Abstract
Despite the variety of statistical methods available for static modeling of plant distribution, few studies directly compare methods on a common data set. In this paper, the predictive power of Generalized Linear Models (GLM) versus Canonical Correspondence Analysis (CCA) models of plant distribution in the Spring Mountains of Nevada, USA, are compared. Results show that GLM models give better predictions than CCA models because a species-specific subset of explanatory variables can be selected in GLM, while in CCA, all species are modeled using the same set of composite environmental variables (axes). Although both techniques can be readily ported to a Geographical Information System (GIS), CCA models are more readily implemented for many species at once. Predictions from both techniques rank the species models in the same order of quality; i.e. a species whose distribution is well modeled by GLM is also well modeled by CCA and vice-versa. In both cases, species for which model predictions have the poorest accuracy are either disturbance or fire related, or species for which too few observations were available to calibrate and evaluate the model. Each technique has its advantages and drawbacks. In general GLM will provide better species specific-models, but CCA will provide a broader overview of multiple species, diversity, and plant communities.
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Austin, M. P. & Smith T. M. 1989. A new model for the continuum concept. Vegetatio 83: 35-47.
Austin, M. P., Nicholls, A. O., Doherty, M. D. & Meyers, J. A. 1994. Determining species response functions to an environmental gradient by means of a Beta-function. J. Veg. Sci. 5: 215-228
Brzeziecki, B., Kienast, F. & Wildi, O. 1993. A simulated map of the potential natural forest vegetation of Switzerland. J. Veg. Sci. 4: 499-508.
Burke, I. C., Reiners, W. A., & Olson, R. K. 1989. Topographic control of vegetation in a mountain big sagebrush steppe. Vegetatio 84: 77-86.
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20: 37-46.
Ferrer-Castán, D., Calvo, J. F., Esteve-Selma, M. A., Torres-Martinez, A. & Ramirez-Diaz, L. 1995. On the use of three performance measures for fitting species response curves. J. Veg. Sci. 6: 57-62.
Fitzgerald, R. W. & Lees, B.G. 1992. The application of Neural Networks to the floristic classification of remote sensing and GIS data in complex terrain (I). Vol. 3, Pp. 2-10. In: Proceedings of the 6th Australian Remote Sensing Conference, Wellington, N.Z., November 1992.
Frank, T. D. 1988. Mapping Dominant Vegetation Communities in the Colorado Rocky Mountain Front Range with Landsat Thematic mapper and Digital Terrain Data. Photogr. Eng. 54: 1727-1734.
Franklin, J. 1995. Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Prog. Phys. Geog. 19: 474-499.
Gauch, H.G. 1982. Multivariate Analysis in Community Ecology. Cambridge University Press, Cambridge.
Gottfried, M., Pauli, H. & Grabherr, G. 1998. Predictions of vegetation patterns at the limits of plant life: a new view of the alpine-nival ecotone. Arctic Alpine Res. 30: 207-221.
Greenacre, M. J. 1984. Theory and Applications of Correspondence Analysis. Academic Press, London.
Guisan, A. 1997. Distribution de taxons végétaux dans un environnement alpin: Application de modélisations statistiques dans un système d'information géographique. Thèse de doctorat présenté à la Faculté des Sciences de l'Université de Genève (PhD Dissertation, University of Geneva). No 2892. 186 pp. C annexes and maps.
Guisan, A., Theurillat, J.-P. & Kienast, F. 1998. Using static modeling to predict potential distributions of species in an alpine environment. J. Veg. Sci. 9: 65-74.
Heikkinen, R. K. 1996. Predicting patterns of vascular plant species richness with composite variables: a meso-scale study in Finnish Lapland. Vegetatio 126: 151-165.
Hetrick, W. A., Rich, P. M, Barnes, F. J., & Weiss, S. B. 1993. GIS-based solar radiation flux models. Pp. 132-143. In: American Society for Photogrammetery and Remote Sensing Technical Papers. Vol. 3, GIS photogrammetry, and modeling. Hill, M. O. 1974. Correspondence analysis: a neglected multivariate method. Appl. Stat. 23: 340-354.
Hill, M. O. 1991. Patterns of species distribution in Britain elucidated by canonical correspondence analysis. J. Biog. 18: 247-255.
