Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

Analytical method for calculating the sensitivity index of system parameters

This work proposes an analytical method for calculating sensitivity index. The proposed method is based on oneat-a-time measurements and calculates the difference between output values and the base solution to define the impact caused by each parameter in the system. The results indicated that the analytical method provides adequate values for the sensitivity index even when the parameters have minor variation in relation to the base value, presenting coherent response with graphical analysis. The proposed method satisfies a wide range of systems and assigns value to the influence of the parameters, contributing to the decision-making process, calculation of system complexity, identification of systems, among other situations