Species distribution models and ecological theory: A critical assessment and some possible new approaches

Ecological Modelling

Volumn 200, Issue 1-2, 2007, Pages 1-19

  Austin, Mike ^a  

^a CSIRO   (Australia)

Author keywords

Competition; Environmental gradients; Generalized additive model; Generalized linear model; Geographically weighted regression; Quantile regression; Species response curves; Structural equation modelling

Indexed keywords

CLIMATE CHANGE; COMPUTER SIMULATION; ECOLOGY; ENVIRONMENTAL IMPACT; MATHEMATICAL MODELS; POPULATION STATISTICS; REGRESSION ANALYSIS; STATISTICAL TESTS;

ECOLOGICAL THEORY; GENERALISED ADDITIVE MODELS (GAM); GENERALISED LINEAR MODELS (GLM); SPECIES DISTRIBUTION MODELS; STATISTICAL MODELS; STRUCTURAL EQUATION MODELLING (SEM);

BIODIVERSITY;

ASSESSMENT METHOD; CONSERVATION MANAGEMENT; DATA ACQUISITION; ECOLOGICAL MODELING; ENVIRONMENTAL FACTOR; SPECIES OCCURRENCE; STATISTICAL ANALYSIS;

EID: 33847675260     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2006.07.005     Document Type: Review

References (150)

