Local and global approaches to spatial data analysis in ecology

Local and global approaches to spatial data analysis in ecology Introduction Geographic analyses in ecology may be separated into those that attempt generalizations to achieve ‘global’ insights, and those that attempt to explore and document local variation. Ecological studies at the broad scale usually set out to test specific hypotheses (such as the effect of energy on species richness) and focus on establishing global relationships before examining local residual variation. However, geographical pattern in model residuals ( Jetz & Rahbek, 2002 ; Fig. 1c) can also lead to important insights. In a recent issue of Global Ecology and Biogeography , a study by Foody (2004 ) illustrates how a method for estimating local variation in model parameters, geographically weighted regression (GWR, Fotheringham ., 2002 ), may enhance data exploration. Standard global methods, such as linear or logistic multiple regression, estimate a single parameter for each explanatory variable. In contrast, GWR allows parameter values to vary continuously in geographical space, and local parameter values are estimated by assigning higher weights to nearby observations than more distant ones. The user varies the ‘bandwidth’ in GWR, which determines the rate at which weights decrease with distance. GWR Foody uses GWR to analyse the same 1599 bird species distributions that we used http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Global Ecology and Biogeography Wiley

Local and global approaches to spatial data analysis in ecology

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Publisher
Wiley
Copyright
Copyright © 2005 Wiley Subscription Services, Inc., A Wiley Company
ISSN
1466-822X
eISSN
1466-8238
DOI
10.1111/j.1466-822X.2004.00129.x
Publisher site
See Article on Publisher Site

Abstract

Introduction Geographic analyses in ecology may be separated into those that attempt generalizations to achieve ‘global’ insights, and those that attempt to explore and document local variation. Ecological studies at the broad scale usually set out to test specific hypotheses (such as the effect of energy on species richness) and focus on establishing global relationships before examining local residual variation. However, geographical pattern in model residuals ( Jetz & Rahbek, 2002 ; Fig. 1c) can also lead to important insights. In a recent issue of Global Ecology and Biogeography , a study by Foody (2004 ) illustrates how a method for estimating local variation in model parameters, geographically weighted regression (GWR, Fotheringham ., 2002 ), may enhance data exploration. Standard global methods, such as linear or logistic multiple regression, estimate a single parameter for each explanatory variable. In contrast, GWR allows parameter values to vary continuously in geographical space, and local parameter values are estimated by assigning higher weights to nearby observations than more distant ones. The user varies the ‘bandwidth’ in GWR, which determines the rate at which weights decrease with distance. GWR Foody uses GWR to analyse the same 1599 bird species distributions that we used

Journal

Global Ecology and BiogeographyWiley

Published: Jan 1, 2005

References

  • Geometric constraints explain much of the species richness pattern in African birds
    Jetz, W.; Rahbek, C.
  • Geographic range size and determinants of avian species richness
    Jetz, W.; Rahbek, C.

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