Potential Distribution Modelling Using Machine Learning

  • Ana C. Lorena
  • Marinez F. de Siqueira
  • Renato De Giovanni
  • André C. P. L. F. de Carvalho
  • Ronaldo C. Prati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)


Potential distribution modelling has been widely used to predict and to understand the geographical distribution of species. These models are generally produced by retrieving the environmental conditions where the species is known to be present or absent and feeding this data into a modelling algorithm. This paper investigates the use of Machine Learning techniques in the potential distribution modelling of plant species Stryphnodendron obovatum Benth (MIMOSACEAE). Three techniques were used: Support Vector Machines, Genetic Algorithms and Decision Trees. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species being considered.


Ecological niche modelling Potential distribution modelling Machine Learning 


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  1. 1.
    Egbert, S.L., Peterson, A.T., Sanchez-Cordero, C., Price, K.P.: Modeling conservation priorities in Veracruz, Mexico. In: GIS in natural resource management: Balancing the technical-political equation, pp. 141–150 (1998)Google Scholar
  2. 2.
    Ortega-Huerta, M.A., Peterson, A.T.: Modelling spatial patterns of biodiversity for conservation prioritization in north-eastern Mexico. Diversity and Distributions 10, 39–54 (2004)CrossRefGoogle Scholar
  3. 3.
    Peterson, A.T., Vieglais, D.A.: Predicting species invasions using ecological niche modeling: New approaches from bioinformatics attack a pressing problem. BioScience 51(5), 363–371 (2001)CrossRefGoogle Scholar
  4. 4.
    Peterson, A.T., Papes, M., Kluza, D.A.: Predicting the potential invasive distributions of four alien plant species in north America. Weed Science 51(6), 863–868 (2003)CrossRefGoogle Scholar
  5. 5.
    Huntley, B., Berry, P.M., Cramer, W., McDonald, A.P.: Modelling present and potential future ranges of some european higher plants using climate response surfaces. J. Biogeography 22, 967–1001 (1995)CrossRefGoogle Scholar
  6. 6.
    Pearson, R.G., Dawson, T.P., Berry, P.M., Harrison, P.A.: Species: A spatial evaluation of climate impact on the envelope of species. Ecological Modelling 154, 289–300 (2002)CrossRefGoogle Scholar
  7. 7.
    Peterson, A.T., Ortega-Huerta, M.A., Bartley, J., Sanchez-Cordero, V., Buddemeier, R.H., Stockwell, D.R.B.: Future projections for mexican faunas under global climate change scenarios. Nature 416, 626–629 (2002)CrossRefGoogle Scholar
  8. 8.
    Guisan, A., Thuiller, W.: Predicting species distribution: offering more than simple habitat models. Ecology Letters 8, 993–1009 (2005)CrossRefGoogle Scholar
  9. 9.
    Peterson, A.T., Sanchez-Cordero, V., Beard, C.B., Ramsey, J.M.: Ecologic niche modeling and potential reservoirs for chagas disease, Mexico. Emerging Infectious Diseases 8, 662–667 (2002)Google Scholar
  10. 10.
    Stockwell, D.R.B., Peters, D.P.: The GARP modelling system: Problems and solutions to automated spatial prediction. International Journal of Geographic Information Systems 13, 143–158 (1999)CrossRefGoogle Scholar
  11. 11.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)Google Scholar
  12. 12.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  13. 13.
    Elith, J., Crahanm, C.H., Anderson, R.P., Miroslav, M.D., Ferrier, S., Guisan, A., Hijmans, R.J., Hucttmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, G., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T., Phillips, S.J., Richardson, K., Scachettti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S., Zimmermann, N.: Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006)CrossRefGoogle Scholar
