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Many authors have pointed out the need to firm up the 'fuzzy' ecosystem management paradigm and develop operationally practical processes to allow forest managers to accommodate more effectively the continuing rapid change in societal perspectives and goals. There are three spatial scales where clear, precise, practical ecosystem management processes are needed: the regional assessment scale, the forest-level scale, and the project-level scale. This paper proposes a practical decision analysis process for ecosystem management at the project-level scale. Goals are the focal point of management. To achieve them requires a formal, structured goal hierarchy, desired future conditions, several interesting alternatives, scenario analysis, and monitoring and evaluation of the results. The proposed process is firmly grounded in the body of theory and practice organized in the scientific literature under the heading of multi-objective decision analysis. An illustrative example of this decision analysis process is presented using the Bent Creek Experimental Forest of the Pisgah National Forest near Asheville, NC as a test case.

Stochastic point processes and associated methodology provide a means for the statistical analysis and modeling of the spatial point pattern formed from forest tree stem locations. Stochastic Gibbs point processes were explored as models that could simulate short-range clustering arising from reproduction of trees by stump sprouting, and intermediate-range inhibition of trees that may result from competition for light and growing space. This study developed and compared three pairwise interaction processes with parametric models for 2nd-order potentials and three triplets processes with models for 2nd- and 3rd-order potentials applied to a mixed-species hardwood forest in the Southern Appalachian Mountains of western North Carolina. Although the 2nd-order potentials of both the pairwise interaction and triplets processes were allowed to be purely or partially attractive, the proposed Gibbs point process models were demonstrated to be locally stable. The proposed Gibbs point processes were simulated using Markov Chain Monte Carlo (MCMC) methods; in particular, a reversible-jump Metropolis-Hastings algorithm with birth, death, and shift proposals was utilized. Parameters for the models were estimated by a Bayesian inferential procedure that utilizes MCMC methods to draw samples from the Gibbs posterior density. Two Metropolis-Hastings algorithms that do this sampling were compared; one that estimated ratios of intractable normalizing constants of the Gibbs likelihood by importance sampling and another that introduced an auxiliary variable to cancel the normalizing constants with those in the auxiliary variable's proposal distribution. Results from this research indicated that attractive pairwise interaction models easily degenerate into excessively clustered patterns, whereas triplets processes with attractive 2nd-order and repulsive 3rd-order interactions are more robust against excessive clustering. Bayesian inference for the proposed triplets models was found to be very computationally expensive. Slow mixing of both algorithms used for the inference combined with the long iteration times limited the practicality of the Bayesian approach. However the results obtained here indicate that triplets processes can be used to draw inference for and simulate patterns of mixed-species Appalachian hardwood forests. Ph. D.

The Southern Appalachian hardwood forests contain a wide diversity of flora and fauna. Understanding processes that affect nutrient availability in these forests is essential for sound forest management. Three interconnected research projects regarding soil resource heterogeneity were designed to increase our understanding of this ecosystem. The objective of these projects were as follows: 1) to examine and quantify the role of decaying stumps in regards to total carbon (C) and nitrogen (N) pools and fine-root dynamics, 2) compare and contrast the use of ground-penetrating radar (GPR) vs. a soil auger for estimating soil depth and site quality and 3) to evaluate how eastern red-backed salamanders (Plethodon cinereus) affect N-availability. For the stump study, results show that decomposing stumps occupy approximately 1.2% of the total soil volume and constitute 4% and 10% of total soil N and C pools. Significant differences in N (p = 0.0114), C (p = 0.0172), microbial biomass C (p = 0.0004), potentially mineralizable N (p = 0.0042), and extractable NH4+ (p = 0.0312) concentrations were observed when compared to mineral soil horizons. In particular, potentially mineralizable N was 2.5 times greater in stump soil than the A-horizon (103 vs. 39 mg kg-1), 2.7 times greater for extractable NH4+ (16 vs. 6 mg kg-1) and almost 4 times greater for MBC (1528 vs. 397 mg kg-1). These measured properties suggest higher N-availability, organic matter turnover and N uptake in stump soil versus the bulk soil. 19% of the total fine root length and 14% of fine root surface area also occurred in the stump soil. The increased fine root length suggests higher concentrations of labile nutrient in the stumps since roots often proliferate in areas with higher nutrient availability. Significant differences occurred in N and C concentrations between all four decay classes and the A-horizon, which validated the use of this system and the need to calculate weighted averages based on the frequency and soil volume influenced by each decay class. In the GPR Study, depth estimations were shallower using a soil auger compared to estimates obtained using GPR across all plots (p = 0.0002; Figure 3.4). On a soil volume basis, this was equivalent to about 3500 m3 of soil per hectare unaccounted for using traditional methods. In regards to using soil depth as a predictor for site quality, no significant relationships were observed with soil depth estimations obtained from the auger (Table 3.3). On the other hand, depth measurements from GPR explained significant amounts of variation across all sites and by physiographic region. Across all sites, soil depth estimates from GPR explained 45.5% of the residual variation (p = 0.001; Table 3.3). When the data were stratified by physiographic region, a higher amount of variation was explained by the regression equations; 85% for the Cumberland Plateau (p = 0.009), 86.7% for the Allegheny Plateau (0.007) and 66.7% for the Ridge and Valley (p = 0.013), respectively (Table 4.2). Results from this study demonstrate how inaccurate current methods can be for estimating soil depth rocky forests soils. Furthermore, depth estimations from GPR can be used to increase the accuracy of site quality in the southern Appalachians. In the salamander study, no significant salamander density treatment or treatment by time effects were observed over the entire study period (p < 0.05). However, when the data were separated by individual sampling periods a few significant treatment by time interactions occurred: 1) during August 2006 for available NH4+ under the forest floor (i.e. horizontal cation membranes; p = 0.001), 2) August and 3) September 2006 for available NH4+ in the A-horizon (p = 0.026), and 4) May 2007 for available NO3- under the forest floor (p = 0.011). As a result of these trends, an index of cumulative N-availability (i.e. NH4+ and NO3-) under the forest floor and in the A-horizon was examined through the entire study period. Cumulative N-availability under the forest floor was consistently higher in the low- and medium-density salamander treatments compared to the high-density treatment. For cumulative N-availability in the A-horizon, a gradient of high to low N-availability existed as salamander density increased. Factors such as a prolonged drought in 2007 may have affected our ability to accurately assess the effects of salamanders on N-availability. We concluded that higher salamander densities do not increase N-availability. Implementing methodologies that accurately account for soil nutrient pools such as stump soil, physical properties such as depth and fauna such as salamanders, increase our understanding of factors that regulate site productivity in these ecosystems. As a result, landscape-level and stand-level management decisions can be conducted more effectively. Ph. D.