Using stand-structure data from older protected forests to validate models for identifying late-successional and older forests in large-scale surveys

Background/Question/Methods

Large-scale forest surveys, conducted by the USDA Forest Service, Forest Inventory and Analysis (FIA) program, are important in assessing the status of the forests across the U.S. These surveys can be used to monitor important structural and functional elements of forests such as species composition, stand structure, growth, mortality, and harvesting. Additionally important for ecosystem management, these assessments can be used to determine the amounts of old growth and late successional forests across the landscape. However, identifying these older stands using survey data is subjective and problematic. To validate our older-forest identification model, we compared the older stands selected from FIA plots across Braun’s Western Mesophytic (WM) and Mixed Mesophytic (MM) Forest Regions to FIA plots collected in the Great Smoky Mountain National Park (GSMNP). We operated under the premise that the GSMNP plots sampled relatively undisturbed forests in one of the largest contiguous tracts of protected forests in the eastern U.S. Our objective was to empirically determine how similar (structurally and dimensionally) the unprotected older forests from the MM and WM regions were, in comparison to the GSMNP forests.

Results/Conclusions

Stands identified using model thresholds of ≥30.0 cm quadratic mean diameter (QMD) and ≥10 sample trees per plot were structurally larger in the GSMNP than forests in the MM and WM regions. Basal area (BA), density, and QMD were all significantly larger for the GSMNP than the MM, and WM (BA [sq. m/ha] = 37.0, 26.2, 25.8 [GSMNP, MM, and WM, respectively]; density [trees/ha] = 357, 278, 282; QMD = 35.7, 33.5, 33.0). Of all the forest plots sampled in the GSMNP, 45 percent fell within the model thresholds whereas only 23 and 13 percent qualified in the MM and WM, respectively. Anthropogenic disturbance is a primary reason for the lower proportions in the MM and WM regions. Identifying structural attributes of older stands across the landscape is complicated by the influence of differing site characteristics, regional climates, and regional species dynamics. In addition, temporal forest dynamics (natural disturbance and senescence) rotate portions of the forest into and out of an old-growth state. The FIA sample lacks fine resolution at the local scale but is able to portray relative forest conditions across the landscape. The model performed reasonably well in identifying older stands in the MM and WM forest regions but further calibrations are needed to adapt to community types in other areas.