Statistical approaches to interpreting diversity patterns in the Norwegian mountain flora

Statistical approaches to interpreting diversity patterns in the Norwegian mountain flora The richness of Norwegian mountain plants in 75 grid squares is mapped from published distributional data for 109 species. Eleven explanatory variables representing bedrock geology, geography and topography, climate, and history (relative abundance of unglaciated areas) Tor each square are used in multiple regression analysis with associated Monte Carlo permutation tests to find statistically significant predictor variables for species richness. The variance in richness explained by the four major groups or explanatory variables is established by (partial) multiple regression analysis in which the groups of predictors are entered in different orders. The variance in species richness explained by the predictor variables is partitioned into four independent components. A predictive model for species richness using partial least squares regression and all explanatory variables has a coefficient of determination (R2) of 0.79. The statistical results consistently show that species‐richness patterns are well explained by modern‐day factors such as climate, geology, elevation, and geography without recourse to historical variables. The nunatak hypothesis of plant survival on unglaciated areas within Norway does not explain the observed richness patterns when modern ecological factors are considered first. The nunatak hypothesis thus appears to be redundant, a view supported by recent palaeobotanical. biosystematical, and evolutionary studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecography Wiley

Statistical approaches to interpreting diversity patterns in the Norwegian mountain flora

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Abstract

The richness of Norwegian mountain plants in 75 grid squares is mapped from published distributional data for 109 species. Eleven explanatory variables representing bedrock geology, geography and topography, climate, and history (relative abundance of unglaciated areas) Tor each square are used in multiple regression analysis with associated Monte Carlo permutation tests to find statistically significant predictor variables for species richness. The variance in richness explained by the four major groups or explanatory variables is established by (partial) multiple regression analysis in which the groups of predictors are entered in different orders. The variance in species richness explained by the predictor variables is partitioned into four independent components. A predictive model for species richness using partial least squares regression and all explanatory variables has a coefficient of determination (R2) of 0.79. The statistical results consistently show that species‐richness patterns are well explained by modern‐day factors such as climate, geology, elevation, and geography without recourse to historical variables. The nunatak hypothesis of plant survival on unglaciated areas within Norway does not explain the observed richness patterns when modern ecological factors are considered first. The nunatak hypothesis thus appears to be redundant, a view supported by recent palaeobotanical. biosystematical, and evolutionary studies.

Journal

EcographyWiley

Published: Jan 1, 1996

References

  • Partialling out the spatial component of ecological variation
    Borcard, Borcard; Legendre, Legendre; Drapeau, Drapeau
  • On different types of unglaciated areas during the Ice Ages and their significance to phytogeography
    Dahl, Dahl
  • On the statistical analysis of vegetation change: a wetland affected by water extraction and soil acidification
    Braak, Braak; Wiertz, Wiertz

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