Forest Health Monitoring In The Interior West

Although forest health may be difficult to define and measure, a strong demand exists for assessment of forest conditions at various state, regional, and national scales. Forest Health Monitoring (FHM) is a national program designed to measure the status, changes, and trends of forest conditions annually. This report presents a broad view of forest health issues affecting the Interior West region of Montana, Idaho, Wyoming, Utah, Nevada, Colorado, New Mexico, and Arizona. We found that the forests of the Interior West have changed considerably in the past century. What is more difficult to assess is whether humans have promoted change that is irreversible, or whether the change we see in the forested landscape is within healthy bounds. Discussions of forest health and forest cover change, the developed and wildland interface, insect and disease disturbances, watershed health, biodiversity, and air quality comprise the body of this report. This initial report sets the stage for more in-depth reports on forest health in the Interior West by introducing the FHM program, defining "the forest" regionally, discussing prominent issues, and displaying summary FHM data taken from 1996-1999. A website address is provided on the inside back cover of this report to solicit reader suggestions for improving future FHM reports.

"The Forest Health Monitoring (FHM) Program's annual national report uses FHM data, as well as data from a variety of other programs, to provide an overview of forest health based on the criteria and indicators of sustainable forestry framework of the Santiago Declaration. It presents information about the status of and trends in various forest health indicators nationwide and uses statistically valid analysis methods applicable to large-scale ecological assessments. Five main sections correspond to the Santiago criteria: Biological Diversity, Productive Capacity, Health and Vitality, Conservation of Soil, and Carbon Cycling. A variety of indicators contribute information about the status of each forest ecosystem considered. Many indicators use data collected from ground plots. Such indicators include species diversity (tree and lichens), bioindicator species (lichens and vascular plants sensitive to ozone), changes in trees (crown condition, damage, and mortality), physical and chemical soil characteristics, and aboveground and belowground carbon pools. Additional information about forest health status and change is derived from data that are used to measure forest extent; data about insects and pathogens; and remotely sensed and/or ground-based data about forest fragmentation, fire, and air pollution. A sixth section presents and discusses a multivariate analysis of the indicators. The technique provides a composite picture of forest health, based on statistically significant principal components."--P. ii.

The Forest Health Monitoring Program's annual national reports present results from forest health data analyses focusing on a national perspective. The Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests are used as a reporting framework. This report has five main sections. The first contains introductory material. The next three sections, S2Landscape Structure, S3 S2Abiotic and Biotic Factors, S3 and S2Forest Conditions, S3 contain results of data analyses. Some of the indicators discussed use data collected from ground plots. These include ozone bioindicator plants; changes in trees (crown condition, mortality, and stand age); and soils (forest floor depth). Other indicators or indicator groups use data about insects and diseases, and remotely sensed or ground-based data about distance to roads, forest edge, interior forest, drought, fire, and air pollution (sulfates, nitrates, and ozone). Identifying patterns and observing possible relationships is an important part of national level analysis and reporting. The fifth section S2Integrated Look at Forest Health IndicatorsS3 presents results of analyses designed to evaluate whether or not individual indicators or linear combinations of indicators discriminate between crowns in poor condition and crowns not in poor condition.