1. Antoine M.E., and McCune B. Contrasting fundamental and realized ecological niches with epiphytic lichen transplants in an old-growth Pseudotsuga forest. Bryologist 107 (2004) 163-173
2. Araujo M.B., Pearson R.G., Thuiller W., and Erhard M. Validation of species-climate impact models under climate change. Global Change Biol. 11 (2005) 1504-1513
3. Arhonditsis G.B., Stow C.A., Steinberg L.J., Kenney M.A., Lathrop R.C., McBride S.J., and Reckhow K.H. Exploring ecological patterns with structural equation modelling and Bayesian analysis. Ecol. Model. 192 (2006) 385-409
4. Austin M.P. A silent clash of paradigms: some inconsistencies in community ecology. Oikos 86 (1999) 170-178
5. Austin M.P. The potential contribution of vegetation ecology to biodiversity research. Ecography 22 (1999) 465-484
6. Austin M.P. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Model. 157 (2002) 101-118
7. Austin M.P. Case studies of the use of environmental gradients in vegetation and fauna modelling: theory and practice in Australia and New Zealand. In: Scott J.M., Heglund P.J., Samson F., Haufler J., Morrison M., Raphael M., and Wall B. (Eds). Predicting Species Occurrences: Issues of Accuracy and Scale (2002), Island Press, Covelo, California 73-82
8. Austin M.P. Vegetation and environment: discontinuities and continuities. In: van der Maarel E. (Ed). Vegetation Ecology (2005), Blackwell Publishing, Oxford 52-84
9. Austin M.P., and Cunningham R.B. Observational analysis of environmental gradients. Proc. Ecol. Soc. Austr. 11 (1981) 109-119
10. Austin M.P., and Nicholls A.O. To fix or not to fix the species limits, that is the ecological question: response to Jari Oksanen. J. Veg. Sci. 8 (1997) 743-748
11. Austin M.P., and Smith T.M. A new model for the continuum concept. Vegetatio 83 (1989) 35-47
12. Austin M.P., Nicholls A.O., and Margules C.R. Measurement of the realised qualitative niche: environmental niches of five Eucalyptus species. Ecol. Monogr. 60 (1990) 161-177
13. Austin, M.P., Belbin, L. Meyers, J.A., Doherty, M.D., Luoto, M. (2006). Evaluation of statistical models for predicting plant species distributions: role of artificial data and theory. Ecol. Model., doi:10.1016/j.ecolmodel.2006.05.023, in press.
14. Austin M.P., Nicholls A.O., Doherty M.D., and Meyers J.A. Determining species response functions to an environmental gradient by means of a β-function. J. Veg. Sci. 5 (1994) 215-228
15. Austin, M.P., Meyers, J.A., Belbin, L., Doherty, M.D., 1995. Modelling of landscape patterns and processes using biological data. Subproject 5: simulated data case study. Consultancy Report for ERIN, CSIRO Wildlife and Ecology, Canberra.
16. *Bhattarai K.R., Vetaas O.R., and Grytnes J.A. Fern species richness along a central Himalayan elevational gradient, Nepal. J. Biogeogr. 31 (2004) 389-400
17. Bio, A.M.F., 2000. Does vegetation suit our models? Data and model assumptions and the assessment of species distribution in space. Faculteit Ruimtelijke Wetenschappen Universiteit Utrecht. Nederlandse Geografische Studies 265.
18. Bio A., Alkemade R., and Barendregt A. Determining alternative models for vegetation response analysis: a non-parametric approach. J. Veg. Sci. 9 (1998) 5-16
19. Blackburn T.M., Lawton J.H., and Perry J.N. A method of estimating the slope of upper bounds of plots of body size and abundance in natural animal assemblages. Oikos 65 (1992) 107-112
20. Bloom A.J., Chapin F.S., and Mooney H.A. Resource limitationin plants-an economic analogy. Ann. Rev. Ecol. Syst. 16 (1985) 363-392
21. Boyce M.S., Vernier P.R., Nielson S.E., and Schmiegelow F.K.A. Evaluating resource selection functions. Ecol. Model. 157 (2002) 281-300
22. Brotons L., Thuiller W., Araujo M.B., and Hirzel A.H. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27 (2004) 437-448
23. *Bustamante J., and Seoane J. Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps. J. Biogeogr. 31 (2004) 295-306
24. Brzeziecki B. Analysis of vegetation-environment relationships using a simultaneous equations model. Vegetatio 71 (1987) 175-184
25. Cade B.S., and Guo Q. Estimating effects of constraints on plant performance with regression quantiles. Oikos 91 (2000) 245-254
26. Cade B.S., and Noon B.R. A gentle introduction to quantile regression for ecologists. Front. Ecol. Environ. 1 (2003) 412-420
27. Cade B.S., Noon B.R., and Flather C.H. Quantile regression reveals hidden bias and uncertainty in habitat models. Ecology 86 (2005) 786-800
28. Cade B.S., Terrell J.W., and Schroeder R.L. Estimating effects of limiting factors with regression quantiles. Ecology 80 (1999) 311-323
29. Cawsey E.M., Austin M.P., and Baker B.L. Regional vegetation mapping in Australia: a case study in the practical use of statistical modelling. Biodivers. Conserv. 11 (2002) 2239-2274
