Ecologists are concerned with the relationships between species composition and environmental factors, and with spatial structure within those relationships. A dissimilaritybased framework incorporating space explicitly is an extremely flexible tool for answering these questions. The R package ecodist brings together methods for working with dissimilarities, including some not available in other R packages. We present some of the features of ecodist, particularly simple and partial Mantel tests, and make recommendations for their effective use. Although the partial Mantel test is often used to account for the effects of space, the assumption of linearity greatly reduces its effectiveness for complex spatial patterns. We introduce a modification of the Mantel correlogram designed to overcome this restriction and allow consideration of complex nonlinear structures. This extension of the method allows the use of partial multivariate correlograms and tests of relationship between variables at different spatial scales. Some of the possibilities are demonstrated using both artificial data and data from an ongoing study of plant community composition in grazinglands of the northeastern United States.
the plant diversity of natural grassland communities, some producers in the Northeast often plant complex Some producers believe that planting pastures to several forage mixtures of grasses and legumes (Tracy and Sanderson, species benefits sustainability of grazing systems. We conducted a grazing study to determine if forage species diversity in pastures affects 2000; Sanderson et al., 2001) because they believe that herbage productivity and weed invasion. One-hectare pastures were maintaining a highly diverse botanical composition in planted to four mixtures in August 2001 and then grazed with lactating pastures benefits persistence, yield stability, and prodairy cattle during 2002 and 2003. The mixtures were two species [orductivity. chardgrass (Dactylis glomerata L.) and white clover (Trifolium repens
Research and development on atmospheric and topographic correction methods for multispectral satellite data such as Landsat images has far outpaced the availability of those methods in geographic information systems software. As Landsat and other data become more widely available, demand for these improved correction methods will increase. Open source R statistical software can help bridge the gap between research and implementation. Sophisticated spatial data routines are already available, and the ease of program development in R makes it straightforward to implement new correction algorithms and to assess the results. Collecting radiometric, atmospheric, and topographic correction routines into the landsat package will make them readily available for evaluation for particular applications.
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