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).

Fitting ACE Structural Equation Models to Case-Control Family Data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). To this end, we describe a ACE model for binary family data and then introduce an approach to fitting the model to case-control family data. The structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. Our likelihood-based approach to fitting involves conditioning on the proband’s disease status, as well as setting prevalence equal to a pre-specified value that can be estimated from the data themselves if necessary. Simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly-made assumptions hold. These assumptions include: the usual assumptions for the classic ACE and liability-threshold models; assumptions about shared family environment for relative pairs; and assumptions about the case-control family sampling, including single ascertainment. When our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.