United States Department of Agriculture

National Insect

and Disease

FOREST RISK ASSESSMENT

2013 – 2027

Frank J. Krist Jr.

James R. Ellenwood

Meghan E. Woods

Andrew J. McMahan

John P. Cowardin

Daniel E. Ryerson

Frank J. Sapio

Mark O. Zweifler

Sheryl A. Romero

Forest Service Forest Health Technology Enterprise Team FHTET-14-01 January 2014
The Forest Health Technology Enterprise Team (FHTET) was created in 1995 by the Deputy Chief for State and Private 

Forestry, USDA, Forest Service, to develop and deliver technologies to protect and improve the health of American forests. This 

book was published by FHTET as part of the technology transfer series.

Forest Health Technology Enterprise Team

2150 Centre Blvd., Bldg. A, Suite 331

Fort Collins, CO 80526

http://www.fs.fed.us/foresthealth/technology

Cover Silhouette of a white bark pine on top of the 

National 2012 Composite Insect and Disease Risk Map. 

Cover design by Sheryl A. Romero.

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, 

national origin, sex, religion, age, disability, political beliefs, sexual orientation, or marital or family status. (Not all prohibited bases 

apply to all programs.) Persons with disabilities who require alternative means for communication of program information (Braille, 

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

discrimination, write USDA, Director, Office of Civil Rights, Room 326-W, Whitten Building, 1400 Independence Avenue, SW, 

Washington, D.C. 20250-9410 or call 202-720-5964 (voice and TDD). USDA is an equal opportunity provider and employer.

The use of trade, firm, or corporation names in this publication is for the information and convenience of the reader. Such use 

does not constitute an official endorsement or approval by the U.S. Department of Agriculture of any product or service to the 

exclusion of others that may be suitable.

Disclaimer of External Links

The appearance of external hyperlinks does not constitute endorsement by the U.S. Department of Agriculture of the linked 

website, or the information, products or services contained therein. Unless otherwise specified, the Department does not 

exercise any editorial control over the information you may find at these locations. All links are provided with the intent of 

meeting the mission of the Department and Forest Service.

Disclaimer of Liability

With respect to documents available from the server, neither the United States Government nor any of its employees, makes any 

warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, or assumes any 

legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, products, or process 

disclosed, or represents that its use would not infringe privately owned rights.

Federal Recycling Program

Printed on recycled paper
2013–2027

National Insect and Disease

Forest Risk Assessment

Frank J. Krist Jr.1, James R. Ellenwood1, Meghan E. Woods3, 

Andrew J. McMahan2, John P. Cowardin2, Daniel E. Ryerson4,

Frank J. Sapio1, Mark O. Zweifler2, and Sheryl A. Romero2

United States Forest Service

1

Forest Health Protection (FHP)‐Forest Health Technology Enterprise Team (FHTET)

2

Cherokee Nation Technologies (CNT)-FHTET

3 Sanborn Map Company, Inc.-Pacific Southwest Region (R5) 

4

Forest Health Protection-Southwest Region (R3) 

Key Contributors

Coordination and Modeling

Borys M. Tkacz, FHP-Washington Office

Dale Starkey, FHP-Southern Region (R8)

Sheri Lee Smith, FHP-R5

Michael L. Simpson, FHP-Pacific Northwest Region (R6)

Lia Spiegel, FHP-R6

James R. Steinman, FHP-Northeastern Area (NA)

Tricia Wurtz, FHP-Alaska Region (R10)

Holly S. J. Kearns, FHP-R6
2013–2027 National Insect and Disease Forest Risk Assessment

ii Executive Summary

This report on the 2012 National Insect and Disease Risk Map 

(NIDRM) contains a nationwide strategic assessment of the 

hazard of tree mortality due to insects and diseases, displayed 

as a series of maps. Risk, or more appropriately termed hazard, is defined 

as: the expectation that, without remediation, at least 25% of 

standing live basal area greater than one inch in diameter will die 

over a 15-year time frame (2013 to 2027) due to insects and diseases.

NIDRM is more than just maps: It is a nationwide, science-based, 

administrative planning tool that is the product of a process whereby, 

every five years, the forest health community works together to 

determine the severity and extent of tree-mortality hazard due to 

insects and diseases.

NIDRM represents 186 individual insect and disease hazard models, 

integrated within a common GIS-based, multi-criteria framework, 

that can account for regional variations in forest health. Applied to all 

50 states, and based on the best-available science and data, NIDRM’s 

modeling process provides a consistent, repeatable, transparent, peerreviewed process through which interactive spatial and temporal 

hazard assessments can be conducted. This process is consistent 

with the 2006 effort, allows for flexible analysis to produce hazard 

assessments for specific insects and diseases, and can be used to 

inform other agency assessments such as the Integrated Resource 

Restoration, Watershed Condition Framework, Terrestrial Ecosystem 

Condition Assessment, Existing Vegetation Classification Mapping 

and Inventory, and Hazardous Fuels Prioritization Allocation System.

NIDRM products are compiled on a national extent with a 240-meter 

(approximately 14 acres) spatial resolution and can be updated as new 

data and/or models become available. This “live” or near-real-time 

approach will greatly facilitate the production of new hazard maps.

PURPOSE NIDRM’s primary purpose is as a strategic, broad-scale 

planning tool that can be used for administrative activities and work 

planning. In certain landscapes and at appropriate scales, NIDRM 

maps may be helpful for on-the-ground tactical management. 

NIDRM was a highly collaborative process led by the Forest Health 

Monitoring program (FHM) of the USDA Forest Service (Forest 

Service), with participation from FHM staffs from all Regions, State 

forestry agencies, Forest Service Forest Health Protection, and Forest 

Service Research and Development.

DATA IMPLICATIONS FOR PARTNERSHIPS To develop 

NIDRM involved an enormous data-production effort. In turn, the 

data created for NIDRM have enormous value across the Federal 

Government and its partners, and can be used across a myriad of 

projects and applications. An organized all lands Spatial Data Library, 

with over 600 data layers, is available through the Forest Health 

Technology Enterprise Team (FHTET). Tree species maps—

including basal area, stand density index, average diameter, and percent 

host at 30- and 240-meter resolution—are available to partners. The 

NIDRM data stack supports forest planning and forest-health hazard 

assessments at national and regional scales.

DATA SOURCES AND PROCESSING Previous NIDRM 

assessments defined forests as lands containing at least 10% tree 

canopy cover, including land that formerly had such tree cover and 

will be naturally or artificially regenerated. By this definition there are 

approximately 749 million acres of forested land in the coterminous 

United States and Alaska. For the 2012 hazard assessment, we 

extended these limits and modeled 1.2 billion acres of treed land (i.e. 

areas of measurable tree presence) across the US—whether or not 

these treed lands met some standard definition of forested. This 

approach improves coverage for rural areas of the Great Plains, as 

well as urban areas nationally. The 2012 hazard assessment estimates 

that 81 million of these acres are in a hazardous condition for insects 

and diseases. Almost 72 million acres are in the coterminous United 

States, and 9.5 million hazardous acres are in Alaska. In Hawaii, not 

previously assessed, just under a half-million acres are estimated to be 

in a hazardous condition. These estimates do not include hazard due to 

projected climate changes, although this NIDRM report includes an 

examination of future climate impacts on insect and disease hazards.

With significant improvements in coverage, accuracy, and precision 

of the data, the 2012 NIDRM was better able to model risk in the 

Great Plains, urban areas, and national parks. These improvements 

also allowed us to model pests, such as emerald ash borer and laurel 

wilt, that infest rare and/or widely distributed host species. The 

change from a 1-kilometer to a 240-meter spatial resolution moves 

the 2012 NIDRM closer to a product that can be used to inform 

local and regional decision making. This table displays some of the 

differences in acreage between the 2006 and 2012 efforts.

MAJOR HAZARDS Collectively, root diseases, bark beetles, and 

oak decline were the leading contributor to the risk of mortality in 

the coterminous United States, while spruce beetle was the most 

significant contributor in Alaska. The confluence of bark beetles and 

root diseases has resulted in large contiguous areas at risk across much 

of the western United States. Emerald ash borer is the most significant 

exotic forest pest. Tree species with the potential to lose more than 

50% of their host volume include redbay and whitebark pine. 

While future climate change is not modeled within NIDRM, we 

expect that the climate changes projected over the next 15 years will 

significantly increase the number of acres at risk, and will include 

elevated risk from already highly destructive pests such as mountain 

pine beetle and engraver beetles (Ips spp.). Host trees such as whitebark 

pine would be at increased risk in future climate-change scenarios. ♦

For more information and access to data

visit the 2012 NIDRM website

http://www.fs.fed.us/foresthealth/technology/nidrm2012.shtml

Millions of Acres

FACTOR

2006 

NIDRM

2012 

NIDRM

Difference

Hazardous conditions mapped in

the coterminous United States 55.2 71.7 16.5

Sources of differences

Host data gaps filled, and non-forested but treed 

areas modeled (2012) 13

Increased hazards from new models and 

improvements in process (2012) 3.5

Hazardous conditions mapped in Alaska 2.8 9.5 6.7

Sources of differences

Improved host maps and new models (2012) 6.7

Hazardous condition mapped in Hawaii 0 0.1 0.1

Sources of differences

Newly modeled (2012) 0.1

2012 NIDRM TOTAL HAZARD, all states 81.3 23.3
2013–2027 National Insect and Disease Forest Risk Assessment

National 2012 Composite Insect and Disease Risk Map — Alaska and Hawaii iii

9.5 million acres at risk in Alaska

0.4 million acres at risk in Hawaii

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

Risk of mortality

Treed areas
2013–2027 National Insect and Disease Forest Risk Assessment

iv National 2012 Composite Insect and Disease Risk Map

71.7 million acres at risk in the coterminous United States
2013–2027 National Insect and Disease Forest Risk Assessment

v

Risk of mortality

Treed areas
2013–2027 National Insect and Disease Forest Risk Assessment

vi Table of Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . ii

National 2012 Composite Insect and Disease 

Risk Map—Alaska and Hawaii . . . . . . . . . . iii

National 2012 Composite Insect and Disease 

Risk Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

List of Tables and Figures . . . . . . . . . . . . . . . . vii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

The Team Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Principal Objectives and Project Requirements. . 1

Mortality, Risk, and Hazard. . . . . . . . . . . . . . . . . . . . . 2

Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Forest Host-Tree Species Parameter Development. . . 5

Predictor Layers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Climate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Soils. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Parameter Samples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Parameter Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Growth and Mortality Adjustments. . . . . . . . . . . . . . . . 9

Additional Model Inputs. . . . . . . . . . . . . . . . . . . . . . . 10

Results/Discussion . . . . . . . . . . . . . . . . . . . . . . 11

Data Use Guidelines. . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Data Flexibility and Watershed Analysis Units. . . 11

Acres at Risk and Contributing Agents. . . . . . . . . . 12

Impacts on Individual Tree Species. . . . . . . . . . . . . 37

Redbay (Persea borbonia). . . . . . . . . . . . . . . . . . . . . . . . 37

Whitebark Pine (Pinus albicaulis). . . . . . . . . . . . . . . . . . 37

Limber Pine (Pinus flexilis). . . . . . . . . . . . . . . . . . . . . . . . 37

Lodgepole Pine (Pinus contorta). . . . . . . . . . . . . . . . . . 37

Significant Improvements in Data Coverage. . . . 45

Hawaii. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Myoporum thrips (Klambothrips myopori). . . . . . . . . . . . 45

Erythrina gall wasp (Quadrastichus erythrinae). . . . . . . 45

Koa wilt (Fusarium oxysporum f. sp. koae). . . . . . . . . . . . 45

Ohia rust (Puccinia psidii). . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Great Plains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Urban Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

National Parks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Accounting for Future Climate . . . . . . . . . . . 55

Future Climate Datasets. . . . . . . . . . . . . . . . . . . . . . . 55

Future Climate Predictions. . . . . . . . . . . . . . . . . . . . 56

Armillaria Root Disease (genus Armillaria). . . . . . . . 56

Hemlock Woolly Adelgid (Adelges tsugae). . . . . . . . . 59

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Forest Insect and Disease Risk Map Atlas . . 63

Index of Atlas Maps . . . . . . . . . . . . . . . . . . . . . 77

Atlas Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Photo Credits . . . . . . . . . . . . . . . . . . . . . . . . . 186

Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

Appendix A: Project Members and 

Acknowledgements . . . . . . . . . . . . . . . . . . . 198
2013–2027 National Insect and Disease Forest Risk Assessment

List of Tables and Figures vii

List of Tables

TABLE 1 Usage Frequency for 2012 NIDRM Model 

Criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

TABLE 2 Mortality Agents Ranked by Guild. . . . . . . . . . . . . . 34

TABLE 3 Amount of Area Where Individual-Agent

Mortality Rates are 25% or More. . . . . . . . . . . . . . . . . . . . . . . 35

TABLE 4 Acres and Percentage of Treed Land at Risk

 by State. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

TABLE 5 NIDRM Estimated Impacts on Host Tree Species. . . 38

TABLE 6 Top Mortality Agents by Rank for Treed Areas 

Within the Great Plains and Adjacent Prairies. . . . . . . . . . . 48

TABLE 7 National Park Units with the Most Significant 

Risk. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

TABLE 8 National Forests with the Most Significant Risk. . . . 51

TABLE 9 Agents and Hosts Modeled in NIDRM that Include 

Climate Drivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

TABLE A Criteria Used to Model Risk Agents and Their 

Importance in Model Inputs. . . . . . . . . . . . . . . . . . . . . . . . . . . 72

TABLE B Host Species Modeled for Each Agent. . . . . . . . . . 74

List of Figures

FIGURE 1 Conceptual risk assessment process. . . . . . . . . . . 3

FIGURE 2 RMAP application-parameter selections. . . . . . . . 3

FIGURE 3 RMAP application-mortality display criteria. . . . . 3

FIGURE 4 RMAP application-modeling results. . . . . . . . . . . 3

FIGURE 5 Risk modeling overview. . . . . . . . . . . . . . . . . . . . . . . . . 4

FIGURE 6 Overview of individual tree species parameter 

development. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

FIGURE 7 Distribution of FIA plot data ased to create

tree species parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

FIGURE 8 Acquisition date differences between FIA plot 

data and Landsat imagery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

FIGURE 9 Areas at Risk in Sam Houston National Forest, 

Texas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

FIGURE 10 Number of Mortality Risk Agents Modeled

 on Any Given Location. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

FIGURE 11 Number of Mortality Risk Agents Modeled

 on Any Given Location—Alaska and Hawaii. . . . . . . . . . . . 16

FIGURE 12 National 2012 Composite Insect and Disease 

Risk Map—Alaska and Hawaii. . . . . . . . . . . . . . . . . . . . . . . 17, 62 

FIGURE 13 National 2012 Composite Insect and 

 Disease Risk Map. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18, 64

FIGURE 14 Percentage of Treed Area at Risk by

 Watershed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20, 66

FIGURE 15 Percentage of Treed Area at Risk by Watershed—

Alaska and Hawaii. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22, 68

FIGURE 16 Watersheds Ranked by Basal Area Loss 

Hazard—Alaska and Hawaii. . . . . . . . . . . . . . . . . . . . . . . 23, 69

FIGURE 17 Watersheds Ranked by Basal Area Loss

Hazard. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24, 70

FIGURE 18 Risk by Ownership. . . . . . . . . . . . . . . . . . . . . . . . . . . 26

FIGURE 19 Risk by Ownership—Alaska and Hawaii. . . . . 28

FIGURE 20 Major Risk Agents Contributing to the 2012 

NIDRM—Alaska and Hawaii. . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

FIGURE 21 Major Risk Agents Contributing to the 2012 

NIDRM. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

FIGURE 22 Insect and Disease Risk on American Indian 

Lands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

FIGURE 23 Percentage of Redbay Basal Area Loss. . . . . . . . . . 40

FIGURE 24 Percentage of Whitebark Pine Basal Area Loss. . 41

FIGURE 25 Percentage of Limber Pine Basal Area Loss. . . 41

FIGURE 26 Percentage of Lodgepole Pine Basal Area Loss . . 43

FIGURE 27 Percentage of Eastern and Carolina Hemlock

Basal Area Loss. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

FIGURE 28 Areas of Hawaii Likely to be Impacted. . . . . . 46

FIGURE 29 Potential for Emerald Ash Borer in the Chicago 

Area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

FIGURE 30 Areas at Risk in Yellowstone National Park. . . . 52

FIGURE 31 Areas at Risk in the Great Smoky Mountains 

National Park. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

FIGURE 32 Estimated Increase in Mean Annual 

Temperature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

FIGURE 33 Estimated Change in Mean Annual 

Precipitation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

FIGURE 34 Accounting for Future Climate–Armillaria 

Root Disease. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

FIGURE 35 January Tmin and Areas of Increased Mortality

from Hemlock Woolly Adelgid under Future Climate. . . . . . . 59

FIGURE 36 2012 National Insect and Disease Survey by 

Watersheds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

CHARTS Basal Area Loss Rates in Watersheds Most 

Impacted by Each Agent. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
2013–2027 National Insect and Disease Forest Risk Assessment

viii
2013–2027 National Insect and Disease Forest Risk Assessment

IntroductIon 1

This 2012 National Insect and Disease Risk Map (NIDRM) 

report is the third in a series (see Lewis 2002; Krist et al. 2007) 

that use Geographic Information Systems (GIS) technology 

to identify the potential impact of both endemic and non-endemic 

forest pests in the coterminous Unites States, Alaska, and (new in 

2012) Hawaii. Each risk assessment provides a five-year strategic 

appraisal of the risk of tree mortality due to insects and diseases; 

each new mapping effort has focused on improving the process and 

data upon which assessments are conducted.

The 2006 and 2012 NIDRM efforts are noteworthy for moving beyond 

the typical cartographic uses of GIS to the realm of advanced spatial 

analysis. In 2006, a GIS-based, multi-criteria modeling framework 

was introduced along with national, standardized, geospatial datasets. 

These improvements provided a consistent, repeatable, transparent 

modeling process that generates comparable results across geographic 

regions and individual pest assessments. Despite these improvements, 

a significant challenge for the 2012 risk map effort was to make 

the sophisticated tools of multi-criteria/weighted-overlay spatial 

modeling accessible to a broad range of forest health professionals 

who, in most cases, have little or no GIS training or experience. 

The Forest Health Technology Enterprise Team (FHTET) defined 

procedures and built datasets and intuitive custom tools to get these 

partners directly involved in the risk-mapping process.

Several weaknesses in data availability and data quality identified 

during the 2006 NIDRM effort have been resolved in the 2012 

NIDRM. These include development of spatially explicit, nationwide, 

individual tree-species parameter layers, such as basal area (BA) (Avery 

and Burkhart 2002), quadratic mean diameter (QMD) (Reineke 

1933), and the stand density index (SDI) (Reineke 1933). Without 

such host-species information, spatially based risk assessments can 

be less than optimal even with very accurate models of risk agent 

effects. Also, with the development of 240-meter resolution forestparameter layers in 2012, we were able to address data gaps in the 

Great Plains, national parks, interior Alaska, and urban areas.

NIDRM’s assessment exclusively focuses on forest mortality due 

to pests and pathogens. This focus is not meant to diminish the 

importance of other factors that contribute to tree mortality or 

growth loss. For example, browsing from ungulates, defoliation, and 

competition from invasive exotic plants pose a significant threat to 

tree health in many regions, but are not included in NIDRM. And 

while climatological data were included as criteria in many insect 

and disease models, drought, storm damage, fire, and other abiotic 

disturbances were not directly modeled as risk agents.

THE TEAM APPROACH

The creation of NIDRM was a collaborative process coordinated by 

the Forest Health Monitoring (FHM) Program of the Forest Service. 

Staff from Forest Service FHM Regions, Forest Health Protection, 

and Research and Development; state agencies; and universities were 

invited to participate in creating NIDRM.

Three teams were created to support the effort. A Risk Map 

Oversight Team (RMOT) was formed to define risk assessment 

products, provide general process guidance, and schedule project 

development activities. A Data Development Team (DDT) was 

created to procure and create geospatial data layers required for input 

into risk map models. The Model Development Team (MDT) was 

assembled to design and improve quantitative models that estimate 

risk from interactive effects among multiple criteria represented 

in geospatial data layers. A list of RMOT, DDT, and MDT team 

members and other key participants can be found in Appendix A.

Beginning in 2009, forest health experts met regularly to review 

published information and to reach consensus on NIDRM models. 

In April 2011, forest health and GIS experts participated in a 

national workshop in Loveland, Colorado, to review and run models, 

display results, and adjust the models. The process culminated with 

a collective review of results by the RMOT, DDT, MDT, and FHM 

partners. Model deficiencies and applicability were discussed and 

plans were made to improve models and input data prior to the final 

review of models, which took place at the annual FHM meeting in 

April of 2012.

PRINCIPAL OBJECTIVES AND PROJECT

REQUIREMENTS

Considering the lessons learned since the 2006 NIDRM, the goals of 

the 2012 NIDRM are to:

1. Provide a five-year strategic appraisal (update) for the risk 

of tree mortality while greatly improving the local utility of 

NIDRM.

2. Increase participation and expedite NIDRM model development through the development of a custom desktop 

ArcGIS Risk Modeling Application (RMAP) that allows 

seamless transfer of GIS multi-criteria modeling technology 

to resource managers engaged in risk assessments.

3. Develop a set of national, standardized, 240-meter resolution, forest-parameter layers, including layers for Alaska 

and Hawaii, and other supporting geospatial data for use in 

NIDRM model development.

4. Provide information on

a. forest insects and diseases currently of concern,

b. conditions under which forested areas are at risk from 

agents of concern,

c. locations of such conditions and presence of risk agents 

of concern, and

d. the identification of significant impacts to individual tree 

species.

5. Involve state and federal partners in

a. developing, testing, and implementing RMAP,

b. identifying important disturbance agents and the host 

species on which they act,

c. developing, evaluating, and updating high-resolution, 

individual, tree-species parameter data,

d. updating existing, and developing new, forest pest models 

using the best available information, and

e. maintaining species-level information on disturbance 

agents and their hosts.

This report focuses on results and important changes since 2006, 

including newly added areas, such as Hawaii, national parks, and 

regions, such as the Great Plains and urban areas, where our ability to 

model risk improved significantly. This report also describes the new 

desktop application and geospatial layers utilized in the construction 

of the 2012 NIDRM. Finally, this report makes limited mention of 
2013–2027 National Insect and Disease Forest Risk Assessment

2 Introduction

the core modeling methodology that has been retained from the 2006 

NIDRM. We recommend two publications for detailed information 

on methods and overall processes.

A Multi-criteria Framework for Producing Local, Regional, and National 

Insect and Disease Risk Maps.

Frank J. Krist Jr., Sapio, F.J., Tkacz, B.M. Fort Collins, Colorado: U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team. 2010. 16 pp.

Online at http://www.fs.fed.us/foresthealth/technology/

pdfs/pnw_gtr802vol2_krist.pdf.

Mapping Risk from Forest Insects and Diseases, 2006.

Frank J. Krist Jr., Sapio, F.J., Tkacz, B.M. FHTET 2007-06. 

Fort Collins, Colorado: U.S. Department of Agriculture, 

Forest Service, Forest Health Technology Enterprise Team. 

2007. 115 p.

Online at http://www.fs.fed.us/foresthealth/technology/

pdfs/FHTET2007-06_RiskMap.pdf.

MORTALITY, RISK, AND HAZARD

Some level of tree mortality occurs in all forests, but usually at low 

and predictable rates that are typically offset by growth of residual 

trees (Smith et al. 2001). Losses from native insects and diseases are 

often widely scattered throughout the landscape and do not always 

result in large tracts of dead trees. However, more wide-spread and 

intensive tree mortality occurs in some areas, particularly when native 

and non-native (exotic) pests reach epidemic levels. 

A key objective is to identify areas at risk of insect- and diseasecaused tree mortality at rates well above average background rates. 

For the 2012 NIDRM, the background mortality rate was defined 

as “the average growing stock volume of timber dying over a given 

time due to natural causes, such as insects, disease, fire, and wind 

throw” (Smith et al. 2001). We used a national average rate of 0.89% 

per year (Smith et al. 2009), although local or regional background 

mortality rates can deviate significantly from this level. 

During the literature review for this project, the MDT was confronted 

with issues concerning the terminology used in environmental 

risk assessment. Risk and hazard are often described differently 

depending on the discipline (NRC 1983, EPA 1998). Rather than 

attempt to reconcile the various definitions of risk and hazard, we 

use a mortality potential paradigm, described below.

As it relates to forest health, risk is often composed of two parts: 

the probability of a forest being attacked and the probability of resulting tree 

mortality, referred to as susceptibility and vulnerability, respectively (Mott 

1963). Although we accept Mott’s distinction between susceptibility 

and vulnerability, due to lack of data it is difficult to assign probabilities to insect and disease activity at specific locations. Thus, 

NIDRM does not represent a probabilistic risk assessment. Instead, 

we define risk as the potential for mortality. Therefore, the 2012 NIDRM 

represents a hazard assessment rather than a true risk assessment.

Our threshold value for mapping risk is defined as the expectation 

that, without remediation, at least 25% of standing live basal 

area greater than one inch in diameter will die over a 15-

year time frame (2013 to 2027) due to insects and diseases. It 

is important to note that for NIDRM, risk of mortality does not 

include impacts from natural causes other than insects and diseases, 

although in many areas mortality resulting from fire, invasive plants, 

drought, browsing, storms and other factors can be very significant.

As in the 2002 and 2006 risk assessments, a basal area (BA) mortality 

rate ≥25% was deemed to represent “an uncommon, rather 

extraordinarily high amount of mortality.” The 15-year period for 

risk assessment is consistent with the 2002 and 2006 risk assessments 

and represents “a horizon long enough to avoid being too specific on 

the timing of outbreaks, yet short enough to be meaningful from a 

strategic planning standpoint” (Lewis 2002, Krist et al. 2007). 

Note: throughout this report, expected mortality is presented in 

either of two units of measure: either as

• a total BA loss (expressed in square feet) attributed to each risk 

agent, or 

• a total area at risk (expressed in acres) attributed to all (or 

specifically selected) risk agents present.

The first measure allows us to compare and rank risk agents according 

to the BA loss attributable to each agent; the second allows us to 

aggregate the acres that meet the 25% BA mortality threshold. ♦
2013–2027 National Insect and Disease Forest Risk Assessment

Methods 3

FIGURE 2 RMAP application-parameter selections

Select (A) an area of interest, (B) a risk agent, (C) a target host, and 

(D) the maximum amount of expected mortality.

FIGURE 3 RMAP application-mortality display criteria

Select (A) a criterion, (B) a rank, and (C) a weight that determine 

susceptibility and vulnerability to each risk agent.

FIGURE 4 RMAP application-modeling results

Run models and view results through maps, charts, and tabular 

RMAP outputs.

*

When more than one agent was responsible for mortality within a single host tree species, the final host-specific BA mortality rate was derived by summing the individual 

agent mortality rates, and truncating the sum such that it did not exceed 100%. This procedure differs from what is described in Krist et al. 2010, and was used in both the 

2006 and 2012 NIDRMs.

FIGURES 1–4

The 2012 NIDRM employs the same five-step, GIS-based, 

multi-criteria process used in 2006 (FIGURE 1). For details 

see Krist et al. 2007, 2010. The 2012 effort is distinguished by 

its use of improved data and the introduction of FHTET’s custom 

RMAP (FIGURES 2–4), which produced a standardized national 

depiction of risk at a 240-meter (approximately 14 acres) pixel/grid 

cell resolution. Briefly, here is the five-step process.

1. Compile a list of forest pests (risk agents), their target host 

species and the locations (ecoregions) where the pests pose 

significant threats to the host.

2. Identify, rank, and weight criteria (GIS layers acting as factors and 

constraints) that determine host susceptibility and vulnerability 

to each risk agent.

3. Re-scale from 0 to 10 the risk agent criteria values on each 

GIS layer and combine the resultant maps in a model of risk 

potential using a series of weighted overlays.

4. Convert modeled values representing potential risk of mortality 

for each agent to a predicted BA loss over a 15-year period.

5. Compile the resultant values from Step 4 and identify areas (at 

a 240-meter pixel resolution) on a national base map that are at 

risk of encountering a 25% or greater loss of total BA in the 

next 15 years.*

FIGURE 1 Conceptual risk assessment process
2013–2027 National Insect and Disease Forest Risk Assessment

4 Methods

RMAP enables forest insect and disease risk mapping specialists 

to create multi-criteria risk models, run the models against selected 

datasets, and view the results in multiple ways. The application’s user 

interface includes a map canvas that allows users to inspect spatial 

data inputs and outputs. 

RMAP provides a common, integrative framework that a large and 

diverse group of forest health specialists, most of whom have limited 

GIS experience, can use to evaluate criteria and reach consensus on 

the construction of pest models. This tool also supports a transparent 

and consistently repeatable process for integrating individual models 

from all geographic regions into a national composite map showing 

cumulative risk of tree mortality.

The software is interactive and easy to use. Users can make changes 

to data inputs or weightings and quickly assess updated results 

through maps, charts, and tabular RMAP outputs. To support 

modeling forest health risk at a national scale, FHTET compiled and 

organized a Spatial Data Library (SDL) with over 600 thematic raster 

datasets aligned to a common 240-meter resolution snap grid. (Data 

for Hawaii were assembled at a 30-meter resolution.) The SDL is 

integrated with RMAP, which facilitates locating and adding desired 

criteria datasets. Individual tree-species parameters and climate 

datasets are the primary inputs to forest health risk models (TABLE 1).

RMAP automatically maintains metadata on model parameters 

(criteria datasets, weights, thresholds, hosts, pests, ecoregion-area 

of interest, citations, etc.) and maintains model-author information; 

users are allowed to inspect components of all models, and can 

edit those models they have created. This database can be queried 

to find all models that meet search criteria, such as all the models 

for a specific damage agent, host, and ecoregion, singly or in any 

combination.

Within RMAP, individual tree species serve as insect and disease 

hosts. Host-tree species parameters, such as BA, SDI, percent host 

(proportion of host tree species BA relative to total BA) and QMD 

(reflecting tree size and age), along with other types of criteria within 

the SDL, such as soils, climate, elevation, etc., are combined to create 

the individual pest and pathogen models (FIGURE 5). These individual 

models are compiled to create the composite 2012 NIDRM. Forest 

host-tree species parameter development is discussed in the next 

section.

TABLE 1 Usage Frequency for 2012 NIDRM Model Criteria

CRITERIA CATEGORY

% of 

MODELS

Diameter 66%

QMD by species or species group

Species frequency by diameter class thresholds

BA / Density 63%

BA by species, species group or total

SDI by species, species group, or total

Canopy closure

Trees per acre

Host Prevalence 59%

Percentage of host by species or species group

Frequency of species or species group presence 

Climate 46%

Drought frequency

Annual, monthly and growing season precipitation

Monthly temperature (min, max, average)

Monthly relative humidity

Frost-free period, last frost

Proximity to Infestation 40%

Proximity to infestation by pest range

Soils 22%

Soil drainage index

Soil nutrient index

Soil moisture regimes

Topography 17%

Slope

Elevation

Aspect

Curvature, position index

Other 10%

Distance to roads, streams, urban areas, coast

Housing density, fenced area, harvested stands

Latitude

TABLE 1 FIGURE 5

FIGURE 5 Risk modeling overview
2013–2027 National Insect and Disease Forest Risk Assessment

Methods 5

FOREST HOST-TREE SPECIES PARAMETER 

DEVELOPMENT

Large portions of this section are taken from an in-process 

manuscript under the lead authorship of FHTET Remote Sensing 

Program Manager James Ellenwood (Ellenwood et al. In press).

The identification of areas at risk to a particular forest pest or pathogen 

first requires the production of host-tree species distributions and 

parameters, such as basal area (BA), stand density index (SDI), and 

quadratic mean diameter (QMD) (Krist et al. 2007). The accuracy 

and the precision of the risk models are largely determined by the 

quality of these host-tree datasets. FIGURE 6 shows a simplified 

overview of how individual tree-species parameters were generated 

and used for risk modeling. The sections below provide important 

details on the host-parameter development process.

