Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species (Quercus suber) and two temperate species (Ilex aquifolium and Taxus baccata). Model evaluation was measured by MSE, Goodman-Kruskal and sensitivity statistics and map outputs based on the minimal predicted area criterion. All the models performed well, confirming the validity of this approach when dealing with species characterized by narrow and specialized niches and when adequate data (more than 40–50 samples) and environmental and climatic variables, recognized as important determinants of plant distribution patterns, are available. Based on the evaluation processes, RF resulted the most accurate algorithm thanks to bootstrap-resampling, trees averaging, randomization of predictors and smoother response surface.
Gaussian processes with radial basis kernel functions
Multivariate Adaptive Regression Splines
Regression Tree Analysis
Support Vector Regression
Araújo, M. B. and A. Guisan. 2006. Five (or so) challenges for species distribution modeling. J. Biogeogr. 33: 1677–1688.
Araújo, M. B. and M. New. 2007. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22(1): 42–47.
Attorre, F., M. Alfò, M. De Sanctis, F. Francesconi and F. Bruno. 2007a. Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. International J. Climatol. 27: 1825–1843.
Attorre, F., F. Francesconi, N. Taleb, P. Scholte, A. Saed, M. Alfo and F. Bruno. 2007b. Will dragonblood survive the next period of climate change? Current and future potential distribution of Dracaena cinnabari (Socotra, Yemen). Biol. Conserv. 138: 430–439.
Beaumont, L.J., L. Hughes, and M. Poulsen. 2005. Predicting species distributions: use of climatic parameters in BIOCLIM and its impact on predictions of species’ current and future distributions. Ecol. Model. 186: 250–269.
Benito Garzòn, M., R. Blazek, M. Neteler, R. Sánchez de Dios, H. Sainz Ollero and C. Furlanello. 2006. Machine learning models for predicting species habitat distribution suitability: An example with Pinus sylvestris L. for the Iberian Peninsula. Ecol. Model. 197: 383–393.
Benito Garzòn, M., R. Sánchez de Dios and H. Sainz Ollero. 2008. Effects of climate change on the distribution of Iberian tree species. Appl. Veg. Sci. 11: 169–178.
Breiman, L. 2001. Random forests. Machine Learning 45: 5–32.
Breiman, L., J. H. Friedman, R. A. Olshen and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth, Belmont, CA.
Chefaoui, R.M. and J.M. Lobo. 2008. Assessing the effects of pseudo-absences on predictive distribution model performance. Ecol. Model. 210: 478–486.
Drake, J.M., C. Randin and A. Guisan. 2006. Modelling ecological niches with support vector machines. J. Appl. Ecol. 43: 424–432.
Drucker, H., C.J.C. Burges, L. Kaufman, A. Smola and V. Vapnik. 1997. Support Vector Regression Machines. Advances in Neural Information Processing Systems 9, NIPS 1996, pp. 155–161.
Elith, J., M.A. Burgman and H.M. Regan. 2002. Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecol. Model. 157: 313–330.
Elith, J., C. H. Graham, R. P. Anderson, M. Dudýk, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J.R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. McC. Overton, A. Townsend Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberòn, S. Williams, M. S. Wisz and N.E. Zimmermann. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129–151.
Elith, J. and J. Leathwick. 2007. Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Divers. Distrib. 13: 265–275.
Engler, R., A. Guisan and L. Rechsteiner. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41: 263–274.
Farber, O. and R. Kadmon. 2003. Assessment of alternative approaches for bioclimatic modelling with special emphasis on the Mahalanobis distance. Ecol. Model. 160: 115–130.
Friedman, J. 1991. Multivariate adaptive regression splines. Ann. Stat. 19: 1–141
Guisan, A. and N. E. Zimmerman. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135: 147–186.
Guisan A., T.C. Edwards and T. Hastie. 2002. Generalized linear and generalized additive models in studies of species distribution: setting the scene. Ecol. Model. 157: 89–100.
Guisan, A. and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8: 993–1009.
Guisan, A., O. Broennimann, R. Engler, M. Vust, N.G. Yoccoz, A. Lehmann and N.E. Zimmermann. 2006. Using niche-based models to improve the sampling of rare species. Conserv. Biol. 20: 501–511.
Guisan, A., N. E. Zimmermann, J. Elith, C. H. Graham, S. Phillips and A. T. Peterson. 2007. What matters for predicting the occurrences of trees: techniques, data or species characteristics? Ecol. Monogr. 77: 615–630.
Guo, Q., M. Kelly and C. H. Graham. 2005. Support vector machines for predicting distribution of Sudden Oak Death in California. Ecol. Model. 182: 75–90.
Hastie, T., R. Tibshirani, and J. Friedman. 2001. The Elements of Statistical Learning. Springer, New York.
Hernandez, P. A., C.H. Graham, L.L. Master and D. L. Albert. 2006. The effect of sample size and species characteristics on performance of different species distribution modelling methods. Ecography 29: 773–785.
Hidalgo, P.J., M.J. Marìn, J. Quiijada and J.M. Moreira. 2008. A spatial distribution model of cork oak (Quercus suber) in southwestern Spain: a suitable tool for reforestation. Forest Ecol. Manage. 255: 25–34.
Hirzel, A. H., J. Hausser, D. Chessel and N. Perrin. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83: 2027–2036.
Iverson, L.R. and A.M. Prasad. 1998. Predicting abundance of 80 tree species following climate change in the Eastern United States. Ecol. Monogr. 68: 465–485.
Iverson, L.R. and A.M. Prasad. 2002. Potential redistribution of tree species habitat under five climate change scenarios in the Eastern United States. Forest Ecol. Manage. 155: 205–222.
Iverson, L.R., A.M. Prasad and M.K. Schwartz. 1999. Modelling potential future individual tree species distributions in the Eastern United States under a climate change scenario: a case study with Pinus virginiana. Ecol. Model. 115: 77–93.
Leathwick, J. R., D. Rowe, J. Richardson, J. Elith and T. Hastie. 2005. Using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshwater Biol. 50: 2034–2052.
Lehmann, A., J. M. Overton and M. P. Austin. 2002. Regression models for spatial prediction: their role for biodiversity and conservation. Biodivers. Conserv. 11: 2085–2092.
Luoto, M., J. Pöyry, R. K. Heikkinen and K. Saarinen. 2005. Uncertainty of bioclimate envelope models based on the geographical distribution of species. Global Ecol. Biogeogr. 14: 575–584.
Magri, D., G. G. Vendramin, B. Comps, I. Dupanloup, T. Geburek, D. Gomory, M. Latalowa, T. Litt, L. Paule, J. M. Roure, I. Tantau, W. O. Van der Knaap, R. J. Petit and J. L. De Beaulieu. 2006. A new scenario for the Quaternary history of European beech populations: palaeobotanical evidence and genetic consequences. New Phytol. 171: 199–221.
Müller, K. R., S. Mika, G. Rätsch, K. Tsuda. and B.Schölkopf. 2001. An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12: 181–202.
Pearce, J. and M. Boyce. 2006. Modelling distribution and abundance with presence-only data. J. Appl. Ecol. 43: 405–412.
Pearson, R.G., T.P. Dawson, P.M. Berry and P.A. Harrison. 2002. SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecol. Model. 154: 289–300.
Peterson, A.T., V. Sanchez-Cordero, J. Soberòn, J. Bartley, R. W. Buddemeier and A. G. Navarro-Sigüenza. 2001. Effects of global climate change on geographic distributions of Mexican Cracidae. Ecol. Model. 144: 21–30.
Peterson, A.T., M.A. Ortega-Huerta, Bartley J. V. Sánchez-Cordero, J. Soberón, R. H. Buddemeier and D. R. B. Stockwell. 2002. Future projections for Mexican faunas under global climate change scenarios. Nature 416: 626–629.
Prasad, A. M., L. R. Iverson and A. Liaw. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9: 181–199.
Recknagel, F. 2001. Applications of machine learning to ecological modelling. Ecol. Model. 146: 303–310.
Rouget, M., D. M. Richardson, J. L. Nel, D. C. Le Maitre, B. Egoh and T. Mgidi. 2004. Mapping the potential ranges of major plant invaders in South Africa, Lesotho and Swaziland using climatic suitability. Divers. Distrib. 10: 475–484.
Scarnati, L., F. Attorre, M. De Sanctis, A. Farcomeni, F. Francesconi, M. Mancini and F. Bruno. 2009. A multiple approach for the evaluation of the spatial distribution and dynamics of a forest habitat: the case of Apennine beech forests with Taxus baccata and Ilex aquifolium. Biodivers. Conserv. Doi: 10.1007/s10531-009-9629-z
Segurado, P. and M. B. Araujo. 2004. An evaluation of methods for modelling species distributions. J. Biogeogr. 31: 1555–1568.
Thuiller, W. 2003. BIOMOD – Optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biol. 9: 1353–1362.
Thuiller, W., J. Vayreda, J. Pino, S. Sabate, S. Lavorel and C.Gracia. 2003. Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain). Global Ecol. Biogeogr. 12: 313–325.
Thuiller, W., S. Lavorel, G.F. Midgley, S. Lavergne and A.G. Rebelo. 2004. Relating plant traits and species distributions along bioclimatic gradients for 88 Leucadendron species in the Cape Floristic Region. Ecology 85: 1688–1699.
Tsoar, A., O. Allouche, O. Steinitz, D. Rotem and R. Kadmon. 2007. A comparative evaluation of presence only methods for modelling species distribution. Divers. Distrib. 13: 397–405.
Vayssieres, M.P., R.E. Richard and B.H. Allen-Diaz. 2000. Classification trees: an alternative non-parametric approach for predicting species distribution. J. Veg. Sci. 11: 679–694.
Ward, G., T. Hastie, S. Barry, J. Elith, and J. Leathwick. 2009. Presence-only data and the EM algorithm. Biometrics 65: 554–563.
Williams, C. K. I. and D. Barber. 1998: Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 20: 1342–1351.
Zaniewski, A.E., A. Lehmann and J. Overton. 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol. Model. 157: 261–280.
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Scarnati, L., Attorre, F., Farcomeni, A. et al. Modelling the spatial distribution of tree species with fragmented populations from abundance data. COMMUNITY ECOLOGY 10, 215–224 (2009). https://doi.org/10.1556/ComEc.10.2009.2.12
- Ilex aquifolium
- Gaussian processes with radial basis kernel functions
- Multivariate adaptive regression splines
- Potential areas
- Random forest
- Regression tree analysis
- Quercus suber
- Spatial modelling
- Support vector regression
- Taxus baccata