30. *Clarke E.D., Spear L.B., McCracken M.L., Marques F.F.C., Brochers D.L., Buckland S.T., and Ainley D.G. Validating the use of generalized additive models and at-sea surveys to estimate size and temporal trends of seabird populations. J. Appl. Ecol. 40 (2003) 278-292
31. Cleveland W.S., and Devlin S.J. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83 (1988) 596-610
32. Coudun C., and Gegout J. Ecological behaviour of herbaceous forest species along a pH gradient: a comparison between oceanic and semicontinental regions in northern France. Global Ecol. Biogeogr. 14 (2005) 263-270
33. Cressie N.A.C. Statistics for Spatial Data (1993), Wiley, New York
34. Drake J.M., Randin C., and Guisan A. Modelling ecological niches with support vector machines. J. Appl. Ecol. 43 (2006) 424-432
35. Dubayah R., and Rich P.M. Topographic solar radiation models for GIS. Int. J. Geogr. Inf. Syst. 9 (1995) 405-419
36. Elith E., Burgman M.A., and Regan H.M. Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecol. Model. 157 (2002) 313-329
37. Elith J., Ferrier S., Huettmann F., and Leathwick J. The evaluation strip: a new and robust method for plotting predicted responses from species distribution models. Ecol. Model. 186 (2005) 280-289
38. Elith J., et al. Novel methods improve prediction of species distributions from occurrence data. Ecography 29 (2006) 129-151
39. *Engler R., Guisan A., and Rechsteiner L. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41 (2004) 263-274
40. Ejrnaes R. Can we trust gradients extracted by detrended correspondence analysis?. J. Veg. Sci. 11 (2000) 565-572
41. Ferrier S., Watson G., Pearce J., and Drielsma M. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. 1. Species-level modelling. Biodivers. Conserv. 11 (2002) 2275-2307
42. Fitzgerald R.W., and Lees B.G. The application of neural networks to the floristic classification of remote sensing and GIS data in complex terrain. Proceedings of the XVII Congress of the International Society for Photogrammetry and Remote Sensing. Washington, USA (1992) 570-573
43. Foody G.M. Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI-rainfall relationship. Remote Sens. Environ. 88 (2003) 283-293
44. Foody G.M. Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna. Global Ecol. Biogeogr. 13 (2004) 315-320
45. Foody G.M. Clarifications on local and global data analysis. Global Ecol. Biogeogr. 14 (2005) 99-100
46. Foody G.M. Mapping the richness and composition of British breeding birds from coarse spatial resolution satellite sensor imagery. Int. J. Remote Sens. 26 (2005) 3943-3956
47. Fotheringham A.S., Brunsdon C., and Charlton M. Geographical Weighted Regression: The Analysis of Spatially Relationships (2002), Wiley, Chichester
48. Franklin J. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Prog. Phys. Geogr. 19 (1995) 474-499
49. Friedman J.H. Multivariate adaptive regression splines. Ann. Stat. 19 (1991) 1-141 (with discussion)
50. Friedman J.H., Hastie T., and Tibshirani R. Additve logistic regression: a statistical view of boosting. Ann. Stat. 28 (2000) 337-407
51. Garrido J.L., Rey P.J., and Herrera C.M. Pre- and post-germination determinants of spatial variation in recruitment in the perennial herb Helleborus foetidus L. (Ranunculaceae). J. Ecol. 93 (2005) 60-66
52. Gegout J., Coudon C., Bailly G., and Jabiol B. EcoPlant: A forest site database linking floristic data with soil and climate variables. J. Veg. Sci. 16 (2005) 257-260
53. *Gibson L.A., Wilson B.A., Cahill D.M., and Hill J. Spatial prediction of rufous bristlebird habitat in a coastal heathland: a GIS-based approach. J. Appl. Ecol. 41 (2004) 213-223
54. Giller J. Community Structure and the Niche (1984), Chapman and Hall, London
55. Grace J.B., and Pugesek B.H. A structural equation model of plant species richness and its application to a coastal wetland. Am. Nat. 149 (1997) 436-460
56. Graham C.H., Ferrier S., Huettman F., Moritz C., and Peterson A.T. New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol. Evol. 19 (2004) 497-503
57. Guisan A., and Harrell F.E. Ordinal response regression models in ecology. J. Veg. Sci. 11 (2000) 617-626
58. Guisan A., and Zimmermann N.E. Predictive habitat distribution models in ecology. Ecol. Model. 135 (2000) 147-186
59. Guisan A., Edwards T.C., and Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Model. 157 (2002) 89-100
60. Guisan A., and Thuiller W. Predicting species distribution: offering more than simple habitat models?. Ecol. Lett. 8 (2005) 993-1009
61. Harrington R., Woiwod I., and Sparks T. Climate change and trophic interactions. Trends Ecol. Evol. 14 (1999) 146-150
62. Hastie T., and Tibshirani R. Generalised Additive Models (1990), Chapman and Hall, London