Numerous forest-type and range maps are available for the United 

States (e.g., Little 1971, 1977); however, these maps typically use 

classification schemes that describe commonly occurring species 

associations. For example, the “Northern Hardwood” association 

may contain sugar maple, red maple, white ash, hemlock, yellow birch, 

and American beech. Such generalized forest type datasets fail to 

provide key information on the density and distribution of individual 

host species, so they are of limited use in predicting pest behavior 

(Krist et al. 2007). In addition, many forest pests and pathogens are 

highly host-specific, and an individual tree species may or may not 

be present throughout the mapped association unit. Therefore, to 

adequately represent forest health risk and hazards, individual tree 

species distributions and densities are needed. To meet this need, the 

RMOT developed its own individual tree species (i.e. host) parameter 

layers for both the 2006 and 2012 assessments. The 2012 dataset 

involved a massive, multi-year effort by the FHTET Remote Sensing 

Program to develop raster surfaces of forest parameters for each of 

the tree species measured in the Forest Service’s Forest Inventory 

and Analysis (FIA) program.

Statistical methods for modeling tree species extents tease out forest 

attributes by identifying unique site-specific spectral signatures from 

satellite imagery and patterns in parameters such as climate, terrain, 

and soil indices (Ruefenacht et al. 2008). For example, red and white 

pines may be difficult or impossible to distinguish through a spectral 

signature alone; however, when that signature is combined with a 

limiting factor, such as a known soil type, individual pine species can 

often be distinguished more precisely.

The previous versions of NIDRM utilized coarse renditions of forest 

host maps. The 2000 NIDRM employed a national forest-type map 

developed at a 1-kilometer scale (Lewis 2002). The map categories 

had broad definitions, which did not allow for modeling individual 

pests using forest parameters. The 2006 NIDRM utilized an Inverse 

Distance Weighting (IDW) interpolation of FIA plot data to produce 

1-kilometer resolution surfaces depicting forest parameters (BA, SDI, 

and QMD) by tree species. Although host parameters were coarsely 

represented, it allowed forest health specialists to build models that 

were more representative of actual forest health conditions. The 

desire to improve the resolution of pest and pathogen models was 

the prevailing impetus for developing host layers at their original 

resolution of 30 meters. Due to data-processing concerns associated 

with using very large, national-extent, 30-meter datasets, all host 

layers were re-sampled to a 240-meter resolution prior to forest pest 

and pathogen model development in RMAP.

Unlike spatial surfacing method employed in the IDW in 2006, the 

2012 NIDRM statistically models host distributions. The statistical 

modeling approach takes advantage of data-mining software and an 

archive of geospatial information to find the complex relationships 

between GIS layers and the presence/abundance of tree species as 

measured over 300,000 FIA plot locations. To describe in simple 

terms the difference between the 2006 surfacing and the 2012 

statistical modeling methods, consider the challenge of estimating 

ponderosa pine BA between two plot locations: on one plot, 100 

square feet per acre of BA is measured, while a neighboring plot 

measures 50 square feet per acre. An IDW surface would estimate 75 

square feet per acre of ponderosa pine at a forested location midway 

between these two plots. In contrast, the 2012 statistical modeling 

approach generates a simple predictive model from what is known 

about ponderosa pine distribution and density, based on a GIS 

overlay analysis of FIA plots with thematic layers such as soils, slope, 

aspect, and imagery characteristics. The predictive model generated 

from this overlay analysis is then used to model (predict) ponderosa 

pine BA for our hypothetical midpoint using that location’s values 

from the same set of thematic layers. 

PREDICTOR LAYERS

The occurrence of an individual tree species within any given area 

depends upon a number of factors related to the environmental 

conditions, cultural practices, and the biogeography of the species. 

The approach for many species distribution models has been to model 

representations of these factors. A number of different predictive 

techniques and variables have been analyzed, and few standard 

modeling approaches have been accepted (Austin 2007, Elith and 

Graham 2009). Nearly all species-distribution model approaches 

focus on characterizing the presence/absence or relative dominance 

of the species of interest and very seldom address density measures 

such as BA or SDI. 

Three domains are important with respect to tree species modeling: 

presence, density, and dominance. The presence of a species is an 

indication of its ability to become established on a site; the density 

of a species is an indication of how well a species can occupy a 

site; and the dominance of a species is an indication of how well it 

competes with other tree species on a site. 

In order to expedite the development of host-tree species data layers 

and the 2012 NIDRM, existing national datasets were used to develop 

host-predictor datasets at 30-meter resolution and site-parameter 

datasets of non-host criteria (elevation, slope, aspect, soil moisture, 

etc.) at 240-meter resolution. These national datasets included the

1. National Land Cover Database (NLCD) 30-meter resolution, three-season, Landsat dataset (Homer et al. 2004);

2. USDA National Resource Conservation Service (NRCS) 

localized soils dataset, Soil Survey Geographic Database 

(SSURGO), and a regionalized dataset, STATSGO2 (Digital General Soil Map of the United States) (http://soils.usda.

gov/survey/geography/ssurgo/index.html);

3. USDA Forest Service FIA nationwide forest inventory 

(Woudenberg et al. 2010);

4. National Elevation Dataset (Gesch et al. 2009); and

5. National Climate Data Center US standard normal data for 

7,937 climate stations in the US (NOAA-NCDC 2001).
2013–2027 National Insect and Disease Forest Risk Assessment

6 Methods

STEP A: Predictor layers and parameter samples

Establish relationship between predictor layers (Soils, Climate, Terrain, 

Landat satellite reflectance values) and individual tree species Basal 

Area (BA) and Density (SDI) measurements from hundreds of thousands 

of inventory plot locations.

STEP B: Parameter modeling

• Use statistical models to apply that relationship and generate initial BA 

and SDI data surfaces for 289 tree species.

• Outputs are adjusted to ensure, for example, that the sum of each 

individual tree species’ BA at each location does not exceed a 

separately modeled total for all species’ BA data layer.

Stand Density Index

STEP C: Tree growth and mortality layer used to adjust tree species parameters

• The vintage of the Landsat satellite imagery and the inventory plots is roughly 2002. To account for recent (2001 -2012) growth and mortality, MODIS 

satellite phenology datasets are used to further adjust BA and SDI outputs from step B.

• The final adjusted BA and SDI layers for each tree species are used to derive other forest parameters useful for risk modeling such as average diameter 

(QMD) and trees per acre.

FIGURE 6 Overview of individual tree species parameter development

FIGURE 6

Basal Area 

Substantial mortality adjustment

Little or no adjustment

Substantial growth adjustment
2013–2027 National Insect and Disease Forest Risk Assessment

Methods 7

Climate

Often, climate variables are significant for modeling individual 

tree species extents. Because climate is a broad characterization of 

conditions over a long period of time, it is difficult to represent local 

climate with any degree of certainty. Local climate at the fine scale 

is considered micro-climate and can be represented through terrain 

variables. 

Several national climate layers exist; unfortunately, they range from 

800 meters to two kilometers in resolution. In order to support development of host maps at 30- and 240-meter resolutions and simulate the potential effects of climate change within pest and pathogen 

models (discussed later in this report), we constructed 12 monthly 

climate variables each for precipitation, average mean temperature, 

average maximum temperature, and average minimum temperature, 

which could be used both as predictor layers for host parameter surfaces and as stand-alone climate criteria for NIDRM modeling.

Using the ANUSPLIN application (Hutchinson 1991) and 

techniques developed by Rehfeldt (2006), climate variables were 

simulated using 7,939 monthly station normals extracted from a 

CLIM81 (Climatography of the U.S. No. 81) 30-year (1971–2000) 

climate-normal dataset (NOAA-NCDC 2002). The monthly station 

normals were separated into two sets: one for the coterminous United 

States and the other for Alaska. For the coterminous United States, 

regression splines were built for precipitation from 7,467 stations. For 

three temperature variables (monthly average mean, monthly average 

maximum, and monthly average minimum), regression splines were 

built using 5,332 stations. For Alaska, regression splines were built 

for precipitation from the 124 stations. For the three temperature 

variables, regression splines were built using 119 stations. For the 

Southeast Alaska panhandle, the narrow nature of the landform 

in relation to the climate data stations proved to be problematic 

in creating splined surfaces that reflect expected climate surfaces. 

Instead, Rehfeldt’s (2006) original spline models for western North 

America were utilized for this area. Seasonal moisture index and 

seasonal moisture precipitation were derived from the ANUSPLINgenerated climate variables utilizing techniques from Crookston (in 

Rehfeldt 2006). 

Soils

Two components of soils are significant in forest type mapping: water 

holding capacity and productivity (Schaetzl et al. 2009). However, 

current metrics available from the NRCS, such as available water 

holding capacity (AWC), do not adequately describe natural soil 

wetness. This is because measures such as AWC reflect only the 

ability of a soil series to retain and release water to plants, not the 

long-term mean amount of water that is in the soil. 

In order to address this data gap, a soil drainage index (DI) layer 

was developed from SSURGO and STATSGO2 soil databases by 

Schaetzl et al. (2009) for use in forest pest and pathogen modeling 

(Krist et al. 2007) and forest host modeling. The DI indicates the 

relative amount of water (wetness) that a soil contains long-term and 

makes available to plants under normal climatic conditions. It is 

not meant to mimic the concept of plant-available water, which is 

largely dependent on soil texture. The DI only secondarily takes soil 

texture into consideration. 

The main factors affecting DI are the depth to the water table, soil 

moisture regime, volume available for rooting, and soil texture. 

The DI is calculated primarily from the soil’s taxonomic subgroup 

classification in the U.S. system of soil taxonomy (Soil Survey Staff, 

1999). Drainage index values range from 0 to 99; the higher the DI, 

the more water a soil can supply to plants. Sites with a DI of 99 are 

almost constantly waterlogged, while a soil with a DI value of 0 is 

almost thin and dry enough to be bare rock or raw sand. Because 

a soil’s taxonomic classification is not (initially) affected by such 

factors as irrigation or artificial drainage, the DI does not change 

as soils are irrigated or drained, unless the long-term effects of this 

involve a change in the soil’s taxonomic classification. Instead, the 

DI reflects the soil’s natural wetness condition.

The Productivity Index (PI) is an ordinal measure that represents the 

productivity of a soil (Schaetzl et al. 2012). A layer for PI also was 

developed from the SSURGO and STATSGO2 soil databases. The 

PI uses family-level soil taxonomy information (i.e., interpretations 

of taxonomic features or properties that tend to be associated with 

low or high soil productivity) to rank soils from 0 (least productive) 

to 19 (most productive). The index has wide application because, 

unlike competing indices, it does not require copious amounts 

of soil data (pH, organic matter, or cation exchange capacity, etc) 

in its derivation. For regionally extensive applications, such as 

NIDRM and host modeling, the PI may be as useful and robust 

as other productivity indexes that have much more exacting data 

requirements.

PARAMETER SAMPLES

FIA data were extracted from FIADB v4.0 (Woudenberg et al. 2010) 

for the plot and tree data. Plot data were limited to state inventory 

cycles that were aligned most closely with the imagery dates and 

generally ranged from 1999 to 2005, with some plots in the western 

United States sampled as late as 2009 (FIGURE 7). Approximately 

80% of the FIA plots were sampled within five years of collection 

dates for corresponding NLCD project, 30-meter, three-season, 

Landsat imagery (FIGURE 8). Tree data were limited to live trees 

and trees of one inch diameter at breast height (DBH) or greater. 

For Alaska, annualized FIA inventories were limited to southcentral and southeastern-panhandle areas. Interior Alaska plots were 

installed in limited areas and do not provide complete coverage of 

the forested area. These interior Alaska plots date from 1968 to 1991 

and utilized an older variable plot-radius cluster design. 

A Memorandum of Understanding was signed by each of the FIA unit 

directors and the FHTET director to allow FHTET to utilize actual 

FIA plot coordinates. Spatial data for the requested cycles in each 

of the States were acquired from the FIA Spatial Data Services unit. 

Best-available plot coordinates were extracted from FIA in February 

of 2011.

FIA plots were installed using the annualized FIA plot design, 

consisting of four 1/24th-acre subplots for larger trees (≥ 5 inches 

DBH) nested with 1/300th-acre subplots for saplings (1 inch to 

< 5 inches DBH) (Bechtold and Patterson 2005). FIA subplots 

were used in the forest parameter modeling to improve the precision 

of the parameter samples. Sub-plots were linked to the FIADB 

header data and the installation date was used to calculate magnetic 

declination for each sub-plot location using the U.S. Geological 

Survey (USGS) magnetic declination and secular variation datasets 

(Tarr 2000a, 2000b). Sub-plot locations were determined based 

upon the calculated magnetic declination and the plot design using 

an equidistant-azimuthal projection from the plot center.
2013–2027 National Insect and Disease Forest Risk Assessment

8 Methods

FIGURE 7 Distribution of FIA plot data used to create tree species parameters

FIGURE 7

To supplement the FIA plots, tree inventory data were also acquired 

from Forest Service Pacific Northwest and Southwest Regions, the 

Great Plains Initiative (GPI) non-forestry inventory (Lister et al. 2012), 

and the Bureau of Land Management (BLM) Oregon State Office. 

Plot locations identical to FIA locations were eliminated, with the FIA 

data being given priority. The GPI dataset used single isolated points, 

while the other non-FIA datasets used a five-point, cluster-plot design 

installed on an intensified grid. Predictor sample files were linked to 

the subplot coordinates and parameter summaries. Parameter null 

values were set to 0 to reflect the absence of a given species on a subplot.

PARAMETER MODELING

Tree species presence/absence, BA, and SDI were constructed from 

a stack of independent variables (predictor layers discussed above) 

using a classification and regression tree (CART) modeling method. 

FIA researchers have frequently used CART for modeling forest 

type and parameters (Blackard et al. 2008, Ruefenacht et al. 2008). 

Loh (2011) describes CART as, “...machine-learning methods for 

constructing prediction models from data. The models are obtained 

by recursively partitioning the data space and fitting a simple 

prediction model within each partition. As a result, the partitioning 

can be represented graphically as a decision tree.”

The advantage of a species-specific approach is that models are 

optimized for a given species. The disadvantage of this approach 

is that each individual species model is created independently of 

the others and anomalies between models occur. We decided the 

species-specific approach would provide a better risk assessment, 

because insect and disease risk models are keyed to individual treespecies parameters.

The presence/absence of total (all species) live tree basal area greater 

than one inch DBH was independently modeled from the predictor 

sample files using See5, version 2.06. The model derived from 

Plot

FIA plot densities
2013–2027 National Insect and Disease Forest Risk Assessment

Methods 9

FIGUR

E 8

See5 was converted to a raster surface using the RSAC Cubist/See5 

toolset (Ruefenacht et al. 2008). We consider this layer a geospatial 

representation of treed area, and it was utilized as a subsequent mask 

for all other host layers. The minimum density that can be measured 

on an FIA sub-plot is 1.7 square feet per acre of trees over one inch 

DBH. Therefore, a single pixel cannot represent less than 1.7 square 

feet per acre. 

A set of models estimating BA and SDI for all tree species was 

generated from Cubist, version 2.07, using selected layers available in 

each of the predictor sample files. The RSAC Cubist/See5 toolset was 

used to convert models from Cubist to geospatial representations of 

host parameters. 

Methods for developing forest parameter layers for Hawaii differed 

significantly from the process used for the rest of the United States 

The absence of FIA plots and limited validation data led to a reliance 

on species range maps obtained from a variety of sources, including 

the 2004 HI-GAP land cover data (US Geological Survey 2011), 

LANDFIRE Existing Vegetation Type and Existing Vegetation Cover 

data (Rollins and Frame 2006), and the NLCD canopy closure layer 

(Homer 2004). Host presence maps were then reviewed for accuracy 

by local forestry and forest health experts. Local experts also provided 

hand-digitized polygons depicting areas of host presence and density. 

Reviewer comments were incorporated into a new iteration of host 

maps; some areas were removed, others added, and then new maps 

were made available for additional review. All host presence maps 

were assembled at a 30-meter resolution.

GROWTH AND MORTALITY ADJUSTMENTS

For the coterminous United States, host parameter layers were derived 

from ground inventories and imagery datasets that contain information 

collected from various time frames. With image collection dates ranging 

from 1985 to 2005, and a mean collection date for treed areas of 2002, 

the vintage of the resultant parameter datasets should be considered 

as 2002. Plot-data evaluation datasets were purposely selected to 

closely correspond to the satellite imagery (FIGURE 8). However, for 

any specific area, the mismatch between ground measurements and 

imagery collection dates can be significant. 

For most eastern states, the annualized inventory has been installed 

on a 5-year cycle, while nine of the twelve western states’ inventories 

have been installed on a 10-year cycle. For this project, ground data 

included inventory panels collected prior to the completion of the 

western panels (up to the 2009 inventory year). Given these regional 

differences in survey frequency and plot intensity in the United States, 

the differences in imagery collection and plot measurement dates in the 

East are less substantial than in the West. In California, Oregon, and 

Washington, intensified plots on Forest Service and BLM lands were 

FIGURE 8 Acquisition date differences between FIA plot data and Landsat imagery

FIA plot data collected 8–11 years prior to imagery

Plots collected 4–7 years prior to imagery

Plots collected within 3 years of imagery collect

Plots collected 4–7 years after imagery

Plots collected 8–11 years after imagery

Inventory date vs. imagery date
2013–2027 National Insect and Disease Forest Risk Assessment

10 Methods

included to offset the incomplete representation of FIA annualized 

panels. Though not annualized, inventories in Nevada, Wyoming, 

and New Mexico are older but have a vintage that is close to satellite 

imagery collection dates. New Mexico plots date from 1985 to 1999; 

as a result, they may be the least representative of current forest 

conditions. 

Despite these date discrepancies, a county-based validation of the 

2002-vintage parameters shows that modeled cumulative BA appears 

to be within -3% of the 2011 FIA estimate (current as of 2/28/2013). 

In order to better represent 2012 conditions, forest parameter outputs 

were then post-processed to account for growth and mortality that 

occurred between 2002 and 2012. With this adjustment, the modeled 

cumulative BA is within -4% of the current 2011 FIA estimate. Due 

to the frequency and nature of the FIA inventory, recent mortality is 

often under-represented in FIA estimates.

The adjustment process for the coterminous United States utilized 

a MODIS phenology dataset (2001–2010) from NASA-Stennis 

(Hargrove et al. 2009, McKellip et al. 2010) and the FHTET Pest 

Portal Forest Disturbance Mapper (FDM) (http://foresthealth.

fs.usda.gov/portal/FDM) dataset (2008–2012). The NASA-Stennis 

dataset was utilized for the entire coterminous United States, whereas 

the FDM dataset was utilized in key portions of Colorado, Utah, and 

Wyoming to account for recent fire and mountain pine beetle mortality. 

Building upon techniques developed by Hargrove et al. (2012), a linear 

regression was performed on a stack of the annual 80% Normalized 

Difference Vegetation Index (NDVI) layer for the years ranging from 

2001 to 2010. Geospatial products of the resultant regression slope 

(greenness trend) and regression r-square were created.

A second set of regressions was created in order to scale the 

phenology regression layers. A collection of approximately 170,000 

re-measured FIA sub-plots was used to create an annual BA percent 

change (including both growth and mortality), by subtracting currentperiod BA from the previous-period BA and dividing the result by 

the period length between plot measurements. 

Two versions of annualized change (growth and mortality) models 

were created. The first version employed the phenology slope and 

the r-square layers as independent variables with annualized change 

as the dependent variable. Visual inspections with the past insect and 

disease aerial survey data and large-fire occurrences indicated that the 

layer generated from this annualized change model underestimated 

mortality. A second model was created with the phenology slope 

independent variable weighted by the r-square variable to predict 

annualized change. In an effort to compensate for the underrepresentation of mortality, the annualized change regression was 

shifted to set the dependent variable intercept to 0. 

For each county, the average ground-plot collection year was 

subtracted from 2011 to compute an average inventory age (in years) 

and limited from 1 to 10 years. This inventory age layer was used 

to weight the annualized BA percent-change layers. From the two 

annualized change layers, a composited annualized-change product 

was generated by giving preference to the greater negative change 

(mortality) over the positive change (growth). The composited 

BA percent-change layer was applied to each of the host layers to 

account for growth and mortality as observed from the phenology 

change layers, thus giving the input forest parameters datasets a 2012 

time-stamp. The final tree growth and mortality layer used to adjust 

the host layers in this assessment are represented in FIGURE 6, page 6.

As with the coterminous United States, Alaska host-parameter 

layers were derived from ground inventories and imagery datasets 

that contain information from various time frames. Image collection 

was limited to early summer and late summer, with collection dates 

from 1994 to 2006. Coastal Alaska ground samples were collected 

on a ten-year annualized inventory with collection dates from 2004 

to 2009. Interior Alaska had a very limited dataset, with dates 

from 1968 to 1994. Given the age of the Interior Alaska dataset 

and the lack of coverage, the representation of the current forest 

conditions is less than ideal, and a vintage is difficult to determine.

Unlike adjustments for the coterminous United States, the growth 

and mortality adjustment for Alaska was limited to fire-related 

mortality. Input for fire-related mortality was taken from the 

Monitoring Trends in Burn Severity (MTBS) archive (Eidenshink 

et al. 2007). All MTBS data were collected between 2001 and 2008 

(the most recently available data in 2011). Each burn severity class 

was assigned a percent BA-loss rate: Unburned/Very Low (0% 

loss); Low Severity (25% loss); Moderate Severity (50% loss); and 

Severe Severity (75% loss). The 30-meter dataset was up-scaled 

to 240-meter resolution by applying an average of the quantitative 

percent-loss values. The 240-meter percent-loss dataset was 

applied to each of the forest parameter layers to yield a dataset 

with an approximate representation of forest conditions in 2008.

ADDITIONAL MODEL INPUTS

Many forest health risk and hazard models utilize metrics such as 

percent-host composition, quadratic mean diameter (QMD), and 

trees per acre (TABLE 1, page 4). However, these metrics are difficult 

to model directly; therefore, they were derived from modeled SDI 

and BA parameters, instead.

While host layers are the most critical model inputs, a variety of 

other data sources are important for running pest and pathogen risk 

models. Apart from host parameters specifically modeled for the 

2012 NIDRM, over 600 layers representing various characteristics 

important to understanding risk agent behavior were included 

in the SDL for use in RMAP. These layers include monthly and 

annual climate parameters, pest and pathogen ranges, land use, soil 

characteristics, topography, census data, and plant hardiness zones, 

among others. Several of these layers were discussed in detail in the 

previous section and are grouped in TABLE 1 by general category. 

Drought layers were not used to model the forest host surfaces; 

however, drought was a key criterion in many of the NIDRM models 

and merits further explanation. Drought index and frequency data layers 

were derived from PRISM climate data (www.prism.oregonstate.edu) 

consisting of precipitation and temperature grids for every month from 

January 1895 to October 2009. From the temperature grids, together 

with latitude, monthly potential evapotranspiration (PET) grids were 

derived. From PET and precipitation grids, a dimensionless monthly 

moisture index (scaled between -1 and 1) was computed for each grid 

cell. Twelve-, 36-, and 60-month (1-, 3-, and 5-year) running average 

moisture indices were derived for each year, and then normalized to 

derive 1-, 3-, and 5-year moisture-index departure scores, which can 

be classified into moisture deficit or surplus classes that range from 

extreme moisture surplus to extreme drought. Drought frequencies 

of 1-, 3-, and 5-years were derived from the drought index departure 

layers, and represent the proportion of times grid cells experience 

moderate, severe, or extreme drought (Koch et al., in press). ♦
2013–2027 National Insect and Disease Forest Risk Assessment

Results/Discussion 11

The change from a 1-kilometer to 240-meter spatial resolution 

moves the 2012 NIDRM closer to a product that may be 

used to inform local and regional decision-making. 

DATA USE GUIDELINES

We offer two basic guidelines for the appropriate use of information 

derived from the 2012 risk assessment.

1. Maximum Display Scale To highlight meaningful patterns 

of risk and avoid maps that appear pixilated, NIDRM 

and any of its derivative products should be displayed at a 

maximum scale ranging from 1:250,000 to 1:500,000. At the 

1:250,000-scale, a typical eastern US county will plot onto 

an 8.5 x 11-inch sheet of paper (a map extent that covers 

approximately 500,000 acres or 800 square miles). At this 

resolution, users with local knowledge can view enough 

map area to understand broader landscape-level magnitudes 

and patterns of risk and still be zoomed-in close enough to 

associate blocks of risk with familiar forest landscapes. A 

240-meter pixel covers approximately 14 acres, a typical 

area for delineating forest stands. Zooming in closer than 

1:250,000-scale invites a per-pixel, stand-level type analysis 

that is not the intended or recommended use of NIDRM.

2. Minimum Analysis Unit NIDRM is primarily a national 

planning tool designed to describe broad regional and national 

trends. Inquiries regarding units smaller than a county or 

national forest should be posed to regional and state experts, 

who may have conducted finer-resolution risk assessments 

and are familiar with local variation. Local implementation of 

finer-resolution assessments will be encouraged by continued 

development of 30-meter datasets and the RMAP application.

DATA FLEXIBILITY AND WATERSHED 

ANALYSIS UNITS

A major advantage of the NIDRM modeling framework is that, in 

addition to its representation of the discrete risk/no-risk classes, it 

allows for the production of maps depicting continuous data, such 

as BA losses. In turn, this allows the data to be summarized in a 

variety of ways at local, regional, and national scales. Depending 

upon analysis objectives, potential pest and pathogen impacts 

might be characterized by individual tree and pest species (or their 

combination) in multiple ways. For example, it may not be practical 

to treat specific areas with the highest risk potential, while areas at 

or near an intermediate mortality threshold may be better candidates 

for management. Here are some common examples.

• Acres with any amount of host impact by pest or pest 

combination can be used to determine the affected area or 

“footprint” on the ground.

• Acres with impacts over any user-defined BA loss threshold can 

be used to prioritize areas for management and restoration.

• BA loss by geographic region, by pest, host, or combinations 

of these can be used to identify areas

◦ where individual tree host species may be extirpated or 

severely impacted,

◦ that require further monitoring, or

◦ that have management or restoration potential.

• The proportion of host BA loss and/or total BA loss can be 

used to determine overall impacts on the forest.

It can be difficult to clearly display regional patterns when 240-meter 

cells are used to represent risk. Throughout the remainder of this 

report, NIDRM outputs are often summarized by 12-digit or 6thlevel USGS subwatersheds, which we refer to as “watersheds.” 

These 10,000- to 40,000-acre units are consistent with those in the 

Forest Service Watershed Condition Framework (Potyondy and Geier 

2011). At regional and national scales, watershed summarizations 

make it easier to visualize these patterns and provide a good basis 

for discussing how best to target local resources and respond to 

detailed inquiries. There are two types of cumulative (i.e. combined 

hazard from all agents) watershed summaries presented in this 

report.

1. Percentage of treed area at risk—allows comparisons between watersheds regardless of their differences in overall 

BA or proportion of treed area. 

2. Ranking of watersheds by the amount of BA loss—identifies the watersheds with the greatest potential BA loss.

To create the percentage of treed area at risk by watershed, first we 

counted the number of 240-meter cells in each watershed that are 

at risk, and then determined the area these cells represent. Then, 

we determined the proportion of treed land at risk by dividing the 

area of the cells at risk in a watershed by the total treed area in a 

watershed. 

We calculated the ranking of potential BA loss by adding together 

all the 240-meter cells with BA loss occupying each watershed. 

Once summed, we divided the BA losses across all watersheds into 

100 classes through an equal-area stretch. The equal-area stretch 

begins by ranking watersheds based on the amount of BA loss. 

Next, the equal-area stretch assigns each watershed to a category 

and ensures that a nearly equal number of watersheds occupy each 

category. Watersheds are assigned to only one category and are 

not split among categories. Depending on watershed size, some 

classes may contain slightly more area than other classes. Finally, 

we divided the 100 classes into five categories: 1) little or no loss, 

2) the 49% least impacted watersheds, 3) 50% –74%, 4) 75%–95%, 

and 5) top 5% - most severely impacted watersheds. 

In some cases, watersheds that are categorized as having little or no 

risk as a percentage of treed area at risk may still have significant 

potential BA losses. For example, in the Southeast Alaska panhandle, 

where there are very productive forests, potential BA losses may be 

substantial but never meet or exceed the risk threshold of 25% BA 

loss over the next 15 years for any 240-meter pixel.

Summarizing by watershed enables integration with the Forest 

Service Watershed Condition Framework (WCF) (Potyondy and 

Geier 2011). In an effort to focus resources, the WCF establishes 

a consistent, comparable, and credible process for assessing and 

improving the health of America’s watersheds. In 2011, priority 

watersheds were identified and Watershed Restoration Action Plans 

were issued for the National Forest System. The 2006 NIDRM 

was part of this watershed prioritization process, and it is our 

intent to continue to support the WCF through the development 

of watershed summaries for the 2012 assessment.
2013–2027 National Insect and Disease Forest Risk Assessment

12 Results/Discussion

Watershed summaries by agent and composite, all-agent, risk-pixel 

maps are useful for clear portrayals of NIDRM outputs at a national 

extent. However, behind these summaries are NIDRM outputs that 

show continuous estimated BA loss values for individual pests and 

pathogens at a 240-meter cell resolution. These data can be valuable 

for local analyses approaching our recommended maximum scales 

and are available from the 2012 NIDRM website www.fs.fed.us/

foresthealth/technology/nidrm2012.shtml. These full-resolution, 

agent-specific datasets for the ~500,000 acre Sam Houston National 

Forest in east Texas are shown in FIGURE 9.

ACRES AT RISK AND CONTRIBUTING 

AGENTS

The 2012 national risk assessment employed 186 risk-agent models, 

representing 43 agents or groups of agents acting on over 60 tree 

species or species groups. It is common for multiple pests to act on 

a single location (FIGURES 10 and 11, pages 14, 16). Outputs from 

all models were composited into NIDRM, resulting in about 81 

million acres at risk (FIGURES 12 and 13, pages 17, 18). Watersheds 

by percentages of treed land at risk are shown in FIGURES 14 and

15, pages 20, 22); watersheds by the absolute amount of BA loss are 

ranked in FIGURES 16 and 17** (pages 23, 24). 

NIDRM displays approximately 9.5 million acres at or above the 

25%-mortality threshold in Alaska, almost 72 million acres in the 

coterminous United States, and just under a half a million acres 

in Hawaii. The combined total of approximately 81 million acres 

represents about 6.7% of the 1.2 billion acres of modeled treed lands 

within the United States. Of the combined risk, 44% is distributed 

across state, private, tribal, and other non-federal ownerships 

(FIGURES 18 and 19, pages 26, 28), while almost 56% is located 

on National Forest System, and other federal lands. Insect and 

disease risk on tribal lands— 2.3 million acres— is highlighted in 

FIGURE 22 (page 32). Many of these tribal areas occupy arid and/

or sparsely forested landscapes; therefore, the risk is generally not 

as widespread as on adjacent lands. However, pinyon ips on the 

Navajo Reservation in Arizona, Douglas-fir beetle and root disease 

on the Colville Reservation in northeastern Washington, and forest 

tent caterpillar, emerald ash borer and oak decline on tribal lands in 

northern Minnesota are areas of concern.

We included 43 risk agents or groups of agents in this study. 

Predicted basal area (BA) losses were summed for risk agents, such 

as mountain pine beetle, a native bark beetle, that required multiple 

models to represent different ecoregions and hosts. These losses 

include all areas with potential for activity both above and below 

the 25% BA mortality threshold. The risk agents, categorized into 

guilds, facilitate comparisons between and within guilds. Although 

all root diseases collectively present the greatest individual agentlevel hazard, all bark beetles collectively are projected to be 

responsible for nearly three times the BA mortality of root disease 

over the next 15 years (TABLE 2, page 34).

