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Community Ecology

, Volume 7, Issue 1, pp 117–127 | Cite as

Quadrat size dependence, spatial autocorrelation and the classification of community data

  • J. PodaniEmail author
  • P. Csontos
Open Access
Article

Abstract

The paper evaluates spatial autocorrelation structure in grassland vegetation at the community level. We address the following main issues: 1) How quadrat size affects the measurement of spatial autocorrelation for presence/absence and cover data? 2) What is the relationship between spatial autocorrelation and classification? 3) Is there a temporal change of spatial autocorrelation in the vegetation studied? We found that multivariate variogram shape, variance explained and the sill are different for presence/absence data and cover data, whereas quadrat size increases apparently introduce a stabilizing effect for both. Spatial stationarity is detected for species presence, and non-stationarity for cover. A new graphical tool, the clusterogram is introduced to examine spatial dependence of classification at various numbers of clusters. We found that spatial autocorrelation plays a crucial role in the classification of vegetation and therefore we suggest that its effect should not be removed from clustering. Mutual interpretation of variogram and clusterogram shape may be informative on the number of meaningful clusters present in the data. Spatial autocorrelation structure did not change markedly after 23 years for presence/absence data, indicating that the vegetation of the study area is stationary in time as well. The present study demonstrates that traditional quadrat data are suitable for evaluating spatial autocorrelation, even though field coordinates are recorded several years after sampling is completed.

