Effects of Harvesting on Herbaceous Layer Diversity of a Central Appalachian Hardwood Forest

Clearcutting is a common harvesting practice in many eastern hardwood forests. Among the vegetation strata of these forests, the herbaceous layer is potentially the most sensitive in its response to harvest-mediated disturbances and has the highest species diversity. Thus, it is important to understand the response of herbaceous layer diversity to forest harvesting. Previous work on clearcut and mature stands at the Fernow Experimental Forest (FEF), West Virginia, has shown that, although, harvesting did not alter appreciably herbaceous layer cover, it influenced the relationship of cover to biotic and abiotic factors, such as tree density and soil nutrients, respectively. The purpose of this study was to examine the response of species diversity of the herbaceous layer to harvesting at FEF. Fifteen circular, 0.04 ha sample plots were established in each of four watersheds (60 plots in total) representing two stand age categories: two watersheds with 20 years even-age stands following clearcutting and two watersheds with mature second growth stands. All woody stems ≥2.5 cm diameter at breast height were identified, tallied, and measured for diameter. The herbaceous layer was sampled by identifying all vascular plants ≤1 m in height and estimating cover for each species in each of 10 (1 m2) circular sub-plots per sample plot (600 sub-plots total). Species diversity for each plot was calculated from herbaceous layer data using the ln-based Shannon Index (H′) equation. Ten stand and soil variables also were measured on each plot. Mean herbaceous layer cover for clearcut versus mature stands was 27.2±14.3% versus 20.2±8.1% (P>0.05), respectively and mean H′ was 1.67±0.42 versus 1.55±0.48 (P>0.05), respectively. Herbaceous layer diversity was negatively correlated with cation exchange capacity and extractable Ca and Mg in the mineral soil in clearcut stands. In contrast, herbaceous layer diversity was positively correlated with soil organic matter and clay content. Although, 20 years of recovery after clearcutting did not have significant effects on the species diversity of the herbaceous layer when examining stand age means alone, harvesting did appear to influence the spatial relationships between herbaceous layer diversity and biotic factors (e.g. tree density) and abiotic factors (e.g. soil nutrients).

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.