When summarizing risk by Forest Service region, Region 8 has the 

most acreage at risk, despite having the lowest proportion of treed 

to non-treed area at risk (TABLE 3, page 35; FIGURE 13, page 18). 

Region 8’s risk profile contrasts sharply with Region 1, which has a 

much higher proportion of treed area at risk than any other region. 

This contrast can be attributed to the significant amount of bark 

beetle and root disease activity across Region 1 (FIGURE 21, page 

30; TABLE 3, page 35), where agents individually contribute 25% or 

more to the total BA loss and a higher number of risk agents acted 

on tree species across the western regions (FIGURES 10 and 11, 

pages 14, 16). In addition, a large majority of Region 8 is treed, while 

vast areas of Region 1 contain few or no trees. Also, treed areas 

across Regions 8 and 9 contain a greater mix of tree species, which 

hinders the spread, and reduces the overall damage, of host-specific 

pests and pathogens, such as bark beetles.

Idaho, Montana and Oregon are at the top of the list both in 

absolute acres and proportion of their treed lands at risk (TABLE 4, 

PAGE 36). The top three risk agents in Idaho and Montana are root 

disease, Douglas-fir beetle and mountain pine beetle (FIGURE 21, 

page 30). Oregon has a similar cohort with stronger impacts from 

western spruce budworm as well as mountain and western pine 

beetles. In the eastern United States, Rhode Island, Connecticut 

and Massachusetts together have the largest proportion of treed 

area at risk, principally due to oak decline, winter moth, and 

root disease. Among southern states, Louisiana has the highest, 

projected percentage of treed area at risk, largely due to hazards 

from southern pine beetle, root disease and oak decline.

In Alaska, large areas are subject to spruce beetle and northern 

spruce engraver risk. Myoporum thrips and koa wilt are the two 

most significant risk agents in Hawaii (FIGURE 20, page 29).

A notable change from the 2006 risk assessment is the increase in 

the proportion of acres at risk in Region 6, which can be attributed 

mostly to bark beetle and western spruce budworm activity (TABLE

3, page 35). Across Region 2, and despite recent losses due to 

fires and mountain pine beetle (USDA 2012), a high proportion 

of treed area remains at risk due to spruce beetle, continued bark 

beetle activity, and root disease. Since 2006, Region 10 appears to 

have experienced a notable increase in acres at risk (acres in 2006 

v. acres in 2012) (FIGURE 12, page 17); however, this change is due 

to significant improvements in our ability to model host extents 

and forest parameters, not necessarily an increase in pest and/or 

pathogen activity.

Results/Discussion, continued, page 37

**Most of the national maps in this report use a technique called ‘shaded relief’ that allows us to portray variations in terrain relative to each map’s subject matter. Data 

used to derive the terrestrial shaded relief are from the USGS National Atlas website http://nationalatlas.gov/atlasftp.html. Great Lakes and ocean bathymetry were adapted 

from imagery available at the National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center website http://www.ngdc.noaa.gov/ngdc.html.
2013–2027 National Insect and Disease Forest Risk Assessment

Areas at Risk in Sam Houston National Forest, Texas 13

OVERALL RISK SAM HOUSTON NF LOCATION

Risk of mortality

Sam Houston NF

PEST OR 

PATHOGEN

(All numbers are rounded)

Host’s 

% of 

Total 

BA

Host 

BA 

Loss 

Rate

Total 

BA 

Loss 

Rate

Southern pine 

beetle

70.4% 22.2% 15.6%

Root diseases 70.4% 8.3% 5.8%

Engraver beetles 

(Ips spp.)

70.4% 2.5% 1.8%

Fusiform rust 66.4% 0.3% 0.2%

Oak decline 9.6% 8.9% 0.9%

Emerald ash borer 0.1% 10.4% <0.05%

IMPACT FROM ALL AGENTS 24.3%

FIGURE 9

Extent and density of most prevalent host tree species groups

SOUTHERN PINES HOSTS OAK SPECIES HOSTS

Mortality rates for highest risk mortality agents 

SOUTHERN PINE BEETLE ROOT DISEASE OAK DECLINE

1–5% 

6–15% 

16–24% 

> 25% 

Percent of Total BA

< 20 

20–50 

51–100 

> 100 

Square Feet of BA
2013–2027 National Insect and Disease Forest Risk Assessment

14 Number of Mortality Risk Agents Modeled on Any Given Location

FIGUR

E 10
2013–2027 National Insect and Disease Forest Risk Assessment

15

Treed areas

1–2

3–4

5–12

Number of agents modeled
2013–2027 National Insect and Disease Forest Risk Assessment

16

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

FIGURE 11

Number of Mortality Risk Agents Modeled on Any Given Location

Treed areas

1–2

3–4

5–12

Number of agents modeled
2013–2027 National Insect and Disease Forest Risk Assessment

National 2012 Composite Insect and Disease Risk Map 17

R10

9.5 million acres at risk in Alaska

R5

0.1 million acres at risk in Hawaii

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

FS 

Regions

Area at Risk

(1000 acres)

% of Treed 

Area at Risk

1 13,381 25.2%

2 7,918 12.1%

3 3,509 7.2%

4 6,166 9.4%

5 5,924 11.4%

6 9,422 14.4%

8 14,061 3.5%

9 11,703 4.1%

10 9,521 5.6%

Risk of mortality

Treed areas

FIGUR

E 12
2013–2027 National Insect and Disease Forest Risk Assessment

18 National 2012 Composite Insect and Disease Risk Map

R1

R5

R6

R8 R3

R4 R2

FS

Regions

Area at Risk

(1000 acres)

% of Treed 

Area at Risk

1 13,381 25.2%

2 7,918 12.1%

3 3,509 7.2%

4 6,166 9.4%

5 5,924 11.4%

6 9,422 14.4%

8 14,061 3.5%

9 11,703 4.1%

10 9,521 5.6%

FIGUR

E 13
2013–2027 National Insect and Disease Forest Risk Assessment

19

Risk of mortality

Treed areas

Forest Service regions

R9/NA

R8

71.7 million acrES at riSk in tHE cotErminouS unitEd StatES

Risk of mortality

Treed areas
2013–2027 National Insect and Disease Forest Risk Assessment

20 Percentage of Treed Area at Risk by Watershed

FIGUR

E 14
2013–2027 National Insect and Disease Forest Risk Assessment

21

Little to no risk

1–4%

5–14%

15–24%

25% or greater
2013–2027 National Insect and Disease Forest Risk Assessment

22 Percentage of Treed Area at Risk by Watershed — Alaska and Hawaii

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

Little to no risk

1–4%

5–14%

15–24%

25% or greater

FIGUR

E 15
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard — Alaska and Hawaii 23

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

FIGUR

E 16

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

24 Watersheds Ranked by Basal Area Loss Hazard

FIGUR

E 17
2013–2027 National Insect and Disease Forest Risk Assessment

25

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

26 Risk by Ownership

Treed Acres (Thousands)

Forest Service administered lands 186,002

Other federal lands 68,375

All other ownerships 785,176

Total 1,039,553

Acres at Risk (Thousands)

Forest Service administered lands 37,026

Other federal lands 4,190

All other ownerships 30,489

Total 71,705

% of Treed Land at Risk

Forest Service administered lands 19.9%

Other federal lands 6.1%

All other ownerships 3.9%

Total % at Risk (Acres at Risk / Treed Acres) x 100 6.9%

FIGUR

E 18
2013–2027 National Insect and Disease Forest Risk Assessment

27

Risk of mortality

Forest Service administered lands

Other federal lands
2013–2027 National Insect and Disease Forest Risk Assessment

28 Risk by Ownership — Alaska and Hawaii

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

Treed Acres (Thousands)

Federal lands 657

All other ownerships 9,014

Total 9,670

Acres at Risk (Thousands)

Federal lands 9

All other ownerships 337

Total 346

% Treed Land at Risk

Federal lands 1.3%

All other ownerships 3.7%

Total % at Risk (Acres at Risk / Treed Acres) x 100 3.6%

Treed Acres (Thousands)

Forest Service administered lands 14,557

Other federal lands 91,537

All other ownerships 63,987

Total 170,081

Acres at Risk (Thousands)

Forest Service administered lands 10

Other federal lands 4,030

All other ownerships 5,481

Total 9,521

% of Treed Land at Risk

Forest Service administered lands 0.1%

Other federal lands 4.4%

All other ownerships 8.6%

Total % at Risk (Acres at Risk / Treed Acres) x 100 5.6%

FIGUR

E 19

Risk of mortality

Forest Service administered lands

Other federal lands
2013–2027 National Insect and Disease Forest Risk Assessment

Major Risk Agents Contributing to the 2012 NIDRM — Alaska and Hawaii 29

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

1

2

3

4

1

2

3

1. Eastern larch beetle, spruce beetle

2. Spruce beetle, northern spruce engraver (Ips)

3. Hemlock dwarf mistletoe, yellow cedar decline, stem rot

Agents listed in order of their relative impact, highest to lowest

1. Ohia rust, koa wilt

2. Ohia rust

3. Myoporum thrips

4. Erythrina gall wasp, Myoporum thrips

Agents listed in order of their relative impact, highest to lowest.

Risk of mortality

Treed areas

FIGURE 20
2013–2027 National Insect and Disease Forest Risk Assessment

30 Major Risk Agents Contributing to the 2012 NIDRM

1. Douglas‐fir beetle, root disease

2. Root disease, Douglas‐fir beetle

3. Western spruce budworm, balsam woolly adelgid

4. Root disease, western spruce budworm, Douglas‐fir beetle

5. Mountain pine beetle, western pine beetle

6. Sudden oak death

7. Mountain pine beetle, Jeffrey pine beetle

8. Engraver beetles (Ips spp.)

9. Engraver beetles (Ips spp.), western pine beetle

10. Root disease, spruce beetle, Douglas‐fir beetle

11. Root disease, spruce beetle

12. Spruce beetle, western spruce budworm

Agents listed in order of their relative impact, highest to lowest.

15

16

13

14

1 10 11 8

9

12

4

1

2 3

5

6

8

7

13. Mountain pine beetle

14. Mountain pine beetle, Douglas-fir beetle, spruce beetle

15. Forest tent caterpillar, aspen/cotttonwood decline, emerald ash borer

16. Aspen/cottonwood/oak decline, emerald ash borer

17. Dutch elm disease, oak decline

18. Oak decline

19. Southern pine beetle, root disease

20. Jack pine budworm, oak decline, oak wilt

21. Oak decline, jack pine budworm, oak wilt

22. Jack pine budworm, maple decline, beech bark disease

23. Eastern spruce budworm, beech bark disease, maple decline

24. Oak decline, winter moth, root disease

FIGURE 21
2013–2027 National Insect and Disease Forest Risk Assessment

31

20

17

19

18

21

22

23

24

Risk of mortality

Treed areas
2013–2027 National Insect and Disease Forest Risk Assessment

32 Insect and Disease Risk on American Indian Lands

FIGUR

E 22
2013–2027 National Insect and Disease Forest Risk Assessment

33

Risk of mortality

American Indian lands
2013–2027 National Insect and Disease Forest Risk Assessment

34

TABLE 2

Mortality Agents ranked by Guild

1

Group includes Arizona fivespined ips, eastern fivespined ips, northern 

spruce engraver, pine engraver, pinyon ips, sixspined ips, small southern 

pine engraver, and three western species without common names, Ips 

latidens, Ips knausi and Ips integer. 

2

Group includes annosus, armillaria, laminated root rot, and Port-Orfordcedar root diseases.

3

Group includes American, Douglas-fir, hemlock, larch, limber pine, pineland, 

and pinyon dwarf mistletoes.

4

Host BA loss layers were not created for Hawaiian pests and pathogens.

AGENT

BA Losses

(Millions of Sq. Feet)

BARK BEETLES (TOTAL) 3337.3

Mountain pine beetle 708.1

Southern pine beetle 647.5

Engraver beetles [Ips spp.]1 580.2

Spruce beetle 536.4

Douglas-fir beetle 457.8

Western pine beetle 165.9

Fir engraver 159

Jeffrey pine beetle 28.2

Western balsam bark beetle 22.7

Eastern larch beetle 19.9

Roundheaded pine beetle 11.6

DECLINES (TOTAL) 1425.9

Oak decline and gypsy moth 1109.8

Maple decline 202.5

Aspen and cottonwood decline 101.3

Yellow-cedar decline 12.3

DISEASES (TOTAL) 1156.6

Root diseases—all2 1117.3

Stem rot 18.8

Fusiform rust 17.7

Bur oak blight 2.8

EXOTICS (TOTAL) 840.5

Emerald ash borer 255.2

Balsam woolly adelgid 121.7

Hemlock woolly adelgid 102.5

Beech bark disease 93.6

Sudden oak death 58.3

Dutch elm disease 49.2

Winter moth 45.9

White pine blister rust 34.8

Oak wilt 33

Sirex woodwasp 23.7

Laurel wilt 22.1

Asian longhorned beetle 0.4

Goldspotted oak borer 0.1

AGENT

BA Losses

(Millions of Sq. Feet)

DEFOLIATORS (TOTAL) 422.4

Eastern spruce budworm 233.9

Western spruce budworm 115.8

Forest tent caterpillar 28.7

Douglas-fir tussock moth 28.1

Jack pine budworm 15.9

OTHER (TOTAL) 45.2

Dwarf mistletoes3 44.3

Spruce aphid 0.9

HAWAIIAN PESTS4

Ohia rust N/A

Koa wilt N/A

Myoporum thrips N/A

Erythrina gall wasp N/A
2013–2027 National Insect and Disease Forest Risk Assessment

35

TABL

E 3

Amount of Area Where Individual–Agent Mortality Rates are 25% or More

AGENT

Thousands of Acres by Forest Service Region

R1 R2 R3 R4 R5 R6 R8 R9 R10

BARK BEETLES

Douglas-fir beetle 2369 406 240 1115 - 577 - - -

Eastern larch beetle - - - - - - - 170 234

Engraver beetles [Ips spp.] - 569 1033 1341 10 - tr* - -

Fir engraver 1 - - 3 79 85 - - -

Jeffrey pine beetle - - - 1 7 tr - - -

Mountain pine beetle 2921 2799 4 1568 791 815 - - -

Southern pine beetle - - - - - - 1832 4 -

Spruce beetle 110 1298 138 154 - 23 - - 848

Western balsam bark beetle - 1 - 4 - - - - -

Western pine beetle 7 14 5 61 240 152 - - -

DECLINES

Aspen and cottonwood decline 7 237 1 76 tr - 5 38 -

Maple decline - - - - - - tr 173 -

Oak decline and gypsy moth 1 10 - - - - 2955 3581 -

DISEASES

Fusiform rust - - - - - - 6 - -

Root diseases—all 1903 312 12 1 tr 47 170 134 -

EXOTICS

Asian longhorned beetle - - - - - - - tr -

Balsam woolly adelgid 6 - - 26 - 112 - 19 -

Beech bark disease - - - - - - 47 126 -

Dutch elm disease 13 164 - - - - 18 158 -

Emerald ash borer 29 2 - - - - 66 618 -

Goldspotted oak borer - - - - 5 - - - -

Hemlock woolly adelgid - - - - - - 1 43 -

Laurel wilt - - - - - - 105 - -

Oak wilt - - - - - - 31 183 -

Sudden oak death - - - - 128 30 - - -

White pine blister rust 1 tr - tr tr tr - - -

Winter moth - - - - - - - 201 -

DEFOLIATORS

Eastern spruce budworm - - - - - - - 242 -

Forest tent caterpillar 6 - - - - - - 1025 -

Jack pine budworm - - - - - - - 448 -

HAWAIIAN AGENTS

Erythrina gall wasp - - - - 12 - - - -

Koa wilt - - - - 53 - - - -

Myoporum thrips - - - - 61 - - - -

* In all instances, tr (trace) refers to values less than 500 acres. 
2013–2027 National Insect and Disease Forest Risk Assessment

36

TABLE 4

Acres and Percentage of Treed Land at Risk by State

STATE

Risk Area

(1000 acres)

Treed Area

(1000 acres)

State Area

(1000 acres)

% of State 

with Trees

% of Treed 

Acres at Risk

Idaho 7,680 27,777 53,484 52% 28%

Rhode Island 143 635 696 91% 23%

Montana 7,645 36,244 94,105 39% 21%

Oregon 6,723 37,396 62,128 60% 18%

South Dakota 832 4,700 49,353 10% 18%

Colorado 5,358 32,563 66,620 49% 16%

Wyoming 2,758 17,390 62,600 28% 16%

Connecticut 399 3,093 3,184 97% 13%

Massachusetts 609 4,946 5,199 95% 12%

California 5,697 47,237 101,218 47% 12%

New Mexico 2,363 23,587 77,818 30% 10%

Washington 2,702 27,827 43,279 64% 10%

Louisiana 1,613 20,213 30,013 67% 8%

Nevada 1,288 16,710 70,759 24% 8%

West Virginia 1,054 15,094 15,507 97% 7%

Virginia 1,621 23,696 25,926 91% 7%

South Carolina 1,190 18,436 19,831 93% 6%

New Hampshire 363 5,799 5,930 98% 6%

Arkansas 1,630 26,424 34,035 78% 6%

Michigan 1,810 30,624 37,214 82% 6%

Alaska 9,519 170,082 420,048 40% 6%

Minnesota 1,492 28,582 54,002 53% 5%

Utah 1,358 26,299 54,335 48% 5%

Missouri 1,376 29,959 44,611 67% 5%

Vermont 264 5,767 6,153 94% 5%

Arizona 1,146 25,487 72,959 35% 4%

New Jersey 187 4,363 4,851 90% 4%

Wisconsin 1,088 26,772 35,909 75% 4%

North Carolina 1,138 29,119 31,626 92% 4%

Mississippi 1,029 26,508 30,516 87% 4%

Alabama 1,177 31,101 33,063 94% 4%

Maine 726 20,055 20,841 96% 4%

Hawaii 77 2,151 4,142 52% 4%

New York 972 28,384 31,132 91% 3%

Georgia 1,035 35,217 37,652 94% 3%

Oklahoma 531 22,163 44,736 50% 2%

Kentucky 464 21,177 25,862 82% 2%

Tennessee 514 23,697 26,972 88% 2%

Maryland 110 5,246 6,638 79% 2%

North Dakota 89 4,248 45,247 9% 2%

Nebraska 143 7,230 49,506 15% 2%

Pennsylvania 510 26,444 28,992 91% 2%

Texas 1,854 97,489 169,463 58% 2%

Kansas 154 9,987 52,657 19% 2%

Indiana 160 12,004 23,157 52% 1%

Illinois 168 13,278 36,058 37% 1%

Ohio 200 17,057 26,405 65% 1%

Florida 265 30,238 36,326 83% 1%

Iowa 67 9,348 36,014 26% 1%

Delaware 3 918 1,285 71% <0.5%

District of Columbia 0 36 44 81% 0%
2013–2027 National Insect and Disease Forest Risk Assessment

Results/Discussion 37

IMPACTS ON INDIVIDUAL TREE SPECIES

For the next 15 years (2013–2027), redbay, whitebark pine, limber 

pine, and lodgepole pine will likely continue to decline due to pests 

and pathogens (USDA 2012). Estimated BA losses for these mostat-risk species could range from 40% for lodgepole pine to 90% for 

redbay (TABLE 5, pages 38-39). Although average projected BA losses 

are expected to be less than 30% each for ash, beech, and hemlock, 

local impacts may be much higher. For example, in Michigan vast 

areas have lost nearly 100% of green, white, and black ash due to 

emerald ash borer. Similarly, hemlock woolly adelgid feeding has 

removed large tracts of eastern and Carolina hemlock from the 

Appalachian Mountains. 

REDBAY (Persea borbonia)

Our models predict that over 90% of the redbay BA in the United 

States will die in the next 15 years.

The entire range of redbay, a relative of avocado, is at risk due to a 

recently introduced disease called “laurel wilt.” Redbay and other 

tree species in the family Lauraceae are susceptible. The disease, 

caused by a fungus (Raffaelea lauricola), is introduced into redbay trees 

by the small, non-native, redbay ambrosia beetle (Xyleborus glabratus). 

The fungus interferes with the tree’s water translocation mechanism 

and causes trees to wilt. Laurel wilt has caused widespread and severe 

levels of mortality in the southeastern coastal plain. This mortality is 

expected to continue as this beetle spreads unabated throughout the 

entire redbay range.

Redbay grows along the edge of streams, springs, and swamps from 

eastern Texas to southern Virginia. It has evergreen, aromatic, leathery 

leaves, dark blue fruit hanging on into winter, and reddish bark. Native 

Americans found a host of medicinal uses for this small-to-mediumsized tree. Inhabitants of the South often used its fine-grained, highly 

polished wood as trim for boats and ships.

WHITEBARK PINE (Pinus albicaulis)

Our models estimate BA losses for whitebark pine approaching 60% 

over the next 15 years as a result of mountain pine beetle and white 

pine blister rust.

Whitebark pine is highly vulnerable to the combined effects of white 

pine blister rust, mountain pine beetle, fire suppression, and climate 

change. Indeed, in 2011 the U.S. Fish and Wildlife Service (2011) 

found it warranted being listed as a “Threatened or Endangered” 

species. Many land management agencies have called for actions 

and strategies to address the threats (Keane et al. 2012), and such 

management is seen as necessary to ensure the survival of this 

ecologically important and often long-lived species.

Whitebark pine is a keystone species, meaning that its importance 

in ecosystem function is large relative to its abundance. It is also a 

foundation species, and its presence exerts significant controls on the 

ecological community structure. Whitebark pine seed is an important 

food source for birds, small mammals, and bears. Aided by Clark’s 

nutcracker for seed dispersal, whitebark pine quickly establishes 

itself after disturbance (e.g., fire) and is important in curtailing soil 

erosion, maintaining snowpack, and providing wildlife habitat and 

biodiversity. It exists in the western United States primarily at high 

elevations in a variety of habitat types, mostly on public lands. 

LIMBER PINE (Pinus flexilis)

Basal area losses in limber pine are estimated to exceed 40% 

over the next 15 years, primarily due to mountain pine beetle and 

secondarily to the combination of white pine blister rust and 

dwarf mistletoe.

Given limber pine’s wide adaptability, its vulnerability to climate 

change and mountain pine beetle is lower than that of other highelevation, five-needle pines over much of its range. 

Limber pine exists over very broad geographic and elevation 

ranges; it is found at both upper and lower elevation tree-lines (and 

elevations in between) and is distributed from southern California 

to South Dakota and from Mexico to British Columbia. It is a 

drought-tolerant species widely adapted to a variety of habitat types, 

frequently found co-existing with other species, such as aspen, 

ponderosa pine, lodgepole pine, and bristlecone pine. Occupying 

a variety of ecological niches, limber pine is an important pioneer 

species and is a climax species on harsh (xeric or high-elevation) 

sites (Keane et al. 2011). Like whitebark pine, limber pine seed is 

important in the ecology of birds and mammals, thus contributing 

to the maintenance of biodiversity across a variety of habitats. 

LODGEPOLE PINE (Pinus contorta)

We estimate BA loss due mountain pine beetle and dwarf mistletoe 

to approach 40% over the next 15 years.

While the risk of loss of lodgepole pine remains high, it is unlikely 

that lodgepole pine forests are at risk of extirpation as a result 

of these agents or other disturbances, such as wildfire or climate 

change. Although these latter two disturbances are often associated 

with mountain pine beetle outbreaks (e.g., climate as a contributing 

factor and wildfire as a subsequent event), neither are likely to 

contribute to long-term widespread losses of lodgepole pine forests 

except, perhaps, at population margins, where future climates may 

affect the post-disturbance re-establishment potential of lodgepole 

pine.

Lodgepole pine is a widespread species, ranging from the southern 

to northern Rockies, the Sierras, and southeast Alaska, and existing 

in both pure and mixed-species stands. It is fire-adapted and has 

co-evolved with the mountain pine beetle, a native bark beetle, 

which has killed vast amounts of lodgepole pine trees over large 

areas in recent years (USDA 2012).

 The proportion of projected host BA loss for redbay, whitebark 

pine, limber pine, lodgepole pine, and hemlock, summarized 

by watershed is shown in FIGURES 23–27 (pages 40–44). The 

proportion of potential host lost is calculated by summing the host 

BA losses for all risk agents that affect each host in each watershed; 

the BA host loss is then divided by the total host BA to determine 

the proportion of host BA that may be lost in the next 15 years. 

Watersheds containing each tree species are then grouped within 

the following classes: 0% (little or no host BA loss), 1–4% loss, 

5–14% loss, 15–24% loss, and 25–100% loss.

Continued, page 45
2013–2027 National Insect and Disease Forest Risk Assessment

38 NIDRM Estimated Impacts on Host Tree Species

TABLE 5

HOST SPECIES

Agent (maximum mortality rate)1 Loss Rate2

WESTERN SOFTWOODS

Whitebark pine 58%

Mountain pine beetle (80–85%)

White pine blister rust (25–30%)

Limber pine 44%

Dwarf mistletoe (5%)

Mountain pine beetle (20–80%)

White pine blister rust (10–20%)

Lodgepole pine 39%

Dwarf mistletoe (2%)

Mountain pine beetle (25–85%)

Ponderosa pine 28%

Dwarf mistletoe (8%)

Engraver beetles [Ips spp.] (5–40%)

Mountain pine beetle (10–80%)

Root diseases (10–30%)

Roundheaded pine beetle (5–25%)

Western pine beetle (5–60%)

Pinyon pines (5 species) 27%

Dwarf mistletoe (3%)

Engraver beetles [Ips spp.] (35–70%)

Jeffrey pine 26%

Jeffrey pine beetle (40%)

Root diseases (30%)

Grand fir 25%

Balsam woolly adelgid (15%)

Douglas-fir tussock moth (20%)

Fir engraver (10–25%)

Root diseases (32%)

Western spruce budworm (5%)

Engelmann spruce 23%

Root diseases (15%)

Spruce beetle (80%)

Western spruce budworm (3%)

Western white pine 23%

Mountain pine beetle (50–60%)

White pine blister rust (30%)

Port-Orford-cedar 22%

Root diseases (50–95%)

Subalpine fir 21%

Balsam woolly adelgid (30–50%)

Root diseases (24–43%)

Western balsam bark beetle (10–60%)

Western spruce budworm (5%)

White spruce 21%

Northern spruce engraver (25%)

Root diseases (8%)

Spruce beetle (40%)

HOST SPECIES

Agent (maximum mortality rate)1 Loss Rate2

Sugar pine 19%

Mountain pine beetle (10–30%)

Mountain pine beetle (10–30%)

White fir 18%

Douglas-fir tussock moth (13–20%)

Fir engraver (25–80%)

Western spruce budworm (5%)

Sitka spruce 18%

Spruce aphid (2%)

Spruce beetle (40%)

Stem rot (2.4%)

Tamarack 3 17%

Eastern larch beetle (50%)

Douglas-fir 15%

Douglas-fir beetle (5–60%)

Douglas-fir tussock moth (20%)

Dwarf mistletoe (5%)

Root diseases (18–52%)

Western spruce budworm (5%)

Mountain hemlock 11%

Root diseases (30%)

Coulter pine 10%

Western pine beetle (60%)

Alaska yellow-cedar 10%

Yellow-cedar decline (21%)

California red fir 9%

Fir engraver (20%)

Southwestern white pine 7%

Mountain pine beetle (5–70%)

White pine blister rust (10%)

Grand fir and Douglas-fir 5%

Root diseases (25–30%)

Western spruce budworm (20%)

Western hemlock 5%

Dwarf mistletoe (6%)

Stem rot (2.4%)

Rocky Mountain bristlecone pine 4%

White pine blister rust (10%)

Western red cedar 3%

Root diseases (11%)

Grand fir and white fir 2%

Fir engraver (60%)

Root diseases (30%)

Spruce / fir (17 species) 2%

Root diseases (15–25%)

Pacific silver fir 1%

Balsam woolly adelgid (30%)

Western larch <0.5%

Dwarf mistletoe (3%)

See footnotes, page 39
2013–2027 National Insect and Disease Forest Risk Assessment

NIDRM Estimated Impacts on Host Tree Species 39

TABLE 5, CONT.

1

Maximum mortality rate is the range of maximum allowable host mortality rates across the suite of agent-host-specific models.

2

Estimated BA mortality rates are the average across the entire host tree species’ extent regardless of whether the agent was modeled across that host 

tree species’ entire range.

3

Eastern larch beetle in tamarack was modeled in both Alaska and the eastern United States; both of its listings here reflect the nationwide mortality 

rate estimate. 

HOST SPECIES

Agent (maximum mortality rate)1 Loss Rate2

WESTERN HARDWOODS

Tanoak 24%

Sudden oak death (60%)

California live oak 11%

Goldspotted oak borer (50%)

Sudden oak death (30%)

Quaking aspen 9%

Aspen & cottonwood decline (25–30%)

Live oak 7%

Oak wilt (50%)

Bur oak 3%

Bur oak blight (25%)

California black oak 1%

Goldspotted oak borer (50%)

Sudden oak death (30%)

Canyon live oak <0.5%

Goldspotted oak borer (15%)

EASTERN SOFTWOODS

Balsam fir 27%

Balsam woolly adelgid (35%)

Spruce budworm (50%)

Jack pine 22%

Jack pine budworm (70%)

Root diseases (50%)

Tamarack 3 17%

Eastern larch beetle (50%)

Southern pines (group 1) (6 species) 13%

Southern pine beetle (35%)

Pitch pine 7%

Southern pine beetle (35%)

Eastern and Carolina hemlock 6%

Hemlock woolly adelgid (40%)

Eastern white pine 5%

Root diseases (50%)

Southern pine beetle (25%)

White pine blister rust (15%)

Spruce (8 species) 5%

Spruce budworm (30%)

Red pine 5%

Root diseases (50%)

Southern pines (group 2) (3 species) 4%

Root diseases (35%)

HOST SPECIES

Agent (maximum mortality rate)1 Loss Rate2

Longleaf pine 4%

Root diseases (25%)

Southern pine beetle (25%)

Fir (9 species) 3%

Root diseases (25%)

Southern pines (group 3) (8 species) 2%

Ips engraver beetles (5%)

Sirex pines (10 species) <0.5%

Sirex woodwasp (5%)

Southern pines (group 4) (2 species) <0.5%

Fusiform rust (40%)

EASTERN HARDWOODS

Redbay 90%

Laurel wilt (98%)

Ash (8 species) 27%

Emerald ash borer (50%)

American elm 20%

Dutch elm disease (30–50%)

Red oaks (13 species) 19%

Oak decline (70%)

Oak wilt (15–80%)

White oaks (10 species) 19%

Oak decline (60%)

American beech 18%

Beech bark disease (70%)

Sugar maple 14%

Asian longhorned beetle (30%)

Maple decline (50%)

Cottonwood (6 species) 3%

Aspen and cottonwood decline (50%)

Aspen (2 species) 2%

Forest tent caterpillar (40%)

Sassafras 1%

Laurel wilt (50%)

Oaks (24 species) 1%

Winter moth (50%)

Paper birch <0.5%

Root diseases (22%)

Red maple <0.5%

Asian long-horned beetle (40%)
2013–2027 National Insect and Disease Forest Risk Assessment

40 Percentage of Redbay Basal Area Loss

Causal agent: laurel wilt

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of host BA loss

Host BA loss by watershed

FIGUR

E 23
2013–2027 National Insect and Disease Forest Risk Assessment

41

FIGURE 24

Percentage of Whitebark Pine Basal Area Loss

Causal agents: 

mountain pine beetle, 

white pine blister rust

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of host BA loss

Host BA loss by watershed
2013–2027 National Insect and Disease Forest Risk Assessment

42 Percentage of Limber Pine Basal Area Loss

FIGUR

E 25

Causal agents: 

dwarf mistletoes,

mountain pine beetle, 

white pine blister rust

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of host BA loss

Host BA loss by watershed
2013–2027 National Insect and Disease Forest Risk Assessment

43

FIGURE 26

Percentage of Lodgepole Pine Basal Area Loss

Causal agents: 

dwarf mistletoes,

mountain pine beetle

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of host BA loss

Host BA loss by watershed
2013–2027 National Insect and Disease Forest Risk Assessment

44 Percentage of Eastern and Carolina Hemlock Basal Area Loss

FIGUR

E 27

Causal agent: 

hemlock woolly adelgid

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of host BA loss

Host BA loss by watershed
2013–2027 National Insect and Disease Forest Risk Assessment

Results/Discussion 45

SIGNIFICANT IMPROVEMENTS IN DATA 

COVERAGE

Hawaii was not included in the 2006 risk assessment; however—and 

despite the lack of modeled forest parameter data—risk models for 

Hawaii were constructed for the 2012 assessment. But unlike the 

models for the coterminous United States and Alaska, risk models 

for Hawaii do not yield any estimates of BA loss.

