Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines

Diversity and Distributions

Volumn 13, Issue 3, 2007, Pages 265-275

  Elith, Jane ^a   Leathwick, John ^b  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b NATIONAL INSTITUTE OF WATER AND ATMOSPHERIC RESEARCH   (New Zealand)

Author keywords

Community; Habitat model; MARS; Multiresponse; Presence only; Pseudo absence

Indexed keywords

COMMUNITY COMPOSITION; GIS; HABITAT; HERBARIUM; MODELING; MULTIVARIATE ANALYSIS; MUSEUM; POPULATION DISTRIBUTION; PREDICTION; RECORD;

EID: 34247359908     PISSN: 13669516     EISSN: 14724642     Source Type: Journal    
DOI: 10.1111/j.1472-4642.2007.00340.x     Document Type: Article

References (62)

1. Araújo, M.B., Cabeza, M., Thuiller, W., Hannah, L. Williams, P.H. (2004) Would climate change drive species out of reserves? An assessment of existing reserve-selection methods. Global Change Biology, 10, 1618 1626.
2. Araújo, M.B. Guisan, A. (2006) Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677 1688.
3. Araújo, M.B., Pearson, R.G., Thuiller, W. Erhard, M. (2005) Validation of species-climate impact models under climate change. Global Change Biology, 11, 1504 1513.
4. Brotons, L., Thuiller, W., Araujo, M.B. Hirzel, A.H. (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 437 448.
5. Burnham, K.P. Anderson, D.R. (2002) Model selection and inference: a practical information-theoretic approach, 2nd edn. Springer-Verlag, New York.
6. Busby, J.R. (1991) BIOCLIM - a bioclimate analysis and prediction system. Nature conservation: cost effective biological surveys and data analysis (ed. by C.R. Margules M.P. Austin), pp. 64 68. CSIRO, Canberra, Australia.
7. Carpenter, G., Gillison, A.N. Winter, J. (1993) DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation, 2, 667 680.
8. Cawsey, E.M., Austin, M.P. Baker, B.L. (2002) Regional vegetation mapping in Australia: a case study in the practical use of statistical modelling. Biodiversity and Conservation, 11, 2239 2274.
9. Dennis, R.L.H. Hardy, P.B. (1999) Targeting squares for survey: predicting species richness and incidence of species for a butterfly atlas. Global Ecology and Biogeography, 8, 443 454.
10. Edwards, T.C., Cutler, D.R., Zimmerman, N.E., Geiser, L. Alegria, J. (2005) Model-based stratifications for enhancing the detection of rare ecological events. Ecology, 86, 1081 1090.
11. Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.M., Peterson, A.T., Phillips, S.J., Richardson, K.S., Scachetti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S. Zimmermann, N.E. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, 129 151.
12. Engler, R., Guisan, A. Rechsteiner, L. (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology, 41, 263 274.
13. Ferrier, S. (2002) Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Systematic Biology, 51, 331 363.
14. Ferrier, S. Guisan, A. (2006) Spatial modelling of biodiversity at the community level. Journal of Applied Ecology, 43, 393 404.
15. Ferrier, S., Manion, G., Elith, J. Richardson, K 2007) Using generalised dissimilarity modelling to analyse and predict patterns of beta-diversity in regional biodiversity assessment. Diversity and Distributions.
16. Ferrier, S., Powell, G.V.N., Richardson, K.S., Manion, G et al. (2004) Mapping more of terrestrial biodiversity for global conservation assessment. Bioscience, 54, 1101 1109.
17. Fielding, A.H. Bell, J.F. (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38 49.
18. Freitag, S., Hobson, C., Biggs, H.C. Van Jaarsveld, A.S. (1998) Testing for potential survey bias: the effect of roads, urban areas and nature reserves on a southern African mammal data set. Animal Conservation, 1, 119 127.
19. Frey, J. (2006) Inferring species distributions in the absence of occurrence records: An example considering wolverine (Gulo gulo) and Canada lynx (Lynx canadensis) in New Mexico. Biological Conservation, 130, 16 24.
20. Friedman, J.H. (1991) Mutlivariate adaptive regression splines (with discussion). Annals of Statistics, 19, 1 141.
21. Funk, V. Richardson, K. (2002) Systematic data in biodiversity studies: use it or lose it. Systematic Biology, 51, 303 316.
22. Goolsby, J.A. (2004) Potential distribution of the invasive old world climbing, fern, Lygodium microphyllum in north and south America. Natural Areas Journal, 24, 351 353.
23. Graham, C.H., Ferrier, S., Huettman, F., Moritz, C. Peterson, A.T. (2004b) New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution, 19, 497 503.
24. Graham, C.H., Ron, S.R., Santos, J.C., Schneider, C.J. Moritz, C. (2004a) Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution, 58, 1781 1793.
25. Griffiths, G.H., Eversham, B.C. Roy, D.B. (1999) Integrating species and habitat data for nature conservation in Great Britain: data sources and methods. Global Ecology and Biogeography, 8, 329 345.
26. Guisan, A. Zimmerman, N.E. (2000) Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147 186.
27. Guisan, A., Graham, C., Elith, J., Huettmann, F. NCEAS modelling Group (2007) Sensitivity of predictive species distribution models to change in grain size: insights from an international experiment across five continents. Diversity and Distributions.
28. Hanley, J.A. McNeil, B.J. (1982) The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology, 143, 29 36.
29. Hastie, T. (1991) Generalized additive models. Statistical models in S (ed. by J.M. Chambers T.J. Hastie), pp. 249 308. Wadsworth and Brooks/Cole Advanced Books and Software, California.
30. Hastie, T., Tibshirani, R. Buja, A. (1994) Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 1255 1270.
31. Hastie, T., Tibshirani, R. Friedman, J.H. (2001) The elements of statistical learning: data mining, inference, and prediction. Springer-Verlag, New York.
32. Hortal, J., Garcia-Pereira, P. Garcia-Barros, E. (2004) Butterfly species richness in mainland Portugal: predictive models of geographic distribution patterns. Ecography, 27, 68 82.
33. Hortal, J. Lobo, J.M. (2005) An ED-based protocol for optimal sampling of biodiversity. Biodiversity and Conservation, 14, 2913 2947.
34. Kadmon, R., Farber, O. Danin, A. (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecological Applications, 14, 401 413.
35. Keating, K.A. Cherry, S. (2004) Use and interpretation of logistic regression in habitat selection studies. Journal of Wildlife Management, 68, 774 789.
36. Leathwick, J.R. (1998) Are New Zealand's Nothofagus species in equilibrium with their environment? Journal of Vegetation Science, 9, 719 732.
37. Leathwick, J.R. (2001) New Zealand's potential forest pattern as predicted from current species-environment relationships. New Zealand Journal of Botany, 39, 447 464.
38. Leathwick, J.R., Elith, J. Hastie, T. (2006) Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecological Modelling, 199, 188 196.
39. Leathwick, J.R., Rowe, D., Richardson, J., Elith, J. Hastie, T. (2005) Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshwater Biology, 50, 2034 2052.
40. Legendre, P. (1993) Spatial autocorrelation: trouble or new paradigm? Ecology, 74, 1659 1673.
41. Levin, S.A. (1992) The problem of pattern and scale in ecology. Ecology, 73, 1943 1967.
42. Liu, C., Berry, P.M., Dawson, T.P. Pearson, R.G. (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography, 28, 385 393.
43. Lobo, J.M., Verdú, J.R. Numa, C. (2006) Environmental and geographical factors affecting the Iberian distribution of flightless Jekelius species (Coleoptera: Geotrupidae). Diversity and Distributions, 12, 179 188.
44. Lütolf, M., Kienast, F. Guisan, A. (2006) The ghost of past species occurrence: improving species distribution models for presence-only data. Journal of Applied Ecology, 43, 802 815.
45. Margules, C.R. Pressey, R.L. (2000) Systematic conservation planning. Nature (London 405.
46. McCarthy, M.A. Masters, P. (2005) Profiting from prior information in Bayesian analyses of ecological data. Journal of Applied Ecology, 42, 1012 1019.
47. McPherson, J.M., Jetz, W. Rogers, D.J. (2006) Using coarse-grained occurrence data to predict species distributions at finer spatial resolutions-possibilities and 2 limitations. Ecological Modelling, 192, 499 522. 3.
48. Moisen, G.G. Frescino, T.S. (2002) Comparing five modeling techniques for predicting forest characteristics. Ecological Modelling, 157, 209 225.
49. Olden, J.D. (2003) A species-specific approach to modelling biological communities and its potential for conservation. Conservation Biology, 17, 854 863.
50. Pearce, J.L. Boyce, M.S. (2006) Modelling distribution and abundance with presence-only data. Journal of Applied Ecology, 43, 405 412.
51. Pearce, J. Ferrier, S. (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling, 133, 225 245.
52. Phillips, S.J., Dudik, M. Schapire, R.E. (2004) A maximum entropy approach to species distribution modeling. (ed. by C.E. Brodley), Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada, 2004 472 486, URL http://www.aicml.cs.ualberta.ca/_banff04/icml/pages/accepted. htm
53. R Development Core Team (2004) A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
54. Randin, C.F., Dirnböck, T., Dullinger, S., Zimmerman, N.E., Zappa, M. Guisan, A. (2006) Are niche-based species distribution models transferable in space? Journal of Biogeography, 33, 1689 1703.
55. Reddy, S. Dávalos, L.M. (2003) Geographical sampling bias and its implications for conservation priorities in Asia. Journal of Biogeography, 30, 1719 1727.
56. Robertson, M.P., Peter, C.I., Villet, M.H. Ripley, B.S. (2003) Comparing models for predicting species' potential distributions: a case study using correlative and mechanistic predictive modelling techniques. Ecological Modelling, 164, 153 167.
57. Soberón, J.M. Llorente, J.B. (1993) The use of species accumulation functions for the prediction of species richness. Conservation Biology, 7, 480 488.
58. Spiegelhalter, D., Thomas, A., Best, N. Lunn, D. (2003a) Winbugs user manual, Version 1.4. MRC Biostatistics Unit, Cambridge, UK.
59. Spiegelhalter, D.J., Best, N.G., Carlin, B.P. van der Linde, A. (2003b) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 64, 583 639.
60. Whittaker, R.H. (1967) Gradient analysis of vegetation. Biological Review, 49, 207 264.
61. Wieczorek, J.R., Guo, Q. Hijmans, R.J. (2004) The point-radius method for georeferencing point localities and calculating associated uncertainty. International Journal of Geographic Information Science, 18, 745 767.
62. Zaniewski, A.E., Lehmann, A. Overton, J.M. (2002) Predicting species distribution using presence-only data: a case study of native New Zealand ferns. Ecological Modelling, 157, 261 280.