63. Hirzel A., Helfer V., and Metral F. Assessing habitat-suitability models with a virtual species. Ecol. Model. 145 (2001) 111-121
64. Hirzel A.H., Hausser J., Chessel D., and Perrin N. Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data?. Ecology 83 (2002) 2027-2036
65. Huisman J., Olff H., and Fresco L.F.M. A hierarchical set of models for species response analysis. J. Veg. Sci. 4 (1993) 37-46
66. Huntley B., Berry P.M., Cramer W.P., and McDonald A.P. Modelling present and potential future ranges of some European higher plants using climate response surfaces. J. Biogeogr. 22 (1995) 967-1001
67. *Huntley B., Green R.E., Collingham Y.C., Hill J.K., Willis S.G., Bartlein P.J., Cramer W., Hagemeijer W.J.M., and Thomas C.J. The performance of models relating species geographical distributions to climate is independent of trophic level. Ecol. Lett. 7 (2004) 417-426
68. Huston M.A. Biological Diversity: The Coexistence of Species on Changing Landscapes (1994), Cambridge University Press, Cambridge
69. Huston M.A. Introductory essay: critical issues for improving predictions. In: Scott J.M., Heglund P.J., Samson F., Haufler J., Morrison M., Raphael M., and Wall B. (Eds). Predicting Species Occurrences: Issues of Accuracy and Scale (2002), Island Press, Covelo, California 7-21
70. Iriondo J.M., Albert M.J., and Escudero A. Structural equation modelling: an alternative for assessing causal relationships in threatened plant populations. Biol. Conserv. 113 (2003) 367-377
71. *Jeganathan P., Green R.E., Norris K., Vogiatzakis I.N., Bartsch A., Wotton S.R., Bowden C.G.R., Griffiths G.H., Pain D., and Rahmani A.R. Modelling habitat selection and distribution of the critically endangered Jerdon's courser Rhinoptilus bitorquatus in scrub jungle: an application of a new tracking method. J. Appl. Ecol. 41 (2004) 224-237
72. Jetz W., and Rahbek C. Geographic range size and determinants of avian species richness. Science 297 (2002) 1548-1551
73. Jetz W., Rahbek C., and Lichstein J.W. Local and global approaches to spatial data analysis in ecology. Global Ecol. Biogeogr. 14 (2005) 97-98
74. Johnson M.L., Huggins D.G., and DeNoyelles F. Ecosystem modelling with LISREL: a new approach for measuring direct and indirect effects. Ecol. Appl. 1 (1991) 383-398
75. Kadmon R., Farber O., and Danin A. A systematic analysis of factors affecting the performance of climatic envelope models. Ecol. Appl. 13 (2003) 853-867
76. Kadmon R., Farber O., and Danin A. Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol. Appl. 14 (2004) 401-413
77. Kaiser M.S., Speckman P.L., and Jones J.R. Statistical models for limiting nutrient relations in inland waters. J. Am. Stat. Assoc. 89 (1994) 410-423
78. Knight C.A., and Ackerly D.D. Variation in nuclear DNA content across environmental gradients: a quantile regression analysis. Ecol. Lett. 5 (2002) 66-76
79. Krause-Jensen D., Middelboe A.L., Sand-Jensen K., and Christensen P.B. Eelgrass, Zostera marina, growth along depth gradients; upper boundaries of the variation as a powerful predictive tool. Oikos 91 (2000) 233-244
80. Krebs C.J. Ecology; The Experimental Analysis of Distribution and Abundance. fifth ed. (2001), Benjamin Cummings, San Francisco
81. Kuhn T.S. The Structure of Scientific Revolutions. second ed. (1970), The University of Chicago Press, Chicago
82. Leathwick J.R. Climatic relationships of some New Zealand forest tree species. J. Veg. Sci. 6 (1995) 237-248
83. Leathwick J.R. Are New Zealand's Nothofagus species in equilibrium with their environment?. J. Veg. Sci. 9 (1998) 719-732
84. Leathwick J.R. Intra-generic competition among Nothofagus in New Zealand's primary indigenous forests. Biodivers. Conserv. 11 (2002) 2177-2187
85. Leathwick J.R., and Austin M.P. Competitive interactions between tree species in New Zealand's old-growth indigenous forests. Ecology 82 (2001) 2560-2573
86. Leathwick J.R., and Whitehead D. Soil and atmospheric water deficits and the distributions of New Zealand's indigenous tree species. Funct. Ecol. 15 (2001) 233-242
87. Leathwick, J.R., Elith, J., Hastie, T. Comparative performance of two techniques for statistical modelling of presence-absence data. Ecology, in press.
88. Leathwick J.R., Whitehead D., and McLeod M. Predicting changes in the composition of New Zealand's indigenous forests in response to global warming: a modelling approach. Environ. Software 11 (1996) 81-90
89. Leathwick J.R., Rowe D., Richardson J., Elith J., and Hastie T. Using multivariate adaptive splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshwater Biol. 50 (2005) 2034-2052
90. Leathwick, J.R., Elith, J., Francis, M.P., Hastie, T., Taylor, P. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecol. Prog. Ser., in press.
