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.     

Comparing the Use of Indigenous Knowledge with Classification and Ordination Techniques for Assessing the Species Composition and Structure of Vegetation in a Tropical Forest

dentification of groups that are similar in their floristic composition and structure (habitat types) is essential for conservation and forest managers to allocate high priority areas and to designate areas for reserves, refuges, and other protected areas. In this study, the use of indigenous knowledge for the identification of habitat types in the field was compared against an ecological characterization of habitat types, including their species composition obtained by using classification and ordination techniques for a tropical landscape mosaic in a rural Mayan area of Quintana Roo, Mexico. Plant diversity data calculated from 141 sampled sites chosen randomly on a vegetation class’s thematic map obtained by multispectral satellite image classification were used for this propose. Results indicated high similarity in the categorization of vegetation types between the Mayan classification and those obtained by cluster and detrended correspondence analysis. This suggests that indigenous knowledge has a practical use and can be comparable to that obtained by using science-based methods. Finally, identification and mapping of vegetation classes (habitat types) using satellite image classification allowed us to discriminate significantly different species compositions, in such a way that they can provide a useful mechanism for interpolating diversity values over the entire landscape.