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

Abstract

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.

The detection of thermophilous forest hotspots in Poland using geostatistical interpolation of plant richness

“…It is important to note that the data was analyzed on a large spatial scale, and was collected for many years by many researchers. The results of this study confirm that ordinary kriging is a good method for the identification of spatial patterns in species richness, as previously noted by other studies [4,[38][39][40].…”

Citizen Science as a Tool in Biological Recording—A Case Study of Ailanthus altissima (Mill.) Swingle

“…Invasive species biological recording, mapping, and monitoring are prerequisites for successful biological invasion risk management [31][32][33] and to prevent invasive species expansion into large areas, where eradication becomes very difficult and costly [1]. Among invasive plants, trees are generally easiest to map because of their size [34], although there is currently no methodical, reasonably priced protocol for the estimation of tree invasions that can be used in invasive species management or scientific research [35].…”

Índices de sorción de metales pesados en suelos urbanos: el caso de Morelia, Michoacán

“…Donde: γ (h) denota el valor del semivariograma experimental para todos los pares de datos de los índices de sorción a una distancia h; Z(X i ) muestra los valores de los índices en un sitio X i ; Z(X i + h) e indica los valores de los índices en diferentes puntos, los cuales son separados de X i en distancia discreta h; X i expresa el sitio georeferenciado y el valor de los índices de contaminación; n indica los números de pares separados a la distancia h (Hernández-Stefanoni y Ponce-Hernández, 2006;Delgado et al, 2010;Zhang et al, 2014;Cortés et al, 2015). Se obtuvo el modelo que mejor se ajustó al semivariograma experimental, y por último se realizaron los diagramas de superficie continua con el método de interpolación kriging ordinario (KO).…”