is based on the Spearman 

rank correlation coefficient (Rs) that compares the ranks 

of modeled species occurrence with the area available to 

“binned” modeled prediction ranks (Boyce et al. 2002). A 

good model would predict an increasing ratio of the percentage of species occurrence to the percentage of the modeled 

landscape in each model bin as the bin values increase. An 

Rs of 1.0 indicates a strong positive correlation (Boyce et al. 

2002).

We produced 10 bootstrapped random replicates for 

each modeling region using 25 percent of the training data 

held out to test the model. We reviewed the jackknife graphs 

for mean test gain and AUC from these replicates, which are 

produced by MaxEnt. These graphs illustrated the contribution that each variable made to the overall model (Phillips 

et al. 2006). Based on these graphs, we dropped variables 

that significantly increased mean test gain and AUC when 

excluded. Once this decision was made, a final check for 

model overfitting (see above) was conducted. This process 

entailed increasing the regularization multiplier by increments of 0.5 from the default setting of 1.0 (once the final 

list of variables was agreed to) to minimize the difference 

between the regularized training gain and test gain, while 

maximizing the test AUC and CBI using the held-out testing points.

The final models used for reporting status and trends 

are the average summary statistic model outputs from these 

replicates. MaxEnt also produced other summary statistic 

grids, such as the standard deviation for each cell within 

the modeling region. We used these maps to calculate 

a 95-percent confidence interval (CI) for each cell and 

produced upper and lower limit maps based on it. These 

summary maps were used to generate histograms of the 

model predictions uncertainty for each model region and for 

each bookend (app. C). The maps produced are also useful 

to see where within the modeling region the model predictions are less robust.

Environmental Variables

The environmental variables that influence the spotted 

owl’s distribution in the Pacific Northwest have been well 

studied, and a wealth of information exists in the literature 

on important vegetation characteristics associated with owl 

habitat use. As previously noted, we were restricted to only 

a few basic factors (i.e., tree diameter, canopy cover) for 

the habitat modeling done in the 10-year monitoring cycle 

(Davis and Lint 2005); however, the GNN map products 

provided us a more extensive “menu” of forest vegetation 

variables to consider. Our initial selection of vegetation 

characteristics and environmental variables for habitat 

modeling was based on three things: (1) habitat relationship information in the literature or expert knowledge, (2) 

on-the-ground plot accuracies of the variable, and (3) correlations between the covariates. We chose not to use any 

GNN structural or age variables that had plot correlations 

less than 0.3 for an individual modeling region and <0.5, 

averaged across all modeling regions. For species composition variables, we chose not to include any variables that 

had kappas <0.2 for individual modeling regions or <0.3, 

averaged (as a species group) across all modeling regions. 

In cases where variables were highly correlated (Pearson 

correlation >0.7) with each other we dropped the variable 

with the lower plot accuracy. 
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GENERAL TECHNICAL REPORT PNW-GTR-850

From our initial list of GNN variables, we dropped 

basal area of conifers ≥20 in diameter at breast height 

(d.b.h.) because it was highly correlated with the mean stand 

conifer diameter, stand height, and the diameter diversity 

index, but had the lowest plot accuracies. We also dropped 

the standard deviation of d.b.h. of all live trees for similar 

reasons. We also did not include total canopy cover or stand 

density index variables because both had high correlations 

with conifer cover, which had the highest plot accuracy of 

all GNN variables. We considered, but did not use any GNN 

variables for snags and down wood because of low plot 

accuracies for those types of variables. 

We ended up with a consistent set of five variables that 

reflected forest structure and one forest age variable that we 

included in all of our modeling regions. The accuracy of the 

variables we used is shown in appendix A (table A-2), along 

with Pearson correlations between covariates we selected 

for habitat modeling. We also developed five forest species

composition variables (i.e., subalpine, pine, evergreen hardwoods, oak woodlands, and redwoods) and included them 

as appropriate for each modeling region (app. A, table A-2). 

For instance, we did not include a subalpine variable in the 

California Coast Range modeling region, because none exists in that area. Likewise, we did not include the redwood 

variable in the western Washington/Olympic Peninsula 

modeling region. The final list of variables used in each 

modeling region is provided in appendix C.

Modeling Regions

Based on recommendations from the 10-year report (Davis 

and Lint 2005), we developed habitat modeling regions that 

removed some administrative boundaries (i.e., state lines) 

and framed areas based more on ecological rather than sociopolitical divisions. Our modeling regions were modified 

versions of the standard physiographic provinces developed 

in FEMAT (1993) and used for reporting monitoring results 

(fig. 3-5). Our intent was not to further split the existing 

delineations into smaller areas, but to combine the existing 

delineations based on two things: (1) ecological similarities 

between physiographic provinces and (2) occurrence and 

distribution of spotted owl location data being used for 

model training and testing. We used the ecological region 

(a.k.a. geographic region) information from the population 

monitoring work (app. A in Anthony et al. 2006, Forsman 

et al. 2011) to combine some provinces and Environmental 

Protection Agency level III ecoregions (Omernik 1987) 

to guide final delineations of modeling regions. Modeling 

regions were only used for habitat modeling purposes, but 

we still report on habitat status and trend conditions within 

the physiographic provinces to maintain consistency with 

previous reports. 

Within these modeling regions, our modeling background (the area for which MaxEnt compares the combinations of environmental variables that underlay owl locations 

to the broader area that is available for use) was based on 

a “habitat-capable” mask that we generated specifically 

for habitat modeling purposes. The GNN environmental 

data are modeled from detailed field plot data from forestcapable areas only, and a non-forest-capable “mask” is 

provided by GNN using ancillary land class data from the 

Gap Analysis Program (GAP) and National Land Cover 

Data (NLCD) data sets (Vogelmann et al. 2001). The GAP 

data are based on multiseason satellite imagery (Landsat 

ETM+) from 1999 to 2001 used in conjunction with other 

data sets (i.e., elevation, landform, aspect, etc.) to model the 

distribution of ecological systems (Comer et al. 2003) and 

land cover classes at a 1-ha (2.47-ac) resolution. However, 

upon review, the GNN mask included inconsistent masking 

of urban areas and roads, and also did not mask out areas 

that we felt were not capable of developing into habitat 

(i.e., subalpine parklands and steppes). Therefore, we 

used the “unmasked” GNN data set and applied our own 

customized mask specific to our purposes. The mask we 

developed included the use of the “impervious layer” from 

NLCD (Herold et al. 2003) to consistently exclude areas that 

have been converted into non-habitat-capable conditions 

(i.e., urbanized areas, major roads, etc.) and refined the 

developed open space designations. We then modified the 

existing GNN mask classes to exclude a few additional land 

classes or ecological systems that we felt were not habitat 

capable. Isolated areas less than 2/3 ac (pixel map noise) of 

both mask and nonmask were removed. The intent of our 

mask was to frame our modeling area such that it contained 

lands capable of producing closed-canopy forests that could 
35

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Figure 3-5—Modeling regions used for modeling northern spotted owl habitat.
36

GENERAL TECHNICAL REPORT PNW-GTR-850

be potentially suitable for spotted owl nesting, roosting, 

foraging, or dispersal; however, we suspect that this mask 

contains areas, especially in the higher elevations, that 

might not actually be capable of developing habitat under 

the current climate.

Habitat Map Development and Evaluation

The MaxEnt model output is a logistic probability estimate 

of a site’s suitability for species presence based on environmental conditions from where the species are found, and 

their differences from the surrounding background environmental conditions within the modeling region (Phillips 

and Dudík 2008). In our case, the environmental predictor 

variables used were based on stand-level structural and 

forest-type species conditions associated with nesting and 

roosting use by spotted owls. Therefore, our raw model output maps show a scale of nesting/roosting suitability (from 

low to high) for forested stands based on the stand structure 

and species composition conditions described by the GNN 

data. The mapped logistic probability values will be higher 

where these stand-level conditions are more similar to the 

conditions observed where we have documented nests and 

territorial pair centers (i.e., the training data). A mapped 

logistic probability of 0.5 represents the “average” condition 

where the species occurred (Phillips 2008). 

Our charge is to develop habitat maps that work well 

and to then measure and report on amounts and distribution 

of habitat. The latter requires that we select a threshold 

from the probability values described above to represent 

“suitable” owl nesting/roosting habitat for summarization 

purposes. In the 10-year report, we used the area-adjusted 

frequency (AAF) curves (Boyce et al. 2002) associated

with the habitat suitability output from BioMapper to 

evaluate our habitat models. These curves are among the 

few diagnostic measures designed specifically for measuring the accuracy of habitat models based on presence-only 

data (Hirzel et al. 2006). But in addition to model evaluation, these curves also provide information that can be used 

to reclassify habitat models into discrete habitat classes 

(Hirzel et al. 2006). To conform to the new terminology, we 

now refer to AAF curves as “continuous predicted versus 

expected (P/E) ratio curves” (Hirzel et al. 2006). The continuous P/E curves provide three indications of a model’s 

performance (Hirzel et al. 2006):

1. For replicated model runs that use held-out testing 

methods (i.e., bootstrap or jackknife), the variance 

along the curve gives information about the model’s 

robustness along its range of probabilities. Smaller 

variances indicate more reliable prediction points. 

Large variances indicate the range of prediction values 

that are the least robust. This information allows a 

better understanding of the model’s strengths and 

weaknesses. 

2. The shape of the curve provides clues about the model’s 

predictive power. The Spearman rank correlation 

coefficient (Rs) is used to help us judge the shape of 

the curve and the model’s performance. For fluctuating 

curves, each time the curve dips as the ranks increase, 

Rs decreases. A higher Rs indicates a consistently increasing larger proportion of species presence (versus 

available) being predicted as the model prediction 

output increases. This is indicative of a good model; 

however, note that one can get the same Rs for many 

different-shaped curves (i.e., linear, exponential, and 

sigmoid), and curves with flatter slopes can have the 

same ranks as curves with steep slopes. According to 

Hirzel et al. (2006), a perfect model would have a linear 

P/E curve that monotonically increases as probability 

increases because a perfectly straight line allows for an 

infinite number of classes along the scale of probability 

(i.e., “resolution”). A wavy line lowers the resolution 

because classifying the line depends on these changes 

in the shape and slopes. For exponential models 

(like MaxEnt), an exponentially increasing curve is 

indicative of a good model.

3. The maximum y-axis value reached by the P/E curve 

reflects how much the model differs from chance 

expectation, or deviation from randomness. This score 

reflects the model’s ability to differentiate the species 

niche characteristics from those of the modeled region. 

Caution is needed because this maximum value is 

sensitive to the species niche breadth within the context 
37

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

of the modeled region. In other words: Does the species 

just use a small percentage of what is available in the 

modeled region or is its habitat use more generalized 

within the modeling region? If there is abundant habitat 

available in the modeling region that is being used by 

the species, the model will usually produce a flatter 

curve with lower P/E values. Also, the selection and 

resolution of the environmental variables used for 

modeling can influence the maximum P/E value.

Once the habitat model evaluation process has been 

completed, the P/E curve provides a method for classification of a model into discrete habitat classes (Hirzel et al. 

2006). The point along the model prediction axis (x-axis) 

where the curve crosses P/E = 1 along the y-axis (fig. 3-6) is 

the threshold where the model predicted species occurrence 

is higher than would be expected if there were no selection 

(i.e., habitat use was random). This threshold is often used 

to classify habitat models into binary maps, where logistic 

probability values greater than the P/E = 1 threshold represent “suitable” habitat (Hirzel et al. 2006). We also note that 

in our case, the P/E = 1 threshold was similar to the “maximum specificity and sensitivity threshold”3 (Phillips and 

Dudík 2008) for all model regions. We provide these and 

additional thresholds, that are commonly used, in appendix 

C. We also note that the 10-percentile threshold (app. C) is 

equivalent to where we reported that 90 percent of the owl 

training data occurred in the 10-year report habitat models 

(see fig. 3-11 and table 3-4 in the 10-year report). 

We further divided the continuous scale of probability 

of occurrence from our habitat models into four habitat 

classes that represent from the least to the most suitable 

habitat conditions (fig. 3-6). This was done to produce histograms (app. F) similar to the five-class histograms used to 

profile the continuum of habitat suitability in the 10-year 

report (Davis and Lint 2005). As in the 10-year report, 

tracking the changes in these habitat profiles (app. F) is 

expected to provide useful information for visualizing 

where habitat may be recruited (first two habitat classes) 

3

 Minimizes omission (false absence predictions) and commission 

(false presence predictions) errors.

via forest succession over the next few decades. The only 

difference between the two reports is that the classes in the 

15-year report are based on commonly used thresholds and 

have more biological meaning. These habitat classes are 

defined as follows:

• Unsuitable—MaxEnt logistic output from zero to 

the mean value between zero and the P/E = 1 threshold. This habitat class represents the lowest suitability class and owls will normally avoid using it for 

nesting and roosting.

• Marginal—MaxEnt logistic output from the mean 

value between zero and the P/E = 1 threshold to the 

P/E = 1 threshold. This habitat class represents a 

condition approaching what owls will nest and roost 

in. Occasionally, these habitat characteristics are 

associated with nesting and roosting owls; however, 

this could be due to occurrence of legacy habitat features such as large trees, extreme rarity of suitable 

nesting/roosting habitat, or perhaps interspecific 

competition with barred owls.

• Suitable—MaxEnt logistic output from the P/E = 1 

threshold to 0.5. A MaxEnt logistic output value of 

0.5 represents the “average” environmental condition associated with the owl training data. This 

habitat class represents habitat conditions where the 

probability of owl presence is higher than expected 

by random chance and up to average conditions associated with nesting and roosting.

• Highly suitable—MaxEnt logistic output from 0.5 

to the highest output from the habitat model. This 

habitat class represents the most suitable, or “above 

average,” conditions used by nesting and roosting 

territorial owl pairs.

In some of the modeled regions, the 10-percentile 

threshold occurs within the “marginal” habitat class indicating some owl nesting/roosting use of younger, mid-aged 

stands as noted by Thomas et al. (1990) who stated that as 

forests develop along the continuum from young to old, they 

gradually become more suitable for spotted owl nesting/

roosting. To show this continuum of conditions, and to help 

interpret what these habitat classes represent on the ground, 
38

GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 3-6—The predicted versus expected ratio curve (modified from fig. 6 in Hirzel et al. 2006).

we also provide average stand structure and age attributes 

(table 3-1). It appears that the lowest class of habitat includes 

early- to mid-successional forests and the highest suitability 

class includes the oldest and most structurally complex forests (table 3-1). However, we stress that these simple combinations of forest attributes do not fully describe habitat, and 

it is the complex interaction between them that does. 

Nesting/Roosting Habitat

The importance of mature or late-successional forests for 

nesting, roosting, and foraging of owls in the Pacific Northwest is clear (see reviews in Thomas et al. 1990), with numerous studies documenting both selection of these habitats 

by owls (Carey et al. 1990, Forsman et al. 1984, Glenn et 

al. 2004, Gutiérrez et al. 1984, Hamer et al. 1989) and now 

more recent research linking greater amounts of older forest 

in owl territories to owl fitness (i.e., increased survival and/

or reproductive success) (Dugger et al. 2005, Franklin et 

al. 2000, Olson et al. 2004). High-quality owl habitat was 

described by Thomas et al. (1990) and generally includes 

older, multilayered, structurally complex forests characterized by large-diameter trees, high amounts of canopy cover, 

numerous large snags, and lots of downed wood and debris. 

Although late-successional and old-growth forests are often 

equated with spotted owl habitat, they are not always the 

same. As noted by Thomas et al. (1990), the redwood zone 

in northwestern California is unique in terms of owl habitat 

development. In that portion of the owl’s range, the structural conditions that constitute nesting/roosting habitat develop 

quicker, with suitable conditions occurring in 40 to 60 years 

on some sites and superior conditions in 80 to 100 years. 

Habitat development is not a mechanistic process, and

there is considerable variability in predictions of habitat 

(Courtney et al. 2004). As can be seen in table 3-1 and 

appendix C, the transition from unsuitable to suitable conditions is more complex than a simple increase in a stand’s 

average tree diameter and canopy closure. In addition, species composition is also important; for instance, late-successional/old-growth ponderosa pine forests do not function as 

nesting/roosting habitat, nor do older subalpine forests. 

We consider our “suitable” and “highly suitable” 

habitat classes, as described above, as nesting/roosting 

habitat. It is important to emphasize that our maps are not 

attempting to predict owl occupancy or other demographics 

across the landscape, but rather describe stand-level habitat 

characteristics that are associated with owl pair use that 
39

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Table 3-1–Average (

± standard deviation) habitat variable values (gradient nearest neighbor) for nesting/roosting habitat classes

in each modeling region

Model Habitat Habitat Conifer

Average Large conifers Diameter

Average

Average

region class suitability cover conifer d.b.h.a (≥30-in d.b.h.) diversity stand height stand age

Percent Inches

Trees/acre Index Feet

Years

Washington Unsuitable 0–6 42±29 11±10 1±3 2±2 42±30 40±57

Coast and Marginal 7–25 79±12 17±7 3±6 5±2 76±26 73±76Cascades Suitable 26–50 85±8 24±9 9±8 6±1 94±32 137±89

Highly suitable 51–86 89±5 30±9 15±8 7±1 114±28 205±78

Washington Unsuitable 0–11 48±31 13±7 1±3 3±2 45±25 87±65

Eastern Marginal 12–35 60±15 16±6 2±3 4±1 57±20 88±47Cascades Suitable 36–50 75±10 17±6 3±5 5±1 70±20 106±54

Highly suitable 51–93 81±9 20±7 6±7 6±1 85±23 128±58

Oregon Unsuitable 0–9 37±31 8±9 0±1 2±2 36±26 23±20

Coast Marginal 10–28 61±19 19±9 1±2 4±1 74±22 46±21Range Suitable 29–50 65±15 26±8 7±6 6±1 106±25 74±26

Highly suitable 51–91 70±10 36±8 19±8 7±1 143±26 137±45

Oregon Unsuitable 0–9 38±26 11±9 1±2 2±2 37±26 50±49

California Marginal 10–30 70±15 17±6 2±5 5±1 68±24 82±60Cascades Suitable 31–50 76±11 22±7 7±8 6±1 91±31 123±65

Highly suitable 51–88 82±8 29±6 16±8 7±1 115±31 185±83

Oregon Unsuitable 0–15 24±22 13±10 1±3 2±2 33±21 52±45

California Marginal 16–37 51±20 19±10 3±5 4±2 50±24 76±47Klamaths Suitable 38–50 60±18 25±11 7±7 6±2 66±25 111±102

Highly suitable 51–86 65±17 29±9 11±7 7±1 95±27 151±80

California Unsuitable 0–12 16±21 11±12 1±2 3±2 38±16 35±32

Coast Marginal 13–35 44±20 18±9 1±2 5±2 48±20 47±22

Suitable 36–50 64±20 24±16 5±7 5±2 63±31 57±74Highly suitable 51–86 78±15 24±14 7±8 6±1 84±30 78±88

Note: This table is intended to provide a general sense of stand structure variable gradients from unsuitable to highly suitable. 

a d.b.h. = diameter at breast height.
40

GENERAL TECHNICAL REPORT PNW-GTR-850

approximates a species’ realized niche within a specific 

environmental space (Phillips et al. 2006).

Dispersal Habitat

Dispersal habitat is used by juvenile owls moving away 

from natal areas or by subadults and adults moving between 

territories (Forsman et al. 2002). Spotted owls are capable

of dispersing long distances, and gene flow from one portion of the range to another can occur in a few generations

(Forsman et al. 2002). The network of large reserves 

established under the Plan appeared suitable for maintaining interconnected populations of spotted owls (Lint et 

al. 2005); however, concern remained for disjunct small 

populations that are isolated by large nonforested areas or 

expanses of young managed forests (Forsman et al. 2002). 

Thomas et al. (1990) predicted that much of the forested 

area between owl conservation areas would be suitable for 

passage by dispersing spotted owls as long as at least 50 

percent of the landscape was forested with conifer stands 

with an average d.b.h. of ≥11 in with at least 40 percent 

canopy closure. This definition of a dispersal-capable 

landscape became known as the “50-11-40 rule” (Thomas et 

al. 1990) and was based on information of habitat conditions 

for dispersing juvenile owls (Miller 1989). Older forest 

habitat is more frequently used for natal dispersal, but 

closed-canopy (>60 percent cover) younger forests are also 

used, whereas younger open-canopied (<40 percent cover) 

forests are generally avoided (Miller et al. 1997). Dispersal 

distance is also negatively associated with the amount of 

clearcut forest in the landscape, and large urban and agricultural areas appear to be barriers to dispersal (Forsman et al. 

2002, Miller et al. 1997). Spotted owls use a wide variety of 

forest habitats for dispersal and will traverse very fragmented landscapes (Forsman et al. 2002), but little information exists on how the amount or fragmentation of habitat 

influences dispersal. The results of the latest meta-analysis 

suggest that recruitment into the territorial breeding population may depend on the presence of sufficient amounts of 

high-quality dispersal habitat, enough to ensure survival of 

dispersing owls until they recruit into the territorial population (Forsman et al. 2011). 

We did not use presence locations and MaxEnt to 

model dispersal habitat. Instead we developed dispersal 

habitat maps for both bookend periods using simple GIS 

queries of our GNN variables for conifer d.b.h. ≥11 in and 

conifer cover ≥40 percent, similar to what was done in the 

10-year report (Davis and Lint 2005). We also included both 

suitable habitat classes from our nesting/roosting habitat 

models, because owls obviously disperse through nesting/

roosting habitat. We then analyzed the status and trend of 

this habitat within federal reserved and nonreserved LUAs, 

as well as nonfederal lands.

To detect changes in amounts of dispersal habitat that

 might affect owl movement across the landscape, we conducted a landscape-scale analysis using a spatial framework based on Forsman et al. (2002). Only 8.7 percent of 

dispersing individuals moved more than 31 linear mi and 

only “large expanses” of nonforested or younger forested 

areas appear to pose significant barriers to this movement 

(Forsman et al. 2002). We used this distance to define the 

radius (15.5 mi) for a circular analysis window within 

which we quantified the percentage of dispersal habitat for 

both bookend periods and included all landownerships. 

