A maximum entropy approach to species distribution modeling

Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004

Volumn , Issue , 2004, Pages 655-662

  Phillips, Steven J ^a   Dudík, Miroslav ^b   Schapire, Robert E ^b  

^a AT AND T LABS RESEARCH   (United States)

^b Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSERVATION BIOLOGY; HABITAT DESTRUCTION; SPATIAL CORRELATION; SPECIES DISTRIBUTION;

ALGORITHMS; CELLS; GLOBAL WARMING; ITERATIVE METHODS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NUMERICAL METHODS; PLANTS (BOTANY);

BIODIVERSITY;

EID: 14344261668     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Andersen, R. P., Lew, D., & Peterson, A. T. (2003). Evaluating predictive models of species' distributions: Criteria for selecting optimal models. Ecological Modelling, 162, 211-232.
2. Anderson, R. P., & Martínez-Meyer, E. (2004). Modeling species' geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biological Conservation, 116, 167-179.
3. Berger, A. L., Pietra, S. A. D., & Pietra, V. J. D. (1996). A maximum entropy approach to natural language processing. Computational Linguistics, 22, 39-71.
4. Chen, S. F., & Rosenfeld, R. (2000). A survey of smoothing techniques for ME models. IEEE Trans. on Speech and Audio Processing, 8, 37-50.
5. Collins, M., Schapire, R. E., & Singer, Y. (2002). Logistic regression, AdaBoost and Bregman distances. Machine Learning, 48, 253-285.
6. Darroch, J. N., & Ratcliff, D. (1972). Generalized iterative scaling for log-linear models. The Annals of Math. Statistics, 43, 1470-1480.
7. Della Pietra, S., Della Pietra, V., & Lafferty, J. (1997). Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 1-13.
8. Dudík, M., Phillips, S. J., & Schapire, R. E. (2004). Performance guarantees for regularized maximum entropy density estimation. Proceedings of the 17th Annual Conference on Computational Learning Theory.
9. Elith, J. (2002). Quantitative methods for modeling species habitat: Comparative performance and an application to Australian plants. In S. Ferson and M. Burgman (Eds.), Quantitative methods for conservation biology, 39-58. New York: Springer-Verlag.
10. Goodman, J. (2002). Sequential conditional generalized iterative scaling. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (pp. 9-16).
11. Goodman, J. (2003). Exponential priors for maximum entropy models (Technical Report). Microsoft Research. (Available from http://research. microsoft.com/~joshuago/longexponentialprior.ps).
12. Hutchinson, G. E. (1957). Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology, 22, 415-427.
13. Malouf, R. (2002). A comparison of algorithms for maximum entropy parameter estimation. Proceedings of the Sixth Conference on Natural Language Learning (pp. 49-55).
14. Minka, T. (2001). Algorithms for maximum-likelihood logistic regression (Technical Report). CMU CALD. (Available from http://www.stat.cmu.ed/~minka/ papers/logreg.html).
15. New, M., Hulme, M., & Jones, P. (1999). Representing twentieth-century space-time climate variability. Part 1: Development of a 1961-90 mean monthly terrestrial climatology. Journal of Climate, 12, 829-856.
16. Peterson, A. T. (2001). Predicting species' geographic distributions based on ecological niche modeling. The Condor, 103, 599-605.
17. Peterson, A. T., Papes, M., & Kluza, D. A. (2003). Predicting the potential invasive distributions of four alien plant species in North America. Weed Science, 51, 863-868.
18. Peterson, A. T., & Robins, C. R. (2003). Using ecological-niche modeling to predict barred owl invasions with implications for spotted owl conservation. Conservation Biology, 17, 1161-1165.
19. Peterson, A. T., & Shaw, J. (2003). Lutzomyia vectors for cutaneous leishmaniasis in southern Brazil: ecological niche models, predicted geographic distribution, and climate change effects. International Journal of Parasitology, 33, 919-931.
20. Ponder, W. F., Carter, G. A., Flemons, P., & Chapman, R. R. (2001). Evaluation of museum collection data for use in biodiversity assessment. Conservation Biology, 15, 648-657.
21. Raxworthy, C. J., Martinez-Meyer, E., Horning, N., Nussbaum, R. A., Schneider, G. E., Ortega-Huerta, M. A., & Peterson, A. T. (2004). Predicting distributions of known and unknown reptile species in Madagascar. Nature, 426, 837-841.
22. Salakhutdinov, R., Roweis, S. T., & Ghahramani, Z. (2003). On the convergence of bound optimization algorithms. Uncertainty in Artificial Intelligence 19 (pp. 509-516).
23. Sauer, J. R., Hines, J. E., & Fallen, J. (2001). The North American breeding bird survey, results and analysis 1966-2000, Version 2001.2. http://www.mbr-pwrc.usgs.gov/bbs/bbs.html. USGS Patuxent Wildlife Research Center, Laurel, MD.
24. Stockwell, D., & Peters, D. (1999). The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13, 143-158.
25. Stockwell, D. R. B., & Noble, I. R. (1992). Induction of sets of rules from animal distribution data: a robust and informative method of data analysis. Mathematics and Computers in Simulation, 33, 385-390.
26. Stockwell, D. R. B., & Peterson, A. T. (2002). Controlling bias in biodiversity data. In J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. A. Wall and F. B. Samson (Eds.), Predicting species occurrences: Issues of accuracy and scale, 537-546. Washington, DC: Island Press.
27. Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., Erasmus, B. F. N., de Siqueira, M. F., Grainger, A., Hannah, L., Hughes, L., Huntley, B., van Jaarsveld, A. S., Midgley, G. F., Miles, L., Ortega-Huerta, M. A., Peterson, A. T., Phillips, O. L., & Williams, S. E. (2004). Extinction risk from climate change. Nature, 427, 145-148.
28. Wiley, E. O., McNyset, K. M., Peterson, A. T., Robins, C. R., & Stewart, A. M. (2003). Niche modeling and geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography, 16, 120-127.
29. Williams, P. M. (1995). Bayesian regularization and pruning using a Laplace prior. Neural Computation, 7, 117-143.