In the 2006 NIDRM, many older FIA plots were used to acquire 

the necessary spatial coverage to generate interpolated surfaces that 

reasonably depict natural variation of forest parameter distributions. 

A number of gaps in FIA plot network coverage have subsequently 

been filled. There are still gaps in forest parameter coverage for 

national parks, some wilderness areas, sparsely tree-covered regions 

(e.g., the Great Plains), and urban areas.

In 2012, with the improvement in host modeling methods, we 

significantly enhanced forest parameter information across regions 

that previously relied on generalized renditions of host distribution. 

Despite these improvements, our remotely sensed modeling 

techniques produce less-reliable depictions of host extent in sparsely 

treed areas relative to more heavily forested areas, which are wellcovered by the FIA plot network.

HAWAII

Today, only 5% of Hawaii’s indigenous dry forest ecosystem remains 

intact. Beginning with the arrival of the first Polynesians and 

continuing through the first European contact in the 18th century up 

to today, housing and urban development, agriculture, and introduced 

species have displaced much of Hawaii’s native vegetation.

Isolated by thousands of miles of ocean, the unique forests of Hawaii 

evolved from the extremely rare introduction of plants via wind, 

birds, or flotation. Initial changes began when the Polynesians first 

arrived sometime between 300 and 800 CE, bringing with them 

a handful of useful plants and, more significantly, seed-eating rats 

and land clearing practices. Following European contact, changes in 

vegetation greatly accelerated with the introduction of countless plant 

and ungulate species, as well as more intensive development. 

Today, non-native plants make up the majority of Hawaii’s lowland 

vegetation, with intact, native forests occurring in higher elevations 

where non-native plants have not yet supplanted the native species. 

Indeed, a visitor to the islands might never set eyes on a native 

Hawaiian plant. This wholesale replacement of native vegetation by 

exotic species continues in Hawaii at an alarming rate.

What little remains of Hawaii’s dry forests naturally occurs on leeward 

areas at lower elevations. Fire-prone, invasive grasses aggressively 

spread into these areas and prevent native plant establishment, 

leading to a destructive grass-fire cycle, after which grasses quickly 

replace woody vegetation. Due to the co-location of dry lowland 

forests and Hawaii’s ports of entry, these areas are more susceptible to 

introduction and establishment of non-native forest pests. 

The bulk of remaining native forests occurs in more isolated, highelevation, high-rainfall areas of the state. However, these forests are 

not impervious to invasion by alien plants. Many horticultural and 

forestry species have “jumped the fence” and are now threatening the 

last tracts of native forest. Strawberry guava, miconia, and Himalayan 

ginger are just a few of the most aggressive plants that thrive in the 

cool, wet, upland habitats of Hawaii and quickly out-compete native 

trees. No risk models representing non-native plants were included in 

the 2012 assessment; in order to provide a complete picture of native 

tree mortality, future risk and hazard assessments should address the 

impacts of invasive, non-native plants.

In addition to introduced plants, ungulates continue to destroy 

native Hawaiian forests. Hawaiian plants are not adapted to grazing, 

trampling, and rooting by mammal species, which were entirely absent 

from the native Hawaiian biota (except for a single bat species). In 

addition, ungulates promote the spread of invasive plants by serving 

as seed dispersers and by disturbing the forest floor. Pigs, goats, 

sheep, and various deer species all degrade forest areas where they 

have not been excluded by fencing and active control programs.

Although invasive plants and ungulates have been the primary 

focus of forest conservation efforts in Hawaii, insect and disease 

pests continue to pose significant risks to native forest species. 

Recent introductions (such as the erythrina gall wasp, ohia rust, and 

Myoporum thrips) demonstrate Hawaii’s vulnerability to pest and 

pathogen introductions. Introduction of exotic forest pests is facilitated 

by a high level of domestic imports (including 90% of Hawaii’s food) 

and the economically vital visitor industry. Once established, these 

pests and pathogens are difficult to control; prevention, therefore, is 

paramount.

Local and regional forest health specialists identified four insect 

and disease pests that have a significant impact on Hawaii’s forests: 

Myoporum thrips, erythrina gall wasp, koa wilt, and ohia rust 

(FIGURE 28, page 46).

Myoporum thrips (Klambothrips myopori)

The initial infestation of naio, Myoporum sandwicense, by Myoporum 

thrips, Klambothrips myopori, in Hawaii was reported in March 2009. 

Surveys are ongoing to determine the extent of its distribution on 

the island of Hawaii, where naio mortality has been observed. This 

thrips causes leaf curling and gall-like symptoms on infested plants, 

with high levels of infestation resulting in plant mortality. The loss 

of naio, a native, ecologically important species, would be particularly 

biologically detrimental where the plant is a key component of critical 

habitat for the palila, Loxioides bailleui, a federally-listed endangered 

species of honeycreeper on Mauna Kea.

Erythrina gall wasp (Quadrastichus erythrinae)

The erythrina gall wasp was first detected on Oahu in April 2005. 

Once introduced, this pest spread to all of the main Hawaiian 

Islands within six months, resulting in chronic defoliation and 

mortality of thousands of endemic wiliwili, Erythrina sandwicensis, 

an important tree species in Hawaii’s remaining lowland dry forests, 

recognized as one of the most endangered habitats in Hawaii. 

Erythrina gall wasps are very small, about the size of a typed 

comma; consequently, host injury (severe galling of multiple tissue 

types) is generally detected prior to the observation of adult wasps. 

Plant vigor declines from sequential defoliation, and mortality may 

be observed in one to two years.

Koa wilt (Fusarium oxysporum f. sp. koae)

Koa wilt, a vascular disease, has been found on the islands of Hawaii, 

Maui, Oahu, and Kauai. It is caused by the fungus Fusarium oxysporum

f. sp. koae, which is now commonly found in soils of the Hawaiian 

Islands and can kill trees of all ages. The fungus enters the roots 

and then colonizes the main stem’s conductive tissue. Water supply 

to leaves is cut off within infected trees, resulting in yellowing of 

leaves and crown wilt symptoms. Many plantation failures and high 

mortality rates of young trees have been observed. 

Continued, page 48
2013–2027 National Insect and Disease Forest Risk Assessment

46 Areas of Hawaii Likely to be Impacted by Ohia Rust and Koa Wilt

Ohia Rust

Moderate impact

Significant impact

FIGUR

E 28

Koa Wilt
2013–2027 National Insect and Disease Forest Risk Assessment

Areas of Hawaii Likely to be Impacted by Myoporum Thrips and Erythrina Gall Wasp 47

Myoporum thrips

Moderate impact

Significant impact

Erythrina Gall Wasp
2013–2027 National Insect and Disease Forest Risk Assessment

48 Results/Discussion

Ohia rust (Puccinia psidii)

Ohia rust, also known as guava rust, eucalyptus rust, and myrtle rust, 

is caused by the fungus Puccinia psidii, which was first detected in 

Hawaii in 2005. It occurs on all of the major islands and affects a 

large range of native and non-native plants in the Myrtaceae. The 

rust infects new foliage, and in some host’s reproductive tissues and 

green stems. Ohia (Metrosideros polymorpha) is an endemic tree that 

makes up most of the remaining native forest in Hawaii and is mildly 

susceptible to the rust. Although the rust does not cause dieback in 

mature trees, it can impact seedlings and hinder regeneration.

GREAT PLAINS

Most of the treed area of the Great Plains and adjacent prairies is 

located in riparian areas, along the edges of farmland, and around 

homesteads. Although this region is lightly forested, riparian forests, 

woodlots, and windrows play a critical role in protecting soil and 

water resources. We did not include these areas in the 2006 risk 

assessment due to the lack of FIA plots to account for them; by design 

we modeled risk only within the extent of forested areas. We derived 

the extent of forest from a national forest type dataset developed by 

the FIA and the Forest Service Remote Sensing Applications Center 

(RSAC) (Ruefenacht et al. 2008). 

In 2012, we did not limit the extent of risk models to the distribution 

of forest areas as defined by Ruefenacht et al. (2008). Rather, we 

included any areas where the presence of trees was recorded. This 

greatly improves the representation of treed areas and potential 

risk throughout the Great Plains. Because many of the tree species 

and forest types along riparian areas are similar to their eastern 

counterparts and contain some of the same agents, relevant eastern 

models, such as emerald ash borer, aspen, cottonwood, oak declines, 

and Dutch elm disease, were used for the Great Plains area and 

modified as necessary (TABLE 6). 

We were unable to model certain tree and forest parameters for 

non-native species, such as ponderosa and Austrian pine, planted 

as windbreaks and around homesteads. Thus, we were not able to 

model the risk to these unique treed areas for key pests of concern 

such as diplodia tip blight, dothistroma needle blight, and pine wilt.

URBAN AREAS

There are numerous social, ecological, and economic benefits of 

urban forests (Nowak et al. 2010), and it is important to assess the 

threats to them. Factors similar to those that inhibited our ability 

to properly model risk to the Great Plains in 2006 also apply to our 

ability to model risk to urban areas. Native insects, such as mountain 

pine beetle, can and do cause extensive damage in many urban areas 

(Ellig 2008). Introduction and establishment of exotic and invasive 

forest pests, such as Asian longhorned beetle and emerald ash borer, 

often begin in urban areas (FIGURE 29, page 49). Once established 

exotic and invasive forest pests can move into the wildland-urban 

interface and impact and spread through native forests. For the 

2012 NIDRM, we greatly improved upon our ability to conduct host 

mapping in urban areas. Although we still do not have adequate 

forest inventory plots in urban areas, our host maps do render urban 

forests in a more representative way. Because of this, our individual 

risk models do run across the rural-urban forest continuum 

NATIONAL PARKS

The improved resolution and coverage of the host layers provide 

an opportunity to generate risk maps at a scale useful for resource 

planning and management purposes in many of our National 

Parks. With the enhanced resolution we were able to estimate the 

proportion of most-at-risk tree species across National Parks such 

as Yellowstone and the Great Smoky Mountains. In Yellowstone, we 

estimate that 46% of whitebark pine basal area could be lost due to 

white pine blister rust and mountain pine beetle. In the Great Smoky 

Mountains 18% of hemlock and 26% of beech could be lost due 

to hemlock woolly adelgid and beech bark disease. Mountain pine 

beetle is the most destructive risk agent in Yellowstone with a 35% 

potential loss of the park’s total BA. In the Great Smoky Mountains, 

oak decline is the greatest hazard with potential losses of 31% of 

the park’s oak resource and 9% of its total BA. These mortality 

estimates by agent are combined to develop the overall composite 

risk of mortality (FIGURES 30, 31, pages 52, 53). A summary of 

mortality for the most significantly impacted national parks and 

national forests is provided in TABLES 7 and 8. Spreadsheets listing 

information for all national forests and parks, including details on 

impacts from individual agents, are available from the NIDRM 2012 

website http://www.fs.fed.us/foresthealth/technology/nidrm2012.

shtml. ♦

AGENT NAME

BA LOSSES

(Millions of Sq. Feet)

Oak decline and gypsy moth 31.8

Dutch elm disease 5.9

Emerald ash borer 4.5

Bur oak blight 2.3

Aspen and cottonwood decline 2.1

TABLE 6 Top mortality agents by rank for treed areas 

within the Great Plains and adjacent prairies

TABLE 6
2013–2027 National Insect and Disease Forest Risk Assessment

Potential for Emerald Ash Borer in the Chicago Area 49

FIGURE 29

 EAB Mortality Potential

Steven KatovichKenneth R. LawEric R. DaySteven Katovich
2013–2027 National Insect and Disease Forest Risk Assessment

50 National Park Units with the Most Significant Risk

*% treed = treed area/total unit area. The BA loss rate (sq. ft. per acre) uses treed area as its basis.

NATIONAL PARK SERVICE UNIT*

Treed area 

(1000 acres) % treed 

BA loss 

(1000 sq. ft.) % BA loss

BA loss rate

(sq. ft. per acre)

Risk area 

(1000 acres)

% of treed 

area at risk

ALASKA

Wrangell-St. Elias National Preserve 1928 40% 15,985 16% 8 375 19%

Lake Clark National Preserve 700 50% 3,954 17% 6 234 33%

Denali National Park 1587 34% 4,259 12% 3 183 12%

Wrangell-St. Elias National Park 1066 13% 6,559 16% 6 147 14%

Yukon-Charley Rivers National Preserve 1843 73% 4,863 7% 3 70 4%

Katmai National Park 728 20% 3,699 17% 5 60 8%

Lake Clark National Park 544 21% 2,057 9% 4 41 8%

INTERMOUNTAIN

Yellowstone National Park 1768 80% 33,830 40% 19 450 25%

Glacier National Park 871 86% 6,666 19% 8 110 13%

Rocky Mountain National Park 209 78% 5,044 33% 24 62 30%

Grand Canyon National Park 470 39% 3,668 18% 8 56 12%

Grand Teton National Park 191 61% 2,107 26% 11 37 19%

Canyon de Chelly National Monument 85 92% 1,155 21% 14 20 24%

Great Sand Dunes National Preserve 38 92% 1,120 31% 29 12 32%

John D. Rockefeller, Jr. Memorial Parkway 23 99% 380 28% 16 8 32%

MIDWEST

Ozark National Scenic Riverway 82 100% 1,229 14% 15 12 14%

Sleeping Bear Dunes National Lakeshore 56 80% 722 16% 13 10 17%

Buffalo National River 93 99% 1,253 13% 14 8 9%

Pictured Rocks National Lakeshore 65 88% 764 11% 12 6 9%

Voyageurs National Park 152 74% 525 6% 3 5 3%

Wind Cave National Park 21 62% 201 37% 9 5 23%

Missouri National Recreation River 37 54% 90 13% 2 5 13%

NATIONAL CAPITOL

Chesapeake and Ohio Canal National Historic Park 20 98% 134 10% 7 1.6 8%

NORTHEAST

Shenandoah National Park 193 100% 3,957 16% 20 43 22%

Saint Croix National Scenic River 91 93% 556 13% 6 12 13%

Cape Cod National Seashore 20 49% 230 28% 12 7 35%

Delaware Water Gap National Recreation Area 68 99% 768 12% 11 7 10%

New River Gorge National River 70 100% 1,107 11% 16 5 7%

Acadia National Park 38 97% 592 14% 16 3 8%

PACIFIC WEST

Yosemite National Park 689 92% 20,958 28% 30 286 41%

Kings Canyon National Park 318 69% 8,134 29% 26 120 38%

Sequoia National Park 328 81% 10,430 24% 32 98 30%

Crater Lake National Park 168 92% 5,303 18% 32 32 19%

Lassen Volcanic National Park 104 97% 2,470 23% 24 28 27%

Great Basin National Park 75 97% 872 13% 12 5 7%

North Cascades National Park 399 80% 1,047 3% 3 4 1%

Hawai’i Volcanoes National Park 62 17% n/a n/a n/a 3 4%

SOUTHEAST

Great Smoky Mountains National Park 516 100% 7,763 12% 15 41 8%

Blue Ridge Parkway 90 100% 1,213 12% 13 8 9%

Mammoth Cave National Park 52 100% 750 10% 15 4 8%

Cumberland Gap National Historical Park 25 100% 524 14% 21 3 14%

Big South Fork National River and Recreation Area 123 100% 1,471 11% 12 2 2%

TABL

E 7
2013–2027 National Insect and Disease Forest Risk Assessment

National Forests with the Most Significant Risk 51

TABLE 8

NATIONAL FOREST*

Treed area 

(1000 acres) % treed

Total BA loss

(1000 sq ft) BA loss rate

BA loss rate

(sq ft per acre)

Area at risk

(1000 acres)

% of treed 

area at risk

Northern (Region 1)

Nez Perce 2,210 98% 87,662 38% 40 1,339 61%

Lolo 2,599 99% 67,082 36% 26 1,198 46%

Clearwater 1,731 100% 55,254 31% 32 884 51%

Flathead 2,522 98% 46,786 28% 19 835 33%

Beaverhead 1,780 87% 48,014 39% 27 770 43%

Gallatin 2,103 91% 35,287 29% 17 683 32%

Kaniksu 1,707 98% 50,183 26% 29 673 39%

Kootenai 2,097 99% 48,432 24% 23 664 32%

Lewis and Clark 1,892 95% 34,570 27% 18 602 32%

Bitterroot 1,536 97% 31,463 34% 20 589 38%

Coeur d’Alene 801 100% 30,299 37% 38 541 68%

Rocky Mountain (Region 2)

Black Hills 1,461 96% 46,293 54% 32 786 54%

San Juan 1,948 94% 63,781 32% 33 706 36%

Rio Grande 1,694 91% 50,443 36% 30 489 29%

Pike 1,189 93% 36,877 37% 31 470 40%

Gunnison 1,671 92% 52,208 30% 31 442 26%

Roosevelt 1,046 97% 24,736 32% 24 384 37%

San Isabel 1,107 86% 32,134 33% 29 383 35%

Southwestern (Region 3)

Carson 1,487 93% 33,864 29% 23 442 30%

Santa Fe 1,534 96% 28,553 26% 19 426 28%

Intermountain (Region 4)

Toiyabe 3,443 78% 40,669 20% 12 731 21%

Payette 2,357 97% 20,796 21% 9 458 19%

Salmon 1,619 91% 20,931 24% 13 419 26%

Bridger 1,519 87% 27,126 31% 18 419 28%

Ashley 1,140 85% 31,129 35% 27 345 30%

Targhee 1,468 88% 16,768 23% 11 338 23%

Pacific Southwest (Region 5)

Modoc 1,743 88% 32,005 32% 18 675 39%

Lassen 1,333 97% 44,180 28% 33 651 49%

Sierra 1,317 93% 47,380 27% 36 480 36%

Tahoe 1,199 99% 31,494 18% 26 353 29%

Plumas 1,379 99% 34,569 18% 25 320 23%

Pacific Northwest (Region 6)

Fremont 1,651 96% 37,594 31% 23 906 55%

Deschutes 1,815 98% 38,564 25% 21 763 42%

Umatilla 1,493 99% 29,861 29% 20 701 47%

Whitman 1,536 98% 32,476 26% 21 695 45%

Winema 1,094 99% 30,796 27% 28 563 51%

Wallowa 885 83% 15,563 30% 18 470 53%

Malheur 1,273 98% 20,189 24% 16 460 36%

Southern (Region 8)

George Washington 1,746 99% 40,405 21% 23 522 30%

Eastern (Region 9)

Mark Twain 2,881 97% 39,716 16% 14 398 14%

Manistee 1,284 97% 16,150 19% 13 342 27%

*% treed = treed area/total unit area. The BA loss rate (sq. ft. per acre) uses treed area as its basis.
2013–2027 National Insect and Disease Forest Risk Assessment

52 Areas at Risk in Yellowstone National Park

WHITEBARK PINE LOSSES FROM ALL AGENTS

OVERALL RISK

FIGUR

E 30

TREE SPECIES*

Host’s % 

of Total 

BA

BA

per Acre

on Host

Extent

Host 

BA Loss 

Rate

Lodgepole pine 65.2% 43.6 51.8%

Whitebark pine 3.0% 10.7 46.3%

Douglas-fir 2.3% 16.2 20.9%

Engelmann spruce 11.9% 16.7 15.1%

Subalpine fir 15.1% 15.6 14.8%

Limber pine 0.2% 4.4 27.7%

Quaking aspen 0.01% 2.8 12.5%

ALL TREE SPECIES** 48.1 39.8%

 * Table only shows species with modeled BA losses.

** BA per Acre uses treed area as its basis. Host BA Loss uses 

 total BA (all host tree species) as its basis.

PEST OR PATHOGEN

(All numbers are rounded)

Host’s % 

of Total 

BA

Host 

BA Loss 

Rate

Total 

BA Loss 

Rate

Mountain pine beetle 68.5% 51.0% 34.9%

Root diseases 52.5% 2.9% 1.5%

Spruce beetle 11.9% 9.2% 1.1%

Balsam woolly adelgid 15.1% 4.8% 0.7%

Western spruce budworm

29.3% 1.4% 0.4%

Western balsam bark 

beetle 15.1% 2.2% 0.3%

Douglas-fir beetle 2.3% 13.1% 0.3%

White pine blister rust 3.2% 7.1% 0.2%

Dwarf mistletoes 67.8% 0.1% 0.1%

Aspen / cottonwood 

decline 0.01% 8.0% <0.05%

IMPACT FROM ALL AGENTS 39.8%

NIDRM risk of mortality

TOTAL BA LOSS FROM MOUNTAIN PINE BEETLE

1–25% 

26–50%

51–75%

> 75% 

Estimated BA Loss
2013–2027 National Insect and Disease Forest Risk Assessment

Areas at Risk in the Great Smoky Mountains National Park 53

FIGURE 31

TREE SPECIES* Host’s % of 

Total BA

BA per Acre 

on host 

extent

Host BA 

Loss Rate 

Red oak spp. 14.9% 23.7 34.1%

White oak spp. 13.7% 25.4 27.7%

American beech 2.9% 12.1 26.3%

Hemlock spp. 6.4% 13.6 18.1%

Eastern white pine 3.0% 16.5 9.4%

Ash spp. 0.4% 5.1 18.8%

Southern pines ( 9 species) 5.6% 22.9 1.9%

Sassafras 0.2% 3.4 8.7%

Sirex pines (10 species) 2.6% 14.2 1.6%

ALL TREE SPECIES** 130.8 11.5%

 * Table only shows species with modeled BA losses.

** BA per Acre uses treed area as its basis. Host BA Loss uses total BA (all host tree 

 species) as its basis.

PEST OR PATHOGEN

(All numbers are rounded)

Host’s % 

of Total 

BA

Host BA 

Loss Rate

Total BA 

Loss Rate

Oak decline 28.5% 30.8% 8.8%

Hemlock woolly adelgid 6.4% 18.2% 1.2%

Beech bark disease 2.9% 26.2% 0.8%

Southern pine beetle 5.6% 7.5% 0.4%

Ips spp. 5.6% 1.9% 0.1%

Emerald ash borer 0.4% 18.9% 0.1%

Oak wilt 14.9% 0.5% 0.1%

Root diseases 9.9% 0.6% 0.1%

Sirex woodwasp 2.6% 1.7% <0.05%

Laurel wilt 0.2% 4.0% <0.05%

IMPACT FROM ALL AGENTS 11.5%

HOST LOSS FROM OAK DECLINE

OVERALL RISK

HOST LOSS FROM HEMLOCK WOOLLY ADELGID

NIDRM risk of mortality

1–10% 

11–24%

> 25%

Estimated BA Loss
2013–2027 National Insect and Disease Forest Risk Assessment

54
2013–2027 National Insect and Disease Forest Risk Assessment

Accounting for future climAte 55

Apart from host parameters, climate data are the most 

significant type of criteria used in NIDRM models (TABLE 1, 

page 4).

Climate is generally defined as the average weather conditions of 

an area. In a broad sense, climate integrates a suite of dynamic 

environmental factors surrounding biotic communities, including 

the composition of the atmosphere, the temperature and moisture 

content of the air and soil, and the timing and severity of extreme 

weather events. In a narrower sense, climate can be characterized, 

at least in part, via metrics such as average temperature and 

moisture content (precipitation) for a region over a period of time.

Forest climates exert significant pressures on the population 

structure and dynamics of forest pest agents and their hosts, and 

on the relationships between agents and hosts. The concentration 

of atmospheric carbon dioxide (CO2

) affects plant photosynthetic 

rates and tissue quality, which in turn has cascading effects 

throughout the ecosystem’s food chain. Atmospheric pollutants 

can significantly affect soil chemistry in ways that can be either 

beneficial or detrimental to plants. Temperature is extremely 

important in controlling biotic metabolic rates and exerts 

strong influence over plant, insect, and pathogen development 

and reproductive rates. Timing, duration, and severity of cold 

conditions influence plant and insect cold-hardiness, phenology, 

and survivorship. Ecosystem moisture content strongly regulates a 

community’s productivity and, together with temperature, dictates 

moisture stress (drought) status, which in turn significantly affects 

the potential severity of insect and disease outbreaks. Severe 

weather events can cause widespread mortality of trees. 

Many of the relationships between climate and forest pests are 

well-understood and documented. For example, drought stress is 

long-recognized as contributing to the severity of many bark beetle 

outbreaks; precipitation and humidity affect the spread of foliage 

and root diseases; and the population dynamics of many insects is 

influenced by climatic trends. Although many of these climatepest relationships have been elucidated by forest ecologists, many 

others are poorly understood. 

An abundant body of scientific research suggests that we can 

expect significant shifts in many climate variables over the coming 

decades. Leading the way in summarizing climate research is 

the Intergovernmental Panel on Climate Change (IPCC), whose 

data have been made available via the Program for Climate 

Model Diagnosis and Intercomparison (PCMDI) Climate Model 

Intercomparison Project 3 (CMIP3) (https://esg.llnl.gov:8443/

index.jsp). According to the IPCC’s Fourth Assessment Report 

(AR4) (AR4; IPCC 2007), by the end of the 21st century North 

America is very likely to have experienced an increase in average 

temperature of approximately 4° C. While less certain, projections 

for precipitation over the next century indicate wetter conditions 

in some areas (e.g., the Northeast), drier conditions in others (e.g., 

the Southwest), and generally dryer summers throughout the 

coterminous United States. 

As discussed above, the 2012 NIDRM draws on historical (1971–

2000) climate datasets for risk-model criteria (see Forest Host-Tree 

Species Parameter Development, page 5). Therefore, risk model 

outputs assume that climate over the next 15 years will be more or 

less commensurate with historical climate levels. To the extent that 

this is not the case, that instead climate over the next 15 years may 

be significantly different than the 1971–2000 “climate normal” 

period, it is important to consider the possible effects of a climatealtered future in our estimation of risk.

We did not perform a comprehensive, future, climate-effects analysis 

as part of the 2012 NIDRM assessment; however, for some agents 

we conducted a preliminary analysis of the possible effects of future 

climate on forest-pest risk. Our analysis represents an introductory 

attempt to understand the importance of climate on various agenthost relationships, and to demonstrate the degree to which the 

individual NIDRM models can capture the effects of future climate.

Of the 186 models that comprise NIDRM, 85 (78 in the coterminous 

US) use one or more climate criteria as “drivers” of risk (TABLE 9). 

That some risk models neglected to include climate drivers should 

not be interpreted as an indication that climate is not an important 

driver in any particular agent-host relationship. Future climate 

datasets were not available during model development, and the 

idea of conducting future climate analysis had not been formally 

developed; thus, we did not consider future climate, per se, when 

we designed the individual pest and pathogen models. 

Our procedures for simulating future climate effects consisted 

simply of deriving projected future climate datasets (details 

provided below) and using them in place of historically based 

climate data. We did not attempt to adjust or amend 2012 NIDRM 

models to better account for climate drivers. We then re-ran the 

standard 2012 NIDRM models through RMAP using the future 

climate datasets. As a result, our analyses reflect future climate 

effects as predicted by only the subset of models that explicitly use 

climate criteria. Comparing the standard NIDRM results with the 

results from runs using future climate provides insights into how 

future climate may affect agent-host relationships, and also helps 

us evaluate our models, thereby allowing us to build better models 

in the future. We present results from two individual models. 

FUTURE CLIMATE DATASETS

Future climate datasets were obtained from the Forest Service 

Rocky Mountain Research Station (Coulson et al. 2010). Datasets 

include down-scaled (5 arc minute postings–approximately 

7-kilometer resolution) estimates of monthly climate variables for 

all months of the 21st century for the coterminous United States. 

Data were not available for Alaska and Hawaii. Variables include 

estimates for average monthly and annual maximum temperature 

(Tmax), minimum temperature (Tmin), average temperature (Tavg), 

precipitation (PPT), and potential evapotranspiration (PET). Data 

are expressed both as deltas, representing a change from historical 

(1971–2000) averages, and as future estimates per se, (i.e., as an 

estimate of the metric itself [e.g., PPT], derived by applying the 

delta to the historical average metric at each location). The available 

data are derived from CMIP3-published data and represent output 

from a number of different general circulation models run under 

different CO2

 emission scenarios. We used data from the Canadian 

Centre for Climate Modeling and Analysis, Third Generation 

Coupled Global Climate Model Version 3.1 medium resolution: 

emission scenario A1B (http://www.cccma.ec.gc.ca/data/cgcm3/

cgcm3.shtml). A discussion of the datasets and the downscaling 

procedures can be found in Joyce et al. (2011).
2013–2027 National Insect and Disease Forest Risk Assessment

56 Agents and Hosts Modeled in NIDRM that Include Climate Drivers

AGENT HOST(S) Number of 

models

CLIMATE CATEGORY*

Drought Precip. Temp.

Aspen decline Quaking aspen 2 yes (1) no yes (1)

Balsam woolly adelgid Balsam fir, grand fir, subalpine fir 4 no no yes

Blight Bur oak 1 yes no no

Douglas-fir beetle Douglas-fir 1 yes no no

Fir engraver California red fir, grand/white fir spp. 5 yes (5) yes (4) no

Goldspotted oak borer California black oak, California live oak, canyon live oak 3 yes no no

Hemlock woolly adelgid Eastern hemlock, Carolina hemlock 1 no no yes

Engraver beetles [Ips spp.] Ponderosa pine, southern pines, pinyon spp. 7 yes (7) yes (2) no

Jeffrey pine beetle Jeffrey pine 1 yes no no

Mountain pine beetle Limber pine, lodgepole pine, ponderosa pine, sugar pine, 

whitebark pine 6 yes (5) yes (1) no

Root disease Grand/white/Douglas-fir spp., subalpine fir, fir spp., mountain 

hemlock, Jeffrey pine, ponderosa pine, western red cedar 12 no yes (11) yes (12)

Roundheaded pine beetle Ponderosa pine 3 yes no no

Sudden oak death California black oak, California live oak, tanoak 6 no yes yes

Western balsam bark beetle Subalpine fir 6 yes no no

Western pine beetle Coulter pine, ponderosa pine 9 yes no no

Western spruce budworm Grand/Douglas-fir spp. 1 no yes yes

White pine blister rust Limber pine, Rocky Mountain bristlecone pine, sugar pine, 

western white pine, whitebark pine, eastern white pine 9 no yes (8) yes (8)

Winter moth Oak spp. 1 no no yes

* The number of models using criteria from the climate category is shown in parentheses.

For this analysis, climate data projections representing the 15-

year period, 2012–2026 were used. Temperature and precipitation 

deltas averaged over this period were applied to their corresponding 

NIDRM historical climate data-layers to derive future estimates 

(both annual and monthly) for Tmax, Tmin, Tavg and PPT. The 

future monthly estimates of PET and PPT data (for each month 

in the 15-year period) were used to derive projected, 15-year, 

drought-index and drought-frequency data layers using methods 

similar to those used to generate the standard historically based 

NIDRM drought layers (Koch et al. in press). All available climate 

datasets were resampled to a 240-meter resolution for inclusion 

within RMAP.

FUTURE CLIMATE PREDICTIONS

Our future climate datasets exhibit slight but significant shifts in 

temperature and precipitation, relative to the historical datasets. 