This distance is also comparable to the root-mean-square 

dispersal distance (a measure of gene flow) estimated by 

Barrowclough et al. (2005). We then overlaid linear owl 

dispersal paths from the 10-year report (Lint et al. 2005) 

on the baseline version to measure underlying percentages 

of dispersal habitat in the landscape through which they 

dispersed (fig. 3-7). The mean percentage of dispersal 

habitat for both juvenile and nonjuvenile owls was 55 

percent. We combined results across age classes and used 

the 10-percentile value (40 percent) from all owl dispersal 

paths as a threshold to create binary maps from the roving 

window analysis maps. Thus, the binary maps show where 

there appears to be enough dispersal habitat at the landscape 

scale (≥40 percent within a 15.5-mi radius) to accommodate 

90 percent of known owl movements. We call this footprint 

the “dispersal-capable landscape” and used it to identify 

potential disconnects or bottlenecks for owl movement between large block reserves. We also identified areas across 

the range of the owl where the footprint shrank or expanded 

between our bookends. 
41

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Figure 3.7—Juvenile and nonjuvenile dispersal straight-line paths from Lint et al. (2005) in relation to the amount of dispersal habitat within a 15.5-mi radius that was based on information from Forsman et al. (2002) where only 8.7 percent of dispersing individuals moved more than 31 linear mi. 

Habitat Fragmentation

Although large blocks of contiguous, high-quality habitat 

provide the best configuration for long-term persistence 

of owl populations (Thomas et al. 1990), smaller blocks or 

patches of owl habitat can also be important as dispersal 

habitat (Forsman et al. 2002). These smaller patches help to 

maintain connectivity between the larger blocks of habitat 

that will eventually develop in the reserve system designed 

under the Plan. At the time of the owl’s listing, habitat fragmentation was believed to be a stressor for spotted owls 

because it is associated with habitat loss, and was also 

thought to improve habitat conditions for spotted owl predators, such as the great horned owl (Bubo virginianus) (Carey 

et al. 1992). There is no clear evidence of indirect effects 

of fragmentation through predation, but it remains as a 

possible threat (Courtney et al. 2004). A compilation of the 

recent research on this subject shows that habitat fragmentation can affect occupancy and other demographic factors, 

and may result in isolated populations and interruption of 

gene flow (Courtney et al. 2004). 

In a general sense, habitat can be divided into two 

broad landscape morphological categories: (1) core habitat, 

which occurs only in larger habitat patches and is some 

distance away from the patch edge (sometimes referred to as 

“interior habitat”); and (2) edge habitat, which occurs along 

the margins of larger habitat patches surrounding the core 

habitat or occurs in patches that are too small to contain 

core habitat.

It is not clear how habitat fragmentation affects owl demographics; however, survival and reproduction are higher 

on owl territories with more old-forest habitat centered on 

the nest tree or activity center (Dugger et al. 2005, Franklin 
42

GENERAL TECHNICAL REPORT PNW-GTR-850

et al. 2000, Olson et al. 2004). Edge habitat also appears 

to be important to spotted owls in some portions of their 

range, probably as a source of prey (Franklin et al. 2000, 

Olson et al. 2004; but see exception in Dugger et al. 2005).

Here we define core habitat as the internal portion of a

stand of nesting/roosting habitat that is farther than 100 m 

from the stand edge. Edge habitat is defined as all noncore 

nesting/roosting habitat and is always adjacent to nonhabitat. We do, however, distinguish between two types of 

edge habitat: (1) core-edge habitat, which is the amount of 

nesting/roosting habitat adjacent to and surrounding core 

patches (i.e., the edges of large habitat patches), and (2) all 

other edge habitat that is not directly adjacent to core habitat 

(i.e., small, isolated habitat patches). In juxtaposition, core

and core-edge habitat reflect more contiguous habitat 

blocks, whereas large amounts of non-core-edge habitat 

occur in landscapes that are highly fragmented, with patch 

sizes too small to contain core habitat.

We used GUIDOS v1.3 (Soille and Vogt 2009) to 

conduct a morphological spatial pattern analysis (MSPA)

on 100-m (2.47-ac)-resolution binary raster (grid) maps of 

nesting/roosting habitat for both 1994/96 and 2006/07 to 

assess status and trend in habitat configurations. GUIDOS 

was specifically developed for analysis of forest spatial patterns extracted from satellite images (Soille and Vogt 2009, 

Vogt et al. 2007). It produces simple-to-interpret maps of 

core and edge patterns from binary raster maps, and the 

outputs are pixels with specific core or edge classifications 

(fig. 3-8). From this product, we conducted an area analysis 

that quantifies the area represented by both types of pixels 

(“core” or “edge”); thus, in our analysis, edge is not quantified as a perimeter. Specifically, edge habitat only occurs 

within 1 pixel width, or 100 m (328 ft), from a nonhabitat 

pixel, and, therefore, core habitat pixels are greater than 

328 ft from nonhabitat pixels. This distance is similar to 

that used by Franklin et al. (2000) and Zabel et al. (2003) to 

define their core habitat. Using 100-m (2.47-ac)-resolution 

maps requires a patch of contiguous habitat to be greater 

than 22 ac before it can contain core habitat. Therefore, the 

combination of core plus core-edge pixels shows patterns 

of habitat patches that are at least that large. All patches of 

nesting/roosting habitat smaller than that are essentially 

Figure 3-8—Example of the morphological spatial pattern 

analysis (MSPA) on binary maps of nesting-roosting habitat.
43

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

edge habitat. We report on the status and trend of core 

habitat and changes in the percentage of the sum of [core] + 

[core-edge] habitat to all nesting/roosting habitat. This percentage can serve as an index of landscape habitat fragmentation, as the higher the percentage, the more contiguous the 

habitat is within the landscape and the lower the percentage, 

the more fragmented the habitat (fig. 3-8).

Results

Habitat Suitability Modeling

Our final habitat models and map products (fig. 3-9) represent the mean from 10 bootstrapped replicates. We decided 

to use the means as our product, because the P/E curves that 

are generated for the means provide the users with valuable 

information on how to interpret the model (see the “Habitat 

Map Development and Validation” section and fig. 3-6). We 

also provide the summary statistic maps (i.e., 95-percent CI) 

to supplement this interpretation with area-specific information as discussed in the methods section.

Performance of bookend 1 (1994/96) models was fair

to good (i.e., C+ to B+ grades) with AUCs ranging from

0.78 to 0.88 and Spearman rank correlation coefficients 

>0.9 (P < 0.001) (app. C). Our lowest performing models 

occurred in the Oregon and California Klamath and

California Coast modeling regions, and our best models 

were in the Washington Coast and Cascades and Oregon 

Coast modeling regions. We suspect this is because of the 

rich vegetative diversity in that area that (1) confounds 

remotely sensed data development and (2) produces a more 

complex “definition” of habitat because of the complex 

variable interactions. Regardless of the reason, the model 

AUCs for these regions were 0.78 and 0.81, respectively, and 

therefore provide useful information (Swets 1988).

Our projected models (bookend 2, 2006/07) were tested 

using the 2006/07 owl location data sets not used for model 

training. Spearman ranks based on the continuous Boyce 

index (Hirzel et al. 2006) ranged from 0.63 to 0.98. The

best model projection [extrapolation] occurred in the 

Oregon Cascades modeling region, followed in order by the 

Oregon Coast Range (Rs = 0.95), western Washington and 

Olympic (Rs = 0.93), and, surprisingly, the Klamath Mountain modeling region (Rs = 0.92). The poorest model projections occurred within the Washington Cascades modeling 

region (Rs = 0.74) and California Coast Range (Rs = 0.63). 

During this testing process, we noted interesting differences 

between the average habitat suitability values where spotted 

owls in the demographic study areas occurred in 1994/96 

compared to where they occurred in 2006/07 (fig. 3-10).

We observed consistently lower than average habitat suitability values in 2006/07 compared to 1994/96; however, 

95-percent CIs overlap between periods. We speculate

that spotted owls might be using lower quality habitat in 

2006/07 because they are being displaced from higher 

quality habitats by barred owls, whose density has increased 

steadily since the late 1990s (Forsman et al. 2011). The 

potential for displacement of spotted owls by barred owls 

in the current bookend is the reason we trained our models 

using the 1994/96 spotted owl locations. However, given the 

aforementioned issues on model projection [extrapolation], 

these results, based on our bookend 2 models, should be 

interpreted with some caution.

Nesting/Roosting Habitat

We estimate a rangewide gross loss of about 298,600 ac4 of 

spotted owl nesting/roosting habitat on federal lands (app. 

D). This amounts to about 3.4 percent of what was present in 1994/96 (bookend 1). Most of the loss (79 percent) 

occurred within the reserved allocations, which amounted 

to about 3.7 percent of the reserved areas under the Plan, 

whereas nonreserved allocations experienced a 2.7 percent 

loss of habitat. Wildfires remain the primary cause of 

habitat loss, accounting for about 90 percent of the loss in 

reserved allocations (203,900 ac), and about half of the loss 

in nonreserved allocations (32,600 ac). Timber harvesting 

accounts for about 45 percent of the loss in nonreserved 

allocations (37,400 ac) and 7 percent within reserved

allocations (16,600 ac), and insects and disease outbreaks 

account for about 3 percent of the loss in all allocations

(fig. 3-11). Relative to the baseline maps, and based on 

4

 Acres are rounded up to the nearest 100 ac.
44

GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 3-9—Northern spotted owl habitat suitability map showing the spatial distribution of nesting/roosting habitat as of 

2006 (in Oregon and Washington) and 2007 (in California).
45

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Figure 3-10—Observed differences in average modeled habitat suitability for spotted owl pair locations within demographic 

study areas between 1994/96 and 2006/07.

Figure 3-11—Causes of nesting/roosting habitat loss on federally administered lands.
46

GENERAL TECHNICAL REPORT PNW-GTR-850

LandTrendr change-detection data, the physiographic 

province that experienced the greatest loss of habitat was 

the Oregon Klamath province because of the large Biscuit 

Fire that occurred in 2002. In general, the Klamath and 

eastern Cascades physiographic provinces experienced the 

largest percentage losses of habitat related to wildfires (fig. 

3-12); however, in terms of absolute acreage of habitat lost, 

the Oregon Klamath ranked first (93,600 ac), California 

Klamath ranked second (71,600 ac), and the Oregon western 

Cascades ranked third (28,900 ac) (app. D). Most of the 

Figure 3-12—Nesting/roosting habitat trends (based on the 

LandTredr analysis) from 1994/96 to 2006/07 by physiographic 

province for reserved and nonreserved federal lands.

habitat loss in the Oregon western Cascades occurred in the 

southern half of that province.

Because wildfires appear to be the number one cause 

of habitat loss, we conducted a more indepth analysis of the 

20 largest wildfires that occurred within the owl’s range 

between 1996 and 2006 (years with satellite data across 

the range). Table 3-2 lists these fires in descending order 

of estimated acres of owl habitat lost. Overall, these 20 

fires accounted for almost 200,000 ac of habitat lost. The 

percentage of owl habitat lost within their fire perimeters 

differed among the east and west Cascades (Washington 

and Oregon) and the Klamath Mountains (Oregon and

California) physiographic provinces (fig. 3-13). The percentage lost per fire in the Klamath Mountains and the west 

Cascade provinces were not significantly different (overlapping 90-percent CIs); however, percentage of habitat 

loss per fire was notably higher in the eastern Cascades. 

However, in terms of the amount of nesting/roosting habitat 

burned by these 20 fires, the vast majority of acres lost 

occurred in the Klamath Mountains (143,000 ac), followed 

by the east side of the Cascades (36,000 ac) and the western 

Cascades (20,000 ac). 

Based on Climate, Ecosystem, and Fire Applications 

(CEFA) program data (Brown et al. 2002) and wildfire 

perimeter data (MTBS and GeoMac), wildfires burned an 

estimated 2.6 million ac within the owl’s range between 

1994 and 2007, which frames our analysis period. From our 

observations, it is clear that wildfires do not remove all owl 

nesting/roosting habitats within their perimeters. Fires of 

low to moderate severity can alter this habitat, but do not 

necessarily result in its loss. The commonly used term to 

define this effect is “habitat degradation.” We estimated owl 

habitat degradation, as the number of acres that changed 

from the “highly suitable” to the “suitable” habitat class 

between our bookends (1994/96 to 2006/07). For habitat 

degradation, our analysis showed the reverse trend from 

what we observed for habitat loss (fig. 3-14). These results 

suggest that wildfires in the east Cascades have been more 

destructive (higher amount of habitat loss, lower amount 

of degradation) and that wildfires in the west Cascades and 

Klamath Mountains were less severe, producing a mosaic of 

fire effects indicative of a moderate-severity regime. 
47

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Table 3-2—Estimated fire effects on northern spotted owl nesting/roosting habitat from the 20 largest 

wildfires between 1996 and 2006

Broad Habitat in

physiographic wildfire

Fire name Year province perimeter Habitat lost Habitat degraded

Acres Acres % Acres %

Biscuit Fire 2002 Klamath Mountains 226,230 93,730 41 12,019 5

Megram Fire 1999 Klamath Mountains 76,337 27,520 36 4,589 6

B&B Complex 2003 East Cascades 26,269 16,403 62 907 3

Bake-oven Fire 2006 Klamath Mountains 23,946 8,873 37 581 2

Boulder Fire 2002 West Cascades 34,059 8,460 25 2,074 6

Davis Fire 2003 East Cascades 8,050 6,943 86 5 0

Pigeon Fire 2006 Klamath Mountains 13,896 5,634 41 327 2

Rex Complex 2001 East Cascades 8,548 4,750 56 278 3

Timbered Rock 2002 West Cascades 10,216 4,539 44 569 6

Spring Fire 1996 West Cascades 13,504 3,931 29 858 6

Deep Harbor Fire 2004 East Cascades 5,761 3,930 68 64 1

Hancock Fire 2006 Klamath Mountains 12,712 3,132 25 336 3

Apple Fire 2 2002 West Cascades 12,227 2,810 23 928 8

Fischer Fire 2004 East Cascades 4,479 2,340 52 34 1

Fork Fire 1996 Klamath Mountains 2,962 2,113 71 14 0

Needles Fire 2003 East Cascades 1,946 874 45 1 0

Trough Fire 2001 Klamath Mountains 1,851 798 43 4 0

Hunter Fire 2006 Klamath Mountains 2,236 789 35 40 2

Deer Point Fire 2002 East Cascades 505 380 75 0 0

Tatoosh Complex 2006 East Cascades 666 378 57 0 0

Figure 3-13—Provincial differences in 

nesting/roosting habitat losses from the 

fires in table 3-2. 
48

GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 3-14—Provincial differences in 

nesting/roosting habitat degradation from 

the fires in table 3-2. 

For this report, we were cautious in our use of the

new GNN/LandTrendr data for measuring gains in nesting/

roosting habitat. Although the products we used for our 

analysis (and other remote sensing approaches) have 

demonstrated their ability to detect both losses and gains in 

forest cover (Coops et al. 2010; Hais et al. 2009; Kennedy et 

al. 2007, 2010; Staus et al. 2002), the underlying measurements from passive optical satellite sensors (i.e., those that 

take pictures of sunlight reflected from the Earth’s surface) 

place constraints on the subtlety of coniferous forest change 

that can be reliably captured over a short period (i.e., 10 to 

12 years). Disturbances that result in substantial removal 

or reduction of vegetation cover (usually abrupt changes) 

are easier to discern during change-detection than minor 

disturbances that cause more subtle change, or gradual disturbances that occur over a longer period, such as insect and 

disease disturbances. Vegetation recovery can also be more 

difficult to detect (depending on the type of vegetation and 

timeframe), as it usually recovers gradually over a longer 

period. Increases in tree bole diameter and forest canopy 

cover happen at a faster rate in younger coniferous forests 

than in older forests, however, and the satellite-measured 

signal changes faster as well. Within the 10- to 12-year period of this investigation, mapping of such changes in early 

successional, pre-canopy-closure conditions are relatively 

robust (Kennedy 2010). Much more subtle, however, are 

the satellite signals associated with the structural changes 

as forests progress to maturity and old age. Moreover, 

small-scale forest canopy gap dynamics cannot be directly 

observed with the sensors used in our analysis (Frolking 

et al. 2009). Rather, all structural changes associated with 

maturing forests often must be inferred from changes in 

the spectral signal caused by proxy effects, such as withincanopy shadowing. Therefore, it is difficult to distinguish at 

a given location small changes in forest structure (and any 

associated variables, such as age) from background random 

noise caused by differing sun angles, atmospheric effects, 

and phenological differences, particularly when the interval 

of change is short (as for the 10- to 12-year period here) 

(Kennedy 2010).

During our analysis, we conducted visual and GIS 

examinations of our nesting/roosting habitat maps and variable maps using aerial imagery and noted that commercial 

thinning of young plantations created suspicious changes 

in some of our habitat modeling variables in the bookend 

2006/07 data set. For instance, in some modeling regions, 

stand age increased by three to five decades, or density of 

large conifer (>30 in d.b.h) increased by as much as three to 

four trees per ac, which is not likely within the timeframe of 

our analysis. We suspect that canopy shadowing increased 

owing to the thinning and may have caused some stands to 
49

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

appear older than they actually were, thus making them appear as habitat when the modeling results from the 1994/96 

bookend were extrapolated (projected) to the 2006/07 

bookend. 

We also conducted an analysis of the regional inventory 

plot data, similar to what was done in the 10-year report 

(Davis and Lint 2005), to determine if there were significant 

gains of forest stand conditions that were similar to spotted 

owl nesting/roosting habitat (see table 3-5, page 47 in Davis 

and Lint 2005). The results of this analysis did not show any 

significant gains in “habitat classes” between the initial plot 

measurement and the remeasurement data, which roughly 

covers the same periods as our bookend models (app. H). In 

addition, the net changes between the bookend models were 

well within the 95-percent CIs between periods; therefore, 

it is not possible to state with certainty that we observed 

“real” net changes in nesting/roosting habitat between our 

bookend maps (app. C). For these reasons (plus the need for 

caution when transferring or projecting models discussed 

earlier), we focused on habitat losses, which are more accurately detected with current technologies and were verified 

by LandTrendr change-detection data. For the next round 

of monitoring (20-year report), we hope to use LandTrendr 

for verification of both nesting/roosting habitat losses and 

gains. 

Dispersal Habitat

Although we were cautious in our interpretation of gains in 

nesting/roosting habitat, we feel that the GNN/LandTrendr 

data were better suited for detecting gains in younger forests (as described above), such as dispersal habitat, plus we 

did not develop and then project (extrapolate) a dispersal 

habitat model from one period to another as we did for nesting/roosting habitat (i.e., no model transferability issues). 

Examination of the bookend changes in the two variables 

that were used to define dispersal habitat (d.b.h. and conifer 

cover), and visual examination of the dispersal habitat maps 

overlaid on high-resolution color aerial imagery showed 

realistic changes that one might expect in a 10-to 12-year 

timeframe.

Rangewide, we report an estimated gross loss of about 

417,000 ac of dispersal habitat, most (82 percent) from wildfire (341,800 ac). The causes for dispersal habitat loss were 

similar to those for nesting/roosting habitat losses, with 

wildfire being the main cause in reserved allocations and 

about half of the loss in nonreserved allocations (fig. 3-15). 

Timber harvesting accounts for the other half of the loss

in nonreserved allocations, and insects and disease account 

for a small percentage of loss in all allocations (fig. 3-15). 

However, these losses were offset by a 1.26-million-ac 

gross gain in dispersal habitat on federal land from forest 

Figure 3-15—Causes of dispersal habitat loss on federally administered lands.
50

GENERAL TECHNICAL REPORT PNW-GTR-850

succession, resulting in a 5.2-percent overall net gain of 

dispersal habitat coverage across the owl’s range (app. E). 

In general, the gains in dispersal habitat were higher in 

federal nonreserved allocations than in reserved allocations. 

Only the Oregon Klamath experienced a net decrease in the 

amount of dispersal habitat (-2.6 percent) owing to the large 

Biscuit Fire, which removed more dispersal habitat than was 

recruited for this period (app. E). The biggest net gain (13.1 

percent) in federal dispersal habitat occurred in the Oregon 

Coast Range, which has some of the most productive forests 

in the owl’s range. An example of this recruitment is clearly 

seen in the maps from 1996 and 2006 for the large Oxbow 

Fire of 1966 (fig. 3-16). In 1996, this area was forested with 

stands just about 30 years of age. Based on tree diameter 

growth data for fully stocked, site class 1, Douglas-fir 

(Pseudotsuga menziesii [Mirb.] Franco) forests, stands of 

this age have an average d.b.h. of 9 in and can put on 3 in of 

diameter growth in one decade (McCardle et al. 1961), thus 

crossing the threshold from nondispersal to dispersal habitat 

in a relatively short timeframe. However, not all sources 

of gain for dispersal habitat come from forest succession. 

Sometimes disturbances, such as a moderate-severity 

wildfire, can alter (i.e., opening up the canopy) suitable 

nesting/roosting habitat, making it unsuitable for nesting 

and roosting, but still suitable enough for owl dispersal (see 

table 3-1). 

At the landscape scale, we detected a 5-percent gross 

loss of dispersal-capable landscape, mostly around the 

periphery of the federal forests. We suspect this may be due 

to regeneration timber harvesting occurring in dispersal 

habitats on nonfederal lands that border federal lands. Large 

wildfires on federal lands played a role in this decrease in 

the eastern Cascade provinces and the Oregon Klamath 

Mountain province. We also detected a 4-percent gross 

gain in dispersal-capable landscapes along the periphery of 

some federal forests caused by forest succession in younger 

forests, resulting in an overall net decrease of 1 percent in 

dispersal-capable landscape area (fig. 3-17). 

The most noticeable change in dispersal-capable 

landscapes, that we detected, occurred in the northeastern 

portion of the Washington eastern Cascades; the losses of 

dispersal-capable landscape caused by large wildfires in Figure 3-16—Recruitment of dispersal habitat in the Oxbow 

Fire (1966) in the Oregon Coast Range.
51

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Figure 3-17—Changes in dispersal-capable landscapes across the owl’s range.
52

GENERAL TECHNICAL REPORT PNW-GTR-850

that area may have isolated some of the large LSRs established at the Plan’s implementation (fig. 3-17). This may 

also be the case for the LSR just to the east of the B&B Fire 

where it appears that there has been a 3- to 6-mi contraction 

of dispersal-capable landscape in that area. Overall, the 

large reserved network still appears to be well connected, 

with the exception of three areas. Of primary concern 

are the federal reserved lands on the Olympic Peninsula, 

which are separated from the Cascades by about 75 mi of 

landscape with poor dispersal conditions (fig. 3-17). These 

federal lands are also separated from federal reserves that 

occur about 90 mi to the south in the northern Oregon 

Coast Range physiographic province. The federal reserves 

in the most northern part of the Oregon Coast Range are 

the second area of concern. It appears that regeneration 

timber harvesting on nonfederal land may be narrowing 

the dispersal connection to the rest of the Coast Range’s 

large federal reserved allocations. Finally, the southernmost 

large reserves, which are mainly located on the Mendocino 

National Forest in the California Klamath Mountains 

physiographic province, appear to occur in poor dispersal 

landscapes, and the Marin County northern spotted owl 

population, in particular, appears isolated at the extreme 

southern tip of the owl’s range (fig. 3-17).

Habitat Fragmentation

At the range scale, core habitat accounted for about 19 and

29 percent of baseline nesting/roosting habitat within nonreserved and reserved allocations, respectively, indicating 

that reserved allocations contain larger patches of suitable 

habitat. Between 1994/96 and 2006/07, the amount of 

core habitat on federal lands decreased by 6 percent at the 

range scale, with 4.6 percent of this decrease occurring in 

reserved allocations. The largest decrease (-20.6 percent) 

occurred in the Oregon Klamath province and was largely 

owing to the Biscuit Fire (fig. 3-18). The percentage loss of 

core habitat by physiographic province shown in figure 3-18 

generally follows the same pattern among provinces as for 

nesting/roosting habitat loss (fig. 3-12); however, percentage 

of loss is larger for core habitat, because it is a subset of 

nesting/roosting habitat and confined to a smaller portion

of the landscape.

Figure 3-18—Nesting/roosting “core” habitat trends from 1994/96 

to 2006/07 by physiographic province for reserved and nonreserved federal lands.

The combination of core and core-edge habitat 

constituted about 50 percent of the baseline nesting/

roosting habitat in nonreserved allocations and 61 percent 

in reserved allocations at the range scale, indicating that 

reserved allocations contain more contiguous habitat than 

nonreserved allocations (table 3-3). We report an average 

rangewide decrease of 1 percent in these ratios, signifying 

a small but measureable increase in habitat fragmentation. 

The largest decreases occurred within the federally reserved 

portions of the Klamath provinces in Oregon (-4.3 percent) 

and California (-2.7 percent) and California Cascades

(-3.1 percent), again because of wildfires (table 3-3).
53

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

Table 3-3–Habitat fragmentation status and trends based on the percentage of nesting/roosting habitat in 

core and core-edge habitat

Reserved Nonreserved

Physiographic province 1994/96 2006/07 Trend 1994/96 2006/07 Trend

Percent

Washington Olympic Peninsula 72.76 72.75 -0.01 33.22 32.86 -0.36

California Coast Range 68.77 68.81 0.04 46.64 47.19 0.55

Oregon Western Cascades 68.77 67.72 -1.05 59.51 58.39 -1.12

Oregon Coast Range 62.83 62.53 -0.30 39.41 37.77 -1.64

Oregon Klamath 61.69 57.35 -4.34 38.51 36.80 -1.71

Washington Western Cascades 59.23 59.06 -0.17 49.77 49.44 -0.33

Washington Eastern Cascades 55.69 54.39 -1.30 50.89 50.20 -0.69

Oregon Eastern Cascades 55.00 55.04 0.04 48.94 47.28 -1.66

California Cascades 51.70 50.67 -1.03 48.30 45.16 -3.14

California Klamath 49.01 46.31 -2.70 42.22 41.14 -1.08

Washington Western Lowlands 31.34 30.28 -1.06 0.00 0.00 0.00

Oregon Willamette Valley 25.74 25.93 0.19 27.83 26.13 -1.70

Note: Physiographic provinces are listed in order of least- to most-fragmented federal reserved land allocations based on the status in 

1994/96. Negative trend values indicate increased fragmentation.

Discussion

Substantial progress has been made in 5 years to overcome 

some of the previous limitations of habitat monitoring 

(Davis and Lint 2005). Most importantly among these 

advancements is the development of a consistent set of 

vegetation data that now covers the entire range of the owl. 

As suspected by Davis and Lint (2005), the finer resolution 

(in both spatial scale and attributes) of this new vegetation 

data resulted in lower, but more accurate, estimates of the 

amount of northern spotted owl habitat in California. In 

addition, the development of bookend maps (using the same 

vegetation and modeling techniques) has increased our 

ability to detect trends of habitat losses and gains. For the 

first time, we can estimate not only habitat losses, but also 

habitat degradation—where habitat is altered by a disturbance, but still remains suitable for owl nesting and roosting. The new LandTrendr change-detection data (Kennedy 

et al. 2010) were critical for verifying the habitat losses we 

detected with the bookends and also for assigning a cause 

for the habitat changes. 

Although we were able to detect, measure, and report 

on nesting/roosting habitat loss and degradation, we were 

not able to detect and measure its recruitment during the 

10- to 12-year timeframe of our analysis data. The expectation was that validation of habitat development would be 

part of the new habitat suitability maps developed by the 

interagency monitoring program (Courtney et al. 2004). 

However, validation of habitat development is a difficult 

task, and the transition of a forest age class or size class into 

the next higher class does not always equate to recruitment 

of owl habitat (Courtney et al. 2004). As seen from the 

combinations of vegetation variables we used for habitat 

modeling (app. A and table C-1 in app. C), the definition 

of nesting/roosting habitat is not a simple combination of 

one or two attributes. In reality, it is much more complex, 

and the transition of habitat from unsuitable to suitable 

likely happens over multiple decades (Courtney et al. 2004). 

This was not the case for the younger forest types through 

which owls can disperse. We cautiously accounted for gains 

in dispersal habitats after examination of the dispersal 

habitat maps on aerial imagery and through GIS analysis of 

changes in the tree diameter and canopy cover variables that 

were used in its definition.

So, although Raphael et al. (1994a, 1994b) and Lint 

et al. (1999) did not expect to see any significant gains in 

nesting/roosting habitat for a few decades, an examination 

of our habitat histograms (app. F) shows some gains in the 
54

GENERAL TECHNICAL REPORT PNW-GTR-850

“marginal” suitability class, which is similar to dispersal 

habitat (see table 3-1). Within the next three decades, the 

transition of habitat from the marginal suitability class to 

the suitable habitat class may be detectable given current 

remote sensing technology. In addition, the use of light 

detection and ranging (LIDAR) imagery, which is able to 

map forest canopy biomass, height, and vertical distribution, 

may provide us the ability to detect and monitor changes 

in the older stages of succession with improved accuracies 

(Falkowski et al. 2009).

Maintaining and restoring habitats that keep owl populations well connected across their range is a central goal of 

the Plan and should remain a priority. Our dispersal-capable 

landscape analysis was based on known linear dispersal 

distances (Forsman et al. 2002, Lint et al. 2005), and the 

analysis window we used to quantify amounts of dispersal 

habitat across the landscape had a diameter of 31 mi. This 

distance exceeds both the mean natal dispersal distance for 

males and females (Forsman et al. 2002) and the root-meansquare dispersal distance, which may be the more appropriate measure of gene flow (Barrowclough et al. 2005). Thus, 

our results indicate that most of the large reserved network 

is currently well connected (fig. 3-17) with a few exceptions, 

such as the Olympic Peninsula, the northern Oregon Coast 

Range, and the California Klamath, which we suggest might 

serve as focal areas for future studies on population connectivity and genetics, particularly as recent genetic work 

suggests northern spotted owls have undergone population 

bottlenecks resulting in reduced genetic diversity in several 

parts of their range, including the northern Oregon Coast 

Ranges, and the Klamath Mountains (Funk et al. 2010). 

Strong evidence for population bottlenecks in the Washington eastern Cascades were also reported (Funk et al. 2010) 

consistent with recent population declines in that region 

(Anthony et al. 2006, Forsman et al. 2011), but there is no 

definitive evidence that dispersal habitat is limited (this 

study) or that gene flow is restricted in that region

(Barrowclough et al. 2005).

Summary

Rangewide owl habitat losses on federal lands were 

expected to be about 5 percent per decade, with a loss of 

2.5 percent from timber harvest (USDA and USDI 1994) 

and 2.5 percent from wildfire (FEMAT 1993). We report a 

rangewide loss of 3.4 percent, between 1994/97 to 2006/07 

and conclude that [rangewide] habitat is not declining 

faster than predicted under the Plan. Timber harvesting 

accounted for 0.6 percent of this loss, insects and disease 

0.1 percent, and wildfire 2.7 percent of the habitat loss. Loss 

from timber harvesting is occurring at a fraction of what 

was predicted at Plan implementation, but habitat losses 

from wildfire are very close to what was predicted (FEMAT 

1993). Although rangewide habitat losses have not exceeded 

what was anticipated under the Plan, the trend of habitat 

loss has been greater than 5 percent per decade in some 

physiographic provinces (i.e., Oregon Klamath). If localized 

habitat losses continue at the current rates within some 

provinces, it is unclear what effect this may have on the 

effectiveness of the Plan to maintain well-distributed and 

connected populations of northern spotted owls throughout 

their entire range, specifically the assumption that the large 

reserve network is resilient enough to incur these losses and 

not result in isolation of population segments (Lint et al. 

1999). 

Since implementation of the Plan, the majority of 

habitat loss on federally administered lands has been caused 

by wildfire, and most of that loss has occurred in reserved 

allocations. This seems counter to the Plan’s goal of habitat 

maintenance and restoration within the reserved network. 

However, the reserve network was designed to function 

despite losses to wildfire, which were anticipated (FEMAT 

1993, Murphy and Noon 1992). Although Lint et al. (1999) 

assumed that habitat conditions within large reserves would 

improve over time at a rate controlled by successional 

processes in stands that are not currently nesting/roosting 

habitat, they did not expect it to happen quickly, but over 

a period of several decades (Lint et al. 1999). Our latest 
55

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl 

monitoring shows that maintenance of nesting/roosting 

habitat within some of the large reserves is being challenged 

by the occurrence of large wildfires, and also that largescale restoration of reserved nesting/roosting habitat has not 

yet occurred. 

The monitoring assumption that habitat conditions 

outside of reserved allocations would continue to decline 

because of timber harvesting and other habitat-altering 

disturbances but would still facilitate owl movement across 

the landscape (Lint et al. 1999) is validated by the latest 

monitoring. The rate of nesting/roosting habitat loss outside 

of the reserves from timber harvesting has been lower 

than expected, and we observed both losses and gains in 

dispersal habitat. In our monitoring, we did not observe any 

isolation of owl population segments caused by large-scale 

disturbance; however, we did note both expansions and 

contractions of dispersal-capable landscape and that some 

large reserves in portions of the range have poor dispersal 

conditions and might be focal areas for further investigation 

of population isolation studies. 

Although not included within the timeframe of this 

latest monitoring analysis, the southern portion of the owl’s 

range experienced another 615,000 ac (approx.) of wildfire 

between 2008 and 2009, with most of it occurring within 

reserved land use allocations. If this trend persists, the 

actual decadal loss of habitat from wildfire will continue to 

push against the Plan’s assumption of 2.5 percent per decade 

and, to reemphasize the point made at the beginning of this 

summary, may have unexpected consequences on the effectiveness of portions of the large reserved network. Outside 

of the reserved network, the lack of timber harvesting in the 

nonreserved allocations over the past 15 years has provided 

some cushion from these losses. And finally, although we 

still anticipate that recruitment of nesting/roosting habitat 

from forest succession will eventually begin to offset habitat 

losses from wildfire, forests grow slowly, and, where they 

occur in landscapes prone to wildfire, the nesting/roosting 

habitat conditions may take much longer to develop.

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Kristian Skybak
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Introduction

When Franklin and Dyrness created their map of physiographic provinces in 1973, they noted that the lines drawn 

to reduce the complexity of large geographic areas into 

more manageable proportions are sometimes arbitrary, 

whereas in nature the transition from one condition to 

another is often gradual. A modified version of the Franklin 

and Dyrness (1973) physiographic provinces was used to divide the northern spotted owl’s (Strix occidentalis caurina) 

range, which covers 57 million ac that stretch from Canada 

to northern California, into 10 areas that represented different forest vegetation and environmental characteristics 

(Thomas et al. 1990). Agee and Edmonds (1992) made the 

first attempt to delineate fire disturbance regimes within the 

owl’s range during the initial stages of northern spotted owl 

recovery planning. The spotted owl recovery team (USDI 

Chapter 4: Large Wildfires Within the Owl’s Range

Raymond J. Davis, William C. Aney, Louisa Evers, and Katie M. Dugger

1992) used this and other information to further subdivide 

the range into 12 physiographic provinces, which currently 

provide the framework for monitoring the Northwest Forest 

Plan (the Plan) (FEMAT 1993, Lint et al. 1999). More recent 

attempts to map the “dry, fire-prone” portion of the owl’s 

range (Healey et al. 2008, Rapp 2005, Spies et al. 2006) 

are mainly delineated along these physiographic province 

boundary lines, which were not drawn specifically to define 

the underlying nature of wildfire within the owl’s range. 

The result is a line that often shifts, sometimes considerably, between mapping efforts (fig. 4-1).

This desire to map fire-prone areas in the owl’s range 

stems from a concern by many that wildfire will destroy 

spotted owl habitat. The recent increase in frequency 

of large wildfire occurrence (and area burned) since the 

mid-1980s in the Western United States (Schwind 2008, 

Westerling et al. 2006), and within the owl’s range (fig. 4-2) 

Figure 4-1—Various depictions of the “fire-prone” areas within the range of the northern spotted owl (Agee and Edmonds 1992, 

Rapp 2005, Spies et al. 2006).
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GENERAL TECHNICAL REPORT PNW-GTR-850

has only heightened this concern. There is also evidence 

that along with this increased frequency there has also been 

an increase in the amount of high-severity wildfire (Miller 

et al. 2009, Schwind 2008; but see Hanson et al. 2009). 

However, evidence from recent studies reveals that the 

effects of wildfire on owl habitat and demography are mixed 

(Bond et al. 2009, Clark 2007). In the short term, large 

wildfires may be detrimental to spotted owls by decreasing 

survival and occupancy rates because high-severity1

 fire 

that caused loss and fragmentation of suitable nesting and 

roosting habitat contributed to existing spotted owl sites 

becoming unoccupied (Clark 2007). In addition, California 

spotted owls avoided roosting [breeding season] in forests 

that had experienced moderate to high-severity2 fire effects 

and nested only in stands that were unburned or had experienced low- to moderate-severity fire (Bond et al. 2009). 

However, spotted owls did forage in areas of high-severity 

fire, possibly because prey species are more abundant and 

1

 Clark (2007) defined high-severity as > 70 percent of the 

overstory removed by fire. 

2

 Bond et al. (2009) defined high-severity as areas where dominant 

vegetation had high to complete mortality owing to fire.

accessible in these high-severity burn patches (Bond et al. 

2009, Clark 2007, Franklin et al. 2000). Thus, although 

stand-replacing wildfires certainly remove nesting/roosting habitats described in chapter 3, they may not prevent 

foraging by owls, and only a very large fire that creates a 

large-scale loss of forest canopy and habitat would have a 

significant effect on owl demography and dispersal (see the 

discussion on dispersal habitat in chapter 3). Much more 

research is needed to fully understand the effects of wildfire 

frequency and severity on owls and their prey sources (see 

chapter 5 in this report), but some adaptation to wildfire is 

expected given that this species has evolved with it in some 

parts of its range. 

Although the relationship between wildfire frequency 

and severity on owl demography is not fully understood, 

habitat loss is the primary reason for the owl’s decline and 

subsequent listing as “threatened” under the Endangered 

Species Act (USDI 1990). The habitat monitoring results 

presented in chapter 3 (this report) identified wildfire as the 

leading cause of current spotted owl nesting and roosting 

habitat loss (3.4 percent) and its future recruitment on 

federal lands. This was also the finding in the 10-year

Figure 4-2—Frequency histogram of acres burned by wildfires within the range of the northern spotted owl between 1970 and 2009

(data sources from large wildfire data from this analysis). 
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

monitoring report (Davis and Lint 2005), and since 

completion of that report, several more large wildfires have 

occurred within the owl’s range and more nesting/roosting habitat has been lost. Thus, loss of habitat to wildfire 

remains a significant concern for the management and 

conservation of the spotted owl. In response, the current 

species recovery planning process for the owl (USDI 2008) 

established working groups to develop recovery actions for 

fire-prone areas based on the current map of physiographic 

provinces (USDI 1992). 

Here we present a novel modeling method to map areas 

within the owl’s range that are prone to large wildfires. 

The result is a rangewide map of likelihood (or suitability) 

gradients for large wildfire occurrence. Instead of using 

physiographic province boundaries to define fire-prone 

areas within the owl’s range, the gradient map is further 

classified into a binary map that we believe better represents 

the fire-prone areas. However, the raw model output (fig. 

4-3) maintains the gradual transitions from one condition 

to another so succinctly alluded to by Franklin and Dyrness 

(1973). 

Methods and Data Sources

There are several modeling approaches and methods available for modeling spatial distributions of environmental 

phenomenon, each with their own strengths and weaknesses 

(Guissan and Zimmermann 2000). A recent paper by Elith 

et al. (2011) summarizes many of these issues, including 

an ecological explanation of MaxEnt (Phillips and Dudík 

2008, Phillips et al. 2006) and discussion on the issue of 

using presence-only versus presence-absence data (also see 

page 35 of Davis and Lint 2005). For consistency, we chose 

to use MaxEnt, the same modeling tool used for mapping 

spotted owl habitat suitability in chapter 3 (this report), to 

model and map wildfire suitability (fig. 4-3). This spatial 

distribution modeling software is commonly used to create 

predictive maps of habitat suitability (or likelihood of use) 

based on species location data and a set of environmental 

predictor variables that contribute to the definition of the 

species’ niche (Phillips et al. 2006). The term “niche” is 

used to describe the environmental requirements needed for 

a species to exist (Grinnell 1917). It is the “hypervolume” 

in the multidimensional environmental space (the number 

of dimensions are based on the number of environmental 

variables used to describe the niche) that permits positive 

growth (Hutchinson 1957). Habitat suitability models are 

operational applications of the ecological niche, and use 

multiple environmental variables to predict the likelihood of 

species occurrence (Hirzel and Le Lay 2008). 

Based on our understanding of northern spotted owl 

ecology, we expect them to nest in landscapes that are 

heavily forested with older or structurally diverse stands 

of conifer with relatively closed canopies (see chapter 3 

in this report). We call this combination of environmental 

conditions owl “habitat.” Similarly, environmental conditions commonly associated with large wildfires include 

steep slopes, warm and dry aspects, hot and dry weather, 

and limited access for ground-based firefighting resources 

(hand crews, engines, etc.). These have long been identified 

in the literature as key elements in the development of large 

wildfires (Albini 1976, Albini et al. 1982, Brown and Davis 

1973, Countryman 1964, Deeming et al. 1977, Garfin and 

Morehouse 2001, Gisborne 1936, Hayes 1941, Rothermel 

1983, Schroeder and Buck 1970, Scott and Reinhardt 2001, 

Sugihara et al. 2006, Van Wagner 1977); in decisionsupport planning tools for wildfire response such as the 

National Fire Management Analysis System (NFMAS) and 

its successor, Fire Program Analysis (FPA); and in practice. 

It is no surprise that wildfires grow rapidly and become 

larger in landscapes that have an abundance of these conditions. The combination of these environmental conditions 

might also be considered a “habitat,” not for an animal, but 

one that is suitable for large wildfires as alluded to by Pyne 

(2001, 2004). The analogy of wildfire as a “living organism” is not unheard of (Bond and Keeley 2005, Parisien and 

Moritz 2009), and it seems reasonable that the principles for 

describing the niche of a plant or animal species should be 

no different than for defining the “niche” of large wildfires, 

or for that matter any other natural phenomenon that is 

associated with unique combinations of environmental 

conditions.

Our ability to accurately map the environmental 

conditions that constitute the niche allows us to use modeling software to map the pattern of the relationship between 
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GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 4-3—Although large (≥ 1,000 ac) wildfires are known throughout the entire range of the owl, this wildfire suitability map represents the likelihood for occurrence of these fires based on three decades of large wildfire occurrence and the

underlying combination of “fire environment” variables from where they occurred (see fig. 4-4).
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

these environmental factors and large wildfire occurrence. 

This approach has been used recently to model wildfire’s 

broad geographical distribution patterns across the conterminous United States, the state of California, and five 

wildfire-prone ecoregions within California (Parisien and 

Moritz 2009). To our knowledge, this marks the first time 

that “habitat suitability” software was used to map spatial 

patterns of wildfire likelihood over large landscapes as a 

function of multiple environmental variables. Coarse-scale 

maps of global fire patterns that discriminated between 

“fire-prone” and “fire-free” areas of the world were also 

produced using similar methods (Krawchuk et al. 2009). 

Maps produced by this method have been called “wildfire 

suitability” maps (Parisien and Moritz 2009), and this is the 

term we use to describe our map (fig. 4-3).

It is not uncommon for wildfires that range from 500 to 

1,000 ac and greater to be defined as “large” in the recent 

fire ecology literature (Potter 1996, Eidenshink et al. 2007, 

Preisler and Westerling 2007, Westerling et al. 2003). In the 

10-year report, a “large wildfire” was defined as a fire that 

would affect multiple owl territories (Davis and Lint 2005). 

Here we define “large wildfire” as one that exceeds 1,000 

ac, which is larger than the estimated size of a northern 

spotted owl home range core area3 throughout most of 

its range (Bingham and Noon 1997, Courtney et al. 2004, 

USDI and USDA 2008). 

Environmental Data

At an intermediate spatial scale, weather and topography 

make up two legs of the fire behavior (or environment) 

triangle (Agee 1993, Countryman 1966), whereas at the 

larger (regional) spatial scale, climate, ignitions, and broad 

vegetation patterns define fire regimes (see fig. 1 in Parisien 

and Mortiz 2009). Our spatial scale of modeling combines 

both the intermediate and regional scales, and our set of 

environmental data reflects this, with the exception of fuels 

and vegetation variable groups. We did not include any

fuel variables in our modeling, but the model’s geographic 

3

 An area of concentrated use within a home range that commonly 

includes nest sites, roost sites, refuges, and regions with the most 

dependable food sources (Kaufmann 1962, Samuel et al. 1985).

background consisted only of forest-capable areas, which 

represent “vegetation” in the larger spatial scale. Because 

forest fires are what we were attempting to model, the use 

of this modeling background allowed us to confine the 

interactions of environmental variables to locations where 

forest vegetation and fuels occur. An advantage of not using 

a fuel variable is that we avoided the difficulties that arise 

in accurately mapping them (Stratton 2006). Fuels are a 

dynamic component of the ecosystem, very temporal in 

nature and always changing in response to forest succession 

and disturbances (Agee 1993). The inclusion of a fuel variable would produce a map that would only be good as long 

as the fuel condition remained exactly as modeled. Instead, 

we wanted to produce a model that was relatively stable, 

and based on the underlying conditions of topography and 

climate that support large wildfires. 

The set of environmental variables we used for modeling was based on fire climate4 and environment relationships in the literature and on expert advice (fig. 4-4, app. G).

