Novel methods improve prediction of species’ distributions from occurrence data
Tóm tắt
Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence‐only data to fit models, and independent presence‐absence data to evaluate the predictions. Along with well‐established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species’ distributions. These include machine‐learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species’ occurrence data. Presence‐only data were effective for modelling species’ distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.
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Tài liệu tham khảo
Austin M. P., 1981, Observational analysis of environmental gradients, Proc. Ecol. Soc. Aust., 11, 109
Austin M. P. et al. 1995. Modelling of landscape patterns and processes using biological data. Subproject 5: simulated data case study. – In: Division of Wildlife and Ecology CSIRO.
Barry S. C. and Elith J. in press. Error and uncertainty in habitat models. – J. Appl. Ecol.
Bio A. M. F. 2000. Does vegetation suit our models? Data and model assumptions and the assessment of species distribution in space. – Fac. of Geographical Sciences Utrecht Univ. Netherlands Ph.D. thesis.
Brown J., 1998, Biogeography
Burnham K. P., 2002, Model selection and inference: a practical information – theoretic approach
Busby J. R., 1991, Nature conservation: cost effective biological surveys and data analysis, 64
Cicero C., 2004, Barriers to sympatry between avian sibling species (Paridae: Beolophus) in tenuous secondary contact, Evolution, 58, 1573
Elith J., 2002, Predicting species occurrences: issues of accuracy and scale, 303
Ferrier S. and Watson G. 1997. An evaluation of the effectiveness of environmental surrogates and modelling techniques in predicting the distribution of biological diversity. – Environment Australia Canberra <http://www.deh.gov.au/biodiversity/publications/technical/surrogates/>.
Gómez Pompa A., 1970, La Flora de Veracruz, Anales del Inst. de Biología de la UNAM, Bot., 31, 137
Goolsby J. A., 2004, Potential distribution of the invasive old world climbing, fern, Lygodium microphyllum in north and south America, Nat. Areas J., 24, 351
Leathwick J. R. et al. in press. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. – Mar. Ecol. Progr. Ser.
Pearce J. L. and Boyce M. S. in press. Modelling distribution and abundance with presence‐only data. – J. Appl. Ecol. in press.
Peterson A. T., 2005, Kansas gap analysis: the importance of validating distributional models before using them, 230
Peterson A. T., 2004, Reconstructing the pleistocene geography of the Aphelocoma jays (Corvidae), Biodiv. Res., 10, 237
Phillips S. J. Dudik M. and Schapire R. E. 2004. A maximum entropy approach to species distribution modeling. – In: Proc. of the 21st International Conference on Machine Learning Banff Canada 2004.
Pielke Jr R. A., 2003, Models in ecosystem science, 111
Randin C. F. et al. in press. Are species distribution models transferable in space? – J. Biogeogr.
Rapoport E. H., 1982, Aerography
Ridgeway G., 1999, The state of boosting, Comput. Sci. Stat., 31, 172
Sneath P. H. A., 1973, Numerical taxonomy – the principles and practice of numerical classification
Spiegelhalter D., 2003, WinBUGS user manual, version 1.4
Spiegelhalter D. J., 2003, Bayesian measures of model complexity and fit, J. R. Stat. Soc. Ser. B, 64, 583, 10.1111/1467-9868.00353
Tibshirani R., 1996, Regression shrinkage and selection via the lasso, J. R. Stat. Soc. Ser. B, 58, 267
Wintle B. A. and Bardos D. C. in press. Modelling species habitat relationships with spatially autocorrelated observation data. – Ecol. Appl.