Keywords

Clusterogram Grasslands Sampling Variogram 

Abbreviations

p/a

Presence-Absence

SA

Spatial Autocorrelation

References

  1. Anand, M. and Kadmon, R. 2000. Community-level analyses of spatiotemporal plant dynamics. Écoscience 7: 101–110.CrossRefGoogle Scholar
  2. Bellehumeur, C., Legendre, P. and Marcotte, D. 1997. Variance and spatial scales in a tropical rain forest: Changing the size of sampling units. Plant Ecol. 130: 89–98.CrossRefGoogle Scholar
  3. Bourgault, G., Marcotte, D. and Legendre, P. 1992. The multivariate (co)variogram as a spatial weighting function in classification methods. Math. Geol. 24: 463–478.CrossRefGoogle Scholar
  4. Dale, M. R. T. and Fortin, M.-J. 2002. Spatial autocorrelation and statistical test in ecology. Écoscience 9:162–167.CrossRefGoogle Scholar
  5. Fortin, M.-J. 1999. Effects of sampling unit resolution on the estimation of the spatial autocorrelation. Écoscience 6: 636–641.CrossRefGoogle Scholar
  6. Fortin, M.-J. and Jacquez, G. M. 2000. Randomization tests and spatially autocorrelated data. Bulletin of the ESA 81: 201–206.Google Scholar
  7. Gordon, A. D. 1999. Classification. 2nd ed. Chapman and Hall, London.Google Scholar
  8. Jelinski, D. E. and Wu, J. 1996. The modifiable areal unit problem and implications for landscape ecology. Landscape Ecol. 11: 129–140.CrossRefGoogle Scholar
  9. Kenkel, N. C., Juhász-Nagy, P. and Podani, J. 1989. On sampling procedures in population and community ecology. Vegetatio 83: 195–207.CrossRefGoogle Scholar
  10. Koenig, W. D. 1999. Spatial autocorrelation of ecological phenomena. Trends Ecol. Evol. 14: 22–26.CrossRefGoogle Scholar
  11. Legendre, P. 1987. Constrained clustering. In: Legendre, P. and Legendre, L. (eds), Developments in Numerical Ecology. Springer, Berlin, pp. 289–307.CrossRefGoogle Scholar
  12. Legendre, P. 1993. Spatial autocorrelation: Trouble or new paradigm? Ecology 74: 1659–1673.CrossRefGoogle Scholar
  13. Legendre, P., Dale, M. R. T., Fortin, M.-J., Gurevitch, J., Hohn, M. and Myers, D. 2002. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography 25: 601–616.CrossRefGoogle Scholar
  14. Legendre, P. and Fortin, M.-J. 1989. Spatial pattern and ecological analysis. Vegetatio 80: 107–138.CrossRefGoogle Scholar
  15. Legendre, P. and Legendre, L. 1998. Numerical Ecology. 2nd ed. Elsevier, Amsterdam.Google Scholar
  16. Maestre, F. T., Rodríguez, F., Bautista, S., Cortina, J. and Bellot, J. 2005. Spatial associations and patterns of perennial vegetation in a semi-arid steppe: a multivariate geostatistics approach. Plant Ecol. 179: 133–147.CrossRefGoogle Scholar
  17. Mistral, M., Buck, O., Meier-Behrmann, D. C., Burnett, D. A., Barnfield, T. E., Scott, A. J., Anderson, B. J. and Wilson, J. B. 2000. Direct measurement of spatial autocorrelation at the community level in four plant communities. J. Veg. Sci. 11: 911–916.CrossRefGoogle Scholar
  18. Nekola, J. C. and White, P. S. 1999. The distance decay of similarity in biogeography and ecology. J. Biogeogr. 26: 867–878.CrossRefGoogle Scholar
  19. Noy-Meir, I. 1977. Multivariate analysis of the semiarid vegetation in south-eastern Australia. II. Vegetation catenae and environmental gradients. Australian J. Bot. 22: 115–140.CrossRefGoogle Scholar
  20. Oden, N. L. and Sokal, R. R. 1986. Directional autocorrelation: an extension of spatial correlograms to two dimensions. Syst. Zool. 35: 608–617.CrossRefGoogle Scholar
  21. Persson, S. 1980. Succession in a south Swedish deciduous wood: a numerical approach. Vegetatio 43: 103–122.CrossRefGoogle Scholar
  22. Perry, J.N., Liebhold, A. M., Rosenberg, M. S., Dungan, J., Miriti, M., Jakomulska, A. and Citron-Pousty, S. 2002. Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography 25: 578–600.CrossRefGoogle Scholar
  23. Podani, J. 1984. Spatial processes in the analysis of vegetation: theory and review. Acta Bot. Hung. 30: 75–118.Google Scholar
  24. Podani, J. 1989. Comparison of classifications and ordinations of vegetation data. Vegetatio 83: 111–128.CrossRefGoogle Scholar
  25. Podani, J. 1998. A complex numerical analysis of dolomite rock grasslands of the Sas-hegy Nature Reserve, Budapest, Hungary. In: Csontos, P. (ed.), Botanical Exploration of Rock Grasslands. A Festschrift to B. Zólyomi. Scientia, Budapest, pp. 213–229 (in Hungarian, with English abstract).Google Scholar
  26. Podani, J. 2000. Introduction to the Exploration of Multivariate Biological Data. Backhuys, Leiden.Google Scholar
  27. Podani, J. 2001. SYN-TAX 2000. User’s Guide. Scientia, Budapest.Google Scholar
  28. Podani, J., Csontos, P., Tamás, J. and Miklós, I. 2005. A new multivariate approach to studying temporal changes of vegetation. Plant Ecol. 181: 85–100.CrossRefGoogle Scholar
  29. Royle, A., Clark, I., Brooker, P. I., Parker, H, Journel, A., Rendu, J. M., Sandefur, R. I. and Moussset-Jones, P. 1980. Geostatistics. McGraw-Hill, New York, N. Y.Google Scholar
  30. Shing, Lin. 2003. Optimization of cluster analysis using autocorrelation. Department of Geography, Southwest Texas State University. Presented at UCGIS Summer Assembly, Pacific Grove, CA, 2003. Manuscript available from www.ucgis.org/summer03/studentpapers/shinglin.pdf.Google Scholar
  31. Solie, J. B., Raun, W. R., Whitney, R. W., Stone, M. L. and Ringer, J. D. 1996. Optical sensor based field element size and sensing strategy for nitrogen application. Transactions of the American Society of Agricultural Engineers 39: 1983–1992.CrossRefGoogle Scholar
  32. Wagner, H. H. 2003. Spatial covariance in plant communities: Integrating ordination, geostatistics, and variance testing. Ecology 84: 1045–1057.CrossRefGoogle Scholar
  33. Wiens, J. A. 1989. Spatial scaling in ecology. Funct. Ecol. 3: 385–397.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2006

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Plant Taxonomy and EcologyEötvös UniversityBudapestHungary
  2. 2.Research Group of Theoretical Biology and EcologyMTA-ELTEBudapestHungary

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