Matching the temporal scale of these environmental data 

with the fire training data was an important factor. Fire 

climate variables were derived from “parameter elevated 

regression on independent slope model” (PRISM) maps 

(Oregon Climate Service 2008) that provide averaged 

weather conditions between 1971 and 2000. This timeframe 

coincides with the 1970 to 2002 timeframe of the fire training data set. As our fire climate variables, we initially chose

average maximum temperature in August and summer 

moisture stress (the ratio of summer temperature and 

precipitation). However, because of the high correlation 

between these two variables (r > 0.7), we replaced the 

moisture stress variable with a summer precipitation variable, which is the average amount of precipitation that fell 

between May and September, corresponding to the average 

fire season. 

Lightning is the primary ignition source for wildfires 

around the world (Agee 1993) including the forested regions 

of the Pacific Northwest, especially when it occurs without 

4

 Defined by the National Wildfire Coordinating Group (NWCG) 

as a “composite pattern of weather elements over time that affect 

fire behavior in a given region.”
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GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 4-4—Environmental variables used in the model to define the “niche” of large wildfires in the 

owl’s range.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

significant rainfall (Rorig and Ferguson 1999). Based on 

data from the Climate, Ecosystem, and Fire Applications 

(CEFA) Program (Brown et al. 2002), lightning was the 

cause for approximately 25,000 wildfires within the range 

of the owl from 1970 to 2002. Lightning accounted for 68 

percent of the wildfires that grew to larger than 1,000 ac and 

accounted for 75 percent of the total area burned within the 

owl’s range from 1994 through 2002 (Davis and Lint 2005). 

The geographic patterns of lightning-ignited wildfires in 

the Pacific Northwest are similar today to what they were 

throughout the 1900s (Agee 1993, Komarek 1967, Morris 

1934, Rorig and Ferguson 1999, Sensenig 2002). Therefore, 

a lightning-ignited fire density map was created using the 

CEFA data from 1970 to 2002 and included as one of the 

environmental variables. 

Topographic variables for elevation, slope, and aspect 

were also used in the model. Elevation provides an environmental gradient that relates to local climate conditions and 

vegetation zones, which can affect fire behavior and growth 

(Hayes 1941, Rothermel 1983). Slope is related to fire spread 

rate, and its orientation, or aspect, relates to the amount of 

solar radiation, which also affects the local microclimate 

and vegetation. Southerly aspects in the Northern Hemisphere usually receive more annual solar radiation and 

are hotter and drier than northerly aspects. We used the 

potential relative radiation (PRR) index developed by Pierce 

et al. (2005) as a more realistic measure for solar radiation 

than simple aspect.

The spatial resolution of our environmental data was 

250- by 250-m (15-ac) pixels, which was averaged within a 

1,000-ac circular moving window to correspond with our 

minimum definition of a large wildfire. All “nonforested” 

(i.e., water, rock, etc.) areas were “masked out” to constrain 

the modeling background to only those areas where large 

wildfires are possible. All variables were analyzed for spatial correlations and one variable was dropped or replaced 

for Pearson correlations > 0.7. 

Large Wildfire Data

We chose to train our model using historical occurrence 

data from only large wildfires (as defined above). Wildfires of this size are relatively rare occurrences, but are

responsible for the vast majority of area burned each year. 

For example, of the roughly 25,000 lightning-ignited 

wildfires recorded within the owl’s range between 1970 to 

2002, less than 1 percent were ≥1,000 ac; but these fires accounted for 96 percent of the total 2.5 million ac that burned 

(based on CEFA data) (Brown et al. 2002). This pattern 

of large areas of land being burned by a small percentage 

of large wildfires is a global phenomenon that fits power 

law distributions (Cui and Perera 2008, Stocks et al. 2003, 

Westerling and Bryant 2008). It therefore made more sense 

to focus our modeling on large wildfires because of their 

disproportionate effect on the environment.

To train the distributional model, the spatial point

locations where large wildfires have occurred are linked

to the underlying combinations of environmental variable 

grid cells over which they lay. This relationship between

fire occurrence and environmental gradients is then extrapolated to the rest of the modeled region to “score” environmental conditions based on their similarity to where 

the training data occur. To create a point layer representing 

large wildfires, we assembled 250 polygons of large wildfire 

perimeters that, in total, burned about 2.6 million ac of 

forest lands across the owl’s range between 1970 and 2002. 

Using a geographic information system (GIS), we overlaid 

these polygons on a grid of randomly generated points that 

was produced using Hawth’s Tools (Beyer 2009). Each grid 

point was separated by 1.6 mi to reduce spatial autocorrelation issues, as the modeling environmental variables were 

averaged over a 0.7-mi radius that covered about 1,000 ac, 

representing a “large wildfire unit” (fig. 4-5). A total of 

1,499 random grid points occurred within a large-wildfire 

perimeter; of these, 104 (about 7 percent) were within 

overlapping wildfire perimeters, representing sites that had 

been burned twice during the 32 years represented by our 

training data. Because these points represent separate large 

wildfire occurrence from different years, they were included as additional points in the training data set for a total 

of 1,603 training points. We also generated an independent 

model-testing data set in the same manner, using 146 large 

wildfires that had burned 1.4 million ac between 2003 and 

2009 (n = 903).
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GENERAL TECHNICAL REPORT PNW-GTR-850

It is likely that without wildfire suppression, there 

would have been more large wildfires (Sensenig 2002) that 

burned between 1970 and 2002; thus our training data are 

likely biased. However, we are uncertain how this bias may 

have affected our model. It is possible that the training data 

better represent large wildfires that were more difficult to 

suppress or contain because of inaccessibility owing to 

geography or absence of roads. To address this issue, we 

also included a variable that represents distance from roads. 

We assumed that wildfires were more apt to get bigger when 

farther away from a road because it was more difficult to get 

people and equipment into the area to fight the fire.

Wildfire Suitability Modeling

We chose the same modeling features in MaxEnt that we

used for habitat modeling (linear, product, and hinge 

features) because this combination works well in fitting 

the environmental data to known or expected relationships 

between the environmental variables and wildfires based 

on visual review of response curves generated during the 

modeling procedure. This combination is also a compromise between using features that may be too restrictive for 

complex environmental relationships (i.e., only linear) while 

Figure 4-5—Model training and testing data came from grid 

points that occurred within large-fire perimeters. Points were 

spaced by 2.5 km to reduce spatial autocorrelation of environmental data, which were averaged within 1,000-ac circles as shown in 

this figure.

avoiding features that might allow overfitting the model to 

the data (i.e., threshold). 

Phillips and Dudík (2008) defined the logistic output 

of their modeling software as an “estimate” of probability 

of presence, conditioned on the environmental variables 

used in the modeling. In our case, we used only training 

data from large wildfires and environmental variables that 

are commonly associated with wildfire ignition and growth 

(Albini 1976, Deeming et al. 1977, Gisborne 1936, Hayes 

1941, Rothermel 1991). Therefore, the model’s logistic 

output represents a scale of probability (from low to high) 

of a large wildfire occurring as a function of physical, 

topographic, climatic, and fire ignition history patterns in 

the owl’s range. Where combinations of these variables are 

more similar to where large wildfires have occurred in the 

past, the logistic probability values will be higher. Likewise, 

underlying patterns of environmental variables that do not 

commonly occur where large wildfires have burned will 

have lower probability values.

We ran 10 bootstrapped model replicates using half of 

the training data set for each replicate, and holding out the 

other half to test the model’s predictions. In other words, 

MaxEnt produced 10 models using 10 randomly generated 

subsets of the 1970–2002 large-fire data, each consisting 

of 802 points. Then each of these models was tested using 

the subset of large-fire points held out (n = 801). During this 

process, we increased the regularization multiplier from 

its default of 1.0, which helps to prevent model overfitting, 

by increments of 0.5 to minimize the difference between 

the regularized training gain and test gain, while tryng to 

maximize the test area under the curve (AUC) statistic and 

Spearman rank (Rs

) correlation coefficient on our held-out 

test data. These three statistics (gain, AUC, and Rs) are 

commonly used to measure the discriminative and predictive power of these sorts of models (Boyce et al. 2002, 

Fielding and Bell 1997, Hirzel et al. 2006). 

The gain relates to how different the training or testing 

data are from the background data. It is similar to “deviance” as used in generalized linear modeling (Phillips et al. 

2006), and higher gains indicate larger differences between 

occurrence location environmental conditions and average 

background environmental conditions. The exponent of gain 
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

produces the mean probability value of occurrence compared to random locations selected from the surrounding 

modeled landscape. Large differences between the regularized training and testing gains indicates model overfitting.

The AUC statistic is a measure of the model’s predictive accuracy, and it was originally developed for evaluations using presence and absence data, producing an index 

value from 0.5 to 1 with values close to 0.5 indicating poor 

discrimination and a value of 1 indicating perfect predictions. The AUC values can be interpreted similarly to the 

traditional academic point system where values between 

0.9 and 1.0 indicate an excellent model (A), 0.8 to 0.9 is 

good (B), 0.7 to 0.8 is fair (C), 0.6 to 0.7 is poor (D), and 

AUC values between 0.5 and 0.6 represent failure (F), or 

models that don’t predict much better than a random guess. 

Examples of this interpretation in the field of niche-based 

species distribution models can be found in Araújo et al. 

(2005) and Randin et al. (2006). In our situation, MaxEnt 

uses 10,000 randomly selected background locations (map 

pixels) instead of true absence data, so it is not possible to 

achieve an AUC value of 1.0 (Wiley et al. 2003). However, 

interpretation is similar, with higher AUCs indicating better 

model predictions (Phillips et al. 2006). Specific to our 

case, AUC values represent the percentage of times a large 

wildfire location would have a higher wildfire suitability 

value than a randomly selected location from the modeling 

region.

The Spearman rank correlation coefficient is a nonparametric statistic that, in our situation, compares the 

ranks of large fire occurrence vs. area available to “binned” 

modeled prediction ranks (Boyce et al. 2002). A good model 

would predict an increasing ratio of the percentage of fire 

occurrence to the percentage of the modeled landscape in 

each model bin as the bin values increase, and an Rs of 1.0 

indicates a strong positive correlation (Boyce et al. 2002).

The best model using the training data, and based on 

these statistics was produced using a regularization multiplier of 1.5. We then reran the same model, using the entire 

training data set (n = 1,603) and conducted a final test of 

the model using 7 years of independent test data from large 

fires that occurred between 2003 and 2009. Following the 

same rationale and modeling approach used in chapter 3 

(this report), our final model product is the “average” model 

from our bootstrapped replicates. The predictive qualities of 

the “average” map can be better explained by the diagnostic 

predicted versus expected (P/E) curve (fig. 4.6) (Hirzel et 

al. 2006), and this curve allows users to better interpret the 

modeled values. 

We also analyzed the importance of each environmental variable and its relationship with large wildfire 

occurrence by running jackknifed models (Phillips et al. 

2006) for each of the 10 replicates. For each environmental 

variable, this jackknifing procedure produces a model that 

excludes the variable, and another model based on only that 

variable. The gain and AUC model performance statistics 

from the jackknifed models then inform us on the relationship and importance of each variable in explaining largewildfire occurrence in the area being modeled. 

Results

An average testing gain of 0.80 indicates that our model 

predicted large-wildfire occurrence 2.2 times that expected 

by chance. The testing gain was also similar to the regularized training gain of 0.77 indicating that our model was not 

over-fit to the environmental data. The mean testing data 

AUC, based on 10 bootstrapped replicates, was 0.83, and 

using independent test data from large wildfires from 2003 

to 2009, the AUC was 0.78. The replicate mean predicted 

versus expected (P/E) curve (Hirzel et al. 2006) had an Rs = 

1.0 (P < 0.001) and the test data P/E curve had an Rs = 0.987 

(P < 0.001). The highest mean logistic probability for our 

model was 0.90, which we converted into an integer value 

(90) for GIS mapping purposes by multiplying by a factor 

of 100. The threshold of 31 along this probability gradient 

marks where the predicted probability of large-wildfire occurrence is greater than what would be expected by chance 

(fig. 4-6). One can use that threshold to define the owl’s 

range in binary terms, where mapped values above this 

threshold represent geographic areas that are more prone 

to large wildfire occurrence, based on our 32-year training 

data timeframe, and areas below that threshold are not 

normally prone to large wildfires during that timeframe. 

The strongest environmental variables were August 

maximum temperature, slope, and lightning ignition 
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GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 4-6—Results of model bootstrapped replicates (based on fire data from 1970 to 2002) and 

independent testing (dashed-line based on fire data from 2003 to 2009) are shown in this predicted vs. 

expected curve (Hirzel et al. 2006). The curves indicate that the model performed well in both tests. 

The point at which the mean curve crosses the random frequency line (predicted/expected = 1) is used 

as the threshold modeled likelihood value (>32) for delineating the “fire-prone” areas of the binary 

map from the full gradient map. The gray-shaded area along the mean curve represents the 95-percent 

confidence intervals (95% CI) from the bootstrapped replicates.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

density, explaining 76 percent of the geographical patterns 

of large wildfires. Distance to roads contributed another 15 

percent, and, together, these four variables account for 91 

percent of the information that relates to wildfire suitability 

in our model. Response curves (app. G) suggest suitability 

for large wildfires increases (in almost a logistic fashion) 

as August maximum temperature and slope increases. The 

response curves for lightning ignition density and distance 

to road variable response curves are quadratic in shape, 

showing sharp increases in suitability as the variable value 

increases, but then reaching a wide plateau and eventually 

decreasing (app. G). We suspect this decrease in suitability 

at the high end is related to elevation effects, which also 

exhibited a similar quadratic response curve. Extreme 

distances from roads occurred in many wilderness areas 

located along the Cascades crest, and these remote areas 

tend to be at the highest elevations where late snowmelt 

produces cooler and moister conditions during the fire 

season. Likewise, lightning ignitions tend to be highest at 

high elevations. Of the six model variables used, slope had 

the highest gain when modeled by itself. It also decreased 

the gain the most when omitted from the model, and 

therefore is an important variable in our model and appears 

to have the most information that is not present in the other 

environmental variables.

Discussion

Four decades of history on large-wildfire occurrence fit well 

within our map of “wildfire suitability” gradients (fig. 4-7). 

The binary version of our map (fig. 4-8) has some distinct 

similarities to previously mapped versions of “fire-prone” 

areas in the owl’s range, especially the map by Agee and 

Edmonds (1992). But, it also has some distinct differences; 

most notably, it includes the considerable portions of the 

western Cascades of Oregon, and it excludes large areas of 

the eastern Cascades that are commonly shown on previous 

map versions (fig. 4-1). Based on our map, only the northern 

half of the Washington Eastern Cascades physiographic 

province has substantial land area that appears suitable for 

large-wildfire occurrence. South of this, our map indicates 

a patchy distribution of high-suitability areas along the 

eastern Cascades; yet, much of this area historically was 

covered with ponderosa pine forests, known for its dependency on wildfire. This, and recent occurrences of large 

wildfires in these areas (i.e., B&B Complex, Link Fire, 

Davis Lake Fire, and the Eyerly Fire) may point to potential 

model limitations, which we discuss below. 

To begin with, our map represents suitability for what 

we defined as “large” wildfires, and perhaps ones that are 

harder to suppress and contain, given the potential bias of 

our training data. The map does not represent a suitability 

gradient for all wildfire occurrences, nor behaviors, such 

as fire severity. Secondly, our map was trained with about 

three decades of large wildfire data and therefore, represents the likelihood of large wildfire occurrence within 

that specific timeframe. If we go further back in time, the 

fire history record within the owl’s range clearly shows the 

occurrence of large wildfires in the lower suitability areas 

(“bluer areas”) of our map (fig. 4-3), such as the Yacolt and 

Columbia Fires of 1902, the Tillamook Fire of 1933, and the 

more recent Oxbow Fire of 1966 (see fig. 3-16 in chapter 3). 

As noted above, our model is based on climate and topographic variables that have been relatively stable over the 

last century. The large wildfires that have occurred within 

the lower suitability areas of our map have been consistently 

associated with extreme weather (i.e., high winds) or heavy, 

contiguous, dry fuel (McClure 2005, Morris 1935), which 

could not be included in the model. We suspect a map 

characterizing long-term means of these extreme, episodic 

climatic events would be even more difficult to produce 

than a rangewide fuel map. However, fire ecologists have 

recently divided the range of the owl into five “fire regime 

groups,”5 which represent a coarse spatial integration of fire 

frequency and severity (Keane et al. 2002, Morgan et al. 

2001, Schmidt et al. 2002). Whereas fire regimes relate to 

the frequency, severity, and spatial distribution of historical 

wildfire in the ecosystem (Rollins et al. 2002), our map 

sheds light only on the latter of these three characteristics. 

However, it still shows spatial similarities to the fire regime 

group map, and, in particular, the lower suitability areas 

complement Fire Regime Group V, which represents infrequent fires (>200-year intervals) and mostly occurs in the 

5 LANDFIRE data products and their descriptions are available 

online at http://www.landfire.gov/products_overview.php.
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GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 4-7—The full gradient version of the wildfire suitability model showing locations of large wildfires used to train 

the model (left) and locations of large wildfires that occurred after 2002 (right) that served as our independent testing 

data.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure 4-8—The binary version of the wildfire suitability model showing locations of large wildfires used to train the 

model (left) and locations of large wildfires that occurred after 2002 (right) that served as our independent testing data.
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GENERAL TECHNICAL REPORT PNW-GTR-850

coastal zones and highest elevations of the mountain ranges. 

These areas have been defined as incurring infrequent 

wildfires that, when they do happen, tend to be extremely 

large and severe (Agee 1993, 1998; Morgan et al. 2001; Noss 

et al. 2006).

On the other end of the fire regime group spectrum, 

wildfires were more frequent (<35-year intervals) and less 

severe, maintaining open forests, or a mosaic of differentaged forest seral stages (Hann and Strohm 2003). On our 

map, portions of the Cascades east of the high-elevation 

crest, where ponderosa pine forests historically dominated, 

fit this description. These pine forests were once dependent 

on frequent surface fires that burned heterogeneously 

through the landscape, creating open, parklike distributions of trees that were often clumped into small groups 

(Agee 1994, Graham and Jain 2005). Historically, wildfires 

in ponderosa pine forests were relatively easy to contain 

(Munger 1917). Fire suppression and exclusion in ponderosa 

pine forests produced changes that have been well documented by scientists since the 1990s (Agee 1990, Deeming 

1990, Kauffman 1990, Mitchell 1990, Mutch et al. 1993, 

Wickman 1992). The lack of natural wildfires allows

understory development of shade-tolerant vegetation that 

produces resource-stressed stands, making them more 

susceptible to insects and disease. This, in turn, leads to 

weakened or dead trees, generating fuel loadings that are 

unnaturally heavy and also contiguous over large areas 

(Hessburg et al. 2005). The understory development 

also creates ladder fuels that can lead to crown fires, and 

this fuel combination produces conditions ripe for large 

wildfires (Hessburg et al. 2005). Today, these areas have 

developed fuel characteristics that support larger, and more 

severe, wildfires (Hessburg et al. 2005), and the recent 

wildfires in central eastern Oregon Cascades have been 

larger than those of historical records (Eckert et al. 2008). 

To what extent fire suppression may have biased our 

map is uncertain. We suspect fire suppression has likely 

affected the frequency of large wildfires, but it is much less 

clear that it has affected the distribution of large wildfires 

on the landscape. Studies of large wildfires in the large 

wilderness areas of the southwest and northern Rockies 

(Rollins et al. 2002, 2004) suggest there has not been an 

effect on the distribution, although that evidence is indirect 

as distribution on the landscape was not the focus of any of 

these studies. Because we have almost no data on how the 

distribution of large fires might have differed in the absence 

of suppression actions within the study area, we cannot 

characterize any model bias in that regard.

Using forest health protection aerial survey data from 

1983 to 2008 (USDA 2008), spatial patterns of recent 

western spruce budworm (Choristoneura occidentalis) and 

mountain pine beetle (Dendroctonus ponderosae) outbreaks 

become apparent (fig. 4-9). In 1983, the spruce budworm 

began expanding its distribution in the eastern Cascades of 

Oregon, spreading northward into Washington. It mostly 

ran its course in the eastern Oregon Cascades by 1993, and 

then became more active in the southern portions of the 

Washington Eastern Cascades province. The increased fuel 

loads created by severe insect outbreaks certainly increase 

suitability for large wildfires, especially if the fuels are 

concentrated in a contiguous fashion. In general, the spatial 

pattern of concentrated spruce budworm outbreaks correspond highly with the B&B Complex and Link Fires from 

2003 (fig. 4-9), and also the Lake George, Puzzle, and Black 

Crater Fires from 2006, and the loss of owl habitat in these 

areas has largely been attributed to this spruce budworm 

epidemic and its contribution to the wildfire’s size and 

severity (Courtney et al. 2004). In addition, the Davis Fire 

of 2003 occurred in a concentrated area of recent mountain 

pine beetle outbreaks, and we consider it is likely these 

episodic insect infections added to the suitability of those 

specific areas to support these large wildfires.

Agee (1993) pointed out that fire regimes are dependent 

on the interaction of all parts of the fire behavior triangle—

weather, topography, and fuel. Parisien and Moritz (2009) 

described the fire regime triangle as the interaction of 

climate, ignitions, and vegetation. Our map spans both the 

spatial and temporal scales that these triangles represent 

(see fig. 1 in Parisian and Moritz 2009) and appears to 

reasonably reflect the last four decades of wildfire history 

within the range of the owl. However, we suspect that additional spatial information on long-term means of episodic 

climatic events or insect outbreaks would likely increase its 

accuracy. 
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure 4-9—Episodic outbreaks of insects in the Oregon Cascades provinces beginning in the 1980s, shown above, may help explain 

why portions of the eastern Cascades of Oregon have experienced recent large wildfires in areas where the wildfire suitability map indicates lower wildfire suitability (blue indicates lower amounts in the maps above, such as wildfire suitability or years of insect detection, 

whereas red indicates higher amounts). Large wildfires from 1970 to 2009 are shown as black cross-hatched polygons.

Perhaps one of the most compelling validations of our 

wildfire suitability map is the relationship of the distributions of three fire-dependent pine species (Little 1971, USDI 

1999) with our binary characterization of wildfire suitability 

(fig. 4-10). As a group in general, pines are associated with 

forests where wildfire is an integral part of the environment 

(Fonda 2001). In the range of the owl, sugar pine (Pinus 

lambertiana Dougl.), Jeffrey pine (P. jeffreyi Grev. & 

Balf.), and ponderosa pine (P. ponderosa P. & C. Lawson) 

are common associates with fire-prone ecosystems having 

shorter fire-return intervals (Skinner and Chang 1996, 

Taylor and Skinner 1998). These species are members of the 

“fire-resistant” group of pines (McCune 1988) that evolved 

in fire-prone environments and developed characteristics 

like thick bark to insulate the cambium and long needles 

to insulate buds from the heat of wildfires. Using a map 

comparison technique6 (Visser and de Nijs 2006), we

6

 This analysis was performed by using the Map Comparison Kit 

software (version 3.2) (Netherlands Environmental Assessment 

Agency) developed by the Research Institute for Knowledge 

Systems, and available online at http://www.riks.nl/mck/.
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GENERAL TECHNICAL REPORT PNW-GTR-850

 found that their combined geographic distributions (Little 

1971) coincide (cell to cell) moderately well (kappa = 0.46, 

KLOC = 0.76, KHISTO = 0.60) with the fire-prone areas of 

the binary version of our model. We believe the historical 

distributions of these fire-resistant conifers provide further 

evidence that our binary map effectively identifies portions 

of the owl’s range that wildfire regularly “inhabits.” 

Countryman (1966) described “fire environment” as 

the complex of fuel, topographic, and weather (air mass) 

factors that influences the inception, growth, and behavior 

of fire. He realized that it was a “pattern phenomena,” and 

advised that its pattern “must be considered in order to 

understand and predict a fire’s behavior.” Our map of wildfire suitability is essentially a modeling application of the 

concept Countryman first described over 40 years ago and 

is well-validated by almost 40 years of large wildfire data.

Figure 4-10—Fire-resistant pine distribution maps that were delineated in 1971 (Little 1971, USDI 1999) overlaid on the 

wildfire suitability binary map.

Summary

Our goal was to identify landscape-scale areas within the 

owl’s range where large wildfires are more probable over 

time using factors that are mostly spatially and temporally 

stable. The use of topographic and climate variables that 

summarized weather patterns over multiple years (1970–

2000) resulted in a map that we believe met this goal, as 

evidenced by the map’s moderate to good correlations (AUC 

of 0.78 to 0.83 and Rs ≥ 0.987) with large wildfire locations 

that post-date the wildfire data used to train the model as 

well as historical distribution maps of fire-dependent pine 

species (fig. 4-10). A binary classification of our map (based 

on the threshold where the map predicts large wildfires 

more often than would be expected by chance) provides 

a less arbitrary way to identify “fire-prone” areas of the 

northern spotted owl’s range that normally experience large 

wildfires. 
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

With this knowledge, we can overlay our wildfire suitability map on the current habitat suitability map produced 

in chapter 3, to confirm that the physiographic province 

with the most owl habitat in fire-prone landscapes is the 

California Klamath province (fig. 4-11). The next highest 

province is the Oregon Western Cascades province. However, the recent occurrence and trends of insect outbreaks 

in the eastern Cascades needs to be considered as well. The 

effects of past management practices combined with these 

outbreaks have probably increased the suitability for large 

wildfires of areas that otherwise have underlying physical 

and climatic factors that are not suitable. If this is the case, 

our results suggest that once the current fuel build-ups in 

the eastern Cascade provinces are reduced to more natural 

levels, the occurrence of large wildfires in that area should 

decline. 

The effects of wildfire on owl biology are difficult 

to assess and will likely remain a source of uncertainty 

(Courtney et al. 2004) for some time. Yet, the latest estimates of wildfire’s effect on current and future owl habitat, 

as displayed in chapter 3, indicate wildfires are the major 

source of habitat loss and future recruitment on federal 

lands in certain parts of the owl’s range. Fortunately, our 

capabilities to map owl habitat suitability, wildfire effects 

on vegetation, and wildfire suitability are improving; 

informing us better on what the habitat effects might be and 

where this interaction is most likely to happen. 

A limitation of our map is that, by itself, it does not provide information on where within the range, large wildfires 

may occur as a result of atypical or unusual, infrequent 

conditions or events such as extreme fire weather (Bessie 

and Johnson 1995; Westerling et al. 2003, 2006), fuel conditions, or a combination of the two. There are other tools 

available to monitor and track those conditions. However, 

our map can be used in conjunction with this ancillary data, 

such as insect outbreak maps, to better inform us on where 

the next large wildfires might happen. 

Finally, the inclusion of climate variables that summarize fire weather in our model may give us the ability to 

explore climate change scenarios (Carroll 2010) and what 

effect they may have on patterns of wildfire suitability in 

the future.

Figure 4-11—Fire-prone spotted owl nesting/roosting habitat, both reserved and nonreserved, by physiographic province, 2006/07. 

The majority of the fire-prone habitat occurs within the Klamath provinces, and the southern portions of the Oregon Western

Cascades. Over half is in reserved land use allocations.
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Ray Davis
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Emerging Issues

The 10-year report on the status and trends of northern 

spotted owl (Strix occidentalis caurina) populations and 

habitat recognized that the conservation and recovery of the 

owl is not solely related to the amount and quality of habitat 

across its range (Lint 2005). Other factors including interactions with prey and prey biology, competition with barred 

owls (S. varia), and the emergence of West Nile virus in 

the Pacific Northwest were noted as emerging issues (Lint 

2005). The potential threat of West Nile virus infections to 

spotted owl populations has not been realized, despite early 

evidence that owls in the wild were susceptible to natural 

infection (Fitzgerald et al. 2003). For unknown reasons, 

outbreaks of the West Nile virus in spotted owls that were 

anticipated 5 years ago have not occurred, although the 

virus is present throughout the owls range (Franklin 2010). 

Although West Nile virus has not developed into as much 

of a threat to owl populations as predicted previously 

(Lint 2005), documentation of the negative association 

between the invasive barred owl and spotted owl vital rates 

has con-tinued over the last 5 years (Anthony et al. 2006, 

Dugger et al. 2008, Forsman et al. 2011, Glenn et al. 2010, 

Kroll et al. 2010). The barred owl is now found at significant densities throughout the entire range of the northern 

spotted owl (Livezey 2009), and the range expansion of this 

species constitutes a significant threat to northern spotted 

owl persistence, which was not evident when the spotted 

owl was first listed (Courtney et al. 2004). The proportion 

of spotted owl territories where barred owls have been 

detected has increased steadily since the early 1990s in the 

eight effectiveness monitoring areas administered under 

the Northwest Forest Plan (the Plan) (fig. 5.1) along with 

increased evidence of negative interactions, presumably 

owing to competition or interference between the two 

species (Dugger et al. 2008, 2009; Kroll et al. 2010; Olson 

et al. 2005).

The invasion of barred owls into the range of the 

northern spotted owl has been associated with a decreased 

ability to detect and monitor spotted owls when barred owls 

are present (Dugger et al. 2009, Glenn et al. 2010, Kroll et 

al. 2010, Olson et al. 2005). In addition, the detection of 

Chapter 5: Emerging Issues, Related Research, and Research Needs

Katie M. Dugger and Raymond J. Davis

barred owls on spotted owl territories is associated with 

decreased site occupancy by spotted owls and changes in 

extinction and colonization rates (Dugger et al. 2008, 2009; 

Kroll et al. 2010; Olson et al. 2005). The strongest association is between detections of barred owls and increased 

extinction rates across the entire range of the spotted owl, 

and decreased colonization rates have been reported for 

some study areas as well (Dugger et al. 2008, 2009; Kroll et 

al. 2010; Olson et al. 2005). The most recent meta-analysis 

of spotted owl population dynamics reports a clear negative 

association between barred owl presence and spotted owl 

survival (Forsman et al. 2011; chapter 2, this report). Effects 

on fecundity are less apparent, but declines in spotted owl 

recruitment on four demographic study areas (Olympic, 

H.J. Andrews, Coast Ranges, Tyee) in association with 

barred owl presence has been reported (Glenn et al. 2010). 

Thus, researchers continue to compile negative associations 

between barred owl presence and spotted owl vital rates 

strengthening the evidence that barred owls are negatively 

affecting spotted owl demography. 

Climate change is another emerging issue that may 

affect spotted owl habitat, populations, and the functionality 

of the network of reserved land use allocations across the 

owl’s range (Carroll 2010, Carroll et al. 2009, Glenn et al. 

2010, Spies et al. 2010). Forest Service research objectives 

include developing projections for changes in fire regimes 

and shifts in habitat distributions because altered forest 

structures with increased threats from wildfire and insect 

and disease outbreaks are anticipated in association with 

predicted climate change (USDA 2009). Rate of change in 

spotted owl population was negatively associated with hot, 

dry growing seasons and wet, stormy winters (Glenn et al. 

2010). Climate models for the first half of the 21st century 

predict warmer, wetter winters and hotter, drier summers, 

which could potentially have negative consequences for 

spotted owls (Glenn et al. 2010). Considering the potential 

effects of different climate change scenarios in models predicting wildfire suitability (see chapter 4 in this report) may 

help estimate potential changes in the fire regime within the 

owl’s range and thus potential threats to habitat. In addition, 
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GENERAL TECHNICAL REPORT PNW-GTR-850

Figure 5-1—Annual proportion of northern spotted owl territories with barred owl detections on study areas in

Washington, Oregon, and California. Source: adapted from Forsman et al. (2011).
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

the inclusion of forest type variables in the owl habitat models can also be modified based on climate change scenarios 

and used to explore the effects of climate change on suitable 

owl habitat. As noted by Glenn et al. (2010), however, in the 

face of climate change and barred owl persistence, the best 

management strategy for conserving spotted owl populations is to maintain sufficient, high-quality, suitable habitat 

throughout the species’ range.

Related Research and Research Needs

Current research efforts to further understand the competitive interactions between barred and spotted owls are 

ongoing by D. Wiens, an Oregon State University Ph.D. 

student. In addition, the U.S. Fish and Wildlife Service 

(FWS) is proceeding with a proposal to clarify interactions 

between the two species using an experimental approach 

as part of Recovery Action 29 in the Final Recovery Plan 

for the Northern Spotted Owl (USDI 2008). This recovery 

action calls for the FWS to “Design and implement largescale control experiments in key spotted owl areas to assess 

the effects of barred owl removal on spotted owl site occupancy, reproduction, and survival.” By removing barred 

owls from spotted owl territories, researchers will be able to 

document a clear cause-effect relationship between barred 

owl presence and spotted owl demography (Gutiérrez et al. 

2007). The information gained from this experiment may 

aid in the management and conservation of spotted owls in 

the face of the continued threat posed by the invasion and 

establishment of the barred owl in the Pacific Northwest. 

This proposed research may elucidate new management 

actions or clarify management and conservation limitations 

regarding the negative interactions of these two species.

The extent to which habitat management can affect 

interactions between barred and spotted owls is not clear, 

but barred owls are habitat generalists that can occupy a 

wide variety of forest conditions including late-successional 

forests (Herter and Hicks 2000, Pearson and Livezey 2003, 

Singleton et al. 2010). In addition, their territories are only 

1/4 to 1/9 the size of spotted owl territories (Singleton et al. 

2010), so the ratio of the number of barred to spotted owls 

can be as high as 9 to 1 in some areas. This argues for the 

increased importance of high-quality, contiguous blocks 

of nesting/roosting habitat for spotted owls, and the effects 

of habitat loss cannot be decoupled from the additional 

stressor imposed by the barred owl range expansion (Dugger et al. 2008). 

In particular, the relationship between spotted owl 

fitness and habitat characteristics may have become disconnected through interspecific competition with barred owls 

in the landscape (Dugger et al. 2008). Our comparison of 

habitat suitability at spotted owl pair locations between 

1994/96 and 2006/07 (see chapter 3 in this report) showed 

an average decrease in habitat suitability value of 9.4 

percent across the owl’s range, suggesting that the quality 

of habitat at spotted owl pair locations has decreased over 

time. As loss of suitable nesting/roosting habitat since 

1994/96 has been low (3.4 percent), it is unlikely this decline 

in habitat quality of owl pair locations is the result of general habitat loss, so it is possible this change reflects competition for space with barred owls. Barred owls will use a wide 

variety of forested landscapes (Hamer et al. 2007, Singleton 

et al. 2010) and may be excluding spotted owls from the best 

spotted owl habitat in places where their densities are high 

(Dugger et al. 2008), but this hypothesis needs to be tested 

directly with barred owl removal experiments. It is possible 

that competition with barred owls also might be the reason 

we have had difficulty developing predictive models (see 

following discussion) that provide a clear understanding of 

the relationship between habitat characteristics and spotted 

owl demographics across the species’ range (Anthony et al. 

1998, 2002a, 2002b; but see Dugger et al. 2005, Olson et al. 

2004). 

The effectiveness monitoring plan for spotted owls 

recommended the development of predictive models linking 

survival, fecundity, and occupancy to observed vegetation 

characteristics of owl habitat (Lint et al. 1999). The expectation was that these predictive models could be validated and 

proved to generate owl vital rate predictions with acceptable error. If so, then there would be a shift from intensive 

collection of mark-recapture data via annual field surveys 

to the use of remotely sensed habitat data to monitor owl 

populations on at least some of the eight study areas (Lint 

et al. 1999). The spotted owl monitoring program funded 

a 5-year study to explore the development and feasibility 
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GENERAL TECHNICAL REPORT PNW-GTR-850

of predicting occupancy and demographic performance of 

spotted owls using remotely sensed habitat data (Anthony 

et al. 1998), and most of that work has been completed 

(Anthony et al. 2002a, 2002b; Dugger et al. 2005, 2006, 

2008; Olson et al. 2004, 2005). 

Unfortunately, this component of the effectiveness 

monitoring program has produced mixed results, with only 

a few strong relationships between habitat characteristics 

and survival and fecundity noted for some of the demographic study areas (Dugger et al. 2005, Franklin et al. 

2000, Olson et al. 2004). As noted by Lint (2005) in the 10-

year report, results at that point did not warrant moving on 

to phase II monitoring, where models would be substituted 

for mark-recapture studies. However, although simple, universal models linking habitat characteristics to survival and 

fecundity of owls are likely not possible, these efforts have 

provided more insight into the effects of climate and habitat 

characteristics on owl demography. Some general findings 

include the strong positive effect of late-successional forest 

at the core of an owl’s territory (around the nest site or activity center) on survival and fecundity (Dugger et al. 2005, 

Franklin et al. 2000, Olson et al. 2004). In addition, at least 

on some study areas in the southern portion of the owls’ 

range, some component of edge habitat may be important, 

probably as a source of prey (Franklin et al. 2000, Olson et 

al. 2004). 

Since the 10-year report, models for two study areas 

have been developed linking occupancy dynamics of 

spotted owls to habitat characteristics (Dugger et al. 

2008; Sovern, 2010). The effect of barred owls and habitat 

characteristics on extinction and colonization rates can 

be modeled using multiseason, single-species occupancy 

models (MacKenzie et al. 2006) or even multiple-species 

models within seasons (Bailey et al. 2009, MacKenzie et 

al. 2006). Estimates of annual site occupancy can also be 

derived from these models, which rely on a mark-recapture 

framework with a “site” or owl territory as the sample unit 

and presence/absence data across multiple visits within and 

between years to allow the separation of occupancy dynamics and detection probabilities (MacKenzie et al. 2006). 

Accounting for variations in detection rates of spotted 

owls is important for developing accurate estimates of site 

occupancy (MacKenzie et al. 2006, Olson et al. 2005). In 

addition, understanding the mechanisms or processes that 

drive site occupancy, like the factors that affect the probability that an occupied site becomes unoccupied (i.e., local 

extinction rate) or the probability that an unoccupied site 

becomes occupied (i.e., local colonization rate) are proving 

vital to understanding the impact of barred owls and habitat 

characteristics on spotted owl persistence (Olson et al. 2005; 

Kroll et al. 2010; Dugger et al., in press). Based on these 

models, strong relationships between the amount of oldforest habitat at the core scale (410-ac circle around nest tree 

or activity center) and extinction rates were observed for 

the South Cascades study area; spotted owl territories with 

small amounts of old forest near the site center experienced 

higher extinction rates of owl pairs (Dugger et al. 2008). In 

addition, increased fragmentation of old forest at the home 

range scale (3,700-ac circle around nest site or activity 

center) decreased colonization rates by owl pairs, and both 

occupancy parameters were affected by barred owl presence 

as well (Dugger et al. 2008).

It is unclear why we observed stronger associations 

between habitat characteristics and occupancy parameters 

as compared to habitat characteristics and survival or 

fecundity (Anthony et al. 2002a, 2002b; Dugger et al. 2005, 

2006, 2008; Olson et al. 2004, 2005), but it is possible that 

occupancy reflects the first level of selection by a species, 

and this is where the strongest selections for habitat are 

being made. In other words, an area of habitat selected 

for defense and maintenance of a territory by an owl also 

meets some minimum standard of suitability for survival 

and reproduction; thus, habitat quality most strongly affects 

territory selection, but other factors (climate, age/experience 

of individuals, individual variation) are more important for 

explaining the variation in survival and fecundity. 

Recent advances in the development of remotely sensed 

vegetation (Ohmann and Gregory 2002) and change-detection data (Kennedy et al. 2007) may provide an opportunity 

to investigate habitat relationships across the range of the 

species in conjunction with barred owl influences. Previous 

efforts included a range of map products based on a single 
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

point in time, or limited temporally and of varying quality (Glenn and Ripple 2004), precluding a meta-analysis 

using data from all the study areas. The development of 

this new vegetation layer will now allow us to search for 

and quantify consistent relationships between habitat 

characteristics and owl demography, particularly occupancy 

across the entire range of the species within a meta-analysis 

framework. In addition, the change-detection data provide 

an annual time sequence of vegetation changes that can now 

be linked to annual demographic data. This kind of analysis 

based on data from eight effectiveness monitoring areas and 

conducted in a workshop format as a meta-analysis following previous efforts for survival and fecundity (Anthony et 

al. 2006, Burnham et al. 1996, Forsman et al. 2011, Franklin 

et al. 1999) should be a priority for future research.

A better understanding of the population dynamics of 

many of the important spotted owl prey species across the 

range of the owl will likely be essential to understanding 

patterns and variation in spotted owl fecundity (Courtney et 

al. 2004, Forsman et al. 2011). New monitoring and research 

programs should be initiated to investigate prey cycles and 

their relationship to spotted owl demographics while incorporating the potential competitive effects of barred owls. 

This remains a large gap in our understanding of spotted 

owl ecology, and our lack of baseline information increases 

the difficulty we face trying to manage spotted owl populations in conjunction with the barred owl, most likely a direct 

competitor for food resources.

Another area of much needed research includes the 

effect of fire on owls and their prey, and how fuel reduction treatments proposed to reduce wildfire risk affect owl 

demography. Fire suppression over the last century has 

reduced wildfire’s presence in its “natural habitats” (Agee 

1993, Atzet and Martin 1992, Sensenig 2002), and although 

wildfire risk has not increased dramatically in the moister/

cooler forests, this suppression is believed to have increased 

the risk for severe wildfires in the fire-prone, or drier/warmer forests. The increased frequency of large wildfires since 

the mid-1980s in the Western United States (Westerling et 

al. 2006, Schwind 2008) and within the owl’s range (see 

chapter 4 in this report) have created concern about how 

wildfires might affect efforts to conserve the owl. Hotter, 

drier climates associated with climate change are believed 

to be at least partially responsible for this increase in largewildfire frequency (Westerling et al. 2006), and there is 

also evidence that the amount of high-severity wildfire has 

increased (Miller et al. 2009, Schwind 2008; but see Hanson 

et al. 2009), in some cases, as the result of accumulated 

fuels and higher stand densities (Sensenig 2002).

The relationship between wildfire and owl demography is not well understood, but likely includes a complex 

interaction of fire frequency and severity (Bond et al. 

2009, Clark 2007). Owls use forest stands that have burned 

understories or partially removed overstories, but they tend 

to avoid areas of complete stand replacement for nesting and 

roosting (Clark 2007), although use of high-severity burn 

areas for foraging has been documented for the California 

spotted owl (Strix occidentalis occidentalis) (Bond et al. 

2009). This species has likely evolved the ability to adapt 

and utilize forests that have been subjected to light to 

moderate fire severity, particularly in the fire-prone portions 

of its range (chapter 4, this report), but again short-term 

vs. long-term effects on demography and dispersal are 

unknown. 

Although wildfire has long been a natural agent of 

disturbance, owls evolved with it in historically forested 

landscapes that could accommodate the habitat changes 

caused by it. Today, much of the spotted owl habitat that 

remains has been “squeezed” into federally managed 

lands, covers a much smaller portion of the owl’s historical 

range, is highly fragmented (Davis and Lint 2005), and 

may no longer be able to accommodate large wildfires 

without incurring adverse consequences to the owl. To 

lessen the chances of adverse impacts from occurring, the 

Final Recovery Plan for the Northern Spotted Owl (USDI 

2008) advocated landscape-level treatments to reduce the 

risk of large-scale habitat loss to high-severity wildfire for 

Eastern Cascades and Klamath provinces of the owl’s range 

(USDI 2008). However, it is currently unclear what short- or 

long-term effects these forest thinning and fuel reduction 

treatments will have on northern spotted owl populations.
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GENERAL TECHNICAL REPORT PNW-GTR-850

A case study on a single owl territory in second-growth 

forests in the northern Oregon Coast Range suggests commercial thinning may cause northern spotted owls to alter 

their habitat use and increase the size of their home ranges, 

particularly during the nonbreeding season (Meiman et al. 

2003). This one case study suggested that thinning operations within core-use areas may be detrimental for northern 

spotted owls, at least in the short term. But it is not known 

whether thinning produces long-lasting adverse impacts 

or long-term benefits associated with owl vital rates. No 

other published literature is available on thinning and the 

effects of fuel reductions on habitat use and demography 

of threatened spotted owls. Understanding the relationship 

between wildfire and owl demography and the effect of 

both commercial and noncommercial thinning activities 

(to reduce fire fuel loads) on owl vital rates should be high 

research priorities.

Summary

As we have summarized above, there are several large gaps 

in our understanding of spotted owl ecology, particularly in 

relation to cycles of prey distribution and abundance, disturbance by fire, and forest management activities associated 

with developing future habitat or reducing fire risk. Emerging issues, primarily the competitive interactions with the 

barred owl, are also of very high concern, particularly as the 

negative effect of this invasive species may be in addition 

to, or somewhat independent of, maintenance of high-quality spotted owl habitat. The information we have on these 

issues is dependent on continued research and, in particular, 

the continued long-term monitoring of owl vital rates 

throughout this species’ range. The effectiveness monitoring program for spotted owls was designed to monitor the 

long-term results of the Plan and its effect on owl populations (Lint et al. 1999). This monitoring program has done 

much more, however, as the unique, large-scale demography 

data set resulting from this program has not only allowed 

resource managers to document the effects of management 

activities, but has also contributed valuable information 

regarding basic owl ecology and the factors that affect vital 

rates. In large part, the effectiveness monitoring program 

has been responsible for documentation of the barred owl 

expansion southward into the spotted owl’s range and the 

negative effects of this invasion on spotted owls (Dugger 

et al. 2008, 2009; Olson et al. 2005). Recovery goals and 

actions associated with the Final Recovery Plan for the 

Northern Spotted Owl (USDI 2008) and proposed revisions 

have been informed directly by or are reliant on the demography data collected on the eight effectiveness monitoring 

study areas, as well as the remotely sensed data developed 

for habitat monitoring. Data from this long-term monitoring 

program have also aided researchers in the development of 

new analytical approaches for answering complex demography questions (Bailey et al. 2009, MacKenzie et al. 2006). 

These examples illustrate how the value of the spotted owl 

effectiveness monitoring program reaches far beyond the 

original objectives and is truly vital to management and 

conservation of this species.

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Modeling demographic performance of northern spotted 

owls relative to forest habitat in Oregon. Journal of 

Wildlife Management. 68: 1039–1053. 

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.

Schwind, B., comp. 2008. Monitoring trends in burn 

severity: report on the Pacific Northwest and Pacific 

Southwest fires (1984 to 2005). Unpublished report. 

On file with: USDA Forest Service, Remote Sensing 

Applications Center (RSAC), U.S. Geological Survey, 

Center for Earth Resources Observations and Science 

(EROS). http://mtbs.gov/reports/MTBS_pnw-psw_final.

pdf. (4 January 2011).

Sensenig, T.S. 2002. Development, fire history and current 

and past growth, of old-growth and young-growth forest 

stands in the Cascade, Siskyou and mid-Coast Mountains 

of southwestern Oregon. Corvallis, OR: Oregon State 

University. 180 p. Ph.D. dissertation.

Singleton, P.H.; Lemkuhl, J.F.; Gaines, W.L.; Graham, 

S.A. 2010. Barred owl space use and habitat selection in 

the eastern Cascades, Washington. Journal of Wildlife 

Management. 74: 285–294. 

Sovern, S.G. 2010. Personal communication. Wildlife 

biologist, Cle Elum demographic survey crew leader, 

USDA Forest Service, Pacific Northwest Research 

Station, 802 West Second Street, Cle Elum, WA 98922.
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Spies, T.A.; Giesen, T.W.; Swanson, F.J.; Franklin, 

J.F.; Lach, D.; Johnson, K.N. 2010. Climate change 

adaptation strategies for federal forests of the Pacific 

Northwest, USA: ecological, policy, and socio-economic 

perspectives. Landscape Ecology. 25: 1185–1199.

U.S. Department of Agriculture [USDA]. 2009. Forest 

Service global change research strategy: 2009–2019. 

Washington, DC: U.S. Department of Agriculture,

Forest Service, Research and Development. 20 p.

http://www.fs.fed.us/research/climate/strategy.shtml.

(17 December 2010).

U.S. Department of the Interior [USDI]. 2008. Final 

recovery plan for the northern spotted owl (Strix 

occidentalis caurina). 142 p. On file with: U.S. Department of the Interior, Fish and Wildlife Service, 2600 SE 

98th Ave., Suite 100, Portland, OR 97266.

Westerling, A.L.; Hidalgo, H.G.; Cayan, D.R.; Swetnam, 

T.W. 2006. Warming and earlier spring increase Western 

U.S. forest fire activity. Science. 313: 940–943.

Acknowledgments

This report is the result of a large group effort focused 

on monitoring the effectiveness of the Northwest Forest 

Plan (the Plan). It would not have been possible without 

the continued hard work of many dedicated biologists who 

annually collect the field data and help to generate the 

natural resource information that we periodically analyze to 

produce these monitoring reports on the status and trends 

of northern spotted owls and how they are faring under 

the Plan. This unique set of field data is the cornerstone of 

our population monitoring and would not exist without the 

vision and leadership of the principal investigators of the 

demography study areas—Robert Anthony, USGS (retired); 

Eric Forsman, USDA Forest Service; Alan Franklin, 

Colorado State University: Rocky Gutierrez, University of 

Minnesota; and their many research associates. We thank 

them for their tremendous contribution to this effort. The 

federal demographic survey area crew leaders (from north 

to south) are Brian Biswell, Scott Gremel, Stan Sovern, Tom 

Snetsinger, Steve Ackers, Janice Reid, Robert Horn, Steve 

Andrews, and Peter Carslon, who, in addition to making the 

field work happen, took the time to review and comment on 

the habitat models that we generated from their data. We 

thank them. We also thank Diana Stralberg, Renée Cormier, 

and Dave Press for sharing their northern spotted owl data 

from the southernmost tip of its range.

For the habitat monitoring, we thank Janet Ohmann, 

Matthew Gregory, and Heather Roberts, also known as the 

Landscape, Ecology, Modeling, Mapping, and Analysis 

team for providing us with our first consistent rangewide 

vegetation data sets. This could not have been done without 

the collaborative efforts of Robert Kennedy, Warren Cohen, 

and Zhiqiang Yang from the Laboratory for Applications of 

Remote Sensing in Ecology research laboratory, which also 

produced the change-detection data for our analysis. This 

group was incredibly helpful to our modeling efforts and 

always available to answer questions and offer advice. We 

are grateful for the collaborative support we received from 

the Marbled Murrelet Monitoring Team, our close partners 

in habitat monitoring, particularly Martin Raphael, Gary 

Falxa, Beth Galleher, and Rich Young. Thanks to Carol 

Apple for her advice and assistance in the inventory plot 

analysis. We are also grateful for the data and advice provided by Alexandre Hirzel and Stephen Phillips (developers 

of the BioMapper and MaxEnt habitat modeling software, 

respectively), who patiently answered our many questions as 

we experimented with different habitat modeling methods 

during the early phases of this report.

The chapter on wildfire suitability was the result of a 

long-term collaboration with several people from several 

agencies, including Joe Lint (retired), Jim Thrailkill, Lynn 

Gemlo, Cindy Donegan, Scott Center, Elaine Rybak, and 

Josh Chapman. We thank them for the many discussions, 

ideas, and assistance that eventually led to us being able 

to portray large wildfires in this unique ecological and 

geographical perspective.

This report benefited from the helpful comments of 

several reviewers who read various drafts or chapters of 

it, including Tom Spies, Robert Anthony, Elizabeth Glenn, 

Warren Cohen, Carl Skinner, and Ayn Shlisky. We thank 

Ashley Steel for the statistical review and Jim Baldwin for 
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

his early comments and assistance in developing a spreadsheet for calculating the Continuous Boyce Index. A

special thanks to Shawne Mohoric (retired) and all those involved in the Interagency Regional Monitoring Program—

especially Melinda Moeur (retired) for her efforts to 

continually advance and improve the vegetation data that 

several modules use for monitoring. We greatly appreciate 

the budget and personnel support from the federal partnership that made this all happen, as well as the Interagency 

Senior Managers Group and the Regional Interagency 

Executive Committee for their continuing commitment and 

support for the Northwest Forest Plan effectiveness monitoring program. And last, but certainly not least, we thank 

Joe Lint for laying down a solid spotted owl monitoring 

foundation upon which we hope to continue to build.

Metric Equivalents

When you know: Multiply by: To find:

Inches (in) 25.4 Millimeters (mm)

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)

Degrees Fahrenheit (°F) (F-32)/1.8 Degrees Celsius (°C)
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GENERAL TECHNICAL REPORT PNW-GTR-850

Barred owl.

Peter Carlson
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Table A-1—Environmental variables that were used for habitat modeling

Variable Description Units

Diameter A measure of the structural diversity of a forest stand based on tree densities Index

diversity in different diameter at breast height (d.b.h.) classes. Calculation procedures

index are described in appendix 1 of McComb et al. (2002)a

Canopy cover Percentage of conifer cover in the canopy as calculated using methods in the Percentage

of all conifers Forest Vegetation Simulator

Stand height Average height of dominant and codominant trees Meters

Mean conifer Basal area weighted mean diameter of all live conifers Centimeters

diameter

Density of Estimated tree density for all live conifers ≥ 30 in d.b.h. Trees/ha

large conifers

Stand age Average stand age based on field-recorded ages of dominant and codominant

(no remnants) tree species, and excluding remnant trees Years

Subalpine Stand component of Pacific silver fir (Abies amabilis Dougl. ex Forbes), Percentage

forest subalpine fir (Abies lasiocarpa (Hook.) Nutt.), noble fir (Abies procera of total

 Rehd.), Shasta red fir (Abies shastensis (Lemmon) Lemmon), Alaska cedar basal area

(Chamaecyparis nootkatensis (D. Don) Spach), Engelmann spruce (Picea

engelmannii Parry ex Engelm.), whitebark pine (Pinus albicaulis Engelm.),

and mountain hemlock (Tsuga mertensiana (Bong.) Carr.)

Pine forest Stand component of lodgepole pine (Pinus contorta Dougl. ex Loud.), Percentage

Jeffrey pine (Pinus jeffreyi Grev. & Balf.), Bishop pine (Pinus muricata of total

 D. Don), and ponderosa pine (Pinus ponderosa Dougl. ex Laws.) basal area

Oak woodlands Stand component of blue oak (Quercus douglasii Hook. & Arn.), Percentage

Oregon white oak (Quercus garryana Dougl. ex Hook.), and California of total

black oak (Quercus kelloggii Newb.) basal area

Evergreen Stand component of Pacific madrone (Arbutus menziesii Pursh), tanoak Percentage

hardwoods (Lithocarpus densiflorus Rehd.), California live oak (Quercus agrifolia Née), of total

canyon live oak (Quercus chrysolepis Liebm.), and California laurel basal area

(Umbellularia californica (Hook. & Arn.) Nutt.)

Redwood forest Stand component of redwood (Sequoia sempervirens D. Don) Endl.) Percentage

of total

basal area

a McComb, W.C.; McGrath, M.T.; Spies, T.A.; Vesely, D. 2002. Models for mapping potential habitat at landscape scales: an example using 

northern spotted owls. Forest Science. 48(2): 203–216.

Appendix A: Environmental Variables Used for Habitat Suitability Modeling
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GENERAL TECHNICAL REPORT PNW-GTR-850

Figure A-1–Stand structure and age habitat variable correlation matrix with averaged accuracy 

plot Pearson correlations (SD = standard deviation, d.b.h. = diameter at breast height). These 

six environmental variables were used in all modeling regions.
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Table A-2—Stand species composition variable groupings, with local scale accuracy assessments, used in applicable modeling regions (MR) (GNN DOM SPP = gradient nearest neighbor, dominant species models)

Washington Washington Oregon Oregon and Oregon and

Coast and Eastern Coast California California California

Species GNN Cascades Cascades Range Cascades Klamaths Coast Averagegrouping DOM SPP Scientific name Common name (MR 221) (MR 222) (MR 223) (MR 224) (MR 225) (MR 226) kappa

– – – – – – – – – – – – – – – – – – – – Kappa coefficients – – – – – – – – – – – – – – – – – – – –

Subalpine ABAM Abies amabilis Pacific silver fir 0.53 0.66 n/a 0.59 n/a n/a 0.59

forest ABLA Abies lasiocarpa Subalpine fir 0.48 0.58 n/a 0.39 n/a n/a 0.48

ABPRSH Abies procera/shastensis Noble fir/Shasta red fir 0.32 0.29 n/a 0.52 0.47 n/a 0.40CHNO Chamaecyparis nootkatensis Alaska cedar 0.28 0.29 n/a 0.19 n/a n/a 0.25PIEN Picea engelmannii Engelmann spruce 0.38 0.38 n/a 0.22 n/a n/a 0.33PIAL Pinus albicaulis Whitebark pine 0.32 0.46 n/a 0.34 n/a n/a 0.37TSME Tsuga mertensiana Mountain hemlock 0.50 0.53 n/a 0.62 0.26 n/a 0.48

Pine PICO Pinus contorta Lodgepole pine n/a 0.26 n/a 0.57 0.28 0.21 0.33

forest PIJE Pinus jeffreyi Jeffrey pine n/a n/a n/a 0.27 0.28 0.14 0.23

PIMU Pinus muricata Bishop pine n/a n/a n/a n/a n/a 0.28 0.28PIPO Pinus ponderosa Ponderosa pine n/a 0.62 n/a 0.58 0.34 0.48 0.51

Oak QUDO Quercus douglasii Blue oak n/a n/a n/a n/a 0.68 0.41 0.55

woodlands QUGA4 Quercus garryana Oregon white oak n/a 0.56 0.29 0.52 0.35 0.34 0.41

QUKE Quercus kelloggii California black oak n/a n/a 0.27 0.53 0.38 0.52 0.42

Evergreen ARME Arbutus menziesii Pacific madrone n/a n/a 0.49 0.45 0.43 0.29 0.41

hardwoods LIDE3 Lithocarpus densiflorus Tanoak n/a n/a 0.72 n/a 0.58 0.55 0.61

QUAG Quercus agrifolia California live oak n/a n/a n/a n/a n/a 0.31 0.31QUCH2 Quercus chrysolepis Canyon live oak n/a n/a 0.46 0.17 0.35 0.22 0.30UMCA Umbellularia californica California laurel n/a n/a 0.43 n/a 0.29 0.30 0.34

Redwood forest SESE3 Sequoia sempervirens Redwood n/a n/a n/a n/a n/a 0.59 0.59Note: n/a = not applicable.
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GENERAL TECHNICAL REPORT PNW-GTR-850

Stan Sovern
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

An analysis of nearest neighbor distances (Clark and Evans 

1954) was conducted on several demographic study area 

owl pair location (fig. B–1) data sets from 1994 through 

1997 to correspond with the baseline satellite imagery. The 

purpose of this analysis was to determine biologically relevant distances for use as minimum distance parameters in 

the random sampling of owl pair locations from the 10-year 

report owl presence data set (Davis and Lint 2005). The 

purpose of this sampling was to provide additional habitat 

model training data points, outside of demographic study 

areas, for the habitat modeling described in chapter 3.

Only one location was used to represent each owl 

territory center. To minimize erroneous results, we only 

used owl locations from the 50-percent harmonic mean core 

(Dixon and Chapman 1980) of each study area’s data set. 

This removed outlier locations that would introduce errors 

in the analysis, especially for study areas that have disjunct 

areas or survey areas that are separated by several miles. 

The analysis was conducted in ArcView Spatial Analyst 

using the Animal Movement extension (v2.0) by Hooge and 

Eichenlaub (2000).

Results show a decreasing trend in distance between 

owl pair territories from north to south (fig. B-2). The greatest mean nearest neighbor distance occurs in the Washington Eastern Cascades (4.5 km), and the shortest mean 

distance occurs within the California Coast (1.4 km). The 

longer distances in the northern portions of the range may 

relate to more limited prey resources. Likewise the shorter 

distances in the southern portion of the range may be due 

to increased prey base diversity and abundance associated 

with the presence of mast-producing evergreen hardwoods 

that occur in the coniferous forests of that region.

Figure B-1—Owl territories, 1994 through 1997.

Appendix B: Nearest Neighbor Distance Analysis of Demographic Study Area Data
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GENERAL TECHNICAL REPORT PNW-GTR-850

Table B-1–Summary statistics from the nearest neighbor analysis for each habitat modeling region

Mean Median

Modeling region Data sets used na distance distance StDev

 – – – – – Kilometers – – – – – 

Washington Eastern Cle Elum and T. Fleming study 26 4.5 3.9 2.4

Cascades areas

Washington Coast Olympic and Rainier study areas 53 3.6 3.1 1.7

and Cascades

Oregon and California H.J. Andrews and Southern 57 3.1 2.9 1.5

Cascades Cascades study areas

Oregon Coast Range Oregon Coast Ranges and Tyee 79 2.7 2.5 1.2

study areas

Oregon and California Klamath, Northwest California, 70 2.4 2.1 1.1

Klamaths and Hoopa study areas

California Coast Green Diamond Resources 77 1.4 1.3 0.6

and Marin study areas

a

 Only locations from the 50-percent harmonic core of the study area data set were used. StDev = standard deviation.

Figure B-2—Results of the nearest neighbor distance analysis showing mean distances between northern spotted owl territory 

centers with 95-percent confidence intervals for each habitat modeling region.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

References

Clark P.; Evans, F.C. 1954. Distance to the nearest 

neighbor as a measure of spatial relationship in 

populations. Ecology. 35: 445–453.

Davis, R.; Lint, J. 2005. Habitat status and trend. In: 

Lint, J., tech. coord. 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: 21–82.

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.

Janice Reid
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GENERAL TECHNICAL REPORT PNW-GTR-850

Patrick Kolar
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Appendix C: Habitat Suitability Modeling Replicate Data Table C-1–Results of the MaxEnt bootstrapped replicate habitat suitability models (10 replicates for each modeling region) showing mean percentage for environmental variable model contributions and changes in model test gain associated with inclusion or exclusion of specific environmental variablesStand structure and age variables Species composition variables Lowered Mean Large Diameter gain the Modeling Conifer conifer conifer diversity Stand Stand Subalpine Pine Oak Evergreen Redwood Highest gain most when region cover d.b.h. density index height age forest forest woodland hardwood forest by itself removed – – – – – – – – – – – – – – – – – – – – – – – – – – – – Percentage of contribution – – – – – – – – – – – – – – – – – – – – – – – – – – – – Washington 21.0 1.7 35.2 7.8 3.2 20.2 10.8 n/a n/a n/a n/a Diameter Subalpine Coast and diversity forest Cascades Washington 34.6 0.5 11.0 6.0 7.4 2.7 31.8 4.9 1.3 n/a n/a Diameter Subalpine Eastern diversity forest Cascades index Oregon 13.3 4.6 58.1 5.4 4.6 11.2 n/a n/a 0.9 1.9 n/a Stand Conifer Coast age cover Range Oregon and 15.6 5.2 33.4 21.7 3.8 4.7 6.3 5.5 1.1 2.6 n/a Diameter Subalpine California diversity forest Cascades index Oregon and California 19.1 1.8 15.0 14.1 8.9 5.1 2.4 18.3 4.4 10.9 n/a diversity Pine Klamaths index forest California 43.7 7.1 3.0 2.6 6.3 2.3 n/a 5.7 13.0 10.2 6.2 Conifer Evergreen Coast cover hardwood Note: MaxEnt replicate variable response curve information for each modeling region is available upon request.

d.b.h. = diameter at breast height.
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GENERAL TECHNICAL REPORT PNW-GTR-850

The following sections in this appendix summarize the 

MaxEnt modeling regions and the modeling results of the 

bootstrapped replicates. Model region descriptions are 

largely based on information from the Landscape Ecology 

Modeling, Mapping and Analysis Web site (http://www.fsl.

orst.edu/lemma/main.php?project=nwfp&id=home).

Washington Coast and Cascades Modeling Region

(MR 221)

This modeling region conforms to the Washington Douglasfir ecological region used in the demographic meta-analyses, and contains the Olympic Peninsula and the Rainier 

demographic study areas. It encompasses the Washington 

Olympic Peninsula, Washington Western Lowlands, and 

the Washington Western Cascades physiographic provinces. 

The Olympic Peninsula is dominated by moist, productive coniferous rain forest on the western slope, and drier 

Douglas-fir forest in the rain shadow on the eastern slope. 

Wildfire frequency is very low. Federally managed lands 

occupy the interior half of the province, the core being 

Olympic National Park girded by the Olympic National 

Forest. Most of the Western Lowlands are in private and 

state ownership, with extensive urban and agricultural 

areas. It is dominated by wide, glaciated valleys, except for 

the Willapa Hills in the coastal section. Lowland coniferous 

forest, deciduous forest, and native prairie were its natural 

dominant vegetation types. The Western Cascades lower 

elevation forests are dominated by Douglas-fir (Pseudotsuga 

menziesii (Mirb.) Franco) and western hemlock (Tsuga 

heterophylla (Raf.) Sarg.) grading into Pacific silver fir (Abies procera Rehd.) at midelevations, and mountain hemlock 

(Tsuga mertensiana (Bong.) Carr.) and subalpine vegetation 

at higher elevations. Wildfire frequencies are low to moderate. About two-thirds of the province is administered by 

federal agencies.

Figure C-1–The mean predicted vs. expected curve (solid black line) from the model replicates, showing 95-percent 

confidence intervals (gray-shaded vertical bars) for the Washington Coast and Cascades modeling region. The logistic 

thresholds used to define the four-class habitat map are represented by vertical blue-dashed lines. The P/E = 1 threshold is where the curve crosses the random chance line (red-dashed line). The solid black dot represents the 10-percentile threshold (see fig. C-2 below) indicating where 90 percent of the training data (owl pair site centers) occured 

above that threshold. The mean Spearman rank correlation (Rs) is shown in the upper right-hand corner. See Hirzel et 

al. (2006) for more information.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure C-2–Habitat modeling statistics produced during the MaxEnt model bootstrapped replicates for the Washington Coast

and Cascades modeling region. Bars represent the mean statistic value, and error bars show the 95-percent confidence intervals. The first four bars represent model fit and discrimination statistics; the last four bars are common “thresholds” used to 

classify continuous habitat suitability models into binary maps of “not-suitable” and “suitable” habitat. P/E = predicted vs. 

expected.

Figure C-3–Bookend habitat model area of suitable nesting/roosting habitat for northern spotted owls for the Washington 

Coast and Cascades modeling region. The bars represent the mean estimate of suitable habitat, and error bars show the 

95-percent confidence intervals. This histogram shows net change (losses and gains) between 1996 and 2006. 
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GENERAL TECHNICAL REPORT PNW-GTR-850

Washington Eastern Cascades Modeling Region

(MR 222)

This modeling region conforms to the Washington MixedConifer ecological region used in the demographic metaanalyses, and contains the Cle Elum demographic study 

area. It also conforms to the Washington Eastern Cascades 

physiographic province. The slopes of the Washington 

Eastern Cascades province are dominated by mixed-conifer 

forest and ponderosa pine (Pinus ponderosa Dougl. ex 

Laws.) forest at lower to midelevations, and by true fir

(Abies spp.) and mountain hemlock at higher elevations. 

Forest productivity is low in places owing to poor soils and 

high elevations. Historically, fire frequencies were high 

(≤35-year fire-return intervals). Intensive fire suppression 

practices since the latter half of the 20th century have 

resulted in areas with significant accumulations of fuel and 

shifts in species composition and stand structure. About 

two-thirds of the area is federally managed.

Figure C-4–The mean predicted vs. expected curve (solid black line) from the model replicates, showing 95-percent confidence intervals (gray-shaded vertical bars) for the Washington Eastern Cascades modeling region. The logistic thresholds 

used to define the four-class habitat map are represented by vertical blue-dashed lines. The P/E = 1 threshold is where the 

curve crosses the random chance line (red-dashed line). The solid black dot represents the 10-percentile threshold (see fig. 

C-5 below) indicating where 90 percent of the training data (owl pair site centers) occured above that threshold. The mean 

Spearman rank correlation (Rs) is shown in the upper right-hand corner. See Hirzel et al. (2006) for more information.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure C-5–Habitat modeling statistics produced during the MaxEnt model bootstrapped replicates for the Washington Eastern 

Cascades modeling region. Bars represent the mean statistic value, and error bars show the 95-percent confidence intervals. 

The first four bars represent model fit and discrimination statistics; the last four bars are common “thresholds” used to classify 

continuous habitat suitability models into binary maps of “not-suitable” and “suitable” habitat. P/E = predicted/expected ratio.

Figure C-6–Bookend habitat model area of suitable nesting/roosting habitat for northern spotted owls for the Washington 

Eastern Cascades modeling region. The bars represent the mean estimate of suitable habitat, and error bars show the 95-

percent confidence intervals. This histogram shows net change (losses and gains) between 1996 and 2006. 
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GENERAL TECHNICAL REPORT PNW-GTR-850

Oregon Coast Range Modeling Region (MR 223)

This modeling region conforms to the Oregon Coastal 

Douglas-fir ecological region used in the demographic 

meta-analyses, and contains the Oregon Coast Ranges and 

Tyee demographic study areas. It contains the Oregon Coast 

physiographic province, and also the Willamette Valley 

physiographic province west of the Willamette River, as 

well as the coastal margins of the Oregon Klamath physiographic province. The moist, productive forests in this 

modeling region are dominated by Douglas-fir, western 

hemlock, and western redcedar (Thuja plicata Donn ex D. 

Don). The Forest Service and Bureau of Land Management 

together manage about one-quarter of the land in the region. 

Older forests are highly fragmented, largely as a result of 

infrequent but very large wildfires in the 1800s and 1900s, 

and heavy cutting, as well as checkerboard ownership 

patterns. Most of the Willamette Valley is in private ownership and includes extensive urban and agricultural areas. 

Lowland coniferous forest, deciduous forest, and native 

prairie were the natural dominant vegetation types.

Figure C-7–The mean predicted vs. expected curve (solid black line) from the model replicates, showing 95-percent confidence intervals (gray-shaded vertical bars) for the Oregon Coast Range modeling region. The logistic thresholds used to 

define the four-class habitat map are represented by vertical blue-dashed lines. The P/E = 1 threshold is where the curve 

crosses the random chance line (red-dashed line). The solid black dot represents the 10-percentile threshold (see fig. C-8 

below) indicating where 90 percent of the training data (owl pair site centers) occured above that threshold. The mean 

Spearman rank correlation (Rs) is shown in the upper right-hand corner. See Hirzel et al. (2006) for more information.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure C-8–Habitat modeling statistics produced during the MaxEnt model bootstrapped replicates for the Oregon Coast 

Range modeling region. Bars represent the mean statistic value, and error bars show the 95-percent confidence intervals. The 

first four bars represent model fit and discrimination statistics; the last four bars are common “thresholds” used to classify 

continuous habitat suitability models into binary maps of “not-suitable” and “suitable” habitat.

Figure C-9–Bookend habitat model area of suitable nesting/roosting habitat for northern spotted owls for the Oregon 

Coast Range modeling region. The bars represent the mean estimate of suitable habitat, and error bars show the 95-percent 

confidence intervals. This histogram shows net change (losses and gains) between 1996 and 2006. 
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GENERAL TECHNICAL REPORT PNW-GTR-850

Oregon and California Cascades Modeling Region

(MR 224)

This modeling region conforms to the Oregon Cascades 

Douglas-fir ecological region used in the meta-analyses, 

and contains the H.J. Andrews and South Cascades 

demographic study areas. It encompasses the east and 

west Cascades provinces in Oregon and portions of the 

California Cascades province as delineated along level III 

ecoregion lines. Although there are differences between 

the east and west Cascades, our decision to lump them into 

one modeling region was based on how the east Cascades 

province was originally drawn to define the eastern margin 

of the owl’s range, which extends into the larger eastern 

Cascades ecoregion (as delineated by the Environmental 

Protection Agency). This thin delineation represents the 

ecotone between the East and West Cascades, and not the 

entire East Cascades province. On the west slope, Douglasfir and western hemlock give way to Pacific silver fir at 

midelevations, and mountain hemlock and subalpine fir 

(Abies lasiocarpa (Hook.) Nutt.) at high elevations. The east 

slope is covered by mixed-conifer and ponderosa pine forest 

at lower elevations, and true firs and mountain hemlock at 

higher elevations. The southern portion is mixed-conifer 

and pine forests in fire-adapted landscapes. Fire frequencies 

range from low to high along a north-to-south moisture 

gradient. Fire suppression has resulted in shifts in species 

composition and stand structure. About two-thirds of the 

land is administered by federal agencies.

Figure C-10–The mean predicted vs. expected curve (solid black line) from the model replicates, showing 95-percent 

confidence intervals (gray-shaded vertical bars) for the Oregon and California Cascades modeling region. The logistic 

thresholds used to define the four-class habitat map are represented by vertical blue-dashed lines. The P/E = 1 threshold 

is where the curve crosses the random chance line (red-dashed line). The solid black dot represents the 10-percentile 

threshold (see fig. C-11 below) indicating where 90 percent of the training data (owl pair site centers) occured above that 

threshold. The mean Spearman rank correlation (Rs) is shown in the upper right-hand corner. See Hirzel et al. (2006) for 

more information.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure C-11–Habitat modeling statistics produced during the MaxEnt model bootstrapped replicates for the Oregon and 

California Cascades modeling region. Bars represent the mean statistic value, and error bars show the 95-percent confidence 

intervals. The first four bars represent model fit and discrimination statistics; the last four bars are common “thresholds” 

used to classify continuous habitat suitability models into binary maps of “not-suitable” and “suitable” habitat.

Figure C-12–Bookend habitat model area of suitable nesting/roosting habitat for northern spotted owls for the Oregon and 

California Cascades modeling region. The bars represent the mean estimate of suitable habitat, and error bars show the 

95-percent confidence intervals. This histogram shows net change (losses and gains) between 1994/96 and 2006/07. 
116

GENERAL TECHNICAL REPORT PNW-GTR-850

Oregon and California Klamaths Modeling Region

(MR 225)

This modeling region conforms to the Oregon/California 

Mixed-Conifer ecological region used in the demographic 

meta-analyses, and contains the Klamath and Northwest 

California demographic study areas. It encompasses the 

Klamath physiographic provinces of Oregon and California. 

It is influenced by unique geologic conditions. In many 

areas, serpentine soils formed by the accretion of rocks onto 

the continent control the native vegetation, which is dominated by mixed-conifer and mixed-conifer and hardwood 

forest such as Douglas-fir mixed with tanoak (Lithocarpus 

densiflorus (Hook. & Arn.) Rehd.), and Pacific madrone 

(Arbutus menziesii Pursh). The region is characterized 

by historically high fire frequencies (≤35-year fire-return 

intervals), and fire suppression has resulted in areas with 

significant accumulations of fuel, shifts in species composition, and changes in stand structure. Forests are highly 

fragmented as a result of dry climate, poor soils, and past 

harvest practices, as well as ownership patterns, especially 

in areas of “checkerboard” ownership. Slightly over half of 

the province in Oregon is federally managed. In California, 

national forests cover about three-quarters of the region.

Figure C-13–The mean predicted vs. expected curve (solid black line) from the model replicates, showing 95-percent 

confidence intervals (gray-shaded vertical bars) for the Oregon and California Klamaths modeling region. The logistic 

thresholds used to define the four-class habitat map are represented by vertical blue-dashed lines. The P/E = 1 threshold 

is where the curve crosses the random chance line (red-dashed line). The solid black dot represents the 10-percentile 

threshold (see fig. C-14 below) indicating where 90 percent of the training data (owl pair site centers) occured above that 

threshold. The mean Spearman rank correlation (Rs) is shown in the upper right-hand corner. See Hirzel et al. (2006) for 

more information.
117

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure C-14–Habitat modeling statistics produced during the MaxEnt model bootstrapped replicates for the Oregon and 

California Klamaths modeling region. Bars represent the mean statistic value, and error bars show the 95-percent confidence 

intervals. The first four bars represent model fit and discrimination statistics; the last four bars are common “thresholds” used 

to classify continuous habitat suitability models into binary maps of “not-suitable” and “suitable” habitat.

Figure C-15–Bookend habitat model area of suitable nesting/roosting habitat for northern spotted owls for Oregon and California Klamaths modeling region. The bars represent the mean estimate of suitable habitat, and error bars show the 95-percent 

confidence intervals. This histogram shows net change (losses and gains) between 1994/96 and 2006/07. 
118

GENERAL TECHNICAL REPORT PNW-GTR-850

California Coast Modeling Region (MR 226)

This modeling region conforms to the California Coast 

ecological region used in demographic meta-analyses, and 

contains the independently operated Green Diamond Resources and Hoopa Reservation demographic study areas. 

It conforms to the California Coast Range physiographic 

province, extending slightly into coastal Oregon Klamath 

physiographic province to encompass the coastal redwood 

forests in that area. Moist, productive forests in the California Coast region are dominated by Douglas-fir and western 

hemlock, and contain most of the coastal redwood forests. 

The southeastern portion of this modeling region falls 

within the Central California Chaparral and Oak Woodlands ecoregion. Only a small proportion of the California 

Coast region is administered by federal agencies.

Figure C-16–The mean predicted vs. expected curve (solid black line) from the model replicates, showing 95-percent confidence intervals (gray-shaded vertical bars) for the California Coast modeling region. The logistic thresholds used to define 

the four-class habitat map are represented by vertical blue-dashed lines. The P/E = 1 threshold is where the curve crosses 

the random chance line (red-dashed line). The solid black dot represents the 10-percentile threshold (see fig. C-17 below) 

indicating where 90 percent of the training data (owl pair site centers) occured above that threshold. The mean Spearman 

rank correlation (Rs) is shown in the upper right-hand corner. See Hirzel et al. (2006) for more information.
119

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

References

Hirzel, A.H.; Le Lay, G.; Helfer, V.; Randin, C.; Guisan, A. 2006. Evaluating the ability of habitat suitability models to 

predict species presences. Ecological Modelling. 199: 142–152.

Figure C-17–Habitat modeling statistics produced during the MaxEnt model bootstrapped replicates for the California Coast 

modeling region. Bars represent the mean statistic value, and error bars show the 95-percent confidence intervals. The first 

four bars represent model fit and discrimination statistics; the last four bars are common “thresholds” used to classify continuous habitat suitability models into binary maps of “not-suitable” and “suitable” habitat.

Figure C-18–Bookend habitat model area of suitable nesting/roosting habitat for northern spotted owls for the California 

Coast modeling region. The bars represent the mean estimate of suitable habitat, and error bars show the 95-percent confidence intervals. This histogram shows net change (losses and gains) between 1994 and 2007.
120

GENERAL TECHNICAL REPORT PNW-GTR-850

Jason Mowdy
121

Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Table D-1

–Estimates of nesting/roosting habitat and loss on federal reserved lands using LandTrendr changedetection data

Insects and Gross

Physiographic province 1994/96 Harvest disease Wildfire loss 2006/07 Change

– – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent

Washington:

Olympic Peninsula 729,500 -300

0 -200 -500 729,000 -0.1

Western Lowlands 24,700 -400 0 0 -400 24,300 -1.6Western Cascades 1,035,100 -2,400 -400 -700 -3,500 1,031,600 -0.3Eastern Cascades 484,900 -4,800 -1,800 -15,900 -22,500 462,400 -4.6

State total 2,274,200 -7,900 -2,200 -16,800 -26,900 2,247,300 -1.2

Oregon:

Coast Range 495,700 -1,300 0 0 -1,300 494,400 -0.3Willamette Valley 700 0 0 0 0 700 0.0Western Cascades 1,302,200 -1,900 -600 -24,500 -27,000 1,275,200 -2.1Klamath 636,200 -1,600 -200 -85,000 -86,800 549,400 -13.6Eastern Cascades 264,800 -1,300 -1,700 -13,300 -16,300 248,500 -6.2

State total 2,699,600 -6,100 -2,500 -122,800 -131,400 2,568,200 -4.9

California:

Coast Range 135,200 -300 -100 -1,900 -2,300 132,900 -1.7Klamath 975,500 -1,400 -1,500 -61,700 -64,600 910,900 -6.6Cascades 103,300 -800

0 -800 -1,600 101,700 -1.5

State total 1,214,000 -2,500 -1,600 -64,400 -68,500 1,145,500 -5.6Range total 6,187,800 -16,500 -6,300 -204,000 -226,800 5,961,000 -3.7

Note: Acres are rounded to the nearest 100 acres.

Appendix D: Nesting/Roosting Habitat Status and Trend Tables Based on

LandTrendr Analysis
122

GENERAL TECHNICAL REPORT PNW-GTR-850 Table D-2

–Estimates of nesting/roosting habitat and loss on federal nonreserved lands using LandTrendr changedetection data

Insects and Gross

Physiographic province 1994/96 Harvest disease Wildfire loss 2006/07 Change

– – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent

Washington:

Olympic Peninsula 33,600 -200 0 0 -200 33,400 -0.6Western Lowlands

0

0

0

0

0

0 0.0

Western Cascades 247,900 -1,300 0 0 -1,300 246,600 -0.5Eastern Cascades 188,700 -3,300 -200 -4,100 -7,600 181,100 -4.0

State total 470,200 -4,800 -200 -4,100 -9,100 461,100 -1.9

Oregon:

Coast Range 115,400 -2,000 0 0 -2,000 113,400 -1.7Willamette Valley 2,700 -100 0 0 -100 2,600 -3.7Western Cascades 956,500 -12,000 -500 -4,400 -16,900 939,600 -1.8Klamath 348,800 -5,200 -100 -8,600 -13,900 334,900 -4.0Eastern Cascades 138,000 -4,500 -600 -4,500 -9,600 128,400 -7.0

State total 1,561,400 -23,800 -1,200 -17,500 -42,500 1,518,900 -2.7

California:

Coast Range 10,300

0

0 -200 -200 10,100 -1.9

Klamath 514,200 -3,000 -100 -9,900 -13,000 501,200 -2.5Cascades 109,900 -5,700 -300 -1,000 -7,000 102,900 -6.4

State total 634,400 -8,700 -400 -11,100 -20,200 614,200 -3.2Range total 2,666,000 -37,300 -1,800 -32,700 -71,800 2,594,200 -2.7

Note: Acres are rounded to the nearest 100 acres.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Table D-3

—Estimates of nesting/roosting habitat and loss on all federal lands using LandTrendr change-detection 

data

Insects and Gross

Physiographic province 1994/96 Harvest disease Wildfire loss 2006/07 Change

– – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent

Washington:

Olympic Peninsula 763,100 -500 0 -200 -700 762,400 -0.1Western Lowlands 24,700 -400 0 0 -400 24,300 -1.6Western Cascades 1,283,000 -3,700 -400 -700 -4,800 1,278,200 -0.4Eastern Cascades 673,600 -8,100 -2,000 -20,000 -30,100 643,500 -4.5

State total 2,744,400 -12,700 -2,400 -20,900 -36,000 2,708,400 -1.3

Oregon:

Coast Range 611,200 -3,300

0

0 -3,300 607,900 -0.5

Willamette Valley 3,400 -100 0 0 -100 3,300 -2.9Western Cascades 2,258,700 -13,900 -1,100 -28,900 -43,900 2,214,800 -1.9Klamath 985,000 -6,800 -300 -93,600 -100,700 884,300 -10.2Eastern Cascades 402,900 -5,800 -2,300 -17,800 -25,900 377,000 -6.4

State total 4,261,200 -29,900 -3,700 -140,300 -173,900 4,087,300 -4.1

California:

Coast Range 145,400 -300 -100 -2,100 -2,500 142,900 -1.7Klamath 1,489,800 -4,400 -1,600 -71,600 -77,600 1,412,200 -5.2Cascades 213,200 -6,500 -300 -1,800 -8,600 204,600 -4.0

State total 1,848,400 -11,200 -2,000 -75,500 -88,700 1,759,700 -4.8Range total 8,854,000 -53,800 -8,100 -236,700 -298,600 8,555,400 -3.4

Note: Acres are rounded to the nearest 100 acres.
124

GENERAL TECHNICAL REPORT PNW-GTR-850 Table D-4

–Estimates of nesting/roosting habitat and loss on all habitat-capable lands within the owl’s range using 

LandTrendr change-detection data

Insects and Gross

Land class 1994/96 Harvest disease Wildfire loss 2006/07 Change

– – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent

Federal reserved:

Washington 2,274,200 -7,900 -2,200 -16,800 -26,900 2,247,300 -1.2

Oregon 2,699,600 -6,100 -2,500 -122,800 -131,400 2,568,200 -4.9California 1,214,000 -2,500 -1,600 -64,400 -68,500 1,145,500 -5.6

Range total 6,187,800 -16,500 -6,300 -204,000 -226,800 5,961,000 -3.7

Federal nonreserved:

Washington 470,200 -4,800 -200 -4,100 -9,100 461,100 -1.9

Oregon 1,561,400 -23,800 -1,200 -17,500 -42,500 1,518,900 -2.7California 634,400 -8,700 -400 -11,100 -20,200 614,200 -3.2

Range total 2,666,000 -37,300 -1,800 -32,700 -71,800 2,594,200 -2.7

All federal:

Washington 2,744,400 -12,700 -2,400 -20,900 -36,000 2,708,400 -1.3

Oregon 4,261,200 -29,900 -3,700 -140,300 -173,900 4,087,300 -4.1California 1,848,400 -11,200 -2,000 -75,500 -88,700 1,759,700 -4.8

Range total 8,854,000 -53,800 -8,100 -236,700 -298,600 8,555,400 -3.4

Nonfederal:

Washington 1,258,900 -234,200 -6,000 -2,400 -242,600 1,016,300 -19.3

Oregon 1,382,400 -301,200 -2,700 -5,100 -309,000 1,073,400 -22.4California 1,556,700 -90,200 -1,900 -5,600 -97,700 1,459,000 -6.3

Range total 4,198,000 -625,600 -10,600 -13,100 -649,300 3,548,700 -15.5

All lands:

Washington 4,003,300 -246,900 -8,400 -23,300 -278,600 3,724,700 -7.0

Oregon 5,643,600 -331,100 -6,400 -145,400 -482,900 5,160,700 -8.6California 3,405,100 -101,400 -3,900 -81,100 -186,400 3,218,700 -5.5

Range total 13,052,000 -679,400 -18,700 -249,800 -947,900 12,104,100 -7.3

Note: Acres are rounded to the nearest 100 acres.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Appendix E: Dispersal Habitat Status and Trend Tables Based on LandTrendr Analysis Table E-1–Estimates of gross loss, gross gain, and net change of dispersal habitat on federal reserved lands Insects and Net Physiographic province 1994/96 Harvest disease Wildfire Gain change 2006/07 Change – – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent Washington: Olympic Peninsula 1,052,600 -300 0 -400 46,100 45,400 1,098,000 4.3 Western Lowlands 61,800 -500 0 0 5,100 4,600 66,400 7.4 Western Cascades 1,889,300 -3,200 -400 -600 87,700 83,500 1,972,800 4.4 Eastern Cascades 1,428,400 -3,700 -1,300 -41,600 71,400 24,800 1,453,200 1.7 State total 4,432,100 -7,700 -1,700 -42,600 210,300 158,300 4,590,400 3.6 Oregon: Coast Range 742,200 -1,700 0 -100 90,200 88,400 830,600 11.9 Willamette Valley 2,300 0 0 0 200 200 2,500 8.7 Western Cascades 1,963,800 -2,000 -600 -29,600 93,400 61,200 2,025,000 3.1 Klamath 903,700 -1,700 -400 -121,400 43,900 -79,600 824,100 -8.8 Eastern Cascades 661,500 -3,400 -3,100 -22,100 44,300 15,700 677,200 2.4 State total 4,273,500 -8,800 -4,100 -173,200 272,000 85,900 4,359,400 2.0 California: Coast Range 169,300 -300 -100 -2,300 14,600 11,900 181,200 7.0 Klamath 1,939,600 -2,500 -2,000 -67,600 127,000 54,900 1,994,500 2.8 Cascades 244,400 -700 0 -700 18,600 17,200 261,600 7.0 State total 2,353,300 -3,500 -2,100 -70,600 160,200 84,000 2,437,300 3.6 Range total 11,058,900 -20,000 -7,900 -286,400 642,500 328,200 11,387,100 3.0 Note: Acres are rounded to the nearest 100 acres.
126

GENERAL TECHNICAL REPORT PNW-GTR-850 Table E-2

–Estimates of gross loss, gross gain, and net change of dispersal habitat on federal nonreserved lands

Insects and Net

Physiographic province 1994/96 Harvest disease Wildfire Gain change 2006/07 Change

– – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent

Washington:

Olympic Peninsula 73,400 -200 0 0 15,700 15,500 88,900 21.1Western Lowlands 200

0

0

0 100 100 300 50.0

Western Cascades 443,900 -1,500 0 0 48,900 47,400 491,300 10.7Eastern Cascades 393,900 -3,500 -300 -9,200 45,100 32,100 426,000 8.1

State total 911,400 -5,200 -300 -9,200 109,800 95,100 1,006,500 10.4

Oregon:

Coast Range 260,800 -2,400 0 0 45,800 43,400 304,200 16.6Willamette Valley 10,200 -100 0 0 1,300 1,200 11,400 11.8Western Cascades 1,495,100 -11,000 -700 -4,200 167,100 151,200 1,646,300 10.1Klamath 605,100 -5,800 -300 -16,100 62,200 40,000 645,100 6.6Eastern Cascades 347,000 -6,500 -700 -6,800 57,600 43,600 390,600 12.6

State total 2,718,200 -25,800 -1,700 -27,100 334,000 279,400 2,997,600 10.3

California:

Coast Range 20,600 -100 0 -800 4,100 3,200 23,800 15.5Klamath 996,000 -4,600 -300 -16,200 124,200 103,100 1,099,100 10.4Cascades 333,800 -8,200 -4,000 -2,000 43,600 29,400 363,200 8.8

State total 1,350,400 -12,900 -4,300 -19,000 171,900 135,700 1,486,100 10.0Range total 4,980,000 -43,900 -6,300 -55,300 615,700 510,200 5,490,200 10.2

Note: Acres are rounded to the nearest 100 acres.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Table E-3–Estimates of gross loss, gross gain, and net change of dispersal habitat on all federal lands

Insects and Net

Physiographic province 1994/96 Harvest disease Wildfire Gain change 2006/07 Change 

– – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent

Washington:

