Effects of Silvicultural Practices on Soil Carbon and Nitrogen in a Nitrogen Saturated Central Appalachian (USA) Hardwood Forest Ecosystem

Silvicultural treatments represent disturbances to forest ecosystems often resulting in transient increases in net nitrification and leaching of nitrate and base cations from the soil. Response of soil carbon (C) is more complex, decreasing from enhanced soil respiration and increasing from enhanced postharvest inputs of detritus. Because nitrogen (N) saturation can have similar effects on cation mobility, timber harvesting in N-saturated forests may contribute to a decline in both soil C and base cation fertility, decreasing tree growth. Although studies have addressed effects of either forest harvesting or N saturation separately, few data exist on their combined effects. Our study examined the responses of soil C and N to several commercially used silvicultural treatments within the Fernow Experimental Forest, West Virginia, USA, a site with N-saturated soils. Soil analyses included soil organic matter (SOM), C, N, C/N ratios, pH, and net nitrification. We hypothesized the following gradient of disturbance intensity among silvicultural practices (from most to least intense): even-age with intensive harvesting (EA-I), even-age with extensive harvesting, even-age with commercial harvesting, diameter limit, and single-tree harvesting (ST). We anticipated that effects on soil C and N would be greatest for EA-I and least with ST. Tree species exhibited a response to the gradient of disturbance intensity, with early successional species more predominant in high-intensity treatments and late successional species more predominant in low-intensity treatments. Results for soil variables, however, generally did not support our predictions, with few significant differences among treatments and between treatments and their paired controls for any of the measured soil variables. Multiple regression indicated that the best predictors for net nitrification among samples were SOM (positive relationship) and pH (negative relationship). This finding confirms the challenge of sustainable management of N-saturated forests.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.