Predicting species distribution: Offering more than simple habitat models

Ecology Letters

Volumn 8, Issue 9, 2005, Pages 993-1009

  Guisan, Antoine ^a   Thuiller, Wilfried ^b,c  

^a Laboratoire de Biologie de la Conservation   (Switzerland)

^b SOUTH AFRICAN NATIONAL BIODIVERSITY INSTITUTE   (South Africa)

^c UNIVERSITY OF ÉVORA   (Portugal)

Author keywords

Dispersal; Ecological niche theory; Future projections; Habitat suitability maps; Population dynamics; Prediction errors; Predictive biogeography; Spatial scales; Species distribution models

Indexed keywords

BIOGEOGRAPHY; ECOLOGICAL MODELING; GEOGRAPHICAL DISTRIBUTION; HABITAT QUALITY; PREDICTION;

ANIMALIA;

EID: 24344493920     PISSN: 1461023X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1461-0248.2005.00792.x     Document Type: Review

References (116)

1. Ackerly, D.D. (2003). Community assembly, niche conservatism, and adaptive evolution in changing environments. Int. J. Plant Sci., 164, S165-S184.
2. Andelman, S.J. & Willig, M.R. (2002). Alternative configurations of conservation reserves for Paraguayan bats: considerations of spatial scale. Conserv. Biol., 16, 1352-1363.
3. Anderson, R.P., Peterson, A.T. & Gomez-Laverde, M. (2002). Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos, 98, 3-16.
4. Araújo, M.B. & Pearson, R.G. (2005). Equilibrium of species' distributions with climate. Ecography, in press.
5. Araujo, M.B., Williams, P.H. & Fuller, R.J. (2002). Dynamics of extinction and the selection of nature reserves. Proc. R. Soc. Lond. B Biol. Sci, 269, 1971-1980.
6. 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. Glob. Change Biol., 10, 1618-1626.
7. Araújo, M.B., Pearson, R.G., Thuiller, W. & Erhard, M. (2005a). Validation of species-climate impact models under climate change. Glob. Change Biol., in press.
8. Araújo, M.B., Whittaker, R.J., Ladle, R. & Erhard, M. (2005b). Reducing uncertainty in projections of extinction risk from climate change. Glob. Ecol. Biogeogr., in press.
9. Aspinall, R. (1992). An inductive modeling procedure based on Bayes Theorem for analysis of pattern in spatial data. Int. J. Geogr. Inf. Syst., 6, 105-121.
10. Austin, M.P. (1971). Role of regression analysis in plant ecology. Proc. Ecol. Soc. Aust., 6, 63-75.
11. Austin, M.P. (1992). Modelling the environmental niche of plants - implications for plant community response to elevated CO2 levels. Aust. J .Bot., 40, 615-630.
12. Austin, M.P. (1998). An ecological perspective on biodiversity investigations: example from Australian eucalypt forests. Ann. Missouri Bot. Gard., 85, 2-17.
13. Austin, M.P. (2002). Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Model., 157, 101-118.
14. Austin, M.P. & Gaywood, M.J. (1994). Current problems of environmental gradients and species response curves in relation to continuum theory. J. Veg. Sci., 5, 473-482.
15. Austin, M.P., Nicholls, A.O. & Margules, C.R. (1990). Measurement of the realized qualitative niche: environmental niches of five Eucalyptus species. Ecol. Monogr., 60, 161-177.
16. Bakkenes, M., Alkemade, J.R.M., Ihle, F., Leemans, R. & Latour, J.B. (2002). Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050. Glob. Change Biol., 8, 390-407.
17. Beerling, D.J., Huntley, B. & Bailey, J.P. (1995). Climate and the distribution of Fallopia japonica: use of an introduced species to test the predictive capacity of response surface. J. Veg. Sci., 6, 269-282.
18. Bell, G. (2001). Neutral macroecology. Science, 293, 2413-2418.
19. Boone, R.B. & Krohn, W.B. (2002). Modeling tools and accuracy assessment. In: Predicting Species Occurrences: Issues of Accuracy and Scale (eds Scott, J.M., Heglund, P.J., Samson, F., Haufler, J., Morrison, M., Raphael, M. & Wall, B.). Island Press, Covelo, California, pp. 265-270.
20. Boyce, M.S., Vernier, P.R., Nielsen, S.E. & Schmiegelow, F.K.A. (2002). Evaluating resource selection functions. Ecol. Model., 157, 281-300.
21. 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.
22. Brown, J.H., Stevens, G.C. & Kaufman, D.M. (1996). The geographic range: size, shape, boundaries, and internal structure. Anna. Rev. Ecol. Syst., 27, 597-623.