Olympic Peninsula 1,126,000 -500 0 -400 61,800 60,900 1,186,900 5.4Western Lowlands 62,000 -500 0 0 5,200 4,700 66,700 7.6Western Cascades 2,333,200 -4,700 -400 -600 136,600 130,900 2,464,100 5.6Eastern Cascades 1,822,300 -7,200 -1,600 -50,800 116,500 56,900 1,879,200 3.1

State total 5,343,500 -12,900 -2,000 -51,800 320,100 253,400 5,596,900 4.7

Oregon:

Coast Range 1,003,000 -4,100 0 -100 136,000 131,800 1,134,800 13.1Willamette Valley 12,500 -100 0 0 1,500 1,400 13,900 11.2Western Cascades 3,458,900 -13,000 -1,300 -33,800 260,500 212,400 3,671,300 6.1Klamath 1,508,800 -7,500 -700 -137,500 106,100 -39,600 1,469,200 -2.6Eastern Cascades 1,008,500 -9,900 -3,800 -28,900 101,900 59,300 1,067,800 5.9

State total 6,991,700 -34,600 -5,800 -200,300 606,000 365,300 7,357,000 5.2

California:

Coast Range 189,900 -400 -100 -3,100 18,700 15,100 205,000 8.0Klamath 2,935,600 -7,100 -2,300 -83,800 251,200 158,000 3,093,600 5.4Cascades 578,200 -8,900 -4,000 -2,700 62,200 46,600 624,800 8.1

State total 3,703,700 -16,400 -6,400 -89,600 332,100 219,700 3,923,400 5.9Range total 16,038,900 -63,900 -14,200 -341,700 1,258,200 838,400 16,877,300 5.2

Note: Acres are rounded to the nearest 100 acres.
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GENERAL TECHNICAL REPORT PNW-GTR-850 Table E-4

–Estimates of gross loss, gross gain, and net change of dispersal habitat on all habitat-capable lands within 

the owl’s range

Insects and Gross Gross Net

Land class 1994/96 Harvest disease Wildfire loss gain 2006/07 change

– – – – – – – – – – – – – – – – – – – – Acres – – – – – – – – – – – – – – – – – – – – Percent

Federal reserved: 

Washington 4,432,100 -7,700 -1,700 -42,600 -52,000 210,300 4,590,400 3.6Oregon 4,273,500 -8,800 -4,100 -173,200 -186,100 272,000 4,359,400 2.0California 2,353,300 -3,500 -2,100 -70,600 -76,200 160,200 2,437,300 3.6

Range total 11,058,900 -20,000 -7,900 -286,400 -314,300 642,500 11,387,100 3.0

Federal

nonreserved:

Washington 911,400 -5,200 -300 -9,200 -14,700 109,800 1,006,500 10.4Oregon 2,718,200 -25,800 -1,700 -27,100 -54,600 334,000 2,997,600 10.3California 1,350,400 -12,900 -4,300 -19,000 -36,200 171,900 1,486,100 10.0

Range total 4,980,000 -43,900 -6,300 -55,300 -105,500 615,700 5,490,200 10.2

All federal:

Washington 5,343,500 -12,900 -2,000 -51,800 -66,700 320,100 5,596,900 4.7Oregon 6,991,700 -34,600 -5,800 -200,300 -240,700 606,000 7,357,000 5.2California 3,703,700 -16,400 -6,400 -89,600 -112,400 332,100 3,923,400 5.9

Range total 16,038,900 -63,900 -14,200 -341,700 -419,800 1,258,200 16,877,300 5.2

Nonfederal:

Washington 4,359,100 -689,300 -14,700 -7,000 -711,000 993,000 4,641,100 6.5Oregon 4,129,400 -760,700 -7,200 -10,000 -777,900 971,200 4,322,700 4.7California 2,858,900 108,000 2,900 9,900 120,800 443,900 3,423,600 19.8

Range total 11,347,400 -1,342,000 -19,000 -7,100 -1,368,100 2,408,100 12,387,400 9.2

All lands:

Washington 9,702,600 -702,200 -16,700 -58,800 -777,700 1,313,100 10,238,000 5.5Oregon 11,121,100 -795,300 -13,000 -210,300 -1,018,600 1,577,200 11,679,700 5.0

California 6,562,600 91,600 -3,500 -79,700 8,400 776,000 7,347,000 12.0

Range total 27,386,300 -1,405,900 -33,200 -348,800 -1,787,900 3,666,300 29,264,700 6.9

Note: Acres are rounded to the nearest 100 acres.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

As discussed in chapter 3, we chose to not report on the 

highly suspect suitable habitat gains in nesting/roosting 

habitat for this monitoring cycle given the short timespan 

of our analysis and because of uncertainties with model 

transferability, bookend 2 (2006/07) map reviews with 

1-m color aerial imagery Natonal Agricultural Imagery 

Program, geographic information system (GIS) analysis 

of model variable changes, and an inventory plot analysis 

(app. H). However, we anticipate that our ability to measure 

these gains will improve with the passing of more time to 

separate the bookend maps, and with improved remote sensing technologies. This appendix presents the following table 

to describe the crosswalk we used for creating a modified 

bookend 2 map for the purpose of making “habitat suitability histograms” to help visualize shifts in habitat classes 

Appendix F: Crosswalk for Modifying Bookend 2 (2006/07) Map for Making Habitat

Suitability Histograms

between bookend 1 (1994/96) and bookend 2. This approach 

is similar to what was done in the 10-year report, where 

we had only a baseline map and change-detection data to 

estimate habitat changes. The habitat suitability histograms 

on the following pages are formatted to be similar to the 

histograms in appendix G of the 10-year report. The modified bookend 2 map (as described below) is our best estimate of habitat classes as of 2006/07. It is conservative in 

nature, as it maintains suitable habitat classes (3 and 4) from 

bookend 1, and only shows loss in these suitable classes if

verified by LandTrendr (LT) data. We allow for minor shifts 

within habitat classes that may represent subtle changes but 

do not result in a change between the broader categories of 

“unsuitable/marginal” (i.e., classes 1 and 2) and the “suitable” classes (i.e., classes 3 and 4).

Table F-1–Crosswalk table for modified bookend 2 map

Bookend model Modified 2006/07

habitat classes Assumptions (based on aerial image review habitat class

1994/6 2006/7 and GIS analysis of environmental variables) (modified bookend 2)

1 1 Not suitable either period; no change 1

1 2 Not suitable either period; accept shift from unsuitable to marginal 2

1 3 Trend toward suitable, but highly uncertain gain; limit shift to marginal class 2

1 4 Trend toward suitable, but highly uncertain gain; limit shift to marginal class 2

2 1 Not suitable either period; accept shift from marginal to unsuitable 1

2 2 Not suitable either period; no change 2

2 3 Trend toward suitable, but highly uncertain gain; keep in marginal class 2

2 4 Trend toward suitable, but highly uncertain gain; keep in marginal class 2

3 1 If LTa verified habitat loss, moved to unsuitable; otherwise no change 1 if LT verified,

 otherwise 3

3 2 If LT verified habitat loss, moved to marginal; otherwise no change 2 if LT verified,

 otherwise 3

3 3 Suitable habitat both periods; no change 3

3 4 Suitable habitat both periods; accept shift to highly suitable 4

4 1 If LT verified habitat loss, moved to unsuitable; otherwise no change 1 if LT verified,

otherwise 4

4 2 If LT verified habitat loss, moved to marginal; otherwise no change 2 if LT verified,

otherwise 4

4 3 Suitable habitat both periods; accept shift to suitable (degraded) 3

4 4 Suitable habitat both periods; no change 4

a LT = LandTrendr.

GIS = geographic information system.
130

GENERAL TECHNICAL REPORT PNW-GTR-850

The histograms on the following pages establish the 

format we propose for visually representing status and 

trends in habitat classes for future monitoring efforts. They 

are based on habitat conditions at roughly the time of the 

Northwest Forest Plan (the Plan) implementation (1994/96) 

to the end of our analysis data set in 2006/07. There are four 

pairs of histogram bars, one pair per habitat suitability class 

as described in chapter 3. The first bar in the pair shows 

conditions at time 1 (1994/96); the second bar shows conditions at time 2 (2006/07), based on our modified bookend 

2 map. They also provide a visual on how owl habitat is 

distributed across reserved and nonreserved federal lands. 

The example histogram below is provided to help interpret 

the histograms provided for each physiographic province in 

the following pages.

In the example above, we observe a slight decrease 

(3.5 percent) in unsuitable habitat class 1 between time 1 

and time 2. We also observe a slightly larger (4.4 percent) 

increase in marginal habitat class (but still unsuitable for 

nesting/roosting). There has been a slight decrease in the 

suitable habitat class 3 (1.1 percent) with a very slight (0.2 

percent) increase in the highly suitable habitat class. We can 

conclude that forest succession in habitat class 1 accounted 

for most of the increase in habitat class 2, but some of the 

loss in habitat class 3 may have also accounted for some 

of the changes in habitat class 2, or perhaps offset some 

of the decrease in habitat class 1. The slight increase in 

habitat class 4 may be a result of changes in habitat class 3, 

as seen in the plot analysis (app. H) where there may have 

been some subtle changes. However, this does not result 

in a change in the broader “suitable” class. The simplest 

interpretation indicates that the increase in the marginal 

habitat class (class 2) will continue to progress to the suitable classes with time. This province has very little suitable 

nesting/roosting habitat.

The table under the graphs shows the estimates of 

percentage of forest-capable land changes between habitat 

maps for both periods. The percentages are split into 

nonreserved and reserved land use allocations. The following graphs illustrate our best estimate of how habitat 

is changing (trending) at this early stage of the Plan. 

These graphs are primarily for interpretive purposes. The 

observed change between the bookends is small, with the 

largest changes being increases in the marginal classes. We 

observed similar changes in dispersal habitat (see chapter 3) 

and consider this an indication of noticeable future recruitment from marginal to suitable habitat within the next two 

to three decades.
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GENERAL TECHNICAL REPORT PNW-GTR-850
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl
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GENERAL TECHNICAL REPORT PNW-GTR-850

Ray Davis
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Table G-1–Wildfire suitabilty modeling environmental variables and their model contributions

Environmental Contribution

variable Description Value range Units to model

 Percent

August maximum PRISM (1971–2000)—mean maximum 1,456–3,635 °C (x 100) 27.8

temperature temperature for month of August

Lightning ignition Kernel density map of all lightning- 0–992 Ignitions/km2

density caused fire ignitions 1970–2002, (x100) 24.1

from Brown et al. (2002)a

Slope Percentage of slope based on analysis 0–125 Percent 23.7

of Digital Elevation Model

Distance from road Linear distance to nearest road, based 0–28,300 Meters 14.6

on road layer in Gallo et al. (2011)b

Summer precipitation PRISM (1971–2000)—mean rainfall 3,243–6,884 ln mm 4.4

between May and September, log (x1,000)

transformed

Elevation U.S. Geological Survey 0–2477 Meters 4.2

Digital Elevation Model

Solar radiation Potential relative solar radiation 5,619–20,546 Index 1.1

as derived by Pierce et al. (2005)c

a Brown, T.J.; Hall, B.L.; Mohrle, C.R.; Reinbold, H.J. 2002. 

b Gallo, K.; Lanigan, S.H.; Eldred, P.; Gordon, S.N.; Moyer, C. 2005.

c

 Pierce, K.B.; Lookingbill, T.R.; Urban, D.L. 2005. 

Appendix G: Wildfire Suitability Modeling, MaxEnt Replicate Data

Figure G-1–Correlation matrix (Pearson correlations) for environmental variables used in the model.
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Figure G-2–Averaged model fit, accuracy, and threshold statistics (with 95-percent confidence intervals) from the 

10 bootstrapped model replicates. Note that the predicted vs expected (P/E) = 1 threshold is similar to the maximum 

testing sensitivity plus specificity threshold, which minimizes model omission and commission errors.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Figure G-3–Model response curves showing logistic probability of large wildfire occurrence (y-axis) for each environmental variable, as it is varied in jackknifed models, keeping all other environmental variables at their average sample 

values. PRP = potential relative radiation.
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GENERAL TECHNICAL REPORT PNW-GTR-850

Figure G-4—Jackknife modeling results for variable importance. Note the similarities between the regularized training gain (top) and test gain (middle) graphs. The high level of similarity between them indicates that 

the model is not over-fit.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Literature Cited

Brown, T.J.; Hall, B.L.; Mohrle, C.R.; Reinbold, 

H.J. 2002. Coarse assessment of federal wildland 

fire occurrence data: report for the National Wildfire 

Coordinating Group. Reno, NV: CEFA Report 02-04. 

Program for Climate, Ecosystem and Fire Applications, 

Desert Research Institute, Division of Atmospheric 

Sciences. 31 p.

Gallo, K.; Lanigan, S.H.; Eldred, P.; Gordon, S.N.; 

Moyer, C. 2005. Northwest Forest Plan—the first 

10 years (1994–2003): preliminary assessment of 

the condition of watersheds. Gen. Tech. Rep. PNWGTR-647. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 

133 p.

Pierce, K.B.; Lookingbill, T.R.; Urban, D.L. 2005.

A simple method for estimating potential relative 

radiation (PRR) for landscape-scale vegetation analysis. 

Landscape Ecology. 20: 137–147

Ray Davis
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GENERAL TECHNICAL REPORT PNW-GTR-850

Ray Davis
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

PLOT ANALYSIS

An analysis of regional vegetation inventory plots was 

performed to determine if there have been significant gains 

in northern spotted owl (Strix occidentalis caurina) nesting/

roosting habitat since monitoring was implemented. We 

used plot data from the Current Vegetation Survey (CVS) 

inventory program on USDA Forest Service (FS) and USDI 

Bureau of Land Management (BLM) lands in Oregon and 

Washington. Forest Inventory and Analysis (FIA) program 

data were used for FS lands in California. Data were not 

available for USDI National Park Service lands or BLM 

lands in Washington or California.

The CVS inventory provides comprehensive information on vegetative resources on FS lands in Oregon and 

Washington and BLM lands in the Northwest Forest Plan 

area in Oregon. The CVS plots consist of four grids of field 

plots that are separated by 3.4 mi on a north-south, eastwest direction. These four inventory grids are offset from 

one another to produce one single 1.7-mi grid of plots across 

BLM lands and FS lands, except in wilderness areas where 

the grid density is 3.4 mi. The FIA plots for FS lands in 

California are also distributed geographically on a 3.4-mi 

grid. For specific information on the attributes that are 

collected on FS lands, refer to the Web sites: http://www.

fs.fed.us/r6/survey/ and http://www.fs.fed.us/r5/rsl/projects/

inventory/InvInfo.shtml. Refer to pages 31–36 in Moeur et 

al. (2005) for additional discussion of the CVS and FIA.

A spotted owl nesting/roosting habitat query was 

developed (table H-1) that is similar to what was used in the 

10-year report (Davis and Lint 2005). There were differences that included:

• Use of a slightly different set of plots

• Summarized data at the plot level vs. subplot level

a

 Carol A. Apple, Mathematical statistician, Regional Vegetation Inventory Program, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Region, 333 SW First Ave., Portland, 

OR 97204.

b

 Raymond J. Davis, Northern Spotted Owl Monitoring Module 

Leader, Northwest Forest Plan Interagency Monitoring Program, 

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

Region, 2900 NW Stewart Parkway, Roseburg, OR 97471.

Appendix H: Regional Inventory Plot Analysis

• Top story quadratic mean diameter for only conifer 

and not all species

• Use of a different strata attribute

These differences were made to adjust to the new 

gradient nearest neighbor variables used for the spatial 

habitat modeling (see chapter 3, this report). The intent of 

this analysis was to explore for differences in “query group” 

acres between the initial measurement and remeasurement 

period, and not for differences between the 10-year report 

map results and this report’s map results. In addition, the 

results of this analysis should not be compared to the results 

of the 10-year report plot analysis. 

On Pacific Northwest Region FS lands, nearly all CVS 

plots have had two samples, but on Oregon BLM lands, only 

one-quarter of the original CVS plots had been remeasured 

at the time of this analysis. Based on the numbers of plots 

for each year of measurement, the weighted average year for 

initial plot measurements in Oregon and Washington was 

1995 for initial plot measurements and 2002 for remeasurements. For California, the plot measurement period spans 

1997 to 2005. The first inventory in California was conducted under the older FIA “periodic” sample design. This 

protocol was replaced by FIA’s “annual” sample design, 

which was used for the plot remeasurments. This change in 

inventory protocol confounds inferences on habitat changes 

in California, as “real” change cannot be separated from 

effects related to changing sampling protocols (see Moeur et 

al. 2011) for more discussion.

As in the 10-year report, the “query groups” in table 

H-1 represent a progression of stand conditions, based on 

conifer diameter, total canopy cover, and stand structure 

complexity (strata) that represent habitat similarity to conditions used by spotted owls for nesting and roosting. A query 

was applied to both the initial measurement and remeasurement plot data in each physiographic province that occurred 

within the “habitat capable” areas described in Davis and 

Lint (2005) to assign a group code to each plot. In addition 

to the six groups in table H-1, two combined groups of EF 

and DEF were also assigned.

Carol A. Applea and Raymond J. Davisb
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GENERAL TECHNICAL REPORT PNW-GTR-850

Table H-1—Forest stand condition query for Current Vegetation Survey plot data

Low <------- Spotted owl nesting/roosting 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) All ≤40 >40 >40 41–70 >70

Strata All All 1 ≥2 ≥2 ≥2

Query part 2:

Quadratic mean diameter (inches) NA NA NA ≥20.5 NA NA

Canopy cover (percent) >40

 Strata 1

An analysis was then done using the jackknife method 

to estimate the variance of mean acres for each query group 

by measurement period and province. The variance was 

used in performing a t-test to look at the differences in the 

means between the two periods. This test assumes independence between the two samples, but in reality many of the 

plots were remeasured. Taking that into consideration, this 

test provided conservative results for significance: if it is 

significant, it is very significant.

The results of this analysis did not show any evidence 

of significant habitat recruitment into classes EF or the 

broader class DEF. There were some significant decreases in 

class E, but these were concurrent with significant increases 

in class F; therefore, these changes “cancelled” each other 

out, resulting in no significant change in the EF group. 

Significant, decreases in EF and DEF were observed in 

the California Cascades and Klamath Mountain provinces 

(table H-4); however, as stated above, the change in protocol 

used to collect plot information in California confounds this 

inference. The histograms on the following page display the 

results of the plot analysis for each physiographic province 

with significant amounts of federal lands. The results of the 

t-test are shown in tables H-2 thru H-4.
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl Figure H-1—Habitat changes between plot measurement periods (Time 1 = 1995 to 1997 and Time 2 = 2002 to 2005). Histrograms represent estimate of mean acres with 90-percent confidence intervals for each period.
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GENERAL TECHNICAL REPORT PNW-GTR-850

Table H-2–Test for significant difference (bold-faced) of mean acres between measurement periods by physiographic province by query group at the 0.1 significance level

(t = 1.6448), Washington

Physiographic province Query group t-value Net change

 Acres

Olympic Peninsula A 0.386 -9,300

B 0.379 -1,900

C 0.197 -1,500

D 1.016 24,000

E 1.455 -15,100

F 0.157 3,900

DEF 0.442 12,700

EF 0.438 -11,200

Western Cascades A 1.346 -65,000

B 1.427 -31,200

C 0.559 6,700

D 0.824 44,800

E 2.057 -80,000

F 2.134 115,600

DEF 1.143 80,400

EF 0.562 35,600

Eastern Cascades A 3.584 151,200

B 2.367 -85,000

C 0.337 -1,900

D 1.738 -75,400

E 0.477 14,100

F 0.114 -1,600

DEF 1.254 -63,000

EF 0.384 12,400
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

Table H-3–Test for significant difference (bold-faced) of mean acres between measurement periods by physiographic province by query group at the 0.1 significance level

(t = 1.6448), Oregon 

Physiographic province Query group t-value Net change

 Acres

Coast Range A 1.206 -46,500

B 0.061 900

C 0.295 6,800

D 0.770 -37,600

E 0.318 11,200

F 1.527 52,400

DEF 0.479 26,000

EF 1.374 63,600

Western Cascades A 0.480 30,500

B 1.981 -85,000

C 0.472 -14,500

D 0.945 85,400

E 2.988 -175,300

F 2.993 140,500

DEF 0.526 50,500

EF 0.499 -34,800

Klamath A 1.400 -70,500

B 0.287 7,600

C 0.854 8,400

D 1.758 98,000

E 1.478 -77,700

F 1.281 40,800

DEF 0.924 61,000

EF 0.638 -36,900

Eastern Cascades A 1.290 44,500

B 1.743 -45,700

C 0.272 -5,200

D 0.055 2,900

E 0.034 700

F 0.740 11,300

DEF 0.272 14,900

EF 0.464 12,000
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GENERAL TECHNICAL REPORT PNW-GTR-850

Table H-4—Test for significant difference (bold-faced) of mean acres between measurement periods by physiographic province by query group at the 0.1 significance level

(t = 1.6448), California 

Physiographic province Query group t-value Net change

 Acres

Coast Range A 0.128 1,900

B 1.145 12,300

C — —

D 0.309 -4,200

E 0.229 -2,300

F 0.213 2,400

DEF 0.269 -4,100

EF 0.008 100

Klamath A 2.988 259,400

B 1.404 108,700

C 1.722 25,200

D 0.597 63,700

E 0.901 -78,800

F 4.835 -402,400

DEF 3.887 -417,500

EF 4.496 -481,200

Cascades A 0.929 43,400

B 0.968 46,000

C 0.271 3,500

D 0.994 -57,600

E 1.660 -57,000

F 1.681 -36,400

DEF 2.628 -151,000

EF 2.380 -93,500
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Northwest Forest Plan—the First 15 Years (1994–2008): Status and Trends of Northern Spotted Owl

References

Davis, R.; Lint, J. 2005. Habitat status and trend. In: 

Lint, J., tech. coord. 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: 21–82.

Moeur, M.; Spies, T.A.; Hemstrom, M.; Alegria, J.; 

Browning, J.; Cissel, J.; Cohen, W.B.; Demeo, T.E.; 

Healey, S.; Warbington, R. 2005. Northwest Forest 

Plan—the first 10 years (1994–2003): status and trend 

of late-successional and old-growth forest. Gen. Tech. 

Rep. PNW-GTR-646. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest Research 

Station. 142 p.

Moeur, M.; Ohmann, J.L.; Kennedy, R.E.; Cohen, W.B.; 

Gregory, M.J.; Yang, Z.; Roberts, H.M.; Spies, T.A.; 

Fiorella, M. [In press.] Northwest Forest Plan—status 

and trends of late-successional and old-growth forests 

from 1994 to 2007. Gen. Tech. Rep. Portland, OR: U.S. 

Department of Agriculture, Forest Service, Pacific 

Northwest Research Station.

Jason Mowdy
Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw

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