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Letters to Nature

Nature 426, 837-841 (18 December 2003) | doi:10.1038/nature02205; Received 27 August 2003; Accepted 11 November 2003

Open Innovation Challenges

Predicting distributions of known and unknown reptile species in Madagascar

Christopher J. Raxworthy1, Enrique Martinez-Meyer2, Ned Horning1, Ronald A. Nussbaum3, Gregory E. Schneider3, Miguel A. Ortega-Huerta2 & A. Townsend Peterson4

  1. American Museum of Natural History, Central Park West at 79th Street, New York, New York 10024-5192, USA
  2. Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico
  3. Museum of Zoology, University of Michigan, Ann Arbor, Michigan 48109-1079, USA
  4. Natural History Museum & Biodiversity Research Center, The University of Kansas, Lawrence, Kansas 66045-2454, USA

Correspondence to: Christopher J. Raxworthy1 Email: rax@amnh.org

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Despite the importance of tropical biodiversity1, informative species distributional data are seldom available for biogeographical study or setting conservation priorities2, 3. Modelling ecological niche distributions of species offers a potential soluion4, 5, 6, 7; however, the utility of old locality data from museums, and of more recent remotely sensed satellite data, remains poorly explored, especially for rapidly changing tropical landscapes. Using 29 modern data sets of environmental land coverage and 621 chameleon occurrence localities from Madagascar (historical and recent), here we demonstrate a significant ability of our niche models in predicting species distribution. At 11 recently inventoried sites, highest predictive success (85.1%) was obtained for models based only on modern occurrence data (74.7% and 82.8% predictive success, respectively, for pre-1978 and all data combined). Notably, these models also identified three intersecting areas of over-prediction that recently yielded seven chameleon species new to science. We conclude that ecological niche modelling using recent locality records and readily available environmental coverage data provides informative biogeographical data for poorly known tropical landscapes, and offers innovative potential for the discovery of unknown distributional areas and unknown species.