23. Busby, J.R. (1991). BIOCLIM - a bioclimate analysis and prediction system. In: Nature Conservation: Cost Effective Biological Surveys and Data Analysis (eds Margules, C.R. & Austin, M.P.). CSIRO, Melbourne, pp. 64-68.
24. Cabeza, M., Araújo, M.B., Wilson, R.J., Thomas, C.D., Cowley, M.J.R. & Moilanen, A. (2004). Combining probabilities of occurrence with spatial reserve design. J. Appl. Ecol., 41, 252-262.
25. Carey, P.D. (1996). DISPERSE: a cellular automaton for predicting the distribution of species in a changed climate. Glob. Ecol. Biogeogr. Lett., 5, 217-226.
26. Carpenter, G., Gillison, A.N. & Winter, J. (1993). DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodivers. Conserv., 2, 667-680.
27. Chuine, I., Cambon, G. & Comtois, P. (2000). Scaling phenology from the local to the regional level: advances from species-specific phenological models. Glob. Change Biol., 6, 943-952.
28. Collingham, Y.C. & Huntley, B. (2000). Impacts of habitat fragmentation and patch size upon migration rates. Ecol. Appl., 10, 131-144.
29. Collingham, Y.C., Wadsworth, R.A., Huntley, B. & Hulme, P.E. (2000). Predicting the spatial distribution of non-indigenous riparian weeds: issues of spatial scale and extent. J. Appl. Ecol., 37, 13-27.
30. Davis, A.J., Jenkinson, L.S., Lawton, J.H., Shorrocks, B. & Wood, S. (1998). Making mistakes when predicting shifts in species range in response to global warming. Nature, 391, 783-786.
31. De'Ath, G. (2002). Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology, 83, 1105-1117.
32. Dullinger, S., Dirnbock, T. & Grabherr, G. (2004). Modelling climate change-driven treeline shifts: relative effects of temperature increase, dispersal and invasibility. J. Ecol., 92, 241-252.
33. Elith, J. (2000). Quantitative methods for modeling species habitat: comparative performance and an application to Australian plants. In: Quantitative Methods in Conservation Biology (eds Ferson, S. & Burgman, M.A.). Springer, New York, pp. 39-58.
34. Elith, J. & Burgman, M.A. (2002). Predictions and their validation: rare plants in the Central Highlands, Victoria, Australia. In: Predicting Species Occurrences: Issues of Accuracy and Scale (eds Scott, J.M., Heglund, P.J., Morrison, M.L., Raphael, M.G., Wall, W.A. & Samson, F.B.). Island Press, Covelo, CA, pp. 303-314.
35. 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. J. Appl. Ecol., 41, 263-274.
36. Ferrier, S. (1984). The status of the Rufous Scrub-Bird Atrichornis rufescens: habitat, geographical variation and abundance. PhD Thesis, University of New England, Armidale, Australia.
37. Ferrier, S. (2002). Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Syst. Biol., 51, 331-363.
38. Ferrier, S., Drielsma, M., Manion, G. & Watson, G. (2002). Extended statistical approaches to modelling spatial pattern in biodiversity in north-east New South Wales. II. Community-level modelling. Biodivers. Conserv., 11, 2309-2338.
39. Ferrier, S., Powell, G.V.N., Richardson, K.S., Manion, G., Overton, J.M., Allnut, T.F. et al. (2004). Mapping more of terrestrial biodiversity for global conservation assessment: a new approach to integrating disparate sources of biological and environmental data. Bioscience, 54, 1101-1109.
40. Fielding, A.H. (2002). What are the appropriate characteristics of an accuracy measure? In: Predicting Species Occurrences: Issues of Accuracy and Scale (eds Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. & Samson, F.B.). Island Press, Covelo, CA, pp. 271-280.
41. Franklin, J. (1995). Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Prog. Phys. Geogr., 19, 474-499.
42. Friedman, J.H., Hastie, T. & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. Ann. Stat., 28, 337-374.
43. Gelfand, A.E., Schmidt, A.M., Wu, S., Silander, J.A., Latimer, A. & Rebelo, A.G. (2005). Modelling species diversity through species level hierarchical modelling. J. R. Stat. Soc. C Appl. Stat., 54, 1-20.
44. Graham, C.H., Ferrier, S., Huettman, F., Moritz, C. & Peterson, A.T. (2004a). New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol. Evol., 19, 497-503.
45. Graham, C.H., Ron, S.R., Santos, J.C., Schneider, C.J. & Moritz, C. (2004b). Integrating phylogenetics and environmental niche models to explore speciation mechanisms in dendrobatid frogs. Evolution, 58, 1781-1793.
46. Guisan, A. & Harrell, F.E. (2000). Ordinal response regression models in ecology. J. Veg. Sci., 11, 617-626.
47. Guisan, A. & Theurillat, J.-P. (2000). Equilibrium modeling of alpine plant distribution: how far can we go? Phytocoenologia, 30, 353-384.
48. Guisan, A. & Zimmermann, N.E. (2000). Predictive habitat distribution models in ecology. Ecol. Model., 135, 147-186.
49. Guisan, A., Theurillat, J.P. & Kienast, F. (1998). Predicting the potential distribution of plant species in an Alpine environment. J. Veg. Sci., 9, 65-74.
50. Guisan, A., Weiss, S.B. & Weiss, A.D. (1999). GLM versus CCA spatial modeling of plant distribution. Plant Ecol., 143, 107-122.
51. Guisan, A., Edwards, J., Thomas, C. & Hastie, T. (2002). Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Model., 157, 89-100.
52. Hewitson, B. (2003). Developing perturbations for climate change impact assessments. EOS, 84, 337-339.
53. Hijmans, R.J., Guarino, L., Cruz, M. & Rojas, E. (2001). Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genet. Resour. Newsl., 127, 15-19.
54. Hirzel, A. & Guisan, A. (2002). Which is the optimal sampling strategy for habitat suitability modelling. Ecol. Model., 157, 331-341.
55. Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. (2002). Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology, 83, 2027-2036.
56. Hubbell, S.P. (2001). A Unified Neutral Theory of Biodiversity and Biogeography. Princeton University Press, Princeton, NJ.
57. Huntley, B., Cramer, W., Morgan, A.V., Prentice, I.C. & Allen, J.R.M. (1997). Predicting the response of terrestrial biota to future environmental changes. In: Past and Future Rapid Environmental Changes: the Spatial and Evolutionary Responses of Terrestrial Biota (eds Huntley, B., Cramer, W., Morgan, A.V., Prentice, I.C. & Allen, J.R.M.). Springer-Verlag, Berlin, pp. 487-504.
58. Huntley, B., Green, R.E., Collingham, Y.C., Hill, J.K., Willis, S.G., Bartlein, P.J. et al. (2004). The performance of models relating species geographical distributions to climate is independent of trophic level. Ecol. Lett., 1, 417-426.
59. Huston, M.A. (2002). Introductory essay: critical issues for improving predictions. In: Predicting Species Occurrences: Issues of Accuracy and Scale (eds Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. & Samson, F.B.). Island Press, Covelo, CA, pp. 7-21.
60. Iverson, L.R., Prasad, A. & Schwartz, M.K. (1999). Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginiana. Ecol. Model., 115, 77-93.
61. Iverson, L.R., Schwartz, M.W. & Prasad, A. (2004). How fast and far might tree species migrate in the eastern United States due to climate change? Glob. Ecol. Biogeogr., 13, 209-219.
62. Jaberg, C. & Guisan, A. (2001). Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment. J. Appl. Ecol., 38, 1169-1181.
63. Johnson, J.B. & Omland, K.S. (2004). Model selection in ecology and evolution. Trends Ecol. Evol., 19, 101-108.
64. Kadmon, R., Farber, O. & Danin, A. (2003). A systematic analysis of factors affecting the performance of climatic envelope models. Ecol. Appl., 13, 853-867.
65. Kearney, M. & Porter, W.P. (2004). Mapping the fundamental niche: physiology, climate, and the distribution of a nocturnal lizard. Ecology, 85, 3119-3131.
66. Keddy, P.A. (1992). Assembly and response rules: two goals for predictive community ecology. J. Veg. Sci., 3, 157-164.
67. Leathwick, J.R. (1998). Are New Zealand's Nothofagus species in equilibrium with their environment? J. Veg. Sci., 9, 719-732.
68. Leathwick, J.R. & Austin, M.P. (2001). Competitive interactions between tree species in New Zealand's old-growth indigenous forests. Ecology, 82, 2560-2573.
69. Leathwick, J.R., Whitehead, D. & McLeod, M. (1996). Predicting changes in the composition of New Zealand's indigenous forests in response to global warming: a modelling approach. Environ. Softw., 11, 81-90.
