We integrate cutting-edge research methods to build forest management tools for Armillaria root disease.

A distinguishing feature of our research is the integration of field-, laboratory-, and modeling-based approaches. We draw on molecular diagnostics, genomic data, and climate-driven modeling to translate fundamental pathogen biology into applied management tools for Armillaria root disease that support forest productivity.

Photo Credit

USDA Forest Service photo by John W. Hanna

Mycelial fan of Armillaria associated with whitebark pine (Pinus albicaulis) in the Jarbidge Wilderness, Humboldt–Toiyabe National Forest, Nevada.

We collect information that improves pathogen and disease detection across the West.

Across the forests of the western United States, we work with partners to conduct field surveys in a range of forest types and geographic regions to document the presence, distribution, and host associations of root disease pathogens. We collect samples from both symptomatic and asymptomatic trees to improve pathogen detection on healthy-appearing trees where the pathogen is easily overlooked. We obtain fungal isolates from infected tree tissues and soil-associated materials before we characterize them using DNA-based methods to ensure accurate species-level identification.

Photo Credit

USDA Forest Service Photo by John W. Hanna

Natalia Risso Fonseca demonstrating molecular diagnostic techniques for Armillaria identification in the laboratory

In the lab, we use genetic tools to better understand the risks of Armillaria root disease in forests.

We use DNA-based analyses – including single-gene and multi-gene sequencing approaches – to differentiate pathogenic, weakly pathogenic, and potentially beneficial species. Then, we incorporate the resulting information into spatial analyses to better understand species distributions, host ranges, and ecological roles. These steps make it easier to assess Armillaria disease risk and potential management activities that will support forest productivity.

We use advanced spatial modeling techniques to identify future areas for targeted monitoring of Armillaria root disease.

To evaluate how temperature and moisture patterns influence Armillaria root disease risk and forest productivity, we integrate pathogen and tree host species occurrence data with climate variables using bioclimatic modeling techniques. We apply these models to assess current and projected future suitability of climate variables on pathogen and tree host distributions under multiple climate scenarios. Our modeling results help identify areas where forest productivity losses from Armillaria root disease may increase and where monitoring and management actions may be most effective.

Photo Credit

USDA Forest Service photo by John W. Hanna

Sara Ashliglar collecting Armillaria rhizomorphs at Cave Lake on the Modoc National Forest, California

We translate our research results into tools and guidance for land managers.

Our forest management-focused research examines interactions among pathogens, hosts, soil microbial communities, and disturbance factors, such as fire and drought. We place a particular emphasis on understanding conditions under which naturally occurring biological control agents and other beneficial microbial communities may suppress severity of Armillaria root disease and reduce losses in forest productivity. We translate our findings into forest management-relevant guidance for detection, monitoring, and adaptive decision-making in relation to Armillaria root disease of wide-ranging forests.

Our partnerships are the key to solving the challenge of Armillaria root diseases.

We conduct this research in close collaboration with personnel from across the Forest Service, including the National Forest System and Forest Health Protection (part of State, Private, & Tribal Forestry). This approach helps to ensure that our methods, data products, and interpretations directly inform forest management needs. Our ongoing coordination with state forestry agencies, university partners, and industry collaborators supports shared field sampling, data exchange, and co-development of forest management-relevant tools. These partnerships help translate research findings into practical applications for forest root disease detection, risk assessment, forest species selection, and long-term forest productivity across many different types of land ownership.

Future directions

Our ongoing and future work will expand genomic resources, refine climate-risk projections, and evaluate management strategies that enhance forest resilience and productivity in the presence of persistent root-disease pathogens.

We provide land managers with critical information about Armillaria root disease.

Photo Credit

USDA Forest Service photo by Raini C. Rippy

Basidiocarps (fruiting bodies) of Armillaria are observed on the Nez Perce-Clearwater National Forest, Idaho

We help managers identify high-risk areas.

Forest root diseases cause substantially reduced growth, increased mortality, and long-term productivity losses, yet they are often difficult to detect before obvious damage occurs. By improving detection, accurate identification, and predictions for root disease pathogens, this project helps managers distinguish forest sites with a high risk of Armillaria root disease from those sites with benign or low-impact conditions.

Photo Credit

USDA Forest Service photo by John W. Hanna

Armillaria root disease causing selective mortality of fir within a mixed pine-fir stand at McBride Plantation, Shasta–Trinity National Forest, California. Armillaria gallica was recovered from mycelial fans associated with affected trees.

