So far, detailed species richness maps based on species ranges Selleck Vactosertib of large numbers of species cover only parts of the Neotropics or lack quantification of uncertainty due to heterogeneous sampling effort over area (Kress et al. 1998;

Hopkins, 2007; Morawetz and Raedig 2007; Schulman et al. 2007). Here we introduce an interpolation approach, which can be applied for scant data, and which does not require more than the available pure species occurrence data. Our goal is to make the application of this approach independent of detailed knowledge of the ecological demands of the species. The resulting patterns are only an approximation of ‘real’ distribution patterns, but produced in a standardized, reproducible way. The aim of this study is (i) to present a method tailored to map distribution patterns of Neotropical angiosperm species based selleck screening library on scarce, yet taxonomically reliable monographic occurrence data, (ii) to estimate the distribution

patterns of Neotropical angiosperm species and (iii) to explore whether the method presented is appropriate for the identification of centers of diversity and narrow endemism. Methods Our analysis is based on distribution data of angiosperm species taken from monographs or similar thoroughly revised treatments covering the Neotropical realm (see Appendix 1). The database was presented in a previous work (Morawetz and Raedig 2007) and since then has been complemented with a further 340 species. It now contains 4,055 species, in 230 genera and 66 families, with ~77% woody and 23% herbaceous species. Species

occurrence data were taken from distribution maps and transferred to a grid with 1° grid resolution containing 2,519 quadrats sized ~100 km × 100 km (varying from 12,550 km2 at the equator to 8,250 km2 at Tierra del Fuego). The species recorded in the database represent about 5% of all Neotropical angiosperm species. It should be stressed that species richness numbers and patterns derived here are indices of species richness, not estimates of absolute numbers. Due to the special characteristics check details of our database, we had to PR-171 cost design a novel interpolation approach. Firstly, because our data set only includes presence data (not presence/absence data), the choice of suitable habitat quality models was already strongly limited (e.g. Graham et al. 2004; Phillips et al. 2006). Secondly, many species are represented in very few quadrats. Although ecological niche models have successfully been applied for species with only five records (Pearson et al. 2007), exclusion of species having less than five occurrences would exclude about 50% of the species of our data set. Thirdly, the rule of the thumb that each explanatory variable requires about ten data points (Harrell 2001; Reineking and Schröder 2006) would exclude 90% of the species in our database, even if we used a small predictor set of only three environmental variables.