70. Lehmann, A., Overton, J.M. & Leathwick, J.R. (2002). GRASP: generalized regression analysis and spatial prediction. Ecol. Model., 157, 189-207.
71. Leibold, M.A. (1995). The niche concept revisited: mechanistic models and community context. Ecology, 76, 1371-1382.
72. Lichstein, J.W., Simons, T.R., Shriner, S.A. & Franzreb, K.E. (2002). Spatial autocorrelation and autoregressive models in ecology. Ecol. Monogr., 72, 445-463.
73. Mac Nally, R. (2002). Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables. Biodivers. Consent., 11, 1397-1401.
74. Mackey, B.G. & Lindenmayer, D.B. (2001). Towards a hierarchical framework for modelling the spatial distribution of animals. J. Biogeogr., 28, 1147-1166.
75. Manel, S., Williams, H.C. & Ormerod, S.J. (2001). Evaluating presence-absence models in ecology: the need to account for prevalence. J. Appl. Ecol., 38, 921-931.
76. Margules, C.R. & Austin, M.P. (1991). Nature Conservation: Cost Effective Biological Survey and Data Analysis. CSIRO, Canberra, Australia.
77. Midgley, G.F., Hannah, L., Millar, D., Thuiller, W. & Booth, A. (2003). Developing regional and species-level assessments of climate change impacts on biodiversity in the Cape Floristic Region. Biol. Conserv., 112, 87-97.
78. Moisen, G.G. & Frescino, T.S. (2002). Comparing five modelling techniques for predicting forest characteristics. Ecol. Model., 157, 209-225.
79. Nix, H., McMahon, J. & Mackenzie, D. (1977). Potential areas of production and the future of pigeon pea and other grain legumes in Australia. In: The potential for pigeon pea in Australia. Proceedings of Pigeon Pea (Cajanus cajan (L.) Millsp.) Field Day (eds Wallis, E.S. & Whiteman, P.C.). University of Queensland, Queensland, Australia, pp. 5/1-5/12.
80. Pearce, J.L. & Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Model., 133, 225-245.
81. Pearce, J. & Lindenmayer, D. (1998). Bioclimatic analysis to enhance reintroduction biology of the endangered helmeted honeyeater (Lichenostomus melanops cassidix) in southeastern Australia. Rester. Ecol., 6, 238-243.
82. Pearce, J.L., Venier, L.A., Ferrier, S. & McKenney, D.W. (2002). Measuring prediction uncertainty in models of species distribution. In: Predicting Species Occurrences: Issues of Accuracy and Scale (eds Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. & Samson, F.B.). Island Press, Covelo, CA, pp. 383-397.
83. Pearson, R.G. & Dawson, T.E. (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr., 12, 361-372.
84. 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. Ecol. Model., 154, 289-300.
85. Pearson, R.G., Dawson, T.E. & Liu, C. (2004). Modelling species distributions in Britain: a hierarchical integration of climate and land-cover data. Ecography, 27, 285-298.
86. Peng, C.H. (2000). From static biogeographical model to dynamic global vegetation model: a global perspective on modelling vegetation dynamics. Ecol. Model., 135, 33-54.
87. Peters, R.H. (1991). A Critique for Ecology. Cambridge University Press, Cambridge, UK.
88. Peterson, A.T., Ortega-Huerta, M.A., Bartley, J., Sánchez-Cordero, V., Soberon, J., Buddemeier, R.H. & Stockwell, D.R.B. (2002). Future projections for Mexican faunas under global climatic change scenarios. Nature, 416, 626-629.
89. Peterson, A.T. (2003). Predicting the geography of species' invasions via ecological niche modeling. Q. Rev. Biol., 78, 419-433.
90. Phillips, S.J., Dudik, M. & Schapire, R.E. (2005). Maximum entropy modeling of species geographic distributions. In: Twenty-first International Conference on Machine Learning, in press.
91. Pulliam, H.R. (2000). On the relationship between niche and distribution. Ecol. Lett., 3, 349-361.
92. Raxworthy, C.J., Martinez-Meyer, E., Horning, N., Nussbaum, R.A., Schneider, G.E., Ortega-Huerta, M.A. & Peterson, A.T. (2003). Predicting distributions of known and unknown reptile species in Madagascar. Nature, 426, 837-841.
93. Robertson, M.P., Villet, M.H. & Palmer, A.R. (2004). A fuzzy classification technique for predicting species' distributions: application using invasive alien plants and indigenous insects. Divers. Distrib., 10, 461-474.