  14. 14.
    Mitchell, M.: An introduction to Genetic Algorithms. MIT Press, Cambridge (1999)Google Scholar
  15. 15.
    Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)Google Scholar
  16. 16.
    Peterson, A.T.: Predicting species’ geographic distributions based on ecological niche modeling. Condor 103(3), 599–605 (2001)CrossRefGoogle Scholar
  17. 17.
    Siqueira, M.F.: Uso de modelagem de nicho fundamental na avaliação do padrão de distribuição geográfica de espécies vegetais. PhD thesis, Escola de Engenharia de São Carlos, Universidade de São Paulo, São Carlos (2005) (in Portuguese)Google Scholar
  18. 18.
    Soberón, J., Peterson, A.T.: Interpretation of models of fundamental ecological niches and species distributional areas. In: Biodiversity Informatics (2005)Google Scholar
  19. 19.
    Fielding, A.H.: Machine learning methods for ecological applications. Kluwer Academic, Dordrecht (1999)zbMATHGoogle Scholar
  20. 20.
    Lek, S., Guegan, J.F.: Artificial neural networks as a tool in ecological modelling: an introduction. Ecological Modelling 120, 65–73 (1999)CrossRefGoogle Scholar
  21. 21.
    Ath, G.D., Fabricius, K.E.: Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecol. 81(11), 3178–3192 (2000)CrossRefGoogle Scholar
  22. 22.
    Pearson, R.G., Dawson, T.P., Liu, C.: Modelling species distribution in britain: a hierarchical integration of climate and land-cover data. Ecography 27, 285–298 (2004)CrossRefGoogle Scholar
  23. 23.
    Siqueira, M.F.D., Peterson, A.T.: Consequences of global climate change for geographic distributions of cerrado tree species. Biota Neotropica 3(2) (2003),
  24. 24.
    Guo, Q., Kelly, M., Grahan, C.H.: Support vector machines for predicting distribution of sudden oak death in California. Ecol. Modelling 182, 75–90 (2005)CrossRefGoogle Scholar
  25. 25.
    Ratter, J.A., Bridgewater, S., Ribeiro, J.F., Dias, T.A.B., Silva, M.R.: Distribuição das espécies lenhosas da fitofisionomia cerrado sentido restrito nos estados compreendidos no bioma cerrado. Boletim do Herbário Ezechias Paulo Heringer 5, 5–43 (2000) (in portuguese)Google Scholar
  26. 26.
    Durigan, G., Siqueira, M.F., Franco, G.A.D.C., Bridgewater, S., Ratter, J.A.: The vegetation of priority areas for cerrado conservation in são paulo state, brazil. Edinburgh Journal of Botany 60, 217–241 (2003)CrossRefGoogle Scholar
  27. 27.
    Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965–1978 (2005)CrossRefGoogle Scholar
  28. 28.
    Souza, A.L.F., Giarola, A., Gondim, M.A.: Aplicao de mascaramento de nuvens e correção atmosférica na geração do índice de vegetação por diferença normalizada no cptec/inpe. In: XIV Cong. Brasileiro de Agrometeorologia (2005) (in portuguese)Google Scholar
  29. 29.
    Quilan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1988)Google Scholar
  30. 30.
    Vapnik, V.N.: The nature of Statistical learning theory. Springer, New York (1995)zbMATHGoogle Scholar
  31. 31.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2004),
  32. 32.
    Demsar, J.: Statistical comparisons of classifiers over multiple datasets. Journal of Machine Learning Research 7, 1–30 (2006)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ana C. Lorena
    • 1
  • Marinez F. de Siqueira
    • 2
  • Renato De Giovanni
    • 2
  • André C. P. L. F. de Carvalho
    • 3
  • Ronaldo C. Prati
    • 3
  1. 1.Centro de Matemática, Computação e CogniçãoUniversidade Federal do ABCSanto AndréBrazil
  2. 2.Centro de Referência em Informação AmbientalCampinasBrazil
  3. 3.Instituto de Ciências Matemáticas e de ComputaçãoUniversidade de São PauloSão CarlosBrazil

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