We predict where Armillaria root diseases will affect future productivity of forests.

Bioclimatic modeling provides spatially explicit information about where Armillaria root diseases are most likely to affect forest productivity under projected current and future climates. These tools support forest management decisions for root disease, such as selection of tree species, regeneration planning, monitoring priorities, and long-term forest resilience strategies.

We develop cost-effective, ecosystem-based management strategies.

Our research on biological control, soil microbial communities, tree host adaptation, and ecosystem-based management highlights opportunities to mitigate impacts of forest root disease without relying on disruptive or costly interventions. Collectively, this work supports forest management strategies that maintain productivity, resilience, and ecological function in the presence of persistent root-disease pathogens.

Our products and outcomes span many aspects of Armillaria root disease.

For more information about each of these products and outcomes, please refer to the peer-reviewed publications linked on the "publications" tab of this website.

Detection and diagnostics

Photo Credit

USDA Forest Service photo by John W. Hanna

A mycelial fan of Armillaria beneath the bark of an infected tree at Sand Mountain, Nez Perce-Clearwater National Forest, Idaho

• Improved DNA-based methods for detection and identification of forest root disease pathogens (Klopfenstein et al. 2017; Park et al. 2018), including new or updated species descriptions and/or summary information (Elías-Román et al. 2018; Antonín et al. 2021; Kim et al. 2023; Antonín et al. 2025);   

Distribution and risk modeling

• Maps of predicted host ranges and geographic distributions of forest root disease pathogens (e.g., Kim et al. 2021), including first reports of pathogens in a new geographic area or on a new host tree (e.g., Alveshere et al. 2020; Alveshere et al. 2021; Duarte-Mata et al. 2021; Hanna et al. 2021; Cram et al. 2022; Alvarado-Rosales et al. 2023; Kim et al. 2023);
• Bioclimatic models predicting current and future risks of root disease to forest productivity (e.g., Kim et al. 2021); 

Photo Credit

USDA Forest Service photo by John W. Hanna

Mycelial fan of Armillaria solidipes associated with subalpine fir in the Abajo Mountains, Manti-La Sal National Forest, Utah

Biology and genomics

• Genetic and genomic information on Armillaria species that provide a biological and ecological information for development of new management approaches (Ibarra Caballero et al. 2023; Antonín et al. 2025; Stewart et al. 2025); 

Management and application

• Information toward developing biological control and host resistance as a novel approach for managing Armillaria root disease and increasing forest productivity (e.g., Stewart et al. 2018; Elías-Román et al. 2019; Warwell et al. 2019; Stewart et al. 2021; Kim et al. 2022; Kim et al. 2023; Ibarra Caballero et al. 2023; Fitz Axen et al. 2024);
• Management-relevant guidance for monitoring, risk assessment, and adaptive decision-making (Kim et al. 2021; Kim et al. 2022; Filip et al. 2024); and
• Scientific publications that synthesize known information on Armillaria root disease to support applied forest health and productivity management (Kim et al. 2022; Filip et al. 2024).

Collaborators

• Co-Principal Investigators

  • Jane E. Stewart, Colorado State University

  Co-Investigators and Collaborators

  • Geral I. McDonald, USDA Forest Service (retired), Rocky Mountain Research Station
  • Marcus V. Warwell, USDA Forest Service, National Forest System, Southern Region
  • Bradley M. Lalande, USDA Forest Service, Forest Health Protection
  • Ashley E. Hawkins, USDA Forest Service, Forest Health Protection
  • James T. Blodgett, USDA Forest Service, Forest Health Protection
  • Bill Woodruff, USDA Forest Service, Forest Health Protection
  • Jared M. LeBoldus, Oregon State University
  • Jorge R. Ibarra Caballero, Colorado State University
  • Ada J. Fitz Axen (Neupane), Colorado State University
  • Chris Lee, CAL FIRE
  • Kim Corella, CAL FIRE
  • Sara Ashiglar, USDA Forest Service, Nez Perce-Clearwater National Forest
  • Andrew T. Hudak, USDA Forest Service, Rocky Mountain Research Station
  • Duncan Kroese, USDA Forest Service, Pacific Northwest Research Station

  Research Staff and Contributors

  • Rocky Mountain Research Station and cooperating Station research staff involved in field sampling, laboratory analyses, molecular diagnostics, data management, modeling, data collection, data analyses, interpretation, technology transfer, and publication preparation