SEARCH

SEARCH BY CITATION

References

  • Araújo, M.B. & New, M. (2007) Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22, 4247.
  • Brosse, S. & Lek, S. (2000) Modelling roach (Rutilus rutilus) microhabitat using linear and nonlinear techniques. Freshwater Biology, 44, 441452.
  • Davis, J. & Goadrich, M. (2006) The relationship between precision-recall and ROC curves. Proceedings of the 23rd international conference on Machine learning - ICML, 06, 233240.
  • Dimopoulos, I. (1999) Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecological Modelling, 120, 157165.
  • Dimopoulos, Y., Bourret, P. & Lek, S. (1995) Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Processing Letters, 2, 14.
  • Fu, L. & Chen, T. (1993) Sensitivity analysis for input vector in multilayer feed forward neural networks. IEEE International Conference on Neural Networks, 1993. pp. 215218.
  • Gevrey, M., Dimopoulos, I. & Lek, S. (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160, 249264.
  • Gevrey, M., Dimopoulos, I. & Lek, S. (2006a) Two-way interaction of input variables in the sensitivity analysis of neural network models. Ecological Modelling, 195, 4350.
  • Gevrey, M., Lek, S. & Oberdorff, T. (2006b) Utility of sensitivity analysis by artificial neural network models to study patterns of endemic fish species. Ecological Informatics . (ed F. Recknagel), pp. 293306. Springer, Berlin.
  • Lek, S. & Guégan, J.F. (1999) Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling, 120, 6573.
  • Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J. & Aulagnier, S. (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 90, 3952.
  • Lobo, J.M., Jiménez-Valverde, A. & Real, R. (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17, 145151.
  • Olden, J.D. & Jackson, D.A. (2002) Illuminating the ‘black box’: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154, 135150.
  • Olden, J.D., Lawler, J.J. & Poff, N.L. (2008) Machine learning methods without tears: a primer for ecologists. The Quarterly Review of Biology, 83, 171193.
  • Özesmi, S. & Özesmi, U. (1999) An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling, 116, 1531.
  • Özesmi, S., Tan, C. & Özesmi, U. (2006a) Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecological Modelling, 195, 8393.
  • Özesmi, U., Tan, C., Özesmi, S. & Robertson, R. (2006b) Generalizability of artificial neural network models in ecological applications: Predicting nest occurrence and breeding success of the red-winged blackbird Agelaius phoeniceus. Ecological Modelling, 195, 94104.
  • Pearson, R.G., Dawson, T.P., Berry, P.M. & Harrison, P.A. (2002) SPECIES: a spatial evaluation of climate impact on the envelope of species. Ecological Modelling, 154, 289300.
  • Peterson, A.T., Papes, M. & Sober, J. (2007) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecology, 3, 6372.