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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Plant ecology 51 (1983), S. 141-155 
    ISSN: 1573-5052
    Keywords: Correlation matrix ; Dispersion matrix ; Forest model ; Hierarchical structure ; Ordination ; Principal components analysis ; Simulation ; Succession
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract A model of a 1/12th ha forest stand, FORET, generated 10 000 years of simulated species succession. Approximately the first third of these results were analyzed by principal component analysis as if they were collected field data to give the trajectory of the community particle in a collapsed species space. The ordination axis orientation was performed on a dispersion matrix and correlation matrix between species. In both cases, however, the eigen vectors were applied to the data matrix which had not been transformed to unit species variance. This facilitated comparison of species dispersion and correlation structure; it emerged they were very different. Correlation structure gave large weights to understory species while dispersion emphasized the dominant overstory species. This implies a decomposition of simulated stand behavior into overstory and understory, even though such decomposition was not formally built into the model. This decomposition would seem to pertain to real vegetation. Principal component analysis was able to express insightful differences between data structure with and without the unit variance transformation implicit in the correlation matrix. This flexibility of the ordination method proved valuable in uncovering unsuspected ordering principles in the model. Complex simulated data allow the ordination technique to demonstrate its capacity to generate new hypotheses, which hypotheses can then be simply validated by a return to the structure of the model but with the hindsight of the analysis. The generation of new hypotheses is not possible if the simulation is of a simple coenocline; on the other hand, ordination of test field data does not allow the simple validation of new hypotheses, for in the field there is not a defined algorithm to which the researcher can return.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Plant ecology 56 (1984), S. 147-160 
    ISSN: 1573-5052
    Keywords: Data transformation ; Ordination ; Phytoplankton ; Principal components analysis ; Scale
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Data transformation is seen here as an aspect of scaling such that we are less interested in the quirks and properties of each transformation but are more concerned with the general scaling properties and trends of suites of transformations. Over two years of daily phytoplankton abundance data for 30 species from a temperate lake (Llyn Maelog, North Wales) were subjected to a series of scale-ordered transformations. Two major classes of transformation were systematically varied: binary and smoothing. Binary transformation scaled the cutoff threshold between ‘presence’ and ‘absence’ of a species to various levels of abundance. With successively smaller universes and smoothing windows and successive species exclusion, ordinations of sample dates revealed smaller scaled structures in the order: annual cycles of species turnover, seasonal areas of attraction and uniqueness of individual sample dates. Gradual increases in the length of the smoothing window resulted in gradual shifts in the positions of points in sample data ordination, but not necessarily in the species ordinations. Thus sample data structures are more stable with change in scale than are species data structures. These differences in stability are discussed in the context of filtering characteristics of data collection and data analysis. Transformations producing similar species statistics (means, variances and skews) did not generally give similar ordination results, while some transformations giving similar ordinations differed in species statistical parameters.
    Type of Medium: Electronic Resource
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