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).

The Ecological Significance of the Herbaceous Layer in Temperate Forest Ecosystems

Despite a growing awareness that the herbaceous layer serves a special role in maintaining the structure and function of forests, this stratum remainsan underappreciated aspect of forest ecosystems. In this article I review and synthesize information concerning the herb layer’s structure,composition, and dynamics to emphasize its role as an integral component of forest ecosystems. Because species diversity is highest in the herb layeramong all forest strata, forest biodiversity is largely a function of the herb-layer community. Competitive interactions within the herb layer candetermine the initial success of plants occupying higher strata, including the regeneration of dominant overstory tree species. Furthermore, the herblayer and the overstory can become linked through parallel responses to similar environmental gradients. These relationships between strata varyboth spatially and temporally. Because the herb layer responds sensitively to disturbance across broad spatial and temporal scales, its dynamics canprovide important information regarding the site characteristics of forests, including patterns of past land-use practices. Thus, the herb layer has asignificance that belies its diminutive stature.

Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials

Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.

Simpson's Paradox: A Graphical Treatment

It is possible for a pair of dichotomous, categorical variables to have an overall positive association (say) in a stratified population, and at the same time be negatively associated in every stratum. This (essentially) is Simpson's paradox. A graphical device, the risk diagram, provides insight into Simpson's paradox and related concepts.

Causal Inference in Observational Studies with Outcome-Dependent Sampling

In this paper, we consider estimation of the causal effect of a treatment on an outcome from observational data collected in two phases. In the first phase, a simple random sample of individuals are drawn from a population. On these individuals, information is obtained on treatment, outcome, and a few low-dimensional confounders. These individuals are then stratified according to these factors. In the second phase, a random sub-sample of individuals are drawn from each stratum, with known, stratum-specific selection probabilities. On these individuals, a rich set of confounding factors are collected. In this setting, we introduce four estimators: (1) simple inverse weighted, (2) locally efficient, (3) doubly robust and (4)enriched inverse weighted. We evaluate the finite-sample performance of these estimators in a simulation study. We also use our methodology to estimate the causal effect of trauma care on in-hospital mortality using data from the National Study of Cost and Outcomes of Trauma.