Plant Ecology

, Volume 187, Issue 2, pp 203–212

Behavior of Vegetation Sampling Methods in the Presence of Spatial Autocorrelation



Spatial autocorrelation in vegetation has been discussed extensively, but little is yet known about how standard plant sampling methods perform when confronted with varying levels of patchiness. Simulated species maps with a range of total abundance and spatial autocorrelation (patchiness) were sampled using four methods: strip transect, randomly located quadrats, the non-nested multiscale modified Whittaker plot and the nested multiscale North Carolina Vegetation Survey (NCVS) plot. Cover and frequency estimates varied widely within and between methods, especially in the presence of high patchiness and for species with moderate abundances. Transect sampling showed the highest variability, returning estimates of 19–94% cover for a species with an actual cover of 50%. Transect and random methods were likely to miss rare species entirely unless large numbers of quadrats were sampled. NCVS plots produced the most accurate cover estimates because they sampled the largest area. Total species richness calculated using semilog species-area curves was overestimated by transect and random sampling. Both multiscale methods, the modified Whittaker and the NCVS plots, overestimated species richness when patchiness was low, and underestimated it when patchiness was high. There was no clear distinction between the nested NCVS or the non-nested modified Whittaker plot for any of the measures assessed. For all sampling methods, cover and especially frequency estimates were highly variable, and depended on both the level of autocorrelation and the sampling method used. The spatial structure of the vegetation must be considered when choosing field sampling protocols or comparing results between studies that used different methods.

Key words

Cover Frequency Modified Whittaker plot North Carolina Vegetation Survey Species-area curves Transect 


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  1. Barnett D.T. and Stohlgren T.J. (2003). A nested-intensity design for surveying plant diversity. Biodivers. Conserv. 12: 255–278CrossRefGoogle Scholar
  2. Bellehumeur C. and Legendre P. (1998). Multiscale sources of variation in ecological variables: modeling spatial dispersion, elaborating sampling designs. Landscape Ecol. 13: 15–25CrossRefGoogle Scholar
  3. Bourdeau P.F. (1953). A test of random versus systematic ecological sampling. Ecology 34: 499–512CrossRefGoogle Scholar
  4. Cain S.A. (1938). The species-area curve. Am. Midl. Nat. 19: 573–581CrossRefGoogle Scholar
  5. Clapham A.R. (1932). The form of the observational unit in quantitative ecology. J. Ecol. 20: 192–197CrossRefGoogle Scholar
  6. Dale M.R.T. and Fortin M.-J. (2002). Spatial autocorrelation and statistical tests in ecology. Écoscience 9: 162–167Google Scholar
  7. Dutilleul P. (1993). Spatial heterogeneity and the design of ecological field experiments. Ecology 74: 1646–1658CrossRefGoogle Scholar
  8. Fortin M.-J., Drapeau P. and Legendre P. (1989). Spatial autocorrelation and sampling design in plant ecology. Vegetatio 83: 209–222CrossRefGoogle Scholar
  9. Grieg-Smith P. (1979). Pattern in vegetation. J. Ecology 67: 755–779CrossRefGoogle Scholar
  10. Jorgensen E.E. and Tunnell S.J. (2001). The effectiveness of quadrats for measuring vascular plant diversity. Tex. J. Sci. 53: 365–368Google Scholar
  11. Korb J.E., Covington W.W. and Ful P.Z. (2003). Sampling techniques influence understory plant trajectories after restoration: An example from Ponderosa pine restoration. Restor. Ecol. 11: 504–515CrossRefGoogle Scholar
  12. Legendre P. (1993). Spatial autocorrelation: trouble or new paradigm?. Ecology 74: 1659–1673CrossRefGoogle Scholar
  13. Legendre P. and Fortin M.-J. (1989). Spatial pattern and ecological analysis. Vegetatio 80: 107–138CrossRefGoogle Scholar
  14. 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: 601–615CrossRefGoogle Scholar
  15. Leis S.A., Engle D.M., Fehmi J.S., Kretzer J. and Leslie D.M. (2003). Comparison of vegetation sampling procedures in a disturbed mixed-grass prairie. Proc. Oklahoma Acad. Sci. 83: 7–15Google Scholar
  16. Levin S.A. (1992). The problem of pattern and scale in ecology. Ecology 73: 1943–1967CrossRefGoogle Scholar
  17. Pebesma E.J. 2001. GSTAT User's Manual. (gstat version 2.3.3). Scholar
  18. Pebesma E.J. (2004). Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30: 683–691CrossRefGoogle Scholar
  19. Peet R.K., Wentworth T.R. and White P.S. (1998). A flexiblemultipurpose method for recording vegetation composition and structure. Castanea 63: 262–274Google Scholar
  20. Rice E.L and Kelting R.W. (1955). The species-area curve. Ecology 36: 7–11CrossRefGoogle Scholar
  21. Rosenzweig M.L. (1995). Species Diversity In Space and Time. Cambridge University Press, Cambridge, UKGoogle Scholar
  22. Stohlgren T.J., Falkner M.B. and Schell L.D. (1995). A Modified-Whittaker nested vegetation sampling method. Vegetatio 117: 113–121CrossRefGoogle Scholar
  23. Stohlgren T.J., Bull K.A. and Otsuki Y. (1998). Comparison of rangeland vegetation sampling techniques in the Central Grasslands. J. Range Manage. 51: 164–172Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  1. 1.USDA-ARS Pasture Systems and Watershed Management Research UnitUniversity ParkUSA

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