Our mean annual temperature (MAT) dataset indicates increased 

temperatures throughout the coterminous United States (FIGURE

32). Our mean annual precipitation (MAP) dataset indicates 

decreased precipitation in much of the Interior West and Southwest, 

and increased amounts in most of the Pacific Northwest and areas 

east of the Mississippi (FIGURE 33). The future drought layers 

indicated significantly greater frequency of drought (data not 

shown). How all of these trends translate into levels of risk to 

trees from insect and pathogen agents depends upon what specific 

climate variable(s) are used to model risk, and the magnitude of 

the variable(s) relative to the levels that constitute risk. We present 

two examples, armillaria root disease in the west and hemlock 

TABL

E 9

woolly adelgid in the east, to demonstrate how subtle shifts in one 

or two climate variables can result in changes in modeled risk and 

mortality.

Armillaria Root Disease (genus Armillaria)

One NIDRM root disease model in the Pacific Northwest represents 

Armillaria root disease in grand fir and Douglas-fir hosts, and 

represents three ecoregions occupying parts of northeast Oregon 

and southeast Washington. This model utilizes two climate variables, 

MAP and MAT, in addition to two forest-parameter variables and 

a soil drainage index. In this model, the risk-response curves for 

MAP and MAT dictate increasing risk as the variable increases 

above a threshold, plateaus at some point, and then decreases as the 

variable continues to increase. The plateau levels (range of values 

where risk remains maximized) for MAP are ~ 35–50 inches of 

precipitation per year, and for MAT, ~ 42–45° F.

Modeled basal area (BA) mortality from this model under future 

climate varies relative to mortality under historical climate, with 

mortality rates increasing in some locations and decreasing in other 

locations (FIGURE 34A). This varied response reflects the variable 

nature of the risk-response curves and the distributions of MAP and 

MAT. Areas of decreasing mortality under future climate generally 

fall in locations where MAT is transitioning to temperatures higher 

than those corresponding to the plateau of the risk-response curve 

(i.e. greater than ~ 45° F) (FIGURE 34B). Areas with increasing 

mortality are generally in locations where future MAP is increasing 

within the upward portion of the risk-response curves (i.e between 

21 and 35 inches of MAP/yr.; FIGURE 34C).
2013–2027 National Insect and Disease Forest Risk Assessment

Accounting for future climAte 57

FIGURE 32 Estimated increase in mean annual temperature

FIGURE 33 Estimated change in mean annual precipitation

FIGUR

ES 32, 33

.6

.7

.8

.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1.7

1.8

1.9

2.0

2.1

2.2

2.3

2.4 

Degrees F

20–30% drier than historical norms

10–20% drier

5–10% drier

1–5% drier

Little predicted change

1–5% wetter than historical norms

5–10% wetter

10–16% Wetter

Change in precipitation
2013–2027 National Insect and Disease Forest Risk Assessment

58 Accounting for Future Climate–Armillaria Root Disease

FIGURE 34

 

A Change in modeled mortality from Armillaria root disease (ARD)

Percent BA mortality (future climate) minus % BA mortality (historical). Hosts are grand fir and Douglas-fir.

B Future MAT and areas of decreased mortality from ARD

Blue pixels represent areas modeled as having less mortality from ARD under future climate relative to historical climate. 

White areas represent the future climate scenario 45–47° F MAT.

Less BA loss in future climate

57–78° F

52–57° F

47–52° F

45–47° F

40–45° F

35–40° F

9–35° F

Decreased mortality and temperature

More BA loss in future climate

> 45”

40–45”

35–40”

20–35”

15–20”

10–15”

< 10” Annual precipitation

Increased mortality and precipitation

C Future MAP and areas of increased mortality from ARD

Red pixels represent areas modeled as having more mortality from ARD under future climate relative to historical climate. 

White areas represent the 20–35 inch MAP band.

MODELED LOCATION

Change in estimated mortality

Increased mortality

2%

1%

Little or no change

1%

2%

Decreased mortality
2013–2027 National Insect and Disease Forest Risk Assessment

Accounting for future climAte 59

FIGURE 35

Hemlock Woolly Adelgid (Adelges tsugae)

The NIDRM model for hemlock woolly adelgid (HWA) in 

hemlock species in the Northeast utilizes one climate criterion, 

January Tmin (FIGURE 35), along with two forest-parameter 

layers and an HWA range distribution layer. It was run in 24 

ecoregions. The model’s risk-response curve for January Tmin 

dictates that risk begins at ~ 10° F, and increases as temperature 

increases, reaching a maximum near 15° F. 

Projected BA mortality in hemlock from HWA under future climate 

increases (overall) relative to the standard NIDRM run, with individual 

pixel changes (future climate mortality rate minus historical climate 

mortality rate) ranging from 0–5% BA. This follows from the 

January Tmin never being lower in any location in the future climate 

dataset relative to the historical dataset (data not shown). Bands 

of increased mortality fall in areas where the future climate January 

Tmin is in the 10–15° range (FIGURE 35).

The two examples above (ARD and HWA) demonstrate a 

contrasting set of agents and risk-response types. HWA is an exotic 

agent from the eastern United States, which had a unidirectional 

response to one climate variable. Armillaria root disease is an agent 

native to the western United States, which had a varied response 

to two climate variables. These two responses typify those seen 

across the suite of NIDRM models that use climate criteria to 

estimate risk and mortality (TABLE 9, page 56).

As for the other NIDRM models utilizing climate criteria: Nearly 

all of the bark beetle models, typically drought-driven, exhibited 

significant increases in projected mortality rates under future 

climate. Drought-driven aspen decline models likewise indicated 

significant increases in mortality in the central and southern 

portions of the Interior West. Other agents, whose NIDRM 

models are in part temperature-driven, that exhibit increased 

mortality under future climate include root diseases, white pine 

blister rust, balsam woolly adelgid, and western spruce budworm, 

predominantly in California, the Pacific Northwest, and northern 

parts of the Interior West.

A number of models predicted decreased levels of mortality 

under future climate. In the western United States, most of the 

projected decreases in mortality were attributable to various root 

disease models that use various temperature and precipitation 

(annual or seasonal) climate drivers. Sudden oak death in coastal 

California was projected to be lower in some locations under 

future climate conditions. Other models exhibiting decreased 

mortality under future climate include those for bur oak blight 

in the Midwest, western spruce budworm in Oregon, and white 

pine blister rust in eastern white pine in the East.

Increased hemlock loss In future climate

10–15° F min January temperature (historical)

10–15° F min January temperature (future)

Increased BA loss relative to January Temperatures

FIGURE 35 January Tmin and areas of increased mortality from hemlock woolly adelgid (HWA) under future climate*

*Red pixels indicate where BA mortality from HWA in hemlock species increases under warmer future climate relative to the NIDRM model 

that used historical climate data
2013–2027 National Insect and Disease Forest Risk Assessment

60 Accounting for future climAte

The analysis we present here is only a partial assessment of the 

possible effects of future climate conditions on forest pest dynamics. 

This assessment demonstrates one approach for performing a 

climate change analysis via the use of climate-driven models 

and data from different future-climate scenarios. The projected 

changes in BA mortality under the future climate scenario reflect a 

number of modeling constructs and assumptions, including those 

embodied within the particular future climate dataset(s) used to 

“drive” the models, as well as the model criteria thresholds used to 

define risk. Although many NIDRM models use climate criteria 

as agent-related drivers of mortality, many of the relationships 

are poorly understood. Further, there are many agent-climatehost relationships that the current suite of NIDRM models fails 

to capture. Future modeling efforts can bring additional ecoclimatological information to bear. For example, phenology 

models, describing insect developmental rates as a function of 

variables such as degree-days, together with improved future 

climate predictions might improve our estimates of the effects of 

future insect outbreak severity.

Land managers and policymakers now operate in a period of 

rapidly changing climate. Although predictions about the timing 

and magnitude of near-future climate changes have inherent 

uncertainty, it is clear that local future climates will differ from 

historical norms. All forest risk modeling efforts that seek to 

provide guidance over medium-term planning horizons (e.g., 

10–30 years) will increasingly depend upon the precision and 

accuracy of future-climate models. To the extent that the RMAP 

models accurately embody effects of climate drivers on agenthost relationships, and the future climate data we used accurately 

portray the direction and magnitude of possible climate shifts, 

these preliminary results indicate that considering future climate 

clearly matters to our estimation of future risks. We intend to 

continue to test and apply climate change analyses to the NIDRM 

results to more fully capture the magnitude and range of possible 

effects on those results. ♦
2013–2027 National Insect and Disease Forest Risk Assessment

ConClusion 61

Over the last eight years, the NIDRM process has been 

guided by a philosophy of continuous quality improvement. 

The 2006 NIDRM focused on bringing FIA inventory data 

into the analysis and developing a modeling process that was more 

transparent and repeatable. For the 2012 NIDRM, we maintained 

those original achievements and redirected some of our effort toward 

improving the host data maps, automating the modeling process, 

and improving access to modeling expertise and data. 

NIDRM integrates well with a larger system of annual forest pest 

and pathogen damage information (FIGURE 36) and current pest 

and pathogen range information provided through the Forest Health 

Protection Mapping and Reporting Portal (http://foresthealth.

fs.usda.gov). Forest health managers may want to prioritize 

remediation efforts by combining NIDRM with insect and disease 

survey data to target areas with both recent forest pest damage and 

high hazard ratings. NIDRM host layers and model outputs can 

inform existing vegetation classification mapping and inventory 

as well as hazardous fuel reduction efforts. While not designed to 

predict forest pest and pathogen hazard relative to changing climate, 

NIDRM is climate-sensitive and outputs can be adjusted to reflect 

differing climate scenarios. 

Ultimately, the 2012 NIDRM is more than a map: It is a strategic 

hazard assessment with national, regional, and local applications. The 

goal of NIDRM is to identify landscape-level patterns of potential 

forest insect and disease activity. This goal is consistent with the FHP 

philosophy that science-based, transparent methods should be used 

to allocate management resources across geographic regions and 

individual pest distributions. In other words: Prioritize investment 

for areas where both hazard is great and effective treatment can be 

efficiently implemented. ♦

FIGURE 36 2012 National Insect and Disease Survey by watersheds

FIGURE 36
2013–2027 National Insect and Disease Forest Risk Assessment

62 National 2012 Composite Insect and Disease Risk Map–Alaska and Hawaii

9.5 million acres at risk in Alaska

0.4 million acres at risk in Hawaii

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

Risk of mortality

Treed areas
2013–2027 National Insect and Disease Forest Risk Assessment

63

Forest Insect and Disease

Risk Map Atlas

The Forest Insect and Disease Risk Map Atlas is a standalone compendium that includes several maps presented in this report and two maps for each risk agent or agent group 

examined in this assessment, summarized by watershed. The risk 

agent maps are arranged in alphabetical order by common name. 

This atlas serves as a tool and reference guide for resource managers 

to quickly identify priority and threatened watersheds and their causal 

agent(s). Watersheds provide a broad overview from which patterns 

across a large landscape can be identified easily.

The first three sets of maps show composite risk of mortality across 

all agents. These maps are shown in the main body of the report 

and are reprinted here for convenient comparison with the individual 

causal agent maps that complete this atlas. Following the standard 

pixel-based composite risk map is a watershed summary based on 

the percentage of treed area at risk. The third map ranks watersheds 

by the cumulative basal area (BA) loss from all agents. 

Following the composite maps are two tables that summarize the 

models behind the Risk Maps. TABLE A (pages 72–73) summarizes 

the criteria (i.e., drivers for risk) used to model each risk agent or 

agent group and describes the importance of these criteria. It also 

lists the maximum realizable mortality rate(s) used in the risk models. 

The maximum realizable mortality rate is a mortality ceiling assigned 

to each risk agent model that defines the level (rate) of host BA 

mortality for the modeled agent that will be attributed to a pixel 

when all of the risk criteria are at their maximum—i.e. have a risk 

score of 10 for every criterion (on a 0–10 risk scale, see Krist et 

al. 2007). The BA mortality rates for risk scores lower than 10 are 

linearly scaled between 0% and the maximum realizable mortality 

rate. For example, if the maximum realizable mortality rate is 

80%, then pixels having the highest possible risk score of 10 would 

experience 80% host mortality; pixels having a risk score of 5 would 

experience a 40% mortality rate, etc. TABLE B (page 74) provides a 

crosswalk between the pests and pathogens and their individual tree 

species hosts, as modeled in the 2012 NIDRM.

We provide two watershed-based maps for each causal agent.

1. Classification of watersheds based on the percentage of 

projected BA loss. Each summarizes the relative impact of that 

pest or pathogen and allows comparison between watersheds 

ranging from sparsely to heavily forested.

2. Ranking of watersheds by the absolute amount of BA loss. 

These identify the watersheds where BA loss potential is 

greatest.

The classification maps are based on the percentage of BA loss, so 

they are helpful for understanding the relative ecological impact of 

individual agents on overall stand ecology. In contrast, the ranking 

maps are useful for identifying those watersheds with the greatest 

BA losses due to each mortality agent, regardless of how much BA 

loss is attributed to each pest or pathogen. For example, while the 

classification map clearly shows that mountain pine beetle is a much 

more wide-ranging and devastating tree-mortality agent than sirex 

woodwasp, the ranking maps make it easy for the user to identify the 

watersheds where risk of mortality from each agent is greatest.

The classification maps are calculated by summing the BA loss for 

each risk agent and the total BA for all tree species in every watershed. 

The summed BA loss by risk agent is then divided by the total BA to 

determine the proportion of total BA that may be lost over the next 

15 years. Proportions are then grouped within five categories: Host 

extent but little or no BA loss, 1–4% loss, 5–14% loss, 15–24% loss, 

and 25–100% loss.

The ranking maps are calculated by summing the BA loss for each 

risk agent in every watershed. Watersheds with a total of 5 square 

feet or less of BA loss (per watershed) are set to zero to eliminate 

noise in the final maps. All watersheds are then grouped and ranked 

into 100 BA loss classes through an equal-area stretch. The equalarea stretch assigns each watershed to a BA loss class and ensures that 

a nearly equal number of watersheds occupy each class. Watersheds 

are assigned to only one of the 100 classes and are not split among 

classes. Depending upon watershed size, some classes may contain 

slightly more area than other classes. Finally, the 100 classes are 

grouped into five categories based on their ranking in the BA loss 

distribution: Host present with < 5 square feet of BA host loss, the 

49% least impacted watersheds, 50%–74% , 75%–95%, and the top 

5% of most severely impacted watersheds.

The CHARTS presented in the Atlas (page 75) show, by risk agent, the 

BA loss rate of the most severely impacted watersheds on the ranking 

maps (i.e. the top 5% category). The ranking maps appear similar in 

that they depict the same amount of area in each impact-category 

regardless of the agent’s absolute mortality level. Accordingly, we 

include a miniaturized version of this chart on all ranking maps to 

provide context about the overall impact of each pest or pathogen. 

The Great Plains and its adjacent prairies are lightly forested. When 

depicted on national maps, it is difficult to differentiate between 

rankings of the most severely impacted areas and watersheds with 

little or no loss. Therefore, several key pests projected to impact 

treed areas of the Great Plains were re-mapped so as to restrict 

the rankings to only those watersheds within the Great Plains and 

adjacent prairie ecosystems. 

Basal area estimates were not calculated for Hawaii. Instead, pest and 

pathogen maps for Hawaii are presented using 30-meter resolution 

cells that represent areas at risk due to each agent. ♦
2013–2027 National Insect and Disease Forest Risk Assessment

64 National 2012 Composite Insect and Disease Risk Map

71.7 million acres at risk in the coterminous United States
2013–2027 National Insect and Disease Forest Risk Assessment

65

Risk of mortality

Treed areas
2013–2027 National Insect and Disease Forest Risk Assessment

66 Percentage of Treed Area at Risk by Watershed
2013–2027 National Insect and Disease Forest Risk Assessment

67

Little to no risk

1–4%

5–14%

15–24%

25% or greater
2013–2027 National Insect and Disease Forest Risk Assessment

68 P

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

Little to no risk

1–4%

5–14%

15–24%

25% or greater
2013–2027 National Insect and Disease Forest Risk Assessment

W 69

Universal Transverse Mercator Projection

Albers Conic Equal-Area Projection

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

70 Watersheds Ranked by Basal Area Loss Hazard
2013–2027 National Insect and Disease Forest Risk Assessment

71

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

72 Criteria Used to Model Risk Agents and Their Importance in Model Inputs

TABLE 

A

# of 

Models

Tree Species Parameters

Climate

Proximity to 

Infestation

Physiographic

Other

*Max.

Mort.

Diameter Rate

Basal Area/

Density

Host

 Prevalence Soils Topography

9 ALASKA

1 Dwarf mistletoe mod 1 mod 1 mod 1 high 1 6

1 Eastern larch beetle v. high 1 v. high 1 50

1 Northern spruce engraver mod 1 low 1 mod 1 mod 1 mod 1 25

1 Spruce aphid low 1 mod 1 mod 1 mod 1 mod 1 2

2 Spruce beetle mod 2 mod 1 mod 2 v. high 1 low 2 high 1 40

2 Stem rot v. high 2 v. high 2 2

1 Yellow-cedar decline mod 1 mod 1 mod 1 high 1 mod 1 21

5 HAWAII

1 Erythrina gall wasp ext. high 1 mod 1 low 1 45

1 Koa Wilt v. high 1 low 1 mod 1 40

2 Myoporum thrips ext. high 2 90

1 Ohia rust v. high 1 v. high 1 40

50 WEST

3 Balsam woolly adelgid mod 3 mod 3 high 1 mod 2 mod 2 30–50

3 Douglas-fir beetle ext. high 3 mod 1 mod 3 5–20

1 Douglas-fir tussock moth mod 1 mod 1 mod 1 mod 1 13

1 Engraver beetles Ips spp. high 1 mod 1 high 1 35

4 Fir engraver high 4 mod 4 high 4 mod 3 20–80

3 Goldspotted oak borer high 3 low 3 high 3 15–50

1 Jeffrey pine beetle mod 1 mod 1 mod 1 mod 1 40

10 Mountain pine beetle high 10 high 10 mod 8 mod 6 10–85

10 Root diseases mod 8 low 8 mod 10 mod 8 mod 8 low 7 ext. high 2 25–95

1 Spruce beetle high 1 mod 1 high 1 mod 1 80

6 Sudden oak death mod 3 v. high 6 mod 6 30–60

3 Western pine beetle mod 3 mod 3 mod 3 mod 3 30–60

1 Western spruce budworm high 1 low 1 mod 1 low 1 20

3 White pine blister rust mod 3 ext. high 3 low 3 30

86 INTERIOR WEST

3 Aspen and cottonwood 

decline v. high 2 mod 2 mod 1 ext. high 2 25–50

2 Balsam woolly adelgid high 2 ext. high 2 15–30

1 Douglas-fir beetle mod 1 mod 1 mod 1 low 1 60

3 Douglas-fir tussock moth mod 3 mod 3 mod 3 mod 3 mod 3 20

1 Dutch elm disease v. high 1 v. high 1 50

6 Dwarf mistletoe ext. high 6 v. high 1 2–8

5 Engraver beetles Ips spp. low 5 mod 5 high 5 mod 5 5–70

4 Fir engraver mod 4 mod 4 mod 3 v. high 1 mod 2 10–25

14 Mountain pine beetle high 14 high 14 high 14 5–80

19 Root diseases high 9 high 7 high 12 high 4 v. high 10 mod 10 mod 3 8–52

3 Roundheaded pine beetle mod 3 mod 3 mod 3 mod 3 5–25

1 Spruce beetle high 1 high 1 high 1 80

6 Western balsam bark 

beetle mod 6 low 6 mod 6 high 6 10–60

6 Western pine beetle mod 6 mod 6 mod 6 mod 6 5–60

5 Western spruce budworm v. high 5 v. high 5 3–5

7 White pine blister rust mod 3 ext. high 5 ext. high 3 10–30

*See footnote, page 73.
2013–2027 National Insect and Disease Forest Risk Assessment

Criteria Used to Model Risk Agents and Their Importance in Model Inputs 73

# of 

Models

Tree Species Parameters

Climate

Proximity to 

Infestation

Physiographic

Other

*Max.

Mort.

Diameter Rate

Basal Area/

Density

Host

 Prevalence Soils Topography

23 NORTHEASTERN AREA

2 Asian longhorned 

beetle ext. high 2 high 2 30-40

1 Balsam woolly adelgid high 1 high 1 high 1 35

1 Beech bark disease high 1 high 1 high 1 70

1 Bur oak blight ext. high 1 high 1 25

1 Dutch elm disease v. high 1 v. high 1 30

1 Eastern larch beetle high 1 high 1 high 1 50

2 Eastern spruce budworm high 2 v. high 2 mod 2 30-50

1 Emerald ash borer high 1 high 1 high 1 50

1 Forest tent caterpillar low 1 high 1 high 1 40

1 Hemlock woolly adelgid mod 1 mod 1 mod 1 mod 1 40

1 Jack pine budworm low 1 v. high 1 high 1 70

1 Maple decline low 1 mod 1 high 1 mod 1 low 1 50

2 Oak decline low 2 mod 2 low 2 mod 2 mod 2 60-70

1 Oak wilt low 1 low 1 mod 1 low 1 v. high 1 80

3 Root diseases high 3 high 3 high 3 50

1 Southern pine beetle mod 1 ext. high 1 mod 1 35

1 White pine blister rust high 1 mod 1 high 1 15

1 Winter moth low 1 mod 1 mod 1 mod 1 50

13 SOUTH

1 Engraver beetles Ips spp. mod 1 v. high 1 high 1 5

1 Fusiform rust mod 1 high 1 mod 1 40

2 Laurel wilt high 2 high 2 high 2 50-98

3 Oak wilt low 3 high 3 mod 2 high 3 mod 1 mod 1 15-50

2 Root diseases mod 2 mod 2 v. high 2 25-35

1 Sirex woodwasp mod 1 v. high 1 high 1 5

3 Southern pine beetle mod 3 ext. high 3 mod 3 25-35

186 Grand Total mod 122 mod 118 mod 109 high 85 high 74 mod 40 high 32 high 18

Weight Class Weight

low 0–15%

mod >15–30%

high >30–45%

very high >45–60%

extremely high >60%

Criteria used as inputs to the risk models (see Methods section, and TABLE 1, page 4, therein) are classified 

according to this table’s column headings. The “Weights” reported here represent the average weight, across 

models, given to the criteria in each region. For any one model, the sum of the weights equals 100%. Numerals 

next to weight classes (above) represent the numbers of models using criteria in the class.

*Maximum Mortality Rate is the range of maximum allowable host mortality rates across the suite of agent-specific models, by region. For maximum mortality 

rates by host/agent combination, see TABLE 5, page 38.

TABLE 

A, CONT.
2013–2027 National Insect and Disease Forest Risk Assessment

74 Host Species Modeled for Each Agent

TABL

E

B

AGENT MODELED HOSTS

Asian longhorned beetle Red maple and sugar maple

Aspen and cottonwood decline Cottonwood species and quaking aspen

Balsam woolly adelgid Balsam, grand, pacific silver and subalpine fir

Beech bark disease American beech

Bur oak blight Bur oak

Douglas-fir beetle Douglas-fir

Douglas-fir tussock moth Douglas-fir, grand fir, and white fir

Dutch elm disease American elm

Dwarf mistletoes1 Douglas-fir, western larch, western hemlock, and the following pines: lodgepole, limber, ponderosa, twoneedle 

pinyon, singleleaf pinyon, border pinyon, Mexican pinyon, and Arizona pinyon

Eastern larch beetle Tamarack (native)

Eastern spruce budworm Balsam fir and spruce species

Emerald ash borer Ash species

Engraver beetle (Ips spp.)2 White spruce and the following pines: eastern white, loblolly, longleaf, pitch pond, shortleaf, slash, Virginia, 

twoneedle pinyon, singleleaf pinyon, border pinyon, Mexican pinyon, Arizona pinyon, and ponderosa

Erythrina gall wasp Wiliwili

Fir engraver California red fir and grand/white fir species

Forest tent caterpillar Aspen species

Fusiform rust Slash pine and loblolly pine

Goldspotted oak borer California black oak, California live oak, and canyon live oak

Hemlock woolly adelgid Eastern hemlock and Carolina hemlock

Jack pine budworm Jack pine

Jeffrey pine beetle Jeffrey pine

Koa wilt Koa

Laurel wilt Redbay and sassafras

Maple decline Sugar maple

Mountain pine beetle Limber, lodgepole, ponderosa, southwestern white, sugar, western white, and whitebark pines

Myoporum thrips Naio

Oak decline and gypsy moth Red oak and white oak species

Oak wilt Live oak and red oak species

Ohia rust Ohia

Root diseases, all3 Spruce and fir species, Douglas-fir, mountain hemlock, Port-Orford-cedar, paper birch, western red cedar, and 

eastern white, jack, Jeffrey, longleaf, ponderosa, red, shortleaf, slash, and loblolly pines 

Roundheaded pine beetle Ponderosa pine

Sirex woodwasp Shortleaf, slash, pitch, pond, Virginia, jack, red, longleaf, loblolly and Scotch pines

Southern pine beetle Eastern white, longleaf, shortleaf, slash, loblolly, pitch, pond, and Virginia pines

Spruce aphid Sitka spruce

Spruce beetle Engelmann, Sitka, and white spruces

Stem rot Sitka spruce and western hemlock

Sudden oak death California black oak, California live oak, and tanoak

Western balsam bark beetle Subalpine fir

Western pine beetle Coulter and ponderosa pines

Western spruce budworm Engelmann spruce, Douglas-fir, grand fir, subalpine fir, and white fir

White pine blister rust Eastern white, limber, Rocky Mountain bristlecone, southwestern white, sugar, western white, and whitebark pines

Winter moth Oak species

Yellow-cedar decline Alaska yellow-cedar

1

Group includes American, Douglas-fir, hemlock, larch, limber pine, pineland and pinyon dwarf mistletoes.

2 Group includes Arizona fivespined ips, eastern fivespined ips, northern spruce engraver, pine engraver, pinyon ips, sixspined ips, small southern pine 

 engraver and three western species without common names; Ips latidens, Ips knausi and Ips integer.

3 Group includes annosus, armillaria, laminated root rot, and Port-Orford-cedar root diseases.
2013–2027 National Insect and Disease Forest Risk Assessment

Basal Area Loss Rates in Watersheds Most Impacted by Each Agent 75

C

HART

S

Coterminous United States

Alaska

A miniature version of these charts accompanies each of the following maps labelled, “Watershed Ranked by Basal Area Loss Hazard” 

(Hawaii and Great Plains maps excluded). In the miniature charts, the bar representing the agent depicted in the map is highlighted. The 

x-axis represents the average basal area mortality rate of the agent’s host tree species in the most severely impacted watersheds (top 5% 

category). See page 63 for details.
2013–2027 National Insect and Disease Forest Risk Assessment

76
2013–2027 National Insect and Disease Forest Risk Assessment

Index of Atlas Maps 77

MAP

S IND E

X

Risk Agents for Hawaii

Ohia rust (Puccinia psidii). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Koa wilt (fusarium oxysporum f. sp. koae). . . . . . . . . . . . . . . . . 78

Myoporum thrips (Klambothrips myopori). . . . . . . . . . . . . . . . 79

Erythrina gall wasp (Quadrastichus erythrinae). . . . . . . . . . . 79

Risk Agents for Alaska and the Coterminous United States

Asian longhorned beetle (Anoplophora glabripennis). . . . 80

Aspen and cottonwood decline. . . . . . . . . . . . . . . . . . . . . . . . . . . 82

Aspen and cottonwood decline — Great Plains. . . . . . . . . . . . 84

Balsam woolly adelgid (Adelges piceae). . . . . . . . . . . . . . . . 86, 88

Beech bark disease (Nectria faginata). . . . . . . . . . . . . . . . . . . . . . 90

Bur oak blight (Tubakia iowensis). . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Bur oak blight (Tubakia iowensis) — Great Plains. . . . . . . . . . . 94

Douglas-fir beetle (Dendroctonus pseudotsugae). . . . . . . . . . . 96

Douglas-fir tussock moth (Orgyia pseudotsugata). . . . . . . . 98

Dutch elm disease (Ophiostoma novo-ulmi). . . . . . . . . . . . . . . . . 100

Dutch elm disease (Ophiostoma novo-ulmi) — Great Plains. . . 102

Dwarf mistletoes (Arceuthobium spp.) — Alaska. . . . . . . . . 104

Dwarf mistletoes (Arceuthobium spp.). . . . . . . . . . . . . . . . . . . . 106

Eastern larch beetle (Dendroctonus simplex) — Alaska. . . 108

Eastern larch beetle (Dendroctonus simplex). . . . . . . . . . . . . 110

Eastern spruce budworm (Choristoneura fumiferana). . . . 112

Emerald ash borer (Agrilus planipennis). . . . . . . . . . . . . 114, 116

Emerald ash borer (Agrilus planipennis) — Great Plains. . . 118

Engraver beetles (Ips spp.). . . . . . . . . . . . . . . . . . . . . . . . . . . 120, 122

Engraver beetles — Northern Spruce Engraver

 (Ips perturbatus). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

Fir engraver (Scolytus ventralis). . . . . . . . . . . . . . . . . . . . . . . . . . . 126

Forest tent caterpillar (Malacosoma disstria). . . . . . . . . . . . . 128

Fusiform rust (Cronartium quercuum f. sp. fusiforme). . . . . 130

Goldspotted oak borer (Agrilus auroguttatus). . . . . . . . . . . . 132

Hemlock woolly adelgid (Adelges tsugae). . . . . . . . . . . . . . . . 134

Jack pine budworm (Choristoneura pinus). . . . . . . . . . . . . . . . 136

Jeffrey pine beetle (Dendroctonus jeffreyi). . . . . . . . . . . . . . . . 138

Laurel wilt (Raffaelea lauricola). . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Maple decline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

Mountain pine beetle (Dendroctonus ponderosae). . . . . . . 144

Oak decline and gypsy moth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

Oak decline and gypsy moth — Great Plains. . . . . . . . . . . . . 148

Oak wilt (Ceratocystis fagacearum). . . . . . . . . . . . . . . . . . . . . . . 150

Root diseases — all. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152, 154

Roundheaded pine beetle (Dendroctonus adjunctus). . . . 156

Sirex woodwasp (Sirex noctilio). . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Southern pine beetle (Dendroctonus frontalis). . . . . . . . . . . 160

Spruce aphid (Elatobium abietinum). . . . . . . . . . . . . . . . . . . . . . 162

Spruce beetle (Dendroctonus rufipennis). . . . . . . . . . . . . . . . . 164

Spruce beetle (Dendroctonus rufipennis) — Alaska. . . . . . . 166

Stem rot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

Sudden oak death (Phytophthora ramorum). . . . . . . . . . . . . 170

Western balsam bark beetle (Dryocoetes confusus). . . . . . 172

Western pine beetle (Dendroctonus brevicomis). . . . . . . . . . 174

Western spruce budworm (Choristoneura occidentalis). . . 176

White pine blister rust (Cronartium ribicola). . . . . . . . . 178, 180

Winter moth (Operophtera brumata). . . . . . . . . . . . . . . . . . . . . 182

Yellow-cedar decline. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
2013–2027 National Insect and Disease Forest Risk Assessment

78 Hawaii

Moderate impact

Significant impact

Ohia rust Puccinia psidii

Modeled host: ohia

Ohia rust was first detected in Hawaii in 2005. The rust occurs on all the major islands and affects many native and nonnative plants in the Myrtaceae family. The rust infects new foliage, and in some hosts the reproductive tissue and green 

stems. Ohia (Metrosideros polymorpha) is an endemic tree that makes up most of the remaining native forest in Hawaii and 

is mildly susceptible to the rust. 

Phil Cannon

Koa wilt Fusarium oxysporum f. sp. koae

Modeled host: koa

Koa wilt has been found on Hawaii (Big Island), Maui, Oahu, and Kauai. This vascular wilt disease is caused by a fungus that 

is now commonly found in soils of the Hawaiian Islands and can kill trees of all ages. The fungal pathogen enters the roots 

and then colonizes the main stem’s conductive tissue, resulting in yellowing of leaves and crown wilt. Many plantation 

Nick Dudley

failures and high mortality rates of young trees have been observed.
2013–2027 National Insect and Disease Forest Risk Assessment

79

Myoporum thrips Klambothrips myopori

Modeled host: naio

The initial infestation of naio, a native, ecologically important species, by myoporum thrips in Hawaii was reported in 2009. 

