Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30371
Assessing Habitat-Suitability Models with a Virtual Species at Khao Nan National Park, Thailand

Authors: M. Jaroensutasinee, K. Jaroensutasinee, W. Srisang


This study examined a habitat-suitability assessment method namely the Ecological Niche Factor Analysis (ENFA). A virtual species was created and then dispatched in a geographic information system model of a real landscape in three historic scenarios: (1) spreading, (2) equilibrium, and (3) overabundance. In each scenario, the virtual species was sampled and these simulated data sets were used as inputs for the ENFA to reconstruct the habitat suitability model. The 'equilibrium' scenario gives the highest quantity and quality among three scenarios. ENFA was sensitive to the distribution scenarios but not sensitive to sample sizes. The use of a virtual species proved to be a very efficient method, allowing one to fully control the quality of the input data as well as to accurately evaluate the predictive power of the analyses.

Keywords: Geographic Information System, climatic factors, Habitat-Suitability Models, Ecological niche factoranalysis

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1503


[1] L. Palma, P. Beja, and M. Rodgrigues, ''The use of sighting data to analyse Iberian lynx habitat and distribution,'' J. Appl. Ecol., vol.36, pp. 812-824, 1999.
[2] J. A. Sanchez-Zapata, J. F. Calvo, ''Raptor distribution in relation to landscape composition in semi-arid Mediterranean habitats,'' J. Appl. Ecol., vol. 36, pp. 254-262, 1999.
[3] D. J. Mladenoff, R. C. Haight, T. A. Sickley, and A. P. Wydeven, ''Causes and implications of species restoration in altered ecosystems. A spatial landscape projection of wolf population recovery,'' Bioscience, vol. 47, pp. 21-23, 1997.
[4] U. Breitenmoser, F. Zimmermann, P. Olsson, A. Ryser, C. Angst, A. Jobin, and C. Breitenmoser-W├╝rsten, Beurteilung des Kantons St. Gallen als Habitat f├╝r den Luchs, KORA, Bern, 1999.
[5] H. R. Akçakaya, and J. L. Atwood, ''A habitat-based metapopulation model of the California Gnatcatcher,'' Conservat. Biol., vol. 11, pp. 422-434, 1997.
[6] H. R. Akçakaya, M. A. McCarthy, and J. L. Pearce, ''Linking landscape data with population viability analysis: management options for the helmeted honeyeater Lichenostomus melanops cassidix,'' Biol. Conservat., vol. 73, pp. 169-176, 1995.
[7] G. Le Lay, P. Clergeau and L. Hubert-Moy, ''Computerized map of risk to manage wildlife species in urban areas,'' Environ. Manage., vol. 27, pp. 451-461, 2001
[8] A. Guisan and N. E. Zimmermann, ''Predictive habitat distribution models in ecology,'' Ecol. Model., vol. 135, pp. 147-186, 2000.
[9] S. Lek, M. Delacoste, P. Baran, I. Dimopoulos, J. Lauga, and S. Aulagnier, ''Application of neural networks to modelling nonlinear relationships in ecology,'' Ecol. Model., vol. 90, pp. 39-52, 1996.
[10] S. Manel, J. M. Dias, S. T. Buckton, and S. J. Ormerod, ''Alternative methods for predicting species distribution: an illustration with Himalayan river birds,'' J. Appl. Ecol., vol. 36, pp. 734-747, 1999.
[11] S. L. Özesmi and U. Özesmi, ''An artificial neural network approach to spatial habitat modelling with interspecific interaction.'' Ecol. Model., vol. 116, pp. 15-31, 1999.
[12] A. H. Hirzel, J. Hausser, D. Chessel, and N. Perrin, ''Ecological-niche factor analysis: how to compute habitat suitability maps without absence data?'' Ecology, vol. 83(7), pp. 2027-2036, 2002.
[13] G. E. Hutchinson, Concluding remarks, Cold Spring Harbor Symposium, Quantitative Biol., vol. 22, pp. 415-427, 1957.
[14] J. Hausser, Mammif?res de la Suisse: Répartition * Biologie * Ecologie. Commission des Mémoires de l'Académie Suisse des Sciences Naturelles, Birkh├ñuser Verlag, Basel, 1995.
[15] R. M. Chefaoui, J. Hortal, and J. M. Lobo, ''Potential distribution modelling, niche characterization and conservation status assessment using GIS tools: a case study of Iberian Copris species,'' Biol. Conservat., vol. 122, pp. 327-338, 2005.
[16] A. H. Hirzel, J. Hausser, and N. Perrin, (2004) Biomapper 3.1, Lab. of Conservation Biology, Department of Ecology and Evolution, University of Lausanne. Available:
[17] S. Wolfram, The Mathematica Book, 5th ed., Wolfram Media, 2003.
[18] R. R. Sokal, and F. J. Rohlf, Biometry: The Principles and Practice of Statistics in Biological Research. New York: W. H. Freeman, 1981.
[19] L. Legendre and P. Legendre, Numerical Ecology, 2nd English ed., Amsterdam, The Netherlands: Elsevier Science BV, 1998.
[20] G. V. Glass and K. D. Hopkins, Statistical methods in education and psychology, 2nd ed., London, UK: Prentice Hall, 1984.
[21] Thailand National Biodiversity Database System. (2006). Available:
[22] A. H. Fielding and J. F. Bell, ''A review of methods for the assessment of prediction errors in conservation presence/absence models,'' Environ. Conservat., vol. 24, pp. 38-49, 1997.
[23] F. Mesplé , M. Troussellier, C. Casellas, and P. Legendre, ''Evaluation of simple statistical criteria to qualify a simulation,'' Ecol. Model., vol. 88, pp. 9-18, 1996.
[24] A. H. Hirzel, V. Helfer, and F. Metral, ''Assessing habitat-suitability models with a virtual species,'' Ecol. Model., vol. 145, pp. 111-121, 2001.
[25] J. R. Alldredge, and J. T. Ratti, ''Further comparison of some statistical techniques for analysis of resource selection,'' J. Wildl. Manag., vol. 56, pp. 1-9, 1992.