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A New Multivariate Approach to Studying Temporal Changes of Vegetation

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Abstract

We emphasize the necessity of a complex approach to evaluating vegetation change at various levels of abstraction. The analytical steps include comparisons at the data, derived variable, distance, ordination and classification levels. A variety of data randomization methods incorporated in testing the significance of changes in raw data are introduced and compared. It is shown that these are true alternatives to Procrustean comparisons, which offer an apparently unfortunate choice in the presence/absence case. We propose to evaluate nearest neighbor relationships among quadrats in a new method, called adjacency analysis, to detect temporal trends that may remain unrevealed, should our attention be paid to full distance structures only. As an illustration, compositional and structural changes in the rock grassland vegetation of the Sas-hegy Nature Reserve (Budapest, Hungary), intensively sampled by quadrats in 1977 and 2000, are evaluated. Permutation tests show that differences between the 2 years are much smaller than expected by chance alone. Such an overall stability in community structure, however, does not mean that minor aspects of vegetation pattern are invariant over the years. Changes in life form and seed mass spectra are explained by the fluctuation of hemicryptophytes and the slight but detectable expansion of annuals and woody species. Classification is slightly rearranged in time, with clearly detectable within-cluster changes, also depicted in ordination scattergrams.

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Podani, J., Csontos, P., Tamás, J. et al. A New Multivariate Approach to Studying Temporal Changes of Vegetation. Plant Ecol 181, 85–100 (2005). https://doi.org/10.1007/s11258-005-3796-0

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Keywords

  • Classification
  • Life forms
  • Manhattan metric
  • Mantel test
  • Matrix rearrangements
  • Nearest neighbors
  • Ordination
  • Procrustes method
  • Seed mass spectra