Surveys are ongoing to determine its extent. This thrips causes leaf curling and gall-like symptoms on infested plants, with 

high levels of infestation resulting in plant mortality. The loss of naio would be particularly detrimental where it is a critical 

University of California

habitat component for the palila, Loxioides bailleui, a federally endangered species of honeycreeper on Mauna Kea. 

Erythrina gall wasp Quadrastichus erythrinae

Modeled host: wiliwili

The erythrina gall wasp was first detected on Oahu in 2005. Once introduced to Hawaii, the wasp spread across all of the 

main islands, resulting in chronic defoliation and mortality of thousands of endemic wiliwili, Erythrina sandwicensis, an 

important tree species in Hawaii’s remaining lowland dry forests recognized as one of the most endangered habitats in 

Hawaii. Plant vigor declines from sequential defoliation and mortality may be observed in one to two years. 

Sheri Smith

Moderate impact

Significant impact
2013–2027 National Insect and Disease Forest Risk Assessment

80 Percentage of Total Basal Area Loss by Watershed

Asian longhorned beetle Anoplophora glabripennis

Modeled hosts: red maple, sugar maple, and other hardwoods

The Asian longhorned beetle is an introduced, destructive, wood-boring pest of maple and other hardwoods. The beetle 

was first discovered in the United States on trees in Brooklyn, New York, in 1996. Populations now exist in Massachusetts, 

New Jersey, New York, and Ohio.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Kenneth R. Law
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 81

Dennis HaugenE. Richard HoebekePennsylvania DNRMichael Bohne

<0.5%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

82 Percentage of Total Basal Area Loss by Watershed

Aspen and cottonwood decline

Modeled hosts: cottonwood species and quaking aspen

In various ecosystems in the western United States and Great Plains, aspen and cottonwood stands are declining at an 

alarming rate. Stem longevity and the associated pathological rotation age seem to be getting shorter. While we know 

cottonwood and aspen to be short-lived, stand decline seems to be occurring at an increasingly younger stand age. Pest 

complexes seem to be changing, with the worst impact on hottest and driest areas: low-lying, south-facing slopes. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss William M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 83 Doug Page William M. Ciesla

6%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

84 Percentage of Total Basal Area Loss by Watershed

Aspen and cottonwood decline — Great Plains

Modeled hosts: cottonwood species and quaking aspen

In various ecosystems in the western United States and Great Plains, aspen and cottonwood stands are declining at an 

alarming rate. Stem longevity and the associated pathological rotation age seem to be getting shorter. While we know 

cottonwood and aspen to be short-lived, stand decline seems to be occurring at an increasingly younger stand age. Pest 

complexes seem to be changing, with the worst impact on hottest and driest areas: low-lying, south-facing slopes. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss William M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 85 William M. Ciesla William M. Ciesla

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

86 Percentage of Total Basal Area Loss by Watershed

Balsam woolly adelgid Adelges piceae

Modeled hosts: balsam fir, grand fir, pacific silver fir, and subalpine fir

The balsam woolly adelgid is a tiny sucking insect that was introduced into North America from Europe in the mid-1950s. 

True firs in the United States have no natural defenses against this pest. In some areas, infestations have been so high, true 

firs have been eliminated entirely from the infested areas. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss USDA FS Southern Archive
2013–2027 National Insect and Disease Forest Risk Assessment

87

David BeckmanScott TunnockLeslie J. MehrhoffRobert L. Anderson
2013–2027 National Insect and Disease Forest Risk Assessment

88 Watersheds Ranked by Basal Area Loss Hazard

Balsam woolly adelgid Adelges piceae

Modeled hosts: balsam fir, grand fir, pacific silver fir, and subalpine fir

The balsam woolly adelgid is a tiny sucking insect that was introduced into North America from Europe in the mid-1950s. 

True firs in the United States have no natural defenses against this pest. In some areas, infestations have been so high, true 

firs have been eliminated entirely from the infested areas. 

Ronald F. Billings

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

89

Dawn Dailey O'BrienLadd LivingstonGSMNP ArchivesUSDA FS Southern Archive

5%
2013–2027 National Insect and Disease Forest Risk Assessment

90 Percentage of Total Basal Area Loss by Watershed

Beech bark disease Nectria faginata

Modeled host: American beech

In the United States, this disease results when the beech scale insect attacks the bark of beech, allowing the introduction 

of two species of fungi to invade the tree through the wound and cause a canker to be formed. As cankers continue to 

form, death of the tree can result. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Joseph O'Brien
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 91

Joseph O'BrienJoseph O'BrienLinda HaugenAndrej Kunca

3%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

92 Percentage of Total Basal Area Loss by Watershed

Bur oak blight Tubakia iowensis

Modeled host: bur oak

As a leafspot fungus that occurs only on bur oaks, this relatively new pest of the upper Midwest. It is associated with early 

spring rainfall and has been reported since the early 1990s. It is not clear if the pathogen is a recent arrival, or if a shift in 

climate has made this disease more noticeable. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Jill Pokorny
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 93

1%

Tom HarringtonTom HarringtonTom Harrington

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

94 Percentage of Total Basal Area Loss by Watershed

Bur oak blight — Great Plains Tubakia iowensis

Modeled host: bur oak

As a leafspot fungus that occurs only on bur oaks, this relatively new pest of the upper Midwest. It is associated with early 

spring rainfall and has been reported since the early 1990s. It is not clear if the pathogen is a recent arrival, or if a shift in 

climate has made this disease more noticeable.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Joseph O'Brien
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 95 Tom Harrington Jill Pokorny Tom Harrington

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

96 Percentage of Total Basal Area Loss by Watershed

Douglas-fir beetle Dendroctonus pseudotsugae

Modeled host: Douglas-fir

Douglas-fir beetle is the single most important bark beetle enemy of Douglas-fir. When outbreaks occur, this beetle can 

kill thousands of seemingly healthy Douglas-fir trees. During outbreaks, groups of trees, ranging from a few to several 

hundred, can be affected. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Malcolm Furniss
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 97

USDA FS Ogden ArchiveUSDA FS Ogden ArchiveWilliam M. CieslaSandy Kegley

10%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

98 Percentage of Total Basal Area Loss by Watershed

Douglas-fir tussock moth Orgyia pseudotsugata

Modeled hosts: Douglas-fir, grand fir, and white fir

The Douglas-fir tussock moth is an important defoliator of true firs, spruces, and Douglas-fir in western North America. 

Insect outbreaks occur rather suddenly; therefore, considerable effort is made to monitor this insect through the use of a 

west-wide system of pheromone traps. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss William M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 99

Dave PowellUSDA FS - NCRSWilliam M. CieslaDavid McComb

6%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

100 Percentage of Total Basal Area Loss by Watershed

Dutch elm disease Ophiostoma novo-ulmi

Modeled host: American elm

Dutch elm disease is one of the most destructive shade tree diseases in North America. The disease affects American 

elms—and other elm species to a lesser extent—killing individual branches and, eventually, the entire tree within one to 

three years. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss USDA FS Archive
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 101

Joseph O'BrienSandra JensenJoseph O'BrienMinnesota DNR Archive

3%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

102 Percentage of Total Basal Area Loss by Watershed

Dutch elm disease — Great Plains Ophiostoma novo-ulmi

Modeled host: American elm

Dutch elm disease is one of the most destructive shade tree diseases in North America. The disease affects American 

elms—and other elm species to a lesser extent—killing individual branches and, eventually, the entire tree within one to 

three years. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Edward L. Barnard
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 103

Joseph O'BrienPaul BachiFred Baker

UK Forestry Comm. Archive

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

104 Percentage of Total Basal Area Loss by Watershed

Dwarf mistletoes — Alaska Arceuthobium spp.

Modeled host: western hemlock

Dwarf mistletoes are the most important vascular plant parasites of conifers in the United States. These shrubby, aerial 

parasites are dispersed by birds or by seed dispersion through explosive fruits. Dwarf mistletoes are obligate parasites, 

dependent on their host for water, nutrients, and some or most of their food. Pathogenic effects on the host include 

deformation of the infected stem, growth loss, and increased susceptibility to other pests. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss USDA FS Archive
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 105

USDA FS ArchiveDavid ConklinWilliam JacobiUSDA FS - R2 Archive

5%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

106 Percentage of Total Basal Area Loss by Watershed

Dwarf mistletoes Arceuthobium spp.

Modeled hosts: Douglas-fir, western larch, western hemlock, lodgepole pine, limber pine, pinyon pine, and 

ponderosa pine 

Dwarf mistletoes are the most important vascular plant parasites of conifers in the United States. These shrubby, aerial 

parasites are dispersed by birds or by seed dispersion through explosive fruits. Dwarf mistletoes are obligate parasites, 

dependent on their host for water, nutrients, and some or most of their food. Pathogenic effects on the host include 

deformation of the infected stem, growth loss, and increased susceptibility to other pests. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss R.S. Peterson
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 107

USDA FS Ogden ArchiveRobert L. AndersonUSDA FS - R2 ArchiveUSDA FS - R2 Archive

1%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

108 Percentage of Total Basal Area Loss by Watershed

Eastern larch beetle — Alaska Dendroctonus simplex

Modeled host: tamarack (native)

The eastern larch beetle is a native North American insect that colonizes the phloem of the main stem, exposed roots, and 

larger branches of tamarack, or eastern larch. Extensive tree mortality has been reported throughout the range of eastern 

larch, and beetle outbreaks have been reported from the late 1800s. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Steven Katovich
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 109 Steven Katovich

7%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

110 Percentage of Total Basal Area Loss by Watershed

Eastern larch beetle Dendroctonus simplex

Modeled host: tamarack (native)

The eastern larch beetle is a native North American insect that colonizes the phloem of the main stem, exposed roots, and 

larger branches of tamarack, or eastern larch. Extensive tree mortality has been reported throughout the range of eastern 

larch, and beetle outbreaks have been reported from the late 1800s. 

Steven Valley

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 111

2%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

112 Percentage of Total Basal Area Loss by Watershed

Eastern spruce budworm Choristoneura fumiferana

Modeled hosts: balsam fir and spruce species

The eastern spruce budworm is one of the most destructive native forest defoliators and is responsible for shaping the 

stand composition and structure of northern spruce and fir forests in the eastern United States and Canada. 

USDA FS Ogden Archive

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 113

Joseph O'BrienJoseph O'BrienEdward H. HolstenSteven Katovich

9% Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

114 Percentage of Total Basal Area Loss by Watershed

Emerald ash borer Agrilus planipennis

Modeled hosts: ash species

Unequivocally a tree-killer in the United States, the emerald ash borer is by far the most destructive invasive exotic species 

to have arrived in North America in quite some time. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Pennsylvania DCNR
2013–2027 National Insect and Disease Forest Risk Assessment

115

Joseph O'BrienPennsylvania DCNREdward CzerwinskiDaniel Herms
2013–2027 National Insect and Disease Forest Risk Assessment

116 Watersheds Ranked by Basal Area Loss Hazard

Emerald ash borer Agrilus planipennis

Modeled hosts: ash species

Unequivocally a tree-killer in the United States, the emerald ash borer is by far the most destructive invasive exotic species 

to have arrived in North America in quite some time. 

Debbie Miller

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

117

Pennsylvania DCNRBrian SullivanChristopher AsaroJoseph O'Brien

3%
2013–2027 National Insect and Disease Forest Risk Assessment

118 Percentage of Total Basal Area Loss by Watershed

Emerald ash borer — Great Plains Agrilus planipennis

Modeled host: ash species

Unequivocally a tree-killer in the United States, the emerald ash borer is by far the most destructive invasive exotic species 

to have arrived in North America in quite some time. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Jared Spokowsky
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 119

Bill McNeeArt WagnerChristopher AsaroKenneth R. Law

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

120 Percentage of Total Basal Area Loss by Watershed

Engraver beetles Ips spp.

Modeled hosts: white spruce and the following pines: pinyon, ponderosa, shortleaf, slash, longleaf, loblolly, 

pitch, pond, Virginia, and eastern white

Engraver beetles belong to the Ips genus of beetles. These beetles are very common throughout the United States, though 

they only contribute to significant pine tree mortality during periods of drought or other environmental stress. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss A. Steven Munson
2013–2027 National Insect and Disease Forest Risk Assessment

121

Dave PowellRoger AndersonTia SmithWilliam M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

122 Watersheds Ranked by Basal Area Loss Hazard

Engraver beetles Ips spp.

Modeled hosts: white spruce and the following pines: pinyon, ponderosa, shortleaf, slash, longleaf, loblolly, 

pitch, pond, Virginia, and eastern white

Engraver beetles belong to the Ips genus of beetles. These beetles are very common throughout the United States, though 

they only contribute to significant pine tree mortality during periods of drought or other environmental stress. 

USDA FS Ogden Archive

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

123

Ronald F. BillingsRonald F. BillingsG. Keith DouceWilliam M. Ciesla

11%
2013–2027 National Insect and Disease Forest Risk Assessment

124 Percentage of Total Basal Area Loss by Watershed

Engraver beetle— Northern spruce engraver Ips perturbatus

Modeled host: white spruce

Engraver beetles belong to the Ips genus of beetles. These beetles are very common throughout the United States, though 

they only contribute to significant spruce tree mortality during periods of drought or other environmental stress. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Edward H. Holsten
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 125 Edward H. Holsten Edward H. Holsten Edward H. Holsten

15%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

126 Percentage of Total Basal Area Loss by Watershed

Fir engraver Scolytus ventralis

Modeled hosts: California red fir and grand/white fir species

Fir engravers are tree-killers of true firs, usually attacking pole-sized to saw-timber sized trees. Outbreaks are associated 

with drought and the presence of root diseases. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss USDA FS Ogden Archive
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 127

Donald OwenDave PowellFred HoningWilliam M. Ciesla

8%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

128 Percentage of Total Basal Area Loss by Watershed

Forest tent caterpillar Malacosoma disstria

Modeled hosts: aspen species

The forest tent caterpillar is native and may be found throughout the United States and Canada wherever hardwoods 

grow—from the Pacific Northwest to the South and the upper Midwest, and along the mid-Atlantic states to New England. 

The favored hosts of this insect are sugar maple, aspen, oaks, water tupelo, sweetgum, blackgum, cottonwood, elms, red 

alder, and willow.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Gerald J. Lenhard
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 129

USDA FS Southern ArchiveSandra JensenUSDA FS Southern ArchiveWhitney Cranshaw

4%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

130 Percentage of Total Basal Area Loss by Watershed

Fusiform rust Cronartium quercuum

Modeled hosts: slash pine and loblolly pine

Fusiform rust is a fungus that causes swellings, called galls, on branches and stems of pines. Mortality is greatest on young 

trees, but the rust galls and cankers deform and weaken older trees as well. The pathogen requires both pine and oak to 

complete its life cycle. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Steven Katovich
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 131

Robert L. AndersonTerry S. PriceUSDA FS Southern ArchiveRobert L. Anderson

1%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

132 Percentage of Total Basal Area Loss by Watershed

Goldspotted oak borer Agrilus auroguttatus

Modeled hosts: California black oak, California live oak, and canyon live oak

The goldspotted oak borer is native to oak forests of southeastern Arizona, and a closely related species is found in Central 

America. Since 2002, the borer is associated with the death of more than 80,000 trees, and this infested area continues to 

expand as borer populations grow and spread. The borer is not a pest in its native range. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Mike Lewis
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 133

Tom ColemanMark S. HoddleMark S. HoddleMike Lewis

2%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

134 Percentage of Total Basal Area Loss by Watershed

Hemlock woolly adelgid Adelges tsugae

Modeled hosts: eastern hemlock and Carolina hemlock

Few pests other than chestnut blight and Dutch elm disease have had such a marked effect on eastern forests. Left unchecked, the hemlock woolly adelgid will likely extirpate most of the native hemlock from eastern North America. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Chris Evans
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 135

USDA FS Southern ArchiveKelly OtenUSDA FS SRS ArchiveLorraine Graney

5%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

136 Percentage of Total Basal Area Loss by Watershed

Jack pine budworm Choristoneura pinus

Modeled host: jack pine

Jack pine budworm is a needle-feeding caterpillar and considered to be the most significant pest of jack pine. Stands older 

than 45 years, that are growing on very sandy sites and suffering from drought or other stresses, are very vulnerable to 

damage. Topkill and, ultimately, mortality result when stressed trees are attacked.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Steven Katovich
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 137

James B. HansonUSDA FS NA ArchiveMinnesota DNRJoseph O'Brien

5%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

138 Percentage of Total Basal Area Loss by Watershed

Jeffrey pine beetle Dendroctonus jeffreyi

Modeled host: Jeffrey pine

The Jeffrey pine beetle kills trees by mining between bark and wood and is the principal bark-beetle enemy of Jeffrey pine. 

The beetle has economic impacts chiefly in California; it is most destructive in older stands in the timber-producing areas 

of northeastern California. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Laura Merrill
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 139

Darren BlackfordTia SmithTia SmithDarren Blackford

6%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

140 Percentage of Total Basal Area Loss by Watershed

Laurel wilt Raffaelea lauricola

Modeled hosts: redbay and sassafras

Laurel wilt is a deadly disease of redbay and other tree species in the Laurel family, such as avocado. The disease is caused 

by a fungus introduced into host trees by the redbay ambrosia beetle (Xyleborus glabratus), a non-native insect. This 

disease is expected to extirpate redbay from southern forests.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Chip Bates
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 141

Albert (Bud) MayfieldRonald F. BillingsRonald F. BillingsR. Scott Cameron

3%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

142 Percentage of Total Basal Area Loss by Watershed

Maple decline

Modeled host: sugar maple

More sporadic and less extensive than oak decline, maple decline is associated with drought and harsh site or exposed 

conditions. Symptoms include slowed radial growth, crown dieback, attack by secondary organisms, and tree mortality.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Jason Sharman
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 143 Randy Cyr Jason Sharman

5%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

144 Percentage of Total Basal Area Loss by Watershed

Mountain pine beetle Dendroctonus ponderosae

Modeled hosts: limber, lodgepole, ponderosa, southwestern white, sugar, western white, and whitebark pine

The mountain pine beetle is the most important biotic change agent in western forests. This insect has been responsible 

for contributing to the death of many millions of acres of trees in lodgepole and ponderosa pine forests. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Dave Powell
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 145

Whitney CranshawSteven KatovichRonald F. BillingsWhitney Cranshaw

29%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

146 Percentage of Total Basal Area Loss by Watershed

Oak decline and gypsy moth

Modeled hosts: red oak and white oak species

Periods of local and regional occurrences of oak decline have been reported since the early 1900s. Trees weakened from 

environmental stresses, such as drought, phloem feeders, root pathogens, sucking insects, and defoliators (notably gypsy 

moth), experience reduced annual growth, canopy dieback, and death.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss USDA FS St. Paul Archive
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 147

Tim TignerTim TignerUSDA APHIS PPQ ArchiveWilliam M. Ciesla

12%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

148 Percentage of Total Basal Area Loss by Watershed

Oak decline and gypsy moth — Great Plains

Modeled hosts: red oak and white oak species

Periods of local and regional occurrences of oak decline have been reported since the early 1900s. Trees weakened from 

environmental stresses, such as drought, phloem feeders, root pathogens, sucking insects, and defoliators (notably gypsy 

moth), experience reduced annual growth, canopy dieback, and death. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Joseph O'Brien
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 149

Tim TignerWilliam M. CieslaJoseph O'BrienJon Yuschock

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

150 Percentage of Total Basal Area Loss by Watershed

Oak wilt Ceratocystis fagacearum

Modeled hosts: live oak and red oak species

Oak wilt is a vascular disease of oak that can quickly kill a tree. It is caused by the fungus Ceratocystis fagacearum. Symptoms 

vary by tree species but generally consist of leaf discoloration, wilt, defoliation, and death. This fungus spreads overland 

on the various insect species that fly to surface wounds or through underground root grafting.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss William M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 151

Joseph O'BrienPaul A. MistrettaJoseph O'BrienPaul A. Mistretta

4%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

152 Percentage of Total Basal Area Loss by Watershed

Root diseases — all

Modeled hosts: spruce and fir species, Douglas-fir, mountain hemlock, Port-Orford-cedar, paper birch, 

western red cedar, and the following pines: eastern white, jack, Jeffrey, longleaf, ponderosa, red, shortleaf, 

slash, and loblolly

Root diseases are the most damaging group of diseases affecting forest trees in the United States. Root diseases kill trees, 

decay wood, slow tree growth, predispose trees to other risk agents, and cause trees to fail or fall over. They impact timber 

volume, forest composition and structure, ecosystem function, personal safety, and carbon sequestration.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Joseph O'Brien
2013–2027 National Insect and Disease Forest Risk Assessment

153

Robert L. JamesAndrej KuncaUSDA FS ArchiveCathy Stewart
2013–2027 National Insect and Disease Forest Risk Assessment

154 Watersheds Ranked by Basal Area Loss Hazard Robert L. Anderson

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses

Root diseases — all

Modeled hosts: spruce and fir species, Douglas-fir, mountain hemlock, Port-Orford-cedar, paper birch, 

western red cedar, and the following pines: eastern white, jack, Jeffrey, longleaf, ponderosa, red, shortleaf, 

slash, and loblolly

Root diseases are the most damaging group of diseases affecting forest trees in the United States. Root diseases kill trees, 

decay wood, slow tree growth, predispose trees to other risk agents, and cause trees to fail or fall over. They impact timber 

volume, forest composition and structure, ecosystem function, personal safety, and carbon sequestration.
2013–2027 National Insect and Disease Forest Risk Assessment

155

John W. SchwandtAndrej KuncaJames W. BylerSusan K. Hagle

11%
2013–2027 National Insect and Disease Forest Risk Assessment

156 Percentage of Total Basal Area Loss by Watershed

Roundheaded pine beetle Dendroctonus adjunctus

Modeled host: ponderosa pine

This pine pest attacks ponderosa pine, primarily in the southwestern United States. Outbreaks of this beetle, at least in the 

recent past, are short-lived and sporadic. Damage is typically be found on ridgetops and other sites with very dry, sandy 

soils. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Steven Valley
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 157

USDA FS Southwestern RegionUSDA FS Southwestern RegionUSDA FS Southwestern RegionUSDA FS Ogden Archive

2%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

158 Percentage of Total Basal Area Loss by Watershed

Sirex woodwasp Sirex noctilio

Modeled hosts: shortleaf, slash, longleaf, loblolly, pitch, pond, Virginia, jack, red, and Scotch pines

The Sirex woodwasp is a species of horntail native to Europe, Asia, and northern Africa. This invasive species is established 

in many parts of the world, including Australia, New Zealand, North America, South America, and South Africa, where it 

can become a significant economic pest of pine. The wasp can attack a wide variety of pine tree species, and stressed trees 

are those most often attacked.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss William M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 159

William M. CieslaDennis HaugenDavid R. LanceVicky Klasmer

1%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

160 Percentage of Total Basal Area Loss by Watershed

Southern pine beetle Dendroctonus frontalis

Modeled hosts: eastern white, longleaf, shortleaf, slash, loblolly, pitch, pond, and Virginia pine

The southern pine beetle is the most destructive bark beetle in the eastern United States. Intensively managed forests and 

active prevention programs have minimized the impact of this potentially explosive pest.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Ronald F. Billings
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 161

USDA FS Southern ArchiveRonald F. BillingsErich G. ValleryRonald F. Billings

Host extent (little to no BA loss) 9%

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

162 Percentage of Total Basal Area Loss by Watershed

Spruce aphid Elatobium abietinum

Modeled host: Sitka spruce

The spruce aphid is thought to have been introduced to North America from Europe. Sitka, Norway, and blue spruce are 

preferred hosts, but other spruce species might also be attacked. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Elizabeth Willhite
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 163

Jill WilsonDonald OwenPetr KapitolaPetr Kapitola

3%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

164 Percentage of Total Basal Area Loss by Watershed

Spruce beetle Dendroctonus rufipennis

Modeled hosts: Engelmann spruce, Sitka spruce, and white spruce

The spruce beetle is the most significant biotic disturbance agent of mature spruce. Outbreaks of spruce beetles have 

dramatically changed the structure and composition of North American spruce forests. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss William M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 165 William M. Ciesla USDA FS Ogden Arcive Whitney Cranshaw

18%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

166 Percentage of Total Basal Area Loss by Watershed

Spruce beetle — Alaska Dendroctonus rufipennis

Modeled hosts: Sitka spruce, and white spruce

The spruce beetle is the most significant biotic disturbance agent of mature spruce. Outbreaks of spruce beetles have 

dramatically changed the structure and composition of North American spruce forests. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Andris Eglitis
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 167 Edward H. Holsten Whitney Cranshaw

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

168 Percentage of Total Basal Area Loss by Watershed

Stem rot

Modeled hosts: Sitka spruce and western hemlock

Stem rot fungi have a major impact on the forests of Southeast Alaska, where roughly a third of the old-growth timber 

volume of live trees is affected. These fungi predispose large old trees to bole breakage. Small-scale canopy gaps created 

by individual tree mortality events serve important ecological functions. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Paul E. Hennon
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 169

3%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

170 Percentage of Total Basal Area Loss by Watershed

Sudden oak death Phytophthora ramorum

Modeled hosts: California black oak, California live oak, and tanoak

Sudden oak death is a tree disease caused by the pathogen Phytophthora ramorum. The disease kills some oak species and 

tanoak, and has had significant effects on forests in California and Oregon. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Joseph O'Brien
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 171

Joseph O'BrienBruce MoltzanJoseph O'BrienJoseph O'Brien

15%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

172 Percentage of Total Basal Area Loss by Watershed

Western balsam bark beetle Dryocoetes confusus

Modeled host: subalpine fir

The western balsam bark beetle is the most conspicuous of a complex of pests which are responsible for high rates of tree 

mortality in sub-alpine fir stands from New Mexico and Arizona through the northern Rocky Mountains. Typically, infestations are chronic, contributing to high rates of subalpine fir mortality in the West. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Javier Mercado
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 173

Elizabeth WillhiteScott TunnockLadd LivingstonUSDA FS - Ogden Archive

2%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

174 Percentage of Total Basal Area Loss by Watershed

Western pine beetle Dendroctonus brevicomis

Modeled hosts: Coulter pine and ponderosa pine

Western pine beetle can be found throughout most of the native range of ponderosa pine and Coulter pine in California. 

As with most Dendroctonus beetles, this pest breeds in larger trees or in trees that have been stressed by drought, disease, 

fire, or overly dense conditions.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Erich G. Vallery
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 175

James EverittKenneth E. GibsonKenneth E. GibsonWilliam M. Ciesla

7%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

176 Percentage of Total Basal Area Loss by Watershed

Western spruce budworm Choristoneura occidentalis

Modeled hosts: Engelmann spruce, Douglas-fir, grand fir, subalpine fir, and white fir

The western spruce budworm is one of the most destructive forest defoliators in western North America. Outbreaks 

have occurred from the central Rockies in the United States to the Coast Mountains in British Columbia, Canada, and the 

panhandle of Alaska.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss William M. Ciesla
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 177

USDA FS Intermountain ArchiveDave PowellWilliam M. CieslaDavid J. Moorhead

3%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

178 Percentage of Total Basal Area Loss by Watershed

White pine blister rust Cronartium ribicola

Modeled hosts: eastern white, limber, Rocky Mountain bristlecone, southwestern white, sugar, western 

white, and whitebark pine 

White pine blister rust is probably the most destructive disease of five-needle pines in North America and is a major threat 

to high-elevation white pines in the western United States. The pathogen is believed to have originated in Asia. By the 

1950s it had spread to most of the commercial white pine regions. It became established in Europe as well, after large 

numbers of highly susceptible American white pines were imported and planted there. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Chris Schnepf
2013–2027 National Insect and Disease Forest Risk Assessment

179

Minnesota DNRUSDA FS Ogden ArchiveJohn W. SchwandtRalph Williams
2013–2027 National Insect and Disease Forest Risk Assessment

180 Watersheds Ranked by Basal Area Loss Hazard Susan K. Hagle

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses

White pine blister rust Cronartium ribicola

Modeled hosts: eastern white, limber, Rocky Mountain bristlecone, southwestern white, sugar, western 

white, and whitebark pine 

White pine blister rust is probably the most destructive disease of five-needle pines in North America and is a major threat 

to high-elevation white pines in the western United States. The pathogen is believed to have originated in Asia. By the 

1950s it had spread to most of the commercial white pine regions. It became established in Europe as well, after large 

numbers of highly susceptible American white pines were imported and planted there. 
2013–2027 National Insect and Disease Forest Risk Assessment

181

USDA FS Ogden ArchiveJoseph O'BrienH.J. LarsenJoseph O'Brien

1%
2013–2027 National Insect and Disease Forest Risk Assessment

182 Percentage of Total Basal Area Loss by Watershed

Winter moth Operophtera brumata

Modeled hosts: oak species

Winter moth is an insect pest that was introduced to North America from Europe. It is now established in eastern Canada, British Columbia, New England, and the Pacific Northwest. Various deciduous trees are susceptible, including oaks, 

maples, basswood, ash, crabapples, apple, blueberry, and certain spruces. Multiple defoliations or interactions with other 

stressors can lead to tree death.

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Dimitrios Avtzis
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 183

Dimitrios AvtzisMilan ZubrikDimitrios AvtzisMilan Zubrik

8% Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses
2013–2027 National Insect and Disease Forest Risk Assessment

184 Percentage of Total Basal Area Loss by Watershed

Yellow-cedar decline

Modeled host: Alaska yellow-cedar

Yellow-cedar decline is not well-understood, but is thought to be associated with root freezing that occurs during very 

cold weather when the ground is not insulated with snow. 

