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GLM versus CCA spatial modeling of plant species distribution

GLM versus CCA spatial modeling of plant species distribution 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Plant Ecology Springer Journals

GLM versus CCA spatial modeling of plant species distribution

Guisan, Antoine; Weiss, Stuart; Weiss, Andrew
Plant Ecology , Volume 143 (1) – Sep 28, 2004
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Publisher
Springer Journals
Copyright
Copyright © 1999 by Kluwer Academic Publishers
Subject
Life Sciences; Plant Sciences
ISSN
1385-0237
eISSN
1573-5052
DOI
10.1023/A:1009841519580
Publisher site
See Article on Publisher Site

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.

Journal

Plant Ecology – Springer Journals

Published: Sep 28, 2004

References

  • A new model for the continuum concept
    Austin, M. P.; Smith, T. M.
  • Determining species response functions to an environmental gradient by means of a Beta-function
    Austin, M. P.; Nicholls, A. O.; Doherty, M. D.; Meyers, J. A.
  • A simulated map of the potential natural forest vegetation of Switzerland
    Brzeziecki, B.; Kienast, F.; Wildi, O.
  • Topographic control of vegetation in a mountain big sagebrush steppe
    Burke, I. C.; Reiners, W. A.; Olson, R. K.
  • A coefficient of agreement for nominal scales
    Cohen, J.
  • On the use of three performance measures for fitting species response curves
    Ferrer-Castán, D.; Calvo, J. F.; Esteve-Selma, M. A.; Torres-Martinez, A.; Ramirez-Diaz, L.
  • Mapping Dominant Vegetation Communities in the Colorado Rocky Mountain Front Range with Landsat Thematic mapper and Digital Terrain Data
    Frank, T. D.
  • Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients
    Franklin, J.
  • Multivariate Analysis in Community Ecology
    Gauch, H.G.
  • Predictions of vegetation patterns at the limits of plant life: a new view of the alpine-nival ecotone
    Gottfried, M.; Pauli, H.; Grabherr, G.
  • Theory and Applications of Correspondence Analysis
    Greenacre, M. J.
  • Using static modeling to predict potential distributions of species in an alpine environment
    Guisan, A.; Theurillat, J.-P.; Kienast, F.
  • Predicting patterns of vascular plant species richness with composite variables: a meso-scale study in Finnish Lapland
    Heikkinen, R. K.
  • GIS-based solar radiation flux models
    Hetrick, W. A.; Rich, P. M.; Barnes, F. J.; Weiss, S. B.
  • Patterns of species distribution in Britain elucidated by canonical correspondence analysis
    Hill, M. O.
  • A hierarchical set of models for species response analysis
    Huisman, J.; Olff, H.; Fresco, L. M. F.
  • A new method for estimating the spatial distribution of mean seasonal and annual rainfall applied to Hunter Valley, New South Wales
    Hutchinson, M. F.; Bischof, R. J.
  • Data Analysis in Community and Landscape Ecology
    Jongman, R. H. G.; Ter Braak, C. J. F.; van Tongeren, O. F. R.
  • Process versus empirical models: which approach for forest ecosystem management?
    Korzukhin, M. D.; Ter-Mikaelian, M.; Wagner, R. G.
  • Trees of the Great Basin: A Natural History
    Lanner, R. M.
  • Ecological responses surfaces for north American tree species and their use in forest classification
    Lenihan, J. M.
  • Comparing global vegetation maps with the Kappa statistic.
    Monserud, R. A.; Leemans, R.
  • How to make biological surveys go further with generalized linear model
    Nicholls, A. O.
  • Why the beta-function cannot be used to estimate skweness of species responses
    Oksanen, J.
  • Putting things in even better order: the advantages of canonical correspondence analysis
    Palmer, M.
  • A comparative ecological study of Norwegian mountain plants in relation to possible future climatic change
    Saetersdal, M.; Birks, H. J. B.
  • Climatic controls of eastern North American coastal tree and shrub distributions
    Shao, G.; Halpin, P. N.
  • Classification of Kangaroo habitat distribution using three GIS models
    Skidmore, A. K.; Gauld, A.; Walker, P.
  • The analysis of vegetation-environment relationships by canonical correspondence analysis
    Ter Braak, C. J. F.
  • CANOCO: an extension of DECORANA to analyze species-environment relationships
    Ter Braak, C. J. F.
  • SIMPLE: an inductive modeling and mapping tool for spatially-oriented data
    Walker, P. A.; Moore, D. M.
  • HABITAT: a procedure for modeling a disjoint environmental envelope for a plant or animal species
    Walker, P. A.; Cocks, D.
  • Proceedings GIS/LIS-92. Bethesda, MD: ASPRS. Weisberg, S. 1980. Applied Linear Regression
    Walker, R. E.; Stoms, D. M.; Davis, F. W.; van Wagtendonk, J.
  • Generalized additive models in plant ecology
    Yee, T. W.; Mitchell, N. D.

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