Mapping the Spatial Variability of Plant Diversity in a Tropical Forest: Comparison of Spatial Interpolation Methods

Knowledge of the spatial distribution of plant species is essential to conservation and forest managers in order to identify high priority areas such as vulnerable species and habitats, and designate areas for reserves, refuges and other protected areas. A reliable map of the diversity of plant species over the landscape is an invaluable tool for such purposes. In this study, the number of species, the exponent Shannon and the reciprocal Simpson indices, calculated from 141 quadrat sites sampled in a tropical forest were used to compare the performance of several spatial interpolation techniques used to prepare a map of plant diversity, starting from sample (point) data over the landscape. Means of mapped classes, inverse distance functions, kriging and co-kriging, both, applied over the entire studied landscape and also applied within vegetation classes, were the procedures compared. Significant differences in plant diversity indices between classes demonstrated the usefulness of boundaries between vegetation types, mapped through satellite image classification, in stratifying the variability of plant diversity over the landscape. These mapped classes, improved the accuracy of the interpolation methods when they were used as prior information for stratification of the area. Spatial interpolation by co-kriging performed among the poorest interpolators due to the poor correlation between the plant diversity variables and vegetation indices computed by remote sensing and used as covariables. This indicated that the latter are not suitable covariates of plant diversity indices. Finally, a within-class kriging interpolator yielded the most accurate estimates of plant diversity values. This interpolator not only provided the most accurate estimates by accounting for the indices' intra-class variability, but also provided additional useful interpretations of the structure of spatial variability of diversity values through the interpretation of their semi-variograms. This additional role was found very useful in aiding decisions in conservation planning.     

CO-KRIGING TO INCORPORATE SCREENING DATA: HUDSON RIVER SEDIMENT PCBs1

ABSTRACT: Kriging methods of geostatistical analysis provide valuable techniques for analysis of sediment contamination problems, including interpolation of concentration maps from point data and estimation of global mean concentrations. Sample collection efforts frequently include preliminary screening data of considerably more extensive coverage than the laboratory analyses on which estimation is usually based. How should these be incorporated in kriging? Screening and laboratory analysis constitute two separate estimates of the same spatial field but of very different characteristics. A modified version of co-kriging is developed to include the imprecise screening information in the analysis of contaminant distribution. Use of the method is demonstrated on a data set of sediment PCB samples from the Upper Hudson River, for which preliminary categorical mass spectrometry screening was used to select a smaller set of samples for gas chromatograph analysis. The method is widely applicable to many situations of contaminant and natural resource estimation.