Host extent (little to no BA loss)

1–4%

5–14%

15–24%

25% or greater of total BA loss Paul E. Hennon
2013–2027 National Insect and Disease Forest Risk Assessment

Watersheds Ranked by Basal Area Loss Hazard 185 Paul E. Hennon

5%

Host extent (little to no BA loss)

Bottom 49%

50–74%

75–95%

Top 5%–highest estimated BA losses

Paul E. Hennon
2013–2027 National Insect and Disease Forest Risk Assessment

186 Photo Credits

PAGE PHOTO CREDIT (photos shown in order of appearance from left to right) DESCRIPTOR AND/OR DESCRIPTION

Asian longhorned beetle

80 Kenneth R. Law, USDA APHIS PPQ, Bugwood.org Adult–Asian longhorned beetle

81 Dennis Haugen, USDA Forest Service, Bugwood.org Damage–Infestation

81 E. Richard Hoebeke, Cornell University, Bugwood.org Adult–Damage

81 Pennsylvania Department of Conservation and Natural Resources–

Forestry Archive, Bugwood.org Damage

81 Michael Bohne, Bugwood.org Damage–Infested poplar

Aspen and cottonwood decline

82 William M. Ciesla, Forest Health Management International, Bugwood.org Symptoms

83 Doug Page, USDI Bureau of Land Management, Bugwood.org Stand–Droughty site and interaction of herbivory leading to decline in 

aspen clone in sagebrush

83 William M. Ciesla, Forest Health Management International, Bugwood.org Symptoms–Tree mortality in aspen stands

84 William M. Ciesla, Forest Health Management International, Bugwood.org Damage–Top dieback of aspen

85 William M. Ciesla, Forest Health Management International, Bugwood.org Damage–Aerial view of declining aspen stands

85 William M. Ciesla, Forest Health Management International, Bugwood.org Infestation–Aerial view of aspen decline

Balsam woolly adelgid

86 USDA Forest Service, Region 8, Southern Archive, USDA Forest Service, 

Bugwood.org Damage–Gouting of Frasier fir from balsam Woolly adelgid

87 David Beckman, Idaho Department of Lands, Bugwood.org Damage–Crown symptoms of subalpine fir being killed by balsam woolly 

adelgid

87 Scott Tunnock, USDA Forest Service, Bugwood.org Infestation

87 Leslie J. Mehrhoff, University of Connecticut, Bugwood.org Damage

87 Robert L. Anderson, USDA Forest Service, Bugwood.org Infestation–Stand mortality at Mt. Mitchell

89 Ronald F. Billings, Texas Forest Service, Bugwood.org Infestation at Mt. Mitchell, North Carolina

89 Dawn Dailey O’Brien, Cornell University, Bugwood.org Damage in the form of swollen, deformed branches

89 Ladd Livingston, Idaho Department of Lands, Bugwood.org Adult(s)–“Wool”-covered females as they appear during summer

89 Great Smoky Mountains National Park Resource Management Archive, 

USDI National Park Service, Bugwood.org Damage–Needle mortality

89 USDA Forest Service, Region 8, Southern Archive, USDA Forest Service, 

Bugwood.org Damage– Frazier fir killed by Balsam woolly adelgid

Beech bark disease

90 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Beech snap, in a stand affected with beech bark disease

91 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Tree mortality in a stand affected by beech bark disease

91 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Multiple cankers on American beech affected by beech bark 

disease

91 Linda Haugen, USDA Forest Service, Bugwood.org Symptoms–Cankered stem of beech following attack by beech scale and 

infection by Neonectria (beech bark disease complex) in Ontario

91 Andrej Kunca, National Forest Centre–Slovakia, Bugwood.org Fruiting bodies

Bur oak blight

92 Jill Pokorny, USDA Forest Service Bur oak blight leaf symptoms

93 Tom Harrington, Iowa State University Affected trees typically occur next to unaffected trees

93 Tom Harrington, Iowa State University Purple-brown lesions develop along the veins on the underside of leaves

93 Tom Harrington, Iowa State University Black pustules develop on infected leaf petioles

94 Joseph O’Brien, U.S. Forest Service Large areas of the leaf may die, resulting in an overall wilted or scorched 

appearance

95 Tom Harrington, Iowa State University

Black fruiting bodies form on dark leaf veins (lower right). Rain-splashed 

spores are produced under a protective hyphal shield (upper right, 

magnified 200X)

95 Jill Pokorny, U.S. Forest Service Dark veins and large wedge-shaped lesions develop

95 Tom Harrington, Iowa State University Many dead leaves remain on the tree throughout the winter

Douglas-fir beetle

96 Malcolm Furniss, Bugwood.org Larva(e)–Two pre-pupal larvae in the phloem

97 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Infestation

97 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Adult–Douglas-fir beetle
2013–2027 National Insect and Disease Forest Risk Assessment

187

PAGE PHOTO CREDIT (photos shown in order of appearance from left to right) DESCRIPTOR AND/OR DESCRIPTION

Douglas-fir beetle, continued

97 William M. Ciesla, Forest Health Management International, Bugwood.org Galleries

97 Sandy Kegley, USDA Forest Service, Bugwood.org Damage–Red-orange boring dust is evidence of successful attack

Douglas-fir tussock moth

98 William M. Ciesla, Forest Health Management International, Bugwood.org Adult–Douglas-fir tussock moth

99 Dave Powell, USDA Forest Service, Bugwood.org Damage–Indian Creek area, Heppner RD, Umatilla NF, 2001

99 USDA Forest Service–North Central Research Station Archive, USDA 

Forest Service, Bugwood.org Damage

99 William M. Ciesla, Forest Health Management International, Bugwood.org Mature larva

99 David McComb, USDA Forest Service, Bugwood.org Damage

Dutch elm disease

100 USDA Forest Service–Northeastern Area Archive, USDA Forest Service, 

Bugwood.org Symptoms

101 Joseph O’Brien, USDA Forest Service, Bugwood.org Early wilt symptoms

101 Sandra Jensen, Cornell University, Bugwood.org Fruiting Bodies–Synnemata

101 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–discoloration in wood; Vascular streaks caused by 

Ophiostoma ulmi in elm wood

101 Minnesota Department of Natural Resources Archive, Minnesota 

Department of Natural Resources, Bugwood.org Symptoms–close-up view of wilting leaves

102 Edward L. Barnard, Florida Department of Agriculture Infestation–trees killed along road

103 Joseph O’Brien, USDA Forest Service, Bugwood.org American elm killed by Dutch elm disease

103 Paul Bachi, University of Kentucky Research and Education Center, 

Bugwood.org

Sign–Characteristic asexual stage or “synnemata”, conidiophores with 

clusters of colorless conidia at the top

103 Fred Baker, Utah State University, Bugwood.org Early wilt symptoms

103 UK Forestry Commission Archive, Bugwood.org Cross section of an Ophiostoma ulmi-infected elm branch, showing 

characteristic dark spots in the wood

Dwarf mistletoes

104 USDA Forest Service Archive, USDA Forest Service, Bugwood.org Infestation on Douglas-fir

105 USDA Forest Service Archive, USDA Forest Service, Bugwood.org Feature(s)–Infections cause spindle-shaped swellings on branches and 

small stems

105 David Conklin, USDA Forest Service, Bugwood.org Damage–Mortality of severely infested tree

105 William Jacobi, Colorado State University, Bugwood.org Plant(s)–Dwarf mistletoe (Arceuthobium sp.) and Cytospora canker 

(Cytospora sp.) on a white fir

105 USDA Forest Service, Region 2, Rocky Mountain Region Archive, USDA 

Forest Service, Bugwood.org Infestation–Infected pine overstory; Gunnison National Forest, Colorado

106 R.S. Peterson, USDA Forest Service, Bugwood.org Plant(s)–bole infection

107 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Fruit(s)–Close-up

107 Robert L. Anderson, USDA Forest Service, Bugwood.org Plant(s)–dwarf mistletoe on pine

107 USDA Forest Service, Region 2, Rocky Mountain Region Archive, USDA 

Forest Service, Bugwood.org Damage–Male and Female plants

107 USDA Forest Service, Region 2, Rocky Mountain Region Archive, USDA 

Forest Service, Bugwood.org

Damage–heavily infected with dwarf mistletoe killed by mountain pine 

beetle

Eastern larch beetle

108 Steven Katovich, USDA Forest Service, Bugwood.org Gallery pattern of the eastern larch beetle under the bark of an infested 

tamarack

109 Steven Katovich, USDA Forest Service, Bugwood.org Damage–Tamarack infested with eastern larch beetle, bark removal in 

late winter due to woodpecker probing

110 Steven Valley, Oregon Department of Agriculture, Bugwood.org Adult(s)–Dendroctonus simplex lateral view

Eastern spruce budworm

112 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Larva(e)–spruce budworm

113 Joseph O’Brien, USDA Forest Service, Bugwood.org Feature(s)–feeding budworm

113 Joseph O’Brien, USDA Forest Service, Bugwood.org Damage

113 Edward H. Holsten, USDA Forest Service, Bugwood.org Adult–spruce budworm

113 Steven Katovich, USDA Forest Service, Bugwood.org Damage–Dead and declining white spruce in northern MN following a 

spruce budworm outbreak
2013–2027 National Insect and Disease Forest Risk Assessment

188 Photo Credits

PAGE PHOTO CREDIT (photos shown in order of appearance from left to right) DESCRIPTOR AND/OR DESCRIPTION

Emerald ash borer

49 Steven Katovich, USDA Forest Service

Feature(s)–Bark removed by woodpeckers searching for emerald ash 

borer larvae and pupae in late winter, early spring. Photo taken in Ann 

Arbor, MI, 2004

49 Kenneth R. Law, USDA APHIS PPQ, Bugwood.org Damage

49 Eric R. Day, Virginia Polytechnic Institute and State University, Bugwood.

org Emerald ash borer adult, in tunnel

49 Steven Katovich, USDA Forest Service Damage–Emerald ash borer infested ash trees along a street in Novi, MI. 

Photo taken in August 2002

114 Pennsylvania Department of Conservation and Natural Resources–

Forestry Archive, Bugwood.org Emerald ash borer adult

115 Joseph O’Brien, USDA Forest Service, Bugwood.org Damage

115 Pennsylvania Department of Conservation and Natural Resources–

Forestry Archive, Bugwood.org Damage

115 Edward Czerwinski, Ontario Ministry of Natural Resources, Bugwood.org Galleries–Larval tunnels

115 Daniel Herms, The Ohio State University, Bugwood.org Damage–Trees removed after infestation

116 Debbie Miller, USDA Forest Service, Bugwood.org Emerald ash borer adult feeding on an ash leaf

117 Pennsylvania Department of Conservation and Natural Resources–

Forestry Archive, Bugwood.org Larva(e) 

117 Brian Sullivan, USDA APHIS PPQ, Bugwood.org Adult–Emerald ash borer

117 Christopher Asaro, Virginia Department of Forestry, Bugwood.org Galleries

117 Joseph O’Brien, USDA Forest Service, Bugwood.org Damage–Ash killed by EAB in southeastern Michigan

118 Jared Spokowsky, Indiana Department of Natural Resources, Bugwood.

org Adult–in flight

119 Bill McNee, Wisconsin Department of Natural Resources, Bugwood.org Emerald ash borer mortality.  Photo taken August 2013

119 Art Wagner, USDA APHIS, Bugwood.org Galleries

119 Christopher Asaro, Virginia Department of Forestry, Bugwood.org Damage

119 Kenneth R. Law, USDA APHIS PPQ, Bugwood.org Damage

Engraver beetle (Ips spp.)

120 A. Steven Munson, USDA Forest Service, Bugwood.org Pinyon ips adult(s)

121 Dave Powell, USDA Forest Service, Bugwood.org Ips engraver beetles damage

121 Roger Anderson, Duke University, Bugwood.org Eastern fivespined ips larva, pupa, and young callow adult

121 Tia Smith, Bugwood.org Pinyon ips damage

121 William M. Ciesla, Forest Health Management International, Bugwood.org Aerial view of scattered tree mortality in a young pine plantation

122 USDA Forest Service, Ogden Archive, USDA Forest Service, Bugwood.org Adult–Engraver beetle

123 Ronald F. Billings, Texas Forest Service, Bugwood.org

On fallen pines or on pine logs, the accumulations of red or brown 

sawdust on the surface of the bark are another sign of attacks by 

secondary bark beetles (Ips spp.)

123 Ronald F. Billings, Texas Forest Service, Bugwood.org Infestation–Infested with ips and black turpentine beetles

123 G. Keith Douce, University of Georgia, Bugwood.org Galleries–With adults and damage

123 William M. Ciesla, Forest Health Management International, Bugwood.org Damage–Top kill caused by pine engraver beetle in Pinus sylvestris var. 

hamata, near Bakuriana

124 Edward H. Holsten, USDA Forest Service, Bugwood.org Adult–Northern spruce engraver

125 Edward H. Holsten, USDA Forest Service, Bugwood.org Galleries

125 Edward H. Holsten, USDA Forest Service, Bugwood.org Infestation–White spruce mortality

125 Edward H. Holsten, USDA Forest Service, Bugwood.org Damage–Frass on bark

Erythrina gall wasp

79 Sheri Smith, USDA Forest Service Damage

Fir engraver

126 USDA Forest Service, Ogden Archive, USDA Forest Service, Bugwood.org Infestation

127 Donald Owen, California Department of Forestry and Fire Protection, 

Bugwood.org Adult–Fir engraver

127 Dave Powell, USDA Forest Service, Bugwood.org Damage–Red foliage stage. Malheur National Forest, northeastern 

Oregon

127 Fred Honing, USDA Forest Service, Bugwood.org Horizontal egg galleries
2013–2027 National Insect and Disease Forest Risk Assessment

189

PAGE PHOTO CREDIT (photos shown in order of appearance from left to right) DESCRIPTOR AND/OR DESCRIPTION

Fir engraver, continued

127 William M. Ciesla, Forest Health Management International, Bugwood.org Damage -Rio Grande National Forest, Colorado

Forest tent caterpillar

128 Gerald J. Lenhard, Louisiana State University, Bugwood.org Larva(e) 

129 USDA Forest Service, Region 8, Southern Archive, USDA Forest Service, 

Bugwood.org Infestation–Forest tent caterpillar defoliation

129 Sandra Jensen, Cornell University, Bugwood.org Larva(e)–injury to dogwood by forest tent caterpillars

129 USDA Forest Service, Region 8, Southern Archive, USDA Forest Service, 

Bugwood.org Forest tent caterpillar–damage up through canopy

129 Whitney Cranshaw, Colorado State University, Bugwood.org Adult–Forest tent caterpillar

Fusiform rust

130 Steven Katovich, USDA Forest Service, Bugwood.org Symptoms–Globose galls typical of the pine-oak and pine-pine gall rusts 

on jack pine

131 Robert L. Anderson, USDA Forest Service, Bugwood.org Sign–pycnia

131 Terry S. Price, Georgia Forestry Commission, Bugwood.org Sign–Branch canker

131 USDA Forest Service, Region 8, Southern Archive, USDA Forest Service, 

Bugwood.org Symptoms–Fusiform rust; stand showing many cankers

131 Robert L. Anderson, USDA Forest Service, Bugwood.org Sign–Fusiform rust; telia on oak leaf; Greenhouse

Goldspotted oak borer

132 Mike Lewis, Center for Invasive Species Research, Bugwood.org Agrilus auroguttatus adults feeding on oak foliage

133 Tom Coleman, USDA Forest Service, Bugwood.org Damage–Staining on the bark surface of coast live oak, Quercus agricola, 

resulting from Agrilus auroguttatus larval feeding

133 Mark S. Hoddle, University of California–Riverside, Bugwood.org Larva(e)–Late instar larva of Agrilus auroguttatus in its feeding tunnel

133 Mark S. Hoddle, University of California–Riverside, Bugwood.org Damage–Woodpecker foraging damage due to Agrilus auroguttatus

infestation

133 Mike Lewis, Center for Invasive Species Research, Bugwood.org Infestation–Tree mortality in San Diego Co., California from Agrilus 

auroguttatus infestation

Hemlock woolly adelgid

134 Chris Evans, Illinois Wildlife Action Plan, Bugwood.org Infestation

135 USDA Forest Service, Region 8, Southern Archive, USDA Forest Service, 

Bugwood.org Damage–HWA on hemlock needles

135 Kelly Oten, Bugwood.org Adult–Hemlock woolly adelgid

135 USDA Forest Service Southern Research Station Archive, USDA Forest 

Service, SRS, Bugwood.org

Damage– Hemlock mortality caused by Hemlock Woolly Adelgid 

(Adelges tsugae)

135 Lorraine Graney, Bartlett Tree Experts, Bugwood.org Egg(s)–egg mass; immature crawler emerging from egg mass

Jack pine budworm

136 Steven Katovich, USDA Forest Service, Bugwood.org Larva(e)–late-instar larvae

137 James B. Hanson, USDA Forest Service, Bugwood.org Damage–first year of moderate to heavy defoliation

137 USDA Forest Service–Northeastern Area Archive, USDA Forest Service, 

Bugwood.org Adult–Jack pine budworm

137 Minnesota Department of Natural Resources Archive, Minnesota 

Department of Natural Resources, Bugwood.org Damage–defoliation

137 Joseph O’Brien, USDA Forest Service, Bugwood.org Damage–mortality

Jeffrey pine beetle

138 Laura Merrill, USDA Forest Service, Bugwood.org Adult(s)–on Jeffrey pine, San Bernardino National Forest, 23 May 2003

139 Darren Blackford, USDA Forest Service, Bugwood.org Infestation

139 Tia Smith, Bugwood.org Pupa(e)–Pupa in a pupal cell within the inner bark

139 Tia Smith, Bugwood.org Galleries–Egg and larval galleries

139 Darren Blackford, USDA Forest Service, Bugwood.org Adult–Jeffrey pine beetle

Koa wilt

78 Nick Dudley, Hawaii Agriculture Research Center Koa wilt, Fusarium oxysporum f. sp. koae

Laurel wilt

140 Chip Bates, Georgia Forestry Commission, Bugwood.org Symptoms–Camphor tree with dieback symptoms caused by laurel wilt 

infection
2013–2027 National Insect and Disease Forest Risk Assessment

190 Photo Credits

PAGE PHOTO CREDIT (photos shown in order of appearance from left to right) DESCRIPTOR AND/OR DESCRIPTION

Laurel wilt, continued

141 Albert (Bud) Mayfield, USDA Forest Service, Bugwood.org Symptoms–Wilted redbay foliage

141 Ronald F. Billings, Texas Forest Service, Bugwood.org Symptoms–Redbay trees dying from laurel wilt disease; center tree 

responding with epicormic sprouting

141 Ronald F. Billings, Texas Forest Service, Bugwood.org Symptoms–Redbay trunk with bark removed exposing sapwood with 

typical black staining caused by laurel wilt disease

141 R. Scott Cameron, Advanced Forest Protection, Inc., Bugwood.org Symptoms–Redbay mortality on Jekyll Island caused by laurel wilt 

disease

Maple decline

142 Jason Sharman, Vitalitree, Bugwood.org Damage–Urban tree decline

143 Randy Cyr, Greentree, Bugwood.org Symptoms–Planted red maples are on the decline since they were 

planted in full sun

143 Jason Sharman, Vitalitree, Bugwood.org Damage–Urban tree decline

Mountain pine beetle

144 Dave Powell, USDA Forest Service Damage–Old group of trees killed, Garber Creek area, South Platte 

Ranger District, Pike National Forest

145 Whitney Cranshaw, Colorado State University, Bugwood.org Infestation–Recently killed and fading lodgepole pine. Rabbit Ears’ Pass, 

Routt County, Colorado. September 3, 2007

145 Steven Katovich, USDA Forest Service, Bugwood.org Damage–Pitch tubes. Medicine Bow Mountains, southeast Wyoming

145 Ronald F. Billings, Texas Forest Service, Bugwood.org Infestation

145 Whitney Cranshaw, Colorado State University, Bugwood.org

Damage– Adult excavating tunnel. Although this tree is still producing 

pitch, it has been heavily attacked by mountain pine beetle, and this 

adult beetle is excavating a larval gallery, treading the oozing pitch

Myoporum thrips

79 Univesity of California Riverside Center for Invasive Species Research Naio thrips, Klambothrips myopori

Oak decline and gypsy moth

146 USDA Forest Service–Forest Health Protection–St. Paul Archive, USDA 

Forest Service, Bugwood.org Symptoms–oak decline

147 Tim Tigner, Virginia Department of Forestry, Bugwood.org Damage–Late instars feeding on oak foliage

147 Tim Tigner, Virginia Department of Forestry, Bugwood.org Infestation–Overstory oak mortality and understory response ensuing 

defoliation

147 USDA APHIS PPQ Archive, USDA APHIS PPQ, Bugwood.org Adult–Male Asian gyspy moth-visually identical in appearance to regular 

gyspy moth-need DNA analysis to distinguish strains

147 William M. Ciesla, Forest Health Management International, Bugwood.org Infestation–Defoliation of oaks and other broadleaf trees

148 Joseph O’Brien, USDA Forest Service, Bugwood.org Moderate to severe decline symptoms in mature red oak

149 Tim Tigner, Virginia Department of Forestry, Bugwood.org Infestation–Heavy defoliation by larvae

149 William M. Ciesla, Forest Health Management International, Bugwood.org Adult(s)–Female adults and egg masses

149 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–oak decline

149 Jon Yuschock, Bugwood.org Gypsy moth caterpillar, Lymantria dispar

Oak wilt

150 William M. Ciesla, Forest Health Management International, Bugwood.org Infestation–Oak killed by oak wilt

151 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–expanding oak wilt pocket

151 Paul A. Mistretta, USDA Forest Service, Bugwood.org Oak wilt, Texas red oak leaf symptoms

151 Joseph O’Brien, USDA Forest Service, Bugwood.org Oak wilt mortality caused by oak wilt

151 Paul A. Mistretta, USDA Forest Service, Bugwood.org Oak wilt, Live oak leaf symptoms

Ohia rust

78 Phil Cannon, USDA Forest Service ‘Ohia rust, Puccinia psidii

Root diseases–all

152 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Windthrow caused by Armillaria root rot in a large bur oak on 

a golf course

153 Robert L. James, USDA Forest Service, Bugwood.org Symptoms–Changes in tree density and clusters of young trees or brush 

associated with tree mortality are good indicators of root disease

153 Andrej Kunca, National Forest Centre–Slovakia, Bugwood.org Symptoms–Root rot 

153 USDA Forest Service Archive, USDA Forest Service, Bugwood.org Laminated root disease infestation–yellow shrubs filling gaps caused by 

conifer mortality
2013–2027 National Insect and Disease Forest Risk Assessment

191

PAGE PHOTO CREDIT (photos shown in order of appearance from left to right) DESCRIPTOR AND/OR DESCRIPTION

Root diseases, continued

153 Cathy Stewart, USDA Forest Service, Bugwood.org Sign–Cream or yellow to cinnamon colored mycelium is found on the 

outer bark of roots with laminated root rot

154 Robert L. Anderson, USDA Forest Service, Bugwood.org Symptoms–Basal canker (with bark removed) on sand pine

155 John W. Schwandt, USDA Forest Service, Bugwood.org Symptoms–Changes in tree density and clusters of young trees or brush

associated with tree mortality are good indicators of root disease

155 Andrej Kunca, National Forest Centre–Slovakia, Bugwood.org Symptoms–Trunk failure

155 James W. Byler, USDA Forest Service, Bugwood.org Symptoms–Root disease mortality in young stands

155 Susan K. Hagle, USDA Forest Service, Bugwood.org Symptoms–Root disease mortality in young stands

Roundheaded pine beetle

156 Steven Valley, Oregon Department of Agriculture, Bugwood.org Adult(s)–Dendroctonus adjunctus female, lateral view

157 USDA Forest Service, Southwestern Region Pitch tubes caused by roundheaded pine beetle attacks on ponderosa 

pine

157 USDA Forest Service, Southwestern Region Ponderosa pine mortality caused by roundheaded pine beetle in the 

Pinaleño Mountains, Arizona

157 USDA Forest Service, Southwestern Region Aerial view of ponderosa pine mortality caused by roundheaded pine 

beetle in the Pinaleño Mountains, Arizona

157 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Galleries

Sirex woodwasp

158 William M. Ciesla, Forest Health Management International, Bugwood.org Adult(s)–Female

159 William M. Ciesla, Forest Health Management International, Bugwood.org Damage–Aerial view of tree mortality in pine plantations

159 Dennis Haugen, Bugwood.org Feature(s)–Resin dribbles from oviposition wounds, 14-year old tree in 

South Australia. June 1988

159 David R. Lance, USDA APHIS PPQ, Bugwood.org Adult(s)–Male

159 Vicky Klasmer, Instituto Nacional de Tecnologia Agropecuaria, Bugwood.

org Damage

Southern pine beetle

160 Ronald F. Billings, Texas Forest Service, Bugwood.org Infestation–Aerial view of spot

161 USDA Forest Service, Region 8, Southern Archive, USDA Forest Service, 

Bugwood.org Adult

161 Ronald F. Billings, Texas Forest Service, Bugwood.org Damage–View of killed pine trees by southern pine beetles

161 Erich G. Vallery, USDA Forest Service–SRS-4552, Bugwood.org Damage

161 Ronald F. Billings, Texas Forest Service, Bugwood.org Infestation–Little Lake Creek Wilderness Octover 7, 1992

Spruce aphid

162 Elizabeth Willhite, USDA Forest Service, Bugwood.org Damage

163 Jill Wilson, USDA Forest Service, Bugwood.org Adult–Aphids are normally wingless, and feed gregariously on spruce or 

Douglas-fir foliage

163 Donald Owen, California Department of Forestry and Fire Protection, 

Bugwood.org Damage

163 Petr Kapitola, State Phytosanitary Administration, Bugwood.org Damage

163 Petr Kapitola, State Phytosanitary Administration, Bugwood.org Damage

Spruce beetle

164 William M. Ciesla, Forest Health Management International, Bugwood.org Adult

165 William M. Ciesla, Forest Health Management International, Bugwood.org Damage–Spruce beetle mortality

165 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Galleries

165 Whitney Cranshaw, Colorado State University, Bugwood.org Damage–Tree mortality along White River in Rio Blanco County

166 Andris Eglitis, USDA Forest Service, Bugwood.org Damage–infested Sitka spruce; early 1980s

167 Edward H. Holsten, USDA Forest Service, Bugwood.org Adult(s) and eggs

167 Whitney Cranshaw, Colorado State University, Bugwood.org Damage–Tree mortality along White River in Rio Blanco County

Stem rot

168 Paul E. Hennon, USDA Forest Service, Bugwood.org Fruiting bodies–on dead Sitka spruce & hemlock in southeast Alaska

Sudden oak death

170 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Coast live oak dying from P. ramorum infection
2013–2027 National Insect and Disease Forest Risk Assessment

192 Photo Credits

PAGE PHOTO CREDIT (photos shown in order of appearance from left to right) DESCRIPTOR AND/OR DESCRIPTION

Sudden oak death, continued

171 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Sudden oak death (ramorum canker) symptoms on coast live 

oak

171 Bruce Moltzan, USDA Forest Service, Bugwood.org Symptoms

171 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Tip droop symptom of sudden oak death on tanoak

171 Joseph O’Brien, USDA Forest Service, Bugwood.org Sign–P. ramorum zone lines on coast live oak

Western balsam bark beetle

172 Javier Mercado, Colorado state University, Bugwood.org Adult

173 Elizabeth Willhite, USDA Forest Service, Bugwood.org Damage

173 Scott Tunnock, USDA Forest Service, Bugwood.org

Galleries–The distinctive gallery pattern results from the male

excavating a central nuptial chamber from which the several females

radiate out to produce their egg galleries

173 Ladd Livingston, Idaho Department of Lands, Bugwood.org Damage–Trees killed typically have bright red crowns for a year or two 

after death

173 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Infestation

Western pine beetle

174 Erich G. Vallery, USDA Forest Service–SRS-4552, Bugwood.org Adult

175 James Everitt, Bugwood.org Damage–A still photograph of site in the Davis Mountains of west Texas 

confirms a stand of dead ponderosa pines killed by western pine beetles

175 Kenneth E. Gibson, USDA Forest Service, Bugwood.org Damage–Pitch tubes

175 Kenneth E. Gibson, USDA Forest Service, Bugwood.org Damage–Bark sloughing off of trees

175 William M. Ciesla, Forest Health Management International, Bugwood.org Damage–Galleries

Western spruce budworm

176 William M. Ciesla, Forest Health Management International, Bugwood.org Infestation–Aerial view of defoliation

177 USDA Forest Service, Region 4, Intermountain Archive, USDA Forest 

Service, Bugwood.org Adult moth

177 Dave Powell, USDA Forest Service, Bugwood.org Damage–Mortality. Aldrich Mountains, Bear Valley Ranger District, 

Malheur National Forest, northeastern Oregon

177 William M. Ciesla, Forest Health Management International, Bugwood.org Larva(e)

177 David J. Moorhead, University of Georgia, Bugwood.org Damage

White pine blister rust

178 Chris Schnepf, University of Idaho, Bugwood.org Sign–Blister rust sporulating

179 Minnesota Department of Natural Resources (MnDNR) Archive, MnDNR, 

Bugwood.org Symptoms

179 USDA Forest Service–Ogden Archive, USDA Forest Service, Bugwood.org Symptoms

179 John W. Schwandt, USDA Forest Service, Bugwood.org Sign–Rust sporulating on the bole of an infected tree

179 Ralph Williams, USDA Forest Service, Bugwood.org Infested stand

180 Susan K. Hagle, USDA Forest Service, Bugwood.org Symptoms–Stem cankers eventually girdle and kill trees

181 USDA Forest Service, Ogden Archive, USDA Forest Service, Bugwood.org Sign–Urediospores on Ribes spp.

181 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms

181 H.J. Larsen, Bugwood.org Sign–Close-up view of the aecia of white pine blister rust (Cronartium 

ribicola) on the branch of a pine tree

181 Joseph O’Brien, USDA Forest Service, Bugwood.org Symptoms–Small “flagged branches”

Winter moth

182 Dimitrios Avtzis, NAGREF-Forest Research Institute, Bugwood.org Larva 

183 Dimitrios Avtzis, NAGREF-Forest Research Institute, Bugwood.org Larva(e)

183 Milan Zubrik, Forest Research Institute, Slovakia, Bugwood.org Adult

183 Dimitrios Avtzis, NAGREF-Forest Research Institute, Bugwood.org Damage

183 Milan Zubrik, Forest Research Institute, Slovakia, Bugwood.org Larva 

Yellow-cedar decline

184 Paul E. Hennon, USDA Forest Service, Bugwood.org Symptoms in southeast Alaska

185 Paul E. Hennon, USDA Forest Service, Bugwood.org Symptoms in Alaska

185 Paul E. Hennon, USDA Forest Service, Bugwood.org Symptoms in southeast Alaska
2013–2027 National Insect and Disease Forest Risk Assessment

A 193

APHIS PPQ Animal and Plant Health Inspection Service, Plant 

Protection and Quarantine

BA Basal Area

BLM Bureau of Land Management

CART Classification and Regression Tree

CMIP3 Climate Model Intercomparison Project 3

DBH Diameter at Breast Height

DDT NIDRM’s Data Development Team

DI Soil Drainage Index

FHM Forest Health Monitoring

FHP Forest Health Protection

FHTET Forest Health Technology Enterprise Team

FIA Forest Inventory and Analysis

GIS Geographic Information System

IDW Inverse Distance Weighting

MAP Mean Annual Precipitation

MAT Mean Annual Temperature 

MDT NIDRM’s Model Development Team

MODIS Moderate Resolution Imaging Spectroradiometer

NDVI Normalized Difference Vegetation Index

NIDRM National Insect and Disease Risk Map

NA Northeast Area (Forest Service: State and Private administrative 

unit for northeastern states)

NLCD National Land Cover Data

NOAA National Oceanic and Atmospheric Association

NOAA-NCDC National Oceanic and Atmospheric Association 

National Climate Data Center

NRCS Natural Resources Conservation Service

PET Potential Evapotranspiration

PI Productivity Index

PRISM Parameter-elevation Regressions on Independent Slopes 

Model

QMD Quadratic Mean Diameter

R1 Region 1 of U.S. Forest Service. (This convention is used for 

other Forest Service regions; e.g. R8 for Region 8)

RMAP Risk Modeling Application

RMOT Risk Map Oversight Team

RSAC Remote Sensing Applications Center

SDI Stand Density Index

SDL Spatial Data Library (Maintained by FHTET)

STATSGO2 State Soil Geographic database

SSURGO Soil Survey Geographic Database

USDA United States Department of Agriculture

USDI United States Department of Interior

USGS United States Geological Survey
2013–2027 National Insect and Disease Forest Risk Assessment

194 G

Basal Area (BA) The cross-sectional area of a tree stem, typically 

measured or estimated at 4.5 feet above the base of the tree, and 

typically expressed in units of square feet. When expressed on a 

per-unit-area basis, basal area values provide a representation of 

tree density. Values for BA cited in this report are expressed on 

various bases, such as square feet per acre or millions of square 

feet per watershed.

Cell Size (See Pixel)

Diameter at Breast Height (DBH) Tree bole diameter (including 

bark) at 4.5 feet above the base of the tree.

Drainage Index (DI) An ordinal measure of the long-term natural 

soil wetness. A GIS surface used as a criterion in several risk models.

Ecoregion A geographic area defined and delineated by climatological 

and physiographic features and conditions. Models in the 2012 

National Insect and Disease Risk Map (NIDRM) are constructed 

and run within specified ecoregions. Ecoregions are organized 

hierarchically into four classes or levels, from a coarse to fine 

scale: Domain, Division, Province, and Section. Ecoregions 

used in NIDRM are at the Section level. 

Endemic Literally “native to a place.” In the context of forest 

disturbance agents, it often describes population reproduction rates 

(of native pests) that are relatively low, constant, and considered 

“normal” or “background.” (See Epidemic and Exotic)

Epidemic A rate of population growth that is substantially above 

what is normal or expected. (See Endemic and Exotic)

Exotic In the context of forest pest agents, exotic describes agents 

that have moved beyond their natural or historic geographic 

range(s) as a result of human activity. Exotic pests are sometimes 

referred to as “introduced.” (See Endemic and Epidemic)

FIA Plot Locations Nationwide plot network on which vegetation 

and fuels examinations have been conducted by the Forest 

Inventory and Analysis (FIA) program of the U.S. Forest Service. 

