SEARCH

SEARCH BY CITATION

References

  • Anderson, R. P. and Martínez-Meyer, E.. 2004. Modeling species’ geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biol. Conserv. 116: 167179.
  • Anderson, R. P. et al. 2003. Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol. Modell. 162: 211232.
  • Anon.. 2001. SPSS for Windows. SPSS, Chicago.
  • Araujo, M. B. and Williams, P. H.. 2000. Selecting areas for species persistence using occurrence data. Biol. Conserv. 96: 331345.
  • Boone, R. B. and Krohn, W. B.. 2002. Modeling tools and accuracy assessment. – In: Scott, J. M. et al (eds), Predicting species occurrences: issues of accuracy and scale. Inland Press, pp. 265270.
  • Brotons, L. et al. 2004. Presence-absence versus presence–only modeling methods for predicting bird habitat suitability. Ecography 27: 437448.
  • Busby, J. R.. 1991. BIOCLIM – a bioclimate analysis and prediction system. – In: Margules, C. R. and Austin, M. P. (eds), Nature conservation: cost effective biological surveys and data analysis. CSIRO, pp. 6468.
  • Carpenter, G. et al. 1993. DOMAIN: a flexible modeling procedure for mapping potential distributions of plants and animals. Biodiv. Conserv. 2: 667680.
  • Corsi, F. et al. 2000. Modelling species distribution with GIS. – In: Boitani, L. and Fuller, T. K. (eds), Research techniques in animal ecology; controversies and consequences. Columbia University Press, pp. 389434.
  • Elith, J. and Burgman, M. A.. 2003. Habitat models for PVA. – In: Brigham, C. A. and Schwartz, M. W. (eds), Population viability in plants. Conservation, management and modeling of rare plants. Springer, pp. 203235.
  • Elith, J. et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129151.
  • Engler, R. et al. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41: 263274.
  • Farber, O. and Kadmon, R.. 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecol. Modell. 160: 115130.
  • Fielding, A. H. and Bell, J. F.. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24: 3849.
  • Fortin, M.-J. et al. 2005. Species ranges and distributional limits: pattern analysis and statistical issues. Oikos 108: 717.
  • Gaston, K. J.. 1997. What is rarity?. – In: Kunin, W. E. and Gaston, K. J. (eds), The biology of rarity. Causes and consequences of rare-common differences. Chapman and Hall, pp. 3047.
  • Gillison, A. N.. 1997. Mapping the potential distribution of plants and animals for wildlife management: the use of the DOMAIN software package. – In: Romimoharto, K. et al (eds), Proceedings of the national seminar on the role of wildlife conservation and its ecosystem in national development. The Indonesian Wildlife Fund (IWF), Jakata,+two maps, pp. 114119.
  • Graham, C. H. et al. 2004. New developments in museum-based informatics and application in biodiversity analysis. Trends Ecol. Evol. 19: 497503.
  • Guisan, A. and Zimmermann, N. E.. 2000. Predictive habitat distribution models in ecology. Ecol. Modell. 135: 147186.
  • Hirzel, A. H. et al. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data?. Ecology 83: 20272036.
  • Johnson, C. M. et al. 2002. Predicting the occurrence of amphibians: an assessment of multiple-scale models. – In: Scott, J. M. et al (eds), Predicting species occurrences: issues of accuracy and scale. Inland Press, pp. 157170.
  • Kadmon, R. et al. 2003. A systematic analysis of factors affecting the performance of climatic envelope models. Ecol. Appl. 13: 853867.
  • Kattan, G. H.. 1992. Rarity and vulnerability: the birds of the cordillera central Columbia. Conserv. Biol. 6: 6470.
  • Lindenmayer, D. B. et al. 1991. The conservation of Leadbeater's possum, Gymnobelideus leadbeateri (McCoy): a case study of the use of bioclimatic modeling. J. Biogeogr. 18: 371383.
  • Loiselle, B. A. et al. 2003. Avoiding pitfalls of using species distribution models in conservation planning. Conserv. Biol. 17: 15911600.
  • Luoto, M. et al. 2005. Uncertainty of bioclimate envelope models based on the geographical distribution of species. Global Ecol. Biogeogr. 14: 575584.
  • Manel, S. et al. 2001. Evaluating presence-absence models in ecology: the need to account for prevalence. J. Appl. Ecol. 38: 921931.
  • McPherson, J. M. et al. 2004. The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artifact?. J. Appl. Ecol. 41: 811823.
  • Nix, H.. 1986. A biogeographic analysis of Australian elapid snakes. – In: Longmore, R. (ed.), Atlas of elapid snakes of Australia. Bureau of Flora and Fauna. Canberra, pp. 415.
  • Pearce, J. and Ferrier, S.. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133: 225245.
  • Phillips, S. J. et al. 2004. A maximum entropy approach to species distribution modeling. – In: Brodley, C. E. (ed.), Machine learning. Proc. of the Twenty-first Century International Conference on Machine Learning, Banff, Canada, 2004. ACM Press, p. 83.
  • Phillips, S. J. et al. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 190: 231259.
  • Rabinowitz, D. et al. 1986. Seven forms of rarity and their frequency in the flora of the British Isles. – In: Soulé, M. E. (ed.), Conservation biology: the science of scarcity and diversity. Sinauer, pp. 182204.
  • Segurado, P. and Araujo, M. B.. 2004. An evaluation of methods for modelling species distributions. J. Biogeogr. 31: 15551568.
  • Stein, B. A. et al. 2000. Precious heritage. Oxford Univ. Press.
  • Stockwell, D. and Peters, D.. 1999. The GARP modeling system problems and solutions to automated spatial prediction. Int. J. Geogr. Inform. Sci. 13: 143158.
  • Stockwell, D. R. B. and Peterson, A. T.. 2002. Effects of sample size on accuracy of species distribution models. Ecol. Modell. 148: 113.
  • Thuiller, W. et al. 2003. Generalized models vs. classification tree analysis: predicting spatial distributions of plant species at different scales. J. Veg. Sci. 14: 669680.
  • Thuiller, W. et al. 2004. Relating plant traits and species distributions along bioclimatic gradients for 88 Leucadendron species in the Cape Floristic Region. Ecology 85: 16881699.
  • Wilson, K. A. et al. 2005. Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biol. Conserv. 122: 99112.