Huisman, J., Olff, H. & Fresco, L. M. F. 1993. A hierarchical set of models for species response analysis. J. Veg. Sci. 4: 37-46.
Hutchinson, M. F. & Bischof, R. J. 1983. A new method for estimating the spatial distribution of mean seasonal and annual rainfall applied to Hunter Valley, New South Wales. Austral. Meteorol. Mag. 31: 179-184.
Jongman, R. H. G., Ter Braak, C. J. F. & van Tongeren, O. F. R. 1995. Data Analysis in Community and Landscape Ecology. Cambridge University Press, Cambridge UK.
Korzukhin, M. D., Ter-Mikaelian, M. & Wagner, R. G. 1996. Process versus empirical models: which approach for forest ecosystem managementčan. J. For. Res. 26: 879-887.
Lanner, R. M. 1983. Trees of the Great Basin: A Natural History. University of Nevada Press, Reno, NV.
Lenihan J. M. 1993. Ecological responses surfaces for north American tree species and their use in forest classification. J. Veg. Sci. 4: 667-680.
McCullagh, P. & Nelder, J. A. 1983. Generalized Linear Models. Monographs on Statistics and Applied Probability, Chapman and Hall, London.
Monserud, R. A. & Leemans, R. 1992. Comparing global vegetation maps with the Kappa statistic. Ecol. Modelling 62: 275-293.
Nachlinger, J. & G. A. Reese. 1996. Plant community classification of the Spring Mountains National Recreation Area, Clark and Nye Counties, Nevada. Unpublished report on file with Toiyabe National Forest, Spring Mountains National Recreation Area, Las Vegas, NV. 104 pp.+ appendix.
Nicholls, A. O. 1989. How to make biological surveys go further with generalized linear model. Biol. Conserv. 50: 51-75.
Oksanen, J. 1997.Why the beta-function cannot be used to estimate skweness of species responses. J. Veg. Sci. 8: 147-152.
Palmer, M. 1993. Putting things in even better order: the advantages of canonical correspondence analysis. Ecology 74: 2215-2230.
Saetersdal, M. & Birks, H. J. B. 1997. A comparative ecological study of Norwegian mountain plants in relation to possible future climatic change. J. Biog. 24: 127-152.
Shao, G. & Halpin, P. N. 1995. Climatic controls of eastern North American coastal tree and shrub distributions. J. Biog. 22: 1083-1089.
Skidmore, A. K., Gauld, A. & Walker, P. 1996. Classification of Kangaroo habitat distribution using three GIS models. Int. J. Geographical Information System 10: 441-454.
Ter Braak C. J. F. 1987. The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio 69: 69-77.
Ter Braak, C. J. F. 1988. CANOCO: an extension of DECORANA to analyze species-environment relationships Vegetatio 75: 159-160.
Ter Braak, C. J. F. & Smilauer, P. 1998. CANOCO Reference Manual and User's Guide to CANOCO for Windows. Software for Canonical Community Ordination (version 4). Centre for Biometry Wageningen (Wageningen, NL) and Microcomputer Power, Ithaca NY, USA, 352 pp.
Walker, P. A. & Moore, D. M. 1988. SIMPLE: an inductive modeling and mapping tool for spatially-oriented data. Int. J. Geog. Inf. System 2: 347-363.
Walker, P. A. & Cocks, D. 1991. HABITAT: a procedure for modeling a disjoint environmental envelope for a plant or animal species. Global Ecol. Biog. Letters 1: 108-118.
Walker, R. E., Stoms, D. M., Davis, F. W. & van Wagtendonk, J. 1992. Modeling potential natural vegetation from a topographic gradient in the southern Sierra Nevada, California. Pp. 794-803. In: Proceedings GIS/LIS-92. Bethesda, MD: ASPRS. Weisberg, S. 1980. Applied Linear Regression. John Wiley and Sons, New York.
Yee, T. W. & Mitchell, N. D. 1991. Generalized additive models in plant ecology. J. Veg. Sci. 2: 587-602.
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Guisan, A., Weiss, S.B. & Weiss, A.D. GLM versus CCA spatial modeling of plant species distribution. Plant Ecology 143, 107–122 (1999). https://doi.org/10.1023/A:1009841519580
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DOI: https://doi.org/10.1023/A:1009841519580