The number of plots per unit area, and re-measurement dates, 

vary by state and FIA region. Typically, one plot represents 6000 

acres and is re-measured every 5 or 10 years.

Forest Health Monitoring (FHM) An annual, national program 

of the U.S. Forest Service designed to determine the status, 

changes, and trends in indicators of forest conditions.

Forest Health Protection (FHP) An organizational unit within the 

U.S. Forest Service primarily responsible for maintaining forest 

health by minimizing the spread of established invasive species and 

lessening the damages caused by native forest insects and diseases.

Forest Inventory and Analysis (FIA) An organizational unit 

within the U.S. Forest Service that collects, analyzes, and reports 

information on the status and trends of America’s forest resources.

Forest Health Technology Enterprise Team (FHTET) An 

organizational unit within Forest Health Protection whose 

mission is to develop and deliver forest health technology 

services to field personnel in public and private organizations. 

(See Forest Health Protection)

Forest Parameters Attributes, such as basal area, quadratic mean 

diameter, and stand density index, that are essential to the 

construction of models used in assembling the 2012 NIDRM 

composite. These attributes are either components of, or calculated 

from, FIA plot data. (See FIA and Site Parameters)

Forest Type A classification, such as a name or label, of a forested 

area characterizing in a general way the composition of its tree 

species. Forest types may represent compositions that contain 

predominantly single species (e.g. loblolly pine), or compositions 

that contain mixed-species (e.g. Englemann spruce-subalpine fir).

Geographic Information System (GIS) A computer system 

capable of integrating, storing, editing, analyzing, sharing and 

displaying geographically referenced information.

Grid (Raster) In a GIS, a geographical representation of an area as 

an array of equally-sized square cells (pixels) arranged in rows 

and columns. Each grid cell is referenced by its geographic x,y 

location and contains one or more attributes (e.g. elevation) 

characterizing the location represented by the pixel. (See Pixel)

Grid Cell (See Pixel)

Host Data Gaps Areas where FIA and other plot data sources 

are lacking within the 2006 NIDRM. Although a few areas are 

lacking data in western and northern Alaska, remotely sensed 

data, GIS technologies, and statistical methodologies are used 

to ensure data gaps do not occur within the forest parameters 

data layers in the 2012 NIDRM. In the 2006 NIDRM, Host 

Data Gaps typically occur within national parks, urban areas, and 

some wilderness areas. 

Layer In a GIS, a collection of similar geographic features that 

represent a particular theme (e.g. roads, streams, or city 

boundaries) displayed on a map. (See Surface) 

Mask In a GIS, a spatial dataset (typically a raster) within which 

analysis, calculations, or other processes are constrained.

Maximum Mortality Rate The maximum realizable basal area 

mortality rate that can be achieved by a specific risk agent acting 

on a specific forest host species in a defined area, as shown 

within a 2012 NIDRM model. The maximum rate is realized 

when all of a risk model’s criteria are evaluated to be at their 

maximum risk level. 

Model (See Risk Model)

Mortality Threshold The minimum percentage of basal area loss 

over an area required to meet the definition of mortality risk. For 

the 2012 NIDRM, the mortality threshold for mapping risk is 

defined as 25% or more of the standing live basal area of trees 

greater than one inch in diameter. (See Maximum Mortality Risk)

Multi-Criteria Modeling A modeling process run in a GIS that 

allows for the combination and weighting of multiple factors to 

derive a new variable or output metric. It provides a common 

framework in which to combine dissimilar information in order 

to produce a single index of evaluation.

Peer Review For the NIDRM project, the peer review process was 

a formal appraisal conducted by those who were acknowledged 

to have sufficient expertise to give critical comment and 

recommendations as to the completeness and accuracy of the 

2012 NIDRM report.

Pixel (Grid Cell) A discretely uniform unit of area such as 240 

meters by 240 meters (pixel size used in the 2012 NIDRM) that 

represents a portion of the earth’s surface. Each pixel has a 

value assigned to corresponding to a feature or characteristic 

of that area, such as elevation, temperature, tree species, or soil 

drainage index.
2013–2027 National Insect and Disease Forest Risk Assessment

195

Quadratic Mean Diameter (QMD) The diameter of a tree having 

the average basal area, over some unit of area. QMD is derived 

by taking the square root of the sums of the squared diameters, 

divided by the number of trees over a given area. Generally, 

QMD is considered a better metric than a straight arithmetic 

mean of tree diameters for characterizing average tree size.

Raster (See Grid)

Remediation Management actions aimed to ameliorate threats to 

forest health. For the 2012 NIDRM, the threats are those from 

insects and diseases, and the modeled scenarios assume that no 

future remediation will be applied. If remediation is undertaken 

by federal, state, or other land managers, modeled risks depicted 

on the 2012 NIDRM may be lowered or eliminated.

Remote Sensing Applications Center (RSAC) An organizational 

unit within the U.S. Forest Service, Salt Lake City, Utah. RSAC 

provides assistance to agency field units in applying the most 

advanced geospatial technology toward improved monitoring 

and mapping of natural resources.

Resolution A level of precision for grids used in the 2012 NIDRM. 

(See Spatial Resolution)

Risk Modeling Application (RMAP) An ArcGIS-based 

application built by FHTET allowing users to create multicriteria risk models against selected datasets and view the results. 

The application’s interface includes a map canvas that allows 

users to inspect spatial data inputs and outputs. The risk model 

outputs comprising the 2012 NIDRM were built using RMAP.

Risk Model In the context of the 2012 NIDRM, a set of weighted 

criteria and associated functions used to derive estimates of risk 

(on a 0–10 scale) and an associated host basal area mortality rate 

(15-year) attributable to a specific risk agent (or class of agents) 

acting on a host species.

Scale The ratio of the distance on a map as related to the true 

distance on the ground or the pixel size. For the 2012 NIDRM 

project, the scale of the input linear or polygonal base map 

features is 1:2,000,000 and the minimum pixel size selected for 

national display is 240-meter. (See Pixel)

Soil Drainage Index (DI) (See Drainage Index)

Spatial Resolution A measure of the smallest object that can be 

represented on the earth’s surface by a pixel. (See Pixel and Scale)

Stand Density Index (SDI) A metric reflecting stand stocking 

levels (i.e. how many trees per unit area), expressed to allow 

comparisons among differently aged (or sized) stands. Reineke 

(1933) derived a relationship between tree density (as trees per 

acre [TPA]) and average tree size (as QMD), finding that TPA is 

proportional to (QMD)-1.6. The SDI is the TPA of a forest stand 

at a standard QMD, which is usually 10 inches. (See Quadratic 

Mean Diameter)

Surface In a GIS, a geographic attribute represented as a set of continuously distributed data, such that every location on the mapped 

plane has a value. Typically, surfaces are grids created by interpolating values between known located values. (See Layer and Grid)

Susceptibility For the 2012 NIDRM, susceptibility is the potential 

for establishment of a forest pest within a tree species, over a 15-

year period. (See Vulnerability)

Treed Area A GIS grid, modeled independently of other forestparameter layers, representing areas having trees at a 240-meter 

resolution. (See Grid)

Vintage In NIDRM, vintage refers to a time period or date, eg., 

The vintage (time period, date) of the resultant parameter datasets should be 

considered as 2002.

Vulnerability For the 2012 NIDRM, if a forest pest were to become 

established, vulnerability would be the potential for mortality of 

a tree species at a given threshold (stated as a percentage) over a 

15-year period. (See Susceptibility)
2013–2027 National Insect and Disease Forest Risk Assessment

196 RefeRences

Austin, Mike. 2007. Species distribution models and ecological 

theory: a critical assessment and some possible new approaches. 

Ecological Modelling. 200: 1-19. Online at: http://people.eri.ucsb.

edu/~fd/misc/595/austin-revu-ecolmod07.pdf. 

Avery, T. A.; Burkhart, H. E. 2002. Forest Measurements (Fifth 

Edition). McGraw-Hill Series in Forest Resources. 456 p.

Bechtold, William A.; Patterson, Paul L., eds. 2005. The enhanced 

forest inventory and analysis program - national sampling design 

and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, 

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

Research Station. 85 p. Online at: www.srs.fs.usda.gov/pubs/gtr/

gtr_srs080/gtr_srs080.pdf. 

Blackard, J. A.; Finco, M. V.; Helmer, E. H.; Holden, G. R.; Hoppus, 

M. L.; Jacobs, D. M.; Lister, A. J.; Moisen, G. G.; Nelson, M. D.; 

Riemann, R.; Ruefenacht, B.; Salajanu, D.; Weyermann, D. L.; 

Winterberger, K. C.; Brandeis, T. J.; Czaplewski, R. L.; McRoberts, 

R. E.; Patterson, P. L.; Tymcio, R. P. 2008. Mapping U.S. forest 

biomass using nationwide forest inventory data and Terra 

MODIS-based information. Remote Sensing of Environment. 

112(4): 1658–1671. Online at: www.fs.fed.us/rm/pubs_other/

rmrs_2008_blackard_j001.pdf. 

Coulson, David P.; Joyce, Linda A.; Price, David T.; McKenney, 

Daniel W.; Siltanen, R. Martin; Papadopol, Pia; Lawrence, Kevin. 

2010. Climate scenarios for the conterminous United States at 

the 5 arc minute grid spatial scale using SRES scenarios A1B and 

A2 and PRISM climatology. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 

Online at: http://dx.doi.org/10.2737/RDS-2010-0017. 

Eidenshink, Jeff; Schwind, Brian; Brewer, Ken; Zhi-Liang, Zhu; 

Quayle, Brad; Howard, Stephen. 2007. A project for monitoring 

trends in burn severity. Fire Ecology Special Issue. 3(1): 3-21.

Elith, Jane; Graham Catherine H. 2009. Do they? How do they? Why 

do they differ? On finding reasons for differing performances of 

species distribution models. Ecography. 32: 66–77. Online at: www.

acera.unimelb.edu.au/materials/publications/Elith_Graham%20

2009.pdf. 

Ellenwood, J. R.; Krist, F. J., Jr.; Sapio, F. J., In press. Modeling 

individual tree species parameters on a national scale. Fort Collins, 

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

Health Technology Enterprise Team.

Ellig, Tracy. 2008. MSU deals with mountain pine beetle infestation. 

Bozeman, MT: Montana State University News Service. 

December 1. Online at: www.montana.edu/cpa/news/nwview.

php?article=6595 (accessed on April, 2013).

Environmental Protection Agency (EPA). 1998. Guidelines for 

ecological risk assessment. Publication EPA/630/R-95/002F. 

Washington, D.C. Online at: www.epa.gov/raf/publications/pdfs/

ECOTXTBX.PDF. 

Gesch, D. B.; Evans, G. A.; Mauck, J.; Hutchinson, J. A.; Carswell, W. 

J., Jr. 2009. The national map—elevation: U.S. Geological Survey 

fact sheet, 2009-3053, 4 p. Online at: http://pubs.usgs.gov/

fs/2009/3053/pdf/fs2009_3053.pdf. 

Hargrove, William W.; Spruce, Joseph P.; Norman, Stephen P.; 

Christie, William M.; Kumar, Jitendra; Hoffman, Forrest M. 

2012. Using land surface phenology for national mapping of 

the occurrence and health of evergreen and deciduous forests. 

Presentation at the Phenology 2012 Conference, September 

10–13, 2012, University of Wisconsin, Milwaukee, WI. Online at: 

www.geobabble.org/~hnw/first/Phen2012/.

Hargrove, William W.; Spruce, Joseph P.; Gasser, Gerald E.; Hoffman, 

Forrest M. 2009. Toward a national early warning system for 

forest disturbances using remotely sensed canopy phenology. 

Photogrammetric Engineering and Remote Sensing. 75: 1150−1156. 

Online at: www.srs.fs.usda.gov/pubs/ja/ja_hargrove002.pdf. 

Homer, Collin; Huang, Chengquan; Yang, Limin; Wylie, Bruce; 

Coan, Michael. 2004. Development of a 2001 national landcover 

database for the United States. Photogrammetric Engineering and 

Remote Sensing. 70(7): 829-840. Online at: www.mrlc.gov/pdf/

July_PERS.pdf. 

Hutchinson, M. F. 1991. Continent wide data assimilation using thin 

plate smoothing splines. In: Jasper, J. D., ed. Data assimilation 

systems: papers presented at the second BMRC modelling 

workshop, September 1990. BMRC Research Report 27, Bureau 

of Meteorology, Melbourne, Australia: 104-113.

IPCC, 2007: Climate Change 2007: Synthesis report. Contribution of 

working groups I, II and III to the fourth assessment report of the 

Intergovernmental Panel on Climate Change [Core Writing Team, 

Pachauri, R.K and Reisinger, A., eds.]. IPCC, Geneva, Switzerland, 

104 pp. Online at: www.ipcc.ch/pdf/assessment-report/ar4/syr/

ar4_syr.pdf. 

Joyce, Linda A.; Price, David T.; McKenney, Daniel W.; Siltanen, 

R. Martin; Papadopol, Pia; Lawrence, Kevin; Coulson, David P. 

2011. High resolution interpolation of climate scenarios for the 

conterminous USA and Alaska derived from general circulation 

model simulations. Gen. Tech. Rep. RMRS-GTR-263. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky 

Mountain Research Station. 87 p. Online at: www.fs.fed.us/rm/

pubs/rmrs_gtr263.pdf. 

Keane, Robert E.; Tomback, D. F.; Aubry, C. A.; Bower, A. D.; 

Campbell, E. M.; Cripps, C. L.; Jenkins, M. B.; Mahalovich, M. 

F.; Manning, M.; McKinney, S. T.; Murray, M. P.; Perkins, D. L.; 

Reinhart, D. P.; Ryan, C.; Schoettle, A.W.; Smith, C.M. 2012. A rangewide restoration strategy for whitebark pine (Pinus albicaulis). Gen. 

Tech. Rep. RMRS-GTR-279. Fort Collins, CO: U.S. Department 

of Agriculture, Forest Service, Rocky Mountain Research Station. 

108 p. Online at: www.fs.fed.us/rm/pubs/rmrs_gtr279.pdf. 

Keane, Robert E.; Tomback, Diana F.; Murray, Michael P.; Smith, 

Cyndi M., eds. 2011. The future of high-elevation, five-needle 

white pines in Western North America: Proceedings of the High 

Five Symposium. 28–30 June 2010; Missoula, MT. Proceedings 

RMRS-P-63. Fort Collins, CO: U.S. Department of Agriculture, 

Forest Service, Rocky Mountain Research Station. 376 p. Online at: 

www.fs.fed.us/rm/pubs/rmrs_p063.html.

Koch, F. H.; Smith, W. D.; Coulston, J. W. In press. An improved 

method for standardized mapping of drought conditions. Chapter 

6 in Potter, K. M.; Conkling, B. L., eds. Forest Health Monitoring 

2010 National Technical Report. Gen. Tech. Rep. SRS-xxx. 

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

Southern Research Station.

Krist, Frank J., Jr.; Sapio, Frank J.; Tkacz, Borys M. 2010. A multicriteria framework for producing local, regional, and national insect 

and disease risk maps. In: Pye, J. M., Rauscher, H. M, Sands, Y., Lee, 

D. C., Beatty, J.S., tech. eds. Advances in threat assessment and their 

application to forest and rangeland management. Gen. Tech. Rep. 

PNW-GTR-802. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest and Southern Research Stations: 

621–636. Online at: www.fs.fed.us/foresthealth/technology/pdfs/

pnw_gtr802vol2_krist.pdf. 
2013–2027 National Insect and Disease Forest Risk Assessment

197

Krist, Frank J., Jr.; Sapio, Frank J.; Tkacz, Borys M. 2007. Mapping 

risk from forest insects and diseases, 2006. FHTET 2007–06. Fort 

Collins, Colorado: U.S. Department of Agriculture, Forest Service, 

Forest Health Technology Enterprise Team. 115 p. Online at: 

www.fs.fed.us/foresthealth/technology/pdfs/FHTET2007-06_

RiskMap.pdf. 

Lewis, J. W. 2002. Mapping risk from forest insects and diseases. FS754. U.S. Department of Agriculture, Forest Service. 60 p. Online at: 

www.fs.fed.us/foresthealth/technology/pdfs/FHTET2007-06_

RiskMap.pdf.

Lister, Andrew J.; Scott, Charles T.; Rasmussen, Steven. 2012. 

Inventory methods for trees in nonforest areas in the great plains 

states. Environmental Monitoring and Assessment. 184: 2465–

2474. Online at: http://link.springer.com/content/pdf/10.1007%

2Fs10661-011-2131-6. 

Little, E. L., Jr. 1971. Atlas of United States trees, volume 1, conifers 

and important hardwoods: Misc. Pub. 1146. Washington, D.C.: U.S. 

Department of Agriculture. 9 p. 200 maps.

Little, E. L., Jr. 1977. Atlas of United States trees, volume 4, minor 

Eastern hardwoods: Misc. Pub. 1342. Washington, D.C.: U.S. 

Department of Agriculture. 17 p. 230 maps.

Loh, Wei-Yin. 2011. Classification and regression trees. Wiley 

Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 

1(1): 14-23. DOI 10.1002/widm.8

McKellip, Rodney D.; Ross, Kenton W.; Spruce, Joseph P.; Smoot, 

James C.; Ryan, Robert E.; Gasser, Gerald E.; Prados, Donald L.; 

Vaughan, Ronald D. 2010. Phenological parameters estimation 

tool. NASA Tech Briefs. 34(9): 36–37. Online at: http://www.

techbriefs.com/component/content/article/8481 (login required).

Moisen, Gretchen G.; Frescino, Tracey S. 2002. Comparing five 

modeling techniques for predicting forest characteristics. Ecological 

Modelling. 157: 209–225. Online at: www.fs.fed.us/rm/pubs_

other/rmrs_2002_moisen_g001.pdf. 

Mott, D. G. 1963. The forest and the spruce budworm. In: Morris, 

R. F., ed. The dynamics of epidemic spruce budworm populations. 

Memoirs of the Entomological Society of Canada. 31: 189–202.

National Climatic Data Center (NCDC). 2001. U.S. Climate Normals 

1971-2000, Products. NOAA’s National Climatic Data Center, 

Asheville, NC

Nowak, David J.; Stein, Susan M.; Randler, Paula B.; Greenfield, Eric 

J.; Comas, Sara J.; Carr, Mary A.; Alig, Ralph J. 2010. Sustaining 

America’s urban trees and forests: a Forests on the Edge report. 

Gen. Tech. Rep. NRS-62. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northern Research Station. 27 

p. Online at: www.fs.fed.us/openspace/fote/reports/nrs-62_

sustaining_americas_urban.pdf. 

National Research Council (NRC). 1983. Risk assessment in the 

federal government: managing the process. National Academy 

Press, Washington, D.C. Online at: www.epa.gov/region9/science/

seminars/2012/red-book.pdf. 

Potyondy, John P.; Geier, Theodore W. 2011. Watershed condition 

classification technical guide. FS-978. U.S. Department of 

Agriculture, Forest Service. 41 p. Online at: www.fs.fed.us/

publications/watershed/watershed_classification_guide.pdf. 

Rehfeldt, Gerald L. 2006. A spline model of climate for the western 

United States. Gen. Tech. Rep. RMRS-GTR-165. Fort Collins, CO: U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research 

Station. 21 p. Online at: www.fs.fed.us/rm/pubs/rmrs_gtr165.pdf.

Reineke, L. H. 1933. Perfecting a stand density index for even aged 

forests. Journal of Agricultural Research. 46: 627–638. Online at: 

http://naldc.nal.usda.gov/download/IND43968212/PDF. 

Rollins, Matthew G.; Frame, Christine K., tech. eds. 2006. The 

LANDFIRE prototype project: nationally consistent and locally 

relevant geospatial data for wildland fire management. Gen. Tech. 

Rep. RMRS-GTR-175. Fort Collins, Colorado: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 416 p. 

Online at: www.fs.fed.us/rm/pubs/rmrs_gtr175.pdf. 

Ruefenacht, B.; Finco, M.V.; Nelson, M.D.; Czaplewski, R.; Helmer, E.H.; 

Blackard, J. A.; Holden, G.R.; Lister, A.J.; Salajanu, D.; Weyermann, 

D.; Winterberger, K. 2008. Conterminous U.S. and Alaska forest type 

mapping using forest inventory and analysis data. Photogrammetric 

Engineering and Remote Sensing. 74(11): 1379–1388. Online at: 

www.nrs.fs.fed.us/pubs/jrnl/2008/nrs_2008_ruefenacht_001.pdf. 

Schaetzl, Randall J.; Krist, Frank J, Jr.; Stanley, Kristine; Hupy, Christina 

M. 2009. The natural soil drainage index: an ordinal estimate of longterm soil wetness. Physical Geography. 30: 383–409. Online at http://

foresthealth.fs.usda.gov/soils/Content/Downloads/DI_paper.pdf. 

Schaetzl, Randall J.; Krist, Frank J., Jr.; Miller, Bradley A. 2012. A 

taxonomically based, ordinal estimate of soil productivity for 

landscape-scale analyses. Soil Science. 177: 288–299. Online at: 

http://foresthealth.fs.usda.gov/soils/Content/Downloads/PIsoilscience.pdf. 

Smith, W. Brad; Darr, David. 2005. U.S. forest resource facts and 

historical trends. FS-801-M: U.S. Department of Agriculture, Forest 

Service. 37 p. Online at: www.fia.fs.fed.us/library/brochures/

docs/2005/Forest_Facts_1952_2002-US%20Metric.pdf. 

Smith, W. Brad; Vissage, John S.; Darr, David R.; Sheffield, Raymond 

M. 2001. Forest resources of the United States, 1997. Gen. Tech. 

Rep. NC-219. St. Paul, MN: U.S. Department of Agriculture, Forest 

Service, North Central Experiment Station. 190 p. Online at: www.

nrs.fs.fed.us/pubs/gtr/gtr_nc219.pdf. 

Smith, W. Brad; Miles, Patrick D.; Perry, Charles H.; Pugh, Scott A. 2009. 

Forest resources of the United States, 2007. Gen. Tech. Rep. WO-78. 

Washington, D.C: U.S. Department of Agriculture, Forest Service, 

Washington Office. 336 p. Online at: www.fs.fed.us/nrs/pubs/gtr/

gtr_wo78.pdf. 

Soil Survey Staff. 1999. Soil taxonomy: a basic system of soil classification 

for making and interpreting soil surveys (2nd ed.). Washington, D.C: 

U.S. Department of Agriculture, Soil Conservation Service.

U.S. Fish and Wildlife Service. 2011. Endangered and threatened wildlife 

and plants; 12-month finding on a petition to list Pinus albicaulis as 

endangered or threatened with critical habitat. Federal Register. 

76(138): 42631-42654

United States Geological Survey (USGS). 2011. Gap Analysis Program 

(GAP). National Land Cover, Version 2.

Woudenberg, Sharon W.; Conkling, Barbara L.; O’Connell, Barbara M.; 

LaPoint, Elizabeth B.; Turner, Jeffery A.; Waddell, Karen L. 2010. 

The forest inventory and analysis database: database description 

and users manual, version 4.0, for phase 2. Gen. Tech. Rep. RMRSGTR-245. Fort Collins, CO: U.S. Department of Agriculture, Forest 

Service, Rocky Mountain Research Station. 336 p. Online at: www.

forestthreats.org/publications/rtp/rmrs_gtr245.pdf.
2013–2027 National Insect and Disease Forest Risk Assessment

198 A

RISKMAP OVERSIGHT TEAM (RMOT)

Team Lead—Borys Tkacz, National Program Manager, FHM

Barbara Burns, VT, Northeast State Representative

Chris Asaro, VA, Southern State Representative

Charles Shaw, Western Wildland Environmental Threat Assessment 

Center

Danny Lee, Eastern Forest Environmental Threat Assessment Center

Frank J. Krist Jr., FHTET, Data Development Team Leader

Frank Sapio, Director, FHTET

Greg Kujawa, Forest Management, Washington Office

Greg Reams, National Program Manager, Forest Inventory and 

Analysis (FIA)

Karen Ripley, WA, Western State Representative

Brian Schwind, Remote Sensing Applications Center

Frank Fay, Fire and Aviation Management, Washington Office

Jim Steinman, Northeastern Area (NA)

MODEL DEVELOPMENT TEAM (MDT)

Team Lead—Jim Steinman (NA)

Regional Modeling Leads

Barbara Schultz, entomologist (VT) and Margaret Weeks, pathologist 

(NA), NA – Northeast FHM Region

Roger Mech, entomologist (MI) and Manfred Mielke, pathologist 

(NA) NA – Northcentral FHM Region

Sheri Smith, entomologist (R5) and Helen Maffei pathologist (R6) 

West Coast, Alaska and Hawaii FHM Region

Dan Ryerson (R3-NM) and Holly Kearns, pathologist (R1), Intermountain FHM Region

Dale Starkey, pathologist (R8) and Steve Clarke, entomologist (R8), 

Southern FHM Region

DATA DEVELOPMENT TEAM (DDT)

Team Leads—Frank J. Krist Jr., GIS and Spatial Analysis Program 

Manager (FHTET) and Jim Ellenwood Remote Sensing Program 

Manager (FHTET)

ACKNOWLEDGEMENTS

FHM coordinators

Borys Tkacz – National Program Manager

Jim Steinman – Northeast Region

Dale Starkey- Southern Region

Jeri Lyn Harris – Intermountain Region

Allison Nelson – Western Region (including Alaska and Hawaii)

Reviewers of the Draft Report

Chris Asaro – Virginia Dept. of Forestry

Ron Billings – Texas Forest Service

Pete Budde – National Parks Service

Bob Edmonds – University of Washington

Chris Fettig – Forest Service: Pacific Southwest Research Station

Everett Hansen – Oregon State University

Robert Hauff – Hawaii Dept. of Land and Natural Resources

Jeffrey Hicke – University of Idaho

Louis Iverson – Forest Service: Northern Research Station

Glenn Kohler – Washington Dept. of Natural Resources

Jim Smith – The Nature Conservancy

Richard Sniezko – Forest Service: Dorena Genetic Resource Center

Dale Starkey – FHP

FHP staff who developed RMAP models

Jim Steinman – Northeast Region

Dale Starkey – Southern Region

Daniel Ryerson – Intermountain Region

Holly Kearns – Intermountain Region

Meghan Woods – Western Region (and Hawaii)

Michael Simpson – Western Region

Lia Spiegel – Western Region

Tricia Wurtz – Western Region (Alaska) 

Nicholas Lisuzzo – Western Region (Alaska) 

Robin Mulvey – Western Region (Alaska) 

Loretta Winton – Western Region (Alaska) 

Data Providers

Arbor Day Foundation

United States Geological Survey 

National Oceanic and Atmospheric Administration

Writer Editors

Chuck Benedict – Cherokee Nations Technologies-FHTET

Mark Riffe – Contractor-FHTET
2013–2027 National Insect and Disease Forest Risk Assessment

199

ACKNOWLEDGEMENTS, continued

FHP

Pete Angwin, Frank Betlejewski, Jim Blodgett, Mike Bohne, Dave 

Bridgwater, Josh Bronson, Billy Bruce, Beverly Bulaon, Kelly Burns, 

Bob Cain, Phil Cannon, Darci Carlson, Stephen Clarke, Danny 

Cluck, Tom Coleman, Mike Connor, Sheryl Costello, Michelle Cram, 

Donald Duerr, Tom Eager, Joel Egan, Andy Eglitis, Mary Lou 

Fairweather, John Ghent, Amanda Grady , Ryan Hanavan, Dennis 

Haugen, Linda Haugen, Paul Hennon, Wood Johnson, William 

Jones, Carl Jorgensen, Steve Katovich, Sandra Kegley, Melody 

Lardner, Laura Lazarus, Donna Leonard, Blakey Lockman, Tom 

Luther, Martin MacKenzie, Joel McMillin, James Meeker, Manfred 

Mielke, Isabel Munck, John Nowak, Steve Oak, Joel O’Brien, Valli 

Peacher, Iral Ragenovich, Carol Randall, Rusty Rhea, Bill Schaupp, 

Noel Schneeberger, Don Scott, Kathy Sheehan, Mike Simpson, 

Sheri Smith, Cynthia Snyder, Chris Steiner, Nancy Sturdevant, 

Dan Twardus, Beth Willhite, Dustin Wittwer, Bill Woodruff, Paul 

Zambino

State partners 

Jana Albers (Minnesota Dept. of Natural Resources), Tiffany Arp 

(South Dakota Dept. of Agriculture), Chris Asaro (Virginia Dept. 

of Forestry), Aaron Bergdahl (North Dakota Forest Service), 

Ron Billings (Texas Forest Service), Barbara Burns (Vermont 

Agency of Natural Resources), Roger Burnside (Alaska Division 

of Forestry), Randy Chapin (Mississippi Forestry Commission ), 

Andrea Diss-Torrance (Wisconsin Dept. of Natural Resources), 

Gail Durham (Nevada Division of Forestry), Tom Eckberg 

(Idaho Dept. of Lands ), Todd Edgerton (Virginia Dept. of 

Forestry), Don Eggen (Pennsylvania Dept. of Conservation), 

Jeff Eickwort (Florida Division of Forestry), Tivon Feeley (Iowa 

Dept. of Natural Resources), Rob Flowers (Oregon Dept. of 

Forestry), Amy Gannon (Montana Dept. of Natural Resources 

and Conservation), Mark Harrell (Nebraska Forest Service), Rob 

Hauff (Hawaii Dept. of Land and Natural Resources), Bob Heyd 

(Michigan Dept. of Natural Resources), Alan Kanaskie (Oregon 

Dept. of Forestry), Mike Kangas (North Dakota Forest Service), 

Colleen Keyes (Utah Dept. of Natural Resources), Cynthia 

King (Hawaii Dept. of Land and Natural Resources), Neal 

Kittelson (Idaho Dept. of Lands ), Les Koch (Wyoming State 

Forestry Division), Glenn Kohler (Washington Dept.of Natural 

Resources), Rob Lawrence (Missouri Dept. of Conservation), 

Kyle Lombard (New Hampshire Division of Forests and Lands), 

Phil Marshall (Indiana Dept. of Natural Resources), Mike 

McWilliams (Oregon Dept. of Forestry), Roger Mech (Michigan 

Dept. of Natural Resources), Jason Moan (North Carolina Forest 

Service ), Danny Norlander (New Mexico Forestry Division ), 

Don Owen (California Dept. of Forestry and Fire Protection ), 

Joe Pase (Texas Forest Service), Chris Pearce (Florida Division 

of Forestry), Nicole Ricci (Kansas Forest Service ), Karen 

Ripley (Washington Dept.of Natural Resources), Tom Smith 

(California Dept. of Forestry and Fire Protection ), Sky Stephens 

(Colorado State Forest Service), Dana Stone (Alabama Forestry 

Commission), Dave Struble (Maine Forest Service), Rob Trickel 

(North Carolina Forest Service ), Julie Yalemar (Hawaii Dept. of 

Agriculture)

Federal (Other than FHP)

Bud Mayfield (Forest Service: Southern Research Station); Elizabeth 

LaPoint and Richard McCullough (Forest Service: Northern Research 

Station -FIA); Lloyd Loope (USGS); Rodney Hart, Jeff Mallory and 

Carlos Ramirez (Forest Service: Region 5 Remote Sensing Lab)

University and Non-Profits 

Betsy Goodrich (Northern Arizona University), Bill Jacobi 

(Colorado State University), John Riggins (Mississippi State 

University), Jim Walla (North Dakota State University), Nick 

Dudley and Tyler Jones (Hawaii Agriculture Research Center)

FHTET

Nathan Edberg, Contractor, Cherokee Nations Technologies

Jeanine Paschke, Contractor, Cherokee Nations Technologies

Finally, we would like to thank Lowell Lewis for helping us to 

get this third installment of NIDRM off the ground.