91. Lee S.Y., Song X.Y., and Poon W.Y. Comparison of approaches in estimating interactions and quadratic effects of latent variables. Multivariate Behav. Res. 39 (2004) 37-67
92. Lehmann A., Overton J.McC., and Austin M.P. Regression models for spatial prediction: their role for biodiversity and conservation. Biodivers. Conserv. 11 (2002) 2085-2092
93. Lehmann A., Overton J.McC., and Leathwick J.R. GRASP: generalized regression analysis and spatial prediction. Ecol. Model. 157 (2002) 189-207
94. Maggini, R., Lehmann, A., Zimmermann, N.E., Guisan, A., 2006. Improving generalized regression analysis for spatial predictions of forest communities. J. Biogeogr., in press.
95. *Malo J.E., Suarez F., and Diez A. Can we mitigate animal-vehicle accidents using predictive models?. J. Appl. Ecol. 41 (2004) 701-710
96. Manel S., Dias J.M., and Ormerod S.J. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with Himalayan river bird. Ecol. Model. 120 (1999) 337-347
97. Marquez A.L., Real R., and Vargas J.M. Dependence of broad-scale geographical variation in fleshy-fruited plant species richness on disperser bird species richness. Global Ecol. Biogeogr. 13 (2004) 295-304
98. McCullagh P., and Nelder J.A. Generalized Linear Models. second ed. (1989), Chapman and Hall, London
99. McCune B., and Grace J.B. Analysis of Ecological Communities (2002), MjM Software Design, Oregon, USA
100. McCune B., Berryman S.D., Cissel J.H., and Gitelman A.I. Use of a smoother to forecast occurrence of epiphytic lichens under alternative forest management plans. Ecol. Appl. 13 (2003) 1110-1123
101. *McPherson J.M., Jetz W., and Rogers D.J. The effects of species' range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?. J. Appl. Ecol. 41 (2004) 811-823
102. Miller J., and Franklin J. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecol. Model. 157 (2002) 227-247
103. Mitchell R.J. Testing evolutionary and ecological hypotheses using path analysis and structural equation modelling. Funct. Ecol. 6 (1992) 123-129
104. Mitchell R.J. Effects of floral traits, pollinator visitation, and plant size on Ipomopsis aggregrata fruit production. Am. Nat. 143 (1994) 870-889
105. Moisen G.G., and Frescino T.S. Comparing five modelling techniques for predicting forest characteristics. Ecol. Model. 157 (2002) 209-225
106. Moisen, G.G., Freeman, E.A., Blackard, J.A., Zimmermann, N.E., Edwards Jr., T.C., 2006. Predicting tree species presence and basal area in Utah-a comparison of generalized additive models, stochastic gradient boosting, and tree-based methods. Ecol. Model., in press.
107. Munoz J., and Felicisimo A.M. Comparison of statistical methods commonly used in predictive modelling. J. Veg. Sci. 15 (2004) 285-292
108. Nicholls A.O. How to make biological surveys go further with generalized linear models. Biol. Conserv. 50 (1989) 51-76
109. Nicholls A.O. Examples of the use of generalized linear models in analysis of survey data for conservation evaluation. In: Margules C.R., and Austin M.P. (Eds). Nature Conservation: Cost Effective Biological Surveys and Data Analysis (1991), CSIRO, Melbourne 191-201
110. Osborne P.E., and Suarez-Seoane S. Should data be partitioned spatially before building large-scale distribution models?. Ecol. Model. 157 (2002) 249-259
111. Pausas J.G., Braithwaite L.W., and Austin M.P. Modelling habitat quality for arboreal marsupials in the South coastal forests of New South Wales, Australia. For. Ecol. Manag. 78 (1995) 39-49
112. Pausas J.G., Austin M.P., and Noble I.R. A forest simulation model for predicting eucalypt dynamics and habitat quality for arboreal marsupials. Ecol. Appl. 7 (1997) 921-933
113. Pearce J., and Ferrier S. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol. Modell. 133 (2000) 225-245
114. Pearson R.G., and Dawson T.P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?. Global Ecol. Biogeogr. 12 (2003) 361-371
115. Phillips S.J., Anderson R.P., and Schapire R.E. Maximum entropy modelling of species geographic distributions. Ecol. Model. 190 (2006) 231-259
116. Prentice I.C., Cramer W., Harrison S.P., Leemans R., Monserud R.A., and Solomon A.M. A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeogr. 19 (1992) 117-134
117. Ricklefs R.E. A comprehensive framework for global patterns in biodiversity. Ecol. Lett. 7 (2004) 1-15
118. Rubio G., Zhu J., and Lynch J.P. A critical test of the two prevailing theories of plant resonse to nutrient availability. Am. J. Bot. 90 (2003) 143-152
119. *Rushton S.P., Ormerod S.J., and Kerby G. New paradigms for modelling species distributions?. J. Appl. Ecol. 41 (2004) 193-200
120. Rydgren K., Okland R.H., and Okland T. Species response curves along environmental gradients. A case study from SE Norwegian swamp forests. J. Veg. Sci. 14 (2003) 869-880
