UNCORRECTED PROOF AMRE [1.30] 2006/11/21 11:19; Prn:7/01/2009; 10:47 F:amre2600.tex; VTeX/Milda p. 1 (46-153) Amphibia-Reptilia 00 (2009): 1-22 Biogeographical patterns derived from remote sensing variables: the amphibians and reptiles of the Iberian Peninsula N. Sillero 1,∗ , J.C. Brito 2 , A.K. Skidmore 3 , A.G. Toxopeus 3 Abstract. The biogeographic patterns in species density of herptiles were analysed in the Iberian Peninsula. Geoclimatic regions were identified using a PCA. Individual habitat suitability (HS) models for 23 amphibians and 35 reptiles at 10 × 10 km scale were calculated with ENFA, using 12 environmental factors established with Remote Sensing (RS) techniques. The species presence proportion in each geoclimatic region was calculated through a cross-tabulation between each potential occurrence model and the geoclimatic regions. Species chorotypes were determined through Hierarchical Cluster Analysis using Jaccard’s index as association measure and by the analysis of marginality and tolerance factors from individual HS models. Predicted species density maps were calculated for each geoclimatic region. Probable under-sampled areas were estimated through differences between the predicted species density maps and observed (Gap analysis). The selected PCA components divided the Iberian Peninsula in two major geoclimatic regions largely corresponding to the Atlantic and Mediterranean climates. The Jaccard’s index clustered herptiles in two main taxonomic groups, with distribution similar to the Atlantic and Mediterranean geoclimatic regions (7 amphibian + 13 reptile species in three Atlantic subgroups and 16 amphibian + 22 reptile species in four Mediterranean subgroups). Marginality and tolerance factor scores identified species groups of herptile specialists and generalists. The highest observed and predicted species density areas were broadly located in identical regions. Predicted gaps are located in north-western, north-east and central Iberia. RS is a useful tool for biogeographical studies, as it provides consistent environmental data from large areas with high accuracy. Keywords: ???. Introduction Since the Rio Conference of 1992, investigation in biodiversity is an important goal focussing in three main research lines: compilation of choro- logical knowledge (Sillero, Celaya and Martín- Alfageme, 2005), identification of new species (Bermingham and Moritz, 1998) and reduction of biodiversity loss (Wilson et al., 2004). The latter research line is very important at a world- wide scale (Houlahan et al., 2000), because the identification of threatened species and the pro- posal of conservation measures requires knowl- 1 - Centro de Investigação em Ciências Geo-Espaciais (CI- CGE) da Universidade do Porto, Departamento de Matemática Aplicada, R. Campo Alegre, 687, 4169-007 Porto, Portugal 2 - CIBIO, Centro de Investigação em Biodiversidade e Re- cursos Genéticos da Universidade do Porto, Campus Agrário de Vairão, R. Padre Armando Quintas, 4485- 661 Vairão, Portugal 3 - ITC, International Institute for Geo-Information Science and Earth Observation, Hengelosestraat 99, Enschede, The Netherlands ∗ Corresponding author; e-mail: neftali.pablos@fc.up.pt edge on species occurrence. However, data on the composition and spatial distribution of bio- diversity is largely insufficient, especially for the worldwide hotspots of biodiversity (Myers et al., 2000). Given the present constraints in time and money for biodiversity assessments, an effi- cient tool is needed for identifying hotspot ar- eas and high diversity loss areas (Luoto, Toivo- nen and Heikkinen, 2002b; Maes et al., 2003; Lobo, Jay-Robert and Lumaret, 2004). Predic- tive modelling combined with Geographical In- formation Systems (GIS) allows the develop- ment of more robust and reliable models, re- lating biological diversity with environmental factors (Brito and Crespo, 2002; Soares and Brito, 2007; Martínez-Freiría et al., 2008). Cur- rently they are framework tools for the estab- lishment of conservation strategies and evalua- tion of management options (Brito et al., 1999; Álvares and Brito, 2006; Santos et al., 2006). Many studies on biodiversity using predic- tive modelling are frequently performed within © Koninklijke Brill NV, Leiden, 2009. Also available online - www.brill.nl/amre