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Recent empirical work in numerous systems has demonstrated the interdependence of spatial and temporal accumulation of species in the species--time--area relationship (STAR). The purpose of this study was to develop a process-based stochastic model for the STAR that assumes species neutrality and to compare the model's expectations to data collected on plant species in a tallgrass prairie. We varied two important aspects of the neutral species assemblage: evenness in the species pool and individual replacement rate (R). When R is larger than ~0.5 and evenness is intermediate to high, the neutral STAR generates patterns qualitatively similar to the empirical STAR. Our model also indicates that space and time were not symmetrical in their effects on species accumulation, except in the special case of R = 1.0. We observed both positive and negative time-by-area interactions in the sampling model, which indicates that nonzero interactions are not necessarily evidence of ecological processes. Furthermore, as accumulated richness approaches the size of the species pool, the time-by-area interaction becomes increasingly negative in our model. This suggests that negative time-by-area interactions should be expected a priori in empirical systems if rates of species accumulation decrease due to increasing rarity of unique species. Given the wide range of STARs that the sampling model generated, the difficulty in estimating key parameters, and the complexity of assessing the relative abundance distribution and scale of the species pool, we cannot refute the sampling effect, and we suggest caution in accepting ecologically oriented explanations of empirical STARs. Key words: biodiversity; grassland; rarefaction effect; sampling effect; scale dependence; scaling; species richness; species--time--area relationship (STAR); species turnover.

We provide algebraic simplifications for the redundancy analysis (RDA) eigenvalue and the canonical correspondence analysis (CCA) eigenvalue in the special case of permanent plots sampled twice. The indices for RDA and CCA are interrelated and are intuitively interpretable. These simplifications also apply to simple split-plot designs and to a balanced design with two independent samples. Key words: canonical correspondence analysis; monitoring; ordination eigenvalues: permanent plots; redundancy analysis; split-plot design.

DAVID B. CLARK [1,3] MICHAEL W. PALMER [2] DEBORAH A. CLARK [1] (1.) Department of Biology, University of Missouri, 8001 Natural Bridge Road, St. Louis, Missouri 63121 USA (2.) Department [...]

We present data that were collected as part of a monitoring project on vascular plant composition at the Tallgrass Prairie Preserve in Osage County, Oklahoma, USA. The purpose of these data are to promote the study of multi-scale patterns of species composition for both theoretical and applied questions. Furthermore, these data will provide a reference point for tallgrass prairie restoration projects in the Flint Hills. Over the course of the 12-year period, we sampled 20 permanent plots annually. The permanent plots were selected semi-randomly from a UTM grid using the criteria that they contain less than 20% of woody cover, standing water, or exposed rock. Plant species presence was recorded at five spatial scales: 0.01, 0.1, 1.0, 10, and 100 [m.sup.2] in each of the four corners of a 100-[m.sup.2] square quadrat. Plant species were assigned to a percent cover class at the 100-[m.sup.2] grain. In addition to information on plant composition, we provide data on topography, soil variables, monthly total rainfall, monthly average temperature, and management records related to fire and grazing history. We hope this data set will stimulate further research into the scaling of biodiversity and insight into the functioning and conservation of tallgrass prairie plant communities. Key words: bison; Flint Hills; restoration: spatial scale; species-time-area relationship; tallgrass prairie; vascular plants; vegetation monitoring.

We present a data set collected in 1989 of vascular plant occurrences in overlapping grids of nested plots in the Oosting Natural Area of the Duke Forest, Orange County, North Carolina, USA. The purpose of these data is to allow the study of vascular plant biodiversity at multiple spatial scales, in terms of both grain and extent. There are eight distinct plot sizes (grains), with size four times that of the next smallest, from 0.015625 [m.sup.2] to 256 [m.sup.2]. Larger grains are possible by aggregating adjacent plots. Extents (linear distances between plots) range from 1 m to 362 m. In addition, trees were mapped over the entire 256 m x 256 m study site in 1990, and again in 1998 to assess the effects of Hurricane Fran in September 1996. Tree species, diameters, and damage descriptors (1998 only) were also recorded. Soils were gathered from the entire grid in 1990 and analyzed for nutrients, pH, and organic matter. These data have already been used to assess scale dependence of plant diversity, to evaluate new methods for the study of spatial scale, and to study the importance of spatial scale on the interpretation of hurricane damage to forests. They are presented as a case study for future scale research. Key words: distance decay; hurricane; North Carolina; soil nutrients; spatial scale; species--area relationships; tree mapping; vascular plants.

Bailey and Gatrell have produced a solid introduction to the field of spatial data analysis. The book focuses on description of pattern, tests of hypotheses, and interpolation, and the authors [...]