Spatial heterogeneity of soil nutrients and plant species in herb-dominated communities of contrasting land use

Recent interest in spatial pattern in terrestrial ecosystems has come from an awareness of theintimate relationship between spatial heterogeneity of soil resources and maintenance of plant species diversity. Soil and vegetation can vary spatially inresponse to several state factors of the system. In this study, we examined fine-scale spatial variability of soil nutrients and vascular plant species in contrasting herb-dominated communities (a pasture and an oldfield) to determine degree of spatial dependenceamong soil variables and plant community characteristics within these communities by sampling at 1-m intervals. Each site was divided into 25 1-m 2 plots. Mineral soil was sampled (2-cm diameter, 5-cm depth) from each of four 0.25-m2 quarters and combined into a single composite sample per plot. Soil organic matter was measured as loss-on-ignition. Extractable NH4 and NO3 were determined before and after laboratory incubation to determine potential net N mineralization and nitrification. Cations were analyzed using inductively coupled plasma emission spectrometry. Vegetation was assessed using estimated percent cover. Most soiland plant variables exhibited sharp contrasts betweenpasture and old-field sites, with the old field having significantly higher net N mineralization/nitrification, pH, Ca, Mg, Al, plant cover, and species diversity, richness, and evenness. Multiple regressions revealedthat all plant variables (species diversity, richness,evenness, and cover) were significantly related to soil characteristics (available nitrogen, organic matter,moisture, pH, Ca, and Mg) in the pasture; in the old field only cover was significantly related to soil characteristics (organic matter and moisture). Both sites contrasted sharply with respect to spatial pattern of soil variables, with the old field exhibiting a higher degree of spatial dependence. These results demonstrate that land-use practices can exert profound influence on spatial heterogeneity of both soil properties and vegetation in herb-dominated communities.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately