RESUMEN
Clear and data-driven bioregionalizations can provide a framework to test hypotheses and base biodiversity conservation. Here we used occurrence and abundance data in combination with objective analytical methods to propose two bioregionalization schemes for tree species of the Cerrado and the Pantanal in South America. We also evaluated the contribution of three sets of determinants of the occurrence- and abundance-based subregions. We compiled data on tree species composition from 894 local assemblages based on species occurrences, and from 658 local assemblages based on species abundances. We used an unconstrained community-level modelling approach and clustering techniques to identify and map tree subregions for the occurrence and the abundance data sets, separately. Hierarchical clustering analyses were conducted to investigate floristic affinities between the subregions and to map broader floristic regions. We used multinomial logistic regression models, deviance partitioning, and rank-sum tests to assess the main subregion correlates. We identified 18 occurrence- and four abundance-based subregions in the Cerrado-Pantanal. The hierarchical classifications grouped the occurrence-based subregions into nine floristic zones and abundance-based subregions into two broad floristic zones. Variation in subregions were explained mainly by environmental factors and spatial structure in both occurrence and abundance data sets. The occurrence- and abundance-based subregions are complementary approaches to disentangle macroecological patterns and to plan conservation efforts in the Cerrado and the Pantanal. Our findings based on occurrence data revealed more complex and interdigitated boundaries between subregions of tree species than previously reported. The environment, historical stability, and human effects act in a synergetic way on the distribution of the subregions. Finally, the relevance of contemporary environmental factors to the subregion patterns we found alert us to the profound impact global warming may have on the spatial organization of the Cerrado-Pantanal tree flora.
RESUMEN
Cerrado is the second largest biome in South America and accounted for the second largest contribution to carbon emissions in Brazil for the last 10 years, mainly due to land-use changes. It comprises approximately 2 million km2 and is divided into 22 ecoregions, based on environmental conditions and vegetation. The most dominant vegetation type is cerrado sensu stricto (cerrado ss), a savanna woodland. Quantifying variation of biomass density of this vegetation is crucial for climate change mitigation policies. Integrating remote sensing data with adequate allometric equations and field-based data sets can provide large-scale estimates of biomass. We developed individual-tree aboveground biomass (AGB) allometric models to compare different regression techniques and explanatory variables. We applied the model with the strongest fit to a comprehensive ground-based data set (77 sites, 893 plots, and 95,484 trees) to describe AGB density variation of cerrado ss. We also investigated the influence of physiographic and climatological variables on AGB density; this analysis was restricted to 68 sites because eight sites could not be classified into a specific ecoregion, and one site had no soil texture data. In addition, we developed two models to estimate plot AGB density based on plot basal area. Our data show that for individual-tree AGB models a) log-log linear models provided better estimates than nonlinear power models; b) including species as a random effect improved model fit; c) diameter at 30 cm above ground was a reliable predictor for individual-tree AGB, and although height significantly improved model fit, species wood density did not. Mean tree AGB density in cerrado ss was 22.9 tons ha-1 (95% confidence interval = ± 2.2) and varied widely between ecoregions (8.8 to 42.2 tons ha-1), within ecoregions (e.g. 4.8 to 39.5 tons ha-1), and even within sites (24.3 to 69.9 tons ha-1). Biomass density tended to be higher in sites close to the Amazon. Ecoregion explained 42% of biomass variation between the 68 sites (P < 0.01) and shows strong potential as a parameter for classifying regional biomass variation in the Cerrado.