94. Rushton, S.P., Ormerod, S.J. & Kerby, G. (2004). New paradigms for modelling species distributions? J. Appl. Ecol., 41, 193-200.
95. Schwartz, M.W., Iverson, L.R. & Prasad, A. (2001). Predicting the potential future distribution of four tree species in Ohio using current habitat availability and climatic forcing. Ecosystems, 4, 568-581.
96. Scott, J.M., Heglund, P.J., Haufler, J.B., Morrison, M., Raphael, M.G., Wall, W.B. et al. (2002). Predicting Species Occurrences: Issues of Accuracy and Scale. Island Press, Covelo, CA.
97. Segurado, P. & Araújo, M.B. (2004). An evaluation of methods for modelling species distributions. J. Biogeogr., 31, 1555-1568.
98. Silvertown, J. (2004). Plant coexistence and the niche. Trends Ecol. Evol., 19, 605-611.
99. Stockwell, D. & Peters, D. (1999). The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci., 13, 143-158.
100. Stockwell, D.R.B. & Peterson, A.T. (2002). Effects of sample size on accuracy of species distribution models. Ecol. Model., 148, 1-13.
101. Svenning, J.-C. & Skov, F. (2004). Limited filling of the potential range in European tree species. Ecol. Lett., 7, 565-573.
102. Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Collingham, Y.C. et al. (2004). Extinction risk from climate change. Nature, 427, 145-147.
103. Thuiller, W. (2003). BIOMOD - optimizing predictions of species distributions and projecting potential future shifts under global change. Glob. Change Biol., 9, 1353-1362.
104. Thuiller, W. (2004). Patterns and uncertainties of species' range shifts under climate change. Gob. Change Biol., 10, 2020-2027.
105. Thuiller, W., Araújo, M.B. & Lavorel, S. (2003). Generalized model vs. classification tree analysis: predicting spatial distributions of plant species at different scales. J. Veg. Sci., 14, 669-680.
106. Thuiller, W., Araújo, M.B. & Lavorel, S. (2004a). Do we need land-cover data to model species distributions in Europe? J. Biogeogr., 31, 353-361.
107. Thuiller, W., Araújo, M.B., Pearson, R.G., Whittaker, R.J., Brotons, L. & Lavorel, S. (2004c). Uncertainty in predictions of extinction risk. Nature, 430, 34.
108. Thuiller, W., Lavorel, S., Midgley, G.F., Lavergne, S. & Rebelo, A.G. (2004b). Relating plant traits and species distributions along bioclimatic gradients for 88 Leucadendron species in the Cape Floristic Region. Ecology, 85, 1688-1699.
109. Van Horn, B. (2002). Approaches to habitat modelling: the tensions between pattern and process and between specificity and generality. In: Predicting Species Occurrences: Issues of Accuracy and Scale (eds Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. & Samson, F.B.). Island Press, Covelo, CA, pp. 63-72.
110. Verner, J., Morrison, M.L. & Ralph, C.J. (1986). Wildlife 2000: Modelling Habitat Relationships of Terrestrial Vertebrates. University of Wisconsin Press, Madison, WI.
111. Vetaas, O.R. (2002). Realized and potential climate niches: a comparison of four Rhododendron tree species. J. Biogeogr., 29, 545-554.
112. Wiens, J.A. (2002). Predicting species occurrences: progress, problems, and prospects. In: Predicting Species Occurrences: Issues of Accuracy and Scale (eds Scott, J.M., Heglund, P.J., Morrison, M.L., Haufler, J.B., Raphael, M.G., Wall, W.A. & Samson, F.B.). Island Press, Covelo, CA, pp. 739-749.
113. Williams, P.H. & Araujo, M.B. (2000). Using probability of persistence to identify important areas for biodiversity conservation. Proc. R. Soc. Lond. B Biol. Sci., 267, 1959-1966.
114. Williams, P.H., Hannah, L., Andelman, S., Midgley, G.F., Araújo, M.B., Hughes, G.O. et al. (in press). Planning for climate change: identifying minimum-dispersal corridors for the Cape Proteaceae. Conserv. Biol.
115. Wilson, K.A., Westphal, M.I., Possingham, H.P. & Elith, J. (2005). Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biol. Conserv., 122, 99-112.
116. Winde, B.A., McCarthy, M.A., Volinsky, C.T. & Kavanagh, R.P. (2003). The use of Bayesian model averaging to better represent uncertainty in ecological models. Conserv. Biol., 17, 1579-1590.