A Geoplatform for improving accessibility to environmental cartography

Geographic variation in range size and beta diversity of groundwater crustaceans: insights from habitats with low thermal seasonality

“…Area of occupancy (AOO) was quantified by counting the number of 20 km × 20 km grid cells in which a species occurred. To analyse the latitudinal patterns of species richness and median range size of all species, we used the grid of 0.9° latitude cells provided by the EDIT geoplatform (Sastre et al ., ). Cell area was kept constant (10,000 km 2 ) all over the grid by adjusting the longitudinal divisions between adjacent cells in each latitudinal band.…”

“…Following Svenning et al . (), larger regions for this analysis corresponded to squares provided by the EDIT geoplatform (Sastre et al ., ), all having the same area (250,000 km 2 ) and latitudinal range (4.5°).…”

Multiple site dissimilarity quantifies compositional heterogeneity among several sites, while average pairwise dissimilarity may be misleading

“…Th erefore, I measured the concordance (Spearman rank correlation, ρ ) between multiple site dissimilarity components derived from turnover or nestedness ( β SIM or β SNE , respectively) and the equivalent averaged pairwise dissimilarities (mean β sim or mean β sne , respectively), calculated in all cases for the pool of 1 ° ϫ 1 ° cells contained within regional squares of 250 000 km 2 (Sastre et al 2009). Th erefore, I measured the concordance (Spearman rank correlation, ρ ) between multiple site dissimilarity components derived from turnover or nestedness ( β SIM or β SNE , respectively) and the equivalent averaged pairwise dissimilarities (mean β sim or mean β sne , respectively), calculated in all cases for the pool of 1 ° ϫ 1 ° cells contained within regional squares of 250 000 km 2 (Sastre et al 2009).…”

How to identify future sampling areas when information is biased and scarce: An example using predictive models for species richness of Iberian water beetles

“…For each cell (2500 km 2 ), nine climatic variables (minimum and maximum monthly mean temperatures, mean annual temperature, total annual rainfall, summer precipitation, mean percentage of sunny hours per year, aridity, annual range of temperature variation and annual precipitation variation); four topographic variables (minimum, maximum and mean altitude, and elevation range), four lithological variables (percentage of area with clay, calcareous, and siliceous substrates, and lithological diversity) and one variable related with the total surface of water bodies in each cell were used. All these data come from the EDIT-Geoplatform (Sastre et al 2009) which is freely accessible at http://edit.csic.es/GISdownloads.html. The scores of these variables at each 50 × 50 UTM square in the Iberian Peninsula were extracted and standardised (to 0 mean and 1 standard deviation) and then submitted to a Principal Component Analysis (PCA) in order to obtain uncorrelated factors (Varimax rotation).…”