Paisagens no Tempo e Espaço (PATES)

Methodological Approach

The acquisition of field botanical data is been made by means of floristic inventories to document species composition and variations, with stratified sampling by geomorphology and preliminary interpretations of geological context.

The geological analysis is been undertaken along outcrops and cores in interfluve areas and include sedimentological data to be used for the reconstruction of the geological history. Samples will be collected for AMS radiogenic dating, optically stimulated luminescence dating (OSL), and isotopic analysis of C and N. Sediment samples will be collected for pollen analysis in order to reconstruct past floristic communities.

Modeling of biodiversity consists of species prediction models that will be built integrating botanical and geological data in georeferenced environment. The objective is to analyze the determinants of species distribution and phylogenetic diversity, and understand how they relate to landscape evolutionary processes. Generalized dissimilarity modeling (GDM) will be used to model non-constant rates of compositional turnover along environmental gradients. The origin, history and interrelationships of disjunct centers of diversity will be examined through community phylogenetic analyses.

We are proposing to do what no one has done before: produce maps of Amazonian biodiversity and paleomorphology that combine a large spatial extent, fine spatial resolution and high thematic accuracy. If successful, these results will be useful to researchers interested in ecology, biogeography and evolution of organisms in Amazonian areas, who have faced the absence of reliable information on environmental and biotic changes within Amazonia. The chances of success are exceptionally high because the project has a highly qualified team and a unique database supported by field observations that can be used to refine and test the models and the maps based on them.

he process itself will be extremely useful, as future projects can build upon our experiences and methodological advances. For example, we will develop protocols for the integration of satellite image and SRTM data over large areas to identify floristic and geologic discontinuities in Amazonia. We will also explore new ways to use legacy data in species distributional model validation, and compare their utility with that of quantitative inventory data. These results will benefit those who need distribution models for species groups other than those we have studied in detail, but cannot dedicate years to collecting field data.