Skip to main content
Log in

Spatial cross-correlation of undisturbed, natural shortleaf pine stands in northern Georgia

  • Papers
  • Published:
Environmental and Ecological Statistics Aims and scope Submit manuscript


In this study a cross-correlation statistic is used to analyse the spatial relationship among stand characteristics of natural, undisturbed shortleaf pine stands sampled during 1961–72 and 1972–82 in northern Georgia. Stand characteristics included stand age, site index, tree density, hardwood competition, and mortality. In each time period, the spatial cross-correlation statistic was used to construct cross-correlograms and cumulative cross-correlograms for all significant pairwise combination of stand characteristics. Both the cross-correlograms and cumulative cross-correlograms identified small-scale clustering and weak directional gradients for different stand characteristics in each time period. The cumulative cross-correlograms, which are based on inverse distance weighting were more sensitive in detecting small-scale clustering than the cross-correlograms based on a 0–1 weighting. Further analysis suggested that the significant cross-correlation observed among basal area growth and other stand characteristics were due, in a large part, on a subset of sample plots located in the northern part of the state, rather than regional or broad-scale variation as first thought. The ability to analyse the spatial relationship between two or more response surfaces should provide valuable insight in the development of ecosystem level models and assist decision makers in formulating pertinent policy on intelligent multiresource management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  • Bechtold, W.A., Ruark, G.A., and Lloyd, F.T. (1991) Changing stand structure and regional growth reductions in Georgia's natural pine stands.Forest Science,37, 703–17.

    Google Scholar 

  • Bocquet-Appel, J.P. and Sokal, R.R. (1989) Spatial autocorrelation analysis of trend residuals in biological data.Systematic Zoology,38, 333–41.

    Google Scholar 

  • Cliff, A.D. and Ord, J.K. (1981)Spatial Processes, Models and Applications. Pion, London.

    Google Scholar 

  • Czaplewski, R.L. and Reich, R.M. (1993) Expected value and variance of Moran's bivariate spatial autocorrelation statistic under permutation. Research Paper RM-309. U.S. Department of Agriculture, Rocky Mountain Forest and Range Experiment Station, Fort Collins, CO.

    Google Scholar 

  • Czaplewski, R.L., Reich, R.M., and Bechtold, W.A. (1994) Spatial autocorrelation in growth of undisturbed natural pine stands across Georgia.Forest Science,40, 314–328.

    Google Scholar 

  • Greig-Smith, P. (1952) The use of random and contiguous quadrats in the study of the structure of plant communities.Annals of Botany, London, N. S.16, 293–316.

    Google Scholar 

  • Haslett, J., Bradley, R., Craig, P., Unwin, A., and Wills, G. (1991) Dynamic graphics for exploring spatial data with application to locating global and local anomalies.American Statistian,45, 234–42.

    Google Scholar 

  • Isaaks, E.H. and Srivastava, R.M. (1989)An Introduction to Applied Geostatistics. Oxford University Press, New York.

    Google Scholar 

  • Klauber, M.R. (1975) Space-time clustering for more than two samples.Biometrics,31, 719–26.

    Google Scholar 

  • Mantel, N. (1967) The detection of disease clustering and a generalized regression approach.Cancer Research,27,209–20.

    Google Scholar 

  • Mielke, P.W., Jr. and You, Y.C. (1990) Ong-sample empirical coverage tests: Exact and simulated null distributions of test statistics with small and moderate sample sizes.Journal of Statistical Computer Simulations,35, 31–9.

    Google Scholar 

  • Moran, P.A.P. (1948) The interpretation of statistical maps.Journal of the Royal Statistical Society Series B,10, 243–51.

    Google Scholar 

  • Orloci, L. (1978)Multivariate Analysis in Vegetation Research. Dr W. Junk Publishers, The Hague.

    Google Scholar 

  • Ripley, B.D. (1981)Spatial Statistics. Wiley, New York.

    Google Scholar 

  • Sokal, R.R. (1979) Ecological parameter inferred from spatial correlograms. InContemporary Quantitative Ecology and Related Econometrics, ed. G.P. Patil and M.L. Rosenzweig, pp. 167–196. International Co-operating Publishing House, Maryland.

    Google Scholar 

  • Sokal, R.R. and Jacquez, G.M. (1991) Testing inferences about microevolutionary processes by menuus of spatial autocorrelation analysis.Evolution,45, 152–168.

    Google Scholar 

  • Stephens, M.A. (1974) EDF statistics for goodness of fit and some comparisons.Journal of the American Statistical Association,69, 730–7.

    Google Scholar 

  • Upton, G.G. and Fingleton, B. (1985)Spatial Data Analysis by Example. Vol. 1: Point Pattern and Quantitative Data. Wiley, New York.

    Google Scholar 

  • Wartenberg, D. (1985) Multivariate spatial correlation: a method for exploratory geographical analysis.Geographical Analysis,17, 263–83.

    Google Scholar 

Download references

Author information

Authors and Affiliations


Rights and permissions

Reprints and permissions

About this article

Cite this article

Reich, R.M., Czaplewski, R.L. & Bechtold, W.A. Spatial cross-correlation of undisturbed, natural shortleaf pine stands in northern Georgia. Environ Ecol Stat 1, 201–217 (1994).

Download citation

  • Received:

  • Issue Date:

  • DOI: