Effects of Atmospheric Nitrogen Deposition on the Herbaceous Layer of a Central Appalachian Hardwood Forest

Additions of nitrogen (N) have been shown to alter species diversity of plant communities, with most experimental studies having been carried out in communities dominated by herbaceous species. We examined seasonal and inter-annual patterns of change in the herbaceous layer of two watersheds of a central Appalachian hardwood forest that differed in experimental treatment. This study was carried out at the Fernow Experimental Forest, West Virginia, using two adjacent watersheds: WS4 (mature, second-growth hardwood stand, untreated reference), and WS3. Seven circular 0.04-ha sample plots were established in eachwatershed to represent its full range of elevation and slope aspect. The herbaceous layer was sampled by identifying and visually estimating cover (%) of all vascular plants. Sampling was carried out in mid-July of 1991 and repeated at approximately the same time in 1992. In 1994, these same plots were sampled each month fromMay to October. Seasonal patterns of herb layer dynamics were assessed for the complete 1994 data set, whereasinter-annual variability was based on plot data from 1991, 1992, and the July sample of 1994. There were nosignificant differences between watersheds for any sample year for any of the other herb layer characteristics measured, including herb layer cover, species richness, evenness, and diversity. Cover on WS4 decreased significantly from 1991 to 1992, followed by no change to 1994. By contrast, herb layer cover did not varysignificantly across years on WS3. Cover of the herbaceous layer of both watersheds increased from early in the growing season to the middle of the growing season, decreasing thereafter, with no significant differencesbetween WS3 and WS4 for any of the monthly cover means in 1994. Similar seasonal patterns found for herblayer cover—and lack of significant differences between watersheds—were also evident for species diversityand richness. By contrast, there was little seasonal change in herb layer species evenness, which was nearlyidentical between watersheds for all months except October. Seasonal patterns for individual species/speciesgroups were closely similar between watersheds, especially for Viola rotundifolia and Viola spp. Species richnessand species diversity were linearly related to herb layer cover for both WS3 and WS4, suggesting that spatialand temporal increases in cover were more related to recruitment of herb layer species than to growth of existingspecies. Results of this study indicate that there have been negligible responses of the herb layer to 6 yr of additions to WS3.

GEOSTATISTICAL INFERENCE UNDER PREFERENTIAL SAMPLING

Geostatistics involves the fitting of spatially continuous models to spatially discrete data (Chil`es and Delfiner, 1999). Preferential sampling arises when the process that determines the data-locations and the process being modelled are stochastically dependent. Conventional geostatistical methods assume, if only implicitly, that sampling is non-preferential. However, these methods are often used in situations where sampling is likely to be preferential. For example, in mineral exploration samples may be concentrated in areas thought likely to yield high-grade ore. We give a general expression for the likelihood function of preferentially sampled geostatistical data and describe how this can be evaluated approximately using Monte Carlo methods. We present a model for preferential sampling, and demonstrate through simulated examples that ignoring preferential sampling can lead to seriously misleading inferences. We describe an application of the model to a set of bio-monitoring data from Galicia, northern Spain, in which making allowance for preferential sampling materially changes the inferences.

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory.