Victor B. Shelburne

The effect of site, stand density, and sapwood permeability on the relationship between leaf area and sapwood area in loblolly pine (Pinus taeda L.)

Abstract The relationship between total leaf area (LA) and sapwood area (SA) was studied in four stands of approximately 30-year-old loblolly pine (Pinus taeda L.) representing extremes of stand basal area and site index. Nine trees (three dominant, three intermediate, and three suppressed) were randomly selected from each stand for determination of LA and SA. Sapwood permeability with respects to height in stem and radial distance from the pith was determined from one other tree from each dominance class in each stand. Stem cross-sectional SA at breast height, mid-stem and base of live crown were linearly related to LA. SA at the base of live crown was a better predictor of LA on some sites than SA at breast height. The effect of including sapwood permeability (averaged sapwood permeability x area of sapwood) in the LA prediction equations was minimal. Regression equations for LA had different slopes between basal area classes but not between sites. Because of the larger regression slope of the LA prediction from SA in the low basal area stands (more leaf area per unit of sapwood), it is hypothesized that carbohydrate production in low basal area stands is allocated proportionally more to leaf or root tissue than sapwood at breast height and mid-stem. This implies that leaf area is the controlling factor in the LA-SA relationship.

Preliminary ecological land classification of the Chauga Ridges Region of South Carolina

An ecological method of integrated multifactor land classification was applied in the Chauga Ridges, a subregion of the Blue Ridge Mountain Province, in northwest South Carolina. Phase I sampling involved measuring 61 reference plots in undisturbed late-successional upland hardwood stands. Four distinct site units (xeric, intermediate, submesic, and mesic) were identified using ordination, cluster analysis, discriminant function analysis, and canonical correspondence analysis. Using cross-validation of a discriminant function, a combination of five environmental features (landform index, terrain shape index, root mat thickness, slope gradient, and slope position) identified site units with an accuracy rate of 82%. A predictive GIS model which used spatial variables only (landform index, terrain shape index, topographic-related moisture index, distance-to-bottom, and slope gradient) was also developed and identified site units with the same success rate of 82%.

Ecological species groups of South Carolina's Jocassee Gorges, southern Appalachian Mountains'

across the landscape favorable for specific species groups. We also tested two multivariate methods for quantifying associations among species groups, and found that Mantel tests using traditional distance measures were inappropriate because of the double-zero problem of species absences, whereas canonical correlation modeled species group associations consistent with species distributions among sites. This study is among the first to develop ecological species groups in the southern United States, and the species group approach was useful for explaining vegetation-environment relationships, identifying groups of ground-flora and tree species that varied together across the landscape, and for determining the environmental gradients most strongly associated with species distributions.

Fuel characterization in the southern Appalachian Mountains: an application of landscape ecosystem classification

Prescribed fire has been widely used in the south-eastern United States to meet forest management objectives, but has only recently been reintroduced to the southern Appalachian Mountains. Fuel information is not available to forest managers in this region and direct measurement is often impractical owing to steep, remote topography. The objective of the present study was to determine whether landscape ecosystem classification (LEC) site units support different types and amounts of fuel in the Chauga Ridges, a subregion of the Blue Ridge Mountain Province. Ecosystem classification identifies vegetation assemblages that are the expressive result of soils, physiography and vegetation, and recur predictably on the landscape. Four fuel complexes were identified using LEC units and field measurements of fuel characteristics. Fuel bed depth, duff (Oe + Oa) thickness, 1000-h fuel loading, and Rhododendron maximum, R. minus, and Vaccinium spp. ground cover were discriminating fuel characteristics of xeric, intermediate, submesic, and mesic site units. Discriminant function analysis provided an overall 64% cross-validation success rate using 138 undisturbed, randomly located plots. This method of characterizing fuel complexes may also be possible in other forested ecosystems where LECs or other ecological vegetation classifications have been developed.

Does Sampler Size Affect Southeastern Piedmont Forest Soil Bulk Density Estimation

ABSTRACT Destructive core sampling is the most recognized technique for soil bulk density estimation to monitor soil compaction. This study was conducted to determine if soil bulk density sampler size would impact value estimates and silvicultural treatment distinctions in southeastern Piedmont forest soils of the Clemson Experimental Forest, Clemson, South Carolina, USA. In 2004, six hundred soils were sampled using both an Oakfield Soil Probe (diameter 2.5 cm) and an AMS, Inc. Slide Hammer (diameter 5.1 cm). The smaller, Oakfield Soil Probe values were greater than the larger, AMS Inc. Slide Hammer values (p < 0.0001) and values from both samplers were greater in the thin and burn treatments than the control treatments (p < 0.007). Strong correlation of the values between the samplers was also demonstrated (p < 00001; r2 = 0.62). These results highlight the utility of the smaller Oakfield Soil Probe when accessibility and intensity may impose sampling constraints. Abbreviations: AMS=AMS Inc. Slide Hammer; CEF=Clemson Experimental Forest; Db=soil bulk density; FFS=National Fire and Fire Surrogate Study; OSP=Oakfield Model H Soil Probe; SOC=soil organic carbon