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  • 1
    ISSN: 1573-5052
    Keywords: Amplitude ; Gaussian logit curve ; Indicator value ; Logit regression ; Maximum likelihood ; Optimum ; Tolerance ; Unimodal response curve ; Weighted averaging
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Two methods for estimating ecological amplitudes of species with respect to Ellenberg's moisture scale are discussed, one based on weighted averaging and the other on maximum likelihood. Both methods are applied to phytosociological data from the province of Noord-Brabant (The Netherlands), and estimate the range of occurrence of species to be about 4–6 units on the moisture scale. Due to the implicit nature of Ellenberg's definition of moisture, it is impossible to improve the indicator values in a statistically sound way on the basis of floristic data only. The internal consistency of the Ellenberg indicator values is checked by using Gaussian logit regression. For 45 out of the 240 species studied the indicator value is inconsistent with those of the other species. The same method is used to estimate the optima and amplitudes of species considered moisture-indifferent and of some species not mentioned by Ellenberg. Some of these ‘indifferent’ species show a remarkably narrow amplitude. It is concluded that the Ellenberg indicator values for moisture form a reasonably consistent system.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 1573-5052
    Keywords: Canonical correspondence analysis ; Correspondence analysis ; Direct gradient analysis ; Ordination ; Species-environment relation ; Trend surface analysis ; Weighted averaging
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Canonical correspondence analysis (CCA) is introduced as a multivariate extension of weighted averaging ordination, which is a simple method for arranging species along environmental variables. CCA constructs those linear combinations of environmental variables, along which the distributions of the species are maximally separated. The eigenvalues produced by CCA measure this separation. As its name suggests, CCA is also a correspondence analysis technique, but one in which the ordination axes are constrained to be linear combinations of environmental variables. The ordination diagram generated by CCA visualizes not only a pattern of community variation (as in standard ordination) but also the main features of the distributions of species along the environmental variables. Applications demonstrate that CCA can be used both for detecting species-environment relations, and for investigating specific questions about the response of species to environmental variables. Questions in community ecology that have typically been studied by ‘indirect’ gradient analysis (i.e. ordination followed by external interpretation of the axes) can now be answered more directly by CCA.
    Type of Medium: Electronic Resource
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