Land managers require information about the ongoing and potential effects of future climate to coordinate responses for ecosystems, species, and human communities at scales that are operationally meaningful. Our study focused on the vulnerability for all upland ecosystem types of Arizona and New Mexico in the southwestern United States. Local vulnerability across the two‐state area was represented by the level of departure for late 21st‐century climate from the characteristic pre‐1990 climate envelope of the ecosystem type at each given location, resulting in a probability surface of climate impacts for the two‐state area and an uncertainty assessment based on agreement in results among multiple global climate models. Though the results varied from one ecosystem type to the next, the majority of lands were forecast as high vulnerability and low uncertainty, reflecting significant agreement among climate model projections for the southwestern United States. We then tested our results in relation to ongoing ecological processes that have both regional and global change implications and discovered significant relationships with wildfire severity, upward tree species recruitment, and the encroachment of scrub into semidesert grassland. The testing helped determine the efficacy of the vulnerability surface, as a product of relatively high spatial and thematic resolution, in supporting local planning and management decisions. Most important, this study links climate and changes in vegetation by ecosystem processes that are already ongoing. The results affirm the value of climate model downscaling and show that this portable approach to correlative modeling has value in determining the location and magnitude of potential climate‐related impacts.

This is an author-produced, peer-reviewed version of this article. © 2009, Elsevier. Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/). The final, definitive version of this document can be found online at Remote Sensing of Environment, doi: 10.1016Environment, doi: 10. /j.rse.2007 1 NOTICE: This is the author's version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document.Changes may have been made to this work since it was submitted for publication. AbstractThe study and management of biological communities depends on systems of classification and mapping for the organization and communication of resource information. Recent advances in remote sensing technology may enable the mapping forest plant associations using image classification techniques. But few areas outside Europe have alliances and associations described in detail sufficient to support remote sensing-based modeling. Northwestern Montana has one of the few plant association treatments in the United States compliant with the recently established National Vegetation Classification system. This project examined the feasibility of mapping forest plant associations using Landsat Enhanced Thematic Mapper data and advanced remote sensing technology and image classification techniques.Suitable reference data were selected from an extensive regional database of plot records. Fifteen percent of the plot samples were reserved for validation of map products, the remainder of plots designated as training data for map modeling. Key differentia for image classification were identified from a suite of spectral and biophysical variables. Fuzzy rules were formulated for partitioning physiognomic classes in the upper levels of our image classification hierarchy. Nearest neighbor classifiers were developed for classification of lower levels, the alliances and associations, where spectral and biophysical contrasts are less distinct.Maps were produced to reflect nine forest alliances and 24 associations across the study area. Error matrices were constructed for each map based on stratified random selections of map validation samples. Accuracy for the alliance map was estimated at 60%. Association classifiers provide between 54 and 86% accuracy within their respective alliances. Alternative techniques are proposed for aggregating classes and enhancing decision tree classifiers to model alliances and associations for interior forest types.

The flow of ecosystem services derived from forests and grasslands in the Southwestern United States may change in the future. People and communities may be vulnerable if they are exposed, are sensitive, and have limited ability to adapt to ecological changes. Geospatial descriptions of ecosystem services, projected climate-related ecological changes, and socioeconomic conditions are used to assess socioeconomic vulnerability to changes in the provision of ecosystem services by national forests and grasslands in the Southwest. Vulnerability is uneven in the Southwest due to varying projected effects of climate on forest ecosystem services, and different levels of exposure, sensitivity, and adaptive capacity of people in the region.