121. Scharf F.S., Juanes F., and Sutherland M. Inferring ecological relationships from the edges of scatter diagrams: comparison of regression techniques. Ecology 79 (1998) 448-460
122. Schroder H.K., Andersen H.E., and Kiehl K. Rejecting the mean: estimating the response of fen plant species to environmental factors by non-linear quantile regression. J. Veg. Sci. 16 (2005) 373-382
123. Schumacker R.E., and Marcoulides G.A. Interaction and Nonlinear Effects in Structural Equation Modelling (1998), Lawrence Erlbaum Associates, New Jersey
124. Scott J.M., Heglund P.J., Haufler J., Morrison M., Raphael M., Wall B., and Samson F. Predicting Species Occurrences: Issues of Accuracy and Scale (2002), Island Press, Covelo, California
125. *Segurado P., and Araujo M.B. An evaluation of methods for modelling species distributions. J. Biogeogr. 31 (2004) 1555-1568
126. Shipley B. Testing causal explanations in organismal biology: causation, correlation and structural equation modelling. Oikos 86 (1999) 374-382
127. Shipley B. Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations and Causal Inference (2000), Cambridge University Press, Cambridge
128. Shipley B., and Lechowicz M.J. The functional co-ordination of leaf morphology, nitrogen concentration, and gas exchange in 40 wetland species. Ecoscience 7 (2000) 183-194
129. Smith P.A. Autocorrelation in logistic regression modelling of species' distributions. Global Ecol. Biogeogr. Lett. 4 (1994) 47-61
130. Stockwell D.R.B., and Noble I.R. Induction of sets of rules from animal distribution data: a robust and informative method of data analysis. Math. Comput. Simul. 33 (1992) 385-389
131. Termansen M., McClean C.J., and Preston C.D. The use of genetic algorithms and Baysian classification to model species distributions. Ecol. Model. 192 (2006) 410-424
132. Thomson J.D., Weiblen G., Thomson B.A., Alfaro S., and Legendre P. Untangling multiple factors in spatial distributions: lilies, gophers and rocks. Ecology 77 (1996) 1698-1715
133. *Thuiller W. BIOMOD-optimising predictions of species distributions and projecting potential future shifts under global change. Global Change Biol. 9 (2003) 1353-1362
134. *Thuiller W., Vayreda J., Pino J., Sabate S., Lavorel S., and Garcia C. Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain). Global Ecol. Biogeogr. 12 (2003) 313-325
135. Thuiller W., Araujo M.B., and Lavorel S. Generalized models vs. classification tree analysis: predicting spatial distributions of plant species at different scales. J. Veg. Sci. 14 (2003) 669-680
136. *Thuiller W., Araujo M.B., and Lavorel S. Do we need land-cover data to model species distributions in Europe?. J. Biogeogr. 31 (2004) 353-361
137. Van der Ploeg R.R., Bohm W., and Kirkham M.B. On the origin of the theory of mineral nutrition of plants and the law of the minimum. Soil Sci. Soc. Am. J. 63 (1999) 1055-1062
138. Van Neil K.P., Laffan S.W., and Lees B.G. Effect of error in the DEM on environmental variables for predictive vegetation modelling. J. Veg. Sci. 15 (2004) 747-756
139. Van Neil, K.P., Austin, M.P. Predictive vegetation modelling for conservation: impact of error propagation from digital elevation data. Ecol. Appl., in press.
140. *Venier L.A., Pearce J., McKee J.E., McKenny D.W., and Niemi G.J. Climate and satellite-derived land cover for predicting breeding bird distribution in the Great Lakes Basin. J. Biogeogr. 31 (2004) 315-331
141. Vile D., Shipley B., and Garnier E. A structural equation model to integrate changes in functional strategies during old-field succession. Ecology 87 (2006) 504-517
142. Wamelink G.W.W., Goedhart P.W., Van Dobben H.F., and Berendse F. Plant species as predictors of soil pH: replacing expert judgement with measurements. J. Veg. Sci. 16 (2005) 461-470
143. Weiher E. Species richness along multiple gradients: testing a general multivariate model in oak savannas. Oikos 101 (2003) 311-316
144. Weiher E., Forbes S., Schauwecker T., and Grace J.B. Multivariate control of plant species richness and community biomass in blackland prairie. Oikos 106 (2004) 151-157
145. Whittaker R.J., Willis K.J., and Field R. Scale and species richness: towards a general, hierarchical theory of species diversity. J. Biogeogr. 28 (2001) 453-470
146. Wood S.N., and Augustin N.H. GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol. Model. 157 (2002) 157-178
147. Yee T.W., and Mackenzie M. Vector generalized additive models in plant ecology. Ecol. Model. 157 (2002) 141-156
148. Yee T.W., and Mitchell N.D. Generalized additive models in plant ecology. J. Veg. Sci. 2 (1991) 587-602
149. Yu K., and Jones M.C. Local linear quantile regression. J. Am. Stat. Assoc. 93 (1998) 228-237
150. Zaneiwski A.E., Lehmann A., and Overton J. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol. Model. 157 (2002) 261-280