Constrained ordination analysis with flexible response functions |
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| Institution: | 1. Institute of Fisheries Science, National Taiwan University, Taipei, Taiwan;2. Department of Life Science, National Taiwan University, Taipei, Taiwan;3. Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, USA;4. School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA;5. Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan;1. Laboratory of Atmosphere Observation and Water Cycle, School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China;2. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China;3. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands;4. Fundamental Surveying and Mapping Information Center of Anhui Province, Hefei, China;1. Staatliches Museum für Naturkunde Karlsruhe, Erbprinzentraße 13, D-76133 Karlsruhe, Germany;2. Institute of Geography and Geology, Ernst-Moritz-Arndt-University, partner in the Greifswald Mire Centre, Friedrich-Ludwig-Jahn-Straße 16a, D-17487 Greifswald, Germany;3. Institute of Botany and Landscape Ecology, Ernst-Moritz-Arndt-University, partner in the Greifswald Mire Centre, Soldmannstraße 15, D-17487 Greifswald, Germany |
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| Abstract: | Canonical correspondence analysis (CCA) is perhaps the most popular multivariate technique used by environmental ecologists for constrained ordination; it is an approximation to the maximum likelihood solution of the Gaussian response model. In this article, we look at the constrained ordination problem from a slightly different point of view and argue that it is this particular point of view that CCA implicitly adopts. This gives us additional insights into the nature of CCA. We then exploit the new perspective to generalize the Gaussian response model to incorporate more flexible response functions. A real example is presented to illustrate the use of the more flexible model. |
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